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Motivations for personal financial management: A Self-Determination Theory perspective

Stefano i. di domenico.

1 Department of Psychology, University of Toronto Scarborough, Toronto, ON, Canada

Richard M. Ryan

2 Institute for Positive Psychology and Education, Australian Catholic University, Sydney, NSW, Australia

Emma L. Bradshaw

Jasper j. duineveld, associated data.

The datasets presented in this article are not readily available. We are open to sharing the dataset once we have finished publishing all relevant findings from it. Requests to access the datasets should be directed to [email protected] .

Financial knowledge and sound financial decision making are now broadly recognized to be important determinants of both personal and societal prosperity, but research has yet to examine how distinct qualities of motivation may be associated with the way people manage their money. In two studies we applied the framework of Self-Determination Theory (SDT) to examine people's autonomous (volitional) and controlled (pressured) motivation for understanding and managing their finances, as well as their amotivation (lack of motivation) for doing so, and the differential associations these motives have with financial knowledge and financial well-being. American participants (Study 1, N = 516; Study 2, N = 534) completed detailed demographic surveys and questionnaires assessing the financial variables of interest. As hypothesized, SDT's motivational constructs were associated with financial outcomes over and above participants' age, gender, income, household wealth, and educational attainment. Autonomous motivation was positively associated with a host of positive financial behaviors and characteristics (e.g., saving/investing and financial self-efficacy, well-being, and self-awareness). Controlled motivation was negatively associated with financial well-being. Amotivation was positively associated with overspending and negatively associated with financial self-efficacy and well-being. These findings support the relevance of SDT's framework in this domain and suggest that interventions aimed at promoting financial knowledge and wellness may benefit by adopting evidence-supported strategies for optimizing more autonomous motivations and addressing amotivations.

Introduction

Personal finance is inescapable. From paying bills to budgeting, from retirement planning to filing taxes, and from paying the debt to saving and investing, people are continually tasked with managing their finances. Personal finance is also woven into other aspects of people's lives. Evaluating employment opportunities, planning vacations, and deciding where to live—buy or rent—are just a few examples of common goal-setting and decision-making activities that are shaped by the way people manage their money. When personal finances are not at the top of people's minds, news media are often there to put it back: Quantitative easing and inflation, tax cuts and unemployment, the stock market and interest rates, the prices of food, oil, and bitcoin—these and related topics flood the news cycle every day.

Given the pervasiveness and importance of financial decision-making, it is not surprising that policy advisors promote financial literacy (e.g., OECD, 2005 ; Lusardi and Mitchell, 2014 ; Lusardi, 2019 ). Financial literacy has been variously defined and measured (Huston, 2010 ; Cude, 2022 ). Some scholars treat financial literacy as a multidimensional construct that subsumes financial knowledge, numeracy, specific attitudes, and behaviors, as well as financial self-efficacy (Cude, 2022 ). Others use the terms financial literacy and financial knowledge interchangeably (Huston, 2010 ). Financial knowledge broadly refers to the knowledge and skills necessary for setting financial goals and making sound financial decisions. Financial knowledge entails understanding basic economic concepts (e.g., opportunity cost, inflation) and how financial products and services work (e.g., mortgages and interest rates). Because financial knowledge is common to most definitions and assessments of financial literacy (Lusardi and Mitchell, 2007 ; Cude, 2022 ), we here focus on financial knowledge.

People who are more financially knowledgeable and have other characteristics related to the broader financially literacy construct are more likely to avoid credit fees, have better credit scores, take measured investment risks, make better plans, set money aside for emergencies, and have greater financial well-being overall (Fernandes et al., 2014 ; Santini et al., 2019 ). Financial knowledge and literacy may also have broader societal import. According to the U.S. Financial Literacy and Education Commission (2020, p. 2), “Financial education is key to unlocking the foundations of economic opportunities and powering a strong and resilient economy. Americans must acquire financial skills and knowledge to fully participate in our dynamic economy.” In a related vein, Fornero et al. ( 2021 ) recently argued that, in our post-COVID world, financial and economic knowledge is especially important for fuller participation and decision-making in contemporary democratic processes.

Despite the importance of financial knowledge and sound money management for financial wellness, research has yet to systematically examine the motivations that drive people to learn about or better manage their finances. Illustratively, in a review of more than 500 peer-reviewed journal articles, Goyal and Kumar ( 2020 ) found three major research themes in the personal finance literature: the degree of financial literacy amongst distinct cohorts, and the influence of financial literacy on financial behavior, and the impact of financial education. Goyal and Kumar ( 2020 ) also reported that motivational constructs (e.g., risk tolerance, financial attitudes, and financial self-confidence) are diffusely represented in the extant literature, as either antecedent of financial literacy or as correlates and outcomes of financial education.

The absence of a comprehensive framework for researching people's motivations for personal finance is somewhat surprising since psychologists have long understood that people can behave in suboptimal ways, even when they “know better,” when they lack high-quality motivation (e.g., Sternberg, 2002 ). Understanding people's motivations for managing their finances is therefore of fundamental importance. Doing so promises to not only increase our understanding of why people manage their finances the way they do, but it could also help identify useful targets for enhancing financial education and advising.

In working toward understanding people's motivations for understanding and managing their finances, insights may be gleaned from the broader fields of motivational psychology and personality development, and in particular from Self-Determination Theory (SDT; Ryan and Deci, 2017 ). SDT is a longstanding framework for the study of human motivation. It differentiates autonomous (volitional) qualities of motivation from controlled (pressured) qualities of motivation, as well as amotivation (a lack of motivation), as different ways people may engage with their personal finances. Research across a variety of applied domains (e.g., work, education, healthcare) indicates that more autonomous forms of motivation are reliably associated with better performance and wellness outcomes (Ryan et al., 2021 ). We expect the same is true for the domain of personal finance. Importantly, SDT offers practical insights for enhancing people's motivation—a point to which we return in the Discussion. We, therefore, believe that SDT has much to offer basic and applied research in personal finance. The main purpose of the present article is to bridge research from personal finance and public advocacy with SDT to more comprehensively examine the motivations that underlie people's financial behaviors and well-being.

A closer look at motivation: Self-Determination Theory

SDT makes the broad distinction between autonomous and controlled qualities of motivation. Autonomously motivated behaviors are enacted with a sense of volition. When a person feels autonomous, he or she concurs with or is willing to engage in particular activities. Intrinsic motivation is a type of autonomous motivation. It refers to people's engagement with activities that are driven by an interest or by the satisfaction that people experience during the enactment of the activities (e.g., an individual enjoys learning about financial “life hacks”). Identified motivation is another type of autonomous motivation that is evidenced when one consciously values activity and endorses its underlying goals (e.g., an individual builds an emergency savings fund upon assenting to its importance). In contrast to these autonomous motivations, SDT describes controlled motivations as those in which a person feels compelled or pressured to act. Introjected motivation is one type of controlled motivation. It describes behavior that is internally controlled by a sense that one “should” or “must” do something. Introjected motivations are often focused on obtaining approval from oneself or others and driven by shame, guilt, or anxiety (e.g., an individual adheres to a budget to avoid the personal shame of failing to do so). External motivation is another type of controlled motivation. It refers to activities that people do in order to obtain external rewards or avoid punishments. Such behaviors are dependent upon external contingencies; when the contingencies are removed, people lose their motivation to persist (e.g., an individual makes sure to pay their bills on time because they expect their spouse to be angry with them if they do not; when the spouse is not present, they are not concerned with their bills).

Both autonomous and controlled motivations describe the reasons why people act in particular ways (Ryan and Deci, 2017 ). In contrast, SDT characterizes amotivation as the lack of intention to act (i.e., a lack of motivation). When a person is amotivated, they either do not value a behavior or outcome (e.g., an individual does not care to learn about mutual funds because they do not perceive any utility in doing so), or believe that they are incapable of performing the activity (e.g., an individual does not invest in the stock market because they do not believe they have enough money to do so). The latter type of amotivation, which connotes feelings of hopelessness or a lack of efficacy, is typically associated with the poorest performance and affective outcomes.

SDT's model of motivation affords a differentiated perspective from which to examine how people manage their finances. Consider, for example, the varied motives one may have for monitoring their budget. An individual may be curious about their spending habits (intrinsic motivation), may find budgeting helpful for allocating funds to important purchases (identified motivation), may compulsively watch their spending to manage anxiety and guilt (introjected motivation) and may follow a budget because others pressure them to do so (external motivation), or may feel that following a budget is too hard or pointless (amotivation). How might these various motives impact one's financial behaviors and well-being?

The qualities of motivation specified by SDT have been researched across a variety of cultural settings, developmental epochs, and life domains (Ryan and Deci, 2017 ). Studies have consistently found that more autonomous ( vs . controlled and amotivation) qualities of motivation are associated with greater behavioral persistence, deeper learning, and more effective performance, especially in activities that are difficult or that require long-term effort. Autonomous motivation is also reliably associated with more positive experiences and greater well-being. This literature is now voluminous (Ryan et al., 2019 ) so we cite a few examples for illustrative purposes.

In education, Domenico and Fournier ( 2015 ) found that university students who were more autonomously motivated for their schoolwork had higher grade-point averages, a statistical relationship that remained even when students' intelligence scores were taken into account. In healthcare, research shows that when people are autonomously motivated for health-related changes (e.g., smoking cessation, improving diet, exercising regularly, managing glucose), they are more successful and more likely to maintain positive changes over time (Ng et al., 2012 ). The benefits of autonomous motivation ( vs . both controlled motivation and amotivation) in workplace settings have also been well documented (Deci et al., 2017 ) and supported meta-analytically (Slemp et al., 2018 ). In a study of school principals, Fernet et al. ( 2012 ) found that autonomous motivation was negatively related to work exhaustion but positively related to work commitment, whereas controlled motivation was positively related to exhaustion. SDT's model of motivation has also been applied to examining people's idiographic goal strivings. In this research, people list the personal goals that they are currently working toward and rate the motivation quality for each goal. Studies in this line show that more autonomous goals are associated with greater goal progress and psychological well-being (e.g., Holding et al., 2017 ). Given the research findings described above, we expect autonomous motivation to be associated with sound financial behaviors and greater financial well-being.

Overview of the present research

The goal of the present research was to apply SDT's motivational framework to the domain of personal finance. Specifically, we aimed to adapt items from existing SDT questionnaires to assess respondents' quality of motivation for (a) monitoring their budgets, (b) paying bills in a timely manner, and (c) learning about new financial products and services. We hypothesized that the subscales measuring SDT's specific qualities of motivation—intrinsic, identified, introjected, external, and amotivation—would evidence internal reliability and a simplex-like pattern of inter-correlations, such that the largest correlations would appear along the main diagonal of the matrix (Ryan and Connell, 1989 ). This simplex-like association would be consistent with previous SDT studies and provide evidence of construct validity by suggesting that the varied motives are ordered along a continuum of relative autonomy (Howard et al., 2017 ; Donald et al., 2020 ).

We also hypothesized that the broad categories of autonomous motivation, controlled motivation, and amotivation would evidence a differentiated pattern of associations with respondents' degree of financial knowledge, financial behaviors (e.g., saving and investing, spending, financial self-awareness), financial self-efficacy, financial well-being, and general psychological well-being. Specifically, we hypothesized that autonomous motivation would be positively associated with financial knowledge, better financial behaviors, financial well-being, and general psychological well-being, and we expected controlled motivation and amotivation to evidence a suboptimal pattern of associations with these variables. These hypotheses were tested across two correlation studies that used diverse measures of financial behaviors and well-being.

Study 1 method

Participants.

The study received institutional review board approval from the Human Research Ethics Committee at the Australian Catholic University. Participants were 516 American adults recruited by Qualtrics, a professional panel company. We targeted adults between the ages of 30 and 50 years to ensure that the study participants would have meaningful amounts of experience managing their personal finances. Participants completed an online consent form before entering the study. Table 1 summarizes the general demographic information of the sample.

Demographic information and frequency distributions.

	516	534
	52	50
	30–50	18–55
	39	39
	5.75	10.03
% White	83	71
% Black	8	14
% Other	9	15
% employed full-time	59	46
% employed part-time	8	10
% self-employed	7	10
% unemployed	9	15
% retired	2	2
% sickness benefit (incapacity for work)	4	6
% other	11	11
% < $15,000	19	26
% $15,001–$25,000	13	16
% $25,001–$35,000	10	14
% $35,001–$50,000	9	16
% $50,001–$75,000	11	13
% $75,001–$100,000	12	6
% >$100,000	26	7
% < $15,000	10	19
% $15,001–$25,000	10	13
% $25,001–$35,000	8	13
% $35,001–$50,000	11	16
% $50,001–$75,000	15	16
% $75,001–$100,000	14	12
% >$100,000	32	11
% some high school	2	5
% high school diploma or equivalent	15	28
% business or trade school	2	4
% some college	15	20
% Associate's degree	9	9
% Bachelor's degree	27	22
% some graduate or professional school	3	1
% graduate or professional degree	27	11

All percentages are rounded to the nearest whole number.

A list of the constructs in this study, along with the descriptive statistics of their measures, is provided in Table 2 . We provide a brief description of each below.

Study 1 means, standard deviations, and internal reliabilities for study questionnaires.

Intrinsic motivation	4.97	1.23	0.87
Identified motivation	5.45	1.13	0.89
Introjected motivation	4.08	1.32	0.83
External motivation	3.19	1.14	0.78
Amotivation	2.64	1.29	0.86
	2.57	1.59	0.60
Saving and investing	1.60	1.20	0.73
Overspending	1.91	0.83	–
Financial self-awareness	4.69	1.03	0.72
	0.85	0.36	–
	0.00	0.79	0.85

Overspending was measured using a single Likert-type item. Financial Self-Efficacy was measured using a single binary-response item. Financial Well-Being was scored as the aggregate of five standardized items. *Chowdhry and Dholakia (2019; Study 3) used data from the 2015 National Financial Capability Study (NFCS; N = 27,564), in which the mean score across the six Financial Knowledge questions was M = 3.29 (SD = 1.67). The lower mean in the present sample may have been produced by sampling error given our relatively small sample. Alternatively, these mean differences may reflect genuine differences across the NFCS participants and the present community sample.

Participants reported their Personal Income and Household Incomes for the 2020 calendar year by selecting one of nine categories that corresponded most closely with their annual earnings before the deduction of tax. The frequency distributions of these variables are presented in Table 1 . The median household income bracket of our sample ($50, 001–75, 000) was consistent with the median income of $64, 994 for households between 2016 and 2020 (U.S. Census Bureau, 2022 ).

Household wealth

Participants reported their Household Wealth , which was described to them as “the amount of money you would have if you cashed in all of your household's assets—e.g., house, car, caravan, boat, jewelry, etc.—and paid off all the debts.” Participants selected one of 23 wealth brackets that most closely matched their household's wealth. The five most commonly selected wealth brackets were “less than $25,000,” “$25,001-$50,000,” “$50,001-$100,000,” “$100,001–$150,000,” and “$200,001–$250,001,” with 27.13%, 10.65%, 10.65%, 6.78% and 6.78% response proportions, respectively.

Financial knowledge

Participants were presented with a 6-question multiple-choice test that assessed their financial knowledge. These six questions were obtained from Lusardi and Mitchell's ( 2007 ) widely recognized measure of “financial literacy” (Lusardi and Mitchell, 2007 ). This measure has been adopted by the FINRA Foundation's National Financial Capability Studies (FINRA Investor Education Foundation, 2022 ) and has been used in other publications to validly assess respondents' financial knowledge (e.g., Chowdhry and Dholakia, 2019 ). These questions concerned financial concepts such as interest rates, differences between stocks and mutual funds, and basic economic concepts. A sample question is: “If interest rates rise, what will typically happen to bond prices?” Response options to this question were, “They will rise,” They will fall,” They will stay the same,” “There is no relationship between bond prices and the interest rate,” “I don't know,” and “Prefer not to answer.” Correct responses were coded as 1; others were coded as 0.

Saving and investing

Three items measured saving and investing behavior (Chowdhry and Dholakia, 2019 ): “Do you regularly contribute to a retirement account like a 401(k) or IRA?”; “Have you set aside emergency or rainy day funds that would cover your expenses for 3 months in case of sickness, job loss, economic downturn, or other emergencies?”; and “Not including retirement accounts, do you have any investments in bonds, mutual funds, or other investments?” “Yes” responses were coded with a value of 1 and the sum of “yes” responses was used to quantify participants' saving and investing.

Overspending

Spending patterns were measured with the question, “Over the past year, would you say your spending was less than, more than, or equal to your income?” Following Chowdhry and Dholakia ( 2019 ), “spending less than income” was coded as 1, “spending about equal to income” was coded as 2, and “spending more than income” was coded as 3.

Financial self-awareness

Nineteen items assessed financial self-awareness (Chowdhry and Dholakia, 2019 ). Participants were instructed to “indicate how well you know the exact value (dollar amount, interest rate, etc.)” for items like “Your total net worth,” “Your student loan debt,” and “The amount of money in your savings account(s).” They then indicated whether they knew the exact value, the approximate value, or did not know at all, coded respectively as 2, 1, and 0. Participants could also indicate if a particular question was not applicable to them. Scores were computed as the mean across applicable items.

Financial self-efficacy

This construct refers to feelings of confidence about successfully handling of financial matters (Cude, 2022 ). A single item, “I am good at dealing with day-to-day financial matters, such as checking accounts, credit and debit cards, and tracking expenses,” was used to assess participants' financial self-efficacy (Chowdhry and Dholakia, 2019 ). This item was presented with a binary response format: “Yes” (coded as 1; “No” coded as 0).

Financial well-being

Five items were standardized and aggregated to form a composite measure of financial well-being. These items were adapted from questionnaire packages used in FINRA Foundation's National Financial Capability Studies (FINRA Investor Education Foundation, 2022 ). The first item read, “Thinking of the next 10 years, how financially secure do you feel?” which participants rated on a 1–4 scale from “Insecure” to “Secure.” The second item read, “All things considered, how satisfied or dissatisfied are you with your standard of living?” The response scale for this item ranged from “Very Dissatisfied,” coded as 1, to “Very Satisfied,” coded as 7. The third item read, “Overall, thinking of your assets, debts, and savings, how satisfied are you with your current financial situation?” with the scale for this item ranging from 1, “Not at All Satisfied,” to 7, “Very Satisfied.” The fourth item read, “How often does it happen that you do not have enough money to afford the kind of food or clothing that you/your family should have?” rated on a scale from “Almost Never,” coded as 1, to “Almost Always,” coded as 5. This item was reverse scored. Finally, the fifth item read, “How much difficulty do you have in meeting the payment of bills?” with responses ranging from 1, “No Difficulty at All,” to 5, “A Lot of Difficulties”, also reverse scored.

Financial motivation

We developed a 45-item financial motivation questionnaire that was closely based on previous SDT-based assessments (Center for Self-Determination Theory, 2022 ). The questionnaire's instructions stated:

People have different reasons for managing their personal finances. By personal finances, we mean spending, budgeting, saving, and investing. In other words, all the different ways you can use your money. Please rate each of the following statements in terms of how true it is for you using the scale below .

Participants rated each of the statements using a Likert scale anchored from “Not at all True,” coded as 1, to “Very True,” coded as 7. To broadly represent the domain of personal finance, the statements on this questionnaire began with one of three stems: “I try to monitor my budget because…,” “I try to pay my bills on time because…,” and “I try to learn about financial products and services (e.g., different types of investments and savings accounts) because…” Each regulatory style was assessed with 3 items for each question stem, for a total of 9 items each for intrinsic, identified, introjected, external, and amotivation. The complete list of items is presented in Table 3 . Table 4 shows that the different types of motivation evidenced an expected simplex-like pattern of associations, such that the largest correlations appeared along the main diagonal of the matrix (Ryan and Connell, 1989 ). Following previous SDT studies, autonomous regulation was scored as the average of intrinsic and identified motivation, and controlled regulation as the average of introjected and external motivation (Ryan and Deci, 2017 ).

Financial motivation PLOC items.

