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Biology LibreTexts

10.7: Homeostasis and Feedback

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  • Page ID 17075

  • Suzanne Wakim & Mandeep Grewal
  • Butte College

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Steady as She Goes

This device looks simple, but it controls a complex system that keeps a home at a steady temperature. The device is an old-fashioned thermostat. The dial shows the current temperature in the room and also allows the occupant to set the thermostat to the desired temperature. A thermostat is a commonly cited model of how living systems, including the human body, maintain a steady state called homeostasis.

Honeywell thermostat

What is Homeostasis?

Homeostasis is the condition in which a system such as the human body is maintained in a more-or-less steady state. It is the job of cells, tissues, organs, and organ systems throughout the body to maintain many different variables within narrow ranges that are compatible with life. Keeping a stable internal environment requires continuous monitoring of the internal environment and constantly making adjustments to keep things in balance.

Setpoint and Normal Range

For any given variable, such as body temperature or blood glucose level, there is a particular setpoint that is the physiological optimum value. For example, the setpoint for human body temperature is about 37 ºC (98.6 ºF). As the body works to maintain homeostasis for temperature or any other internal variable, the value typically fluctuates around the set point. Such fluctuations are normal as long as they do not become too extreme. The spread of values within which such fluctuations are considered insignificant is called the normal range . In the case of body temperature, for example, the normal range for an adult is about 36.5 to 37.5 ºC (97.7 to 99.5 ºF).

Maintaining Homeostasis

Homeostasis is normally maintained in the human body by an extremely complex balancing act. Regardless of the variable being kept within its normal range, maintaining homeostasis requires at least four interacting components: stimulus, sensor, control center, and effector.

  • The stimulus is provided by the variable that is being regulated. Generally, the stimulus indicates that the value of the variable has moved away from the set point or has left the normal range.
  • The sensor monitors the values of the variable and sends data on it to the control center.
  • The control center matches the data with normal values. If the value is not at the set point or is outside the normal range, the control center sends a signal to the effector.
  • The effector is an organ, gland, muscle, or other structure that acts on the signal from the control center to move the variable back toward the set point.

Each of these components is illustrated in Figure \(\PageIndex{2}\). The diagram on the left is a general model showing how the components interact to maintain homeostasis. The stimulus activates the sensor. The sensor activates the control system that regulates the effector. The diagram on the right shows the example of body temperature. From the diagrams, you can see that maintaining homeostasis involves feedback, which is data that feeds back to control a response. High body temperature may stimulate the temperature regulatory center of the brain to activate the sweat glands to bring the body temperature down. When body temperature reaches normal range, it acts as negative feedback to stop the process. Feedback may be negative or positive. All the feedback mechanisms that maintain homeostasis use negative feedback. Biological examples of positive feedback are much less common.

 Negative Feedback Loops of human body

Negative Feedback

In a negative feedback loop , feedback serves to reduce an excessive response and keep a variable within the normal range. Examples of processes controlled by negative feedback include body temperature regulation and control of blood glucose.

Body Temperature

Body temperature regulation involves negative feedback whether it lowers the temperature or raises it (Figure \(\PageIndex{3}\)).

Cooling Down

The human body’s temperature regulatory center is the hypothalamus in the brain. When the hypothalamus receives data from sensors in the skin and brain that body temperature is higher than the setpoint, it sets into motion the following responses:

  • Blood vessels in the skin dilate (vasodilation) to allow more blood from the warm body core to flow close to the surface of the body, so heat can be radiated into the environment.
  • As blood flow to the skin increases, sweat glands in the skin are activated to increase their output of sweat (diaphoresis). When the sweat evaporates from the skin surface into the surrounding air, it takes the heat with it.
  • Breathing becomes deeper, and the person may breathe through the mouth instead of the nasal passages. This increases heat loss from the lungs.

Temperature Regulation via negative feedback

When the brain’s temperature regulatory center receives data that body temperature is lower than the setpoint, it sets into motion the following responses:

  • Blood vessels in the skin contract (vasoconstriction) to prevent blood from flowing close to the surface of the body. This reduces heat loss from the surface.
  • As the temperature falls lower, random signals to skeletal muscles are triggered, causing them to contract. This causes shivering, which generates a small amount of heat.
  • The thyroid gland may be stimulated by the brain (via the pituitary gland) to secrete more thyroid hormones. This hormone increases metabolic activity and heat production in cells throughout the body.
  • The adrenal glands may also be stimulated to secrete the hormone adrenaline. This hormone causes the breakdown of glycogen (the carbohydrate used for energy storage in animals) to glucose, which can be used as an energy source. This catabolic chemical process is exothermic, or heat producing.

Blood Glucose

In the control of the blood glucose level, certain endocrine cells in the pancreas called alpha and beta cells, detect the level of glucose in the blood. Then they respond appropriately to keep the level of blood glucose within the normal range.

  • If the blood glucose level rises above the normal range, pancreatic beta cells release the hormone insulin into the bloodstream. Insulin signals cells to take up the excess glucose from the blood until the level of blood glucose decreases to the normal range.
  • If the blood glucose level falls below the normal range, pancreatic alpha cells release the hormone glucagon into the bloodstream. Glucagon signals cells to break down stored glycogen to glucose and release the glucose into the blood until the level of blood glucose increases to the normal range.

Positive Feedback

In a positive feedback loop , feedback serves to intensify a response until an endpoint is reached. Examples of processes controlled by positive feedback in the human body include blood clotting and childbirth.

Blood Clotting

When a wound causes bleeding, the body responds with a positive feedback loop to clot the blood and stop blood loss. Substances released by the injured blood vessel wall begin the process of blood clotting. Platelets in the blood start to cling to the injured site and release chemicals that attract additional platelets. As the platelets continue to amass, more of the chemicals are released and more platelets are attracted to the site of the clot. The positive feedback accelerates the process of clotting until the clot is large enough to stop the bleeding.

Childbirth via Positive Feedback

Figure \(\PageIndex{4}\) shows the positive feedback loop that controls childbirth. The process normally begins when the head of the infant pushes against the cervix. This stimulates nerve impulses, which travel from the cervix to the hypothalamus in the brain. In response, the hypothalamus sends the hormone oxytocin to the pituitary gland, which secretes it into the bloodstream so it can be carried to the uterus. Oxytocin stimulates uterine contractions, which push the baby harder against the cervix. In response, the cervix starts to dilate in preparation for the passage of the baby. This cycle of positive feedback continues, with increasing levels of oxytocin, stronger uterine contractions, and wider dilation of the cervix until the baby is pushed through the birth canal and out of the body. At that point, the cervix is no longer stimulated to send nerve impulses to the brain, and the entire process stops.

When Homeostasis Fails

Homeostatic mechanisms work continuously to maintain stable conditions in the human body. Sometimes, however, the mechanisms fail. When they do, homeostatic imbalance may result, in which cells may not get everything they need or toxic wastes may accumulate in the body. If homeostasis is not restored, the imbalance may lead to disease or even death. Diabetes is an example of a disease caused by homeostatic imbalance. In the case of diabetes, blood glucose levels are no longer regulated and may be dangerously high. Medical intervention can help restore homeostasis and possibly prevent permanent damage to the organism.

Feature: My Human Body

Diabetes is diagnosed in people who have abnormally high levels of blood glucose after fasting for at least 12 hours. A fasting level of blood glucose below 100 is normal. A level between 100 and 125 places you in the pre-diabetes category, and a level higher than 125 results in a diagnosis of diabetes.

Of the two types of diabetes, type 2 diabetes is the most common, accounting for about 90 percent of all cases of diabetes in the United States. Type 2 diabetes typically starts after the age of 40. However, because of the dramatic increase in recent decades in obesity in younger people, the age at which type 2 diabetes is diagnosed has fallen. Even children are now being diagnosed with type 2 diabetes. Today, about 30 million Americans have type 2 diabetes, and another 90 million have pre-diabetes.

You are likely to have your blood glucose level tested during a routine medical exam. If your blood glucose level indicates that you have diabetes, it may come as a shock to you because you may not have any symptoms of the disease. You are not alone, because as many as one in four diabetics does not know they have the disease. Once the diagnosis of diabetes sinks in, you may be devastated by the news. Diabetes can lead to heart attacks, strokes, blindness, kidney failure, and loss of toes or feet. The risk of death in adults with diabetes is 50 percent greater than it is in adults without diabetes, and diabetes is the seventh leading cause of death in adults. In addition, controlling diabetes usually requires frequent blood glucose testing, watching what and when you eat and taking medications or even insulin injections. All of this may seem overwhelming.

The good news is that changing your lifestyle may stop the progression of type 2 diabetes or even reverse it. Here’s how:

  • Lose weight. Any weight loss is beneficial. Losing as little as seven percent of your weight may be all that is needed to stop diabetes in its tracks. It is especially important to eliminate excess weight around your waist.
  • Exercise regularly. You should try to exercise five days a week for at least 30 minutes. This will not only lower your blood sugar and help your insulin work better; it will also lower your blood pressure and improve your heart health. Another bonus of exercise is that it will help you lose weight by increasing your basal metabolic rate.
  • Adopt a healthy diet. Decrease your consumption of refined carbohydrates such as sweets and sugary drinks. Increase your intake of fiber-rich foods such as fruits, vegetables, and whole grains. About a quarter of each meal should consist of high-protein foods, such as fish, chicken, dairy products, legumes, or nuts.
  • Control stress. Stress can increase your blood glucose and also raise your blood pressure and risk of heart disease. When you feel stressed out, do breathing exercises or take a brisk walk or jog. Also, try to replace stressful thoughts with more calming ones.
  • Establish a support system. Enlist the help and support of loved ones as well as medical professionals such as a nutritionist and diabetes educator. Having a support system will help ensure that you are on the path to wellness and that you can stick to your plan.
  • What is homeostasis?
  • Define the setpoint and normal range for physiological measures.
  • Identify and define the four interacting components that maintain homeostasis in feedback loops.
  • Compare and contrast negative and positive feedback loops.
  • Explain how negative feedback controls body temperature.
  • Give two examples of physiological processes that are controlled by positive feedback loops.
  • brings a variable’s level back to a normal range
  • can lower, but not raise, body temperature
  • is the type of feedback involved in blood clotting
  • Is this an example of negative or positive feedback? Explain your answer.
  • What do you think might be the evolutionary benefit of the milk production regulation mechanism described in part a?
  • Explain why homeostasis is regulated by negative feedback loops, rather than positive feedback loops.
  • the top of a normal range
  • the bottom of a normal range
  • in the middle of a normal range
  • the point at which changes can no longer occur
  • What is the stimulus in this system? Explain your answer.
  • What is the control center in this system? Explain your answer.
  • What is the pituitary considered in this system: stimulus, sensor, control center, or effector? Explain your answer.

Explore More


  • Honeywell thermostat by Vincent de Groot , licensed CC BY 4.0 via Wikimedia Commons
  • Negative feedback loop by OpenStax , licensed CC BY 4.0 via Wikimedia Commons
  • Temperature Regulation dedicated CC0 via Wikimedia Commons
  • Pregnancy-Positive Feedback by OpenStax, licensed CC BY 4.0 via Wikimedia Commons
  • Text adapted from Human Biology by CK-12 licensed CC BY-NC 3.0

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AP®︎/College Biology

Course: ap®︎/college biology   >   unit 4, homeostasis.

  • Hormone concentration metabolism and negative feedback

feedback mechanism essay

  • Homeostasis is the tendency to resist change in order to maintain a stable, relatively constant internal environment.
  • Homeostasis typically involves negative feedback loops that counteract changes of various properties from their target values, known as set points .
  • In contrast to negative feedback loops, positive feedback loops amplify their initiating stimuli, in other words, they move the system away from its starting state.


Maintaining homeostasis.

  • One is activated when a parameter—like body temperature—is above the set point and is designed to bring it back down.
  • One is activated when the parameter is below the set point and is designed to bring it back up.

Homeostatic responses in temperature regulation

Disruptions to feedback disrupt homeostasis..

  • Muscle and fat cells don't get enough glucose, or fuel. This can make people feel tired and even cause muscle and fat tissues to waste away.
  • High blood sugar causes symptoms like increased urination, thirst, and even dehydration. Over time, it can lead to more serious complications. 4 , 5 ‍  

Positive feedback loops


  • " Homeostasis " by OpenStax College, Biology, CC BY 4.0 ; download the original article for free at[email protected]
  • " Homeostasis " by OpenStax College, Anatomy & Physiology, CC BY 4.0 ; download the original article for free at[email protected] .
  • " The endocrine pancreas " by OpenStax College, Anatomy & Physiology, CC BY 4.0 ; download the original article for free at[email protected]

Works cited

  • "Human Body Temperature," Wikipedia, last modified June 18, 2016, .
  • "Circadian Rhythm," WIkipedia, last modified June 29, 2016, .
  • David E. Sadava, David M. Hillis, H. Craig Heller, and May Berenbaum, "Physiology, Homeostasis, and Temperature Regulation," in Life: The Science of Biology , 9th ed. (Sunderland: Sinauer Associates, 2009), 847.
  • "Causes of Diabetes," National Institute of Diabetes and Digestive and Kidney Diseases, last modified June 2014, .
  • Mayo Clinic Staff, "Hyperglycemia in Diabetes," last modified April 18, 2015, Mayo Clinic, .

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Microbiology Notes

Microbiology Notes

Feedback Mechanism – Definition, Types, Mechanism, Examples

Table of Contents

What is Feedback mechanism?

  • A feedback mechanism, rooted in the principles of cybernetics, is a fundamental regulatory system prevalent in both biological and non-biological systems. It operates as a loop, responding to perturbations or changes either by amplifying (positive feedback) or counteracting them (negative feedback). This mechanism is quintessential for maintaining a state of equilibrium or homeostasis, especially in biological systems.
  • In the realm of biology, a feedback mechanism is not merely a loop but a complex interplay of biological processes, signals, and mechanisms. These processes either initiate and accelerate or inhibit and decelerate a particular response.
  • For instance, the secretion of the hormone oxytocin during the onset of childbirth contractions exemplifies a positive feedback loop. The hormone’s release intensifies the contractions, thus perpetuating the cycle. Conversely, the regulation of blood glucose levels showcases a negative feedback loop.
  • Elevated blood glucose levels can be detrimental, potentially leading to conditions like diabetes. Hence, the body employs negative feedback mechanisms to regulate and maintain glucose levels within a safe range.
  • Feedback mechanisms are not limited to biological systems alone. The term originated in the field of cybernetics, emphasizing a system’s capacity to adjust its output based on received input. In essence, it is a system’s inherent ability to self-regulate and adapt.
  • To further elucidate, feedback mechanisms can be categorized into two primary types: positive and negative. In a positive feedback mechanism, the activation of one component stimulates the activation of another, reinforcing the initial stimulus. This often leads to an amplification of the response.
  • On the other hand, a negative feedback mechanism operates in a counteractive manner. Here, the activation of one component results in the suppression or inactivation of another, ensuring that the system returns to its baseline or equilibrium state.
  • In summary, feedback mechanisms, whether positive or negative, play a pivotal role in maintaining the stability and functionality of various systems, from biological entities to intricate cybernetic systems. Their precise and dynamic nature underscores their significance in ensuring the harmonious operation of complex systems.

Definition of Feedback mechanism

A feedback mechanism is a regulatory system within a process or organism that responds to changes by either amplifying (positive feedback) or counteracting (negative feedback) them to maintain equilibrium or homeostasis.

Open and Closed-Loop Feedback Mechanisms

Feedback loops are integral components in the maintenance of homeostasis within biological systems. These loops are meticulously designed systems comprising three primary components: the receptor (sensor), the control center (integrator or comparator), and the effector. The receptor identifies and relays physiological deviations to the control center, which then evaluates the data against a predetermined setpoint. If a significant deviation is detected, the control center activates the effector to instigate corrective measures, ensuring the system returns to its optimal state.

The communication pathways between these components are vital for the effective functioning of the feedback loop. Typically, these pathways are facilitated by neural or hormonal signals. However, in certain instances where the receptor and control center are unified structures, direct signaling mechanisms may not be necessary.

Feedback loops are broadly classified into two categories: open-loop and closed-loop mechanisms, which are further delineated into positive and negative feedback loops.

  • Positive Feedback Loops : These loops are characterized by a self-amplifying cycle where an initial change leads to a series of events that further accentuate the change. In essence, a deviation in one direction prompts further deviations in the same direction. While the term “positive feedback” is universally recognized when a variable augments itself, it’s essential to note that such loops can potentially lead to uncontrolled conditions. This is because the continuous amplification can spiral beyond control. However, in specific scenarios, positive feedback can be beneficial when moderated and controlled.
  • Negative Feedback Loops : Contrary to positive feedback loops, negative feedback loops work to counteract deviations. When a change occurs in one direction, the system responds with changes in the opposite direction, aiming to restore the system to its setpoint. This counteractive mechanism inherently provides stability to the system. Due to various external and internal stimuli that can influence a variable, negative feedback loops often result in oscillations around the setpoint, ensuring the system remains within a controlled range. Classical examples of negative feedback loops include the regulation of body temperature and blood glucose levels.

In summary, feedback loops, whether positive or negative, play a pivotal role in ensuring the stability and homeostasis of biological systems. Their intricate design and precise functioning underscore their significance in the complex orchestration of physiological processes.

Feedback Mechanism Types

Feedback mechanisms are fundamental regulatory systems that maintain stability and homeostasis within various processes. These mechanisms respond to deviations in physiological parameters or input changes, ensuring that systems operate within their optimal limits. Based on the nature of their response to these deviations, feedback mechanisms are categorized into two primary types:

I. Positive Feedback Mechanism : In this type of mechanism, the response amplifies the initial deviation or change. Essentially, when a system encounters a perturbation, the positive feedback mechanism reinforces and augments this change, pushing the system further away from its equilibrium. This can lead to an escalating cycle until a specific endpoint or external factor intervenes. It’s crucial to understand that while the term “positive” might suggest a beneficial outcome, this is not always the case. In certain scenarios, unchecked positive feedback can lead to undesirable or even harmful outcomes. However, in controlled circumstances, positive feedback can be instrumental in achieving specific physiological goals or responses.

II. Negative Feedback Mechanism : Contrastingly, the negative feedback mechanism operates by counteracting deviations from a set point or equilibrium. When a system’s parameters stray from their desired range, the negative feedback mechanism initiates processes to reverse the direction of the deviation, bringing the system back towards its set point. This self-regulating mechanism ensures that systems remain stable and within their optimal operational limits. The inherent nature of negative feedback to resist change provides a buffering effect, allowing systems to maintain homeostasis in the face of internal or external perturbations.

In essence, while both positive and negative feedback mechanisms serve to regulate systems, their modes of action differ significantly. Positive feedback amplifies changes, often leading to a specific endpoint, while negative feedback resists changes, ensuring stability and equilibrium. Both mechanisms, with their distinct characteristics, play vital roles in the intricate balance of physiological processes.

1. Positive feedback mechanism

  • A positive feedback mechanism, within the context of regulatory systems, is a process that accentuates or amplifies the initial deviation or change in a system. Contrary to what the term “positive” might suggest, this mechanism does not necessarily denote a beneficial outcome. Instead, it refers to the mechanism’s propensity to enhance the initial stimulus, pushing the system further away from its equilibrium or homeostatic state.
  • In essence, when a variation in output occurs, the positive feedback mechanism responds by intensifying this variation, leading to an even greater change in the same direction. This amplification can be visualized as a loop where the output reinforces the initial stimulus, causing a continuous escalation until the stimulus is removed or another regulatory mechanism intervenes.
  • While positive feedback mechanisms are less prevalent than their negative counterparts, they are not entirely absent in biological systems. Their rarity can be attributed to the fact that they move the system away from the desired state of homeostasis. However, when rapid and efficient responses are required, positive feedback mechanisms come into play, ensuring swift and amplified reactions to specific stimuli. For instance, certain biological processes necessitate a rapid escalation, and in such scenarios, the positive feedback mechanism ensures that the stimulus’s influence is magnified.
  • One can conceptualize a positive feedback loop as either a singular component that stimulates its own activity or a complex interplay of multiple components with both direct and indirect interactions. While these mechanisms are infrequent in intricate systems like the human body, they are discernible in specific environmental processes. A classic example is the ripening of fruit, where the onset of ripening in one fruit can stimulate the ripening of its neighbors.
  • In summation, positive feedback mechanisms play a pivotal role in specific scenarios requiring rapid and amplified responses, despite their infrequent occurrence in comparison to negative feedback mechanisms. Their unique ability to magnify deviations offers both challenges and advantages, depending on the context in which they operate.

Positive Feedback

Steps of positive feedback mechanism

The positive feedback mechanism operates as a regulatory system, amplifying deviations from a set point or equilibrium. This mechanism, while less common than its negative counterpart, plays a crucial role in specific biological processes requiring rapid and intensified responses. The steps or processes involved in a positive feedback mechanism can be delineated as follows:

  • Stimulation : The inception of the positive feedback loop is marked by an initiating stimulus. In biological contexts, this often manifests as hormones secreted by various organs in response to a particular event or change. This stimulus serves as the catalyst, setting the feedback loop into motion.
  • Reception : Following the initial stimulation, the system’s receptors detect the change. These receptors, predominantly nerve cells, are responsible for capturing the stimulus and transmitting the associated signals to a control center. In humans, the brain typically functions as this control center, processing and interpreting the incoming signals.
  • Processing : Once the control center, such as the brain, receives the signals from the receptors, it undertakes the task of information analysis. The control center evaluates the incoming data against a predefined set point or range. If the received stimulus deviates significantly from this set point, the control center prepares to generate an appropriate output.
  • Amplification of Stimuli : The final step in the positive feedback loop involves the transmission of directives from the control center to the target site or effector. This transmission, often facilitated by neural pathways, instructs the effector to respond to the initial stimulus. However, in the context of a positive feedback mechanism, this response serves to further amplify or enhance the initial deviation. The system continues to intensify the stimulus until a certain endpoint is reached or an external factor intervenes to halt the process.

In essence, the positive feedback mechanism operates as a self-reinforcing loop, where each cycle intensifies the deviation from the norm. While this can lead to rapid and pronounced changes, it is essential for processes that require swift and decisive actions.

Positive feedback mechanism examples

Positive feedback mechanisms are regulatory systems that amplify or enhance deviations from a set point or equilibrium. These mechanisms are crucial in specific biological processes that necessitate rapid and pronounced responses. Here are some illustrative examples of positive feedback mechanisms:

Blood Clotting Mechanism during Positive feedback system.

  • Blood Clotting : When an injury results in bleeding, the body’s immediate response is to initiate the clotting process to prevent excessive blood loss. The injured blood vessel releases substances that activate clotting. Platelets adhere to the injury site and release chemicals that attract more platelets. This accumulation and chemical release amplify the clotting process, a classic example of a positive feedback loop, until a clot forms sufficiently to stop the bleeding.
  • Childbirth : Childbirth in humans is regulated by a positive feedback mechanism. The process begins when the baby exerts pressure against the cervix. This pressure is sensed and transmitted as nerve impulses to the brain, prompting the release of the hormone oxytocin. Oxytocin induces uterine contractions, pushing the baby further against the cervix and intensifying the stimulus. This cycle continues, with increasing oxytocin levels and stronger contractions, until the baby is delivered.
  • Menstrual Cycle : At the onset of the menstrual cycle, the ovaries release the hormone estrogen. This release acts as a stimulus for the brain, leading the hypothalamus to release gonadotrophin-releasing hormone and the pituitary gland to release luteinizing hormone. These hormones, in turn, stimulate the ovaries to produce more estrogen. This cycle repeats, amplifying the hormonal response until follicle-stimulating hormone is released, culminating in ovulation and the commencement of the menstrual cycle.
  • Fruit Ripening : A fascinating phenomenon in nature is the synchronized ripening of fruits on a tree. The initial ripening fruit releases ethylene gas, which acts as a stimulus for neighboring fruits to ripen. As these fruits ripen, they too release ethylene, perpetuating the ripening process in a wave-like manner across the tree. This positive feedback loop is harnessed commercially, with fruits being exposed to ethylene gas to expedite ripening.

Childbirth Mechanism during Positive feedback system.

In summary, positive feedback mechanisms play pivotal roles in various biological processes, ensuring swift and decisive actions in response to specific stimuli. These mechanisms, while amplifying deviations, are crucial for processes that require rapid and intensified responses.

2. Negative feedback mechanism

  • A negative feedback mechanism is a regulatory system integral to maintaining stability and equilibrium within biological and other systems. Contrary to positive feedback mechanisms that amplify deviations, negative feedback mechanisms counteract and mitigate changes, ensuring that a system remains close to a desired set point or equilibrium.
  • At its core, a negative feedback mechanism operates by detecting deviations in output. Once a deviation is identified, the mechanism initiates corrective actions that produce changes in the opposite direction of the initial deviation. This counteractive response ensures that the system returns to its desired state or homeostasis.
  • For instance, when a system’s output exceeds its desired set point, the negative feedback mechanism reduces the output. Conversely, if the output falls below the desired set point, the mechanism increases the output. This self-regulating nature of negative feedback mechanisms ensures that systems remain stable and resist abrupt or drastic changes.
  • The control unit plays a pivotal role in the functioning of the negative feedback mechanism. It continuously monitors and analyzes the system’s current state, comparing it to the desired set point. If discrepancies arise, the control unit orchestrates the necessary corrective actions to bring the system back to equilibrium.
  • In comparison to positive feedback mechanisms, which are designed to amplify deviations and are less common, negative feedback mechanisms are ubiquitous. Their prevalence can be attributed to their inherent ability to stabilize systems, making them indispensable in maintaining homeostasis in various biological processes.
  • In summary, negative feedback mechanisms are foundational to the stability and equilibrium of systems. By counteracting deviations and driving systems towards homeostasis, they ensure consistent and optimal functioning.

Negative Feedback

Steps in a negative feedback mechanism

A negative feedback mechanism is a fundamental regulatory process that ensures stability and homeostasis within systems. This mechanism operates by counteracting deviations from a set point, ensuring that systems remain within desired parameters. The steps involved in a negative feedback mechanism are as follows:

  • Stimulation : The initiation of a negative feedback mechanism begins with the detection of deviations in physiological parameters from their established norms. These deviations can arise due to various internal or external factors and can occur in either direction—increasing or decreasing from the set point.
  • Reception : Once a deviation is detected, it is relayed to the control unit through specialized receptors distributed throughout the system. These receptors, which can be nerves, thermoreceptors, or other sensory cells, are adept at sensing changes and transmitting this information to the central control unit, typically the brain.
  • Processing : The control unit, upon receiving the information about the deviation, processes it to determine the magnitude and direction of the change. Based on this analysis, the control unit decides whether to activate or inhibit the feedback loop. It formulates a response strategy to address the detected deviation, ensuring that the system returns to its desired state.
  • Counteraction : In the final step, the control unit dispatches corrective signals aimed at neutralizing the effects of the initial deviation. These signals are directed towards specific effectors or organs that can bring about the necessary changes to restore equilibrium. Depending on the nature of the deviation, these corrective actions can involve increasing or decreasing certain physiological activities, releasing specific hormones, or adjusting metabolic rates.

In essence, a negative feedback mechanism operates as a self-regulating system, continuously monitoring and adjusting to maintain stability and homeostasis. By promptly detecting and counteracting deviations, it ensures that systems function optimally and remain within their desired parameters.

Examples of negative feedback

Negative feedback mechanisms are pivotal in maintaining homeostasis within biological systems. These mechanisms detect deviations from a set point and initiate responses to counteract and restore the system to its desired state. Here are some illustrative examples of negative feedback in action:

 Temperature Regulation under Negative Feedback System.

  • Thermoregulation : The human body employs a sophisticated negative feedback system to regulate its internal temperature, ensuring it remains close to an average of 37°C (98.6°F). When the body’s internal temperature rises above this set point, thermoreceptors in the skin and brain detect this change. In response, the hypothalamus, a region in the brain, triggers mechanisms like vasodilation (widening of blood vessels) and sweating to dissipate excess heat. Conversely, if the body’s temperature drops below the set point, the hypothalamus activates mechanisms like vasoconstriction (narrowing of blood vessels) and shivering to generate and conserve heat. These actions ensure that the body’s temperature remains stable and within a narrow range, despite external environmental fluctuations.
  • Glycemic Control : The regulation of blood glucose levels is another quintessential example of a negative feedback mechanism. When blood glucose levels rise post-meal, the pancreas secretes insulin, a hormone that facilitates the uptake of glucose by cells, particularly muscle and liver cells. This uptake reduces the glucose concentration in the bloodstream. On the other hand, when blood glucose levels drop, perhaps due to fasting or strenuous activity, the pancreas releases glucagon. This hormone stimulates the liver to convert stored glycogen into glucose, which is then released into the bloodstream. Through these opposing actions of insulin and glucagon, the body ensures that blood glucose levels remain within a tight range, providing a steady energy supply to cells.

Blood Glucose Level Regulation under Negative Feedback System

These examples underscore the importance of negative feedback mechanisms in preserving the stability and functionality of biological systems. By continuously monitoring and adjusting, these mechanisms ensure that physiological parameters remain within optimal ranges, safeguarding the overall health and well-being of the organism.

Differences Between Positive vs. Negative Feedback Mechanism

Distinguishing between positive and negative feedback mechanisms is crucial for understanding how biological systems maintain equilibrium and respond to external stimuli. These mechanisms, while both integral to homeostasis, operate in fundamentally different ways. Here’s a comprehensive comparison between the two:

Positive Feedback Mechanism vs. Negative Feedback Mechanism :

  • Positive Feedback Mechanism : Leads to an amplification or enhancement of the initial change or deviation.
  • Negative Feedback Mechanism : Counteracts the initial change, working to restore the system to its set point or equilibrium.
  • Positive Feedback Mechanism : Less commonly observed in biological systems.
  • Negative Feedback Mechanism : More prevalent, as it’s essential for maintaining homeostasis in various physiological processes.
  • Positive Feedback Mechanism : Reinforces or intensifies the stimulus, leading to a further deviation from the set point.
  • Negative Feedback Mechanism : Opposes or diminishes the stimulus, aiming to bring the system back to its desired state or set point.
  • Positive Feedback Mechanism : Generally less stable, as it can lead to extreme changes unless externally regulated or until a specific endpoint is reached.
  • Negative Feedback Mechanism : Promotes stability by constantly adjusting and maintaining parameters within a narrow range.
  • Positive Feedback Mechanism : Processes like blood clotting, fruit ripening, childbirth in mammals, and certain phases of the menstrual cycle.
  • Negative Feedback Mechanism : Examples include the regulation of body temperature and the control of blood glucose levels.

In essence, while positive feedback mechanisms amplify changes and can drive processes to completion, negative feedback mechanisms are the body’s primary tool for maintaining stability and homeostasis. Both types of feedback play vital roles in ensuring the proper functioning and adaptability of biological systems.

Importance of Feedback mechanism

Feedback mechanisms are fundamental to the stability and functionality of biological, ecological, and even technological systems. Their importance can be understood through the following points:

  • Maintenance of Homeostasis : Feedback mechanisms, especially negative feedback loops, are essential for maintaining homeostasis in organisms. Homeostasis ensures that internal conditions remain stable and relatively constant, allowing organisms to function optimally despite external changes.
  • Adaptation to Changes : Feedback mechanisms allow systems to adapt to changes in the environment or internal conditions. This adaptability is crucial for the survival of organisms in varying environmental conditions.
  • Efficiency : Positive feedback mechanisms can amplify specific processes, making them more efficient. For instance, the clotting of blood is expedited through a positive feedback loop, ensuring rapid wound healing.
  • Regulation of Growth and Development : Feedback mechanisms regulate growth and developmental processes in organisms, ensuring that they occur at the right time and in the right sequence.
  • Protection : Feedback mechanisms can protect organisms from harm. For instance, when body temperature rises, sweating (a negative feedback mechanism) helps cool the body down, preventing overheating.
  • Dynamic Equilibrium in Ecosystems : In ecological systems, feedback mechanisms help maintain a dynamic equilibrium. For example, predator-prey relationships are governed by feedback loops that ensure neither population grows too large or too small.
  • Technological Applications : Feedback mechanisms are not limited to biological systems. They are widely used in technology, especially in control systems, to ensure devices operate within desired parameters. For instance, thermostats use feedback mechanisms to maintain room temperature.
  • Learning and Decision Making : Feedback is crucial in learning processes and decision-making, both for humans and artificial intelligence systems. Receiving feedback helps in refining strategies, improving performance, and achieving desired outcomes.
  • Stabilization of Economic Systems : In economics, feedback mechanisms help stabilize markets. For example, when demand for a product increases, prices may rise, leading to increased production, which eventually brings prices back down.
  • Enhancement of Processes : In certain situations, it’s beneficial for a process to be amplified or accelerated. Positive feedback mechanisms can enhance such processes until a specific endpoint or goal is achieved.

