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International Journal of Innovation Science

ISSN : 1757-2223

Article publication date: 5 April 2021

Issue publication date: 5 September 2022

The purpose of this paper is to review the literature on the topic “impact of digital marketing” over the span of past eight years, published in various prominent research journals in the past eight years. The purpose of this paper is to act as a starting point for several further researches in this area of study, and also to get the overview of the research that has happened and understand the relevant research gaps that exist in the area of digital marketing.

Design/methodology/approach

Scopus database is used to search the research publications on the selected topic. The papers selected for this paper have been published in the past eight years (2012–2020).

It has been concluded by many of the research papers reviewed that “Digital Marketing Efforts” influence the purchase intention of the customer. It can be also inferred that the distinction between the “marketing” and “digital marketing” is soon fading as every type of marketing effort will have an element of “digital marketing” in it.

Research limitations/implications

The approach to the review is theoretical and no primary data have been collected. This bibliometric review is expected to provide overview of the research that has happened over the span of past eight years in the area of digital marketing.

Practical implications

Many of the papers have expressed the limitations and opportunities for the future research. Few of the prominent and relevant research gaps are listed in this paper. This paper is expected to lay a foundation for several further studies in this area of study.

Originality/value

The paper is original in terms of reviewing the literature published on the topic, “impact of digital marketing”, between years 2012 to 2020. As the world has been forced to go digital due to COVID-19 outbreak, it has become all the more significant to take an account of developments in the field of “digital marketing”.

Purchase intention
Online marketing
Digital marketing
Touchpoints

Dunakhe, K. and Panse, C. (2022), "Impact of digital marketing – a bibliometric review", International Journal of Innovation Science , Vol. 14 No. 3/4, pp. 506-518. https://doi.org/10.1108/IJIS-11-2020-0263

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Effectiveness of Digital Marketing in the Challenging Age: An Empirical Study

Marketers are faced with new challenges and opportunities within this digital age. Digital marketing is the utilization of electronic media by the marketers to promote the products or services into the market. The main objective of digital marketing is attracting customers and allowing them to interact with the brand through digital media. This article focuses on the importance of digital marketing for both marketers and consumers. We examine the effect of digital marketing on the firms' sales. Additionally the differences between traditional marketing and digital marketing in this paper are presented. This study has described various forms of digital marketing, effectiveness of it and the impact it has on firm's sales. The examined sample consists of one hundred fifty firms and fifty executives which have been randomly selected to prove the effectiveness of digital marketing. Collected data has been analyzed with the help of various statistical tools and techniques.

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HYPOTHESIS AND THEORY article

Evaluating the effectiveness of digital content marketing under mixed reality training platform on the online purchase intention.

1 School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, Hong Kong SAR, China
2 Division of Business and Hospitality Management, College of Professional and Continuing Education, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
3 Institute of Industrial Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou, China
4 Re-Industrialisation, Hong Kong Science and Technology Parks Cooperation, Hong Kong, Hong Kong SAR, China
5 Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, China

The purpose of this research is to investigate the effectiveness of Digital Content Marketing (DCM) on a Mixed Reality (MR) training platform environment with the consideration of online purchase intention (OPI) through social media. E-commerce today encounters several common issues that cause customers to have reservations to purchase online. With the absence of physical contact points, customers often perceive more risks when making purchase decisions. Furthermore, online retailers often find it hard to engage customers and develop long-term relationships. In this research, a Structural Equation Model (SEM) is proposed to examine the efficacy of DCM from both immediate and long-term OPI. The results examine whether adopting DCM on an MR training platform environment through social media brings positive results in OPI. Empirical research was carried out through online questionnaires collected in 2021 and 2022. A total of 374 questionnaires were qualified for data analysis in this study, conducted with IBM SPSS and AMOS. The results imply that DCM is critical to stimulating both immediate and long-term OPI. The immediate OPI is positively affected by increasing perceived value through MR in DCM. Regarding the long-term OPI, increased customer engagement with DCM under MR environment can cultivate brand trust and significantly affect the long-term OPI.