1. I'm curious about tracking my own spending habits. (Intrinsic) 2. I enjoy understanding the ways I spend my money. (Intrinsic) 3. I enjoy planning how to spend my money. (Intrinsic) 4. It helps me spend my money in ways that I believe are most important. (Identified) 5. It helps me reach the important goals that I have for myself and my family. (Identified) 6. It helps me make sure I dedicate enough money for important purchases. (Identified) 7. I'd feel bad about myself if I didn't. (Introjection) 8. I'd feel like guilty if I didn't. (Introjection) 9. I don't want other people to think that I'm “too loose” with my money. (Introjection) 10. Others would get angry with me if I didn't. (External) 11. I don't want others to think I'm irresponsible. (External) 12. I have no choice; others force to. (External) 13. Actually, I gave up trying to keep track of a budget. (Amotivation) 14. Actually, I don't have a budget; I don't believe I really need one. (Amotivation) 15. Honestly, I don't have a budget; I don't know how to create one. (Amotivation)

16. I enjoy the feeling of staying on top of my bills. (Intrinsic) 17. I feel a sense of accomplishment when I pay bills. (Intrinsic) 18. I'm interested to see how my bills change from month-to-month. (Intrinsic) 19. Paying bills on time helps me keep my spending well-organized. (Identified) 20. I believe in the importance of respectfully paying others for the services they provide me. (Identified) 21. Maintaining and improving my credit score will help me achieve the financial goals that are important to me. (Identified) 22. I'd be embarrassed if I didn't. (Introjection) 23. I'd feel like a “deadbeat” or “loser” if I didn't. (Introjection) 24. I'd get angry with myself if I didn't. (Introjection) 25. When I get a bill notification, it feels like a threat or warning. (External) 26. I feel threatened at the possibility of getting a bad credit score. (External) 27. Other people would get angry with me if I didn't. (External) 28. Actually, I easily forget to pay bills on time. (Amotivation) 29. Honestly, I don't really try; I just wait for notifications. (Amotivation). 30. I'm not sure; I sometimes wonder what would happen if I didn't pay. (Amotivation)

31. I find these topics really interesting. (Intrinsic) 32. It's fun to learn new, and sometimes better, ways to manage my money. (Intrinsic) 33. I'm just naturally curious about these things. (Intrinsic) 34. Keeping up to date with such matters helps me plan for the future. (Identified) 35. Learning about such matters helps me make the best financial decisions. (Identified)

36. I think it's really useful to stay informed about such matters. (Identified) 37. I want others to think I'm knowledgeable about these types of things. (Introjection) 38. I don't want to sound like “an idiot” to others when such topics come up in conversation. (Introjection) 39. I'd feel guilty if I didn't keep up to date on such matters. (Introjection) 40. I'm worried that others (e.g., my employer, banks, credit card companies) might take advantage of me. (External) 41. I'm not a “sucker”; I stay informed to get the best for myself and my family. (External) 42. I feel like I'm forced to; others depend on me to know about finances. (External) 43. To be honest, I tried to learn but I gave up. (Amotivation) 44. Actually, I don't try to learn about these things; it's just not important to me. (Amotivation) 45. Actually, I kind of gave up trying to learn about these things; I usually get overwhelmed when I try. (Amotivation)

Study 1 correlations among the five motivation subscales (below the diagonal) and 95% confidence intervals (above the diagonal).

	–	[0.78,0.84]	[0.42,0.55]	[0.20,0.36]	[−0.13,0.04]
		0.81	–	[0.39,0.53]	[0.11,0.28]	[−0.32, −0.16]
		0.49		0.46	–	[0.55,0.67]	[0.13,0.29]
		0.28		0.20		0.60	–	[0.54,0.65]
	–	0.04	–	0.24		0.21		–

Data analytic approach

We conducted two types of statistical analyses. First, we conducted correlational analyses to broadly examine the relationships between different qualities of financial motivation, personal finance variables, and well-being. We report the 95% confidence intervals of the correlations to compare the magnitudes of the different relationships; non-overlapping intervals allow for the inference that correlations differ in magnitude. We then conducted a hierarchical multiple regression procedure to examine the extent to which autonomous motivation, controlled motivation, and amotivation predict financial knowledge, saving and investing, spending, financial self-efficacy, and financial well-being after controlling for a host of factors from age to income. Partial F -tests were utilized to examine whether models explained significantly greater amounts of variance in the dependent variables.

Study 1 results

Correlational analyses.

Table 5 displays correlations between the financial motivations and all other variables. With respect to demographic characteristics, an inspection of the 95% confidence intervals indicated that autonomous motivation held a greater positive association with income, household wealth, and educational attainment than amotivation; autonomous and controlled motivation did not differ in their associations with these demographic characteristics. Age was negatively correlated with both controlled motivation and amotivation. These patterns of associations indicated the importance of controlling for demographic characteristics in subsequent analyses.

Study 1 correlations of SDT financial motivations ( N = 516).

Age	−0.09 [−0.19,0.00]	−0.17 [−0.26, −0.08]	−0.19 [−0.27, −0.09]
Gender	0.15 [0.06,0.23]	0.16 [0.07,0.24]	0.14 [0.05,0.22]
Income	0.31 [0.23,0.38]	0.26 [0.17,0.34]	0.13 [0.04,0.21]
Household wealth	0.26 [0.18,0.34]	0.19 [0.11,0.28]	0.07 [−0.01,0.16]
Educational attainment	0.28 [0.20,0.36]	0.18 [0.10,0.27]	0.02 [−0.07,0.10]
Financial knowledge	0.12 [0.04,0.21]	0.04 [−0.05,0.12]	−0.15 [−0.23, −0.06]
Saving and Investing	0.38 [0.30,0.45]	0.24 [0.16,0.32]	0.05 [−0.04,0.14]
Overspending	−0.05 [−0.14,0.04]	0.06 [−0.02,0.15]	0.26 [0.17,0.34]
Financial self-awareness	0.39 [0.32,0.46]	0.11 [0.03,0.20]	−0.13 [−0.22, −0.05]
Financial self-efficacy	0.31 [0.23,0.38]	0.05 [−0.04,0.14]	−0.15 [−0.24, −0.07]
Financial well-being	0.40 [0.33,0.47]	0.07 [−0.02,0.15]	−0.16 [−0.24, −0.07]

The autonomous motivation was positively associated with financial knowledge, whereas amotivation was negatively related, and controlled motivation was unrelated. Interestingly, whereas autonomous and controlled motivation were both associated with saving and investing, amotivation was significantly correlated with spending exceeding earnings. And whereas autonomous motivation had significant positive associations with financial self-efficacy and financial well-being, amotivation was negatively correlated with these variables. Finally, all three types of motivation held significant associations with financial self-awareness, but autonomous motivation had the strongest positive association and amotivation was negatively associated.

Regression analyses

We used hierarchical regression to examine the incremental validity of autonomous motivation, controlled motivation, and amotivation in the prediction of financially relevant characteristics and outcomes over and above relevant participant demographics, namely, age, gender, income, household wealth, and educational attainment. Whereas these demographic characteristics were entered in Step 1, all three qualities of motivation were entered at Step 2. Partial F -tests were used to assess the incremental prediction of the overall models across Steps 1 and 2. In the unstandardized models, each predictor was group-mean centered so that the intercept could be interpreted as the unstandardized value of the outcome at the mean of all predictors. The variance inflation factors (VIFs) for each predictor in the models were computed to check for multicollinearity and each predictor's VIF was well below Cohen et al.'s ( 2003 ) rule of thumb of VIF <10, indicating that multicollinearity was not an issue. The residual plots of the regression models were visually inspected and no evidence of heteroscedasticity was found. The results of these regression analyses are summarized in Table 6 .

Study 1 regression analyses examining the incremental associations of SDT motivations on financial outcomes.

									.						.
	(SE)		(SE)		(SE)		(SE)		(SE)		(SE)		(SE)		(SE)
	0.06(0.01)	0.23	0.03(0.08)	0.02	−0.01(0.02)	−0.05	0.00(0.06)	0	0.22(0.04)	0.29	0.08(0.08)	0.05	0.06(0.09)	0.04	−0.20(0.07)	−0.16
	= 0.12, = 11.93, < 0.001								= 0.15, = 9.54, < 0.001
	−0.01(0.01)	−0.06	0.03(0.05)	0.02	0.02(0.01)	0.10	0.29(0.04)	0.46	0.10(0.03)	0.18	0.18(0.05)	0.17	−0.03(0.05)	−0.03	0.02(0.04)	0.02
	, ,								= 0.51, = 54.89, < 0.001
	−0.02(0.01)	−0.15	0.07(0.05)	0.08	0.00(0.01)	0	0.00(0.03)	0	−0.01(0.03)	−0.04	0.01(0.04)	0.02	−0.04(0.05)	−0.05	0.16(0.04)	0.25
	= 0.04, = 2.96, = 0.012								= 0.09, = 4.68, < 0.001
	0.00(0.01)	0.09	0.01(0.04)	0.12	0.02(0.01)	0.02	0.16(0.03)	0.05	0.01(0.02)	0.08	0.21(0.03)	0.41	−0.15(0.04)	−0.03	−0.05(0.03)	−0.02
	= 0.28, = 32.70, < 0.001								= 0.39, = 33.40, < 0.001
	0.01(0.00)	0.11	0.03(0.02)	0.10	0.00(0.00)	−0.02	0.04(0.01)	0.20	0.01(0.01)	0.06	0.09(0.02)	0.27	−0.03(0.02)	−0.08	−0.03(0.01)	−0.11
	= 0.08, = 7.87, < 0.001								= 0.17, = 11.14, < 0.001
	0.00(0.01)	0.02	0.01(0.04)	0.01	0.02(0.01)	0.14	0.16(0.03)	0.39	0.01(0.02)	0.02	0.21(0.03)	0.30	−0.15(0.04)	−0.20	−0.05(0.03)	−0.09
	= 0.28, = 32.70, < 0.001								= 0.39, = 33.40, < 0.001

Gender: Female = −1, Male = 1; The effect size b(SE) is the unstandardized regression coefficients with mean-centered predictors; b* is the standardized regression coefficient. The p-values are rounded to the nearest thousandth. All other statistics are rounded to the nearest hundredth.

In Step 1, both age and educational attainment were significantly predictive of financial knowledge. In Step 2, amotivation evidenced a significant negative association with financial knowledge. This significant incremental effect was further supported with a partial F -test that formally compared the models at Step 1 and 2, Δ R 2 = 0.03, F (3, 426) = 5.00, p = 0.002.

In Step 1, household wealth, income, and educational attainment were significant positive predictors of saving and investing. In Step 2, autonomous motivation evinced a significant positive association with saving and investing. This significant incremental effect was further supported with a partial F -test that formally compared the models at Step 1 and 2, Δ R 2 = 0.02, F (3,426) = 6.61, p < 0.001.

In Step 1, age held a significant negative relationship with overspending. In Step 2, amotivation was a significant positive predictor of spending. This significant incremental effect was supported with a partial F -test that formally compared the models at Step 1 and 2, Δ R 2 = 0.04, F (3,399) = 7.32, p < 0.001.

In Step 1, income held a significant positive relationship with financial self-efficacy. In Step 2, autonomous motivation was a significant positive predictor of financial self-efficacy and amotivation held a significant negative association with financial self-efficacy. These significant incremental effects were supported with a partial F -test that formally compared the models at Step 1 and 2, Δ R 2 = 0.08, F (3,426) = 4.79, p < 0.001.

In Step 1, gender held a significant positive relationship with financial self-awareness. In Step 2, autonomous motivation was a significant positive predictor of financial self-awareness. This significant incremental effect was supported with a partial F -test that formally compared the models at Step 1 and 2, Δ R 2 = 0.14, F (3,424) = 24.98, p < 0.001.

In Step 1, household wealth and income each held significant positive relationships with financial well-being. In Step 2, autonomous motivation evinced a significant positive association with financial well-being, whereas controlled motivation evinced a significant negative association with financial well-being. Amotivation was negatively associated with financial well-being, though this relationship was of marginal statistical significance. These significant incremental effects were supported with a partial F -test that formally compared the models at Step 1 and 2, Δ R 2 = 0.10, F (3,426) = 25.30, p = <0.001.

Study 1 brief discussion

The results of Study 1 were generally supportive of our hypotheses. Financial motives evidenced a simplex-like pattern of associations, consistent with previous research in SDT showing that these qualities of motivation are ordered along a continuum of relative autonomy (Howard et al., 2017 ). Regression analyses controlling for demographic characteristics showed that: (a) Autonomous motivation was positively associated with saving and investing, financial self-awareness, financial self-efficacy, and financial well-being; (b) Controlled motivation was negatively associated with financial well-being, and (c) Amotivation was negatively associated with financial knowledge and financial self-efficacy and positively associated with overspending.

In Study 2, we aimed to conceptually replicate and extend the findings from Study 1 using a different set of instruments to measure financial behaviors and well-being. Specifically, in addition to assessing the simplex-like pattern of associations among the different qualities of motivation and examining the differential associations that autonomous motivation, controlled motivation, and amotivation hold with financial knowledge, we used the Financial Health Network's FinHealth Score ® Toolkit (Financial Health Network, 2022a ). This instrument is widely used by human resource professionals to assess and promote financial well-being among stakeholders. According to the Financial Health Network, “Financial health is a composite measurement of an individual's financial life. Unlike narrow metrics such as credit scores, financial health assesses whether people are spending, saving, borrowing, and planning in ways that will enable them to be resilient and pursue opportunities” (Financial Health Network, 2022b ). The FinHealth Score ® Toolkit assesses respondents' financial health across four domains: Spending (e.g., spending less than income), saving (e.g., having sufficient liquid funds), borrowing (e.g., having manageable debt), and planning (e.g., being financially prepared). We also measured respondents' general psychological wellness by assessing their feelings of vitality and depletion (Ryan and Frederick, 1997 ) and their life satisfaction (Diener et al., 1985 ).

Hypotheses for Study 2 were preregistered ( https://osf.io/qsker ). We hypothesized that autonomous motivation would be positively associated with financial knowledge and indicators of healthy financial management whereas amotivation would be negatively associated with these outcomes, direct replication of Study 1. Previous work in SDT has shown that autonomous motivation is especially beneficial for endeavors that are complex, require sustained effort, and have a longer time horizon (Ryan and Deci, 2017 ). We accordingly hypothesized that autonomous motivation would evidence the strongest positive associations with the more deliberate and effortful aspects of financial health, namely, planning and borrowing. We expected amotivation to hold the strongest negative associations with these aspects of financial health. Finally, we hypothesized that whereas autonomous motivation would be associated with greater vitality, less depletion, and more life satisfaction, amotivation would have the opposite associations with these variables.

Study 2 method

The study received institutional review board approval from the Human Research Ethics Committee at the Australian Catholic University. Participants were 534 American adults recruited by Qualtrics, a professional panel company. Participants completed an online consent form before entering the study. Table 1 summarizes the general demographic information of the sample.

A list of the assessments in this study, along with their descriptive statistics, is provided in Table 7 . We provide a brief description of each below.

Study 2 means, standard deviations, and internal reliabilities for study questionnaires.

Intrinsic motivation	4.75	1.29	0.88
Identified motivation	5.13	1.20	0.87
Introjected motivation	3.85	1.39	0.86
External motivation	3.22	1.20	0.78
Amotivation	2.72	1.29	0.86
	2.58	1.63	0.62
Spend	68.16	25.41	–
Save	57.47	30.79	–
Borrow	63.59	26.74	–
Plan	58.19	27.49	–
	3.38	1.37	0.85
	2.78	1.24	0.85
	4.20	1.58	0.89

Participants reported their Personal Income and Household Incomes for the 2020 calendar year using the question used in Study 1. Again, the median household income bracket of our sample was $50,001–$75,000. Personal and household income were highly correlated ( r = 0.73, df = 532, p < 0.0001) and were accordingly standardized and aggregated into a composite measure of income.

Participants also reported their Household Wealth using the questionnaire used in Study 1. The five most commonly selected wealth brackets were “ < $25,000,” “$25,001–$50,000,” “$50,001–$100,000,” “$100,001–$150,000,” and “$150,001–$200,000,” with 41.20%, 13.67%, 10.30%, 7.30%, and 3.93% response proportions, respectively. This distribution was similar to that reported in Study 1.

Participants completed the financial knowledge test that was used in Study 1.

We administered the same 45-item financial motivation questionnaire developed in Study 1. Table 8 shows that the different types of motivation once again produced the expected simplex-like pattern of associations. The point estimates of these correlations were very similar to those obtained in Study 1. Autonomous regulation was again computed as the average of intrinsic and identified motivation and controlled regulation as the average of introjected and external motivation.

Study 2 correlations among the five motivation subscales (below the diagonal) and 95% confidence intervals (above the diagonal).

	–	[0.78,0.84]	[0.43,0.56]	[0.25,0.41]	[−0.09,0.00]
		0.82	–	[0.40,0.53]	[0.17,0.33]	[−0.31, −0.15]
		0.50		0.47	–	[0.55,0.67]	[0.15,0.31]
		0.33		0.25		0.66	–	[0.49,0.61]
	–	0.09	–	0.23		0.23	0.55	–

FinHealth Toolkit ®

This 8-item questionnaire was developed by the Financial Health Network (Financial Health Network, 2022a ) to assess four domains of financial health, each assessed with two items. Respondents are presented with a series of questions to which they may respond by selecting an answer that is most descriptive for them. The instrument's scoring manual assigns a specific score for each possible answer. Scores are computed as the mean across the two items for that domain. A sample item reads as follows: “How would you rate your credit score?” to which respondents and answer, “Excellent” (100 points), “Very good” (80 points), “Good” (60 points), “Fair” (40 points), “Poor” (0 points), and “I don't know” (0 points).

Psychological well-being

Subjective vitality was measured with Ryan and Frederick's ( 1997 ) 6-item scale. The scale's three positively worded items were used to assess feelings of Vitality proper (e.g., I have a lot of positive energy and initiative.”). The scale's three negatively worded items were used to assess feelings of Depletion (e.g., “I feel drained.”). The 5-item Satisfaction With Life Scale (Diener et al., 1985 ) asked participants to rate their agreement with each item on a 7-point Likert scale ranging from “Strongly Disagree” to “Strongly Agree.” A sample item is as follows: “In most ways, my life is close to my ideal.”

Study 2 results

Consistent with Study 1 and with the broader SDT literature (Ryan and Deci, 2017 ), Table 8 displays that the different types of motivation once again evidenced the expected simplex-like pattern of associations, such that the largest correlations appeared along the main diagonal of the matrix (Ryan and Connell, 1989 ).

Table 9 displays correlations between the financial motivations and all other variables. The pattern of correlations between the financial motivations and the demographic variables was directionally consistent with the results obtained in Study 1 with a few notable exceptions. In this sample, the motivations were unrelated to gender, whereas in Study 1 females tended to score lower on all types of financial motivation. Furthermore, in the present sample, amotivation was negatively associated with income.

Study 2 correlations of SDT financial motivations ( N = 534).

Age	−0.14 [-0.22, −0.06]	−0.17 [−0.25, −0.09]	−0.06 [−0.14, 0.03]
Gender	−0.01 [−0.09, 0.08]	0.03 [−0.05, 0.12]	−0.06 [−0.14, 0.03]
Income	0.20 [0.11, 0.28]	0.07 [−0.01, 0.16]	−0.12 [−0.20, −0.03]
Household wealth	0.11 [0.02, 0.19]	0.00 [−0.08, 0.08]	−0.10 [−0.18, 0.05]
Educational attainment	0.09 [0.01, 0.18]	0.09 [0.01, 0.18]	−0.03 [−0.12, 0.10]
Financial knowledge	0.07 [−0.01, 0.16]	0.05 [−0.03, 0.14]	−0.20 [−0.27, −0.11]
Spend	0.18 [0.10, 0.27]	−0.01 [−0.09, 0.08]	−0.20 [−0.28, −0.12]
Save	0.30 [0.22, 0.37]	0.16 [0.07, 0.24]	0.02 [−0.06, 0.11]
Borrow	0.27 [0.19, 0.34]	0.11 [0.02, 0.19]	−0.12 [−0.20, −0.03]
Plan	0.37 [0.30, 0.45]	0.17 [0.09, 0.25]	−0.03 [−0.11, 0.05]
Vitality	0.45 [0.39, 0.52]	0.14 [0.05, 0.22]	−0.05 [−0.14, 0.03]
Depletion	−0.21 [−0.29, −0.13]	0.23 [0.14, 0.31]	0.38 [0.30, 0.45]
Life satisfaction	0.35 [0.27, 0.42]	0.14 [0.06, 0.23]	0.01 [−0.08, 0.09]

Gender: −1 = Female, 1 = Male; 95% confidence intervals displayed beneath correlation coefficients in brackets.