In summary, feedback mechanisms are integral to the functioning and stability of various systems, from cellular processes in our bodies to global ecological cycles. They ensure that systems can adapt, respond, and evolve in the face of changing conditions.

Quiz Practice

Which of the following best describes a feedback mechanism? a) A process that amplifies system changes b) A process that only reduces system changes c) A system’s response to a change to return to a set point d) A system’s response to permanently change its set point

Which feedback mechanism tends to stabilize a system? a) Positive feedback b) Negative feedback c) Neutral feedback d) None of the above

Which of the following is an example of a positive feedback mechanism? a) Body temperature regulation b) Blood clotting c) Blood glucose level regulation d) Breathing rate adjustment

What is the primary purpose of negative feedback mechanisms in the human body? a) To amplify deviations b) To maintain homeostasis c) To accelerate processes d) To disrupt stability

In which feedback mechanism does the response enhance the original stimulus? a) Negative feedback b) Neutral feedback c) Positive feedback d) None of the above

Which component of a feedback mechanism detects changes in the environment? a) Effector b) Control center c) Receptor d) Stimulus

In the regulation of blood glucose levels, which hormone lowers blood sugar when it’s too high? a) Glucagon b) Insulin c) Adrenaline d) Cortisol

Which of the following processes is regulated by a positive feedback mechanism? a) Blood pressure regulation b) Childbirth c) Heart rate regulation d) Respiratory rate

What happens in a negative feedback mechanism when the set point is achieved? a) The response is amplified b) The response is reduced or stopped c) The set point changes d) The stimulus is enhanced

In which feedback mechanism can the response often lead to an extreme or uncontrolled situation? a) Neutral feedback b) Negative feedback c) Positive feedback d) All of the above

What is a feedback mechanism?

A feedback mechanism is a process in which a system responds to a change by either returning to its original state or by amplifying the change.

How do feedback mechanisms maintain homeostasis?

Feedback mechanisms help maintain homeostasis by detecting deviations from a set point and triggering responses to bring the system back to that set point.

What is the difference between positive and negative feedback mechanisms?

Positive feedback amplifies changes, often leading to an extreme or uncontrolled situation, while negative feedback opposes changes, bringing the system back to its set point.

Are positive feedback mechanisms harmful?

Not necessarily. While positive feedback can lead to extreme situations, it is essential in certain processes like childbirth and blood clotting.

Why is negative feedback more common in biological systems?

Negative feedback mechanisms are more common because they help maintain stability and homeostasis, which are vital for the survival of organisms.

Can one system have both positive and negative feedback mechanisms?

Yes, many systems, especially in biology, can have both types of feedback mechanisms operating under different conditions.

What role do receptors play in feedback mechanisms?

Receptors detect changes in the environment or within the system and send this information to the control center to initiate a response.

How does the body regulate temperature using feedback mechanisms?

When the body’s temperature deviates from the set point, receptors detect this change and send signals to the brain, which then triggers responses like sweating (to cool down) or shivering (to warm up) to return to the set point.

Are feedback mechanisms only found in biological systems?

No, feedback mechanisms are also found in non-biological systems, such as in engineering, electronics, and economics.

Why is understanding feedback mechanisms important in medicine?

Understanding feedback mechanisms is crucial in medicine to diagnose diseases, understand body responses to treatments, and develop therapeutic interventions that can modify or harness these feedback loops for better health outcomes.

  • Biga, L., Dawson, S., Harwell, A., Hopkins, R., Kaufmann, J., LeMaster, M., Matern, P., Graham, K., Quick, D., & Runyeon, J. (2021). Anatomy and Physiology . Link
  • Lumen Learning. (2019). Homeostasis from Boundless Anatomy and Physiology . Link
  • Kahn Academy. (2019). Homeostasis . In Human Body Systems . Link
  • Torday, J. S. (2021). Homeostasis as the Mechanism of Evolution. Biology, 4 (3), 573-90. DOI: 10.3390/biology4030573
  • Wakim, S., & Grewal, M. (2021). Homeostasis and Feedback . Link

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1.5 Homeostasis

Learning objectives.

By the end of this section, you will be able to:

  • Discuss the role of homeostasis in healthy functioning
  • Contrast negative and positive feedback, giving one physiologic example of each mechanism

Maintaining homeostasis requires that the body continuously monitor its internal conditions. From body temperature to blood pressure to levels of certain nutrients, each physiological condition has a particular set point. A set point is the physiological value around which the normal range fluctuates. A normal range is the restricted set of values that is optimally healthful and stable. For example, the set point for normal human body temperature is approximately 37°C (98.6°F) Physiological parameters, such as body temperature and blood pressure, tend to fluctuate within a normal range a few degrees above and below that point. Control centers in the brain and other parts of the body monitor and react to deviations from homeostasis using negative feedback. Negative feedback is a mechanism that reverses a deviation from the set point. Therefore, negative feedback maintains body parameters within their normal range. The maintenance of homeostasis by negative feedback goes on throughout the body at all times, and an understanding of negative feedback is thus fundamental to an understanding of human physiology.

Negative Feedback

A negative feedback system has three basic components ( Figure 1.10 a ). A sensor , also referred to a receptor, is a component of a feedback system that monitors a physiological value. This value is reported to the control center. The control center is the component in a feedback system that compares the value to the normal range. If the value deviates too much from the set point, then the control center activates an effector. An effector is the component in a feedback system that causes a change to reverse the situation and return the value to the normal range.

In order to set the system in motion, a stimulus must drive a physiological parameter beyond its normal range (that is, beyond homeostasis). This stimulus is “heard” by a specific sensor. For example, in the control of blood glucose, specific endocrine cells in the pancreas detect excess glucose (the stimulus) in the bloodstream. These pancreatic beta cells respond to the increased level of blood glucose by releasing the hormone insulin into the bloodstream. The insulin signals skeletal muscle fibers, fat cells (adipocytes), and liver cells to take up the excess glucose, removing it from the bloodstream. As glucose concentration in the bloodstream drops, the decrease in concentration—the actual negative feedback—is detected by pancreatic alpha cells, and insulin release stops. This prevents blood sugar levels from continuing to drop below the normal range.

Humans have a similar temperature regulation feedback system that works by promoting either heat loss or heat gain ( Figure 1.10 b ). When the brain’s temperature regulation center receives data from the sensors indicating that the body’s temperature exceeds its normal range, it stimulates a cluster of brain cells referred to as the “heat-loss center.” This stimulation has three major effects:

  • Blood vessels in the skin begin to dilate allowing more blood from the body core to flow to the surface of the skin allowing the heat to radiate into the environment.
  • As blood flow to the skin increases, sweat glands are activated to increase their output. As the sweat evaporates from the skin surface into the surrounding air, it takes heat with it.
  • The depth of respiration increases, and a person may breathe through an open mouth instead of through the nasal passageways. This further increases heat loss from the lungs.

In contrast, activation of the brain’s heat-gain center by exposure to cold reduces blood flow to the skin, and blood returning from the limbs is diverted into a network of deep veins. This arrangement traps heat closer to the body core and restricts heat loss. If heat loss is severe, the brain triggers an increase in random signals to skeletal muscles, causing them to contract and producing shivering. The muscle contractions of shivering release heat while using up ATP. The brain triggers the thyroid gland in the endocrine system to release thyroid hormone, which increases metabolic activity and heat production in cells throughout the body. The brain also signals the adrenal glands to release epinephrine (adrenaline), a hormone that causes the breakdown of glycogen into glucose, which can be used as an energy source. The breakdown of glycogen into glucose also results in increased metabolism and heat production.

Interactive Link

Water concentration in the body is critical for proper functioning. A person’s body retains very tight control on water levels without conscious control by the person. Watch this video to learn more about water concentration in the body. Which organ has primary control over the amount of water in the body?

Positive Feedback

Positive feedback intensifies a change in the body’s physiological condition rather than reversing it. A deviation from the normal range results in more change, and the system moves farther away from the normal range. Positive feedback in the body is normal only when there is a definite end point. Childbirth and the body’s response to blood loss are two examples of positive feedback loops that are normal but are activated only when needed.

Childbirth at full term is an example of a situation in which the maintenance of the existing body state is not desired. Enormous changes in a person’s body are required to expel the baby at the end of pregnancy. And the events of childbirth, once begun, must progress rapidly to a conclusion or the life of a person giving birth and the baby are at risk. The extreme muscular work of labor and delivery are the result of a positive feedback system ( Figure 1.11 ).

The first contractions of labor (the stimulus) push the baby toward the cervix (the lowest part of the uterus). The cervix contains stretch-sensitive nerve cells that monitor the degree of stretching (the sensors). These nerve cells send messages to the brain, which in turn causes the pituitary gland at the base of the brain to release the hormone oxytocin into the bloodstream. Oxytocin causes stronger contractions of the smooth muscles in of the uterus (the effectors), pushing the baby further down the birth canal. This causes even greater stretching of the cervix. The cycle of stretching, oxytocin release, and increasingly more forceful contractions stops only when the baby is born. At this point, the stretching of the cervix halts, stopping the release of oxytocin.

A second example of positive feedback centers on reversing extreme damage to the body. Following a penetrating wound, the most immediate threat is excessive blood loss. Less blood circulating means reduced blood pressure and reduced perfusion (penetration of blood) to the brain and other vital organs. If perfusion is severely reduced, vital organs will shut down and the person will die. The body responds to this potential catastrophe by releasing substances in the injured blood vessel wall that begin the process of blood clotting. As each step of clotting occurs, it stimulates the release of more clotting substances. This accelerates the processes of clotting and sealing off the damaged area. Clotting is contained in a local area based on the tightly controlled availability of clotting proteins. This is an adaptive, life-saving cascade of events.

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Feedback mechanism

Feedback mechanism definition

Feedback mechanism n., plural: feedback mechanisms [ˈfiːdˌbæk ˈmɛkəˌnɪzəm] Definition: a loop system wherein the system responds to a perturbation

Table of Contents

Feedback Mechanism Definition

What is a feedback mechanism? A feedback mechanism is a physiological regulation system in a living body that works to return the body to its normal internal state, or commonly known as homeostasis . In nature, feedback mechanisms can be found in a variety of environments and animal types. In a living system, the feedback mechanism takes the shape of a loop, which aids in maintaining homeostasis.

The feedback mechanism is triggered when the system undergoes a change that causes an output. The biochemical control system in living beings is made up of a variety of components, including chemicals, genes, and their regulatory connections.

When the activation of one component leads to the activation of another, the interaction between the components is said to be positive . If the activation of one component results in the inactivation of another, it is labeled as negative .

The term “feedback mechanism” was first used in cybernetics to characterize a control system’s ability to change its output in response to an input.

There are two types of feedback mechanisms; these are positive and negative feedback mechanisms.

positive Feedback mechanismand negative Feedback mechanism

Open and Closed-Loop Feedback Mechanisms

Homeostasis is often achieved in the body through the use of feedback loops that regulate the body’s internal circumstances. A feedback loop is a system that uses an identified receptor (sensor), the control center (integrator or comparator), effectors , and communication means to control the level of a variable.

Communication methods between the components of a feedback loop are required for it to function. This is usually accomplished through nerves or hormones, but in some circumstances, receptors and control centers are the same structures; therefore, these signaling mechanisms are not required in that phase of the loop.

The three common components of a feedback loop are the receptor (sensor), the control center (integrator or comparator), and effectors. A sensor, or commonly known as a receptor, detects and transmits a physiological value to the control center. The value is compared to the typical range by the control center. If the value deviates significantly from the setpoint, the control system stimulates an effector. A change is caused by an effector, which causes the situation to be reversed and the value to return to its normal range.

major components of homeostasis diagram

Feedback loops are commonly divided into two main types; opened-loop mechanism and closed-loop mechanism.

1. Positive feedback loops occur when a change in one direction is followed by another change in the same direction. A sensor, or commonly known as a receptor, detects and transmits a physiological value to the control center. Positive feedback loop examples can result in uncontrolled conditions since a change in an input generates reactions that cause further modifications in the same manner. Even if the components of a loop (receptor, control center, and effector) are not immediately recognizable, the term “positive feedback” is widely accepted when a variable has the ability to increase itself. Positive feedback is often damaging, however, there are a few occasions where it can help people function normally when used in moderation.

2. Negative feedback loops occur when a change in one direction produces a change in the other. For instance, a rise in a substance’s concentrations produces feedback, which causes the substance’s content to reduce. Negative feedback loops are mechanisms that seem to be naturally stable. When combined with the many stimuli that can affect a variable, negative feedback loops usually result in the value oscillating about the set point. Negative feedback loop examples include temperature and blood glucose level regulation.

READ: Sugar Homeostasis – Biology Tutorial

Feedback Mechanism Types

There are two types of feedback mechanisms, depending on whether the input changes or the physiological parameters deviate from their limits. Although the reactions of various processes to changes in variables varied, the loop’s components are similar.

I. Positive feedback mechanism

A positive feedback mechanism involves more stimulation or the acceleration of the process. Let’s find more about it below.

Positive feedback mechanism definition

What is a positive feedback mechanism? As the name implies, a positive feedback mechanism or positive feedback homeostasis is a pathway that, in response to an output variation, causes the output to vary even more in the direction of the initial deviation. A positive feedback system amplifies deviations and causes output state changes. Because it moves the body away from homeostasis, positive feedback mechanisms are significantly less common than negative feedback mechanisms. As long as the stimulus (example: the presence of the stimulant) is maintained, the positive feedback system gradually increases the reaction. A single component that activates its own activity or numerous components with direct and indirect interactions might make up a positive feedback loop. Positive feedback loops in biological processes are common in processes that need to happen fast and efficiently, as the output tends to magnify the stimulus’ influence. Positive mechanisms are rare in living systems such as the human body, but they can be found in the environment, such as in the instance of fruit ripening.

Positive Feedback Anatomy

Steps / Process / Mechanism of positive feedback mechanism

The process of a positive feedback loop consists of a control system that consists of various components, working in a circular pathway to stimulate or inhibit one another. The overall process can be described in terms of the components of the system.

  • Stimulation . The stimulation that initiates the positive feedback loop in order to complete a process is the initial step. Hormones released by various organs as a result of the start of a process are the most common stimuli in the human body.
  • Reception . The second step in the loop is the reception of stimuli via various sensors, which provide data to the control unit. These receptors are mostly nerves that transmit signals from the stimulus location to the control unit, which is the brain in humans.
  • Processing . The processing of information supplied to the control unit by the receptors is the next phase in the loop. The control unit tallies the data and displays an output if the stimulus is outside the typical range of the value.
  • Stimuli are activated even more . In order to induce an output in response to the stimulus, information from the brain is conveyed to the location of action via several nerves. The brain’s messages tend to activate the stimulus even more in the direction of deviation in the case of a positive feedback loop.

Positive feedback mechanism examples

  • Blood Clotting

When a wound creates bleeding, the body responds by clotting the blood and preventing blood loss through a positive feedback loop. The wounded blood vessel wall releases substances that start the clotting process. Platelets in the blood begin to adhere to the wounded area and produce substances that attract more platelets. As the platelets continue to accumulate, more chemicals are released, and more platelets are drawn to the clot location. The clotting process is accelerated by the positive feedback until the clot is large enough to halt the bleeding.

Blood Clotting Mechanism during Positive feedback system

In humans, a positive feedback mechanism is noticed during childbirth, which is caused by the baby pressing against the ovary wall. The brain receives the pushing feeling via several nerves, and the pituitary is stimulated to generate oxytocin in response. The oxytocin feedback loop is responsible for uterine muscle contractions, which cause the fetus to come closer to the cervix, thereby increasing the stimulation. Until the baby is born, the positive feedback loop continues.

The positive feedback loop that regulates childbirth is seen in the diagram above. When the infant’s head bumps up against the cervix, the procedure usually starts. Nerve impulses flow from the cervix to the hypothalamus in the brain as a result of this stimulation. The hypothalamus responds by sending the hormone oxytocin to the pituitary gland, which secretes it into the bloodstream to reach the uterus. Oxytocin causes uterine contractions to increase, pushing the baby closer to the cervix. As a result, the cervix begins to dilate in preparation for the baby’s passage. Increased levels of oxytocin, stronger uterine contractions, and wider cervix dilatation continue this cycle of positive feedback until the baby is pushed through the delivery canal and out of the body. The cervix is no longer stimulated to send nerve impulses to the brain at this stage, and the entire process comes to a halt.

Childbirth Mechanism during Positive feedback system

  • Menstrual cycle

The hormone estrogen is released by the ovaries at the start of the menstrual cycle. The estrogen operates as a positive feedback loop stimulation. The information is delivered to the brain, which prompts the hypothalamus to release gonadotrophin-releasing hormone and the pituitary to release luteinizing hormone. The control unit releases these hormones in response to the stimulation. These hormones then cause the ovaries to release estrogen, and the cycle repeats itself until the levels of these hormones are high enough to trigger the release of follicle-stimulating hormone. After the release of follicle-stimulating hormone, ovulation occurs, and the menstrual cycle begins. The rise in one element causes the output to move in the same direction until the task is done, which is an example of a positive feedback process.

Flow of Menstrual Cycle

  • Fruit Ripening

A tree or bush will suddenly ripen all of its fruit or vegetables without any visible warning, which is a startling event in nature. This is the first time a positive biological feedback loop has been observed in action. In the flash of an eye, an apple tree with many apples appears to go from unripe to ripe to overripe. This will start with the very first ripe apple. When fully ripe, it emits the gas ethylene (C2H4) via its skin. When apples are exposed to this gas, they ripen as well. They, too, generate ethylene once ripe, which continues to ripen the rest of the tree in a wave-like action. This feedback loop is commonly utilized in the fruit industry, with apples being exposed to ethylene gas to increase ripening.

Process of Fruit Ripening under Positive Feedback Mechanism

II. Negative feedback mechanism

Let’s take a look now at the negative feedback mechanism, particularly its steps (mechanisms) and examples.

Negative feedback mechanism definition

What is a negative feedback mechanism? A negative feedback mechanism, often known as negative feedback homeostasis, is a pathway that is triggered by a deviation in output and produces changes in output in the opposite direction of the initial deviation. After the control unit analyzes the magnitude of the deviation, the negative feedback mechanism drives the variable factors towards a stable state or homeostasis. Positive feedback loops are less prevalent than negative feedback loops because negative feedback loops tend to stabilize the system.

Negative feedback definition - schematic diagram

Steps in a negative feedback mechanism

The negative feedback system works in a similar way to the positive feedback loop in that it is activated by stimuli and eventually leads to modifications that tend to cancel out those impulses. The following is a summary of the overall procedure:

  • Stimulation . The development of stimuli as a result of physiological parameter deviations from the normal value is the initial stage in the negative feedback loop. Physiological parameters can deviate from the norm in either direction.
  • Reception . The control unit receives changes in physiological parameters through a variety of receptors located throughout the body. Nerves and other thermoreceptors are examples of common receptors engaged in stimulus transmission.
  • Processing . The brain serves as the loop’s control unit, determining whether a change in a physiological parameter necessitates loop activation or inhibition. The brain sends out signals to erase the alterations in different ways depending on the direction of departure.
  • Counteract on the stimulus . The control unit sends out signals at the end of the loop to cancel out the impacts that cause changes in physiological variables. Changes can take several forms and be directed at different sections of the body.

Examples of negative feedback

  • Regulating Temperature

A typical example of a negative feedback mechanism in the human body is the regulation of body temperature via endotherms. When the body’s temperature rises above normal, the brain sends signals to various organs, including the skin, to release heat in the form of sweat. These physiological actions cause the temperature to drop to the point where the negative feedback mechanism’s pathways are shut down. When the body temperature rises above its typical level in order to preserve homeostasis, a similar mechanism happens.

temperature regulation under negative feedback system - diagram

  • Regulating Blood Glucose Level

A negative feedback mechanism regulates the concentration of glucose in the blood. More glucose is absorbed in the gut and stored in the form of glycogen in the liver when blood glucose levels rise above the usual range. Insulin secretion from the pancreas is in charge of conversion and conservation. Insulin encourages glucose absorption in the muscles and liver. When blood glucose levels drop and more glucose is needed in the blood, insulin release is suppressed, which reduces blood glucose absorption.

Blood Glucose Level Regulation under Negative Feedback System

Positive vs. Negative Feedback Mechanism

Here is a summary of the differences between a positive feedback mechanism and a negative feedback mechanism.

Choose the best answer. 

Send Your Results (Optional)


  • Biga, L.,Dawson, S.,Harwell A.,Hopkins, R.,Kaufmann J.,LeMaster, M.,Matern, P.,Graham K., Quick, D. and Runyeon, J. (2021). Anatomy and Physiology. Retrieved from
  • Lumen Learning. (2019). Homeostasis from Boundless Anatomy and Physiology. Retrieved from
  • Kahn Academy (2019) Homeostasis. In Human Body Systems. Retrieved from
  • Torday, J. S. (2021). “Homeostasis as the Mechanism of Evolution.” Biology vol. 4,3 573-90.  doi:10.3390/biology4030573
  • Wakim, S. and Grewal, M. (2021). Homeostasis and Feedback. Retrieved from

© Content provided and moderated by Biology Online Editors.

Last updated on June 16th, 2022

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Positive Feedback Mechanism Research Paper

From a natural sciences perspective, a positive feedback mechanism is often referred as a feedback loop. Therefore, a certain action can be referred as a causative factor of a specific reaction. The reaction within the systems also leads to further action by a system as it reacts. In natural sciences, positive feedback mechanism is responsible for explosive reactions in certain elements of the ecosystem.

Sometimes, a positive feedback mechanism can cause a positive or a negative reaction. An example of a negative result from a positive feedback mechanism is global warming. This research paper introduces positive feedback in clouds as an accelerating factor in global warming.

Scientifically, high-level clouds have been found to have a net cooling effect (Pickering and Owen 36). From various researches, it has been found that a decrease in low-cloud cover is caused by warm atmospheric pressure. This warm atmospheric pressure is evidenced once the sea surface becomes warmer. However, a consequent rise in temperatures causes a negative feedback mechanism.

This means that more clouds will be formed as a result of warm sea temperatures. Eventually, the feedback mechanisms from the rising sea temperatures directly harness global warming. From a scientific point of view, atmospheric water is responsible for the upper-level clouds. In this respect, a positive feedback mechanism from clouds is harnessed by the net greenhouse effect from the upper-level clouds.

Another reaction as a result of the upper-level clouds is an increase of the global temperatures and carbon dioxide in the atmosphere (Woodward 39). Another fact that validates the assumption that clouds harness global warming is the ability to reflect incoming sunlight. High-level clouds have the ability to prevent a direct radiation of heat from the earth and water surfaces.

In this respect, an impact on the global energy equation is created. It is also important to note that high-level clouds releases heat, once they start to precipitate. From a scientific perspective, such heat is transferred into the atmosphere and affects the equation of the global heat. However, a feedback loop is evidenced once the clouds start to react to the prevailing climatic conditions.

Global warming can be described as the prevailing earth temperature. However, low-level clouds can alter this temperature. The interaction between low-level clouds and wind systems is considered to be a contributory factor towards global warming. Low-level clouds are thick and have the ability to trap heat than upper-level clouds. However, this depends on the control mechanism fostered by atmospheric winds.

There are two common types of high-level clouds. The first high-level cloud is known as cirrus. Cirrus is characterized by weak reflection and strong greenhouse. The second high-level cloud is altostratus. Altostratus is characterized by intermediate reflection and intermediate greenhouse.

From the above discussions, it is important to note the following cloud feedbacks. First, any cloud feedback can also cause certain changes in cloud properties. Secondly, cloud sensitivity and emissivity is affected by a change in water properties.

For example, there is a change in cloud properties once the ice changes to liquid and eventually to vapor. Thirdly, the reflectivity of clouds increases with an increase in condensation of water. All these factors are crucial elements in feedback mechanism that leads to an increase in global temperatures.

Works Cited

Pickering, T., Kevin and Owen, A., Lewis. An introduction to global environmental issues . Routledge, 1997. Print.

Woodward, I. F. Advances in ecological research V22: The ecological consequences of global climate change . Academic Press, 1992. Print.

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IvyPanda. (2019, July 3). Positive Feedback Mechanism.

"Positive Feedback Mechanism." IvyPanda , 3 July 2019,

IvyPanda . (2019) 'Positive Feedback Mechanism'. 3 July.

IvyPanda . 2019. "Positive Feedback Mechanism." July 3, 2019.

1. IvyPanda . "Positive Feedback Mechanism." July 3, 2019.


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Feedback Mechanisms Essays

The operation of negative and positive feedback mechanisms in maintaining homeostasis, popular essay topics.

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Biology on feedback mechanisms

Biology on feedback mechanisms

Biology essay on feedback mechanisms BY Deep Homeostasis is the ‘maintenance of equilibrium in a biological system by means of an automatic mechanism that counteracts influences tending towards disequilibrium’. Homeostasis mechanisms operate at all levels within living systems, including the molecular, cellular, and population levels. In humans homeostasis involves the constant monitoring and regulating of numerous factors including, oxygen and carbon dioxide levels, nutrient and hormone levels and inorganic and organic substances.

The concentrations of these substances in the body fluid remain unchanged, within limits, despite changes in the external environment. There are two general ways in which the body can respond to changes, these being positive and negative feedback. Negative Feedback Negative feedback causes the body to respond in such a way as to reverse the direction of a change and this tends to keep the internal environment at a constant, thus maintaining homeostasis. Sensors and receptors are what bring about a reaction to ensure conditions within the body remain favorable.

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Examples of negative feedback are as follows. Blood glucose levels The receptors of the pancreas are responsible for monitoring the blood glucose levels. The alpha-cells of the Islets of Lanterns release glucose when glucose levels are low. Glucose promote the conversion of glycogen into glucose; the lack of glucose can then be compensated for by the new supply of glucose brought about from glucose. The beta cells of the Islets of Lanterns release insulin when the levels of glucose in the blood is high.

Insulin promotes the conversion of glucose into logged and this can be stored in the liver for later use. Fight or flight In emergencies adrenaline is released by the body to override the homeostasis control of glucose levels and promotes the breakdown of glycogen into glucose. Adrenaline is secreted by the adrenal gland and its secretion leads to increased heart rate, breathing and metabolism. Once the emergency is over adrenaline levels drop, and the homeostasis controls are back in place. Water regulation Homeostasis control of water is also controlled by negative feedback.

Competitors palpable of detecting water concentration are situated on the hypothalamus. The hypothalamus sends chemical signals to the pituitary gland, which secretes anti- diuretic hormone ( nee DAD on reaching the kidney causes the tubules to become more or less permeable to water. If more water is required in the blood stream then high. Concentrations are released and this makes the tubules of the kidney more permeable to water. If less water is required in the blood stream then low. Concentrations are released and this makes the tubules of the kidney less permeable to water.

Temperature control Animals, which are capable of temperature control, are called homeostasis. The hypothalamus acts as the temperature control centre and detects any change in the temperature with thermometer. One of the most obvious physical responses to overheating is sweating; this cools the body by making more moisture on the skin available for evaporation. Other responses, perhaps less obvious, is vacillation of blood vessels. The blood vessels close to the skins surface become more dilated meaning there is a larger surface area for heat loss to the external environment.

If the body is cooled then sweating is reduced, body hair stands on end to trap air close to the skin, shivering occurs, the metabolic rate decreases and vasoconstriction of blood vessels occurs to prevent the further loss of heat. Positive Feedback Positive feedback mechanisms increase the departure from the normal even more. Positive feedback can have both beneficial and harmful consequences. Digestion Positive feedback occurs in some digestive enzymes such as pepsin. Pepsin is a protein-digesting enzyme that works in the stomach, the stomach does not secrete pepsin instead it secretes an inactive form, called possession.

When one possession molecule becomes activated, it helps to activate other possession molecules nearby, which in turn can activate others. In this way, the number of active pepsin molecules can rapidly increase, by using positive feedback. The advantage of pepsin being present in an inactive form is the prevention of self-digestion. Temperature One harmful effect of positive feedback is if an individuals temperature is very high the negative feedback system ceases to work and the increase temperature speeds p the body chemistry, which causes the temperature to rise even more, which in turn increases the temperature and so forth.

This is the vicious cycle of positive feedback and can only lead to death if not stopped Overall, homeostasis is a very important mechanism, and complex systems must have homeostasis to maintain stability to survive. The thickening of fur in winter, the darkening tot skin in sunlight, the seeking tot shade in neat, and the production tot more red blood cells at high altitude are all examples of adaptations animals can make in order to maintain homeostasis

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Review article, a review of feedback models and theories: descriptions, definitions, and conclusions.

  • 1 Queens College and the Graduate Center, City University of New York, New York, NY, United States
  • 2 Ikerbasque, Basque Foundation for Science and Universidad de Deusto, Bilbao, Spain

The positive effect of feedback on students’ performance and learning is no longer disputed. For this reason, scholars have been working on developing models and theories that explain how feedback works and which variables may contribute to student engagement with it. Our aim with this review was to describe the most prominent models and theories, identified using a systematic, three-step approach. We selected 14 publications and described definitions, models, their background, and specific underlying mechanisms of feedback processes. We concluded the review with eight main points reached from our analysis of the models. The goal of this paper is to inform the field and to help both scholars and educators to select appropriate models to frame their research and intervention development. In our complementary review ( Panadero and Lipnevich, 2021 ) we further analyzed and compared the fourteen models with the goal to classify and integrate shared elements into a new comprehensive model.

A Review of Feedback Models and Theories: Descriptions, Definitions, Empirical Evidence, and Conclusions

For many decades, researchers and practitioners alike have been examining how information presented to students about their performance on a task may affect their learning ( Black and Wiliam, 1998 ; Lipnevich and Smith, 2018 ). The term feedback was appropriated into instructional contexts from the industry ( Wiliam, 2018 ), and the original definitions referred to feedback as information from an output that was looped back into the system. Over the years, definitions and theories of feedback evolved, and scholars in the field continue to accumulate evidence attesting to feedback’s key role in student learning.

To provide the reader with a brief historical overview, we will start from the early 20th century and consider Thorndike’s Law of Effect to be at the very inception of feedback research ( Thorndike, 1927 ; Kluger and DeNisi, 1996 ). Skinner and behaviorism with positive and negative reinforcements and punishments can also be considered as precursors to what the field currently views as instructional feedback ( Wiliam, 2018 ). Further, the value of formative assessment, as it is now known, was first explicated by Benjamin Bloom in his seminal 1968 article, in which he described the benefits of offering students regular feedback on their learning through classroom formative assessments. Bloom described specific strategies teachers could use to implement formative assessments as part of regular classroom instruction, both to improve student learning, to reduce gaps in the achievement of different subgroups of students, and to help teachers to adjust their instruction ( Bloom, 1971 ). Hence, extending ideas of Scriven (1967) who proposed the dichotomy of formative and summative evaluation, Bloom deserves the credit for introducing the concept of formative assessment ( Guskey, 2018 ).

Importantly, the arrival of cognitive and constructivist theories started to change the general approach to feedback ( Panadero et al., 2018 ), with researchers moving from a monolithic idea of feedback as “it is done to the students to change their behavior” to “it should give information to the students to process and construct knowledge.” So, in the late seventies and most of the eighties, there was a push to investigate the type of feedback that would be most beneficial to students’ learning. Even though the first publications were heavily influenced by behaviorism (e.g. Kulhavy, 1977 ), by the end of the 80s there was a pedagogical push to turn feedback into opportunities for learning (e.g., Sadler, 1989 ).

It was in the nineties when the “new” learning theories gained major traction in psychological and educational literature on feedback. Around that time, cognitive models of feedback were developed such as the ones by Butler and Winne (1995) and Kluger and DeNisi (1996) . These theories focused on cognitive processes that were central to the processing of feedback, and the mechanisms, through which feedback affected cognitive processes and students’ subsequent behavior, were also explored. The key point in the development of the field, however, was Black and Wiliam (1998) publication of their thematic review that expedited and reshaped the field of formative assessment. The main message of their review still stands: across instructional settings assessment should be used to provide information to both the learner and the teacher (or other instructional agent) about how to improve learning and teaching, with feedback being the main vehicle to achieve it. This idea may seem simple but it is neither fully implemented nor sufficiently integrated within the summative functions of assessment ( Panadero et al., 2018 ). After two decades following Black and Wiliam’s (1998) publication, a lot of progress has been made, with classroom assessment literature being fused with learning theories such as self-regulated learning ( Panadero et al., 2018 ), cognitive load ( Sweller et al., 1998 ), and control-value theory of achievement emotions ( Goetz et al., 2018 ; Pekrun, 2007). In the current review we do not intend to delve into these voluminous strands of research, but we encourage the reader to conduct future exploration to expand on these.