Introduction

Marketing strategy is crucial in a business plan. Apart from triggering short-term sales, it determines corporate image and acts as a bridge between customers and sellers so that both parties can communicate and build a relationship. Advertising draws people’s attention to a brand’s message on a product, service, information, or idea. Traditional advertising is often displayed on billboards, external walls of buildings, magazines, newspapers, leaflets, and TV commercials. Digital Content Marketing (DCM) is a way of marketing by creating and distributing content online to deliver valuable and engaging content to customers ( Rowley, 2008 ; Holliman, 2014 ). Since the last decade, digital advertising has become much more critical than before as it is more affordable and can reach larger audiences. 5G networking is being adopted worldwide increasingly so the access speed of digital content can be instantaneous with the aid of the Internet of Things ( Keung et al., 2018 , 2020 , 2021 , 2022a , b ; Lee et al., 2018b ; Liu et al., 2019 ; Li et al., 2021b , c , d ; Xia et al., 2021 ; Zheng et al., 2021 ; Fan et al., 2022 ; Zhang et al., 2022 ). DCM aims to build a close connection with customers through continuous conversation to convince their leads over time. A company can show its expertise and strengths through delivered content so that customers may be convinced that its offering is valuable and worth purchasing ( Hollebeek and Macky, 2019 ) and can realize more on their actual needs of a product ( Thomson and Laing, 2003 ; Grant et al., 2007 ; Dawes and Nenycz-Thiel, 2014 ). Consequently, DCM can reach more potential customers, boost online purchase intention (OPI), and retain customer loyalty ( Rowley, 2008 ; Holliman, 2014 ). Considering the online seller’s perspective, a reliable and valuable e-commerce environment can retain customers, reach higher customer retention and boost sales, as the traditional marketing industry is progressively burdensome and cost-ineffective ( Khan and Siddiqui, 2013 ).

Meanwhile, misleading advertising becomes an increasing concern for digital marketing, as customers cannot do a hands-on inspection to ensure product quality. It may create false belief in the expected product performance, which varies from the actual product information to deceit, hidden contracts, fees, inexistent benefits, and exaggeration ( Sharma and Chander, 2011 ; Kumar and Gunaseelan, 2016 ). Sharma and Chander (2011) found that even though people understood the existence of misleading advertising, they could not always distinguish the trustfulness and authenticity of the product. This phenomenon may eventually affect customers’ decision-making on whether to repurchase a product or not ( Jacoby et al., 1982 ). Compared with shopping in a physical store, consumers perceive more risk of misleading and deceptive practices when online shopping ( Ahmad et al., 2016 ; Consumer Council, 2016 ). In the long term, it will affect the customer benefit and satisfaction and lead to low OPI and consumer confidence in the future ( Hollebeek et al., 2016 ; Marbach et al., 2016 ; Azer and Alexander, 2020 ).

The second issue in digital marketing is that its payoff is not straightly associated with an advertisement’s spending. Paid advertisements are too intrusive, so that customers may feel annoyed and ignore them; therefore, the effectiveness of paid advertisements is likely to become minimal ( Barreto, 2013 ). Tudoran (2019) found that the majority of people had a negative perception of intrusive marketing, as it is always one-way and irrelevant. Some responses received by Truong and Simmons (2010) stated that the responders were rarely concerned with banner advertisements and felt annoyed when they were searching for other valuable information. Internet users can access thousands of information every day, distinguishing the authenticity of an advertisement’s information. Most content is provided free of charge or at a low cost since web 3.0 started. Therefore, selling a product to Internet users becomes more challenging through traditional marketing strategies. If the contents of an advertisement bores its audience or fails to give confidence to the audience, most of them will close the pop-up commercials as soon as possible and even install pop-up blockers. In contrast, Internet users today appreciate advertising messages that are customized, valuable, and under control ( Xu et al., 2020 ; Ali et al., 2021 ; Liu et al., 2021 ; Siddique et al., 2021 ; Jamil et al., 2022 ; Shiyong et al., 2022 ; Wang et al., 2022 ).