We hypothesized that autonomous motivation would again be positively associated with financial knowledge and that amotivation would again be negatively associated with financial knowledge. Autonomous motivation did not evidence this association. As a further exploratory analysis, we separately examined intrinsic motivation (i.e., acting out of enjoyment or interest) and identified motivation (i.e., acting consistently with abiding values or attributed importance). We found that whereas intrinsic motivation had no association with financial knowledge ( r = 0.02, df = 532, p = 0.601), identified motivation did ( r = 0.12, df = 532, p = 0.004). Amotivation held the expected negative association with financial knowledge.

Continuing with the correlations in Table 9 , we further hypothesized that autonomous motivation would evidence the strongest positive associations with planning and borrowing, the ostensibly more deliberate and effortful aspects of financial health. We also expected amotivation to hold the strongest negative associations with these outcomes since deliberation and effort are depleted by amotivation. Contrary to our expectations, autonomous motivation held pronounced positive associations with each component of financial health. Moreover, amotivation held the strongest negative associations with participants' spend and borrow scores. The controlled motivation was positively correlated with three of the four components of financial health, though inspection of the correlation confidence intervals indicated that the magnitude of these correlations tended to be smaller than those obtained for autonomous motivation.

As predicted, autonomous motivation was associated with greater vitality, less depletion, and more life satisfaction. Amotivation was positively associated with depletion but did not evince significant associations with the other psychological well-being variables. We note that controlled motivation was positively associated with both vitality and life satisfaction, albeit, to a lesser degree than autonomous motivation, yet also positively associated with depletion. Though not hypothesized, we note that this latter association with depletion is consistent with controlled motivation as a suboptimal quality of motivation that is experienced as effortful compliance with external demands and internal pressures (Ryan and Deci, 2017 ).

We again used hierarchical regression to examine the incremental validity of autonomous motivation, controlled motivation, and amotivation in the prediction of financial knowledge and well-being. Like Study 1, demographic characteristics were entered in Step 1 and the three qualities of motivation were entered in Step 2. Partial F -tests were used to assess the incremental prediction of the overall models across Steps 1 and 2. In the unstandardized models, each predictor was group-mean centered so that the intercept could be interpreted as the unstandardized value of the outcome at the mean of all predictors. The VIFs for each predictor in the models were checked and problematic multicollinearity was not found, nor did we find evidence of heteroscedasticity in the model residual plots. The results of these regression analyses are summarized in Table 10 .

Study 2 regression analyses examining the incremental associations of SDT motivations on financial outcomes.

									.						.
	(SE)		(SE)		(SE)		(SE)		(SE)		(SE)		(SE)		(SE)
	0.01(0.01)	0.12	0.38(0.07)	0.23	0.01(0.02)	0.01	0.08(0.04)	0.12	0.19(0.03)	0.25	−0.10(0.07)	−0.07	0.23(0.07)	0.17	−0.30(0.06)	−0.24
	, ,								, ,
	−0.07(0.11)	−0.03	0.65(1.11)	0.03	0.18(0.37)	0.24	2.36(0.72)	0.24	−0.06(0.56)	−0.01	2.47(1.13)	0.12	−0.36(1.22)	−0.02	−2.90(0.99)	−0.15
	, ,								, ,
	−0.34(0.13)	−0.11	2.34(1.29)	0.08	0.47(0.43)	0.07	3.38(0.83)	0.30	0.26(0.66)	0.02	7.12(1.30)	0.27	−1.44(1.40)	−0.06	3.09(1.14)	0.13
	, ,								, ,
	−0.22(0.11)	−0.08	1.52(1.14)	0.06	0.46(0.39)	0.08	2.71(0.74)	0.26	0.21(0.58)	0.02	4.20(1.16)	0.19	0.61(1.26)	0.03	−1.33(1.02)	−0.06
	, ,								, ,
	−0.33(0.12)	−0.12	1.80(1.19)	0.07	0.36(0.40)	0.06	2.71(0.77)	0.25	−0.01(0.60)	0	8.51(1.16)	0.37	−1.32(1.26)	−0.06	1.71(1.02)	0.08
	, ,								, ,

N = 526, eight observations omitted because of missing values for gender;

Gender: Female = −1, Male = 1; The effect size b(SE) is the unstandardized regression coefficients with mean-centered predictors; b * is the standardized regression coefficient. The p-values are rounded to the nearest thousandth. All other statistics are rounded to the nearest hundredth.

The results for financial knowledge mirrored those obtained in Study 1. In Step 1, both age and educational attainment were significantly predictive of financial knowledge. Inconsistent with the results of Study 1, gender was also significantly associated with financial knowledge in Step 1. In Step 2, amotivation once again evidenced a significant negative association with financial knowledge. Deviating from the results of Study 1, controlled motivation evidenced a positive association with financial knowledge in Step 2. We suspect this effect may have been obtained because of the associations between introjected and external motivation with amotivation ( Table 8 ); with both autonomous motivation and amotivation partialled out of controlled motivation, the residual variance in controlled motivation may be an unstable predictor of financial knowledge. Nonetheless, the significant incremental effects found at Step 2 were supported with a partial F -test that formally compared the models at Step 1 and 2, Δ R 2 = 0.04, F (3,517) = 9.34, p < 0.001.

Income was the only significant predictor of spending scores at Step 1. In Step 2, autonomous motivation showed a significant and positive incremental association with respondents' spend scores, whereas amotivation had a significant and negative incremental association with spend scores. These significant incremental effects were supported with a partial F -test that compared the models at Step 1 and 2, Δ R 2 = 0.04, F (3,517) = 7.78, p < 0.001.

Age was negatively associated with save scores at Step 1 whereas income was positively associated with saving scores at Step 1. In Step 2, autonomous motivation yielded a significant and positive incremental association with respondents' save scores. Unexpectedly, amotivation also held a significant and positive incremental association with saving scores. Like controlled motivation in the prediction of financial knowledge, we suspect that this regression result is an artifact of the correlations among the qualities of motivation and is not discussed further. These significant incremental effects were supported with a partial F -test that compared the models at Step 1 and 2, Δ R 2 = 0.06, F (3,517) = 13.09, p < 0.001.

At Step 1, age held a marginally significant, negative association with borrow scores and income was positively associated with borrow scores. At Step 2, autonomous motivation evidenced an incrementally positively association with borrow scores, Δ R 2 = 0.05, F (3,517) = 8.79, p < 0.001.

Age held a significantly negative association with plan scores at Step 1, whereas income held a significantly positive association with this health measure. In Step 2, autonomous motivation was a significant and positive predictor of plan scores, whereas amotivation held a marginally significant, negative association with plan scores. A partial F -test supported the incremental difference between Steps 1 and 2, Δ R 2 = 0.10, F (3,517) = 22.50, p < 0.001.

Study 2 brief discussion

The correlational and regression analyses in Study 2 were largely consistent with the results obtained in Study 1. The financial motives again displayed the expected simplex-like pattern of associations (Howard et al., 2017 ). Autonomous motivation held consistently positive associations with most indicators of financial health whereas amotivation mostly held negative associations with these variables. Income was a consistent positive predictor of financial health.

General discussion

How people manage their personal finances has become a salient concern for financial decision-making experts, economists, and policy advisors (OECD, 2005 ; Financial Literacy U.S. and Education Commission, 2020 ). The present research utilized SDT (Ryan and Deci, 2017 ) and its differentiated framework of human motivation to elucidate how the quality of people's motivation for managing their finances is related to people's financial knowledge, behaviors such as saving and investing, and financial well-being. Consistent with SDT, we expected that financial motives would array along a continuum of relative autonomy and that more autonomous forms of motivation would be associated with more effective financial management. We also predicted that controlled motivation, and especially amotivation, would be associated with less effective financial management and lower financial well-being. The results across two studies largely supported our hypotheses.

As expected, correlational analyses found that the financial motives were arranged into a simplex-like pattern of associations (Ryan and Connell, 1989 ), such that the largest correlations appeared along the main diagonal of the matrix ( Tables 4 , ​ ,8). 8 ). The simplex-like associations suggest that these motives are systematically ordered along a continuum of relative autonomy as the theory predicts (Howard et al., 2017 ). Comparing autonomous and controlled qualities of motivation, the confidence intervals of correlational analyses also revealed that autonomous motivation was positively and more strongly associated with financial knowledge, financial self-efficacy, awareness, well-being, and overall psychological wellness. Amotivation was negatively associated with financial knowledge, financial self-efficacy, self-awareness, financial well-being, and psychological well-being. These results are broadly consistent with previous SDT studies showing that more autonomous qualities of motivation are predictive of enhanced quality of performance and well-being outcomes (Ryan and Deci, 2017 ).

Across both Studies 1 and 2, age evidenced a significant negative correlation with controlled motivation. It is possible that as people mature and take on more financial responsibilities, they become more accustomed to managing their personal finances and feel less pressured for doing so. Future studies could more closely examine this relationship. Longitudinal designs will be necessary for elucidating the possible developmental mechanisms mediating this negative association between age and controlled motivation.

Regression analyses further revealed that amotivation was negatively associated with financial knowledge over and above key demographic variables (i.e., age, gender, household wealth, annual income, educational attainment). This result highlights the importance of motivational factors in the development of financial knowledge. Specifically, it suggests that feelings of indifference and ineffectiveness are detrimental to the acquisition of financial knowledge. Regression analyses also examined whether autonomous motivation, controlled motivation, and amotivation were predictive of financial behaviors and well-being. Over and above key demographic variables, we found that autonomous motivation was generally positively associated with several financial well-being indicators and that amotivation was generally negatively associated with financial well-being indicators. Together, these results attest to the importance of motivation beyond demographic characteristics such as age, education, and income status.

The present results bear important implications for interventions that aim to improve people's financial knowledge and money management. Field studies and interventions using SDT across a variety of applied domains (e.g., education, work, healthcare) have shown that social-contextual factors can promote the development of more autonomous forms of motivation (Ryan and Deci, 2017 ). Specifically, teachers, managers, and advisors promote the development of autonomous motivation for specific activities by supporting the autonomy and competence of those they wish to motivate (Ng et al., 2012 ; Slemp et al., 2018 ). Autonomy support entails relating to target individuals by taking their perspective, providing a meaningful rationale for a recommended behavior, encouraging initiation, providing meaningful choices, and being responsive to their needs and concerns. Controlling contexts, in contrast, pressure people to think, feel, or behave in specific ways. In fact, SDT specifies an array of strategies to increase the internalization of new values, thus leading to more autonomous motivations and their positive consequences (e.g., Bradshaw et al., 2021 ).

The results also underscore the deleterious associations that amotivation has with both financial knowledge and management. Amotivations arise when people feel incapable or when they do not ascribe value to a particular activity or outcome (Ryan and Deci, 2017 ). Interventions aimed at addressing amotivation may accordingly focus on providing effecting-related feedback to foster a sense of competence. They may also focus on clarifying the possible benefits (and costs) of an activity so that individuals can find personally valued reasons for undertaking it. In light of the current findings, financial educators and service agents may benefit from using SDT as a framework for distinguishing different qualities of motivation and for addressing possible amotivations among their current and prospective clients to help them make fuller use of the financial services available to them.

Amotivations may also be sensitive to broader societal factors. Specifically, macroeconomic conditions (e.g., employment rate, inflation, wealth concentration) may represent contextual influences that can exacerbate or ameliorate amotivations (Di Domenico and Fournier, 2014 ; Ryan et al., 2019 ). For example, in the face of rampant asset inflation (e.g., fast rise in the costs of home ownership relative to increases in wages), individuals may feel increasingly frustrated and hopeless about achieving longer-term financial goals (e.g., saving for a home or retirement) and may be more likely disengage from effective financial practices. Future studies should examine this possibility.

Previous studies in SDT suggest that autonomous motivation, controlled motivation, and amotivation may have synergistic and compensatory interactions with other independent variables in the prediction of some consequential life outcomes (e.g., Di Domenico and Fournier, 2014 ). This may also be the case within the domain of personal finance, especially in light of the fact that some financial literacy scholars see financial literacy as a broad, multidimensional construct that includes relevant financial attitudes and behaviors (Huston, 2010 ; Cude, 2022 ). For example, individuals with greater financial knowledge may have greater financial well-being if they are also autonomously motivated to mobilize their knowledge in the management of their finances (a synergistic interaction). Alternatively, a high degree of financial self-efficacy may be a protective factor among those who feel amotivated, particularly when people do not value personal financial management (a compensatory interaction). Testing for statistically reliable interaction effects require suitably sized samples and the current samples were not collected to examine interaction effects. Future studies with adequately sized samples will be needed.

The current findings combined with previous applied studies in SDT encourage us to envision financial education programs and practices that use autonomy-supportive practices. Such a program would seem very useful. The 2018 National Financial Capability Study (Lin et al., 2019 ), which tested financial knowledge in a nationally representative sample of American Adults, found that financial knowledge continues to decline among Americans. Although 71% of respondents believed that they have a high level of financial knowledge, the study results indicated that financial knowledge dropped from 42% in 2009 to only 34% in 2018. The 2018 study found that only 7% of respondents obtained perfect test scores, that only 43% correctly answered a question about investment risk, and that only 26% were able to correctly identify the relationship between bond prices and interest rates. Given the importance of financial knowledge and active personal finance management for individuals to make sound decisions and fully participate in the economy (Financial Literacy U.S. and Education Commission, 2020 ), these results are alarming and signal a strong need for schools and financial institutions to improve the delivery of financial education. We believe these institutions can make use of SDT principles to enhance the quality of financial education and promote higher qualities of financial motivation.

Limitations

The current study is not without its limitations. We measured respondents' qualities of motivation across three financial domains, namely, monitoring budgets, paying bills, and learning about new financial products and services. People's motivations for other important financial domains—e.g., retirement planning, paying taxes, purchasing insurance, and investing—were not directly captured by our assessment. Future research will be needed to examine if people's quality of motivation in other financial domains is similarly linked to important outcomes. Moreover, we developed the question stems for assessing financial motivations using the style of past SDT-based assessments (Center for Self-Determination Theory, 2022 ) but future studies might benefit by trying to refine our measure, for example, by trialing multiple items stems for each different financial domains.

We utilized self-report personal finance questionnaires used in previous studies. Future research should use more objective, behavioral measures to more precisely assess the associations between people's motivation quality and their financial behaviors (Campbell and Fiske, 1959 ). Another limitation concerns the demographic characteristics of the present samples. We recruited exclusively American participants. The sample was range-restricted in terms of age and the majority of them identified as Caucasian. Future studies should test whether the present findings generalize to distinct segments of the population, including people living in other nations and older adults. Also interesting would be assessing motivation in young adults, who may just be forming their habits and attitudes toward money management. Finally, we highlight the cross-sectional nature of these data. The present findings suggest that financial motivations are predictive of financial outcomes and well-being but longitudinal data are required to decisively evaluate these relationships.

We believe that the present findings advance research on the determinants of personal financial management and demonstrate the potential utility of SDT as a framework for understanding the varied reasons why people enact or fail to enact sound financial practices. The results of the current study may have important implications for the design of effective interventions to enhance people's knowledge and efficacy in dealing with their money, especially given SDT's evidence-supported principles for enhancing autonomous motivation and its associated beneficial outcomes. Given the importance, and the apparent struggles, of people managing their personal finances, a focus on motivation may have potentially broad effects.

Data availability statement

Ethics statement.

The studies involving human participants were reviewed and approved by Human Research Ethics Committee at the Australian Catholic University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

SD and RR conceptualized the studies and wrote the manuscript. SD conducted the data analysis with inputs from all co-authors. EB and JD assisted with data collection and all aspects of manuscript preparation. All authors contributed to the article and approved the submitted version.

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.

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Financial Management Research Paper Topics

Financial management research paper topics have emerged as an essential part of contemporary education in business and economics. As financial management continues to evolve with global economic changes, the need for research and analysis in this area grows. This article provides a comprehensive guide for students who study management and are assigned to write research papers on various aspects of financial management. From understanding the diverse topics to learning how to write an impactful research paper, this page offers valuable insights. Additionally, it introduces iResearchNet’s writing services, specifically tailored to assist students in achieving academic excellence. The content is structured to guide students through topic selection, writing, and leveraging professional services to meet their academic goals. Whether a novice or an advanced student of financial management, this resource offers a multifaceted perspective on the vast and dynamic field of financial management research.

100 Financial Management Research Paper Topics

The field of financial management offers a vast array of research paper topics. This complex discipline touches every aspect of business operations, influencing strategic planning, decision-making, and organizational growth. Below, you will find a comprehensive list of financial management research paper topics, divided into 10 categories. Each category offers 10 unique topics that cater to various interests within financial management. These topics have been carefully selected to reflect the richness and diversity of the subject.

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Get 10% off with 24start discount code, financial planning and control.

• The Role of Budgeting in Financial Planning
• Strategic Financial Management in SMEs
• The Impact of Working Capital Management on Profitability
• Ethical Considerations in Financial Planning
• Risk Management in Financial Planning
• Cost Control Techniques in Manufacturing
• Financial Decision-making Processes in Non-profit Organizations
• The Impact of Inflation on Financial Planning
• International Financial Planning Strategies
• The Relationship between Corporate Governance and Financial Planning

Investment Analysis and Portfolio Management

• The Efficient Market Hypothesis: A Critical Analysis
• The Role of Behavioral Finance in Investment Decisions
• Modern Portfolio Theory and Its Limitations
• Risk and Return Analysis in Emerging Markets
• Socially Responsible Investment Strategies
• The Impact of Political Instability on Investment Decisions
• Real Estate Investment Trusts (REITs): An In-depth Study
• Impact of Technology on Portfolio Management
• Mutual Funds vs. ETFs: A Comparative Study
• The Role of Artificial Intelligence in Investment Management

Corporate Finance

• Capital Structure Decisions in Startups
• The Role of Dividends in Corporate Financial Management
• Mergers and Acquisitions: Strategic Financial Analysis
• Corporate Financing in Developing Economies
• An Analysis of Venture Capital Financing
• The Impact of Corporate Social Responsibility on Financial Performance
• The Role of Financial Management in Business Turnaround Strategies
• Debt Financing vs. Equity Financing: A Comparative Analysis
• Corporate Financial Risk Management Strategies
• Financing Innovation: Challenges and Opportunities

International Financial Management

• Exchange Rate Dynamics and International Financial Decisions
• The Role of International Financial Institutions in Economic Development
• Cross-border Mergers and Acquisitions
• Globalization and Its Impact on Financial Management
• International Tax Planning Strategies
• Challenges in Managing International Financial Risk
• Currency Risk Management in Multinational Corporations
• International Capital Budgeting Decisions
• The Impact of Cultural Differences on International Financial Management
• Foreign Direct Investment Strategies and Financial Management

Financial Markets and Institutions

• The Role of Central Banks in Financial Stability
• The Evolution of Microfinance Institutions
• The Impact of Regulation on Banking Operations
• An Analysis of Stock Market Efficiency
• Financial Derivatives and Risk Management
• The Role of Technology in Financial Services
• A Study of Financial Crises and Regulatory Responses
• Peer-to-Peer Lending Platforms: A New Paradigm
• The Role of Credit Rating Agencies in Financial Markets
• The Future of Cryptocurrency in the Financial Landscape

Personal Finance Management

• Financial Literacy and Personal Investment Decisions
• The Role of Technology in Personal Finance Management
• Retirement Planning Strategies
• Impact of Consumer Behavior on Personal Financial Decisions
• Personal Finance Management in the Gig Economy
• A Study of Personal Bankruptcy Trends
• Credit Card Management Strategies for Individuals
• The Effect of Education on Personal Financial Management
• The Role of Financial Counseling in Personal Finance
• Estate Planning: A Comprehensive Analysis

Risk Management

• Enterprise Risk Management: A Strategic Approach
• The Role of Insurance in Financial Risk Management
• Financial Innovations in Risk Management
• A Study of Credit Risk Management in Banks
• Risk Management Strategies in Supply Chain Finance
• Cyber Risk Management in Financial Institutions
• The Impact of Climate Change on Financial Risks
• A Study of Operational Risk Management in the Healthcare Sector
• Behavioral Aspects of Risk Management
• Crisis Management and Financial Stability

Financial Technology (FinTech)

• The Rise of Blockchain Technology in Finance
• The Impact of FinTech on Traditional Banking
• Regulatory Challenges in the Age of FinTech
• Financial Inclusion through FinTech Innovation
• Artificial Intelligence in Financial Services
• The Future of Cryptocurrencies: Opportunities and Risks
• A Study of Peer-to-Peer Lending Platforms
• FinTech and Consumer Privacy: Ethical Considerations
• Mobile Banking: An Evolutionary Study
• The Role of Big Data Analytics in Financial Decision Making

Ethics and Sustainability in Finance

• Ethical Investing: Trends and Challenges
• Corporate Social Responsibility Reporting in Finance
• Sustainable Finance in Emerging Economies
• Environmental, Social, and Governance (ESG) Criteria in Investment
• The Impact of Business Ethics on Financial Performance
• The Role of Sustainability in Corporate Financial Strategy
• Green Bonds and Financing Sustainable Development
• Social Impact Investing: Opportunities and Challenges
• A Study of Gender Equality in Financial Institutions
• Financial Strategies for Achieving Sustainable Development Goals

Accounting and Finance

• Forensic Accounting: Techniques and Case Studies
• The Role of Management Accounting in Financial Decision-making
• International Financial Reporting Standards (IFRS) Adoption
• The Impact of Taxation on Financial Management
• Accounting Information Systems: An In-depth Analysis
• The Role of Auditing in Corporate Governance
• Accounting Ethics: A Study of Professional Conduct
• Environmental Accounting and Sustainable Development
• The Effect of Automation on Accounting Practices
• A Comparative Study of GAAP and IFRS

The extensive list above offers a broad spectrum of financial management research paper topics. They cater to different academic levels and areas of interest, providing a wealth of opportunities for students to explore the multi-dimensional world of financial management. The selection of these topics can lead to exciting discoveries and insights, pushing the boundaries of existing knowledge in the field. Whether it’s understanding the intricate dynamics of global finance or delving into the ethical considerations in investment decisions, these topics serve as starting points for thought-provoking research that can shape future practices in financial management. By choosing a topic from this comprehensive list, students embark on a journey of intellectual exploration that can contribute to both academic success and the broader understanding of financial management in the modern world.