As the field of formative assessment and feedback was evolving, scholars were devising feedback models to describe processes and mechanisms of feedback. In general, these models have gotten both more comprehensive and focused, depicting more specific cognitive processes ( Narciss and Huth, 2004 ), student responses to feedback ( Lipnevich et al., 2016 ), the context ( Evans, 2013 ), and pedagogical aspects of feedback ( Carless and Boud, 2018 ; Hattie and Timperley, 2007 ; Nicol and Macfarlane-Dick, 2006 ). In contrast to the earlier conception where “feedback was done” to the student, in the most current models the learner is not only at the center of the feedback process, but is now an active agent that does not only process feedback, but responds to it, can generate it, and acquires feedback expertise to engage with it in more advanced ways ( Shute, 2008 ; Stobart, 2018). Additionally, a lot is known about how to involve students in the creation of feedback either as self-feedback ( Andrade, 2018 ; Boud, 2000 ) or peer feedback ( Panadero et al., 2018 ; van Zundert et al., 2010 ), and what key elements influence students’ use of feedback ( Winstone et al., 2017 ; Jonsson and Panadero, 2018 ). Due to the ongoing proliferation of models, theories, and strands of research, now may be a critical moment to examine the most influential models and theories currently utilized by researchers and educators. It will help us to consider how the models have evolved and what the main developments in our conceptions of feedback are after decades of research. In the current review we did just that. Through a rigorous multi-step process we selected, described, and compared 14 prominent models and theories currently discussed and utilized by researchers in the field. Our aim was to provide a guide for researchers for selecting the most suitable model for framing their studies, as well as to provide the newcomers to the field with a starting point to the key theoretical approaches and descriptions of feedback mechanisms. We worked on two reviews simultaneously. In the current one we focus on the description of the fourteen included models, drawing conclusions about definitions and supporting evidence. In the second review ( Panadero and Lipnevich, 2021 ) we compare typologies of feedback and discuss elements of the included models, proposing an integrative model of feedback elements: the MISCA model (Message, Implementation, Students, Context, and Agents). To get the complete picture we strongly recommend that the reader engages with both reviews, starting with the present one.

Selection of Relevant Publications

To select models for review we searched PsycINFO, ERIC, and Google Scholar databases. Unfortunately, this approach was not fruitful as the combination of “feedback + model” and “feedback + model + education” in either title, abstract, or document offered a mix of results from different disciplines in the first combination and focus on variables other than feedback in the second (e.g. self-assessment, peer assessment). Therefore, we established a three-way search method. First, we used our own reference libraries to locate the feedback models we had previously identified. This exercise resulted in ten models. Second, we consulted colleagues to get their opinion on models that they thought had an impact in feedback literature. We sent emails to 38 colleagues asking the following questions: what were the feedback models they knew of, have they developed any model themselves, and would they be available for further consultation. Our instructions intentionally did not include either a strict definition or operationalization of what constituted a feedback model; this was done purposefully to allow for individual interpretation. To select the 38 colleagues, we first contacted all the first authors of the models we had already identified, the authors of the two existing reviews, and internationally reputed feedback researchers. We obtained 29 responses, but two of them did not provide enough information and were rejected. Through this procedure we identified 65 models, including the ten models that were already selected from our reference libraries. Finally, we used two previous feedback model reviews ( Mory, 2004 ; Shute, 2008 ) to identify models that might not have been included in the two previous steps, selecting two more. Importantly, one of the models included in Mory (2004) that had not been identified earlier, had a very limited impact in the field. Clariana (1999) had only been cited 39 times since 1999, thus this model was no longer considered for inclusion. The resulting number of feedback models was 67.

Acknowledging that so many models could not have been meaningfully represented in our review, we tasked ourselves with further reduction of the database. We used a combination of two inclusion criteria. First, we selected all but one model that had been included in the two previous feedback reviews ( Mory, 2004 ; Shute, 2008 ). Application of this criterion produced a total of six models. Second, in addition to the previous criterion, we established a minimum threshold of three votes by the consulted colleagues to add new models, which resulted in ten models. After deleting duplicates, a total of 14 models were included in this review ( see Figure 1 ). The final list is: Ramaprasad (1983) ; Kulhavy and Stock (1989) ; Sadler (1989) ; Bangert-Drowns et al. (1991) ; Butler and Winne (1995) ; Kluger and DeNisi (1996) ; Tunstall and Gipps (1996) ; Mason and Bruning (2001) ; Narciss (2044, 2008) ; Nicol and Macfarlane-Dick (2006) ; Hattie and Timperley (2007) ; Evans (2013) ; Lipnevich et al. (2016) ; and Carless and Boud (2018) .

FIGURE 1 . Search and selection criteria process.

The fourteen selected publications are different in their nature. Some of them are meta-analytic reviews, others are narrative reviews, in addition to several empirical papers. Importantly, two of the papers present theoretical work without any empirical evidence and without describing any links among variables related to feedback ( Ramaprasad, 1983 ; Sadler, 1989 ). Hence, we would like to alert the reader that some of the articles discussed in this review do not present anything that would fit with the definition of a model. Traditionally, a model explains existing relations among components and is typically pictorially represented ( Gall, Borg, and Gall, 1996 ). Some of the manuscripts that we included neither described relations nor offered graphical representations of feedback mechanisms. Our decision in favor of inclusion was based exclusively on the criteria that we described above. For the purposes of simplicity, we will refer to the fourteen scholarly contributions as models, but will explain the type of each contribution in the upcoming sections.

Extracting, Coding, Analyzing Data and Consulting Models’ Authors

The data were extracted from the included publications for the following categories: 1 ) Reference, 2 ) Definition of feedback, 3 ) Theoretical framework, 4 ) Citations of previous models, 5 ) Areas of feedback covered, 6 ) Model description, 7 ) Empirical evidence supporting the model, 8 ) Pictorial representation, 9 ) References to formative assessment, 10 ) Notes first author, 11 ) Notes second author, and 12 ) Summaries of the model from other publications. We coded the sources in a descriptive manner for most categories except for the definition of feedback category, where we copied them directly from the original source. Both authors coded the articles and there was a total agreement in the assignment of separate components to the aforementioned categories.

Finally, we contacted authors of all the included publications except for one (the two authors were in retirement and not accessible). Twelve authors replied (two from the same publication) but only seven agreed and scheduled an interview (Boud, Carless, Kluger, Narciss, Ramaprasad, Sadler, and Winne). The interviews were recorded and helped us to better understand the models. Also, to ensure adequate interpretation of the models we shared our descriptions with the authors, which they returned with their approval and, in some cases, suggestions for revisions.

We would like to alert the reader that the Method section is shared with the second part of this review ( Panadero and Lipnevich, 2021 ).

Models and Theories: Descriptions

In this section we present descriptions of the models and theories included in the current systematic review. We do not claim to provide a comprehensive overview of each model or theory. Rather, we give the reader a flavor of what they represent. Due to the fact that most summaries were checked by the corresponding authors, we believe that our account is fairly accurate and unbiased, and in this section we try to withhold our own interpretation or opinions about the utility of each model. We will follow this section with a detailed analysis of definitions, summary of existing empirical evidence, as well as conclusions and recommendations. We would also like to direct the reader to the second review, wherein we integrate the fourteen models into a comprehensive taxonomy ( Panadero and Lipnevich, 2021 ).

Ramaprasad (1983) : Clarifying the Purpose of Feedback From Outside the Field

Ramaprasad’s work has been crucial for the current conceptualization of feedback in educational settings with his paper on the definition of feedback being cited over 1500 times, more frequently from educational psychology and education.

Theoretical Framework

Ramaprasad’s seminal article was published in Behavioral Science (now called “Systems Research and Behavioral Science”), which is the official journal of the Society for General Systems Research (SGSR/ISSS). The paper does not contain a single educational source, drawing upon management, behavioral, I/O, and social psychology.


Ramaprasad’s article does not describe a model in its traditional sense. Rather, the author presents a theoretical overview of feedback, focusing on mechanisms, valence, and consequences of feedback for subsequent performance. His work is influential in education because of his definition of feedback, but it contains other concepts that have been somewhat overlooked by educators. We will concentrate on three main aspects of the paper.

1) The definition. Ramaprasad’s definition of feedback is very well-known and is referenced extensively. According to the author: “Feedback is information about the gap between the actual level and the reference level of a system parameter which is used to alter the gap in some way” (p. 4). There are three key points that need to be emphasized: 1 ) the focus of feedback may be any system parameter, 2 ) the necessary conditions for feedback are the existence of data on the reference level of the parameter, data on the actual level of the parameter, and a mechanism for comparing the two to generate information about the gap between the two levels, and 3 ) the information on the gap between the actual level and the reference level is considered to be feedback only when it is used to alter the gap.

2) Three key ideas. Due to the fact that Ramaprasad comes from a different academic field, it is important to clarify the main ideas that are present in his definition. The first one concerns the reference level and is a representation of a position that can be used for assessing a product or performance. According to Ramaprasad, it can vary along two continua bounded by explicit and implicit, and quantitative and qualitative. “When reference levels are implicit and/or qualitative, comparison and consequent feedback is rendered difficult. Despite the above fact, implicit and qualitative reference levels are extremely important in management and cannot be ignored.” (p. 6). In educational settings this would refer to the importance of explicit criteria and standards.

The second key idea is the comparison of reference and actual levels. Here, Ramaprasad states that “irrespective of the unit performing the comparison, a basic and obvious requirement is that the unit should have data on the reference level and the actual level of the system parameter it is comparing. In the absence of either comparison it is impossible” (p. 7). This aspect emphasizes the critical role of the two key questions that Hattie and Timperley (2007) discussed in their work: “where am I going?” and “where am I supposed to be?”

Further, using information about the gap to alter the gap is an idea that is critical in instructional contexts. Ramaprasad maintains that only if the information about the gap is used (and the decision can be to not do anything about it) it can be considered feedback. If it is just information that gets stored but nothing happens with it, then it is just information, and, hence, cannot be considered feedback. It is important to remember that Ramaprasad viewed feedback through the lens of the systems and management perspective. From the learning sciences perspective, if information gets stored in the long term memory, a change has already occurred and feedback has had an effect, even if no external changes had been observed. On the other hand, if the process and the outcome of storing the information is interpreted as ignoring the feedback, then it has not had any effect.

3) Positive and negative feedback. Ramaprasad’s description of positive and negative feedback differs from a more traditional interpretation in terms of its affective valence. From Ramaprasad’s perspective: “if the action triggered by feedback widens the gap between the reference and the actual levels of the system called positive feedback; ...if the action reduces the gap between the two negative” (p. 9). Ramaprasad did acknowledge alternative interpretations of positive and negative feedback, with the valence being determined by the emotions triggered in the feedback receiver (e.g., positive for enjoyment and pride, negative for disappointment and anxiety), as well as within the parameters of positive and negative reinforcement from Skinner. In the context of education, we usually describe feedback as being positive or negative depending on the emotions it elicits.

Kulhavy and Stock (1989) : A Model From Information Processing

Kulhavy and Stock’s (1989) seminal work introduced the idea of multiple feedback cycles, considered types, form, and content of feedback, and explicitly equated feedback with general information. The authors consider feedback from the information processing perspective, juxtaposing it with earlier studies that used the behaviorist approach as their foundation.

According to the authors, many of the early studies conducted within the behaviorist perspective viewed feedback as corrective information that strengthened correct responses through reinforcement, and weakened incorrect responses through punishment. This somewhat mechanistic perspective stressed the importance of minimizing errors, but no description of error correction, nor the means for it were presented. Feedback following an instructional response was viewed as fitting the sequence of events of the Thorndike’s Law of Effect ( Thorndike, 1927 , 1997 ), and was construed as the driving force of human learning. The fact that a learner 1 ) received a task, 2 ) produced a response, and 3 ) received feedback indicating whether the answer was correct or not (punishment or reinforcement) provided a superficial parallel to the familiar sequence of the 1 ) stimulus, 2 ) response, and 3 ) reinforcement. However, as Kulhavy and Stock (1989) noted, people involved in instructional tasks were not under the powerful stimulus control found in the laboratory, which, along with constantly changing stimuli and responses, carried very little resemblance to the typical operant setting. Hence, presentation of corrective feedback following an incorrect response may carry no effect on the learner. Thus, errors that students made were ignored, and instructors’ attention was directed to students’ correct responding only.

From the information-processing perspective, on the contrary, errors were of central importance, as this approach described the exact mechanisms through which external feedback helped to correct mistakes in the products of a learning activity. Both direct and mediated feedback could be distinguished according to its content on two vectors of verification and elaboration. Verification represents students’ evaluation of whether a particular feedback message matches their response, whereas elaboration can be classified according to load, form, and type of information . Load is represented by the amount of information provided in the feedback message that can range from a letter grade to a detailed narrative account of students’ performance. Type of information is reflected in the dichotomy of process-related, or descriptive feedback, and outcome-related, or evaluative feedback. Form is defined as changes in stimulus structure between instruction and the feedback message that a learner receives.

Although Kulhavy and Stock (1989) did not provide a clear definition of feedback, they adhered to the one offered by Kulhavy (1977) . Kulhavy (1977) defined feedback as “any of the numerous procedures that are used to tell a learner if an instructional response is right or wrong” ( Kulhavy, 1977 , p. 211). In its simple form, this would imply simply indicating whether a learner’s response to an instructional prompt was correct or not whereas more complex forms of feedback included messages that provided the learner with additional information on what needed improvement (“correctional review”). Interestingly, the authors also noted that with increasing complexity, feedback would inevitably become indistinguishable from instruction.

The central idea of Kulhavy and Stock’s model is that of response certitude ( Figure 2 ). The authors defined it as a degree to which the learner expected his or her response to be a correct one. This central tenet of the model is only indirectly related to the classification of the response as right or wrong, so the model is not limited to a basic error analysis. In other words, it seeks to make predictions about student performance. In addition to response certitude, the authors discuss response durability, which is the likelihood that an instructional response will be available for the learner’s use at some later point in time. Thus, the key underlying premise of Kulhavy and Stock (1989) model is that certitude estimates and response durability are positively related, so in situations where feedback is unavailable, the magnitude of certitude increases, and the probability of selecting the same response (often incorrect) increases also.

FIGURE 2 . Kulhavy and Stock (1989) feedback components.

The model itself comprises three cycles, each of which includes an iteration loop ( Figure 3 ). The first cycle depicts instructional task demands, the second represents feedback message, and the third is a criterion task demand. In the first cycle the perceived task demand is compared to the set of existing cognitive referents available to the learner. In the second cycle, the feedback message is compared to the cognitive referents retained from the initial cycle. These two cycles are followed by cycle three, where the perceived stimulus is again the original task demand that is compared to the cognitive referents which have been modified by the feedback message.

FIGURE 3 . Adapted from Kulhavy and Stock (1989) three-cycle feedback model.

Kulhavy and Stock (1989) also suggested that an introduction of a small delay between responses and feedback helped to eliminate proactive interference and thus increased the impact of error-correcting feedback.

Sadler (1989) : Seminal Work for Formative Assessment

Sadler’s work has been foundational for the conceptualization of feedback, assessment criteria, and assessment philosophy. His seminal paper outlines a theory of formative assessment. Sadler does not describe an actual model of feedback but focuses on feedback’s formative features. However, a significant portion of his paper is dedicated to feedback.

Before developing and extending his own definition of feedback, Sadler referred to earlier writings on formative assessment and feedback, specifically those by Kulhavy (1977) , Kulik and Kulik (1988) , and the much earlier work, Thorndike’s Law of Effect ( Thorndike, 1913 ; Thorndike, 1927 ). These authors equated feedback with learners’ knowledge of results, whereas Sadler adopted a much broader view. His theoretical exploration built upon a definition of feedback offered by Ramaprasad (1983) , which referred to feedback as it is found in many different contexts – not specifically in education. Sadler brought Ramaprasad’s definition into education and extended it into such areas as writing assessment or qualitative judgment, which are characterized by multidimensional criteria that cannot be evaluated as correct or incorrect (Sadler and Ramaprasad, October 2019, personal communication).

Sadler referred to feedback as: “a key element in formative assessment… usually defined in terms of information about how successfully something has been or is being done.” (p. 120). Sadler applied Ramaprasad’s conceptualization of feedback to situations, in which the teacher provides feedback, and learners are the main actors who have to understand the feedback in order to improve their work. If information from the teacher is too complex, if students do not possess knowledge or opportunity to use the information, then this feedback is no more than “dangling data” and, hence, highly ineffective. For the feedback to be effective students should be familiar with assessment criteria, should be able to monitor the quality of their work, and should have a wide arsenal of strategies from which they can draw to improve their work. Sadler emphasized the importance of continuous self-assessment, which he described as judgments of quality of one’s own work at any given time.

Sadler also discussed three conditions that had to be satisfied for the feedback to be effective. According to Sadler, the first condition had to do with a standard, toward which students aimed as they worked on a task at hand. The second condition required students to compare their actual levels of performance with the standard, and the third emphasized student engagement in actions that eventually closed the gap. Sadler stressed that all three conditions had to be satisfied for any feedback to be effective.

Sadler made an interesting distinction that we did not encounter in any other writings of scholars included in the current review. He juxtaposed feedback and self-monitoring, with the former defined as information that came from an external source, and the latter being self-generated by a learner. He further suggested that one of the key instructional goals was to move learners away from feedback and have them fully rely on self-monitoring.

Sadler also proposed that it was difficult to evaluate students’ work on a dichotomous scale of correct or incorrect. Effective evaluations resulted from “direct qualitative human judgments.” Consequently, Sadler broadened the definition of feedback as information about the quality of performance to include: “…knowledge of the standard or goal, skills in making multicriterion comparisons, and the development of ways and means for reducing the discrepancy between what is produced and what is aimed for.” (p. 142).

Sadler discussed a variety of tools and approaches that should help students with effective self-monitoring. He addressed such topics as peer assessment, the use of exemplars, continuous assessment, Bloom’s taxonomy, grading on the curve, and curriculum structure. In his account, Sadler’s primary focus was on the need to help students to develop effective evaluative skills, so they could transition from their complete reliance on teacher-delivered evaluations to students’ own self-monitoring.

Bangert-Drowns et al. (1991) : The First Attempt to Meta-Analyzing (CAP) the Effects of Feedback

Bangert-Drowns et al. (1991) are well-known for a series of a meta-analyses and empirical reviews that they published throughout the 80s and mid 90s on a range of topics (e.g. coaching aptitude tests, computer-based education, frequent classroom testing). This particular meta-analysis to our knowledge, was the first to aggregate results of empirical studies on the effects of feedback on meaningful educational outcomes.

This meta-analysis is grounded in early feedback research, especially research conducted by Kulhavy and Stock (1989) . The paper also draws upon behaviorist ( Thorndike, 1913 ) and cognitive psychology ( Shuell, 1986 ) ideas on how feedback may affect learning.

In their paper the authors did not present a clear definition of feedback. They discussed previous research on feedback, going back to the first decade of the 19th century, without ever operationalizing feedback. Nevertheless, they presented a typology of feedback that included three main categories that characterized feedback:

1. Intentionality: feedback can be intentional, that is, delivered via interpersonal action or through intervening agents such as computer, or informal, which is more incidental in nature.

2. Target: feedback can influence affective dimensions, for example, motivation, it can scaffold self-regulated learning, and it can signal whether the student has correctly applied concepts, procedures, and retrieved the correct information.

3. Content: characterized by load , which is the total amount of information given in the feedback message, ranging from simple yes/no statements to extended explanations; form , defined as the structural similarity between information as presented in feedback compared to the instructional presentation; and type of information indicating whether feedback restated information from the original task, referred to information given elsewhere in the instruction, or provided new information.

Additionally, the authors presented a five-stage model describing the feedback process. The five stages were: 1 ) Learner’s initial state defined by four elements of interest, goal orientation, self-efficacy, and prior knowledge; 2 ) a question (or task) that activated the search and retrieval strategies, 3 ) the learner’s response to the question, 4 ) followed by the learner’s evaluation of the response and its comparison to the information offered in the feedback, and 5 ) learners’ subsequent adjustments from this evaluation to their knowledge, self-efficacy, and interest.

Finally, in their meta-analytic review the authors used additional moderators that included the “type of feedback” and the “timing of feedback.” Regarding the former, the researchers found that just indicating the correctness of responses was less powerful than providing an explanation. In regards to the latter, the authors found superior effects of delayed feedback. All of the studies included in this meta-analysis had been published before 1990.

Butler and Winne (1995) : Answers From Self-Regulated Learning Theory (SRL)

Butler and Winne (1995) model served a twofold purpose: it explained differential effects of feedback at the cognitive processing level and, at the same time, it represented one of the widely cited SRL models (for a comparison of SRL models see Panadero, 2017 ).

In their model, Butler and Winne (1995) attempted to explain how internal and external feedback influenced students’ learning. Their main theoretical lens was information processing, with their focus expanding to include motivational factors in later years ( Winne and Hadwin, 2008 ). Most of the references the authors used came from the domain of cognitive psychology (e.g. Balzer et al., 1989 ; Borkowski, 1992 ), SRL theory (e.g. Zimmerman, 1989 ), and attribution theory (e.g. Schunk, 1982 ).

The authors did not include an explicit definition of feedback, but in the section “Four views on feedback…” the authors provided a broad description of the processes related to feedback. The model depicted mental processes that students activated when self-regulating during their execution of a task. Figure 4 is the original version, whereas Figure 5 represents the modified version of the model. Our subsequent discussion is based on the more current model depicted in Figure 5 .

FIGURE 4 . First version of Winne’s SRL model (extracted from Winne, 1996 ).

FIGURE 5 . Modified version of Winne’s SRL model (extracted from Panadero et al., 2019 ).

According to this model, there is a number of antecedent variables that affect students’ later performance. These are task conditions that are processed through the learner’s cognitive conditions such as “domain knowledge” or “knowledge of study tactics and strategies” along with motivational conditions. When students begin their performance, there are four different phases that take place: 1 ) defining the task, 2 ) establishing goals and plans, 3 ) applying study tactics and strategies by searching, monitoring, assembling, rehearsing, and translating (SMART) and 4 ) adapting. Throughout the whole process students monitor and control their progress. When they receive an external evaluation and feedback that comes with it, their initial conditions get updated.

Butler and Winne (1995) , drawing upon Carver and Scheier’s (1990) ideas, viewed feedback as an internal source, with students undergoing loops of feedback through monitoring and control. The loop related their interpretation about the product of monitoring (e.g. successful/unsuccessful, slow/fast, satisfying/disappointing) to the learner’s decision to maintain or adapt their thinking or actions in light of the product of monitoring. Feedback was then the information learners perceived about aspects of thought (e.g., accuracy of beliefs or calibration) and performance (e.g., comparison to a standard or a norm). Feedback could come from external sources when additional information was provided by an external agent. At any given point of the performance, students generated internal feedback comparing the profile of their current state to their ideal profile of the goal. According to Butler and Winne (1995) it happened via self-assessment by comparing different features of a task and through learners’ active engagement with the task. With both internal and external feedback learners could undergo small-scale adaptations represented by basic modifications in their current performance, or large-scale adaptations that would subsequently affect their future performance on the task. This model, with some modifications, has been used in at least two other publications to anchor self-regulated learning and different assessment practices ( Nicol and MacFarlane-Dick, 2006 ; Panadero et al., 2018 ).

Kluger and DeNisi (1996) : An Ambitious Meta-Analysis Exploring Moderators of Feedback Interventions

Kluger and DeNisi (1996) paper is regarded as a seminal piece in feedback research literature. It is frequently referenced to support a somewhat counter-intuitive finding – the fact that in 1/3 of cases feedback may have negative effect on performance. The authors did a thorough job reviewing 3000 publications on feedback to reduce it to the final set of 131. The number of considered moderators is also quite impressive and supersedes those examined in other meta-analyses. The paper quantitatively synthesized research into feedback interventions and proposed a new Feedback Intervention Theory with the goal to integrate multiple theoretical perspectives.

This review is one of the most thorough syntheses of the psychological feedback literature. Kluger and DeNisi (1996) carefully summarized work into knowledge of results and knowledge of performance, and stressed the key relevance of Thorndike’s Law of Effect, cybernetics ( Annett, 1969 ), goal setting theory ( Locke and Latham, 1990 ), social cognitive theory ( Bandura, 1991 ), learned helplessness ( Mikulincer, 1994 ), and multiple-cue probability learning paradigm ( Balzer, Doherty, and O’Connor, 1989 ). Interestingly, this review did not reference Kulhavy and Stock (1989) , Bangert-Drowns et al. (1991) , or Sadler (1989) Ramaprasad — all of whom came from the field of educational assessment. It shows that until recently, feedback research in psychology and education was conducted largely in parallel, despite a range of commonly shared ideas. For example, Kulhavy and Stock (1989) idea of “response certitude” has a clear overlap with “discrepancy,” which is a foundational idea of the Feedback Intervention Theory model.

Kluger and DeNisi (1996) did not offer a clear definition of feedback but did define feedback intervention as: “…actions taken by (an) external agent (s) to provide information regarding some aspect(s) of one’s task performance.” (p. 225). The model has several pictorial representations ( see Figure 6 ). Additional diagrams represented the effects of feedback intervention for more specific processes, such as attention and task-motivation processes.

FIGURE 6 . A schematic overview of feedback intervention theory by Kluger and DeNisi (1996) .

Their model focused on feedback that provided information about the discrepancy between the individual’s current level of performance and goals or standards. Kluger and DeNisi further proposed that individuals may have varying goals activated at the same time. For example, they could be comparing their performance to an external standard, to their own prior performance, performance of other reference groups, and their ideal goals. These discrepancies may be averaged or summed into an overall evaluation of feedback. The Feedback Intervention Theory also suggested that when the discrepancy between current and desired performance was established, the individual could: 1 ) choose to work harder, 2 ) lower the standard, 3 ) reject the feedback altogether, or 4 ) abandon their efforts to achieve the standard. Option selection depends upon how committed individuals are to the goal, whether the goal is clear, and how likely success will be if more effort is applied.

In the Feedback Intervention Theory, when an individual received feedback indicating that a goal had not been met, individuals’ attention could be focused on one of three levels: 1 ) the details of how to do the task, 2 ) the task as a whole, and 3 ) processes that the individual engages in doing the task (meta-task processes). Kluger and DeNisi (1996) argued that individuals typically processed feedback at the task level, but that the feedback could influence the level at which the feedback was received and attended to. Similarly to many educational researchers, Kluger and DeNisi claimed that if a task was clear to the individual, receiving feedback containing too many task-specific details could be detrimental to performance.

The impact of Kluger and DeNisi’s work could be seen in much of the theoretical work that followed it (e.g., Hattie and Timperley, (2007) model). The Feedback Intervention Theory model generally focused on feedback that communicated to individuals whether they were doing a particular task at an expected or desired level, thus assuming that individuals knew how to do the task. This is not often the case in educational settings, wherein the development of new skills is often the main goal. The authors acknowledged limitations of the model in that its breadth made the theory hardly falsifiable.

The main and frequently cited finding of their meta-analysis was that feedback interventions increased individuals’ performance by 0.4 standard deviations. At the same time, there was a great deal of variability of results, with 1/3 of studies showing a negative influence on performance. Based on the results of their meta-analysis and close examination of moderators, Kluger and DeNisi demonstrated the utility of the Feedback Intervention Theory.

Tunstall and Gipps (1996) : A Typology for Elementary School Students

This publication was included based on three votes, cast by the consulted experts. Unlike other models, its contribution may be more limited in scope. First, it is not a model that describes links and interactions but a typology. The authors’ primary goal was to categorize different types of feedback that they observed in classrooms. Second, its theoretical framework and links to the literature are rather limited. And third, the typology was developed based on a sample of 6 and 7 year old students, hence, it is only applicable to early primary grades. We decided to keep this publication in the current review to be consistent with our specified inclusion criteria. It may be beneficial for those researchers who are interested in the downward extension of feedback studies, as primary school samples are generally scarce in the field of instructional feedback and assessment research ( Lipnevich and Smith, 2018 ).

The publication has a short theoretical introduction, with the authors referencing studies in early childhood education (e.g. Bennett and Kell, 1989 ), feedback (e.g. Brophy, 1981 ), and educational psychology (e.g. Dweck, 1986 ). They authors do not frame their study within any specific theoretical approach.

Despite the fact that this paper described a typology of feedback, the authors did not present a definition of feedback. Tunstall and Gipps (1996) conducted their study in six schools and selected 49 students for detailed examination. Based on document analyses, recordings of dialogues and observations, they derived a typology that included five different types of feedback and their valence ( Figure 7 ). These types were organized around two more general aims: Feedback and socialization (i.e. Type S) and Feedback in relationship to assessment. The former included four types that differed according their purpose: classroom/individual management, performance orientation, mastery orientation, and learning orientation. Feedback in relationship to assessment was differentiated into feedback that is rewarding, approving, specifying attainment, and constructing achievement. The authors also included dimensions of positive and negative feedback, as well as achievement and improvement feedback. The typology appears to be very descriptive with multiple overlapping categories.

FIGURE 7 . Feedback typology by Tunstall and Gipps (1996) .

Mason and Bruning (2001) : Considering Individual Differences in a Model for Computer Based Instruction

Mason and Bruning’s (2001) contribution is unique in that it is the first one introducing the role of individual differences in the context of the Computer Based Instruction. The authors present a framework for decision making about feedback options in computerized instruction.

The authors review literature on feedback in traditional instructional contexts, summarize research into computer based education (e.g. Cohen, 1985 ), and discuss publications combining studies of both regular and computer-based instructional settings (e.g. Mory, 1994 ).

The authors provided the following definition of feedback: “In general terms, feedback is any message generated in response to a learner’s action.” (p. 3). They started off by describing eight types of feedback that came from the literature, differentiated based on two main vectors of verification and elaboration: 1 ) No Feedback, which presents only a performance score; 2 ) Knowledge of response, which communicates whether the answer was correct or incorrect; 3 ) Answer until correct, which provides verification but no elaboration; 4 ) Knowledge of correct response, which provides verification and knowledge of correct answer; 5 ) Topic contingent, which delivers verification and elaboration regarding the topic; 6 ) Response contingent, which includes both verification and item specific elaboration; 7 ) Bug related, which presents verification and addresses errors and 8 ) Attribute isolation, which focuses learner on key components. In this categorization, the authors considered the instructional context, and some of these types are more common in computer based instruction than others (e.g., “answer until correct”).

This paper’s main contribution is the model that differentiates among types of feedback based on learners’ characteristics, prior knowledge, and the timing of feedback. The pictorial representation includes a flowchart starting at the student achievement level and going down to the complexity of the task, and the type of feedback ( Figure 8 ). The model offers clear guidelines on how to deliver better feedback based on previous empirical research, while considering a range of key variables, such as student level of achievement and prior knowledge, as well as the timing of feedback. Interestingly, “attitude towards feedback” and “learner control,” two additional individual student characteristics that the authors explored in their introduction, were not incorporated into the model.

FIGURE 8 . Mason and Bruning (2001) model.

Narciss and Huth (2004 , 2008 ): An Ambitious Model Created for Computer Supported Learning

This model is probably the most ambitious out of those described in this review. It explores both the reception and processing of feedback. There are connections between this model and Butler and Winne’s, however, this model has a range of unique contributions. This model was presented first in two publications: one from 2004 and an updated and much more specific version from 2008. In later publications Narciss introduced minor changes in the figures to make them easier to understand.

The model is based upon the cybernetic paradigm from systems theory, at the same time having aspects of “notions of competencies and models of self-regulated learning” ( Narciss, 2017 ). The Interactive Tutoring Feedback model, also known as interactive-two-feedback-loops model, is heavily steeped in vast research base of general feedback literature. The model represents interacting processes and factors of the two feedback loops that may account for a large variety of feedback. The model also focuses on computer supported learning with a strong emphasis on tutoring systems that adapt feedback to students’ needs. Despite its strong focus on tutoring systems, the contentions of this model can be applied to face-to-face learning situations (2017).

Narciss describes feedback as follows: “In instructional contexts the term feedback refers to all post-response information which informs the learner on his/her actual state of learning or performance in order to regulate the further process of learning in the direction of the learning standards strived for (e.g., Narciss, 2008 ; Shute, 2008 ). This notion of feedback can be traced back to early cybernetic views of feedback (e.g., Wiener, 1954 ) and emphasizes that a core aim of feedback in instructional contexts is to reduce gaps between current and desired states of learning ( see also Ramaprasad, 1983 ; Sadler, 1989 ; Hattie, 2009 ).” ( Narciss, 2017 p. 174). As it can be seen, it is an ambitious definition that includes aspects from multiple theories.

The model presents factors and processes of both the external and internal loop and how their potential interactions may influence the effects of feedback. When a student receives feedback it is not just the characteristics of the feedback message that will explain student responses. Rather, it is an interactive process in which the students and instructional characteristics create a particular type of feedback processing. Narciss presented three main components that had to be considered when designing feedback strategies ( Figure 9 ): 1 ) characteristics of the feedback strategy (e.g., function, content, and presentation); 2 ) learner’s individual factors (e.g. goals, motivation); and 3 ) instructional factors (e.g. goals, type of task). Hence, the model integrates multiple factors that influence if and how feedback from an external source is processed effectively. Additionally, in 2013 and 2017, Narciss elaborated upon the individual and instructional factors and added specific conditions of the feedback source useful for designing efficient feedback strategies.

FIGURE 9 . Narciss (2008) model of factors and effects of external feedback.

The model is represented in more detail in Figure 10 . As one can see, learners’ engagement with feedback is influenced by both their characteristics and teacher, peer, and instructional medium. Narciss juxtaposes internal and external standards, competencies, and task requirements, as well as internal and external reference values. So, for example, an external controller compares external standards to feedback and communicates this information to the learner’ internal controller, which, in turn, generates internal feedback via self-assessment. This leads to different actions such as control actuator and controlled variables. If we go back to Butler and Winne’s model, the interactive processes described in Narciss’ model explain in a similar way the adaptation (small and large scale) proposed by Butler and Winne.

FIGURE 10 . Narciss (2013) feedback model explaining interactions.

Additionally, Narciss (2008) provided what is probably the most specific taxonomy of feedback from all the models herein included, based on a multidimensional approach to describing the many ways feedback can be designed and provided. According to her typology, feedback can have three functions: 1 ) cognitive (informative, completion, corrective, differentiation, restructuring); 2 ) metacognitive (informative, specification, corrective, guiding); and 3 ) motivational (incentive, task facilitation, self-efficacy enhancing, and reattribution). Additionally, feedback can be classified by its content with an evaluative component or an informative component, with eight different categories ( Narciss, 2008 . Table 11.2tbl112 p. 135). And finally, the presentation of feedback can vary in timing, schedule, and adaptivity. This multidimensional classification, which also represents ways of designing feedback, is extremely detailed and stems from Narciss’ extensive work in the subfield of feedback.

Nicol and McFarlane-Dick (2006) : Connecting Formative Assessment With Self-Regulated Learning

This article has become one of the most important readings in the formative assessment literature. The article connects self-regulated learning theory, more specifically the model developed by Winne (2011) , and seven principles that are introduced as “good feedback practices.” Nicol and MacFarlane-Dick were among the first authors to provide specific connections between the two fields of self-regulated learning and formative assessment ( Panadero et al., 2018 ).

The theoretical framework of the paper draws upon the two fields of self-regulated learning and formative assessment, combining assessment literature ( Sadler, 1998 ; Boud, 2000 ) with studies coming from self-regulated learning scholars (e.g. Pintrich, 1995 ; Zimmerman and Schunk, 2001 ).

According to Nicol and MacFarlane-Dick, “Feedback is information about how the student’s present state (of learning and performance) relates to goals and standards. Students generate internal feedback as they monitor their engagement with learning activities and tasks and assess progress towards goals. Those more effective at self-regulation, however, produce better feedback or are abler to use the feedback they generate to achieve their desired goals ( Butler and Winne, 1995 )” (p. 200).

Additionally, they referred to the seminal work of Sadler (1989) and Black and Wiliam (1998) and emphasized the importance of the three conditions that must be explicated for students to benefit from feedback: 1 ) the desired performance; 2 ) the current performance; 3 ) how to close the gap between the two.

Their model is largely based on Winne’s model of self-regulated learning, and it describes how feedback interacts within each of the components of the model. For example, the authors suggested that comparisons of goals to outcomes generated internal feedback at cognitive, motivational, and behavioral levels, and this information prompted the student to change the process or continue as it was. They emphasized that self-generated feedback about the potential discrepancy between the goal and the performance may result in revisions of the task, changes in internal goals or strategies. The model also presented variable sources of feedback, which could be provided by the teacher, peer, or by other means (e.g. a computer). Just like Sadler (1989) , Nicol and MacFarlane-Dick emphasized the importance of active engagement with feedback.

Their model can be categorized as instructional and pedagogical as it presented seven feedback principles that influenced self-regulated learning. According to the authors, good feedback that may influence self-regulated learning:

1. helps clarify what good performance is (goals, criteria, expected standards);

2. facilitates the development of self-assessment (reflection) in learning;

3. delivers high quality information to students about their learning;

4. encourages teacher and peer dialogue around learning;

5. encourages positive motivational beliefs and self-esteem;

6. provides opportunities to close the gap between current and desired performance;

7. provides information to teachers that can be used to help shape teaching.

These principles are among the main instructional practices that the formative assessment literature has been emphasizing for years (e.g. Lipnevich and Smith, 2018 ; Black and Wiliam, 1998 ; Black et al., 2003 ; Dochy and McDowell, 1997 ). However, the clarity of the presentation of the feedback practices in relationship to self-regulated learning ( Figure 11 ) turns this model into a very accessible one. Additionally, each principle is presented in detail describing empirical support and instructional recommendations.

FIGURE 11 . Nicol and MacFarlane-Dick (2006) model.

Hattie and Timperley (2007) : A Typology Model Supported by Meta-Analytic (CAP) Evidence

This is by far the most cited model of feedback not only in terms of the number of citations (+14000) but also in terms of expert selections (all consulted experts identified this model). It also both a model and a typology because it established instructional recommendations while linking them to four different types of feedback.

The theoretical framework of this paper builds upon previous feedback reviews and meta-analyses, ideas presented in Hattie (1999) , and general educational psychology literature (e.g. Deci and Ryan, 1985 ).

The authors presented a simple definition that is applicable to a wide range of behaviors, contexts, and instructional situations: “... feedback is conceptualized as information provided by an agent (e.g., teacher, peer, book, parent, self, experience) regarding aspects of one’s performance or understanding.” (p. 81).

The model is based on the following proposition: Feedback should serve the purpose of reducing the gap between the desired goal and the current performance. To this end, Hattie and Timperley (2007) proposed different ways, in which the students and teachers can reduce this gap ( Figure 12 ). For the feedback to be more effective, it should answer three questions, each of them representing a type of feedback: where am I going? = feed up; how am I going? = feed back; and where to next? = feed forward. The authors claimed that the last type was the least frequently delivered and it was the one having the greatest impact, and when the authors asked students what they meant by feedback this was the one the students overwhelmingly desired (Hattie, personal communication, 30/11/2019). This, in itself, could be considered a typology differentiating feedback based on the context and the content of it. However, the typology that resonated the most with the field places feedback into four levels of task, process, self-regulation, and self. Most of the feedback given in an instructional setting is at the task level (i.e., specific comments relating to the task itself) and the self level (i.e., personal comments), despite the fact the process (i.e., comments on processes needed to perform the task) and self-regulation (i.e., higher-order comments relating to self-monitoring and regulation of actions and affect) are the ones with more potential for improvement. The authors also noted that the self level feedback (e.g., generic, person-level praise) is almost never conducive to enhancing performance regardless of its valence. Self-level feedback may interfere with the task-, process-, or self-regulation feedback by taking individuals’ attention away from those other types. This review tackles a range of additional topics, describing feedback timing, effects of positive and negative feedback, teacher role in feedback, and feedback as part of a larger scheme of assessment.

FIGURE 12 . Hattie and Timperley (2007) feedback model.

Importantly, through personal communication with the authors (Hattie, personal communication, 30/11/2019), Hattie stated: “The BIG idea we missed in the earlier review was that we needed to conceptualize feedback more in terms of what is received as opposed to what is given.” This line of reasoning is prominently featured in Hattie’s recent work ( Hattie and Clarke, 2019 ).

Evans (2013) : Reviewing the Literature on Assessment Feedback in Higher Education

Evans (2013) publication presents a compelling review of literature on feedback in higher education settings. The author showed an excellent understanding and pedagogical reading of the field covering different areas of formative assessment ranging from lecturers’ instructional activities, peer, and self-assessment.

The primary purpose of this article was to review current literature focusing on feedback in the context of higher education. Evans described feedback from socio-constructivist, co-constructivist, and cognitivist perspectives, to name a few, and reviewed characteristics of feedback that were most pertinent in the context of higher education.

Evans spent substantial amount of time reviewing definitions of feedback. She proposed that “Assessment feedback therefore includes all feedback exchanges generated within assessment design, occurring within and beyond the immediate learning context, being overt or covert (actively and/or passively sought and/or received), and importantly, drawing from a range of sources.” (p. 71). Evans systematically reviewed principles of effective feedback and provided an excellent overview of methodological approaches employed in feedback research.

Towards the end of her review, Evans presented a model entitled “The feedback landscape.” The pictorial representation of the model is presented in Figure 13 . The underlying idea of the model is in the close interaction between students and lecturers. Evans suggested that feedback was moderated by 12 variables shared by feedback receivers and givers (e.g. ability, personality, etc.), with three additional mediators selected for lecturers (i.e., awareness of other contexts, alignment of other modules, and knowledge of students). Surrounding this interaction there is the academic learning community (e.g. resources, academic peers, etc.) and an emphasis of temporal and special variability of mediators. Interestingly, most of these variables are not explored in detail in the review but are presented to the reader as part of the model. In general, presentation of the model was not among the articulated goals of the manuscript and its description is somewhat cursory.

FIGURE 13 . Evans (2013) Feedback landscape model.

Nevertheless, the publication provided a remarkable amount of information about instructional applications of feedback. For example, the author presented a table with a list of key principles of effective feedback practice. For each principle, Evans provided a significant number of references. Additionally, she summarized these practices into “12 pragmatic actions”:

1. ensuring an appropriate range and choice of assessment opportunities throughout a program of study;

2. ensuring guidance about assessment is integrated into all teaching sessions;

3. ensuring all resources are available to students via virtual learning environments and other sources from the start of a program to enable students to take responsibility for organizing their own learning;

4. clarifying with students how all elements of assessment fit together and why they are relevant and valuable;

5. providing explicit guidance to students on the requirements of assessment;

6. clarifying with students the different forms and sources of feedback available including e-learning opportunities;

7. ensuring early opportunities for students to undertake assessment and obtain feedback;

8. clarifying the role of the student in the feedback process as an active participant and not as purely receiver of feedback and with sufficient knowledge to engage in feedback;

9. providing opportunities for students to work with assessment criteria and to work with examples of good work;

10. giving clear and focused feedback on how students can improve their work including signposting the most important areas to address;

11. ensuring support is in place to help students develop self-assessment skills including training in peer feedback possibilities including peer support groups;

12. ensuring training opportunities for staff to enhance shared understanding of assessment requirements.

She delivered multiple lists and tables containing recommendations for peer feedback (p. 92), the basics about the feedback landscape (p. 100), and a list of potential avenues for future research (p. 107). These instructional recommendations are arguably more relevant than the model itself as the model is not sufficiently developed in the text.

Lipnevich, Berg, and Smith (2016) : Describing Students – Feedback Interaction

Lipnevich et al. (2016) model is one of the more recent models and it has been first presented in a chapter of a Handbook. The model, however, was selected by five experts as one of the models with which they are most familiar. The model has been recently revised and expanded to incorporate empirical findings and recent developments in the field of feedback.

The authors ground their model in the literature on feedback, reporting a thorough review of the field. The definition that Lipnevich et al. (2016) used came from Shute (2008) who defined feedback as “information communicated to the learner that is intended to modify his or her thinking or behavior for the purpose of improving learning” (p. 154). Lipnevich et al. (2016) emphasized students’ affective responses and used Pekrun’s Control-Value theory of achievement emotions to frame their discussion. In their revision of the model, Lipnevich et al. (2013) proposed the following definition of feedback: “Instructional feedback is any information about a performance that learners can use to improve their performance or learning. Feedback might come from teachers, peers, or the task itself. It may include information on where the learner is, where the learner is going, or what steps should be taken and strategies employed to get there.”

In their walk through the model the authors emphasized that feedback was always received in context ( Figure 14 ). The same type of feedback would be processed differentially depending on a class, academic domain, or consequential nature of the task. Within the context, the feedback is delivered to the student. Students will inevitably vary on their personality, general cognitive ability, receptivity to feedback, prior knowledge, and motivation. The feedback itself may be detailed or sparse, aligned with the students’ level of knowledge or not. It may be direct but delivered in a supportive fashion or may be unpleasantly critical. It may match what the student is expecting or be highly below or above those expectations. All these characteristics will contribute to students’ differential processing of feedback. In their new version of the model Lipnevich et al. (2013) included the source of feedback as a separate variable. The authors reported evidence that the feedback from the teacher, computer, peer, and the task itself may be perceived very differently, and may variably interact with student characteristics.

FIGURE 14 . Lipnevich et al. (2016) Feedback – student interaction Model.

When students receive the feedback message, they produce cognitive and affective responses that are often tightly interdependent. The student may cognitively appraise the situation, deciding whether the task is of interest and importance and whether they have control over the outcome, and make a judgment of whether the feedback is clear and understandable. That is, in reading through the feedback, students might be baffled by the comments, or may fully comprehend them. These appraisals result in a range of emotions, which, in turn, lead to some sort of behavioral responses. Students may engage in adaptive or maladaptive behavioral responses which will have a bearing on performance on a task and, possibly, learning. In the revision of the model the authors differentiated between learning and performance discussing potential effects of feedback on short-term changes on a task and long-term transfer to subsequent tasks. The authors also showed that both the response to the feedback and the actions that the student takes reflect on who the student was, what the student knew and could do in this area, and how the student would respond in the next cycle of feedback. Lipnevich et al. (2016) described feedback as a conversation between the teacher and a student, and cautioned scholars and practitioners that utterance that each party uses would be highly consequential for future student-teacher interactions and student learning progress.

The authors recently revised their model to further emphasize the three types of student processing: cognitive, affective, and behavioral 1 . Figure 15 of the revised model shows that message, student characteristics, and cognitive, affective, and behavioral responses contribute to an action that may alter student performance and learning 1 . Emphasized three questions, important for student receptivity of feedback: Do I understand feedback? How do I feel about feedback? What am I going to do about feedback? Importantly, the authors suggest that all feedback that comes from any external source will have to be internalized and converted into self- or inner feedback ( Nicol, 2021 ; Panadero et al., 2019; Andrade, 2018 ). The efficiency of this internal feedback would vary depending on a variety of factors.

FIGURE 15 . Student-feedback interaction Model (Revised).

Carless and Boud (2018) : A Proposal for Students’ Feedback Literacy

Carless’ work was voted in favor of inclusion by three experts but it was difficult to decide on a specific model, because he had proposed four ( Carless et al., 2011 ; Yang and Carless, 2013 ; Carless, 2018 ; Carless and Boud, 2018 ). The feedback literacy model included into the current review was suggested by the author as being both influential and matching with the goals of the current paper. In its short time since publication this model has achieved a significant number of citations.

The theoretical framework is based on social constructivist learning principles (e.g. Palincsar, 1998 ; Rust, O’Donovan and Price, 2005 ) and previous assessment research literature (e.g., Sadler, 1989 ; Price et al., 2011 ), with a clear grounding in higher education (31 out of the 53 cites come from journals with “higher education” in the title, with 18 of them from Assessment and Evaluation in Higher Education) and a few references to general feedback literature (e.g. Hattie and Timperley, 2007 ; Lipnevich et al., 2016 ).

Carless and Boud provided the following definition of feedback: “Building on previous definitions ( Boud and Molloy, 2013 ; Carless, 2015 ), feedback is defined as a process through which learners make sense of information from various sources and use it to enhance their work or learning strategies. This definition goes beyond notions that feedback is principally about teachers informing students about strengths, weaknesses and how to improve, and highlights the centrality of the student role in sense-making and using comments to improve subsequent work.” (p. 1). The authors then continued by defining student feedback literacy “…as the understandings, capacities and dispositions needed to make sense of information and use it to enhance work or learning strategies.”

The model and the main propositions are straightforward ( Figure 16 ). The model is composed of four inter-related elements. For students to develop feedback literacy they need to 1 ) appreciate the value and processes of feedback, 2 ) make judgments about their work and that of others, 3 ) manage the affect feedback can trigger in them, and all of this leads towards 4 ) taking action in response to such feedback. However, how these elements are operationalized could have been explicated in further detail as strategies are not explicit in the model. Carless and Boud provided a few illustrative examples of activities needed to develop feedback literacy that included peer feedback and analyzing exemplars, and described teachers’ role in the development of student feedback literacy. Some parts remain at a general level of description and the paper is concisely packaged, with some concepts and practices being mentioned but not described in detail.

FIGURE 16 . Carless and Boud (2018) Feedback literacy model.

Feedback Models Comparison

In the upcoming sections we will compare the models, focusing on definitions and empirical evidence behind them. Importantly, the comparison continues in Panadero and Lipnevich (2021) where we synthesize typologies and models, and propose a new integrative feedback model. Page limitations prevented us from having both articles in one, but we are hopeful that the reader will find them useful.

To offer the reader an idea about the historical and temporal continuity of the publications, we developed Table 1 that shows cross-citations among the fourteen publications discussed in this review. There is a clear evidence of cross-citations among the included publications. This is reassuring because a number of fields suffer from isolated pockets of research that do not inform each other ( Lipnevich and Roberts, 2014 ). An evidence to that is the model by Kluger and DeNisi (1996) that came to us from the field of industrial-organizational psychology: The authors did not reference any of the models published before them, most of which were situated within the field of education. Also, the only publication that was voted to be included but had not been cited was that of Tunstall and Gipps (1996) , possibly due to its limited demographic focus (i.e., early elementary students). However, for most included models the cross-citation is high and especially evident in the latest ones (e.g. Evans, 2013 , cited seven previous models).

TABLE 1 . Cross-citations of articles included in the current review.

When it comes to the number of citations of the models ( Table 2 ), it is clear that feedback models generate significant attention in the field of educational research. Interestingly, it does not matter whether the models are definitional (e.g. Ramaprasad, Sadler), are based on meta-analyses (e.g. Bangert-Drowns et al., Hattie and Timperley) or are linking multiple subfields (e.g. Butler and Winne, Nicol and McFarlane-Dick). Furthermore, it is clear that the field is constantly evolving with new models being developed that reflect the current focus of feedback research. We would like to encourage researchers to frame their studies within models that are currently available to avoid proliferation of redundant depictions of the feedback phenomena. For example, the field of psychosocial skills research currently has 136 models and taxonomies discussed by researchers and practitioners ( Berg et al., 2017 ). Obviously, it is not humanly possible to make sense of all of them, so the utility of proposing new models is very limited. Rather, validating existing models would be a more fruitful investment of researchers’ time. In Panadero and Lipnevich (2021) we integrate the fourteen models, selecting the most prominent elements of Message, Implementation, Student, Context, and Agents (MISCA). We hope the reader will find it instrumental.

TABLE 2 . Models characteristics.

The Definitions of Feedback

The problem of defining feedback has occupied minds of many feedback scholars and it has been a contested area, especially in the confluence of educational psychology and education. There are hundreds of definitions of educational feedback. This is not unique to the field of feedback and is common for other psychological and educational constructs, where lacking agreement on definitions stifles scientific developments.

When it comes to feedback, there appear to be opposing camps with some researchers arguing that feedback is information that is presented to a learner, whereas others viewing feedback as an interactive process of exchange between a student and an agent. There are also more extreme positions that describe feedback as the process where students put the information to use. Hence, according to this view, if not utilized, information delivered to students cannot be regarded as feedback (e.g. Boud and Molloy, 2013 ). However, there seems to be some common ground with some educational psychologists emphasizing the importance of the receptivity of feedback yet acknowledging that not acting upon feedback may be a valid situational response to it ( Lipnevich et al., 2016 ; Winstone et al., 2016 ; Hattie and Clarke, 2019 ; Jonson and Panadero, 2018 ). This tension shows that how researchers define feedback directly influence how they operationalize research. Therefore, it is crucial to explore how the models actually define feedback.

To achieve such goal, we looked in the included publications for sentences or paragraphs that were clearly indicative of a definition (e.g. “feedback is…”, “our definition of feedback is…”). We intentionally tried not to borrow definitions from subsequent or previous work of the authors and focused exclusively on what was presented in publications included in this review. Table 3 contains all definitions and Table 4 a comparison which we further develop below. Four publications did not include definitions ( Kulhavy and Stock, 1989 ; Bangert-Drowns et al., 1991 ; Tunstall and Gipps, 1996 ; Butler and Winne, 1995 ).

TABLE 3 . Definitions of feedback used in the different models.

TABLE 4 . Elements of the definitions of feedback used in the different models.

To offer the authors of the included models a chance at presenting their evolved ideas, in our interviews or email communication we asked whether they still agreed with their definitions or wanted to present one if it was missing in the original manuscript. This way we obtained a definition from Winne. Additionally, Lipnevich et al. (2016) , who used the unaltered Shute’s (2008) definition of feedback, put forward a new definition in their model revision ( Lipnevich et al., 2013 ). After analyzing the definitions, we identified five elements that are present in multiple definitions across the included publications ( Table 4 ). Our conclusions will be anchored to them.

The first conclusion is that the definitions of feedback are getting more comprehensive as more recent definitions include more elements than older definitions. For example, whereas the first chronological definition included two of the six elements we identified in the definitions ( Ramaprasad, 1983 ) the latest included six of the elements (Winne, 2019 via personal communication with authors). Table 4 shows the increase in the number of elements that occurs with the more recent models. This is an important reflection of the evolution and maturity of the feedback field where we are now looking at such aspects as feedback sources or the degree of student involvement in the feedback process. It appears that the expansion of the formative assessment field after the publication of Black and Wiliam (1998) played a major role in the development of definitions, and the models that followed this publication were more likely to include references to formative assessment. This, of course, makes sense. For formative assessment and assessment for learning theories ( Wiliam, 2011 ) feedback has to aim at improving students’ learning, to help students to process such feedback, and to become active agents in the process. Hence, within the realm of formative assessment, the field moved from a static understanding where feedback is “done” to the students (e.g., just indicating their level of performance) to our current understanding of the complex process that feedback involves.

A second conclusion takes us to the individual analysis of the definitional elements. So, all of the definitions discuss feedback as information that is exchanged or produced. This is a crucial component of feedback as without information there is nothing to process and, thus, it simply cannot be successful. This information may range from detailed qualitative commentary to a score, or from being delivered face to face to notes scribbled on the students’ work. Information is the essence of feedback, or, as many would maintain, is feedback.

One of the definitions presents a notable exception: Carless and Boud (2018) , building upon Boud and Molloy (2013) definition, argue that information is not feedback in itself but feedback is what students do with that information. From our perspective, although this pedagogical premise is powerful and instructors need to increase the likelihood of students using such information (e.g. allowing for resubmission of work), we do not fully agree with this position. According to Boud and Molloy’s definition if a student decides not to use the information then the information would not be considered feedback. The latter view conflicts with learner’s autonomy as the student might decide not to react to the feedback and not to improve a piece of work because she is happy with the score and does not want to invest more effort. For us, the information has been delivered and feedback has reached the student. All in all, the element of information is shared by the definitions.

Regarding all the other elements, seven models discuss the gap as the distance between the goal or the standard and student current performance. Feedback is thus construed as the information that is intended to close this gap (e.g. Hattie and Timperley, 2007 ). It is a powerful image for the teachers reminding them of the importance of the final goal and analyzing the students’ performance in relationship to it.

Another element is the process, explicitly mentioned by four of the models, which refers to the understanding that processing feedback involves cognitive, affective, and regulatory steps. Further, seven of the models describe the involvement of different educational agents that provide feedback (e.g. teacher, peer, computer, etc.). Hence, current definitions acknowledge that feedback can be delivered not only by teachers but by other agents too emphasizing the broadening of the definition. Finally, students’ active processing, which refers to the student as an active recipient of feedback, is also a key component of several definitions. This idea was brought into focus by the work of Sadler (1989) and was substantially expanded by Butler and Winne (1995) , who introduced the idea of internal feedback. Internal feedback is defined as feedback produced by the learner. These two elements, mentioned explicitly in four of the models, are very interrelated but can be differentiated. Interestingly, although some papers do not focus on student active processing in their definitions, this idea is very central to their model (e.g., Lipnevich et al., 2016 ). In the upcoming sections of this review we will devote more attention to these two elements.

A third conclusion is that there appears to be a higher level of consensus than could have been expected, with more recent definitions including different agents and stressing the active role of students. Although there are more extreme pedagogical positions (e.g. Boud and Molloy, 2013 ), the latest definitions are generally well aligned. The discussion in the field seems to be moving towards how to help students to use the feedback ( Winstone et al., 2017 ; Jonsson and Panadero, 2018 ).

Following this overview, we would like to aggregate definitions that were presented in the reviewed publications. We propose that: feedback is information that includes all or several components: students’ current state, information about where they are, where they are headed and how to get there, and can be presented by different agents (i.e., peer, teacher, self, task itself, computer). This information is expected to have a stronger effect on performance and learning if it encourages students to engage in active processing .

The Empirical Evidence Supporting the Models

It is important to analyze the models by looking at empirical support behind them. Theoretical explorations have their utility and serve as a starting point for subsequent studies. However, models that are derived based on data or have support after they have been presented have more value to the field. Among our selected models, there are three that are theoretical in nature without empirical evidence behind them: 1 ) Ramaprasad (1983) is a theoretical exploration of feedback definitions; 2 ) Sadler’s (1989) work is purely theoretical also, although Sadler claims that “Empirically, they [formative assessment and feedback practices] are known to produce results.” (p. 143); and 3 ) Carless and Boud (2018) present their first attempt at describing the model, so there are no studies known to date that attempted to validate this model.

The second group of models is also theoretical but these authors make explicit links to prior research. First, Mason and Bruning (2001) derived their model from previous theoretical and empirical work, so there was some support for links among variables depicted in the model. This model serves as a guide for designers of computer based instruction and explicates variables that would matter in the process. Second, Nicol and McFarlane-Dick (2006) model was based on prior empirical research that supported the seven described principles. It is, therefore, a theoretical contribution framed in previous empirical research. And, third, Evans (2013) presented a high number of relevant references to empirical studies to support instructional recommendations. The model itself represents an organizing framework, but it may be useful to devise studies that would examine, for example, the roles of mediators within corresponding buffer zones for lecturers and students. There is a third group of models that is based on authors’ previous work to derive their models. So, Kulhavy and Stock (1989) reported three previous studies, in which students were asked to report their response certitude (or make confidence judgments) following each response to various tasks. The researchers hypothesized that when students were certain that their answer was correct, they would spend little time analyzing feedback, and when students were certain their answer was incorrect, they would spend more time interacting with feedback. Practical implications of their findings are rather simple. That is, educators are to provide elaborated feedback for students who are more certain that their answer is wrong and deliver more limited feedback for those with high certitude of correct answers. Although their own studies supported their hypotheses, other studies did not replicate these findings. For example, Mory (1994) tried to replicate Kulhavy and Stock’s (1989) results and found that although there were differences in the amount of time students studied feedback, there was no significant effect for feedback tailored to response certitude and correctness in terms of student learning.

Similarly, Tunstall and Gipps (1996) derived their typology from their empirical data reported. Their method description is not detailed and it is not clear which data sources were used to arrive at specific categories. The information that we do have suggests that the sample was limited, and, to our knowledge, no studies have attempted to validate this typology.

Narciss also derived her models based on the extensive overview of theoretical and empirical studies conducted by others and herself. Due to the inherent complexity of these models, studies would need to systematically select specific components and test them separately. Modeling all included variables may not be feasible. In a personal communication with the author (3/12/2019), Narciss mentioned that her 2013 paper summarized empirical evidence her team had found so far using the model as a framework for designing and evaluating feedback strategies for digital learning environments. Additionally, many feedback studies conducted by others provided some empirical evidence for the model; yet, so far only a few of them had been explicitly linked to the model.

Finally, there is a number of studies that examined different aspects of the Lipnevich et al. (2016) model. So, Lipnevich and Smith (2009a , 2009b) demonstrated variable effects of differential feedback on student individual characteristics and subsequent responses. Further, Lipnevich et al. (2021) examined mediational role of emotions in the link between different types of feedback and student responses. Hence, there is initial evidence suggesting viability of the model, and due to the recency of it, more studies will be coming out soon.

The fourth group comprises models that are based on meta-analytic data. First, Bangert-Drowns et al. (1991) synthesized a number of relevant studies and emphasized the idea of mindfulness as a key approach to the effective receptivity of feedback. Further, Kluger and DeNisi (1996) meta-analysis “provided partial support to Feedback Intervention Theory” (p. 275). The researchers tested the propositions of the FIT and there was a reasonable support for the Feedback Intervention Theory. Future empirical work in the field of instructional feedback can be used to support the validity of the FIT. Finally, Hattie and Timperley (2007) model emanates from existing empirical research into what constitutes the most useful feedback characteristics. Additionally, there have been studies such as the ones by Harris et al. (2014) with teacher feedback, Harris et al. (2015) with peer and self-assessment, and Lipneviche et al. (2013) with principal’s perception of feedback that found the evidence that Hattie and Timperley’s model typology can be used to effectively categorize feedback in classroom settings.

Finally, Butler and Winne (1995) are in their own special category, in which other scholars reviewed the empirical evidence behind the original model. Greene and Azevedo (2007) published what they considered a theoretical review, where they reviewed 113 studies providing empirical evidence for this model. The researchers found evidence supporting most of the processes from the model, as can be seen in the original Table 2 .

In sum, the general conclusion is that the empirical support for the fourteen included models is variable. More empirical studies are needed, and we implore researchers in the field to invest time into model validation. The and into conducting studies that investigate propositions of existing models.

In this section we will discuss the main conclusions of our review.

1. The models have different aims and focus . Although the models share the purpose of explaining the process of feedback and its effects, they are quite different from each other in their aim or purpose. The focus of these models varies from being descriptive to presenting the specifics of processing of feedback or offering detailed pedagogical recommendations. In our work, which builds upon this review ( Panadero and Lipnevich, 2021 ), we provided a framework organized around five elements: (feedback) message, implementation, (instructional) context, (feedback) agents and student (characteristics). We hope the reader will find it useful.

2. Choosing the “right” model and theory . Some models are better suited for guiding empirical or theoretical investigations. Therefore, feedback researchers may want to choose the model that better suits their aims. For example, if a researcher is interested in exploring the mechanisms of feedback receptivity, it would be better to anchor the work in the interactional models, that describe specifics of student feedback processing. If instructional interventions are the goal, then pedagogical models will be of higher value. Another aspect to consider is that some models are more general and might be easier for teachers to apply to a range of instructional scenarios (e.g. Hattie and Timperley, 2007 ) whereas others might be better suited for specific contexts and more specific interventions (e.g. computer, Narciss, 2013 ).

3. Typologies: wide variety but more research is needed ( Panadero and Lipnevich, 2021 ) . There seems to be a consensus in terms of possible functions of feedback, whereas the content of feedback is a more contested category. Jointly, these two categories of feedback have received considerable attention, whereas the importance of presentation seemed to be less emphasized. Presentation influences students’ receptivity and use of feedback, and, hence, future theoretical explorations may devote more attention to it (e.g., Jonsson, 2013 ). Additionally, the source of feedback represents a useful variable to consider in future typologies.

4. Focus on feedback receptivity . The actual efficiency of any feedback message depends on what the student does next with this information. After all, if instructors or peers prepare the best kind of feedback and students simply dismiss it, the effort will be wasted and no benefit will follow. Many of the models, starting with Ramaprasad (1983) and Sadler (1989) , and ending with the more recent ones of Hattie and Timperley (2007) , Lipnevich et al. (2016) , and Carless and Boud (2018) all stress the role of the student and emphasize the recursive nature of student-teacher (or other agent) interaction. Bringing the role of the student into the focus is critical for the field, and examining the ways in which learners make sense and use feedback to inform their progress is of key importance also. Thoughtful application of the reviewed models may help us to better understand how feedback leads to learning, where it might hinder learning, and which characteristics and contexts of feedback would be more likely to encourage students to actively engage with it.