Financial Management and the Range of Research Paper Topics

Financial management is a multifaceted discipline that stands at the intersection of economics, business administration, and finance. It governs the planning, organizing, directing, and controlling of financial activities within an organization or individual framework. In an ever-changing global economy, the importance of financial management cannot be overstated. It empowers organizations and individuals to make informed decisions, manage risks, and achieve financial stability and growth. This article delves into the vast domain of financial management and explores the wide array of research paper topics it offers.

A. Definition and Core Concepts of Financial Management

Financial management refers to the efficient and effective management of money to achieve specific objectives. It involves processes and tasks such as budgeting, forecasting, investment analysis, risk management, and financial reporting. The primary goals are to maximize shareholder value, ensure liquidity, and maintain solvency.

• Budgeting and Forecasting : These processes involve planning and estimating future financial needs and outcomes. They guide decision-making and help in aligning resources with organizational goals.
• Investment Analysis : This includes evaluating investment opportunities and determining the most profitable and sustainable investments.
• Risk Management : This aspect focuses on identifying, evaluating, and mitigating financial risks, including market risk, credit risk, and operational risk.
• Financial Reporting : This entails the preparation and presentation of financial statements that accurately reflect the financial position of an organization.

B. Importance of Financial Management

• Ensuring Financial Stability : Effective financial management helps in maintaining the financial health of an organization or individual by ensuring a balance between income and expenditure.
• Optimizing Resources : It enables the optimal utilization of resources by aligning them with short-term and long-term goals.
• Strategic Planning : Financial management plays a key role in strategic planning by providing insights into financial capabilities and constraints.
• Enhancing Profitability : By making informed investment and operational decisions, financial management enhances the profitability of an organization.

C. Modern Trends and Challenges in Financial Management

The evolution of technology, globalization, regulatory changes, and societal expectations have shaped modern financial management. Some noteworthy trends and challenges include:

• Financial Technology (FinTech) : The integration of technology into financial services has revolutionized banking, investing, and risk management.
• Globalization : The interconnectedness of global markets presents both opportunities and challenges in managing international financial operations.
• Sustainability and Ethics : The growing focus on environmental, social, and governance (ESG) criteria has led to ethical investing and sustainable finance.
• Regulatory Compliance : Navigating the complex regulatory landscape is a challenge that requires constant adaptation and vigilance.

D. Range of Research Paper Topics in Financial Management

The vastness of financial management offers a rich source of research paper topics. From exploring the intricacies of investment analysis to understanding the ethical dimensions of finance, the possibilities are endless. The following are some broad categories:

• Corporate Finance : Topics related to capital structure, mergers and acquisitions, dividend policies, and more.
• Investment and Portfolio Management : Including research on investment strategies, portfolio optimization, risk and return analysis, etc.
• International Financial Management : This encompasses studies on exchange rate dynamics, global financial strategies, cross-border investments, etc.
• Risk Management : Topics include various risk management techniques, insurance, financial innovations in risk management, etc.
• Personal Finance Management : This field covers financial planning for individuals, retirement strategies, credit management, etc.
• Financial Technology : Blockchain, cryptocurrencies, mobile banking, and more fall under this innovative domain.
• Ethics and Sustainability in Finance : Research in this area may focus on ethical investing, corporate social responsibility, green financing, etc.

Financial management is an expansive and dynamic field that intertwines various elements of finance, economics, and business administration. Its importance in today’s world is immense, given the complexities of the global financial system. The array of research paper topics that this subject offers is indicative of its diversity and depth.

From traditional concepts like budgeting and investment analysis to modern phenomena like FinTech and sustainability, the world of financial management continues to evolve. It invites scholars, practitioners, and students to explore, question, and contribute to its understanding.

The range of research paper topics in financial management offers avenues for academic inquiry and practical application. Whether it’s investigating the effects of globalization on financial strategies or exploring personal finance management in the gig economy, there’s a topic to spark curiosity and inspire research. These research endeavors not only enrich academic literature but also play a crucial role in shaping the future of financial management. In a rapidly changing world, continuous exploration and learning in this field are essential to remain relevant, innovative, and responsible.

How to Choose Financial Management Research Paper Topics

Choosing the right research paper topic in the field of financial management is a critical step in the research process. The chosen topic can shape the direction, depth, and impact of the research. Given the wide array of subfields within financial management, selecting a suitable topic can be both exciting and challenging. Here’s a comprehensive guide to assist you in choosing the ideal financial management research paper topic.

1. Understand Your Interest and Strengths

• Assess Your Interests : Consider what aspects of financial management intrigue you the most. Your enthusiasm for a subject can greatly enhance the research process.
• Identify Your Strengths : Understanding where your skills and knowledge lie can guide you towards a topic that you can explore competently.
• Connect with Real-world Issues : Relating your interests to current industry challenges or trends can make your research more relevant and engaging.

2. Consider the Scope and Depth

• Define the Scope : A clear understanding of the scope helps in keeping the research focused. Too broad a topic can make the research vague, while too narrow may limit your exploration.
• Determine the Depth : Decide how deep you want to delve into the topic. Some subjects may require extensive quantitative analysis, while others may be more theoretical.

3. Examine Academic and Industry Relevance

• Align with Academic Requirements : Ensure that the topic aligns with your course objectives and academic requirements.
• Analyze Industry Needs : Consider how your research could contribute to the industry or address specific financial management challenges.

4. Evaluate Available Resources and Data

• Assess Data Accessibility : Ensure that you can access the necessary data and information to conduct your research.
• Consider Resource Limitations : Be mindful of the time, financial, and technological resources that you’ll need to complete your research.

5. Review Existing Literature

• Analyze Previous Research : Review related literature to identify gaps, controversies, or emerging trends that you can explore.
• Avoid Duplication : Ensure that your chosen topic is unique and not merely a repetition of existing studies.

6. Consult with Experts and Peers

• Seek Guidance from Faculty : Consulting with faculty or mentors can provide valuable insights and direction.
• Collaborate with Peers : Discussions with classmates or colleagues can help in refining ideas and getting diverse perspectives.

7. Consider Ethical Implications

• Evaluate Ethical Considerations : Ensure that your research complies with ethical guidelines, especially if it involves human subjects or sensitive data.
• Reflect on Social Impact : Consider how your research might influence society, policy, or industry standards.

8. Test the Feasibility

• Conduct a Preliminary Study : A small-scale preliminary study or analysis can help in gauging the feasibility of the research.
• Set Realistic Goals : Ensure that your research objectives are achievable within the constraints of time, resources, and expertise.

9. Align with Career Goals

• Consider Future Applications : Think about how this research might align with your career goals or professional development.
• Build on Past Experience : Leveraging your past experiences or projects can lend depth and continuity to your research.

10. Stay Flexible and Adaptable

• Be Open to Change : Research is often an iterative process; being flexible allows for adaptation as new insights or challenges emerge.
• Maintain a Balanced Perspective : While focusing on your chosen topic, keep an open mind to interrelated areas that might enrich your research.

Choosing the right financial management research paper topic is a nuanced process that requires careful consideration of various factors. From understanding personal interests and academic needs to evaluating resources, ethics, and feasibility, each aspect plays a significant role in shaping the research journey.

By following this comprehensive guide, students can navigate the complexities of selecting a suitable research paper topic in financial management. The ultimate goal is to find a topic that resonates with one’s interests, aligns with academic and professional objectives, and contributes meaningfully to the field of financial management. Whether delving into the dynamics of risk management or exploring the impact of FinTech innovations, the chosen topic should be a catalyst for inquiry, creativity, and growth.

How to Write a Financial Management Research Paper

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Some key developments in international financial management

• Open access
• Published: 29 June 2021
• Volume 91 , pages 595–615, ( 2021 )

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• Wolfgang Breuer   ORCID: orcid.org/0000-0001-5470-4263 1 &
• Santiago Ruiz de Vargas   ORCID: orcid.org/0000-0002-3349-4874 2  

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1 International financial management and valuation: an important field of financial research

International financial management is about investment and financing decisions confronting the management of multinational companies due to the international context of their activities. Investment and financing decisions involve the valuation of uncertain future cash flows (Ross 2004 , p. 1). A key element of the international context is that these decisions are affected by exchange rate risk. Therefore, the methodology used to support rational decision making in an international context must capture the additional complexity that exchange rate risk and exchange rate forecasting pose.

Forty years ago, in a special issue of this journal, the areas of research pertinent to international financial management were addressed (Stehle 1981 , pp. 75–76) and a status report on the theory of international capital markets was given (Franke 1981 , pp. 51–66). After four decades, this special issue presents articles of current interest that address important aspects of the research field of international financial management and valuation. But before presenting these, we would like to offer a brief update on where we stand today in the field of international financial management.

In these 40 years, the number of countries and currencies has increased substantially, to more than 220 countries with more than 150 currencies as of the beginning of this decade. Footnote 1 The world has experienced rapid internationalization and widespread integration of capital markets (e.g. Obstfeld and Taylor 2004 ; Das 2004 , 2006 ; Frieden 2020 ). This has caused business and investor cross-border activities to experience enormous expansion. Trading volumes on international capital markets have been boosted considerably, and the array of available financing instruments has substantially increased. The triennial central survey of the Bank of International Settlements reports a daily total OTC foreign exchange turnover of US$ 6.6 trillion for April 2019 (“net-net” basis, Bank for International Settlements 2019 , Table 1, p. 1) in comparison to US$ 0.6 trillion in April 1989, i.e. three decades earlier (grand total, Bank for International Settlements 1996 , p. 3).

The framework for investment and financing decisions in an international context is given by the international monetary system and the integration, depth and breadth of international financial markets. Of course, the growing integration of real goods and services markets and the intensity of international trade also play an important role in international financing and investment decisions. Since the breakdown of the Bretton Woods system of fixed exchange rates 50 years ago, most developed countries have adopted a free float, and a comprehensive liberalization of capital and current-account transactions has taken place. This still reflects the current overall trend (see International Monetary Fund (IMF) 2020 , p. 3). However, some developed countries in Europe introduced a common currency in 1999, the euro, thus establishing a fixed-rate regime for their common market, while accepting a free float of their currency in relation to countries outside this monetary union (Baldwin and Wyplosz 2020 ; Brasche 2013 ; Wagener and Eger 2014 ). Consequently, managing exchange rate risk and forecasting future exchange rates have become essential aspects of international financial management decision making since the breakdown of the Bretton Woods system.

These developments have been accompanied by wide-ranging theoretical and empirical research on international financial management, most of which applies and extends the concepts and methodology that have been developed in financial economics. In particular, a theory of international capital markets establishing equilibrium conditions based on the absence of arbitrage (see e.g. Ross 2004 ; Kruschwitz and Löffler 2020 ; Franke and Hax 2009 ) has been developed (Solnik 1974 ; Grauer et al. 1976 ; Mehra 1978 ; Sercu 1980 ; Stulz 1981a ; Adler and Dumas 1983 ; Ross and Walsh 1983 , among others).

In the next section, we will give a brief overview of the relevance of internationally related topics at the annual meetings of the European Finance Association (EFA). It shows that aspects of foreign exchange management and valuation issues play a crucial role in papers on finance with an international focus. Against this background, we will take a closer look at the contents of a selection of important textbooks on international financial management in Sect.  3 . Section  4 presents this special issue’s four papers, and Sect.  5 concludes.

2 International finance at the annual meetings of the European Finance Association

Just to get an idea of the importance of the international dimension in the field of finance, we have taken a look at the annual meetings of the EFA from 2009 to 2020. Along with the annual meetings of the Western Finance Association and the American Finance Association, the EFA conferences are some of the most prestigious worldwide. On average, around 232 papers are presented at each annual meeting, of which around 5.78% have a pronounced international focus as highlighted by the term “international” in their title, their key words, or their session heading. This does not rule out the possibility of an international “flavor” in some of the other papers. Nevertheless, in such cases, this typically will not be the main focus of the respective study. Rather interestingly, this percentage of papers as identified according to our criteria as “internationally related” was somewhat higher from 2009 to 2014 (6.8%) than from 2015 to 2021 (4.8%). However, these figures are generally quite stable over time. The number of downloads of these contributions that are available at the Social Science Research Network is almost the same. This means that papers with an international focus are of rather “average” interest for the scientific community.

We started with a co-occurrences analysis of paper titles, highlighting which words commonly occur together. Figure  1 documents the results for the entire timespan from 2009 to 2020 as well as for the two subperiods 2009–2014 and 2015–2020. It shows that, in addition to the relatively isolated field of monetary policy, most paper titles seem to center around topics related to foreign exchange, currency, market risk, and asset prices.

Bi-grams of EFA conference papers on international finance. This figure shows the most common word pairs in working paper titles presented at EFA conferences between 2009 and 2014 as well as between 2015 and 2020. The number n of co-occurrences is indicated by the thickness of the connection lines; the most common words are located in the center

In order to gain additional insights, we applied the state-of-the-art machine learning method “Latent Dirichlet Allocation” (LDA) (Blei et al. 2003 ) to the abstracts of the EFA conference papers separately for the papers presented from 2009 to 2014 and those presented from 2015 to 2020. The LDA is an advanced textual analysis technique that views each abstract as a union of topics and each topic as a union of words. This approach mimics human language processing, as it classifies documents into natural groups without any pre-specified topics. The LDA applies mathematics to reduce the dimensionality of datasets and thus is similar to a factor analysis (Dyer et al. 2017 ). This reduction is achieved by defining a topic as a collection of words where each word is assigned a probability of belonging to a topic. Thus, the LDA connects documents with probability distributions belonging to topics, so that one document can contain several topics. We restricted our analysis to identifying the five main topics for both subperiods.

The results are shown in Fig.  2 , which lists relevant topics and their most important words. One problem is that we have to determine a suitable heading for each topic “manually”, as this cannot be generated automatically. Our suggestions are also presented in Fig.  2 . For each topic of our two subsamples, Fig.  3 presents the probability γ that a given abstract deals with a given topic. For most topics, it is an either-or decision whether a paper is assigned to this topic, since probabilities are usually close to 0 or 1.

LDA analysis of EFA conference papers on international finance. This figure shows the top 8 words for each of the 5 topics found by the LDA (Latent Dirichlet Allocation) in the abstracts of all papers from 2009 to 2014 (Panel A ) as well as from 2015 to 2020 (Panel B ) presented at the EFA conferences. Each word is connected with a probability β of that word belonging to that topic

Probability of an abstract belonging to a topic. This figure shows the probability γ that an abstract from the EFA conference papers belongs to a specific topic. The y-axis counts the number of abstracts and is log(10) scaled. Results in Panel A are based on all papers presented at the EFA conferences between 2009 and 2014, while Panel B analyzes papers from 2015 to 2020

If we restrict our examination to those papers which can be assigned to one topic with a probability of at least 80%, we get the following frequency distribution for 2009–2014: asset pricing (11.11%), currency risk models (13.33%), global portfolio management (21.11%), investment management (22.22%), and U.S. banking crisis (20%). Of the 89 papers, 12.22% could not be assigned to one of these five categories with sufficient precision. The corresponding results for 2015–2020 are: asset pricing (23.19%), bank risk taking (17.39%), corporate debt financing (15.94%), currency risk models (17.39%), and mergers and acquisitions (17.39%). For 8.70% of the 69 papers, it was not possible to determine just one dominant topic with sufficient precision.

As financing and investment decisions are also closely related to valuation and risk issues, we will rely primarily on these aspects for our analysis of textbooks in the next section.

3 Insights from recent textbooks on international financial management

While it is impossible to present all of the developments in international financial management made in the last four decades, the fruits of this theoretical and empirical research can be found in numerous textbooks and handbooks that cover investment and financing decisions in an international context.

We conducted a literature review on current textbooks and handbooks on international financial management. In order to reflect an up-to-date perspective of current knowledge on international financial management, we have restricted our review to books published in the last 20 years that we are aware of. Footnote 2 To keep the assessment manageable, books on international macroeconomics, international business or international management have been excluded. Of course, the resulting sample of literature is selective and incomplete. Footnote 3 Nevertheless, in our opinion, the resulting sample of 14 textbooks reflects the current state of basic knowledge on international financial management that academics deem necessary to convey to future researchers and practitioners (see also Boland 1997 , pp. 109–110).

Our, admittedly rather limited, literature review aims to answer the following key methodologically driven questions on international finance: (1) What theoretical framework is presented for exchange rate determination and forecasting? (2) What exchange rate forecasting method is applied when presenting valuation examples? (3) What valuation methods do textbooks propose for conducting cross-border valuations? (4) What method is proposed to calculate the cost of equity? (5) How do the textbooks evaluate the hypothesis that the home currency and foreign currency approaches are equivalent? Footnote 4 We acknowledge that these five questions are not at all comprehensive, and that other interesting questions have not been considered. However, we believe that the questions addressed in our review are highly relevant in a vast majority of practical and empirical cases.

Table 1 presents the results of our limited literature review on the first two questions regarding the methodology used to forecast future exchange rates.

The sample of currently available textbooks presented in Table 1 emphasizes the relevance of exchange rate determination and forecasting and uses international parity relations (IPR) as the relevant theoretical framework. Footnote 5 In contrast, textbooks on international financial management available shortly before and after the breakdown of the Bretton Woods regime did not cover this area. Footnote 6 With a free-float regime, it became more and more necessary to forecast exchange rates (see Giddy and Dufey 1975 ). Footnote 7 An early coherent presentation of international parity conditions as an integrated theory of exchange rate equilibrium based on expectations can be found in Giddy ( 1976 ), Aliber ( 1978 ), and Roll and Solnik ( 1979 ). This framework still underpins the theoretical exposition of the current textbooks presented in Table 1 . Our sample confirms that the theoretical foundation of exchange rate determination and forecasting based on international parity conditions has remained the relevant paradigm since the breakdown of the Bretton Woods system of fixed rates.