5. The need for more empirical evidence . Some of the models included in this review have some empirical evidence supporting their validity (e.g. Hattie and Timperley), whereas others do not. Some models are inherently not testable because they do not describe relations and are purely definitional (e.g., Ramaprasad and Sadler), whereas others include multiple components and relations that are virtually impossible to define and investigate in a single study ( Narciss, 2013 ). It is our hope that future research tasks itself with providing empirical evidence for models included in this review. Instructional feedback does not exist in a vacuum, so a host of variables described by the reviewed models needs to be taken into consideration. Conducting such investigations lies at the crux of art and science of research. We should be able to specify and validate models in classroom settings, conducting studies that are not laboratory-sterile, but those that can be applied and replicated in typical instructional settings. At the same time, we should be able to disambiguate relations so that we can make clear attributions of causes and make conclusions about feedback effects with some degree of certainty. The review showed that theoretical developments have been impressive—now more good quality empirical work is needed. At the same time, examining practical applications of models and theories herein reported is of key importance to the field.

6. Lack of an output of the feedback effects: performance versus learning . A number of models does not explicitly describe outcome variables, focusing exclusively on characteristics of feedback itself. In education, there has been a large debate about whether what we measure in our classrooms and in most educational research is learning or performance ( Soderstrom and Bjork, 2015 ). Most commonly, it is the performance that gets measured. Unfortunately, measuring learning is a complex enterprise that implies designing studies that capture transfer from one task to the next, and such investigations are rather costly. Nevertheless, it is crucial that when considering the effects of feedback and student receptivity, researchers start evaluating effects on learning and not just on their performance on the immediate task. Unless we as a field commit to this goal there will be a multitude of unanswered questions about the utility and the general promise of feedback. In other words, we need to measure the impact of feedback interventions on academic achievement.

7. A final remark: do we need more models? This review included fourteen models after leaving a number of important ones because they did not fulfill our selection criteria. If we would just consider the number of models, then the probable answer would be “we have enough.” Nevertheless, the situation is more complex than that. The newer models cover aspects that the previous ones had not or offer new perspectives about already known aspects. Take for example, Lipnevich et al. (2016) that brings to the forefront the mechanisms of how the feedback and students characteristics may jointly influence responses and actions that students perform, while also considering the context. The rule of thumb for the creation of new models could be: “Does my model cover an area in need of an explanation? Does my model explain or clarify aspects the existing models do not?.” If the answer is yes, then it might be worth giving it a try and letting the research community decide.

Although researchers agree that feedback is essential for improved performance and can contribute to enhanced achievement on the task (reported effect sizes are as high as 0.73), we also know that 1 ) learners often dread it and dismiss it, and 2 ) the effectiveness of feedback varies depending on specific characteristic of feedback messages that learners receive ( see , e.g., Hattie and Timperley, 2007 ; Lipnevich and Smith, 2018 ; Shute, 2008 ). Many studies have attempted to identify what constitutes good feedback, which characteristics are most critical for students’ receptivity, and how to encourage students to effectively utilize it – often with inconsistent results (e.g., Lipnevich and Smith, 2018 ). Part of the reason for such inconsistency may be attributed to studies coming from different methodological perspectives and using disparate terminology to label relevant student-, feedback- or context-level factors that link feedback to improved performance (i.e., the “jingle–jangle fallacy,” see Block, 1995 ). To bring more clarity into the field, many researchers have attempted to propose models and theories that describe feedback, student interaction with it, along with specific conditions that make feedback effective. At this time, however, the models are proliferating and the field is missing clarity on what feedback models are available and how can they be used for the development of instructional activities, assessments, and interventions. With this review we attempted to describe the most prominent models in the field and summarize main conclusions along with recommendations for future research and Panadero and Lipnevich (2021) extended this discussion with an attempt to integrate the fourteen included models and theories. We hope this review will be a good resource to both experts and novices who work or are considering joining the exciting field of feedback research.

Author Contributions

AL and EP both contributed to conceptualization, preparation, and writing up of the study.

Review funded by UAM-SANTANDER collaboration with the USA (Reference: 2017/EEUU/12).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


To our feedback experts: Rola Ajjawi, Heidi Andrade, David Boud, Susan Brookhart, Gavin Brown, David Carless, Roy Clariana, Siv Gamlem, Mark Gan, Steve Graham, John Hattie, Avraham Kluger, Angela Lui, Jackie Murray, Susanne Narciss, David Nicol, Ron Pat-El, Reinhard Pekrun, Elizabeth Peterson, Joan Sargeant, Mien Segers, Valerie Shute, Jan-Willem Strijbos, Jeroen Van Merriënboer, Dylan Wiliam, Phil Winne, and Naomi Winstone. Thanks to our interviewees: David Boud, David Carless, Avraham Kluger, Susanne Narciss, Arkalgud Ramaprasad, Royce Sadler and Phil Winne. Thanks to the authors who were not available for an interview but with whom we exchanged emails: Robert Bangert-Drowns, Carol Evans, Caroline Gipps, John Hattie and David Nicol. Special thanks to Dylan Wiliam for providing comments on earlier versions of the manuscript and thanks to Emilian Lipnevich for helping us to redraw some of the figures.

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Keywords: feedback, review, definition, feedback model, feedback theory, feedback mechanism

Citation: Lipnevich AA and Panadero E (2021) A Review of Feedback Models and Theories: Descriptions, Definitions, and Conclusions. Front. Educ. 6:720195. doi: 10.3389/feduc.2021.720195

Received: 03 June 2021; Accepted: 02 November 2021; Published: 31 December 2021.

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Copyright © 2021 Lipnevich and Panadero. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Anastasiya A. Lipnevich, [email protected]

Properties of feedback mechanisms on digital platforms: an exploratory study

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feedback mechanism essay

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Many digital platforms implement feedback mechanisms as a means to control the behavior of their users. However, there is a lack of theoretical explanation regarding the interrelation between design characteristics of feedback mechanisms and their effects. In this study, we interpret feedback mechanisms as a specific type of management control to propose properties as a new theoretical perspective on this problem. Our exploratory study has two objectives. First, we analyze how digital platforms design their feedback mechanisms. Second, we examine to what extent feedback mechanisms comply with standards given in the management control literature for our newly introduced properties. Analyzing the 102 most widely used platforms in Germany, we find dominant patterns in nearly all design characteristics (e.g., query method, submission category and scale level). Furthermore, we find mixed compliance of feedback mechanisms with our introduced properties (e.g., low precision but high sensitivity and verifiability). For a deeper understanding of these results, especially the reasons for the design choices, we conduct 14 semi-structured expert interviews. We find simplicity and inspiration from other platforms to be dominant drivers for design choices.

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1 Introduction

In recent years, there has been growing interest in digital platforms in research as well as in practice. Driven by the fact that some of the highest-valued companies (e.g., Apple and Amazon), have implemented successful platforms, many others have been following these examples (Evans and Schmalensee 2016 ; Parker et al. 2016 ). Digital platforms such as marketplaces, social media platforms, and Internet-of-Things platforms represent “a new business model that uses technology to connect people, organisations, and resources in an interactive ecosystem in which amazing amounts of value can be created and exchanged” (Parker et al. 2016 ).

An example of such a platform is eBay, which matches sellers of products and buyers via its marketplace. The buyers and sellers are also called market sides (Cabral and Hortaçsu 2010 ; Melnik and Alm 2002 ). The sellers can present their products and additional information about them (e.g., price and condition) and can also upload photos. Customers can buy products either in the form of an auction or at a fixed price (Cabral and Hortaçsu 2010 ). After the transaction, both the buyer and the seller can provide feedback. As is usual in the context of platforms, we consider users to be both buyers and sellers.

The products offered by the sellers, often referred to as external resources, are crucial to the platform because, without them, there would not be any transaction (van Alstyne et al. 2016 ). The sellers and buyers often do not know each other before the transaction, which results in information asymmetries. After an agreement between the buyer and the seller, the buyer can pay or not. However, whether and when the buyer will pay is unclear to the seller. The seller can avoid negative consequences from this information asymmetry by waiting for the payment before shipping. In addition, the buyer also has an information deficit, as the buyer does not know whether he or she will receive the product and whether its quality will be as described. However, buyers cannot solve the problems resulting from information asymmetries. The resulting decrease in external resource quality and possible negative behavior can lead to users losing confidence in the platform (Bolton et al. 2013 ) and consequently ceasing to use it.

In practice, there are several control mechanisms for this problem, such as gatekeeping, recommender systems (Tiwana 2014 ), and feedback mechanisms (Bolton et al. 2013 ). In this paper, we focus on feedback mechanisms because they represent the best-known application (Dellarocas 2003 ) and a widely established mechanism (Gutt et al. 2019 ). For instance, eBay uses a feedback mechanism that many studies have analyzed (e.g., Bolton et al. 2013 ; Dellarocas et al. 2004 ). Within this feedback mechanism, both sellers and buyers can evaluate each other after a successful transaction (Bolton et al. 2013 ). In particular, they can specify whether the transaction was positive, neutral or negative (Kornberger et al. 2017 ). Further, buyers can give an additional detailed seller rating (Bolton et al. 2013 ; Kornberger et al. 2017 ) and rate several categories such as whether the item was as described, communication, shipping time and shipping charges. The sensitivity relates among the design characteristics reciprocity and scale level (Kornberger et al. 2017 ).

The positive effects of well-designed feedback mechanisms have been shown in many studies; for example, the effects on revenue are well-known (e.g., Ba and Pavlou 2002 ; Bajari and Hortaçsu 2003 ; Dellarocas et al. 2004 ; McDonald and Slawson 2002 ; Melnik and Alm 2002 ; Resnick et al. 2006 ; Resnick and Zeckhauser 2002 ). However, these feedback mechanisms cause several dysfunctional effects. For example, almost all feedback on eBay’s marketplace is positive (Dellarocas and Wood 2008 ; Resnick and Zeckhauser 2002 ). This frequent positive feedback is not necessarily related to good transactions and user behavior. Indeed, users may take “revenge” for negative feedback; thus, for example, a buyer may give a seller positive feedback even on an unsatisfactory transaction to ensure that the buyer’s reputation is not damaged by retaliatory negative feedback from the seller (Bolton et al. 2013 ). Moreover, such dysfunctional effects extend beyond the mechanism of eBay. For instance, manipulations such as inconsistent feedback occur within other feedback mechanisms (e.g., Amazon, TripAdvisor and (Fazzolari et al. 2017 ; Mayzlin et al. 2014 ; Mudambi et al. 2014 ). These dysfunctional feedback mechanisms may lead to a decline in users’ motivation to provide feedback (McDonald and Slawson 2002 ) and, as a result, may cause a negative attitude towards feedback (Abramova et al. 2016 ).

Although it seems obvious that dysfunctional effects of feedback mechanisms can be seen as a consequence of their design, there is a lack of theoretical explanation of this interrelation. In this study, we draw on the management control literature to overcome this gap. Management control systems exist in multiple forms, in which a manager tries to align a subordinate’s performance with the business objectives (Anthony et al. 2014 ). Control mechanisms are implemented to influence the behavior of subordinates to achieve the organization’s objectives (Merchant and van der Stede 2017 ). We interpret feedback mechanisms as a specific type of control mechanism that is implemented as a means to influence users to implement the platform provider’s strategy (e.g., to improve transaction quality). The literature on management control has established several standards to avoid dysfunctional effects that can be used as a theoretical basis in this study. Therefore, we propose the following research questions:

How do digital platforms design their feedback mechanisms?

To what extent do digital platforms’ feedback mechanisms comply with the standards given in the literature on management control systems properties?

To answer our first research questions, we introduce a morphological box of design characteristics obtained during a literature analysis, as well as theoretical considerations. Based on the morphological box, we conduct a descriptive analysis of various feedback mechanisms and their design characteristics.

Furthermore, we adapt properties that are discussed in management control literature as necessary to design management control systems. These properties form the basis of the subsequent analysis of whether the feedback mechanisms of the digital platforms comply with the standards given in the literature on the management control systems properties.

Based on the results of the descriptive analysis, we interview experts to gain insight into the specific feedback mechanism and to unravel reasons for different design choices and the compliance with the standards given in the literature on management control systems properties.

Through our research, we contribute to the development and structure of the research field of feedback mechanisms. We show how digital platforms design their feedback mechanisms and provide insight into their decisions. In particular, our paper offers a morphological box of those design characteristics and provides a framework for operators to design, implement, or redesign feedback mechanisms. Furthermore, our analysis shows to what extent existing feedback mechanisms comply with the properties of management control systems. Moreover, it reveals reasons for compliance based on expert interviews and highlights trade-offs in designing feedback mechanisms.

The remainder of this paper is organized as follows: Based on the design characteristics of feedback mechanisms and the standards given in the management control literature, we develop a framework for designing feedback mechanisms in Sect.  2 . Section  3 presents the methods we use to analyze existing feedback mechanisms with regard to their compliance with the framework’s properties and the method of the qualitative approach. Our results are presented in Sect.  4 . We discuss results and paths for future research in Sect.  5 . Section  6 summarizes and concludes.

2 Literature review and theoretical framework

2.1 design characteristics of feedback mechanisms on digital platforms.

Feedback mechanism research is diverse and includes, among others management control systems, behavioral economics, digital platforms, and information systems research. The corresponding literature covers many terms to describe feedback mechanisms. In addition to “feedback mechanisms” (e.g., Ba and Pavlou 2002 ; Chen et al. 2017 ; Dellarocas 2003 ; Dellarocas and Wood 2008 ) the literature refers to the terms “evaluating infrastructure” (e.g., Kornberger et al. 2017 ), “feedback systems”  (e.g., Bolton et al. 2013 , 2018 ), “ratings” (e.g., Abramova et al. 2016 ), “online reviews” (e.g., Gutt et al. 2019 ; Mayzlin et al. 2014 ), and “reputation systems” (e.g., Bharadwaj and Al-Shamri 2009 ; Resnick and Zeckhauser 2002 ). Due to the diversity of research, there is no standard definition of feedback mechanisms. Similar to Kornberger et al. ( 2017 ), we consider feedback mechanisms to be a type of formal control mechanism.

Feedback mechanisms should ensure proper transaction quality, in terms of both the behavior of users and the conditions of external resources. Feedback helps users to receive information about the conditions of previous transactions, such as product or service quality, and about potential opportunistic behavior of users (Nosko and Tadelis 2015 ). Therefore, feedback mechanisms aim to reduce information asymmetries between users (McDonald and Slawson 2002 ), increase trust between users (Ba and Pavlou 2002 ), and avoid the problem of adverse selection and moral hazard (Hui et al. 2019 ). Furthermore, feedback mechanisms enable users to be ranked and compared. Therefore, users facing a transaction decision can decide more quickly, resulting in decreased search and transaction costs (Chen et al. 2017 ; Hagiu 2009 ; Zhang et al. 2016 ; Zhang and Sarvary 2015 ).

In summary, feedback mechanisms have information and incentive functions. Feedback mechanisms as a formal control mechanism must be distinguished from word-of-mouth because these mechanisms have an unpredictable scale and low costs. The lower costs of feedback mechanisms compared to word-of-mouth are related to platform users’ reciprocal evaluation and the resulting control and monitoring of the users’ performance by automatic feedback mediators (Dellarocas 2003 ).

Feedback mechanisms are designed in various forms. To get a better understanding of feedback mechanisms, we describe different design characteristics of feedback mechanisms using a morphological box. The morphological box is based on several specifications obtained during the literature analysis and theoretical considerations. We separate criteria that primarily contribute to the provision of feedback from criteria that influence users in consuming feedback for their transaction decisions.

Table 1 shows various design characteristics of feedback mechanisms and their possible specifications. The design characteristics of feedback mechanisms include reciprocity, submission restriction, query method, submission categories, scale level, feedback evaluation, filter, sorting, symbol, and color. In detail, we describe the design characteristics of feedback mechanisms.

Feedback mechanisms differ with regard to their reciprocity. The submission may concern only the users of one market side (one-sided) or the users of two or more market sides (multi-sided) (Bolton et al. 2004 ). Platforms use one-sided feedback to evaluate users of different market sides (Chua and Banerjee 2015 ; Einav et al. 2015 ; Tadelis 2016 ). For example, within Amazon’s marketplace, customers evaluate sellers’ performance. However, an evaluation of the buyer is not possible. In contrast to one-sided feedback, reciprocal feedback is related to a mutual rating of different market sides (Bolton et al. 2013 ). For instance, Airbnb provides reciprocal feedback in which guests evaluate the hosts’ service and vice versa.

Another characteristic that refers to users providing feedback is that of submission restriction. That is, the operator can restrict users from giving feedback. In some cases, there may be no restrictions, so that all users can give feedback regardless of whether or not they interacted, while in other cases users may be permitted to provide feedback only after a transaction. Moreover, this feedback may be voluntarily or required.

Another characteristic, the query method, refers to the type of feedback that is requested. This query can be qualitative, quantitative, or both. Qualitative feedback includes textual information, while quantitative feedback is given using a predefined scale level (Chevalier and Mayzlin 2006 ; Mudambi et al. 2014 ).

Closely linked to the query method is the characteristic of submission categories. Users can give feedback for the entire transaction (overall rating) or for two or more categories. While the overall rating is easy to provide feedback, which in turn reduces rating costs for users, the use of several categories provides more accurate information for the rated users as well as other users who are deciding about a possible transaction. However, this information is limited to the defined categories. Specific evaluation categories could still be weighted according to the platform’s objectives. However, it should be guaranteed that the users providing feedback, as well as users who are facing an interaction decision on the platform, understand the evaluation categories of the platform. Furthermore, the overall rating can be weighted according to price, rating skills, and timing (Panagopoulos et al. 2017 ). Based upon individual quantitative feedback, platform operators can display several measures according to the reputation of each user. For example, the number of previous ratings of each scale level or the percentage of positive feedback of total provided feedback could be displayed (Nosko and Tadelis 2015 ).

The platform can use different scales (e.g., five-point scale) for the evaluation (Jiang and Guo 2015 ; Sparling and Sen 2011 ). However, selecting an incorrect scale may cause dysfunctional effects. For example, a feedback scale consisting of only one positive and one negative option may discourage a user from providing any feedback if the user perceives the transaction as neutral. This kind of measurement may cause extreme values. The advantages of quantitative feedback are the straightforward measurement and the possibility of aggregating the individual ratings to an overall rating per user.

Feedback evaluation is another criterion that enables users consuming feedback to react to and evaluate existing feedback (Mudambi and Schuff 2010 ). This evaluation is based on the same characteristics described for the feedback mechanisms. For example, this evaluation can contain several categories (e.g., usefulness) and can be qualitative or quantitative.

In addition to the evaluation of existing feedback, there are differences in the display of feedback (Gutt et al. 2019 ). For instance, platforms can implement a filter to limit the display of current feedback according to defined categories, such as keywords within qualitative feedback. Furthermore, there are filters for different rating levels, submission categories, user types, transaction types, or others. Platforms can also use multiple filters. In addition to filters, operators can also offer feedback sorting according to date, rating level, usefulness, relevance, or user type. Multiple sorting is also possible.

There are further criteria that can also be considered when designing these mechanisms. Operators can use different symbols and colors within the feedback mechanism (Berger and Schmitt 2005 ). For instance, some platforms use thumbs, smiles, stars, or sliders as symbols (Sparling and Sen 2011 ). Moreover, operators use different colors (e.g., yellow, red, blue) to display the rating or vary the colors within the scale. The specification of the evaluation can be highlighted using different colors (Kornberger et al. 2017 ; Sänger and Pernul 2018 ).

In the literature, several publications have provided an overview of the feedback mechanism characteristics (e.g., Gutt et al. 2019 ). However, different design approaches affect the willingness to provide feedback in different ways. For instance, a platform should only use multi-sided feedback if the provision of multi-sided feedback is simultaneous or blind. In other cases, contributors within multi-sided mechanisms often wait to give their ratings in order to potentially take revenge for negative feedback. This dysfunctional effect results in higher scores and less negative feedback (Bolton et al. 2013 ). Another type of feedback mechanism that sets an incentive to provide negative feedback is the withdrawal option. This option offers the opportunity for compensation and allows both users to withdraw the feedback later. Consequently, this option can be used to turn an unhappy user into a happier one, thereby increasing trust (Bolton et al. 2018 ).

Another characteristic affecting the provision of feedback is the query method (e.g., qualitative and quantitative feedback). Qualitative feedback has many benefits. For example, it can enable users to give more open and detailed feedback, providing more informative evaluations of users, which can also help users who are about to make an interaction decision. However, the information content of feedback differs (Mudambi et al. 2014 ). Therefore, in practice, further mechanisms have been developed to make it possible to identify relevant information among individual pieces of feedback more quickly (Liu and Park 2015 ; Mudambi and Schuff 2010 ; Schindler and Bickart 2012 ). A precise evaluation is possible using various categories. However, these predefined categories cannot include all topics that are relevant for users. Thus, quantitative components are easier to handle than multiple categories.

The consumption of feedback and the related transaction decisions of users can also be affected by different design approaches. For instance, both filter and sorting options allow users to change the way existing feedback is displayed so that they can quickly find the feedback they need in order to make an interaction decision. Therefore, filter and sorting options contribute to the reduction of search costs. Moreover, different symbols and colors within feedback may influence the perception of the feedback for users as to whether the feedback is positive or not. Symbols may also affect the provision of feedback. Nevertheless, platforms use symbols, colors, and scales only to display quantitative feedback.

In addition to the characteristics that influence the provision of feedback, outcomes also influence the function of a feedback mechanism. In this stream of research, it is well known that feedback mechanisms have a positive influence on trust in the market or on the platform and thus on the price and transaction volume (e.g., Ba and Pavlou 2002 ; Bajari and Hortaçsu 2003 ; Bolton et al. 2004 ; Dellarocas et al. 2004 ; McDonald and Slawson 2002 ; Melnik and Alm 2002 ; Resnick et al. 2006 ; Resnick and Zeckhauser 2002 ). Although there is a free-rider problem, most users provide feedback; however, most of it is positive (Hu et al. 2017 ; Resnick and Zeckhauser 2002 ; Zervas et al. 2015 ). Furthermore, users weight negative feedback more strongly than positive feedback and weight recent feedback more strongly than older feedback (Bolton et al. 2004 ). Possible reasons for this dysfunctional effect include incorrectly designed feedback mechanisms, such as reciprocal mechanisms in which users take revenge for negative feedback (Bolton et al. 2013 ). In addition to the behavioral economic aspects, there is further literature on various forms of manipulation, their detection, and their design to prevent manipulation (e.g., Hoffman et al. 2009 ; Jøsang et al. 2007 ; Mayzlin et al. 2014 ; Sänger and Pernul 2018 ). Platform operators should seek to avoid manipulations, such as ballot stuffing, bad-mouthing and Sybil attacks (Hoffman et al. 2009 ).

2.2 Management control systems

Management control systems aim to implement business strategy (Anthony et al. 2014 ; Merchant and van der Stede 2017 ). Management control is thus “the systematic process by which the organization’s higher-level managers influence the organization’s lower-level managers to implement the organization’s strategies” (Anthony et al. 2014 ). This separation of high-level and lower-level managers is crucial with regard to the decentralized organization of companies. However, a decentralized or lower-level manager does not always act in line with organizational goals. This divergence results from lower-level managers not entirely understanding, not agreeing with, or not having the resources to achieve the goals or strategies of the higher level (Anthony et al. 2014 ). Therefore, management control systems help to uncover whether lower-level managers behave in accordance with organizational strategy (Merchant and van der Stede 2017 ; Simons 1995 ). Moreover, management control systems “provide information that is intended to be useful to managers in performing their jobs and assisting organizations in developing and maintaining viable patterns of behavior” (Otley 1999 ). In the literature, various terms are used for high-level and low-level managers of a company (e.g., senior management and decentralized manager) (Anthony et al. 2014 ). In this paper, we use the terms “managers” for high-level managers and “subordinates” for lower-level managers.

Management control systems exist in many different forms, in which the manager tries to align the subordinate’s performance with the business objectives (Anthony et al. 2014 ). For example, control mechanisms can be divided into formal and informal control mechanisms (Chenhall 2003 ; Ferreira and Otley  2009 ; Langfield-Smith 1997 ), both of which influence the behavior of subordinates to achieve the organization’s objectives (Merchant and van der Stede 2017 ). Formal control mechanisms comprise specific rules and standard procedures for organizations and allow a manager to control the organization’s objectives (Langfield-Smith 1997 ). Informal control mechanisms are not explicitly designed. They contain the organization’s unwritten rules and often derive from the organization’s culture (Langfield-Smith  1997 ). Altogether, management control systems include strategic planning, budgeting, resource allocation, performance measurement, and pricing (Merchant and van der Stede 2017 ).

However, management control systems are not flawless, and dysfunctional effects can occur when the wrong design is used. An appropriate design is essential, because management control systems are directly related to organizational commitment and trust in managers (Magner et al. 2006 ). Furthermore, perceived fairness influences the behavior of a subordinate (Klein et al. 2019 ; Langevin and Mendoza 2013 ; Little et al. 2002 ) and leads to less budgetary slack, higher job performance, and greater helping behavior (Magner et al. 2006 ). Conversely, dysfunction results in unethical behavior (Langevin and Mendoza 2013 ; Merchant and van der Stede 2017 ), which mainly consists of budgetary slack (e.g., Dunk 1993 ; Libby 2003 ; Merchant 1985 ) and data manipulation (e.g., DeFond and Park 1997 ; Merchant and Rockness 1994 ; Merchant and van der Stede 2017 ).

The negative attitudes and behavior result from subordinates’ perception of injustice (Langevin and Mendoza 2013 ). Such injustice includes a lack of fairness in the distribution of resources and procedural components, such as the distribution of rewards and evaluations, and a lack of appropriate treatment of subordinates, such as efforts to foster respect and dignity (Alexander and Ruderman 1987 ; Folger and Konovsky 1989 ; Langevin and Mendoza 2013 ; Lindquist 1995 ; Roberson and Stewart 2006 ).

Langevin and Mendoza ( 2013 ) developed a framework to avoid perceived injustice and, thus, negative attitudes and behavior among subordinates. This framework includes the properties of participation in target-setting, the controllability principle, the use of multiple performance measures, and feedback quality. These properties form the basis for the later development of the theoretical framework for analyzing properties of feedback mechanisms on digital platforms. We will examine the properties of the framework (see Table 2 ) in more detail.

A property of the framework of Langevin and Mendoza ( 2013 ) is controllability, which implies that the measured indicator should be within the subordinate’s control or influence (Holmström 1979 ; Merchant 2006 ). Performance measures that are not influenceable are related to lower motivation and therefore cause dysfunctional behavior as well as lower performance of the subordinate (Dent 1987 ; Giraud et al. 2008 ; Huffman and Cain 2000 ; McNally 1980 ; Merchant 2006 ; Simons 1995 ). However, the controllability of measures is not observable and is based on the properties of precision and sensitivity (Bisbe et al. 2007 ). Therefore, further properties should be considered to ensure the quality of performance measurement. These properties include precision, sensitivity, and verifiability (Groen et al. 2017 ; Moers 2006 ).

Precision describes the lack of noise or variability of a performance measure (Banker and Datar 1989 ; Burkert et al. 2011 ). Therefore, precision implies the correct representation of what is to be measured. For accurate representation, there should be no uncontrollable factors for the subordinate that cause noise (Burkert et al. 2011 ). If a measure is not precise, a subordinate might be motivated to do the wrong things by false incentives (Kerr 1975 ; Merchant 1990 ). The variance of the actual performance can be demotivating for the subordinate, since performance is evaluated based on the incorrect measure or measures that the subordinate cannot influence (Moers 2006 ).

Sensitivity implies that the measure covers changes triggered by the subordinate’s action (Burkert et al. 2011 ). Therefore, the effort level of the subordinates should be reflected in the indicator (Banker and Datar 1989 ; Burkert et al. 2011 ). Subsequently, if the subordinate improves his/her performance, the measure should increase as well (Moers 2006 ). If the measurement is not sensitive, this could be demotivating for the evaluated subordinate.

Verifiability describes whether the measurement process is objective and verifiable by the subordinate (Moers 2006 ). Consequently, the subordinate should understand what the measure reflects and how it is calculated (Merchant 2006 ). In order to achieve objectivity, a measure should be collected and measured neutrally and free of subjective biases (Globerson 1985 ; Merchant 2006 ; Neely et al. 1997 ; Simons 1995 ). For subjective measures, actual performance may differ from measured performance (Merchant 2006 ; Simons 1995 ). Subjective measures are often associated with an inadequate response to the type of assessment. Subjective measures may make it difficult for subordinates to understand and have confidence in the measurements, potentially leading to a culture of excuses (Merchant and van der Stede 2017 ). Furthermore, subjective measures could lead to lenient ratings and less differentiation of users’ performance (Moers 2005 ). However, using multiple performance measures can reduce the problems of subjectivity (Bommer et al. 1995 ; Henemann 1986 ; Henemann et al. 1987 ; Prendergast and Topel 1996 ).

The next property of the framework of Langevin and Mendoza ( 2013 ) only refers to the use of performance measures, which constitute one type of management control system. In particular, the framework recommends the use of multiple performance measures, because a single measure may not reflect the actual conditions accurately and therefore may not be perceived as fair (Langevin and Mendoza 2013 ). For example, a subordinate whose performance is only measured by single performance measures will maximize his/her bonus, which leads to single short-term results (Ittner et al. 2003 ). Maximizing the bonus does not always reflect the increase in the companies’ value. Therefore, management control systems should include both financial and non-financial measures to cover subordinates’ performance more comprehensively (Burney et al. 2009 ; Ittner et al. 2003 ; Kaplan and Norton 1996 ).

A further property is participation in target-setting. Participation refers to the ability of a subordinate within a target-setting process to influence the objectives (Langevin and Mendoza 2013 ). This influence covers both the targets within their defined process and the control of the outcome (Brownell 1982 ; Langevin and Mendoza 2013 ; Milani 1975 ). Additionally, participation positively affects the perceived fairness of the measure because it causes subordinates to believe that their opinion is considered (Langevin and Mendoza 2013 ).

Feedback quality, the last property of the framework, relates to the managers’ response to the subordinates’ performance (Langevin and Mendoza 2013 ). This response should be clear, timely, and accurate (Magner et al. 2006 ). If there is a gap between the subordinate’s behavior and the measurement, this can lead to less motivation and lower performance on the part of the subordinate (Merchant 2006 ). Furthermore, feedback should cover information about the subordinate’s past behavior (Ilgen et al. 1979 ). In particular, consistency and accuracy are essential for the quality of feedback on a subordinate’s performance, as the subordinate perceives the feedback as informationally fair within a feedback procedure (Roberson and Stewart 2006 ). Higher feedback quality is directly related to a greater trust of subordinates in the management control system (Hartmann and Slapničar 2009 ).

Currently, there is a discussion in the literature on management control systems. Specific mechanisms can be regarded as a bundle or as part of a system (Grabner and Moers 2013 ). Therefore, a particular management control system affects the perceived fairness and, thus, the attitude and behavior of the subordinate. Moreover, all management control systems and the resulting interdependencies affect the subordinate collectively. Consequently, other characteristics should be considered in addition to those listed by Langevin and Mendoza ( 2013 ).

2.3 Theoretical framework—taking a management control perspective for analyzing properties of feedback mechanisms on digital platforms

To analyze the effects of design characteristics on the function of feedback mechanisms, we take a management control perspective. Therefore, we use properties given in a management control literature as a link between specific design characteristics and function.

However, the properties within the management control literature, such as the framework identified by Langevin and Mendoza ( 2013 ) refer to the control or performance measurement of subordinates within an organization. In the context of feedback mechanisms of digital platforms, this subordinate exists only figuratively; that is, in this context, the term “subordinates” refers to the users who interact via the platform. The design of the feedback mechanism can affect only the operator of products or services (one-sided feedback) or the users of all market sides (multi-sided feedback). We translate the management control system properties (introduced in Sect.  2.2 ), to the context of feedback mechanisms with regard to the design characteristics (introduced in Sect.  2.1 ) because, similar to the organizational setting of the management control systems, the specific behavior of the users, or dysfunctional effects can be caused by design on the part of the operator. We do not transfer these dysfunctional effects to an instruction on how to give feedback, because the users react to certain arrangements, and by a specific design, these dysfunctional effects can be avoided in advance.

Based on differences between a subordinate and a user of a platform, there might also be differences or performance measures within organizations. Based upon the properties and the differences between feedback mechanisms and management control systems, we develop a theoretical framework, illustrate individual components, and discuss negative behavior of platform users caused by non-compliance with management control systems properties. These properties include precision (e.g., Burkert et al. 2011 ; Groen et al. 2017 ; Moers 2006 ), sensitivity (e.g., Burkert et al. 2011 ; Groen et al. 2017 ; Moers 2006 ), verifiability (e.g., Groen et al. 2017 ; Moers 2006 ), multiple performance measures (e.g., Klein et al. 2019 ; Langevin and Mendoza 2013 ; Ittner et al. 2003 ), participation in target setting (e.g., Langevin and Mendoza 2013 ), and quality of feedback (e.g., Klein et al. 2019 ; Langevin and Mendoza 2013 ; Magner et al. 2006 ; Roberson and Stewart 2006 ). Table 3 shows the properties of the framework with their explanations and differentiations from the properties of management control systems.

Precision within a feedback mechanism means that the low scores are not affected by noise (i.e., factors outside the control of the rated user). Depending on the feedback mechanisms’ reciprocity, the mechanisms measure the performance of the different market sides. If the measured submission categories are beyond the control of the rated users, and these users cannot influence the requirements, this can lead to non-compliance with the property. In addition to uncontrollable factors (e.g., submission categories beyond the user's control), noise can also result from qualitative feedback consisting of ambiguous texts (Mudambi et al. 2014 ), which in turn includes content that cannot be influenced. Precision is associated with the incentive to use performance measures (Moers 2006 ). Disregarding precision in the context of feedback mechanisms could, therefore, result in users using feedback neither to improve performance nor to decide on transactions. Consequently, the quality of the external resources would not increase.

Sensitivity is a property that is directly related to feedback mechanisms. Better performance of a platform’s users should lead to better feedback. However, if sensitivity is not ensured, it might be demotivating, and users will no longer attempt to improve their performance. Since each piece of feedback is subjective, better performance should be represented by the diversity of provided feedback. For example, disregarding sensitivity leads to the evaluation not accurately reflecting the user’s efforts. Although an improvement might not be reflected in every rating due to the subjectivity of single evaluations, an increase in performance should at least lead to a change in the overall rating. Depending on the mechanisms’ reciprocity, this change should comprise all market sides included in the evaluation. Moreover, sensitivity could be affected by the scale level.

In the context of feedback mechanisms, verifiability implies that users should be able to understand each piece of feedback. Feedback only consisting of a summary of all given feedback is not verifiable, because the evaluated user does not know how the feedback mechanism calculates the overall rating. For instance, outliers could be present. In addition to the possibility of inspecting all single instances of feedback, the evaluated user should understand the scoring rule (i.e., how the overall rating is calculated). For example, each feedback could be weighted differently for various categories or their actuality. In such cases, users should understand how individual feedback or categories are included in the overall rating. Moreover, the use of the design characteristics filter, sorting, symbol, or color could affect the perception of the rating and thus the verifiability of a mechanism.

Due to the feedback being provided by the platform’s users, personal feedback is not objective and represents a variety of different opinions. This subjectivity may cause demotivation (Ittner et al. 2003 ; Prendergast and Topel 1993 ). Also, tensions and anger can arise between users (Merchant and van der Stede 2017 ) and may even lead to favoritism (Prendergast and Topel 1996 ). Furthermore, the outcome effect and the hindsight effect could occur (Butler and Ghosh 2015 ; Ghosh 2005 ). However, this subjectivity might be reduced by the presence of a large amount of feedback and the use of a variety of measures.

Regarding feedback mechanisms, multiple measures require that the mechanism comprise several categories rather than only an overall rating. If there is only one overall rating within a feedback mechanism, no conclusion can be drawn about the actual score. For instance, if a user receives a rating of three on a scale of five, it is not clear whether the issue with the interaction was poor communication, poor service, or an unacceptable product. Therefore, this user does not know how to improve his/her performance and tries to maximize the overall rating without considering categories that are important for the entire platform ecosystem. Consequently, quantitative feedback should be kept simple by including multiple categories to help users understand the feedback and to ensure its quality. Unless several quantitative measures exist, feedback mechanisms should additionally include qualitative feedback. Similar to other management control systems, using various measures within a platform’s feedback mechanism should help to decrease dysfunctional effects caused by subjectivity, such as favoritism.

In the context of feedback mechanisms, the property of target-setting differs from the target-setting within the performance measurement of subordinates. In the case of subordinates, managers can specify goals individually. Conversely, in the context of feedback mechanisms, operators can set minimum standards, but they do not define a specific target for users. Target-setting within qualitative feedback could include the limitation of negative feedback. Within quantitative feedback, performance is measured using predefined scales. The objective, in this case, is to receive the highest value on a given level. If the operator of a digital platform involves the users in the target-setting, this will result in specific benefits for many users. However, other users who are facing an interaction decision might no longer be able to compare users based upon feedback, which may result in an increase in search costs and weaken or remove the essential advantage of feedback mechanisms. In summary, users cannot participate in the objectives of the platform operator. Consequently, this control mechanism property is not transferable to platforms.

The property of feedback quality also exists within a platform’s feedback mechanism. Feedback quality does not refer to the individual user that provided feedback but instead to the evaluations of the feedback given. For example, users can evaluate whether the feedback is useful or not. In particular, the quantification of the perceived usefulness of feedback is vital for future interaction decisions (Lee et al. 2018 ; Liu and Park 2015 ; Mudambi and Schuff 2010 ; Schindler and Bickart 2012 ). Like feedback from a manager on a subordinate’s performance, platform users should provide feedback promptly and accurately. This kind of feedback is essential for users facing a transaction, because they put more weight on recent feedback (Bolton et al. 2004 ). Feedback evaluation helps to identify actual and accurate quantitative feedback. Since users do not directly consider temporal changes, this causes a self-selection bias in interaction decisions (Li and Hitt 2008 ). The evaluation of perceived usefulness by the platform’s users sorts the comments with variable content according to their importance at low cost. This evaluation makes the comments that are identified as useful visible first, which can reduce the users’ search costs, build reputation and trust, and reduce the cost of individual interactions (Ba and Pavlou 2002 ; McDonald and Slawson 2002 ; Melnik and Alm 2002 ). Additionally, users can comment on qualitative feedback. Consequently, other users can use the feedback evaluation to check for feedback noise.

To answer our research questions, we conducted a descriptive analysis of various feedback mechanisms. We analyzed their characteristics and compliance with the standards. Based on the results of the descriptive analysis, we accomplished expert interviews to gain insight into the specific feedback mechanism and to unravel reasons for different choices.

3.1 Descriptive analysis

The theoretical framework was developed by translating standards given in the management control system literature to avoid dysfunctional effects such as negative attitudes and behavior. Our analysis focused on the feedback mechanism properties of precision, sensitivity, verifiability, multiple measures and feedback evaluation. However, we did not consider interdependencies with other management control systems.

Inspired by Steur and Bayrle ( 2020 ), we chose a selection principle similar to that of Dorfer ( 2016 ) to examine the specific platform feedback mechanisms’ compliance with our theoretical framework design properties. The selection of platforms with a feedback mechanism to be evaluated was based on data from Alexa Internet Inc. Alexa provided a ranking of the most popular websites from Germany based on a global traffic panel consisting of millions of Internet users. The rank of each website was calculated using average daily visitors and the estimated number of page views over the last three months (Alexa 2018 ). We used the ranking of top sites in Germany on September 25th, 2018. The German ranking was chosen following Dorfer ( 2016 ). The restriction to the German ranking enabled a similar selection and helped to deal with legally dubious websites. The ranking, limited to 500 websites, was not representative of all platforms using feedback mechanisms. However, it was an approximate representation of the most popular sites and their corresponding business models in Germany (Becker et al. 2009 ; Dorfer 2016 ). Consequently, we assumed that the sample included the most important and widely used platforms, enabling us to cover a wide range of different platforms. Within this descriptive study, this approach provided internal validity and is free of bias (Dorfer 2016 ).

Starting from 500 websites, we selected relevant platforms using three steps and then evaluated their feedback mechanisms. Figure  1 illustrates the selection process of platforms with feedback mechanisms and the evaluation of these mechanisms. In particular, it shows the selection at the individual stages as well as the evaluation criteria of the unique properties. Appendix A shows the different websites and, where relevant, the specific reasons for exclusion.

figure 1

Evaluation method

First, we carried out a formal examination. We removed duplicates that used multiple domains. Websites with pornographic or legally dubious content were also excluded from the analysis, as cybercriminal activities were often associated with them (Dorfer 2016 ). To review the websites, we used the evaluations of Dorfer ( 2016 ) and  Steur and Bayrle ( 2020 ) and web reputation tools such as Trend Micro Inc. and Web of Trust. Like Steur and Bayrle ( 2020 ), we excluded sites that did not offer a German, English or French version due to language limitations. We also excluded platforms that required confirmation of a Google account or phone number or that did not provide free access to the platform. There were three reasons for this exclusion: First, the websites without free access could not be checked for the presence of a platform-based business model. Second, we could not verify these websites for the presence of a feedback mechanism because this mechanism was only visible after logging in. Third, these websites could not be analyzed for compliance with the framework’s properties.

In the next step, we checked the remaining 336 websites for the existence of a digital platform using the same characteristics than Steur and Bayrle ( 2020 ). In particular, a platform required the presence of two or more market sides, an intermediary providing a digital infrastructure, interactions between the different market sides, and network effects (Armstrong 2006 ; Brousseau and Penard 2007 ; Rochet and Tirole 2003 ; Tiwana 2014 ). For the selection, we used a similar classification of websites developed by Dorfer ( 2016 ). Editorial content websites, internet sites of non-profit organizations, mail services, online shops or internet sites of pipeline business models, online marketing domains for web tracking online converters, shortening and link management services and websites of (trans-) governmental institutions did not meet these criteria. Thus, we excluded these types of websites from further analyses. Website types that did represent a digital platform according to the criteria and thus are considered in the further evaluation are as follows: marketplaces, rating platforms, social media platforms, and travel platforms.

Next, we checked the remaining 102 platforms for the existence of a feedback mechanism. For the presence of a feedback mechanism, a qualitative or quantitative mechanism, recognizable by a comment function or different symbols, had to be present. Following the platform selection, we analyzed the properties of the remaining 58 platforms’ feedback mechanisms. The final analysis of the properties was conducted in summer 2019. In a detailed analysis, we categorized the platforms according to their types. For this purpose, we used the different types that we classified in step two. Appendix B summarizes the platforms with a feedback mechanism and the type of platform.

Within the analysis, we used existing items from the management control systems literature to assess the applicability of the frameworks’ properties. All measurement instruments are presented in Appendix C . To check for precision, sensitivity, and verifiability, we used items developed by Moers ( 2006 ) that had already been used by other authors such as Burkert et al. ( 2011 ) and Groen et al. ( 2017 ). Since we utilized single items within the analysis, we selected the particular items of the management control systems quality in accordance with Groen et al. ( 2017 ). Groen et al. ( 2017 ) also analyzed the precision, sensitivity, and verifiability of single items based on the constructs of Moers ( 2006 ). The item for precision related to feedback mechanisms that only measure what the evaluated user can influence. The item for sensitivity referred to the fact that excellent performance of the evaluative user is directly reflected in better feedback. The item of verifiability applied to the measurement of user performance being verifiable.

We checked multiple measures using a construct based on Widener ( 2006 ), who provides a questionnaire to analyze the use of management control systems in bonus compensation. In particular, Widener ( 2006 ) examined plans that include both financial and non-financial measures. Other authors such as Klein et al. ( 2019 ) used the items for multiple measures. In the context of feedback mechanisms, the multiple measures item indicates that the mechanism is based on both multiple quantitative and qualitative measures. We constructed the item on evaluating feedback according to Hartmann and Slapničar ( 2009 ). More precisely, we used question five of their questionnaire as a reference. The item feedback evaluation covered users who had given useful evaluations on previous feedback.

The evaluation of the measuring instruments was not carried out based on a survey of the platform operators or the users. Instead, we evaluated the instruments ourselves. For this purpose, we collected information about the feedback mechanisms on the respective websites provided by the platform operators and examined the structure and design of the previously offered feedback. For a better understanding of each platform’s feedback mechanism, we provided feedback ourselves. Subsequently, we could assess the items. We conducted several steps to ensure reliability. First, two researchers conducted an independent analysis of the platforms. Then, the results were compared, discussed several times, and the platforms were rechecked.

In addition to the properties of our framework, we analyzed the feedback mechanisms of the selected platforms concerning their design characteristics. This analysis provides an overview of the analyzed feedback mechanisms. In particular, we examined the frequency and portion of the feedback characteristics introduced in Table 2 . The analysis of the reciprocity, query method, submission categories, feedback evaluation, filter, and sorting involved the entire sample of 58 platform feedback mechanisms. The symbols, colors, and scale levels, however, applied only to the quantitative feedback display. Therefore, our examination only included a sample of 56 platforms. We did not check the design characteristic “submission restriction” of the feedback mechanisms because the analysis of this characteristic required the execution of transactions on each platform.

3.2 Qualitative analysis

In addition to the descriptive approach, we chose to use a qualitative research approach (Yin 2009 ) based on interviews to identify reasons for different design approaches for a feedback mechanism. Specifically, we used a cross-case analysis (Eisenhardt 1989 ). Therefore, we were able to compare the results among different platforms. The information from all interview transcripts was first synthesized, and the transcripts were analyzed using selective coding. We chose selective coding due to the late period in the study, and the different categories resulted from the descriptive analysis that needed further explanations (Corbin and Strauss 1990 ).

Therefore, we developed a semi-structured interview to include the three categories remaining from the descriptive analysis:

application of a feedback mechanism,

characteristics of feedback mechanism,

feedback mechanism properties.

Within the interviewee selection process, we ensured that the interview partners were part of a platform and thus complied to the same criteria as the platforms within the descriptive analysis. Furthermore, our study focused on companies located in the DACH-region (at least one subsidiary) and covered platforms from different industries. To find reasons why platforms used or did not use feedback mechanisms, the analysis included platforms that currently did not use a feedback mechanism. In total, we contacted 87 platforms and requested for interviews, which resulted in 14 interview partners. Five platforms did not use any feedback mechanism. The interviews were conducted by telephone, recorded and transcribed in autumn 2019 and lasted between 32 and 88 min, with an average of 60 min. To avoid misunderstandings, we then sent the transcripts to the interviewees, who rechecked the transcripts. Table 4 gives an overview of the anonymized interviewees, their platform type, their foundation date, number of employees, the revenue of the platform, and whether they used a feedback mechanism.

In order to ensure the reliability of the data, two researchers conducted the data analysis independently. We then compared and discussed the emerging results several times. These emerging results were mostly similar. Appendix D provides an overview of the individual categories, the associated constructs and corresponding examples.

4.1 Results of the descriptive analysis

The frequency of design characteristics containing the absolute and relative frequencies examined in 58 platform feedback mechanism are shown in Table 5 .

Descriptive analysis reveals that 57% of the analyzed digital platforms use a feedback mechanism. A further examination of the design of the individual feedback types reveals that only 3% of digital platforms use multi-sided feedback. There are differences in the method within the feedback mechanisms. Most platform feedback mechanisms have both qualitative and quantitative elements (91%). A few (8) platforms use either qualitative or quantitative parts. In particular, 5% of digital platforms only use qualitative feedback, whereas 3% only use quantitative feedback options. Within the qualitative feedback, there are differences in the maximum number of characters. However, most feedback mechanisms (73%) contain only an overall rating. The remaining platforms use up to 12 categories within the feedback. Feedback evaluation is possible on 53% of the platforms. The scales used within the quantitative components also vary and consist of up to 25 levels. However, most digital platforms with a feedback mechanism use a five-point scale (52%).

In our sample, 66% of the platforms do not use a filter. Some platforms classify qualitative feedback by keywords (e.g., Amazon’s marketplace). Five platforms provide an overview of the top terms within the qualitative part of their feedback mechanism. However, one of these platforms offers an additional filter corresponding to the rating. The filter by rating is provided by 10% of the platforms. Also, this filter is used in combination with other filters. In addition to the rating, these filters include language (two platforms), transaction type (two platforms), photos (one platform) or date (one platform). Further, filters within feedback mechanisms include the transaction type and the submission categories (one platform each). The platforms also offer different forms of sorting (33%). These forms include multiple sorting (28%) in addition to the date (5%). The date is involved in all offered filters. Other filters include rating, usefulness, language, user activity, photos, and videos.

Digital platforms use star symbols most frequently (48%). Heart symbols (12%), arrow symbols (9%), and thumb symbols (9%) are used less commonly. Platforms most often use a yellow color (31%) followed by a blue color (14%) for the symbols within their feedback mechanism.

The explorative analysis shows different compliance of the platforms with the properties, as well as also differences between platform types (Table 6 ).

Most platforms comply with sensitivity (55 platforms) and verifiability (56 platforms). Platforms less often comply with the other properties. For instance, almost half of the platforms use feedback evaluation, while precision and multiple measures are rarely considered. In particular, only seven feedback mechanisms comply with precision, and 17 feedback mechanisms achieve multiple measures.

We find further differences in compliance with the properties of our framework between different platform types. However, these differences are not apparent with regard to sensitivity and verifiability, as almost all platforms fulfil these properties. Instead, we find differences in precision and multiple measures. Only one of the social media platforms achieves both properties. In contrast, some of the marketplaces, rating platforms and travel platforms fulfil precision (18–22%) and the multiple measures (44–100%).

To obtain first indications about the possible interrelations between the properties, we analyze their co-occurrence (Table 7 ).

Co-occurrence analysis shows further results. For example, whenever a feedback mechanism complies with precision, the mechanism also fulfills sensitivity, verifiability and multiple measures. The feedback evaluation also often co-occurs with precision (71%). In addition to precision, verifiability is usually satisfied if sensitivity is met. However, precision (13%), multiple measures (28%), and feedback evaluation (48%) rarely co-occur with sensitivity. The same results are obtained in relation to the co-occurrence of verifiability. Sensitivity (96%) often co-occurs, while precision (13%), multiple measures (30%), and feedback evaluation (55%) co-occur less frequently. Multiple measures often coincide with sensitivity, verifiability (100% each), and feedback evaluation (65%). However, precision rarely co-occurs (41%). Likewise, we observe a concomitant presence of the feedback evaluation and precision (16%). Sensitivity (97%) and verifiability (100%) often occur concurrently, compared to rare co-occurrence with precision and multiple measures.

4.2 Results of the interviews

This section contains the main findings of the interviews for three categories “application of a feedback mechanism”, “design characteristics of feedback mechanisms”, and “feedback mechanism properties”, which are summarized in Table 8 . In detail, we present the results for the individual categories.

4.2.1 Application of a feedback mechanism

The analysis shows several results regarding the implementation of feedback mechanisms. The results include reasons for but also arguments against the use of feedback mechanisms. One issue that interviewees mention as a reason for introducing a tool is the control of user behavior. The experts point out that feedback mechanisms are primarily related to trust. They justify this argument with the importance of social proof, which implies that users increasingly base their transaction decisions on customer ratings and have more confidence in them than in advertising. The experts assess the feedback mechanisms as necessary not only for users’ decisions regarding transactions but also for the behavior of the suppliers. For example, platform 12 mentions that their “suppliers are very curious about the feedback, especially why and what kind of feedback they get”. A further point about behavior control is the infrastructure used, such as mobile devices. In this context, platform 8 notes that “the faster people are asked for feedback, the more people give feedback.”

However, behavior control can also be used as an argument against the application of a feedback mechanism. Platform 14 explains that they do not use a feedback mechanism because users are very price-sensitive and tend to base their decision on price. Therefore, feedback would not be used for a transaction decision. Further, platform 1 names a negative effect on the conversion rates as a further argument against the usage of a feedback mechanism. Platform 10 mentions another argument against using a feedback mechanism, stating that they do not use any feedback mechanism because “the users were in contact before using the platform”. As a further argument, platform 14 states that there are few suppliers within their branch. If platform 14 had a feedback mechanism and one of the providers got a lot of bad ratings, platform 14 would have to remove that provider. However, this would result in losing a significant part of the offering and content, which would be a considerable disadvantage compared to other competitors.

Another argument against the use of a feedback mechanism represents the development stage of the platform. According to the experts, many platforms are still in the early stages of their development and have therefore not yet implemented a feedback mechanism. For instance, platform 6 states that for the use of “automatic mechanisms, you first need a critical mass [of users], which we do not have at present.” Moreover, the interviewees argue that a platform first needs a sufficient number of users on the platform. Considering a limited budget, the operator focuses on this task first and tries to acquire users. Once sufficient transactions have been made, and sufficient feedback can be obtained, it seems reasonable to introduce a feedback mechanism.

The costs of implementation and monitoring also contribute to the experts’ decision regarding whether to use a feedback mechanism. The experts argue that the mechanism must be integrated procedurally into the platform and that this carries development costs. Besides the implementation, the feedback mechanism also involves rating costs for users, such that some platforms worry about extreme ratings (e.g., only positive and negative feedback). For instance, platforms 9 and 13 recognize such extreme ratings (e.g., 80% of their ratings are positive, and a relevant number of ratings are negative). However, the submission of feedback is not a problem, which is attested to by a relatively good response rate (e.g., 7% to 14%, platform 9). A further aspect of the application of a feedback mechanism is the cost of handling manipulated feedbacks. For instance, platform 9 uses “a large number of software programs that detect manipulation and prevent such ratings from going live at all” and has a separate compliance department, which takes care of manipulations.

The use of other control mechanisms is another explanation for the lack of use of feedback mechanisms. For example, platforms 3 and 10 state that they do not use a feedback mechanism, as they use performance measures or other control mechanisms of supplier evaluation to ensure quality. Platform 3 prefers to use performance measures to provide its suppliers with reliable and objective information.

Moreover, the platform type and the variety of supply are reasons against the application of a feedback mechanism. For instance, platform 3 does not use a feedback mechanism because, in their business-to-business environment, there are few transactions per product offered and the customer of a product is not necessarily the user of that product. However, the platform type can also be a reason for using a feedback mechanism, as this mechanism is part of the business model of rating platforms. In addition to the platform type, the need for an explanation of the products and services offered is a reason for not using a feedback mechanism.

4.2.2 Design characteristics of feedback mechanisms

In the next step of our analysis, we obtain the frequency of the design characteristics of feedback mechanisms (Table 9 ). For example, only platform 2 uses a multi-sided mechanism, noting that “there are always two sides involved in the trade. Therefore, both sides should have the possibility to evaluate the trade.” All platforms use both qualitative and quantitative feedback. Most platforms even have several categories. However, most of the platforms have not implemented the feedback evaluation. Almost all platforms use a scale of five with stars. In contrast, the color used within the mechanism varies. Additionally, most platforms use one or more filters. In terms of sorting, the platforms either have multiple sorting options or do not use any sorting.

In addition to the frequencies, the interviews also reveal reasons for the use of individual design characteristics. These reasons include other platforms, detailed information vs simplicity, psychological factors, and implementation and monitoring costs.

Most interviewees use other platforms as inspiration or have even copied the mechanism of another platform. The experts cite Amazon, and eBay as examples of inspiration. Platform 12 went one step further and even “hired people who previously worked at Amazon”. This orientation towards other platforms is the main reason for the use of multiple categories and star symbols. For instance, platform 2 claims that they use stars because “they are also used on Amazon and eBay”. The orientation to other platforms clarifies the example of platform 11, which previously used black stars. They are currently switching to yellow stars “because they are the common standard and users have learned this”. In summary, the interviewees name the well-established status, the popularity among users, and the simple and fast development of the mechanism as reasons for inspiration.

The experts highlight the trade-off between detailed information and simplicity as another essential factor in designing the feedback mechanism. This trade-off includes the design characteristics of query method, submission categories, scale level, filter, sorting, symbols, and colors. For instance, a simple design should “keep the barriers [to providing feedback] low” (platform 4) and should help to get more feedback. Moreover, this trade-off also refers to users deciding on a transaction and needing detailed information. For example, the experts mention an adverse effect of individual design characteristics (e.g., filters and sorting) on conversion rates as a reason for the design of a feedback mechanism.

To create simplicity, platform 1 is switching to mobile-first so that an app will display the complete transaction. App users are less willing to write text; therefore, the experts plan to introduce several quantitative criteria. The experts explain that spoken feedback also plays a role in this transition. Additionally, simplicity is also related to the platform’s decision regarding the cost of development and the value added by the particular design characteristics. For instance, the experts argue that a filter might not be used because relevant groups or filter categories first have to be defined, which could be very expensive, in the case of high product variety.

Psychological factors are listed as further factors for using symbols and colors. Platform 2, for example, states that they chose their color because it is “complementary to our other colors […] [and the displayed feedback] is not a rating of our platform”. Moreover, the experts claim that yellow stars reflect a kind of value and should symbolize trust. The colors should also support the users in their perception of the feedback. While red indicates a problem, green indicates that everything is okay.

4.2.3 Application of feedback mechanism properties

None of the platforms take the feedback mechanism properties such as precision into account in the design of their mechanisms. However, the interviews indicate that customer experience vs accessibility, detailed information vs simplicity, and supply and diversity are crucial factors in terms of compliance with feedback mechanism properties.

An essential factor for the interview partners is the trade-off between customer experience and accessibility. The customer experience is composed of many variables and of variables that the supplier, cannot control, so the customer experience and accessibility differ (platform 10). For instance, the experts argue that the evaluators should understand the supplier’s business model to assess criteria that can be influenced. Moreover, the rare compliance with feedback evaluation within our descriptive analysis could result from an unclear assignment of the most helpful feedback for the decision and the fact that users base their decision on several instances of feedback (platform 3).

Furthermore, the experts explain the rare compliance of feedback mechanisms with precision and multiple measures within the descriptive analysis using the trade-off between detailed information and simplicity. For example, they note that if precision is fulfilled, the provision of feedback is more complicated, which in turn leads to higher rating costs compared to feedback mechanisms that do not comply with this property. Also, the interviewees mention the faster provision of feedback and the associated evaluation costs as reasons not to comply with the multiple measures. In contrast, the compliance with multiple measures even makes it possible to get detailed information and to compare it with aspects that are important to a user (platform 11).

Another explanation for compliance with feedback mechanism properties is the time and capital invested by users. In particular, these reasons emerge about different platform types. The experts attribute spent time and money both to the use of transaction decisions and to the provision of feedback. For example, the experts state that trips are only made once a year and that this typically involves higher invested capital than in many marketplaces. In this case, the user wants to get as much information as possible about what he or she is spending his or her money on and thus reads the previous evaluations very carefully. The users also honor this information requirements in the feedback. The higher the invested capital, the more aware users are of the importance of feedback and the more willing they will be to give more detailed feedback.

According to the interviewees, the supply and diversity of products and services offered on the platform are essential factors in the fulfilment of the feedback mechanism properties (i.e., precision and multiple measures). In terms of precision, platform 3 mentions that it is difficult for operators to define standardized, influenceable rating categories due to the variety of products. According to platform 5, the precision depends on whether the supplier offers the complete service itself and can thus also influence the complete outcome. Also, platform 8 attributes the fulfilment of this property to the emotionality of the products. Platform 5 identifies the degree of personality as another reason for compliance with multiple measures. The more personal the performance is, the more detailed and differentiated the feedback has to be, or the more open the users are providing detailed feedback. In contrast, within social media platforms, getting feedback is more critical than within the other platform types. Therefore, social media platforms might not comply with precision or multiple measures to get more feedback and a higher distribution reach of the posts on the platform (platform 14).

5 Discussion

Some of our results are counterintuitive. The rare application of feedback mechanisms is surprising considering the relevance of feedback mechanisms for a platform’s control, as mentioned by Kornberger et al. ( 2017 ). The limited use is unexpected because some authors, such as Bolton et al. ( 2004 ) and Resnick and Zeckhauser ( 2002 ), have found out that feedback mechanisms have a positive influence on trust in the market or the platform and thus on the respective price and transaction volume. However, the interviews reveal the reasons for this low usage. The example of Uber provides a further explanation, as Uber’s users do not use the given feedback when making decision interactions (Knee 2018 ). This illustrates that the success of a platform does not necessarily require a feedback mechanism in all cases. Subsequently, we recommend that platform operators check whether their users use feedback mechanisms for their transaction decision, so as to analyze their impact on conversion rates. If they have enough users (i.e., users who could give feedback), they should introduce a feedback mechanism.

Moreover, the rare use of multi-sided feedback is surprising. However, the example of Amazon’s marketplace could provide further insights. Amazon’s marketplace uses other intermediaries (e.g., Visa) to ensure payment and to avoid opportunistic behavior of one market side. Consequently, the additional intermediary guarantees that the respective user of a market side fulfils his/her obligations. Therefore, the operator has more information than the customer and could behave opportunistically. The feedback mechanism should reflect this information asymmetries based on previous transactions and reduce information asymmetries. A reciprocal feedback mechanism is only necessary if users of one market side have more information than users of the other market sides and there might be opportunistic behavior by more than one side. Within the platform Airbnb, for instance, guests can act opportunistically by destroying the inventory or by polluting the apartment. Feedback mechanisms might help to prevent such cases before the transaction.

The low use of reciprocal feedback confirms the results found by Bolton et al. ( 2013 ). Within complementary feedback mechanisms, which inform users about the feedback immediately after the submission, there is dysfunctional user behavior. In this case, the feedback neither improves users’ performance nor supports users in deciding on interactions. Subsequently, there is no overall improvement in the value creation of the platform. Within two-sided blind feedback or an additional one-sided feedback option for customers, operators can avoid this misconduct, even if the amount of provided feedback is lower than without the restriction (Bolton et al. 2013 ). Despite the popular feedback mechanisms designs to prevent the disadvantages of reciprocal feedback, future research can examine when platform operators should use one-sided and multi-sided feedback mechanisms. Due to the well-known behavior within reciprocal mechanisms, we propose that operators in the next step should check how the information asymmetries are distributed. If these asymmetries are only distributed to one market side, the operator should first use a one-sided mechanism. However, if a reciprocal mechanism is used, we recommend using double-blind feedback or even a detailed seller rating in accordance with Bolton et al. ( 2013 ).

The frequent use of star symbols is consistent with the findings of Sparling and Sen ( 2011 ), who also reported that the star is the most preferred symbol. Unlike in Sparling and Sen ( 2011 ), thumb symbols are rarely used in our sample, and heart symbols are used frequently. Most of the experts mention the simplicity and popularity of the symbols as a reason for their frequent use. On the one hand, this result is expectable, since the Internet offers maximum transparency, and therefore competitors can use the same symbols relatively easy. On the other hand, it is surprising that the operators rarely think about the effect of the choice of the symbols on users’ behavior (except for the conversion rates). Not to limit the feedback mechanism using star symbols within the feedback mechanisms simply because most other platforms seems to be an interesting approach. Star symbols could be used in a first version but should be evaluated thereafter for their impact on providing feedback and transaction decisions (e.g., conversion rates).

Since users often give extreme ratings (i.e., if they are very satisfied or very dissatisfied) (Dellarocas and Wood 2008 ), the frequent use of a five-point-scale is unexpected (as, e.g., “very satisfied” and “very dissatisfied” can be shown on a two-point scale). The rare use of scales with more than five levels can be explained by the higher rating costs (Sparling and Sen 2011 ). Nevertheless, a scale level of two could be used more frequently. The evaluation costs for the users would be lower, and many ratings in the middle of the scale (e.g., level 3 in a 5 point scale) (Sparling and Sen 2011 ) could be avoided. Likewise, such a scale can prevent extreme ratings, such as in the case of Airbnb and TripAdvisor, where the users have a proportionally high average score of over 4.5 on a five-point scale (Zervas et al. 2015 ). We suggest operators not merely to adopt a scale of five from other operators within the mechanism. Rather, they should consider the impact of the scale used on the provision of feedback. In the case of extreme ratings, for example, the scale could be reduced to two levels. However, since few experts assess extreme scores, most of them notice normal distribution, and non can see a negative impact on submission, the five-point scales seems to be a good starting point.

The rare compliance with precision is unexpected in the first place and could explain the negative attitude and behavior of users reported by Abramova et al. ( 2016 ). However, the need for influenceable factors is controversial topic in the literature. For instance, Klein et al. ( 2019 ) found that the application of controllability is not significantly related to perceived justice. Although controllability is regarded as necessary, it is rarely used in practice due to its complexity and uncertainty. Controllable measures are optimal for the subordinate, but not for the entire organization, so uncontrollable factors must be measured to bring these factors into focus (Burkert et al. 2011 ). Differences in the application of controllability exist with regard to the subordinate. To achieve complete leadership and to draw the attention of a subordinate to critical points, subordinates could use non-influenceable categories (Burkert et al. 2011 ). Moreover, platform operators could deliberately use non-influenceable categories because they want to set incentives for investment in innovations (Boudreau 2010 ). The low co-occurrence of multiple measures and precision, as well as the results derived from the interviews, reveal a similar phenomenon to that described by Burkert et al. ( 2011 ) in the context of a manager. According to the experts, these categories may be necessary for the entire ecosystem and support users who are facing an interaction decision. Therefore, the platform operators focus on customer expertise and use categories that the supplier cannot control. Submission categories that can be affected by the evaluated users seems to be an interesting approach. However, if other uninfluenceable categories are essential for users for their transaction decision and thus for the conversion rates, we recommend including these categories separately and not including them in the overall rating of the user. For example, a separate view could be introduced for user ratings or travel location ratings within a travel platform. Nevertheless, it should be clear to users what should be measured and how the measurement works. Moreover, the operator should be aware of the time users spend on the platform and the capital they invest when doing a transaction.