In line with the efficient market hypothesis (EMH), Giddy ( 1976 ), p. 883, concluded some years after the breakdown of the Bretton Woods system that interest differentials and forward rates provide “the best forecast of the future spot rate”, respectively. Footnote 8 In current textbooks, the prevalent forecasting method still appears to be the market-based approach, i.e. using forward rates as a direct predictor of future exchange rates when assuming risk neutrality or as the certainty equivalent of future exchange rates when assuming risk aversion. In the latter case some textbooks indicate that, due to small risk-premium estimates, the risk aversion case and the risk neutrality case deliver similar forecasts. However, several textbooks refer to their own or third-party empirical work in the last decades, indicating weaknesses and exchange rate anomalies in contradiction to economic theory. These affect not only the uncovered interest rate parity (“forward premium puzzle”; for a comprehensive review see Miller 2014 ), but other parity conditions as well (for the purchasing power parity (PPP) puzzle, see Rogoff 1996 ). These and other anomalies still challenge economic theory on exchange rate determination and forecasting (e.g. Obstfeld and Rogoff 2000 ). The volatility of exchange rates and these empirical findings make it clear that no forecasting method can promise ex ante forecasting infallibility (Rossi 2013 ). Research reported in these textbooks also shows that, where profitable trading strategies taking advantage of these anomalies are discovered, the anomalies tend to disappear once transaction costs and risks are considered.

Shapiro and Moles ( 2014 ) point out that exchange rate forecasting is to be viewed in relative terms and that any alternative currency forecasting model should be required to outperform “the market’s estimates of currency changes” (p. 169). This is still a “daunting task” (Fama 1998 , p. 284). While alternative approaches to UIP or relative PPP are presented in the textbook literature we reviewed (see Table 1 ), the textbooks do not claim that these alternative approaches can consistently “beat the market”. As Jacque ( 2020 ), p. 470, puts it: The “lack of a definitive answer to the general question of forecasting exchange rates, […], is probably one of the most potent justifications for undertaking costly and at times highly constraining hedging policies against foreign exchange risk.” Thus, exchange rate risk is intertwined not only with exchange rate forecasting, but also with hedging decisions based on it. As a consequence, the distinction between fallacious and valid reasons to hedge (e.g. Levi and Sercu 1991 ) and a proper measurement of currency risk (Adler and Dumas 1984 ; O’Brien 2019 ) is key for sound exchange rate risk management.

Interestingly, in the textbooks we reviewed, it appears that only Sercu ( 2009 ) and Apte ( 2009 ) emphasize the role of the forward rate as a certainty equivalent of the future exchange rate. Based on this, Sercu ( 2009 ) proposes using the forward rate as a guide for commercial and financing decisions (p. 193), as well as for accounting purposes (pp. 190–192). While his proposal of using the forward rate instead of the spot rate in accounting is probably at odds with most accounting standards, this approach treats the decision not to hedge in the same way as the decision to hedge and provides an economically sound division of profits into operating income and financial income. It remains to be seen if his proposal is picked up by standard setters.

Most textbooks briefly present technical analysis and fundamental analysis as additional exchange rate determination and forecasting approaches. Fundamental analysis aims to identify causality relationships that determine the current spot rate based on economic factors. As Eun and Resnick ( 2018 ), pp. 160–161, show, based on a monetary approach, in order to produce a forecast for an exchange rate with a fundamental method, the values to be estimated are not the current values, but the future values of the independent variables. This substantially exacerbates the exchange rate forecasting problem for practical applications due to expanding data requirements in order to estimate an array of variables to formulate a forecast. The rather frequent, albeit usually brief, inclusion of technical analysis, a practitioners’ tool that defies economic fundamentals and the market efficiency concept, is somewhat surprising and probably can only be explained by its relevance to short-term forecasting for many foreign exchange professionals (see Menkhoff and Taylor 2007 , for a comprehensive review). Footnote 9

Table 2 presents the results of our limited literature review on the third to fifth questions in international finance and shows that there is a clear preference for the adjusted present value (APV) method developed by Myers ( 1974 ) over the weighted average cost of capital (WACC) or the flow-to-equity (FTE) approaches. The latter method is only mentioned occasionally. This preference for using the APV approach in an international valuation setting can be traced back to Lessard ( 1985 ). The reasoning is that an international context must reflect different value components of financing side effects (e.g., different currencies, taxes, and subsidies). It is surprising that, in standard textbooks, the advantages of the APV approach (assuming predetermined debt levels) are not highlighted correspondingly, since these advantages do not apply exclusively in an international context. Footnote 10 Most of the textbooks deem it necessary to consider an additional value component resulting from real options that may stem from a capital budgeting problem in an international context, while the difficulties of applying this concept in practice are usually not discussed (e.g. Kruschwitz and Lorenz 2020 , pp. 362–401).

The equivalence of the home currency and foreign currency approaches is only addressed in some of the textbooks, while applying different sets of assumptions. Apte ( 2009 ), Bekaert and Hodrick ( 2018 ), Buckley ( 2012 ), Butler ( 2016 ), Click and Coval ( 2002 ), Levi ( 2009 ) and O’Brien ( 2017b ) show under what conditions the equivalence holds and how differences may indicate the incurrence of a speculative financial position that should not be confused with an operative investment decision.

To estimate the cost of equity, nearly all textbooks advocate the use of the capital asset pricing model (CAPM) in an international context. Footnote 11 To capture the international context in integrated capital markets, the use of the global CAPM is preferred to the more complex version, the international CAPM with additional currency risk factors. Footnote 12 Some textbooks, such as Sercu ( 2009 ), O’Brien ( 2017b ) and Bekaert and Hodrick ( 2018 ) present the international CAPM that incorporates exchange risk as a separate risk factor in addition to the market risk factor of the global CAPM. Footnote 13 Only Jacque ( 2020 ) adds a separate ad hoc country risk factor in the global CAPM. Footnote 14 Other textbooks propose capturing country risk with a scenario analysis at the cash-flow level (e.g. Levi 2009 , p. 435). Market-segmentation effects on the cost of equity consistent with the CAPM are only treated occasionally. Eun and Resnick ( 2018 ) propose an approach that extends the (global) CAPM to incorporate non-tradable assets (see Alexander et al. 1987 ) and foreign ownership restrictions (see Eun and Janakiramanan 1986 ). Other models that capture relevant aspects of partially segmented capital markets consistent with the CAPM, such as Black ( 1974 ), Stulz ( 1981b ), Errunza and Losq ( 1985 ), Merton ( 1987 ), and Bekaert and Harvey ( 1995 ), are not presented in the textbooks we reviewed.

Comparing the 12 models to calculate the international cost of capital that are brought forward in Harvey ( 2005 ) to the models presented in the textbooks, Table 2 shows a clear preference for Model 1 (global CAPM) for valuation in developed countries with sufficiently integrated capital markets; this confirms the recommendation in Harvey ( 2005 ). The remaining 11 models, most of which are not consistent with the CAPM, are not found in the textbook literature. Footnote 15 For valuations in emerging markets, the degree of segmentation plays a major role when choosing an adequate model to estimate the cost of equity. In this respect, Harvey ( 2005 ) stresses that a term structure of country risk should be considered in long-term project evaluation, as segmentation may fade out. Summing up, for the assumption of partially segmented capital markets, no conclusive recommendation for a specific cost-of-equity model can be extracted from our limited review.

4 The contributions in this special issue

The literature review of textbooks in international financial management sheds some light on where we stand today and what knowledge is thought to be of use to future professionals and academics. It shows that theoretical and empirical research has found its way to the applied side of international finance. We can now put forward further interesting advances in international financial management included in this special issue of the Journal of Business Economics .

Schüler ( 2021 ) develops a comprehensive framework for cross-border discounted cash-flow valuation that is not found in the literature. The valuation framework encompasses the derivation of the risk-adjusted rate of return in order to discount future expected cash flow in accordance with the global CAPM while considering relevant risks such as exchange rate risk, business risk, financial risk, the risk related to tax shields, and the risk of default. The paper shows how the foreign currency and home currency approaches are equivalent when assuming uncertainty and risk aversion. This is demonstrated using the adjusted present value (APV) approach. The risk-adjusted discount rates to be used in the corresponding flow-to-equity (FTE) approach and in the weighted average cost of capital (WACC) approach are also presented. This paper is therefore relevant to the valuation of not only a foreign company, but also of a domestic company that generates cash flows in a foreign currency and/or uses debt-financing instruments denominated in a foreign currency.

In the article by Bartram et al. ( 2021 ), the literature on cross-sectional stock-return predictability, with over 450 factors documented for different asset classes, is reviewed. Taking the perspective of an institutional investor, the authors guide the reader through this “zoo of factors”, as they call it, discussing the evidence of factors and their relevance for an institutional investor. Their approach requires a theoretical rationale in order to identify the relevant “true” factors and differentiate them from factors resulting from data mining. According to their review, the performance of many factors depends on the inclusion of small- and micro-cap stocks. However, such securities are usually not relevant from an institutional investor’s perspective, as an investment in these collides with liquidity requirements and involves higher transaction costs. Most factors have been detected in the U.S. equity markets and then reproduced in international markets. In particular, style factors such as value and momentum, but also other predictors appear in several international markets facilitating global factor-based investment strategies. The article presents a set of factors in equities and other asset classes, including currencies, fixed income and commodities, that the authors see as meaningful “ingredients” of factor-based portfolio construction. They also bring forward some key metrics, like the maximum Sharpe ratio and others, that may help to identify the better model. Nonetheless they advert that the choice of test and comparison techniques may drive the results and point out that discerning between risk, mispricing and statistical bias remains a challenge for researchers and investors.

Hammer et al. ( 2021 ), use a dataset of 1149 global private equity transactions to analyze cross-border buyouts. They find that significantly higher valuation multiples are observed for these transactions than for domestic ones. They attribute their finding to informational asymmetries with which foreign acquirers are confronted. They show how the valuation spread encountered there diminishes under circumstances where the information asymmetries decrease (e.g. accounting standards, public listing, local partnering, size of the acquirer and its organizational resources).

While Hammer , Janssen , and Schwetzler analyze the valuation effect of information asymmetries in private equity transactions, the study by Bobenhausen and Salzmann ( 2021 ) examines informational asymmetries and their valuation impact in public equity offerings. Bobenhausen and Salzmann expand previous studies on equity rights offerings and the effects occurring when they are announced. The focus lies on the relationship between the discount of an equity rights offering and the announcement effect. They show that the relationship between a discount that signals the quality of the equity offering and the announcement effect found in previous studies is especially relevant in environments with a particularly low level of capital market transparency, i.e. high informational asymmetry. Their study estimates announcement effects across several countries while considering different transparency environments for equity rights offerings.

5 Conclusion

Certainly, international financial management and valuation is a special discipline in the broader field of finance. According to our analysis of the annual meetings of the European Finance Association, such topics are of persistent relevance, especially with respect to issues of foreign exchange management and asset pricing.

Our limited review of the textbook literature on international financial management has shown that international parity conditions, in particular the uncovered interest rate parity, the unbiasedness forward-rate hypothesis and the efficient markets version of the relative purchasing power parity continue to be the prevalent paradigms since the breakdown of the Bretton Woods system 50 years ago. An enormous amount of empirical research has shown that no exchange rate forecasting method is infallible and that some empirical anomalies still challenge economic theory, at least for some forecasting horizons, exchange rates and periods. However, empirical research also indicates that no pervasive trading strategies to “beat the market” can be devised after considering transaction costs and risk. The assumption of sufficient market integration appears to be the prevalent assumption in most textbooks on international financial management. Cross-border valuation of businesses or projects in less developed countries, where a high degree of segmentation must be considered, remains both a theoretical and practical challenge.

In a similar way, the topics presented in this special issue on international financial management show clearly that this is an area of ongoing research with high demand for practical applications. Schüler presents a comprehensive model for cross-border valuation, while Bartram , Lohre , Poe , and Ranganathan deliver a tour de force through the empirical research on relevant investment factors for different asset classes across the globe from the perspective of an institutional investor. Hammer , Janssen , and Schwetzler as well as Bobenhausen and Salzmann show how information asymmetries in cross-border situations influence the value of the investment target in private transactions or in public offerings. All these contributions give valuable theoretical and empirical insights for investment and financing decisions that management or investors need to consider in an international context. It shows that fruitful developments relating to academic research and its practical application are and will remain a key feature of this field of financial economics.

Data retrieved from the United Nations website, see United Nations ( 2021 ), on January 29, 2021 (access date).

We focused the literature review on publications in English, and did not include general textbooks on corporate finance or financial management. Please note that many such textbooks contain one or more chapters on international financial management issues; see for example standard textbooks such as Berk and DeMarzo ( 2020 , pp. 1097–1112), Brealey et al. ( 2020 , pp. 717–736), Brigham and Daves ( 2010 , pp. 1016–105), Copeland et al. ( 2014 , pp. 785–830), Moles et al. ( 2011 , pp. 837–886) and Ross et al. ( 2020 , pp. 935–957). Valuation handbooks and textbooks such as Koller et al. ( 2020 ) or Holthausen and Zmijewski ( 2020 ), and textbooks on global investments such as Solnik and McLeavey ( 2014 ) were likewise not included. These books basically reflect the same methodological approach as the textbooks presented in our review that specialize in international financial management. Therefore, not including such handbooks and textbooks has no material effect on our conclusions.

We apologize if valuable textbooks and handbooks we are not aware of have not been included in our literature review on international financial management.

According to the home currency approach, the market value of future payoffs in foreign currencies has to be converted into the home currency with the respective expected future exchange rate. This future payoff, now denominated in the home currency, is then discounted using the cost of capital of the home currency to obtain a market value expressed in the home currency. Instead, the foreign currency approach discounts future payoffs in foreign currencies with the foreign currency’s cost of capital. The resulting net present value is then converted to the home currency using the spot rate. Both approaches are deemed to be equivalent under certain conditions.

Please note that in the textbooks under review the term for “forward rate expectations parity” varies significantly from textbook to textbook, where it is also called “unbiasedness hypothesis”, “forward parity”, “forward rate unbiasedness” or “forward unbiasedness hypothesis”, etc. The uncovered interest rate parity (UIP) is sometimes also called “Fisher open”.

For example, Zenoff and Zwick ( 1969 ), a textbook written during the Bretton Woods regime of fixed exchange rates, reduces the exchange rate forecasting problem to the forecasting of inflation as a primary cause of currency devaluations and the imposition of exchange controls. The textbook by Weston and Sorge ( 1972 ), which was written shortly after the breakdown of the Bretton Woods system, likewise does not discuss international parity conditions and their possible applicability to forecasting.

The textbook Rodriguez and Carter ( 1984 ), the third edition of which was written in the mid-1980s, already had a clear focus on exchange rate determination and forecasting and used the concept of efficient markets and prediction based on forward exchange rates, see pp. 114–146; see also Abdullah ( 1987 ), pp. 54–55. The textbook by Evans ( 1992 ) presents also the efficient markets version of the relative purchasing power parity connecting both theories, see Roll ( 1979 ), Shapiro ( 1983 ) and, Adler and Lehmann ( 1983 ).

Levich ( 1979b ), pp. 258–259, presents an early account of studies on the forecasting accuracy of the forward rate conducted after the breakdown of the Bretton Wood system; see also Levich ( 1978 , 1979a , 1980 , 1983 , 1984 ).

The textbooks reviewed usually do not recommend technical analysis and fundamental analysis for practical applications. Technical analysis is deemed to be suitable, if at all, for short forecast horizons, see Eiteman et al. ( 2021 ), p. 285. Most textbooks state that technical analysis is only able to indicate the direction of exchange rate changes and is therefore not suitable for period-specific forecasting.

See for example Drukarczyk and Schüler ( 2016 , pp. 171), Holthausen and Zmijewski ( 2020 , p. 16), and Koller et al. ( 2020 , p. 178). For highly leveraged firms, see Arzac ( 1996 ), and for valuations with changing predetermined debt levels (probably the most common case in practice), see for example also Arzac ( 2008 , pp. 96–102).

In contrast, Kim and Kim ( 2006 ) show no distinguishable preference among the CAPM, a dividend growth rate model and a price-to-earnings ratio model.

The term “global CAPM” is not used uniformly in the textbooks we reviewed; sometimes it is called “world CAPM” or even “international CAPM”. Since the global CAPM assumes integrated capital markets, and not all capital markets in the world are integrated, we prefer the adjective “global” to “world”. We reserve the term “international CAPM” for versions that consider exchange risk as an additional factor, while the global CAPM is a single-factor model.

Sercu ( 2009 ) presents a detailed derivation of the model and its conceptual implications; see also O’Brien ( 2017b ) who provides an extensive demonstration of its application.

Jacque ( 2020 ) presents the approach of Damodaran ( 2003 ) without discussing the lack of conceptual consistency of this risk factor with the global CAPM, see for example Kruschwitz et al. ( 2012 ).

The only exception is Model 7 that corresponds to the (ad hoc) country-risk model of Damodaran ( 2003 ).

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Quantitative Finance

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Treasury Management

The following topic ideas explore treasury management, focusing on modernisation through technological advancements, the impact on firm liquidity, and the intertwined relationship with corporate governance among other crucial areas.

• Analysing the impact of treasury management practices on firm liquidity and profitability
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Financial Technology (FinTech)

The following research topic ideas explore the transformative potential of blockchain, the rise of open banking, and the burgeoning landscape of peer-to-peer lending among other focal areas.

• Evaluating the impact of blockchain technology on financial services
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Commercial Banking

These topic ideas span commercial banking, encompassing digital transformation, support for small and medium-sized enterprises (SMEs), and the evolving regulatory and competitive landscape among other key themes.

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• Analysing the determinants of exchange rate fluctuations and their impact on international trade
• Analysing the influence of global trade agreements on international financial flows and foreign direct investments
• Evaluating the effectiveness of international portfolio diversification strategies in mitigating risks and enhancing returns
• Evaluating the role of international financial institutions in global financial stability
• Investigating the role and implications of offshore financial centres on international financial stability and regulatory harmonisation
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• CAREER FEATURE
• 14 June 2024

Securing your science: the researcher’s guide to financial management

Andy Tay is a freelance writer in Singapore.

You can also search for this author in PubMed   Google Scholar

It helps to mix science and savings when it comes to lab financial management. Credit: Getty

For Michael Monaghan, the road to better laboratory finances came with breaking down walls — literally. When faced with the costly challenge of securing microscope access, Monaghan, a biomedical engineer at Trinity College Dublin, had to get creative.

“The equipment was housed in an isolated quarter and its deconstruction and recalibration would be too costly,” he says. “In the end, we got approval to break down the wall of the microscope room, allowing us to reconfigure the building’s boundaries. By thinking outside the box, we killed two birds with one stone — we got the microscope, and the total cost was only 10% of the predicted relocation fee.”

Lab financial management has perhaps never been more important. The COVID-19 pandemic paired with a slowing world economy have led to budgets being cut globally. During this year alone, the South Korean government has announced a 14.7% reduction in research expenditure , US science agencies are predicted to miss research-spending goals by as much as US$7 billion and the European Union’s flagship science programme, Horizon Europe, is set to lose €2.1 billion ($2.28 billion) of its €95.5-billion budget .

Alongside this, supply-chain disruptions and increasing energy prices are adding to the rising tide of research expenses. The price of scientific equipment has also been driven higher by inflation, while research grants have seen little increment.

Despite these circumstances, financial training for junior lab members is still scarce. “I wish I had received more formal training on how to financially manage a lab when I was a research trainee,” says Kaitlyn Sadtler, an immunoengineering researcher at the National Institutes of Health in Bethesda, Maryland. Nature spoke to researchers about their budget tips. Here’s their advice.

Build a culture of trust

Sadtler was exposed to two financial-management styles during her time as an early-career researcher. Her PhD lab had a culture of responsible spending, managed by her principal investigator, whereas in her postdoctoral lab, each member was allocated a fixed monthly budget to spend. When starting her own lab, Sadtler gravitated towards the first management style, while integrating extra checks and balances.

“At the start of each fiscal year I would gather my lab members to discuss our funds and ongoing projects,” she says. “I find that such a meeting creates a collective sense of ownership, which fosters a culture of responsible spending.”