Using various measures can prevent dysfunctional effects of subjective measurements (Ittner et al. 2003 ; Prendergast and Topel 1993 ). Thus, the rare use of multiple measures is quite surprising. It is questionable how many measures a feedback mechanism should include to avoid subjectivity, such as more lenient scores and less differentiation (Moers 2005 ). However, the trade-off between detailed information and simplicity mentioned by the experts explains the low compliance with this property. We suggest avoiding the use of only an overall or textual evaluation. Operators should pay attention to the trade-off between simplicity and detailed information. In particular, the effects of the feedback mechanism design on feedback provision and conversion rates should be considered.

Another unexpected result is the rare compliance with feedback evaluation. The possibility to evaluate feedback contributes to the reduction of potential dysfunctional effects. Furthermore, subjective performance measurement only operates if trust is high (Simons 1995 ). However, this operation is not clear concerning feedback mechanisms, as these are intended to reduce the information asymmetries between different market sides and create trust. The low compliance with the feedback evaluation is unexpected, because other researchers such as Klein et al. ( 2019 ) report that feedback evaluation has a significant influence on perceived justice. However, feedback evaluation is related to higher rating costs for the users. Therefore, according to the results of the interviews, the platforms do not use feedback evaluation to set incentives for users to give feedback. We propose that operators should introduce feedback evaluation only if this evaluation does not reduce the amount of feedback given. Additionally, the platforms should ensure that users provide sufficient feedback evaluations and that users use these evaluations to decide on transactions.

Table 10 summarizes our results and the differences from the current literature and provides recommendations for designing feedback mechanisms.

6 Conclusion

Our study used a descriptive approach to conceptually outline the field of feedback mechanisms, thereby enabling the understanding and design of this type of control mechanism in research and business practice. Since feedback mechanisms can be interpreted as a specific type of control mechanism refer to the research on control mechanism that is implemented as a means to influence users to implement the platform provider’s strategy, we examined the transferability of management control system properties to a platform’s feedback mechanism. Therefore, we developed a framework for designing a feedback mechanism based on management control system properties. Subsequently, we examined the use of properties in existing feedback mechanisms. For this purpose, specific feedback mechanisms were analyzed, and the compliance with different management control systems properties was evaluated. The findings suggest that platforms have considered some but not all of the properties of management control systems theory. We also discussed within 14 expert interviews reasons for differences in compliance, which revealed a desire for simplicity and the provision of feedback as primary reasons.

This paper contributes to the literature in the following ways. First, it empirically shows how digital platforms design their mechanisms and provides insights into their decisions. In particular, it gives a morphological box of various criteria. It indicates dominant patterns in nearly all design characteristics (e.g., query method, submission category and scale level). Moreover, the interviews reveals that simplicity and the provision of feedback are essential. Second, our analysis shows to what extent existing feedback mechanisms of digital platforms as decentralized control mechanisms were compliant with the properties of management control systems. More precisely, it shows that sensitivity and verifiability were taken into account by most feedback mechanisms. In contrast, other properties such as precision, multiple measures, and feedback evaluation are not important.

Consequently, this paper contributes to the research field of feedback mechanisms. We show how digital platforms design their feedback mechanisms and provide insight into their decisions. In particular, our paper offers a morphological box of those design characteristics and provides a framework for operators to design, implement, or redesign efficient feedback mechanisms. Furthermore, our analysis shows to what extent existing feedback mechanisms comply with the properties of management control systems. Moreover, it reveals reasons for compliance based on expert interviews and highlights trade-offs in designing feedback mechanisms. More specifically, operators should consider interdependencies with other control mechanisms.

Our study is subject to several limitations. First, the selection should preferably be representative, whereas our selection was only based on the most frequently visited websites (e.g., no app-based platforms were part of the analysis). Moreover, business-to-business platforms, such as the Internet-of-Things platforms, were not considered within the analysis. Second, we used single items and did not differentiate within the level of compliance with the properties. Thirdly, the compliance with the properties was analyzed individually, although these properties are used together as a construct. However, this limitation was accepted to demonstrate their compliance, to show feedback mechanism properties and because no correlations were investigated. Fourth, we assumed that platforms implemented feedback mechanisms as a management control system and did not just implement them because they are state-of-the-art.

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Steur, A.J., Seiter, M. Properties of feedback mechanisms on digital platforms: an exploratory study. J Bus Econ 91 , 479–526 (2021).

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Introduction, negative feedback mechanisms, positive feedback mechanisms, clinical implications, conclusions.

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Ovarian feedback, mechanism of action and possible clinical implications

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Ioannis E. Messinis, Ovarian feedback, mechanism of action and possible clinical implications, Human Reproduction Update , Volume 12, Issue 5, September/October 2006, Pages 557–571,

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The secretion of gonadotrophins from the pituitary in women is under ovarian control via negative and positive feedback mechanisms. Steroidal and non-steroidal substances mediate the ovarian effects on the hypothalamic-pituitary system. During the follicular phase of the cycle, estradiol (E 2 ) plays a key role, while circulating progesterone (at low concentrations) and inhibin B contribute to the control of LH and FSH secretion respectively. During the luteal phase, both E 2 and progesterone regulate secretion of the two gonadotrophins, while inhibin A plays a role in FSH secretion. The intercycle rise of FSH is related to changes in the levels of the steroidal and non-steroidal substances during the luteal-follicular transition. In terms of the positive feedback mechanism, E 2 is the main component sensitizing the pituitary to GnRH. Activity of a non-steroidal ovarian substance, named gonadotrophin surge-attenuating factor (GnSAF), has been detected after ovarian stimulation. It is hypothesized that GnSAF, by antagonizing the sensitizing effect of E 2 on the pituitary, regulates the amplitude of the endogenous LH surge at midcycle. Disturbances in the feedback mechanisms can occur in various abnormal conditions or after treatment with pharmaceutical compounds that interfere with the production or the action of endogenous hormones.

Menstrual cyclicity in women is greatly dependent on negative and positive ovarian feedback mechanisms. The activity and strength of these mechanisms change markedly from birth to menopause. Before puberty, only the negative feedback mechanism is in action, whereas after menopause, the two mechanisms are abolished. During the normal menstrual cycle, steroidal and non-steroidal substances mediate the effects of the ovaries on the hypothalamic-pituitary system. In this article, the contribution of these substances to the control of gonadotrophin secretion in women will be discussed based on established knowledge and recent information.

Before puberty, the hypothalamus is extremely sensitive to the suppressing effect of the very low levels of circulating estrogen ( Winter and Faiman, 1973 ), although an as-yet-unclarified central inhibitory mechanism may also contribute to the repression of the gonadostat ( Roth et al ., 1972 ; Plant and Shahab, 2002 ). As puberty approaches, the hypothalamus becomes less sensitive to the suppressing effect of estrogen, while the central mechanism is inhibited ( Foster and Ryan, 1979 ). This results in derepression of the gonadostat and increasing secretion of gonadotrophins. During the normal menstrual cycle, the pattern of changes in FSH and LH levels can be easily explained by changes in circulating steroids ( Ross et al ., 1970 ; Messinis and Templeton, 1988a ; Roseff et al ., 1989 ). Although E 2 in the follicular phase and progesterone in the luteal phase predominate, recent evidence indicates that the whole process is less simplified. First, during the follicular phase, progesterone is also present in the circulation, although at low levels, and second, non-steroidal substances, such as inhibin, activin and follistatin, are also produced by the ovaries.

Follicular phase

The role of ovarian steroids.

There are several ways to investigate the action of ovarian steroids on gonadotrophin secretion in women: by administrating exogenous steroids, by administrating selective estrogen receptor blockers or aromatase inhibitors, by eliminating endogenous hormones through ovariectomy or by enhancing endogenous estrogen activity through ovarian stimulation. Several studies have demonstrated the ability of exogenous estrogen to suppress FSH and LH levels during the follicular phase of the cycle ( Tsai and Yen, 1971 ; Monroe et al ., 1972a ; Young and Jaffe, 1976 ; Messinis and Templeton, 1990 ). It has been suggested that the two gonadotrophins are equally sensitive to the suppressing effect of E 2 ( Messinis and Templeton, 1990 ). However, recent research has shown that the secretion of FSH and LH is differentially controlled by the ovaries ( Dafopoulos et al ., 2004a ). In a series of experiments in which two simulated follicular phases and one simulated luteal phase in between were artificially induced in estrogen-deprived post-menopausal women by exogenous administration of estrogen and progesterone, the elevated serum FSH and LH concentrations gradually declined as E 2 and progesterone values increased. Although by the end of the simulated luteal phase, the levels of LH had been suppressed to the normal premenopausal range, those of FSH were still higher than in the early follicular phase of the normal menstrual cycle ( Dafopoulos et al ., 2004a ). This suggests that E 2 plus progesterone only partly affect FSH secretion and that other already known ovarian substances may also be important as will be discussed in the next section on non-steroidal hormones. In the same study, however, during the second simulated follicular phase, the already reduced levels of FSH remained stable, while those of LH increased gradually despite the increasing concentrations of E 2 ( Dafopoulos et al ., 2004a ). It is suggested from these data that E 2 regulates differentially FSH and LH secretion in women.

The inability of follicular phase E 2 levels to maintain low LH levels in the presence of non-functioning ovaries suggests that during the normal follicular phase this steroid is not the only mediator of the ovarian negative feedback effect on LH secretion. It is likely that endogenous progesterone also contributes, because in the above experiments serum concentrations of this steroid were lower in the post-menopausal women than in the normal follicular phase ( Dafopoulos et al ., 2004a ). Furthermore, treatment of normal women with the antiprogestagen, mifepristone, during the early- and midfollicular phases of the cycle results in a significant increase in basal LH levels ( Kazem et al ., 1996 ). So far, no other studies have particularly investigated the ability of progesterone, in concentrations normally found in the follicular phase of the cycle, to control gonadotrophin secretion in women. In one study, exogenous administration of this steroid to women with polycystic ovary syndrome (PCOS) resulted in a significant reduction of the already elevated serum LH concentrations, but only luteal phase progesterone levels were achieved ( Buckler et al ., 1992 ).

Progesterone is normally produced by luteinizing granulosa and by luteal cells. Although the source of its production during the follicular phase has not been clarified, one study has suggested the adrenal gland ( Judd et al ., 1992 ). However, it has been shown more recently that following ovariectomy, performed in the midfollicular phase of the cycle, serum concentrations of progesterone declined to almost unmeasurable concentrations ( Alexandris et al ., 1997 ). This suggests that the ovaries produce progesterone even during the follicular phase of the cycle. The fact that in the same study ( Alexandris et al ., 1997 ) as well as in previous studies ( Wallach et al ., 1970 ; Yen and Tsai, 1971a ; Monroe et al ., 1972b ; Daw, 1974 ; Chakravarti et al ., 1977 ; Muttukrishna et al ., 2002 ) serum E 2 levels also declined, while FSH and LH levels increased gradually following ovariectomy, provides evidence that endogenous ovarian steroids are important for the control of gonadotrophin secretion.

Ovarain stimulation for IVF is an alternative way to study the role of endogenous estrogens in gonadotrophin secretion. It has been shown that during induction of multiple follicular development with the use of FSH, a rapid decline in the concentrations of basal LH occurs, while E 2 concentrations rise to supraphysiological levels ( Messinis and Templeton, 1987a ; Messinis et al ., 1998 ). At least two studies have suggested that it is the rising E 2 that suppresses LH levels on this occasion. In one of them ( Messinis et al ., 1994 ), a single FSH dose of 450 IU, injected to normal women on cycle day 2, resulted in a temporal but significant increase in serum E 2 values and in a concomitant suppression of basal LH values.

In the second study ( Messinis and Templeton, 1989 ), normally cycling women were treated in one cycle with clomiphene citrate and in another cycle with daily injections of FSH. In both cycles, serum E 2 concentrations increased to supraphysiological levels, but LH levels were suppressed only in the FSH-treated cycles, while in the clomiphene cycles they increased significantly. These two studies provide evidence that the suppressing factor of LH levels during treatment with FSH is endogenous E 2 , which in the case of clomiphene was unable to act due to the occupation of estrogen receptors. The site of action of estrogen is primarily the pituitary; however, an effect on the hypothalamus cannot be excluded ( Nakai et al ., 1978 ; Plant et al ., 1978 ; Kelner and Peck, 1984 ; Richardson et al ., 1992 ).

Although E 2 plays a predominant role in the control of gonadotrophin secretion during the follicular phase, this has not been universally acknowledged. One of the reasons is possibly the fact that despite elevated FSH in normally cycling women, towards the end of their reproductive life, circulating E 2 levels remain within the normal range ( Lee et al ., 1988 ). In addition, a recent study showed that while serum E 2 values on cycle days 3–5 of normally menstruating women aged 40–50 years correlated negatively with FSH, inhibin B was the only independent predictor of FSH values ( Burger et al ., 2000 ).

The role of non-steroidal ovarian hormones

Along with the steroids, the ovaries produce non-steroidal substances, such as inhibins ( Bicsak et al ., 1986 ; Roberts et al ., 1993 ). These proteins by definition suppress only basal FSH secretion from the pituitary. Although in vitro data have shown that under specific circumstances LH secretion may be also affected ( Farnworth et al ., 1988 ), this hormone is much less sensitive to inhibition than FSH ( Attardi et al ., 1991 ). It has been suggested that during the follicle selection process, the follicle destined to become dominant produces inhibin B ( de Kretser et al ., 2002 ), the levels of which are high in the early- to midfollicular phases and very low in the late follicular and luteal phases ( Groome et al ., 1996 ). In contrast, the levels of inhibin A are low in the follicular phase and rise markedly during the luteal phase, indicating that this form is produced mainly by the corpus luteum ( Groome et al ., 1996 ).

In terms of the role of inhibin in the secretion of gonadotrophins, there is little evidence in humans, and only animal data have convincingly indicated that this protein participates in the control of FSH secretion in vivo . In particular, immunoneutralization of inhibin by the injection of antibodies to rats resulted in a significant increase in FSH levels ( Rivier et al ., 1986 ). Also, administration of inhibin antiserum to rats and hamsters increased FSH-β mRNA without affecting LH-β ( Attardi et al ., 1992 ; Kishi et al ., 1996 ). In addition, administration of recombinant inhibin A to rats or to castrated rams blocked the FSH surge and suppressed plasma FSH levels respectively ( Rivier et al ., 1991 ; Tilbrook et al ., 1993 ). Finally, in rhesus monkeys, inhibin A given during the early follicular phase rapidly decreased circulating FSH levels ( Stouffer et al ., 1994 ; Molskness et al ., 1996 ).

Data in humans only indirectly indicate that inhibin participates in the control of FSH secretion. In one study, in which normally cycling women were treated with clomiphene for 15 days during the follicular phase, LH levels increased continuously throughout the treatment, while those of FSH after an initial rise declined to the pretreatment levels ( Messinis and Templeton, 1988b ). As clomiphene is an anti-estrogenic compound, it is suggested that for the control of FSH secretion, non-estrogenic mechanisms are also important, supporting the hypothesis of inhibin participation. In post-menopausal women, elevated FSH declines during treatment with estrogen but never returns to premenopausal levels suggesting that inhibin may be missing in these women ( Lind et al ., 1978 ; Dafopoulos et al ., 2004a ).

Recent studies in normally cycling premenopausal women have shown a significant decline in inhibin, E2 and progesterone concentrations following ovariectomy ( Alexandris et al ., 1997 ; Muttukrishna et al ., 2002 ). When the operation was performed in the follicular phase, the levels of both inhibin A and inhibin B decreased significantly, but when it was performed in the luteal phase only inhibin A declined ( Muttukrishna et al ., 2002 ). These data confirm that inhibin B is mainly produced in the follicular phase and inhibin A in the luteal phase but do not provide a clear relationship between these hormones and FSH. Nevertheless, when older perimenopausal but normally cycling women with increased early follicular FSH levels were compared with younger women with normal FSH levels, it was found that the older women also had lower inhibin B but similar inhibin A concentrations ( Klein et al ., 1996 ; Burger et al ., 1998 ; Klein et al ., 2004 ). Furthermore, in women with imminent premature ovarian failure, but with ovulatory cycles, persistently elevated FSH was accompanied by reduced levels of inhibin B and inhibin A ( Welt et al ., 2005a ). It is likely from these data that inhibin plays a role in the ovarian negative feedback control of FSH secretion in women. However, inhibin B is particularly important during the follicular phase of the cycle (Table I ).

Ovarian hormones that mediate the negative feedback effect on FSH and LH secretion in women

The role of other non-steroidal substances, such as activin and follistatin, is less clear. Activin is a dimeric product of the β-subunit of inhibin and exists in three forms, ‘A’, ‘B’ and ‘AB’ ( Muttukrishna et al ., 2004 ). Animal data have shown that activin stimulates the secretion of FSH from pituitary cells in culture ( Ling et al ., 1986 ; Vale et al ., 1986 ) as well as in vivo ( McLachlan et al ., 1989 ; Rivier and Vale, 1991 ; Stouffer et al ., 1993 ), and therefore, this protein does not have a place in the negative feedback mechanism. In women, due to methodological difficulties, only activin A has been measured in blood during the normal menstrual cycle, and the data have shown fluctuations with higher levels in the early follicular phase, at midcycle and in the late luteal phase ( Muttukrishna et al ., 1996 ). The importance of these changes is not clear, although activin A may contribute to the increase in FSH levels at these particular periods.

Follistatin was initially thought to be an inhibin agonist ( Robertson et al ., 1987 ; Ueno et al ., 1987 ), but it was subsequently found to be a binding protein for activin that may have no other specific biological roles ( Nakamura et al ., 1990 ). Most circulating follistatin in cycling women is activin bound ( McConnell et al ., 1998 ). Data in animals, however, have shown that follistatin may exert actions on FSH secretion in vivo . In particular, administration of human recombinant follistatin-288 to ovariectomized rams reduced the secretion of FSH but not LH ( Tilbrook et al ., 1995 ). Also, in a study in ewes, it was shown that the same recombinant product was able to suppress FSH levels in the peripheral circulation without affecting basal GnRH secretion or the frequency and the amplitude of GnRH pulses ( Padmanabhan et al ., 2002 ). The mechanism of this effect is unclear but is possibly related to the bioavailability of activin ( Padmanabhan et al ., 2002 ).

Data on the mechanism of action of inhibin are lacking in women, and information is derived only from studies in animals. It has been suggested that in the context of the negative feedback mechanism, inhibin acts directly on the pituitary without affecting GnRH secretion, although it may reduce the GnRH-induced FSH secretion ( de Kretser et al ., 2002 ). However, the existence of inhibin/activin and follistatin subunits in the pituitary cells of various species indicates that these proteins may also exert local effects ( Mather et al ., 1992 ; Bilezikjian et al ., 1993 ; Farnworth et al ., 1995 ). Especially, activin A has been shown to be secreted by rat anterior pituitary cells in culture ( Liu et al ., 1996a ), whereas activin B is the major form in such cells ( Bilezikjian et al ., 1994 ). It is possible, therefore, that the effect of these proteins on FSH secretion is exerted via antocrine/paracrine pathways ( Corrigan et al ., 1991 ). Follistatin is also secreted from rat pituitary cells ( Liu et al ., 1996b ), and therefore, its inhibitory effect on FSH secretion may be via the neutralization of pituitary activin ( Ling et al ., 1986 ; Vale et al ., 1986 ; Muttukrishna and Knight, 1991 ).

The role of another non-steroidal substance named anti-Mullerian hormone (AMH) is less clear. In women, AMH is expressed in the granulosa cells of follicles from the primordial to the antral stage until the size of 4–6 mm ( Weenen et al ., 2004 ). Although it appears that serum levels of AMH on cycle day 3 of normally cycling women decline with increasing age ( de Vet et al . 2002 ) and show a negative correlation with FSH ( van Rooij et al ., 2002 ), its role in the ovarian negative feedback system has not been clearly established.

Luteal phase

During the luteal phase, the increased values of progesterone and E 2 play an important role in the maintenance of the low FSH and LH levels (Table I ). This is derived from experiments in women that have shown that after ovariectomy, performed in the midluteal phase, both E 2 and progesterone concentrations decreased significantly within the first 24 h, while those of FSH and LH increased gradually ( Alexandris et al ., 1997 ). Although these data underlie the importance of these two steroids in the control of gonadotrophin secretion in the luteal phase, they do not specify the role of each of them. The latter has been further investigated in a recent study ( Messinis et al ., 2002 ) demonstrating that maintenance of midluteal levels of E 2 with the administration of exogenous estrogen immediately after ovariectomy postponed the expected increase in FSH and LH concentrations by on average 3 days. When, however, the midluteal concentrations of progesterone were also maintained with the exogenous administration of this steroid, the increase in FSH and LH values was prevented ( Messinis et al ., 2002 ). This suggests that it is the combined action of E 2 and progesterone that mediates the negative feedback effect of the ovaries on gonadotrophin secretion during the luteal phase of the cycle. Whether progesterone alone could express a similar action has not been investigated, although under experimental conditions the negative feedback effect of progesterone is apparent in the presence of estrogen ( Soules et al ., 1984 ).

During the luteal phase, the frequency of GnRH pulses decreases, while the amplitude increases ( Filicori et al ., 1986 ). Although this could be due to the high progesterone concentrations ( Soules et al ., 1984 ), it seems that both E 2 and progesterone are required to maintain this pattern ( Nippoldt et al ., 1989 ). The suppressing effect of these two steroids on gonadotrophin secretion is possibly mediated via an increase in β-endorphin activity in the hypothalamus ( Wehrenberg et al ., 1982 ).

Apart from the steroids, no other ovarian substances have been detected to specifically suppress basal LH secretion in women. For FSH, however, inhibin may participate in the negative feedback mechanism during the luteal phase but does not affect LH secretion. This is derived from data in both animals and women. Infusion of inhibin A into rhesus macaques starting at midluteal phase resulted in a progressive decline in FSH levels ( Stouffer et al ., 1994 ). A recent study in women has shown a significant decline in inhibin A values within the first 12 h from ovariectomy performed in the luteal phase that was followed by a gradual but significant increase in serum FSH concentrations ( Muttukrishna et al ., 2002 ). Although in that study E 2 and progesterone levels also declined, a negative correlation between the values of inhibin A and FSH was found. In the same study, inhibin B levels did not change significantly after ovariectomy, suggesting that this protein is not a regular component of the negative feedback mechanism during the luteal phase of the cycle ( Muttukrishna et al ., 2002 ). Nevertheless, previous data have demonstrated that normally cycling premenopausal women with raised FSH values in the early follicular phase had during the luteal phase inhibin A, and during the follicular phase inhibin B, concentrations significantly lower than in women with normal FSH levels ( Danforth et al ., 1998 ; Santoro et al ., 1999 ; Welt et al ., 1999 ; Muttukrishna et al ., 2000 ). These data provide evidence that both forms of inhibin participate in the control of FSH secretion in women with inhibin A being important during the luteal phase and inhibin B during the follicular phase of the cycle.

The role of activin and follistatin in the control of human gonadotrophin secretion during the luteal phase is, as in the follicular phase, not clear. Measurement of follistatin in the circulation of women has not shown any significant alterations throughout the whole menstrual cycle ( Khoury et al ., 1995 ; Kettel et al ., 1996 ; Evans et al ., 1998 ).

Luteal-follicular transition

During the passage from the luteal to the next follicular phase, an increase, or ‘intercycle rise’, in serum FSH concentrations occurs. FSH starts to increase 2–3 days before the onset of the menstrual period, although recent data have shown that the initial rise occurs 4 days before menses ( Miro and Aspinall, 2005 ). FSH remains elevated during the early follicular phase and returns to the basal value in midfollicular phase ( Mais et al ., 1987 ; Messinis et al ., 1993c ). During the period of FSH increase, also named the ‘FSH window’, the selection of the dominant follicle takes place. The described changes in FSH levels were measured by immunoassays, but when a specific in vitro bioassay was used, increased signal was detected earlier, i.e. in midluteal to late luteal phase ( Christin-Maitre et al ., 1996 ).

The intercycle rise of FSH appears to be controlled by ovarian substances (Figure 1 ). Before the onset of the FSH rise, a gradual but significant decline in the levels of inhibin A, E 2 and progesterone takes place ( Roseff et al ., 1989 ; Groome et al ., 1996 ). It is assumed, therefore, that the intercycle rise of FSH starts in late luteal phase as a result of the reduced activity of the negative feedback mechanism that suppressed FSH secretion during the early- and midluteal phases. Inhibin B does not participate in this mechanism. However, from the time FSH levels reach a peak at the onset of menstruation, inhibin B levels increase gradually and significantly ( Groome et al ., 1996 ). It is possible that the increasing concentrations of inhibin B under the influence of FSH during the early follicular phase ( Welt et al ., 1997 ) suppress FSH secretion, narrowing thus the FSH window.


Hormonal dynamics during the luteal-follicular transition. The FSH intercycle rise is the result of the reduced activity of the negative feedback mechanism due to the decrease in estradiol (E 2 ), inhibin A and progesterone (P4) concentrations in late luteal phase. The FSH increase is terminated by the rising E 2 and inhibin B levels produced by the dominant follicle. The diagram, regarding the pattern of hormonal changes, is based on data presented in two references ( Messinis et al ., 1993c ; Groome et al ., 1996 ).

Based on these data, it is assumed that both forms of inhibin are important for the control of FSH secretion during the intercycle period in women with inhibin A playing a role in the process that ‘opens’ and inhibin B in the mechanism that ‘closes’ the FSH window (Table I ). Although this makes sense, a study in women has shown that maintenance of midluteal concentrations of E 2 during the intercycle period postponed the intercycle rise of FSH despite a decline in inhibin concentrations ( Le Nestour et al ., 1993 ). Furthermore, following the selection of the dominant follicle in the normal cycle, serum FSH declined as E 2 levels increased ( van Santbrink et al ., 1995 ). It is suggested from these results that E 2 is possibly more important than inhibin in regulating the FSH window. More recent data in women treated with the anti-estrogenic compound, tamoxifene, have confirmed the greater role of E 2 over inhibin in the negative control of FSH secretion during the luteal phase and the luteal-follicular transition with inhibin B being more important as the follicular phase progresses ( Welt et al ., 2003 ).

The role of progesterone in the control of the intercycle rise of FSH is less clear, although this hormone may participate via an effect on GnRH secretion. Progesterone is believed to reduce the frequency and increase the amplitude of LH pulses during the luteal phase of the cycle ( Soules et al ., 1984 ; Nippoldt et al ., 1989 ). Therefore, the withdrawal of the progesterone effect in late luteal phase increases the frequency of GnRH pulses ( McCartney et al ., 2002 ), an event that stimulates predominantly the secretion of FSH ( Marshall and Kelch, 1986 ; Hall et al ., 1992 ). This is possibly one of the reasons that the increase of LH during the intercycle period is less discernible. In any case, however, the frequency of GnRH pulses contributes to but is not solely responsible for the intercycle rise of FSH in women ( Welt et al ., 1997 ).

Another mechanism that might contribute to the intercycle rise of FSH includes activin A. The concentrations of this protein, despite limitations in the existing methodology, start to increase from the midluteal phase, preceding through the intercycle rise of FSH ( Muttukrishna et al ., 1996 ). Also, aged but normally cycling women with raised FSH values in the early follicular phase had increased concentrations of activin A in the luteal phase ( Muttukrishna et al ., 2000 ). Similarly, a significant increase in serum activin A levels over age has been reported in healthy post-menopausal women ( Baccarelli et al ., 2001 ). In terms of the role of other forms of activin, such as activin B and activin AB, there are no data in the literature regarding their concentrations in the circulation of women.

It has been known for years that E 2 is the main component of the positive feedback effect of the ovaries on the hypothalamic-pituitary system ( Ferin et al ., 1974 ). This steroid sensitizes the pituitary to GnRH and enhances the self-priming action of GnRH on the pituitary gonadotrophs ( Lasley et al ., 1975 ). The interaction between E 2 and GnRH is important for the expression of the endogenous gonadotrophin surge at midcycle ( Hoff et al ., 1977 ).

Pituitary sensitivity to GnRH

Experiments in women involving the i.v. infusion of GnRH or the i.v. administration of consecutive GnRH pulses have differentiated two functional pools of gonadotrophin secretion in the gonadotrophs ( Lasley et al ., 1975 ; Wang et al ., 1976 ; Yen and Lein, 1976 ; Hoff et al ., 1977 ). The first, also named ‘releasable’ pool, releases gonadotrophins immediately after stimulation by GnRH, whereas the second or ‘reserve’ pool, which represents the synthesis of new hormones, requires a longer stimulation by GnRH. Under these circumstances, the reserve is converted to the acutely releasable pool before gonadotrophins are secreted.

During the i.v. administration of two submaximal doses of GnRH, 10 µg each, to normal women 2 h apart, the response to the first pulse is maximum at 30 min and represents the pituitary ‘sensitivity’, while the whole area under the curve lasting 240 min represents the pituitary reserve ( Lasley et al ., 1975 ). The response to the second GnRH pulse is greater than the response to the first pulse, a pattern that is particularly evident in the presence of estrogen and is called ‘the self-priming effect’ of GnRH on the pituitary ( Lasley et al ., 1975 ; Wang et al ., 1976 ; Hoff et al ., 1979 ). Although this reflects increased number of GnRH receptors in the gonadotrophs ( Laws et al ., 1990 ), it may be also related to increased availability of GnRH in the pituitary as E 2 inhibits GnRH metabolism by monkeys and rat pituitary cells ( Danforth et al ., 1990 ). In addition, data in rats have shown that GnRH controls LH biosynthesis by increasing glycosylation and polypeptide synthesis of LH, while E 2 facilitates LH secretion by lowering the concentrations of GnRH needed to stimulate these two processes ( Ramey et al ., 1987 ). The biphasic pattern of LH response to GnRH has been also shown in vitro using rat pituitary cells in a perifusion system ( Evans et al ., 1983 ; Loughlin et al ., 1984 ).

When GnRH experiments were performed during the human menstrual cycle, it was found that the pituitary sensitivity and reserve as well as the self-priming effect were augmented in the late follicular phase as compared to the early follicular phase ( Wang et al ., 1976 ; Hoff et al ., 1977 ). This augmentation of LH secretion as a response to repeated injections of GnRH in an estrogenic environment is related to the rate with which LH molecules are discharged from the pituitary ( Sollenberger et al ., 1990 ), although the duration of the LH secretory events and consequently the LH mass are also augmented ( Quyyumi et al ., 1993 ). It has been found that the pituitary sensitivity to GnRH, estimated as the 30-min response to a single i.v. GnRH dose of 10 µg, does not change significantly in women during the early- and midfollicular phases of the cycle, despite the significant rise in E 2 concentrations, but increases markedly during the late follicular phase ( Messinis et al ., 1994 , Messinis et al ., 1998 ). This suggests that the sensitizing effect of E 2 on the pituitary is inhibited during the early- and midfollicular phases and is facilitated in the late follicular phase of the normal menstrual cycle. Alternatively, the ovaries during the early- and midfollicular phases produce a substance that is able to antagonize the sensitizing effect of E 2 on the pituitary gonadotrophs.

Experiments that were performed in healthy estrogen-deprived post-menopausal women support this assumption ( Dafopoulos et al ., 2004a ). In particular, two simulated follicular phases with a luteal phase in between were created in these women with the exogenous administration of E 2 and progesterone. The experiments lasted for 43 days, and the pituitary sensitivity, expressed as the 30 min response to 10 µg GnRH i.v., was investigated on a daily basis. Immediately after the end of the simulated luteal phase, the response of LH to GnRH was similar to that in the early follicular phase of the normal menstrual cycle. However, from that point onwards, i.e. in the following simulated follicular phase a continuous increase in the pituitary sensitivity to GnRH was seen, which was different from that described in the normal follicular phase (Figure 2 ). These data are compatible with the hypothesis of a missing ovarian substance in the case of post-menopausal women because their ovaries are not functioning.