A member of Michael Monaghan’s lab bulking up on cell-culture media.

However, to make sure that the trust she gives lab members is not exploited and there are funds for rainy days, Sadtler also maintains a carefully monitored spreadsheet containing the spending of each lab member so that the flow of funds is clear. She also slightly overestimates her lab’s spending, to have funds for emergencies or wish-list items. “By the start of the next fiscal year, my lab will calculate how much we have left and allocate the extra funds to our wish-list experiments,” she says. “An example would be single-cell RNA sequencing, which can cost upwards of $1,000 per sample. It was this experiment which helped us to kick start a new study in our lab last year.”

Negotiate discounts and buy in bulk

Negotiation is another important but overlooked skill for lab-budget management, according to Monaghan. His lab regularly orders cell-culture media in bulk, so he can often negotiate discounts of 20–50%. Buying in bulk also comes with other benefits: larger purchases are often prioritized for shipment, and delivery fees — which are calculated on how many items are ordered, not the total weight — are reduced. This process, Monaghan says, also builds a closer relationship with sales representatives, leading to improved technical support and product introduction.

There is also the shopping trend known as group buying, in which researchers place a shared order with the same company. This enables negotiation for better prices and improves the chances of priority shipping. However, Monaghan warns that “bureaucratic policies in institutions can add time to [this process], possibly negating the savings benefits”.

What steps to take when funding starts to run out

The geographical location of a lab also plays a part in spending decisions, says Jeremy Teo, an assistant professor at New York University Abu Dhabi in the United Arab Emirates. “In Abu Dhabi, shipping of chemical and biological materials can take up to three times longer than in the United States, and items might also be transported in non-optimal conditions. Therefore, if the items can be preserved, we try to spend as early as we can to prevent delays.” Teo adds that his lab tends to purchase items in bulk during the winter season, when the weather is cooler, because the extreme heat of the Abu Dhabi summer can damage biological materials en route.

Make it a team effort

Because the day-to-day responsibilities of an investigator can be overwhelming, lab members and institutional staff should be on hand to offer help. “Managing finances is a team effort and bringing experienced staff on board can be useful,” says Sadtler. “One of my technicians is supporting me with lab finances and, because she already has experience handling budgets, she is able to recognize when items are more expensive than they should be and how to get bundle deals.”

Hiring and being hired: faculty members share their stories

Institutions can provide another source of support and junior investigators should consider attending orientations regarding handling finances, reading handbooks and seeking advice from senior investigators. Sadtler also attends monthly meetings with administrative and budget-management staff at her institution to discuss lab expenditures, including spending projections and updates on expiring funds. “This has helped me to stay updated on how my lab is doing financially and to spend funds in a timely manner,” she says.

Teo says that, when he started his assistant professorship, he did not pay attention to e-mails or institutional policies on research spending and only later learnt that he was unable to carry funds forward to the next financial cycle. “Every mistake is a good lesson. I highly advise all new investigators to read e-mails from the finance office and create calendar reminders.”

Learn money management early

“Financial-management skills are not only important for principal investigators, but also for researchers interested in entering different industries,” says Gordon Xiong, assistant director at the Singapore Health Technologies Consortium. “For instance, project managers are often expected to organize timely deliverables and control budgetary spending for institutional and industry-sponsored projects. To prepare researchers for these positions, early exposure to financial and project management is key.”

The right mix: making a hybrid conference work for all

Like most scientists, Xiong learnt budgetary management on the job, while assisting his PhD supervisor with grant applications (see ‘Tips for lab budgeting’) . A postdoctoral stint gave him an opportunity to be a co-investigator on several projects and allowed him to hone his skills further. Today, he manages a programme that at one time was giving out seed funding to university researchers. “Most PhD graduates would have completed multi-year experimental projects, which means employers are confident in their technical and analytical skills. However, to build the financial skills that are also valued in the industry, it’s useful to get involved in managing project budgets early on,” he says.

Sadtler says that, although there is no formal financial-management training programme at her institution, she mentors her students during the grant-writing process. “Getting competing quotes from vendors for large value items and learning how much to budget for personnel prepares my students for future leadership responsibilities.”

Monaghan has supported his students during fellowship applications when they had to budget for expenditures such as salary, materials costs and travel expenses. “Although there are no formal training programmes, mentors can provide informal learning experiences for their students. When my postdocs apply for fellowships, I scrutinize their budget sections carefully, because grantors look for realistic budgeting skills in research trainees. Through this, a few of my postdocs have successfully received fellowships.”

Tips for lab budgeting

Use institutional resources. Ask your institution for support and see what it has to offer. This might include grant-monitoring software, free online videos or training support from its finance departments.

Create a spreadsheet to monitor spending. Having a shareable spreadsheet can help to organize lab inventory and track project costs.

Get your hands on example budgets. Having a reference budget is a helpful guide to navigating personnel costs, equipment, recurring fees and experimental materials. It is advisable to obtain recent examples from grantors or colleagues from a similar field for relevance.

Seek advice from senior colleagues. Senior researchers can provide mentoring by sharing their experiences, including negotiating discounts with vendors and navigating budgetary increases such as pay rises. Research trainees can also request training from their supervisors during fellowship applications and grant writing.

Try to negotiate. Negotiation with vendors, service providers and publishers can help to build relationships and save money for labs. Examples include negotiating for free samples, especially when buying in bulk or during a group order, postponement in payments and discounts for open-access fees.

Build a team culture. Every member in the lab can help with optimizing lab spending and budget planning. They can purchase in bundles to reduce shipping costs, share lab supplies and minimize reagent-expiration costs. Lab heads and managers should try to cultivate a culture of responsible spending and teamwork.

doi: https://doi.org/10.1038/d41586-024-02039-8

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Banks’ climate pledges don’t add up to change, research finds

Deborah Lynn Blumberg

Jul 10, 2024

Like many other large institutions, banks have stepped up their pace in making voluntary commitments to combat climate change in recent years.

Most prominently, more than 138 banks, representing over 40% of global banking assets, have made commitments through the UN-convened Net Zero Banking Alliance . Their pledge: to “align lending and investment portfolios with net-zero emissions by 2050,” with intermediate targets for 2030 or sooner.

But are these types of voluntary climate commitments to accelerate decarbonization and scale up sustainable finance truly shifting the ways in which banks operate?

A new paper, “ Business as Usual: Bank Net Zero Commitments, Lending, and Engagement ,” suggests that the commitments aren’t having much of an impact.

“These voluntary commitments, on their own, seem to not be having the effects that perhaps people are hoping they might have,” said MIT Sloan finance professor Emil Verner,  one of the paper’s co-authors. “There’s been more talk than action.”

Specifically, Verner and co-authors Parinitha Sastry from Columbia University and David Marques-Ibanez from the European Central Bank found that net-zero banks haven’t divested from polluting sectors, haven’t scaled up project financing for renewable power projects, and have failed to influence climate behavior in the firms they lend to.

The results are concerning because many policymakers, activists, and other stakeholders believe that the private sector needs to play a major role in transitioning the economy away from carbon-intensive production. “Banks play a central role in capital allocation, so they are key to financing the green transition,” the authors note in their paper.

Divesting and influencing to meet climate commitments

To arrive at their findings, the researchers used data from the European Central Bank covering more than 300 banks from 2018 to 2023.

Around 10% of those banks had joined the NZBA, the largest and most stringent climate-related banking initiative. NZBA-aligned banks are required to set net-zero emissions-related targets for credit and investment portfolios, with a 2050 achievement date and milestones along the way.

The researchers compared the portfolios of the non-NZBA banks with those of banks that had joined NBZA. They detailed two ways by which institutions can meet their NZBA climate commitments and made key discoveries about those approaches.

Divestment. Banks can divest from polluting firms and then reallocate that capital to less emissions-intensive firms.

But Verner and his colleagues found no evidence that the climate-aligned banks were divesting from sectors on which they pledged to focus their emission-reducing efforts, including power generation, transportation, and oil and gas.

In fact, NZBA financial institutions were 3% more likely to enter into new relationships with firms in high-emissions targeted sectors than were non-NZBA banks.

Engagement. Net-zero banks can continue to lend to polluting firms but engage with them by pushing them to reduce their emissions.

Related Articles

While the researchers found that climate-aligned banks loaned less money to firms in high-emissions targeted sectors, the amounts they loaned weren’t significantly less than loans made by banks with no voluntary climate commitment.

The climate-aligned banks the researchers studied also didn’t charge higher interest rates to high-emissions firms — or lower rates to “green” firms — and their borrowers, in turn, weren’t more likely to set their own decarbonization targets. At the same time, the researchers discovered that NZBA-aligned banks did gain a major benefit from their participation in the initiative: Their ESG rating, as measured by Morgan Stanley Capital International, rose by 0.6 points on average in the two years after they made their climate commitments, which the authors characterized as “a substantial upgrade.” Overall, the results call into question the effectiveness of voluntary climate commitments, Verner said. “It seems like there have been substantial commitments that have not really translated into meaningful changes in these banks’ business models.”

Government policy could help drive behavior

Despite the somewhat disappointing findings, there is still hope that voluntary climate commitments may end up making a real impact, Verner said.

With initial net-zero targets currently several years away, financial institutions do have time to reverse course and make progress. “We know from other circumstances that banks can change their lending very quickly,” he said. To do so, banks need more data plus additional disclosure. Financial institutions could potentially make better-informed climate-friendly lending decisions if they had more information on their borrowers’ emissions.

Banks might also be able to structure loan contracts in ways that could incentivize borrowers to become greener. Influencing borrower behavior in ways like this is key, Verner said.

“This notion of divestment will only get us so far,” he said, “because other lenders can just step in, and you can’t destroy complete industries by not lending to them. Helping industries to become greener is important.”

Another way to encourage climate-committed banks to increase divestment and exert more influence over their borrowers could be to associate a cost with not making progress toward fulfilling the climate pledges they’ve made. Damage to banks’ reputations might be enough to drive change. “But it’s difficult to credibly score how banks are doing,” Verner said. “Currently, banks can almost write their own report cards. Maybe we need government policy to drive behavior.”

Read next: Companies that submit to an audit see their emissions rise. And that’s OK

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Can the ideas and research of management professors change the world.

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“The quality of our thinking cannot be higher than the quality of the information on which it is ... [+] based.” Daniel Kahneman, Nobel laureate in Economics and author of 'Thinking, Fast And Slow' (AP Photo/Jacquelyn Martin, File)

While the influential work of economists is recognized by the Nobel Prize, and mathematicians have the Fields Medal, there is no equivalent for their academic peers in business schools.

But don’t underestimate the ability of professors in finance, strategy, marketing and other disciplines to change the way we think, and certainly how we do business.

The late C.K. Prahalad, author of The Fortune at the Bottom of the Pyramid , was a Professor of Corporate Strategy at the University of Michigan Ross School of Business for over 30 years. With fellow academic, Stuart Hart they made a case in the business journal, Strategy+Business for the fastest growing new markets and entrepreneurial opportunities being found among the billions of poor people 'at the bottom of the financial pyramid’.

The article was followed in 2004 by the book, which according to Bill Gates , "offers an intriguing blueprint for how to fight poverty with profitability.”

The University of Michigan describes the work of one of the school’s most beloved teachers as indispensable reading for executives and scholars who wish to understand emerging markets. "He created the base of the pyramid idea and changed the way the world viewed India’s economic potential."

When it comes to business strategy, INSEAD professors Renée Mauborgne and W.Chan Kim shared their vision of expanding, competitor-free markets in one of the bestselling business books of the century, Blue Ocean Strategy . With more than 4 million copies sold across five continents and translated into 49 languages, Blue Ocean Strategy argues that tomorrow’s leading companies will succeed not by battling competitors, but by creating blue ocean of uncontested market space ripe for growth.

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"The only way to beat the competition is to stop trying to beat the competition." Renee Mauborgne, ... [+] co-author with W. Chan Kim of 'Blue Ocean Strategy', and professor of strategy at INSEAD (Photographer: Craig Ruttle/Bloomberg)

For Nicolas G. Hayek, Cofounder and Chairman of the Board, Swatch Group, Blue Ocean Strategy is an extremely valuable book to read. “It examines the experience of companies in areas as diverse as watches, wine, cement, computers, automobiles, and even the circus to shed new light on the development of future strategies.”

In the past year, Harvard Business School professor Amy Edmondson has challenged the way that we think about failure with her book, Right Kind of Wrong: The Science of Failing Well . Drawing on a lifetime of award-winning research into the science of psychological safety, she shows that the most successful cultures are those in which you can fail openly, without your mistakes being held against you.

Thanks to Edmondson, you’ll never look at failure the same way again. But beyond a handful of bestselling authors, there are many other business school professors whose work and ideas challenges traditional thinking, including the economic theory that has won Nobel Prizes.

Traditional economic theory would have you believe that markets are the primary drivers of wealth and innovation. Nobel Prize winners Kenneth Arrow and Gérard Debreu developed a mathematical model that is central to the theory of general economic equilibrium.

Their work involves equations that remind you of Matt Damon’s character writing on a chalk board in Good Will Hunting .

But research from Professor Ramesh Rao, Professor of Banking and Finance at The University of Texas at Austin’s McCombs School of Business, looks to overturn decades of accepted economic shorthand.

“While proponents of free markets often cite Adam Smith’s “Invisible Hand” as a guarantee of optimality, our framework reveals a more nuanced reality”, he explains.

Rao models differently, and delves into the interactions between innovation, competition, institutions, and organisations.

“Not all innovations are economically viable when production is organised through the markets.” ... [+] Professor Ramesh Rao, Professor of Banking and Finance at UT Austin McCombs

His work argues that traditional value theory is blind to the realities of the world, and that firms can generate more wealth from innovations than markets . “What people aren’t willing to acknowledge is that in this theory, they assume that firms could never go bankrupt – there’s no liquidation, no negative earnings, and essentially no money.”

You don’t have pore through the Wall Street Journal every day to realize that bankruptcy and negative earnings are a reality for the world of business. And a world without money?

“The moment you bring in money, and the moment you bring in firms, the theory fails,” says Professor Rao. His research has brought those imperfections into the scope of analysis. He argues that money has a fundamental role to play in the economy, and that as liquidation is always a threat you cannot overlook.

“In a competitive economy, liquidation is always a threat. Because if somebody comes up with a new technology that can make yours outdated you will struggle to survive.”

MBA students are likely to agree. Pretty much every business school teaches cases that explore the failure of Eastman Kodak to keep up with the digital age, the Borders Group of bookstores losing out to Amazon, and the video rental company Blockbuster being taken out by Netflix.

“These companies had run out of ideas,” explains Rao. “Most people equate bankruptcy with debt, but even without debt if you don’t have idea that can create value you’re dead.”

Beyond the sophistication of complex economic theory, and research that has been published in top academic journals, Ramesh Rao is committed to sharing his expertise with new generations of business students at McCombs. The award-winning finance professor challenges them with the role that institutions and public policy can play alongside free markets.

His research shows policymakers that seek to encourage innovation and long-term prosperity should not only focus on markets, but also create an environment conducive to the formation and efficient functioning of firms.

But the key is to make it about real world situations. The kids coming out of a privileged high school on the west side of Austin might have all the support and networks they need to launch a well-funded business. But Ramesh Rao argues that the government has a role to play to make it easier to register a new business, and help economically disadvantaged individuals to gain access to capital.

“Small business promotes economic welfare,” he concludes. From such ideas, big and small, maybe business school professors really can help to change the world.

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ORIGINAL RESEARCH article

Asset risk assessment and management of large-scale electricity enterprises under the concept of financial sharing.

• Department of Economics and Management, North China Electric Power University, Beijing, China

The power grid is an important industry that is crucial to national security and economic development, and its importance in society continues to grow. As an emerging concept, financial sharing enables internal resource sharing and optimization, thereby improving the efficiency and effectiveness of asset management. This study investigates and analyzes the current situation of asset management in large-scale electricity enterprises in X Province, China, and proposes a comprehensive asset management strategy optimization plan based on the concept of financial sharing. The proposed plan integrates management models such as PDCA and designs an entire information management architecture to enhance resource utilization efficiency, reduce environmental pollution risks, and optimize asset allocation and operational decisions. In addition, it also utilizes the status of assets to assess the risks associated with fixed assets in the power grid. The results indicate that the asset risk assessment method under the concept of financial sharing can reduce power grid asset losses, effectively enhance the competitiveness and sustainable development capabilities of electricity enterprises.

1 Introduction

1.1 background.

With the rapid development of economic globalization and information technology, the number of multinational corporations and economic connections has been increasing, leading to intensified competition among enterprises. To adapt to the challenges of globalization and improve efficiency, many large enterprises have adopted the financial shared services model ( Yang et al., 2021 ). Financial shared services integrate and centralize non-core financial functions of the business into a shared services center, providing specialized financial services. This model supports enterprises in the era of globalization by optimizing financial processes, integrating data and resources, improving workflow efficiency, and reducing costs.

As one of the large-scale enterprises, the power industry also faces challenges from globalization. In the context of domestic economic development and improved living standards, the power industry experiences growing demand for supply and increased requirements for quality. However, traditional financial management systems in power enterprises often have complex organizational structures, outdated operational concepts, and inefficient working environments, making it difficult to meet the rapidly evolving industry demands ( Butt et al., 2021 ).

Therefore, power enterprises need to introduce advanced management concepts and approaches to enhance their financial management capabilities. Financial shared services, as an advanced financial management model, are still in their early stages of adoption and exploration in the power industry ( Gao, 2022 ). Through the implementation of financial shared services, power enterprises can optimize their financial processes, improve management efficiency, reduce costs, and establish a comprehensive financial management framework for their development.

The purpose of this study is to investigate and analyze the financial shared services center within a power generation group enterprise. By examining the current state of financial management and identifying existing problems and their causes, combined with the characteristics of the power industry, this research aims to provide a comprehensive financial management framework, strategies, and optimization plans for the establishment of a financial shared services center. This study can assist power enterprises in adapting to the challenges of globalization, improving their financial management level, and promoting sustainable development.

1.2 Research significance

Effective fixed asset management is crucial for the healthy development of enterprises. It enables businesses to maximize their benefits, improve operational efficiency, and optimize lifecycle costs. Furthermore, ensuring the security and appreciation of fixed assets is a key objective in optimizing fixed asset management. With the advancement of information technology, establishing a financial shared services center through advanced information systems has become a choice for enterprises to enhance their financial management capabilities.

In the era of information and economic globalization, enterprises face increasingly fierce market competition. As companies grow in size and expand their branches, they encounter challenges such as rising costs, increased management complexity, and amplified financial and operational risks. Addressing these issues necessitates organizational and management transformations.

Tang et al. (2022) used data from strategic emerging enterprises in Shanghai and Shenzhen Stock Exchanges from 2011 to 2018 to explore the impact of digital finance on enterprise value. They found that digital finance development, especially the depth of use, has a structural driving effect on the value of strategic emerging enterprises, particularly for those in less developed markets, non-state-owned, and in central and western regions. Digital finance enhances enterprise value by providing funds, reducing risk, and promoting innovation. Liu (2021) analyzed the impact of the COVID-19 pandemic on enterprise financial management and how big data technology can help enterprises effectively respond. The paper discusses how big data is changing traditional business models and financial management practices, and looks forward to trends in financial management informatization, automation, intelligence, and digitalization. Gardi et al. (2021) investigated the effects of financial accounting reports on managerial decision making in small and medium-sized enterprises in Iraq. Through cross-sectional data analysis of 250 respondents, they found that the effectiveness of managerial decisions is significantly influenced by factors such as financial statements, company records, report understandability, and data quality. The understandability, relevance, and quality of financial reports positively mediate the relationship between accounting reports and SME managerial decisions.

This study aims to provide comprehensive solutions for the fixed asset management and construction of financial shared services in the power industry. By investigating and analyzing the current status and challenges in fixed asset management, combined with the characteristics of the power industry, effective strategies and optimization plans for fixed asset management are proposed. In Chapter four, the application of the CBRM theory is calculated and analyzed, proving that the effectiveness of the proposed method. This research will assist power enterprises in meeting the challenges of globalization, enhancing their financial management capabilities, and promoting sustainable development. Moreover, it serves as a valuable reference for enterprises in other industries, fostering reform and innovation in financial management practices.