LH response to GnRH (ΔLH) in women. Comparison between (•) a simulated follicular phase (Group 1) and (▴) a control normal follicular phase (Group 2). In Group 1, estrogen-deprived post-menopausal women were treated with exogenous estradiol valerate and progesterone to induce concentrations of these two steroids similar to those in the follicular phase and the luteal phase of the normal menstrual cycle. Two simulated follicular phases and one luteal phase in between were induced. Days 32–42 correspond to the second simulated follicular phase immediately after the simulated luteal phase. The changes in LH represent the pituitary sensitivity to GnRH (30 min response to an i.v. injection of 10 µg). The different pattern of LH changes is interpreted as indicating that the ovaries (Group 2) produce a substance (gonadotrophin surge-attenuating factor) that in the early follicular and midfollicular phases prevent the increase in pituitary sensitivity to GnRH. This substance was not produced in Group 1 as the ovaries were not functioning.* P < 0.05. Adapted from Dafopoulos et al ., 2004a .

Such a substance could be gonadotrophin surge-attenuating factor (GnSAF) that in superovulated women reduces the pituitary response to GnRH and attenuates the endogenous LH surge ( Messinis et al ., 1985 ; Messinis and Templeton, 1989 ). Another candidate could be progesterone, but this is not likely because when normal women were treated with the antiprogestagen, mifepristone, both the pituitary sensitivity and reserve were significantly attenuated as compared to control cycles of the same women ( Kazem et al ., 1996 ). It is clear from these data that when the progesterone activity is neutralized, the pituitary sensitivity is decreased, and therefore, the lack of progesterone in the post-menopausal women would be expected to result in a decrease and not in an increase in the pituitary responsiveness to GnRH during the administration of E 2 . In other words, progesterone in the follicular phase of the normal cycle, although in low concentrations, probably sensitizes the pituitary to GnRH and in that way facilitates the E 2 positive effect. In agreement with this notion are data in rats in which the sensitizing effect of progesterone on the GnRH self-priming in pituitary monolayers was abolished by coincubation with mifepristone ( Byrne et al ., 1996 ). Even, in the absence of progesterone, according to data in rats, GnRH self-potentiation requires a cross-talk with the progesterone receptor ( Waring and Turgeon, 1992 ).

LH surge onset

As has been shown in several studies, E 2 is the predominant factor that triggers the onset of the endogenous LH surge during the normal menstrual cycle provided a threshold level is exceeded for a certain period of time ( Karsch et al ., 1973 ; Keye and Jaffe, 1975 ; Young and Jaffe, 1976 ; March et al ., 1979 ; Liu and Yen, 1983 ; Karande et al ., 1990 ). It has been demonstrated that this level is around 200 pg/ml, and the period of pituitary exposure to it at least 48 h ( Young and Jaffe, 1976 ; Simon et al ., 1987 ; Karande et al ., 1990 ). Even supraphysiological levels of E 2 induced by the exogenous administration of estrogen are able to stimulate an LH surge ( Messinis et al ., 1992 ). The interaction of E 2 with GnRH is important for the LH surge onset via an increased expression of the GnRH self-priming on the pituitary ( Hoff et al ., 1977 ).

The extent to which other ovarian hormones, such as progesterone, participate in the positive feedback mechanism at midcycle is less clear. Experiments in women have shown that progesterone can induce a positive feedback effect only after pretreatment with estrogen, even when the appropriate threshold for E 2 has not been reached ( Chang and Jaffe, 1978 ; March et al ., 1979 ). At midcycle, the shift in steroidogenesis represents the ability of the preovulatory follicle to produce more progesterone than E 2 ( McNatty et al ., 1979a , b ). There has been debate, however, in the literature as to whether the progesterone secretion and, therefore, its concentrations in the circulation increase a little while before the onset of the midcycle LH surge ( Johansson and Wide, 1969 ; Thorneycroft et al ., 1974 ; Laborde et al ., 1976 ; Landgren et al ., 1977 ; Djahanbakhch et al ., 1984 ). In one study, in which blood samples were taken from normal women every 2 h for 5 days during the periovulatory period, a clear increase in progesterone concentrations was demonstrated before the real onset of the surge ( Hoff et al ., 1983 ).

It is possible, therefore, that at midcycle the role of progesterone is permissive. In experiments performed in women, the administration of progesterone advanced the onset of an E 2 -induced LH surge ( Chang and Jaffe, 1978 ; Liu and Yen, 1983 ; Messinis and Templeton, 1990 ). Also, in rats progesterone enhanced E 2 -induced GnRH secretion from the medial basal hypothalamus ( Miyake et al ., 1982 ), while the antiprogestagen, mifepristone, blocked the midcycle gonadotrophin surge in women when given after the emergence of the dominant follicle ( Batista et al ., 1992 ). Although the role of circulating progesterone in the positive feedback mechanism in women requires clarification, data in ovariectomized estrogen-treated progesterone receptor knockout mice have shown that activation of these receptors is necessary for the expression of the GnRH self-priming effect and the generation of the E 2 -induced gonadotrophin surge ( Chappell et al ., 1999 ). Additional information in ovariectomized and adrenalectomized rats indicates that neuroprogesterone synthesized in the hypothalamus under the influence of E 2 is an obligatory mediator of the positive feedback mechanism that is induced by this steroid ( Micevych et al ., 2003 ). Furthermore, data in rats have shown that estrogens induce de novo synthesis of progesterone from cholesterole in the hypothalamus, which plays a role in the onset of the LH surge ( Soma et al ., 2005 ). It is possible, therefore, that progestestogenic mechanisms involving the progesterone receptor participate in the E 2 positive feedback mechanism, regulating thus the LH surge onset.

The site of action of E 2 for the positive feedback effect is both the hypothalamus and the pituitary ( Xia et al ., 1992 ). A preovulatory surge of GnRH has been detected in the ewe ( Moenter et al ., 1991 ). However, a LH surge has been induced by estrogens in monkeys with no GnRH production ( Ferin et al ., 1979 ). In women, while data are lacking, one study has shown an increase in plasma immunoreactive GnRH, as a result of estrogen administration, that precedes the increase of LH and FSH ( Miyake et al ., 1983 ). It is likely, therefore, that the primary site of the positive feedback effect is the pituitary and that GnRH plays a permissive role ( Knobil, 1988 ). A recent study has shown that in rats a direct action of E 2 on the anterior pituitary is obligatory for the positive feedback effect on LH secretion ( Yin et al ., 2002 ). Progesterone seems also to exert the positive feedback effect via the hypothalamus ( Terasawa et al ., 1982 ).

The mechanism of the E 2 positive effect on gonadotrophin secretion is via the estrogen receptors (ER). Whether this involves ERα or ERβ or both is not clear. Both types exist in the gonadotrophs ( Mitchner et al ., 1998 ), while data in rats have shown that estrogens may regulate their own receptors as well as the pituitary responsiveness to them ( Schreihofer et al ., 2000 ). The effect of E 2 is also mediated by changes in the activity of neurotransmitters in the hypothalamus, such as β-endorphin, whose plasma levels start to increase 2 days before the LH peak ( Laatikainen et al ., 1985 ).

Characterization of GnSAF

It has been suggested that GnSAF attenuates the endogenous LH surge and reduces the pituitary response to GnRH in superovulated women ( Messinis et al ., 1985 , 1986 ; Messinis and Templeton, 1986 , 1989 ) Whether GnSAF is similar to a gonadotrophin surge inhibiting factor (GnSIF) in monkeys ( Sopelak and Hodgen, 1984 ) and rats ( Geiger et al ., 1980 ; Koppenaal et al ., 1992 ) is not known. It may be that GnSAF and GnSIF share some structural similarities, although species differences may be important ( Fowler and Templeton, 1996 ).

Attempts to purify GnSAF/GnSIF from various sources have shown molecular masses ranging from 12.5 to 69 kDa with different aminoacid sequences ( Tio et al ., 1994 ; Danforth and Cheng, 1995 ; Pappa et al ., 1999 ; Fowler et al ., 2002 ). A study using human follicular fluid as a source ( Pappa et al ., 1999 ) has shown a molecular weight of 12.5 kDa and identity to the C-terminal fragment of human serum albumin (HSA).

That GnSAF may be related to HSA was further supported by two recent studies. In one of them ( Tavoulari et al ., 2004 ), the production of recombinant polypeptides of HSA corresponding to subdomain IIIB residues of 490–585 was feasible by using the expression-secretion system of Pichia Pastoris GS115. Different clones were obtained that in the in vitro bioassay system of rat pituitary cells were able to reduce the GnRH-induced LH secretion demonstrating, therefore, GnSAF bioactivity ( Tavoulari et al ., 2004 ). More recently, with the use of RT-PCR and the appropriate primers, the expression of HSA mRNA was found in human granulosa cells obtained from women undergoing IVF treatment ( Karligiotou et al ., 2006 ). All fragments of HSA were expressed in the nucleus of the granulosa cells, and only the promoter and the C-terminal fragment were expressed in the cytoplasm ( Karligiotou et al ., 2006 ). Evidence has been provided that GnSAF is different from inhibin, first because it reduces LH rather than FSH secretion ( Pappa et al ., 1999 ), second because during the process of purification from human follicular fluid inhibin was removed from the system ( Pappa et al ., 1999 ; Fowler et al ., 2002 ) and third because when antibodies against inhibin were used in vitro the bioactivity of GnSAF was preserved ( Balen et al ., 1995a ; Byrne et al ., 1995 ).

Several studies have shown in vivo bioactivity of GnSAF during ovarian stimulation in women with FSH ( Messinis et al ., 1991 , 1993a , b , 1994 , 1996 ). It has been suggested that FSH stimulates the production of GnSAF from growing follicles but not from the corpus luteum ( Messinis et al ., 1996 ).

Physiological role of GnSAF: LH surge amplitude

Although GnSAF bioactivity is particularly evident during ovarian stimulation, it is possible that this factor plays a physiological role during the normal menstrual cycle affecting gonadotrophin secretion. Studies in women during the natural cycle have suggested that GnSAF is produced during the luteal-follicular transition under the influence of the intercycle rise of FSH ( Messinis et al ., 1991 , 1993c , 2002 ) (Figure 3 ). A hypothesis has been developed that the activity of GnSAF in the circulation is high during the early- and midfollicular phases, and this maintains the pituitary in a state of low responsiveness to GnRH ( Messinis and Templeton, 1991 ; Fowler et al ., 2003 ; Messinis, 2003 ). However, in the late follicular phase, there is a decline in GnSAF bioactivity that facilitates the sensitizing effect of E 2 on the pituitary and the full expression of the midcycle LH surge ( Messinis et al ., 1994 ) (Figure 4 ).


Activity of gonadotrophin surge-attenuating factor (GnSAF) during the normal menstrual cycle in relation to inhibin A, inhibin B, estradiol (E 2 ) and progesterone (P4) patterns. It is postulated that GnSAF is produced during the luteal-follicular transition under the influence of the intercycle rise of FSH.


A hypothesis on the physiological role of gonadotrophin surge-attenuating factor (GnSAF) during the normal menstrual cycle. GnSAF interacts with estradiol (E 2 ) on the pituitary to decrease the GnRH-induced LH secretion. This factor is produced mainly by small growing follicles, and therefore, its activity is high in the early- to mid-follicular phase. The activity of GnSAF is reduced in the late follicular phase and at midcycle, facilitating thus the sensitizing effect of E 2 on the pituitary and the full expression of the endogenous LH surge. Progesterone (P4) may sensitize the pituitary to GnRH throughout the follicular phase, while inhibin B may decrease the GnRH-induced FSH secretion in the early- and midfollicular phase of the cycle.

That GnSAF activity is higher in the early- and midfollicular phases than in the late follicular phase is also supported by in vitro studies demonstrating GnSAF activity in human follicular fluid particularly of small- and medium-sized follicles rather than of large follicles ( Fowler et al ., 1990 , 2001 ). According to this hypothesis, the role of GnSAF in humans is to control the amplitude and not the onset of the LH surge. In fact, an endogenous LH surge occurs invariably as a response to the positive feedback effect of E 2 either in the early- or in midfollicular phases of the normal menstrual cycle, but this surge is attenuated as compared to midcycle ( Taylor et al ., 1995 ; Messinis et al ., 2001 ). Therefore, E 2 and GnSAF seem to interact at the pituitary gonadotrophs with the former expressing a sensitizing effect and the latter an antagonistic effect.

Of the other ovarian hormones, progesterone seems also to play a role in the control of the amplitude of the LH surge. Under experimental conditions in normally cycling or in post-menopausal women, exogenous progesterone amplified the LH surge that was induced by the administration of exogenous estrogen ( Liu and Yen, 1983 ; Messinis and Templeton, 1990 ). Data in rats have shown that this action of progesterone is mediated via an enhanced activation of GnRH neurons ( Lee et al ., 1990 ).

Termination of the LH surge

The midcycle LH surge normally has a duration of 48–72 h ( Hoff et al ., 1983 ; Messinis and Templeton, 1988a ; Shoham et al ., 1995 ). The factors, however, that control the termination of the endogenous LH surge during the normal menstrual cycle have not been clarified. After the onset of the LH surge, E 2 concentrations decline. It is rather unlikely that the withdrawal of the E 2 effect terminates gonadotrophin secretion, because termination also occurred in experiments in which high estrogen concentrations were maintained during the LH surge ( Liu and Yen, 1983 ). In addition, animal studies have shown that E 2 is important for triggering the LH surge but is not required after its onset ( Evans et al ., 1997 ). Nevertheless, data in rats have shown that estrogen can reduce ERα and ERβ protein and increase a truncated ER product-1 that suppresses the activity of both types of receptors in vivo and in cell lines, suggesting that this may be a mechanism via which these steroids limit the positive feedback effect ( Schreihofer et al ., 2000 , 2002 ). More recent data, however, have demonstrated that the loss of the LH surges in ovariectomized rats during chronic treatment with E 2 was achieved without any changes in the proportion of GnRH cells expressing ERα or ERβ ( Legan and Tsai, 2003 ).

Serum progesterone levels in women increase gradually from the onset to the end of the LH surge and continuously, thereafter, during the luteal phase of the cycle ( Hoff et al ., 1983 ). It is possible that the rising progesterone levels contribute to the termination of the LH surge via a negative feedback effect. Experimental data in women have shown that when an LH surge was induced with the exogenous administration of E 2 , LH values declined following the peak but went down to the presurge level only after the administration of progesterone ( Messinis and Templeton, 1990 ).

A recent study has provided more information regarding the mechanism that is responsible for the termination of the endogenous LH surge in women ( Dafopoulos et al ., 2006 ). In particular, the positive feedback effect of exogenous E 2 was investigated in two cycles of normally cycling women who underwent ovariectomy on day 3 of the second cycle. An endogenous LH surge was induced by exogenous E 2 given from cycle days 3–5 that was comparable in the two cycles up to the point of the LH peak. A descending limb, however, was evident only in the first cycle with the intact ovaries. In contrast, in the second cycle in which the ovaries were removed, LH and FSH levels did not decline but fluctuated around the peak value of the surge for the rest of the experimental period ( Dafopoulos et al ., 2006 ). These data suggest that the termination of the E 2 -induced LH surge is not related to an exhaustion of the pituitary reserves but is controlled by ovarian factors. As progesterone values decreased after ovariectomy and were lower than during the corresponding days in the first cycle with the intact ovaries, this steroid is a possible candidate for the surge termination. A decrease in GnRH pulse frequency in the late as compared to the early- and mid-portions of the midcycle LH surge ( Adams et al ., 1994 ) can be part of the mechanism of progesterone action on gonadotrophin secretion.

Following menopause, changes in the relationships between the ovaries and the hypothalamic-pituitary system take place. Ovarian failure leading to menopause is a gradual process that usually starts several years before menopause ( Burger et al ., 2002 ). It has been shown that basal FSH concentrations are raised, whereas those of inhibin B are reduced in the early follicular phase of the cycle during the perimenopausal period, although cyclicity is maintained ( Klein et al ., 1996 ; Soules et al ., 1998 ). At the same time, LH values remain normal suggesting that the negative feedback effect of the ovaries is partly attenuated long before menopause. After menopause, however, the negative feedback mechanism is abolished, since following ovariectomy in healthy post-menopausal women FSH, LH, and E 2 values do not change and only testosterone levels decline substantially ( Dafopoulos et al ., 2004b ). Therefore, the post-menopausal ovaries do not produce estrogens but only testosterone ( Sluijmer et al ., 1995 ; Laughlin et al ., 2000 ) which, however, does not contribute to the negative feedback system ( Dafopoulos et al ., 2004b ). The latter can be re-established in post-menopausal women after the exogenous administration of estrogens and progesterone ( Yen and Tsai, 1971b ; Lind et al ., 1978 ; Lutjen et al ., 1986 ). Despite the absence of ovarian feedback, age-related changes occur in the hypothalamic component that are characterized by a decrease in GnRH pulse frequency with age ( Lambalk et al ., 1997 ; Santoro et al ., 1998 ; Hall et al ., 2000 ).

Due to the very low circulating E 2 concentrations in the post-menopausal women, the positive feedback mechanism is also not active. However, the secretion of LH and FSH and, therefore, GnRH is still pulsatile ( Hall et al ., 2000 ). Furthermore, in a recent study, the pituitary sensitivity to GnRH, assessed as the 30 min response to 10 µg GnRH i.v., remained unchanged during the week following ovariectomy as compared to the pre-operative pattern ( Dafopoulos et al ., 2004b ). When, however, exogenous estrogens were given to post-menopausal women in order to achieve concentrations similar to those during the follicular phase of the normal menstrual cycle, the sensitivity of the pituitary to GnRH increased gradually ( Dafopoulos et al ., 2004a ). Also, a LH surge can be induced in post-menopausal women after the exogenous administration of estrogens ( Tsai and Yen, 1971 ; Lachelin and Yen, 1978 ; Liu and Yen, 1983 ; Lutjen et al ., 1986 ). It is clear, therefore, that after menopause the absence of the feedback mechanisms is due to ovarian insufficiency, whereas the hypothalamic-pituitary system remains intact with increased GnRH secretion and able to respond to the negative and positive feedback effects of exogenous steroids ( Gill et al ., 2002 ). Nevertheless, because the magnitude of the response in post-menopausal women has not been quantified in a comparative way with that in premenopausal women, an altered sensitivity to the ovarian steroids cannot be excluded, based on recent data in perimenopausal women suggesting hypothalamic-pituitary insensitivity to estrogens ( Weiss et al ., 2004 ).

Abnormal conditions

During the reproductive years, disturbances in the feedback mechanisms may occur, characterized by menstrual irregularities, such as dysfunctional uterine bleeding, menorrhagia or amenorrhea. In terms of the negative feedback mechanism, a possible abnormality is a reduced suppression of gonadotrophin secretion. This happens when the production of ovarian steroids is inadequate as in primary ovarian failure or after ovariectomy. Women with premature ovarian failure and ovulatory cycles have higher FSH and lower inhibin B and inhibin A levels ( Welt et al ., 2005a ). Although premature ovarian failure is a permanent condition, in some cases the ovaries become only temporarily inactive. When, however, gonadotrophin levels decrease either spontaneously or after treatment with E 2 , normal cyclicity is re-established and even a pregnancy can occur ( Taylor et al ., 1996 ).

The opposite situation, i.e. an augmented negative feedback mechanism has not been described in humans. It is known that in women with PCOS serum FSH is normal, while LH is either normal or elevated ( Balen et al ., 1995b ). The inability of follicles to mature to the preovulatory stage in these patients is due at least in part to the lack of an intercycle rise of FSH, as when FSH concentrations are slightly elevated by the exogenous administration of this hormone, follicle selection can take place ( van der Meer et al ., 1994 ). Although the pathophysiology of this syndrome is in several aspects unclear, the lack of an intercycle rise of FSH may be partly related to an augmentation of the negative feedback effect mediated by elevated inhibin. It has been shown, however, that inhibin levels are not higher in patients with PCOS as compared to controls ( Pigny et al ., 2000 ). On the contrary, the concentrations of both inhibin A and inhibin B in the follicular fluid of PCOS follicles have been found to be significantly lower than in size-matched follicles from normal women ( Welt et al ., 2005b ).

Normally cycling women express a positive feedback effect at midcycle and ovulate invariably. In cases of follicle arrest including hyperandrogenic conditions, PCOS, hyperprolactinaemia, hypogonadotrophic-hypogonadism and premature ovarian failure, there is no regular expression of a positive effect of endogenous E 2 . With the exception of hypogonadotrophic-hypogonadism, in the majority of these cases, a positive feedback effect can be demonstrated during an estrogen provocation test ( Shaw, 1976 ). Therefore, when ovulation is induced in hypogonadotrophic-hypogonadic women, hCG should be given to induce final follicle maturation ( Messinis, 2005 ). It has been suggested that the elevated LH that is seen in the serum of about 40% of patients with PCOS is related to a continuous positive action of E 2 on the pituitary ( Lobo et al ., 1981 ; Waldstreicher et al ., 1988 ). Although this has to be confirmed, such an action does not fulfil the criteria of a positive feedback effect. However, a continuous sensitizing action of E 2 on the pituitary that enhances the pituitary response to GnRH in PCOS patients cannot be excluded, although data in monkeys do not support such a hypothesis ( Richardson et al ., 1992 ).

Administration of pharmaceutical compounds

Changes in the integrity of the feedback system can occur in normal women under the treatment with various pharmaceutical compounds. Oral contraceptives, containing ethinylestradiol and a progestagen, maintain the pituitary in a state of low secretion of gonadotrophins. During the week free of treatment, a FSH rise is usually below the threshold for follicle selection, and due to follicle arrest, an endogenous LH surge does not occur ( Kuhl et al ., 1985 ). Administration of E 2 to normal women during the second half of the luteal phase reduces FSH intercycle levels and the size of antral follicles ( Fanchin et al ., 2003a ). This results in a better synchronization of follicle growth during the ensuing follicular phase under the stimulation by exogenous FSH ( Fanchin et al ., 2003b ).

Selective blockage of the positive feedback mechanism can be achieved in women with the injection of a GnRH antagonist. When such a compound is given in mid follicular or late follicular phases of the cycle, the endogenous LH surge is blocked or becomes abortive ( Leroy et al ., 1994 ). The use, however, of GnRH antagonists as contraceptive means is not feasible due to variability in the time of LH surge onset, as well as the high cost and the inconvenience caused to patients. The GnRH agonists act in a different way than the antagonists and block both the negative and the positive feedback mechanisms ( Fraser, 1988 ). Other drugs that can directly affect the feedback system are the anti-estrogenic compounds, such as clomiphene citrate and tamoxifene. These drugs bind the estrogen receptors and block the negative feedback effect, resulting in increased gonadotrophin secretion ( Shaw, 1976 ). Clomiphene also blocks the positive feedback effect during its administration and for some days after the end of the treatment, while follicle maturation is progressing ( Messinis and Templeton, 1988b ). An attenuation of the negative feedback effect in women can be also achieved with the use of aromatase inhibitors ( Mitwally and Casper, 2001 ).

Changes in the feedback mechanisms in women can also occur as a result of increased ovarian activity that takes place during ovarian stimulation for IVF. In these cases, the negative feedback is augmented and the positive feedback action is markedly attenuated. During ovarian stimulation, the concentrations of E 2 in the circulation become supraphysiological ( Messinis et al ., 1985 ), while inhibin concentrations also increase excessively ( Tsonis et al ., 1988 ). These result in a significant suppression of LH and FSH secretion ( Messinis and Templeton, 1987a , Messinis et al ., 1998 ). Gonadotrophin secretion is also markedly reduced during the endogenous LH surge despite the very high E 2 concentrations ( Messinis et al ., 1985 ). This has been related to the increased production of GnSAF by the stimulated ovaries, which antagonizes the stimulating effect of E 2 on the pituitary ( Messinis and Templeton, 1989 ).

Nevertheless, during multiple follicular development, the endogenous LH surge is blocked on several occasions ( Messinis and Templeton, 1987a ). When a LH surge occurs, it is either premature or on time in relation to the size of the preovulatory follicle ( Messinis and Templeton, 1986 ; Templeton et al ., 1986 ). In clinical terms, a premature LH surge may result in premature luteinization and therefore in a low success rate during IVF treatment. The LH surge in these cases can be prevented by the administration of GnRH analogues, although with the antagonists premature LH peaks >10 IU/l have been reported in a rate from 3 to 35% of the cycles ( Albano et al ., 2000 ; Borm and Mannaerts, 2000 ; Felberbaum et al ., 2000 ; European and Middle East Orgalutran Study Group, 2001 ; Engel et al ., 2002 ; Messinis et al ., 2005 ). Luteinization, however, occurs in a small percentage of the cases with raised LH.

The importance of LH suppression during ovarian stimulation induction has not been clarified because data in the literature are conflicting ( Westergaard et al ., 2000 ; Humaidan et al ., 2002 ; Kolibianakis et al ., 2004 ; Huirne et al ., 2005 ). LH secretion is also suppressed during the luteal phase of stimulated cycles ( Messinis and Templeton, 1987b ; Tavaniotou et al ., 2001 ). It seems that the suppression of gonadotrophin secretion during ovarian stimulation is a continuous process starting in the follicular phase and ending up in a disrupted luteal phase. The latter is inadequate not only after an attenuated LH surge ( Messinis and Templeton, 1987b ) but also after hCG, recombinant LH or a GnRH agonist administration for final follicle maturation ( Beckers et al ., 2003 ).

The two feedback mechanisms are important determinants of the relationships between the ovaries and the hypothalamic-pituitary system. The ovarian steroids, E 2 and progesterone, are the principal mediators of the suppressing effect on gonadotrophin secretion during the normal menstrual cycle. Evidence has been provided that for FSH secretion non-steroidal substances, such as inhibin A and B, also play an important role. E 2 is the main component of the positive feedback mechanism that induces the midcycle endogenous LH surge. Further research is required to clarify the specific role of each of the steroidal and non-steroidal substances in the context of the feedback mechanisms.

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  • gonadotropins
  • gonadotropin-releasing hormone
  • bodily secretions
  • menstrual cycle, proliferative phase
  • hypothalamus
  • luteal phase
  • pharmacokinetics
  • pituitary gland
  • progesterone

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Homeostasis and feedback mechanisms.

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             Before we can refer to the two different Feedback Mechanisms as it relates to Homeostasis, we first have to define and discuss the meaning of the word Homeostasis. The word Homeostasis is described as the body's ability to maintain relatively stable internal conditions even thought the outside world changes continuously. A Physiologist in the early twentieth century, talked about certain "wisdom of the body", calling it Homeostasis. This man's name was Walter Cannon. The body needs to communicate to its different parts to maintain Homeostasis. This communication is mainly sought through by the endocrine and nervous systems. These systems use hormones or even nerves to transport electrical impulses to each different organ of the body. .              There are two different Feedback Mechanisms in our body. The most common.              control mechanism is called the Negative Feedback Mechanism. In this mechanism, "the output system shuts off the original stimulus or reduces its intensity."" The negative feedback mechanism causes whatever is happening to change in the opposite direction, so it returns to its model value or homeostasis. A good example of this is a heater system in a home connected to a thermostat. When the house becomes to cold, the thermostat detects it, and turns on the heater. Making it go the opposite way. .              The other Feedback Mechanism in our body is obviously called the Positive.              Feedback Mechanism, being the opposite of the Negative Feedback Mechanism. .              Don't let the name "Positive"" fool you, this Feedback Mechanism is.              anything but positive. "This feedback mechanism is said to be "positive.              because the change that occurs proceeds in the same direction as the initial.              disturbance, causing the variable to deviate further and further from its.              original value. This Positive Feedback mechanism isn't as common as the.              Negative Feedback, which is good since it usually disrupts homeostasis.

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The mechanisms of homeostasis occurs when the external environment changes; the body either sweats or shivers to keep the internal temperature close to the set point (98.6 degrees Fahrenheit). ... Homeostasis is controlled by a stimulus or a change variable, that can detect and respond to any changes in the external environment and it has two important feedback systems, negative and positive feedback. ... A positive feedback in homeostasis is the complete opposite of the negative feedback mechanism. ... An example of a positive feedback in homeostasis would be when a mother needs to produce mi...

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    There are 2 types of feedback mechanisms - positive and negative. Positive feedback is like praising a person for a task they do. Negative feedback is like reprimanding a person. It discourages them from performing the said task. The human body is like any other system. It carries out a myriad of functions, and like any functioning entity, it ...

  9. Feedback mechanism

    A feedback mechanism is a physiological regulation system in a living body that works to return the body to its normal internal state, or commonly known as homeostasis. In nature, feedback mechanisms can be found in a variety of environments and animal types. In a living system, the feedback mechanism takes the shape of a loop, which aids in ...

  10. 6.4.2 Negative Feedback

    Negative feedback control loops involve: A receptor (or sensor) - to detect a stimulus that is involved with a condition / physiological factor. A coordination system (nervous system and endocrine system) - to transfer information between different parts of the body. An effector (muscles and glands) - to carry out a response.

  11. Positive Feedback Mechanism

    An example of a negative result from a positive feedback mechanism is global warming. This research paper introduces positive feedback in clouds as an accelerating factor in global warming. Scientifically, high-level clouds have been found to have a net cooling effect (Pickering and Owen 36).

  12. Feedback Mechanisms Essay Examples

    Feedback Mechanisms Essays. The Operation of Negative and Positive Feedback Mechanisms in Maintaining Homeostasis. Introduction Ensuring equilibrium in biological systems necessitates incorporating positive and negative feedback mechanisms. Nonetheless, these processes have different impacts and methods of operation (Calabrese & Kozumbo, 2021).

  13. ⇉Biology on feedback mechanisms Essay Example

    Biology essay on feedback mechanisms BY Deep Homeostasis is the 'maintenance of equilibrium in a biological system by means of an automatic mechanism that counteracts influences tending towards disequilibrium'. Homeostasis mechanisms operate at all levels within living systems, including the molecular, cellular, and population levels. ...

  14. The usefulness of feedback

    This can be as simple as providing feedback comments on an essay draft, which learners can then use in a final essay. ... In this light, it is plausible that on-campus students build additional mechanisms for feedback which results in them re-evaluating the usefulness of the educator comments, while in comparison, online students remain ...


    The role of feedback in organizational change has not been fully explored or understood. Most of the explicit attempts to date have focused only on negative feedback. Implicit in these attempts is ...

  16. Frontiers

    The positive effect of feedback on students' performance and learning is no longer disputed. For this reason, scholars have been working on developing models and theories that explain how feedback works and which variables may contribute to student engagement with it. Our aim with this review was to describe the most prominent models and theories, identified using a systematic, three-step ...

  17. Biology essay on feedback mechanisms

    Biology essay on feedback mechanisms. Homeostasis is the 'maintenance of equilibrium in a biological system by means of an automatic mechanism that counteracts influences tending towards disequilibria'. Homeostatic mechanisms operate at all levels within living systems, ...

  18. Quiz & Worksheet

    Take a quick interactive quiz on the concepts in Homeostasis & Biological Feedback Mechanisms or print the worksheet to practice offline. These practice questions will help you master the material ...

  19. AI-Enabled Assessment and Feedback Mechanisms for ...

    AI-Enabled Assessment and Feedback Mechanisms for Language Learning: Transforming Pedagogy and Learner Experience December 2023 DOI: 10.4018/978-1-6684-9893-4.ch002

  20. Properties of feedback mechanisms on digital platforms: an exploratory

    Feedback mechanisms differ with regard to their reciprocity. The submission may concern only the users of one market side (one-sided) or the users of two or more market sides (multi-sided) (Bolton et al. 2004).Platforms use one-sided feedback to evaluate users of different market sides (Chua and Banerjee 2015; Einav et al. 2015; Tadelis 2016).For example, within Amazon's marketplace ...

  21. Ovarian feedback, mechanism of action and possible clinical

    The role of other non-steroidal substances, such as activin and follistatin, is less clear. Activin is a dimeric product of the β-subunit of inhibin and exists in three forms, 'A', 'B' and 'AB' (Muttukrishna et al., 2004).Animal data have shown that activin stimulates the secretion of FSH from pituitary cells in culture (Ling et al., 1986; Vale et al., 1986) as well as in vivo ...

  22. FREE Homeostasis and Feedback Mechanisms Essay

    The body maintains homeostasis through its own set of checks and balances called negative feedback. Negative feedback is a self regulated mechanism of the body that is activated by an imbalance, or a change from a normal set point, and acts to correct it by causing changes which tend to return conditions to normal.

  23. Religions

    This paper considers the influence of Platonism and Neoplatonism on the British Romantic poet and theologian Samuel Taylor Coleridge (1772-1834) and how they informed his reverence for nature. Coleridge did not see this reverence as merely personal but sought to call an increasingly materialist and industrializing England back to a Platonic social imagination that would better revere the ...