1.3 Chapter arrangement

The research content and ideas of this paper are arranged as follows. The first chapter of this paper introduces the research background of the concept of financial sharing and the asset management strategy of power enterprises. The second chapter introduces the related work, including the current situation and existing problems of fixed asset management in power enterprises, and the concept of financial sharing. The third chapter introduces the enterprise financial management model based on big data and IoT technology and the process of inventory, addition and scrapping of fixed assets. The fourth chapter introduces the principle and process of asset risk assessment of power grid enterprises based on CBRM theory. The fifth chapter summarizes the work of the whole paper.

2 Related work

2.1 status of fixed asset management.

Fixed assets play a vital role in power grid enterprises, accounting for approximately 50% in provincial-level power grid companies and even reaching 80% in some county-level power supply companies ( Song et al., 2018 ). Although fixed assets maintain their physical form during usage, they experience wear and tear, and the reduction in their value is partly transferred to the produced electrical energy, becoming one of the elements constituting the value of the energy product. Traditionally, fixed assets and physical assets have been considered identical, but this viewpoint is too simplistic. Due to the large number and frequent changes of assets in power grid enterprises, the fixed asset cards often fail to achieve synchronized management of asset value and physical records, leading to complex situations of inconsistency between accounts, cards, and physical assets. Fixed assets possess two important characteristics ( Biryukov et al., 2019 ):

Dual Attributes of Value and Physicality: Fixed assets are a concept in accounting with value attributes, while also corresponding to tangible objects with physical attributes. In practice, the management of fixed asset value is typically handled by the finance department, while the technical management of physical assets is the responsibility of the production technology department.

Relative Physical Forms of Fixed Assets: Managing the value of fixed assets requires categorizing assets into specific objects. Each fixed asset has a defined scope and content, representing the absolute nature of fixed asset value ( Mykolaitiene et al., 2010 ). However, the categorization of physical assets is not as straightforward as that of fixed assets because a single physical asset usually consists of multiple components with different functionalities. Especially for power grid enterprises with highly integrated and interconnected assets, where all electrical network assets collectively form a whole, it becomes challenging to categorize these assets as independent entities. Therefore, the physical form of fixed assets not only has an absolute nature but also exhibits relative characteristics.

Given the aforementioned situations, traditional fixed asset management methods such as fixed asset cards and single fixed asset catalogs are no longer effective for managing assets in power grid enterprises. Consequently, it is necessary to explore new management models to address the challenges of fixed asset management in power grid enterprises.

According to Accounting Principles published by State Grid Corporation of China, fixed assets in power grid enterprises can be categorized based on their functions, including transmission lines, transformer devices, distribution lines and equipment, electricity metering equipment, communication lines and equipment, automation control equipment, manufacturing and maintenance equipment, production management tools, transportation equipment, and auxiliary production equipment. Analyzing the operational characteristics of electrical networks and asset functionalities, assets in power grid enterprises can be simplified into three main categories ( Degefa et al., 2021 ), as shown in Figure 1 :

(1) Primary Assets: Transmission lines, transformer devices, and distribution lines and equipment. These assets directly connect power sources to electricity users, playing a crucial role in electricity transmission and distribution, serving as the core assets of power grid enterprises.

(2) Intelligent Assets: Equipment assets related to information processing, including devices with functions such as data collection, communication, control, and processing. Examples include automation control systems, electricity metering and collection systems, communication lines and equipment, and information processing devices. While these assets are not directly involved in electrical energy transmission and distribution, they play a vital supporting role in operational monitoring, marketing services, and enterprise management.

(3) Other Assets: Mainly comprising equipment related to maintenance, repair, transportation, as well as buildings and structures. These assets function relatively independently, with no direct technical connections among them.

Figure 1 . Classification method for fixed assets of power grid enterprises.

2.2 Problems in asset management

In the asset management of large power enterprises, fixed assets account for a large proportion of the total investment, so the management of fixed assets is of great significance to the enterprise. However, there are currently some urgent problems that need to be solved ( Rathor and Saxena, 2020 ). Taking the power grid enterprise in X Province, China as an example, the main problems in asset management during the planning and construction phase include the following aspects:

(1) Lack of systematic cost control management: The main business of power grid enterprises is power grid construction. However, at different stages of power grid construction, it involves the cooperation of multiple departments, but these stages and departments are relatively independent of each other’s work, resulting in cost savings in certain stages and overall cost overruns.

(2) The content of fixed asset management is limited: Currently, X Province’s power grid enterprises only carry out physical management work after the formation of fixed assets, fail to consider the overall cost from the entire life cycle of assets, and lack early planning awareness.

(3) The cost control system during the asset construction phase is not perfect: In the construction of X Province’s power grid, engineering management is divided according to specialties, and there are significant differences between different specialties, often making it difficult to connect. Engineering construction management focuses more on technical agreements, schedule management, and coordination of external conditions, resulting in a relatively lagging management of cost work. In terms of financial management, there is a lack of effective monitoring methods for budget execution, often resulting in budget overruns.

In addition, X Province has the following issues in asset management during the daily management phase ( Jung et al., 2019 ):

(1) The ineffective connection between asset physical management and value management: Different departments of X Province’s power grid enterprise are unable to share asset information in a timely manner, resulting in difficulties in achieving one-to-one correspondence of asset equipment. When production, infrastructure, materials, and finance departments manage fixed assets, there is often inconsistency in the management objects and standards, leading to a disconnect between the physical and value of assets.

(2) Lack of scientific rationality in fixed asset budgeting work: X Province’s power grid enterprises lack scientific rationality in fixed asset budgeting work, lack reference basis for standard operating cost management, and insufficient input-output analysis of technical transformation and major repair projects. Taking the standard for determining daily operation and maintenance repair costs as an example, its determination method is not rigorous enough.

(3) Failure to manage asset scrapping in a timely and efficient manner: X Province’s power grid enterprises often encounter assets that have been depreciated but have not yet completed the scrapping procedures in their financial management process. The actual situation of these assets is often difficult to determine, and there may be situations where they are used beyond their expected period or have already been scrapped, resulting in discrepancies between the accounts and reality.

(4) The application of material standards is not standardized: Currently, X Province’s power grid enterprises have not established a sound data integration mapping relationship between relevant information systems such as design, bidding, and procurement, resulting in a lack of corresponding foundation for material and equipment ledgers. The procurement requirements submitted by the construction department lack change process management compared to the actual procurement, resulting in inconsistent information. The management process for retired assets needs further improvement, and the unified management and allocation of idle assets throughout the company have not yet been achieved. The utilization rate of assets needs to be improved.

In the presence of these problems, large power enterprises face a series of challenges in asset management, which require optimization and improvement to improve management level and efficiency, reduce costs, and achieve reasonable management of the entire life cycle of assets.

2.3 Development of financial management

With the continuous development of group business and the advancement of information technology, the financial management business model of enterprises has also undergone changes ( Karadag, 2015 ). According to the sequence of development time, enterprise financial management can be divided into the following three stages: decentralized financial management, centralized financial management, and financial shared services, as shown in Figure 2 . The following will introduce the development process of these three stages.

Figure 2 . Development of enterprise financial management methods.

2.3.1 Decentralized financial management

Decentralized financial management refers to a model in which branch organizations within a corporate group independently make financial management decisions and operate under the guidance and supervision of the headquarters ( Kutsyk et al., 2020 ). Each branch independently maintains accounting books and conducts accounting calculations, submitting financial statements to the group management at the end of the accounting period. The understanding of the group’s overall financial situation is achieved through consolidated financial statements. Under the decentralized financial management model, each branch establishes independent bank accounts as needed and independently manages cash, liquidity, and small-scale short-term financing activities. Only long-term strategic financing activities are centrally controlled by the headquarters. Decentralized financial management has been a prevalent model among international corporations due to its advantages. These advantages include the flexibility for branches to handle cash issues according to their regional and individual conditions and to manage relationships with banks effectively. Financial personnel at each branch can better understand the local economic environment and the financial status of the company, enabling them to provide timely financial services to the branch and facilitate cooperation with the responsible departments.

2.3.2 Centralized financial management

As the organizational and managerial development of corporate groups evolves along with the economic and technological progress in China since the reform and opening-up, enterprises are striving for rapid development, market-oriented operations, and customer-centric objectives. Therefore, in the process of their development, groups need to grant branch organizations greater autonomy to carry out business operations flexibly. In this context, it becomes necessary to strengthen financial monitoring of branch organizations to prevent management crises. The development of computer technology provides support for transforming the financial management model ( Long and Liu, 2016 ). Centralized financial management has replaced the previous decentralized model and has become an essential component of financial management.

2.3.3 Financial shared services

Financial shared services centralize resources from different organizations within a group to provide financial services to various entities at lower operating costs and with higher service quality, aiming to enhance corporate value. Another way to describe financial shared services is to detach decentralized financial operations from basic financial units and centralize them in a new financial unit for unified processing, which is referred to as the financial shared services center. Financial shared services represent an advanced form of centralized financial management ( Li, 2020 ). It involves two aspects of intensification: concentration, which primarily refers to the centralization of accounting personnel and accounting information across different organizations within the enterprise, and integration, which mainly refers to integrating business functions. The types of services provided by financial shared services cover almost all financial accounting and financial management operations that can be consolidated and streamlined, potentially transcending the functional divisions of traditional responsibility centers.

2.4 Theories related to financial sharing

2.4.1 theory of office automation.

Office automation is a comprehensive technology that applies computer technology, systems, and behavioral science to data processing or non digital information processing. The development of office automation has reduced the burden on workers, improved work efficiency, and is an important turning point in improving national productivity ( Papagiannidis and Marikyan, 2020 ). In the context of financial sharing for power grids, automating financial processes and data sharing between different entities in the grid can streamline operations. For example, using software to automatically reconcile transactions and share financial data can reduce manual work and errors.

2.4.2 Process reengineering theory

Process reengineering is an enterprise activity that examines business processes and involves internal personnel in change. It aims to establish an efficient and collaborative process oriented organization, improving operational efficiency by optimizing and integrating business processes. This could involve redesigning how costs and revenues are allocated, how payments are processed, or how financial risk is managed across the shared grid infrastructure. The goal is to make the financial flows more efficient and transparent. ( Mandych et al., 2021 ).

2.4.3 Sharing economy theory

The sharing economy refers to a system of direct exchange of goods and services between various entities, which utilizes the Internet to achieve sharing in various aspects of society, including hitchhiking, renting houses, and exchanging idle items. In a financially shared power grid, the different stakeholders share the costs, revenues, risks and benefits of the grid rather than each managing their own infrastructure. This allows for more efficient utilization of capital and potentially lower costs for all through economies of scale. ( Wang et al., 2021 ).

2.4.4 Cognitive surplus theory

Cognitive surplus refers to the use of idle time for content creation and sharing, which generates value far beyond consumption. The essence of cognitive surplus is a behavior of time sharing, which enables multiple users to jointly handle transactions and the same account to be operated by different people at the same time. In the financial shared service center, the application of cognitive surplus enables multiple users to handle transactions together, and the account can be used by multiple people, thereby improving work efficiency. With the development of digital technology, the accumulation of information and data has increased data complexity, and the advent of the fragmented era has also provided convenience for financial sharing. Employees can complete reimbursement and other matters through online media without the need to personally go to the office.

3 Methodology of fixed asset management in enterprises

3.1 design ideas and principles.

State Grid Corporation of China (hereinafter referred to as State Grid) is a public utility unit that is strictly regulated by the state. The scale and quality of power grid assets have a significant impact on power grid safety, power supply quality, as well as enterprise income and profitability. Engineering financial management plays a bridging role between funds and assets, and has practical significance for leveraging value leadership and achieving digital transformation. The State Grid System of China aims to become a world-class enterprise, driven by digital transformation, and has built a financial management system for the entire process of smart sharing projects with one center construction, two sharing concepts, four service principles, and four smart mechanisms as its core.

Based on the theory of financial sharing, an important concept is the concentration of information. We use IoT technology to optimize the fixed asset management of enterprises, so that they can level, transmit, and process information as much as possible within a system. This involves asset Life Cycle Management (LCM), which is a theory that evolves, extends, and enriches from Life Cycle Costing (LCC). Life Cycle Management is a comprehensive management approach aimed at optimizing and maximizing the value and benefits of a product or asset throughout its entire lifecycle, from design, production, use to disposal. The goal of LCM is to achieve sustainable development and environmental protection by comprehensively considering all aspects from resource collection to waste disposal, and to create greater value at the economic and social levels.

For local problem-solving and process improvement, we consider the PDCA closed-loop management process, which is a management method used for quality management and continuous improvement ( Isniah et al., 2020 ). PDCA management theory divides management into four stages: Planning, Do, Check, and Act, as shown in Figure 3 . Its significance lies in the retention of successful or mature management models or experiences through a cycle of repetition, and the retention of immature ones for the next cycle to solve, in a stepwise upward pattern.

Figure 3 . PDCA closed-loop management process.

During the P (Plan) phase, it is necessary to develop goals and processes that can achieve expected results in combination with company policies and customer needs. The D (Do) phase executes specific actions based on plans and standards. Analyze and summarize the execution effect in the C (Check) stage, and verify whether the implementation plan has achieved the goals. In the A (Act) stage, summarize the results of the inspection, confirm the results and standardize them, while addressing any remaining issues. The PDCA management theory systematizes, systematizes, and scientific work ideas and steps, and is applicable to multiple levels and links of the company’s overall project and management.

At the end of each cycle, there will be results or problems discovered, and continuous cycles will continuously improve corporate governance or project operations. However, although PDCA management theory provides a scientific and systematic thinking and management mode, it may also lead to inertia thinking and weaken creativity, with certain limitations.

3.2 Fixed asset management system

3.2.1 system overview.

Based on LCM theory and PDCA management philosophy, we design a management system. Figure 4 is a financial sharing center asset control framework model based on cloud accounting and big data technology ( Teoh et al., 2021 ). It includes the technical features of cloud accounting and big data, and consists of five levels from top to bottom: user layer, application layer, service layer, data layer, and infrastructure layer.

Figure 4 . Asset management framework model for financial shared center.

The user layer is mainly the decision-makers of the group enterprise, including the group company and its subsidiaries, branches, and related companies. It is necessary to combine the financial decision-making plan of the application layer with the actual situation of the group enterprise and choose according to the optimal principle.

The main task of the service layer is to receive, process, utilize, integrate, classify (portal organization), and transmit (basic services) data from data centers or data warehouses. It is aimed at decision-makers and their subsidiaries, branches, and related stakeholders in the user layer group enterprise. Based on their asset management and control decision-making needs, it constructs a decision-making application platform for internal asset allocation decisions, asset service decisions, and other aspects.

The main task of the data layer is to extract, transform, and load data from ODS industries such as DBMS, File, HDFS, and NoSQL associated with downstream business layers, while utilizing big data technologies such as Hadoop, HPCC, and Storm for standardization, programming, and processing. Using asset management as the framework system, establish a multi-dimensional data center or data warehouse that includes fixed assets, inventory, intangible assets, procurement management, production management, sales management, and warehousing management.

The infrastructure layer is mainly supported by software and hardware environments. Based on cloud accounting technology, traditional intelligent terminals, servers, storage, networks, and security devices are connected to the cloud. At the micro level, management systems can be provided for various links in the business layer (procurement, sales, production, and assets). At the macro level, data from related industries can be obtained from the cloud, providing support for the upstream data layer The application layer collects the required asset management control data.

3.2.2 Fixed asset inventory

X Province Power Enterprise enhances the quality of fixed asset data and improves the level of asset management by deepening the application of asset information systems. The company regularly organizes physical management departments and user departments to conduct comprehensive inventory of fixed assets to ensure the consistency between accounts, cards, and physical assets ( Wang, 2018 ). The finance department leads the effort to standardize and govern the company’s system-wide asset data. Relevant departments and units are organized to implement specific tasks for standardizing asset data, ensuring the completion of the standardization process according to the schedule. The physical asset management department is responsible for verifying the equipment ledger information in the respective asset-use and storage departments and performing clean-up and improvement of the equipment ledger in the ERP system. They assist the finance department in the standardization and governance of asset data, coordinate the verification and correspondence of physical equipment ledgers and corresponding software equipment cards in the storage departments, and assist the finance department in verifying, splitting, and merging asset cards. They also coordinate any organizational, personnel, and scheduling issues that arise during the standardization process. The physical management department prepares the asset inventory report for their department and assists the finance department in preparing the company’s asset inventory report. The equipment operations and maintenance unit verifies whether the physical assets, equipment ledgers, and asset cards correspond to each other, and generates foundational data tables such as the physical asset inventory list, inventory gains and losses, and asset information change table to support the finance department in creating the company’s inventory report. After approval from company leadership, the corresponding asset information is modified, increased, or disposed of; the finance department is primarily responsible for value adjustment, while the physical management department and user departments are primarily responsible for equipment ledger adjustments, ensuring the consistency of asset records, cards, and physical assets. The specific process is shown in Figure 5 .

Figure 5 . Fixed asset inventory process diagram.

Unified identity coding is a crucial foundational task. Only by establishing a solid foundation can technologies such as the Internet of Things (IoT) and big data truly play their roles. Building a unified coding standard system and assigning a unique, consistent identity card to devices is essential for facilitating the transition of asset management from physical to digital. Currently, X Province Power Company is carrying out the coding work for existing physical devices. The main process involves equipment management personnel generating physical ID codes through the production management system according to the established procedures. The electronic version of the code is exported and completed by the manufacturer during production. The equipment management personnel are responsible for installing the codes on the devices and completing the related system processes. By the year 2020, the goal is to complete over 55% of the coding work for the 14 categories of main network equipment and two categories of distribution network equipment. The 14 categories of main network equipment include main transformers, circuit breakers, switchgear, disconnecting switches, current transformers, voltage transformers, reactors, power capacitors, coupling capacitors, grounding transformers, station transformers, switchboards, surge arresters, and arc quenching coils. The two categories of distribution network equipment refer to distribution transformers and ring main units.

3.2.3 Fixed asset additions

In accordance with relevant regulations of State Grid Corporation of China, fixed asset additions primarily come from engineering projects, sporadic purchases, donations, free transfers, allocations, and transfers. After the completion of a project, the construction management unit organizes the project acceptance, with participation from the implementing unit, physical asset management department, finance department, and equipment operations and maintenance unit. The construction management unit provides a list of equipment for project completion acceptance, and the project owner department and construction unit count the physical assets on-site. The finance department reviews the equipment acceptance list and supplements financial information. The physical asset management department and equipment operations and maintenance unit verify the physical assets on-site. After passing the on-site acceptance, the equipment is put into operation. The equipment operations and maintenance unit establishes corresponding equipment ledgers in their respective management systems and completes the classification and creation of asset numbers according to the requirements of the finance department. The finance department conducts temporary capitalization based on the actual costs incurred in the project and finalizes the assets after accurate final audit. The equipment operations and maintenance unit verifies the equipment ledgers and on-site assets based on the asset inventory provided by the finance department, completing the corresponding work for fixed assets. The specific process is shown in Figure 6 .

Figure 6 . Flow chart of new fixed asset management.

3.2.4 Fixed asset disposal

X Province Power Company strictly follows the relevant regulations for the retirement technical assessment of physical assets in the power grid to establish the basic principles, assessment responsibilities of each level and department, assessment process, and the content to be included in the assessment report. The principle of specialized assessment and graded identification is used to determine equipment disposal. The application for asset disposal is initially submitted by the equipment operations and maintenance unit, which then conducts a technical assessment and provides recommendations for the disposal of relevant equipment (reuse or disposal). The technical assessment report includes information such as the equipment manufacturer, factory date, commissioning date, operating environment, identified defects, equipment evaluation status, equipment ledger information, and asset card information. After being reviewed and approved by the physical management department, it is then submitted to the finance department for approval. The finance department submits it to company leadership for further approval, and after approval from the provincial company, the on-site retirement of equipment is completed. The specialized management system’s ledger is also updated to reflect the retirement. The equipment operations and maintenance unit processes the necessary return procedures with the materials department, which disposes of the obsolete materials. The finance department handles the relevant accounting procedures, and thus, the asset disposal process is completed. For equipment identified as eligible for reuse in the technical assessment, the physical management department handles its unified management, enabling internal asset reuse within the company. The departments involved in transfer and handover complete the equipment ledger updates and the finance department cooperates in modifying the asset card information and facilitating the necessary transfers. The specific process is shown in Figure 7 .

Figure 7 . Fixed asset scrapping process diagram.

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4 Risk assessment of fixed assets in the power grid

4.1 objectives of fixed asset risk assessment.

Through the asset management model established based on the concept of financial sharing, we assess the health status of the effective assets in the model. The objective of risk management is to define the risk management objectives for a specific fixed asset, namely, the CBRM risk management objectives of the power grid under the current established service level, as follows:

(1) Categorize the risk management objectives of the equipment. The assessment of a single equipment typically consists of two parts: non-status assessment and status assessment. The goal is to classify the risks of the equipment based on its unique and specific potential risks, including risks related to the performance of the power grid itself, personal safety, financial aspects, and environmental factors.

(2) Analyze the effectiveness of the profit and loss resulting from the assessed risks of the fixed asset equipment. Firstly, provide relevant analysis tools to conduct cost-effectiveness analysis of the risk investment scheme, in order to develop the optimal solution that maximizes the utilization of limited resources while reducing risks. Secondly, transparent and auditable evidence should be provided for the replacement cost of power equipment or lines.

(3) By evaluating the equipment status and assessing the risk of fixed assets, we gain insights into the information, processes, and system requirements related to asset management, thereby enhancing the overall level of asset management for power grid enterprises. Additionally, we provide specific equipment experiences and information to power grid enterprises, supplying them with the necessary data and materials for risk management and control.

4.2 Principles of CBRM theory

CBRM (Condition Based Risk Management) was originally developed by the Electricity Association (EA) of the UK’s National Power Research Institute, based on years of research on power grid equipment and analysis of databases from the United Kingdom National Reliability Center and the North America Reliability Center ( Mehairjan et al., 2015 ). Over the years, EA Technology has collaborated extensively with major power companies worldwide, gradually developing and enhancing this approach. It has been successfully applied to individual fixed assets in foreign power enterprises numerous times, effectively assisting power companies around the world in asset-related risk management.

CBRM is a risk management principle based on equipment condition, structured as a systematic process. The CBRM system predicts and assesses risk types and levels by evaluating the current and future conditions of power grid equipment, providing a reliable basis for risk decision-making and risk control in power grid enterprises. The risk assessment process of CBRM combines the asset information of individual effective assets, evaluates the status, performance, safety, and economic aspects of power grid assets based on relevant engineering knowledge, and monetizes the assessed risks based on expert experience. The fundamental characteristic of this process is the ability to combine and consolidate assets with diverse characteristics under different operating environments ( Zaldivar et al., 2023 ).

CBRM is a comprehensive risk assessment system. When applying this system for risk assessment, it is necessary to systematically analyze and consolidate information such as technical parameters of power equipment, series of test data, equipment load conditions, environmental factors, visual descriptions of equipment, fault situations, and defect levels. Then, using predetermined quantitative criteria, this information is transformed into a series of numerical codes. The system calculates the overall health index of the equipment based on technical parameters, environmental conditions, series of test data, and load conditions. The health index is further adjusted based on the visual conditions, fault defect levels, and engineering principles, combined with the practical work experience of on-site engineers. By systematically identifying factors that may affect the actual operating status of power grid equipment and formulating a single quantitative health index (HI, 0–10), the health index represents the current and future health conditions of each individual equipment. Using the health index, it is possible to calculate the remaining service life of the equipment, as well as the types and probabilities of potential failure. Thus, the changing trends of failure types and frequencies for the entire equipment group can be scientifically predicted and understood. The essence of CBRM lies in the effective integration of engineering knowledge, practical experience, detailed equipment information, investment plans, and implementation processes. The wide range of engineering knowledge and experience is related to the following equipment information: quality, faults, condition assessment, performance and environmental impacts, load, and the initial specification of assets. With this information, we can define the current and future states and performance of these assets, which is crucial for guiding us in making economically efficient investment plans.

4.2.1 Evaluation of investment cost for effective assets

By compounding and discounting the current asset replacement cost, the future investment cost for the next few years can be calculated. The reduction in investment cost over time can be calculated using an equation. This number can easily be adjusted within an effective asset project. The relationship between the reduction in investment cost and time can be derived from the results of the equation:

Where, I n v 0 is the replacement cost of current assets. r is conversion rate. t is number of the year in the future.

Assuming the conversion rate r = 6%, the relationship between the reduction of investment costs and practice can be obtained from the results of the equation, as shown in the curve in the financial optimization example in Figure 8 .

Figure 8 . Financial optimization example.

4.2.2 Risk accumulation assessment of effective assets

We use the concept of cumulative discounts Δ R i s k to define the optimal replacement period for a given grid’s effective assets.

Δ R i s k represents the difference between the risk of an aging asset and the risk of a new asset, that is, the net risk between an aging asset that bears the same function and a new asset. Its quantification formula is represented as follows:

Where, Δ R i s k t is the net risk of a given aging asset in the next t  years. R i s k t is the overall risk of aging assets in the next t years. R i s k n e w is the overall risk of a given new asset.

For a certain year in the future, the overall conversion of Δ R i s k t can already be calculated within t  year (i.e., the cumulative risk of assets within a given t year is represented by the current value). Refer to Eq. 3 for details:

As mentioned above, the risk of an asset increases year after year until the health index reaches the region of failure rate. The compound conversion increase of Δ R i s k is also very similar to it, as we can see from the curve in Figure 8 .

4.2.3 Evaluation of equipment replacement costs

For a given effective asset, the overall replacement cost within year t can be considered as the total discount on investment costs within year t and the cumulative discount it bears. The sum of formulas (1) and (3)

Eq. 4 is depicted by the blue curve in Figure 8 . From this, it can be seen that the curve depicted by this relationship has a minimum value. For a given asset, when the inflection point occurs, the increase in asset discount from year t -1 to year  t exceeds the cost of asset refurbishment rather than replacement from year t -1 to year  t .

For power grid assets, choosing to replace them with the health index at its minimum value within the next t years is economically optimal. It should be noted that the overall replacement cost curve is displayed by creating a visual inflection point. This inflection point is obtained by the interaction between the current risk value and the investment cost, and by displaying whether the complex relationship has changed over a relatively long or short period of time. The gray curve represents the current value of the actual replacement cost of the asset in time t .

4.3 CBRM risk management process

The CBRM risk management process, as shown in Figure 9 , consists of the following steps ( Rodkumnerd and Hongesombut, 2019 ):

(1) Define the basic condition of effective fixed assets in the power grid enterprise. Evaluate the condition of each individual asset in different asset groups, which is typically referred to as the indicator of asset condition risk, known as the Health Index (HI). The HI ranges from 0 to 10, with 10 indicating the best condition and 0 indicating the worst condition.

(2) Establish the relationship between the HI and the failure rate by linking the current condition of an effective fixed asset in the power grid enterprise with its operational reliability and safety performance indicators. The health index is calibrated based on the Probability of Fault (POF). The HI table is matched with the current POF, and the relationship between the HI and POF is determined.

(3) Estimate the future health status and performance of assets by utilizing knowledge related to the aging process for asset age computation. The aging rate of an individual asset depends on its initial health index and operational condition. The future failure probability can be calculated based on the relationship between aging health index data and the previously determined relationship between health index and failure rate.

(4) Evaluate potential control measures based on the failure probability. Changes in replacement, refurbishment, or maintenance plans may have potential impacts on the equipment condition. By modeling these impacts, the degree of influence from different strategies can be determined. The future health index and failure rate can be adjusted based on this relationship.

(5) Determine and assess the consequences of failures. We define and construct a general framework to evaluate the severity of consequences in various domains, including power grid performance, safety, economic, and environmental aspects. The consequence types are weighted using a unified monetary unit.

(6) Establish a risk model to quantify the risk of an individual equipment. By multiplying the severity of failure and the probability of failure, the risk can be quantified. Therefore, the total risk of an asset group is the sum of risks of each individual asset it contains.

(7) Assess potential control measures based on the risk. The impacts of potential actions such as replacement, refurbishment, or maintenance plan changes can be quantified through modeling, enabling the adoption of different strategies to mitigate potential risks. Review and improve information and processes. Building and managing a risk-based process based on specific asset information is not a one-time process. The initial application is based on existing equipment information and provides a foundation and operational framework for future applications. Continuous improvement is especially important in establishing an evolving asset information framework.

Figure 9 . Analysis of CBRM fixed asset management process.

In summary, the CBRM risk management process aims to determine the condition and performance of assets and provide a systematic process to determine and predict asset life. Future expenditure plans can be linked to the probability of failure and failure levels. Additionally, risks are defined and quantified based on the consequences of failure and the importance level of equipment, integrated with the failure rate (POF). By separating these two process steps, the output of the system and its connection with relevant engineering knowledge and experience can be clearly demonstrated. It emphasizes that establishing and implementing a condition-based risk management system is not a one-time process. Based on existing equipment information, an initial application can be established, providing a foundation and framework for future asset updates and development. Importantly, the accumulated experience during operation will continue to be updated and incorporated into this system.

5 Conclusion

This paper presents a method for assessing and managing asset risks in large power enterprises based on the concept of financial sharing. Firstly, the current status and existing issues of fixed asset management in power enterprises are introduced. Subsequently, the development process of financial management and the theory of financial sharing are presented. Based on the concepts of asset lifecycle management and the Plan-Do-Check-Act (PDCA) closed-loop management process, a model for fixed asset management system in power enterprises is designed, centralizingaccounting information. In addition, the processes of inventory, addition, and scrapping of fixed assets, based on the concept of financial sharing, are integrated to streamline financial management operations as much as possible. Lastly, based on this system and the CBRM theory, the risks of fixed assets are assessed to maximize their benefits. The research findings have significant implications for the policies and practices within the power industry. By adopting a method based on financial sharing concepts for asset risk assessment and management, power enterprises can achieve more centralized and streamlined financial operations. This approach enhances the accuracy and efficiency of fixed asset management, which is essential for maintaining the reliability and sustainability of power infrastructure. The integration of asset lifecycle management and the Plan-Do-Check-Act (PDCA) closed-loop management process allows for continuous improvement and better risk mitigation strategies. This model could influence policy by encouraging the adoption of more standardized and efficient asset management frameworks across the industry. Practically, it could lead to improved financial transparency, reduced operational costs, and better allocation of resources, ultimately driving innovation and competitiveness within the power sector.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

MB: Conceptualization, Data curation, Formal Analysis, Methodology, Project administration, Validation, Writing–original draft, Writing–review and editing. XY: Project administration, Software, Supervision, Writing–original draft.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

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.

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Keywords: financial sharing, electricity enterprises, asset management, risk assessment, strategy optimization

Citation: Minyue B and XiuE Y (2024) Asset risk assessment and management of large-scale electricity enterprises under the concept of financial sharing. Front. Energy Res. 12:1430562. doi: 10.3389/fenrg.2024.1430562

Received: 10 May 2024; Accepted: 31 May 2024; Published: 08 July 2024.

Reviewed by:

Copyright © 2024 Minyue and XiuE. 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: Bai Minyue, [email protected]

Disclaimer: 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.

Mak Financial Management System a Timely addition to Digitalisation Drive

The Makerere University Financial Management System launched on Wednesday 10 th July 2024 by the Chairperson, Finance, Planning, Administration and Investment Committee (FPAIC) of Council, Mr. Bruce Kabaasa has been heralded a timely addition to our transformation into a digitally-driven research-led University. Since the beginning of the year, the University has launched systems to digitalise Research Information Management (RIMS), Academic Records and Processes (DARP), Student and Staff Attendance Management (SAMS) and to amalgamate records from all the above, the Makerere Data Repository (MakDATA).

The system developed in-house by the Directorate for ICT Support (DICTS) is part of the University Council’s strategy to adopt a cost-effective and sustainable approach to Makerere ’s business automation needs. It will enable the Finance Department to streamline budget management at cost centres and budgeting units at the University as well as automate the requisitions and generation of digital reports for decision making.

Presiding over the event held in the Council Room, Mr. Kabaasa congratulated the University Management upon taking yet another step in improving the efficiency of the University “I am very happy with your leadership Mr. Vice Chancellor. The processes of improving financial management continue to come a long with your guidance.”

He equally paid tribute to the University Bursar, Director DICTS and their respective teams on a job well done in developing the system but nevertheless challenged offices directly in charge of financial management to review the line manual so as to cater for the growth in development financing and other grants. “We must have a robust framework within which all these policies that speak to finances are grounded.”

Mr. Kabaasa further called for the appreciation that timelines and deliverables are very pertinent in a Public-funded University such as Makerere and called upon the offices in charge to go beyond release funds for procurement to ensuring that the goods and services purchased meet the requisite quality and timeliness of delivery. In the spirit of ensuring transparency, he urged that the system should grant limited access to all users to view amounts and dates when releases are effected.

Adding his voice to Mr. Kabaasa’s, the Vice Chancellor Prof. Barnabas Nawangwe acknowledged that the launch of the system is testament to Makerere ’s commitment not to be left behind in the global digitalisation drive. Furthermore, he noted that the system is a game changer in the drive to reduce the paper trail generated by business operations especially in light of inefficient search processes associated with physical documents.

The Vice Chancellor therefore paid tribute to the Director DICTS, Mr. Samuel Mugabi and his team of young developers that have been at the helm of implementing the University Council’s in-house software development strategy. He nevertheless tasked the developers to incorporate alerts in the system that are reflective of the University Organisational Manual’s stipulation of the maximum period within which documents ought to be cleared by the concerned offices.

Prof. Nawangwe thanked the University Council for creating an environment conducive for the digialisation of business processes. “I am happy that Council put its foot down and said ‘we must digitalise’ and we can now see how many systems we have in such a short time; what remains is to integrate all these (systems) so that they speak to each other and I urge DICTS to expedite that.”

“At Makerere we want to be the best and if we want to be the best, we must make sure that we are efficient” continued the Acting Deputy Vice Chancellor (Finance and Administration), Prof. Henry Alinaitwe. He added that the system would greatly enhance transparency in the administration of budgets as relevant offices will be able to view real-time balances during implementation.

Pre-empting the notion that too many systems were being launched, the University Secretary Mr. Yusuf Kiranda explained that “digitalisation is only coming to translate what we have been doing on paper onto a digital platform, which improves efficiency and most importantly for Makerere , transparency.”

He therefore thanked the Vice Chancellor for leading the drive to digitalise the University and FPAIC for the strong interest taken in budget processes and enabling Makerere to achieve a stronger compliance. “Lastly, I thank the University Bursar and Director DICTS for working under pressure to deliver the system.”

The University Bursar, Mr. Evarist Bainomugisha noted that the system launch was a key milestone for the Finance Department, University Council and Makerere . He explained that whereas the University is already implementing the Integrated Financial Management System (IFMS), the system consolidates all administrative units into one pool which makes managing budgets at unit level complicated. He further noted that IFMS neither processes payment vouchers for various requisitions nor has the ability to capture supporting documents.

“I therefore thank the University Council and Management led by the Vice Chancellor for the support and DICTS for all the technical backing” acknowledged Mr. Bainomugisha.

The event attended by Members of Central Management, College Principals and staff from the Finance Department was moderated by the Principal Public Relations Officer, Ms. Ritah Namisango.

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Freshers’ Orientation and Registration For 2024/2025 Academic Year

To All First Year Students; First Year students (Freshers) are by tradition given an “acclimatization” period of one week which is referred to as the “Orientation Week”. The Freshers report on Campus one week earlier than the Continuing students and during this week they are introduced to the key facilities in the University as well as other important aspects of life at the University.

Admission Lists -Disability, Sports and District Quota Schemes 2024/25

The Office of Academic Registrar, Makerere University has released the admission list of candidates admitted under the  Disability, Sports and District Quota Schemes  with Government sponsorship 2024/25 Academic Year.

Kindly follow the links below to access the lists:-

• Disability Scheme
• Talented Sports Persons Scheme
• District Quota Scheme including APPEALS (Admitted pending verification by Districts)

Undergraduate Admission Lists under Government Sponsorship 2024/2025

The  Office of the Academic Registrar,   Makerere University  has released admission lists for Government sponsored students for the Academic Year 2024/2025. The Office has also released  Cut Off Points for Government Admissions .

Below are lists of candidates admitted to the respective courses tenable at  Makerere University  and  Makerere University Business School :

The list for Government Sponsorship for admission and Cut-Off Points in other public universities 2024/25 i.e. Busitema, Gulu, Kabale, Kyambogo, Mbarara and Soroti University can be accessed by following the links below:

• Government Sponsorship Admission Lists for other Public Universities 2024/25 AY
• Government Sponsorship CUT-OFF points for other Universities

UPDATE 23 rd May 2024

Admission List for Government Sponsorship and Cut-Off Points 2024/25 for Lira and Mountains of the Moon University

• Government Sponsorship Admission Lists (Lira and MMU)
• Government Sponsorship CUT-OFF points (Lira and MMU)

UPDATE 12 th July 2024

Admission list arising from APPEALS under National Merit

• Candidates recommended for Admission under NATIONAL MERIT

MBA Shortlist for Graduate Admission Test (GAT) 2024/2025

Makerere University Students’ Internship Evaluation

Call For Applications: Small Scale Action/Field Research Grants on TELLS Project for Senior Researchers at Makerere University

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Waste Management (WM) Gains As Market Dips: What You Should Know

In the latest trading session, Waste Management ( WM Quick Quote WM - Free Report ) closed at $211.77, marking a +0.28% move from the previous day. This change outpaced the S&P 500's 0.88% loss on the day. Meanwhile, the Dow experienced a rise of 0.08%, and the technology-dominated Nasdaq saw a decrease of 1.95%.

Shares of the garbage and recycling hauler witnessed a gain of 5.13% over the previous month, beating the performance of the Business Services sector with its gain of 0.29% and the S&P 500's gain of 5.11%.

Analysts and investors alike will be keeping a close eye on the performance of Waste Management in its upcoming earnings disclosure. The company's earnings report is set to go public on July 24, 2024. The company is forecasted to report an EPS of $1.81, showcasing a 19.87% upward movement from the corresponding quarter of the prior year. In the meantime, our current consensus estimate forecasts the revenue to be $5.41 billion, indicating a 5.7% growth compared to the corresponding quarter of the prior year.

Looking at the full year, the Zacks Consensus Estimates suggest analysts are expecting earnings of $7.31 per share and revenue of $21.55 billion. These totals would mark changes of +18.09% and +5.48%, respectively, from last year.

Investors might also notice recent changes to analyst estimates for Waste Management. These revisions typically reflect the latest short-term business trends, which can change frequently. As a result, upbeat changes in estimates indicate analysts' favorable outlook on the company's business health and profitability.

Empirical research indicates that these revisions in estimates have a direct correlation with impending stock price performance. To take advantage of this, we've established the Zacks Rank, an exclusive model that considers these estimated changes and delivers an operational rating system.

The Zacks Rank system ranges from #1 (Strong Buy) to #5 (Strong Sell). It has a remarkable, outside-audited track record of success, with #1 stocks delivering an average annual return of +25% since 1988. Over the past month, the Zacks Consensus EPS estimate has remained steady. At present, Waste Management boasts a Zacks Rank of #2 (Buy).

Digging into valuation, Waste Management currently has a Forward P/E ratio of 28.9. This valuation marks a premium compared to its industry's average Forward P/E of 27.93.

Also, we should mention that WM has a PEG ratio of 2.46. This metric is used similarly to the famous P/E ratio, but the PEG ratio also takes into account the stock's expected earnings growth rate. The Waste Removal Services was holding an average PEG ratio of 2.67 at yesterday's closing price.

The Waste Removal Services industry is part of the Business Services sector. This industry currently has a Zacks Industry Rank of 93, which puts it in the top 37% of all 250+ industries.

The Zacks Industry Rank gauges the strength of our industry groups by measuring the average Zacks Rank of the individual stocks within the groups. Our research shows that the top 50% rated industries outperform the bottom half by a factor of 2 to 1.

Remember to apply Zacks.com to follow these and more stock-moving metrics during the upcoming trading sessions.

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