Mixed Reality (MR) is an integration of augmented and virtual reality. As the latest immersive technology among the three, MR is involved in functional mockups, military training, medical care, and many other fields. It combines digital and real worlds to unblock the linkage between human, computer, and environment interaction. Users can communicate with digital items placed in the physical world in real-time ( Liu et al., 2017 ; Li et al., 2021d ). The virtual objects will be able to respond to users when they are equipped with the necessary equipment. For example, the MR Headset is adopted to deliver a credible and three-dimensional mixed-reality experience. To enhance the overall customer experience for online shopping, MR technologies could be adopted to create technology-enhanced customer experiences ( Alcañiz et al., 2019 ; Flavián et al., 2019 ). Castillo and Bigne (2021) proposed a model that extends the technology acceptance model by introducing factors that affect the consumers’ acceptance of augmented reality (AR) self-service technologies, providing new understandings for retailers on the adoption of AR at the point of sale. Wedel et al. (2020) proposed a conceptual framework for VR/AR research in consumer marketing that intensifies around customer experiences provided by VR/AR implementation along the consumer journey and the effectiveness of such VR/AR implementation toward consumer marketing. Alcañiz et al. (2019) further extended the VR in marketing and proposed a research agenda for VR in marketing. However, the current literature has not considered the MR-based platform for the DCM, primarily through social media. The current hypothesis models have not been tested under an MR-based platform for DCM. When compared to traditional DCM-based research with questionnaires, we further extend the scope of the field for exploring the DCM strategies that will affect the immediate and long-term OPI under the MR training platform.

Besides achieving the immediate purchase intention (in terms of product/service) by delivering helpful content to the audience, DCM can cultivate trust and customer loyalty by customer engagement. An effective way to retain customer loyalty is to build the relationship through many conversations and deliver valuable and accurate information to the audience. This way, companies can affect the mindset of the audiences over a long duration of time. Compared to traditional marketing, digital marketing technology is more affordable and easy to use. With DCM, even the SMEs can achieve an effective marketing campaign and access to their targeted customer with great content. Li et al. (2002) pointed out that reducing intrusiveness has a significant positive impact on advertising effectiveness and customer engagement. Therefore, e-commerce can capture their customer’s favor, a massive amount of data during the conversation and provide a customized product. During the first one and half years, a paid search campaign is effective. However, leads from paid search campaigns are constant, while content marketing has exponential growth. Content marketing can produce three times more than a paid search campaign in the last month of the third year. Thus, the SMEs should not give up on developing DCM, and they cannot initially observe a decisive result. The long-tail effect of DCM under an MR-based training platform will surprise everyone, as it requires time to have an exponential effect. Social media networks are the most popular way people are willing to grasp information. Users are willing to search, follow, like, and comment on a post they are interested in; hence, user-generated content can be developed. Therefore, DCM seems able to present selling messages to their targeted customer effectively, avoiding the issues of traditional paid advertisements, and at the same time is price valued. With extraordinary performance, DCM can achieve a company’s marketing objectives at a low cost. SMEs should involve the DCM in their marketing activities. The aims of this paper include:

• To evaluate the effectiveness of DCM through social media under the MR-based platform to immediate and long-term OPI.

• To evaluate the mediating effect of perceived value, customer engagement, and brand trust.

• To discuss the managerial implications of using DCM in an MR-based environment.

Adopting DCM with social media under an MR-based environment provides valuable and engaging content to raise immediate OPI and enables customer engagement to build trust and long-term OPI. This study develops a hypothesis model of DCM under an MR-based platform to conduct the empirical study for evaluating the effectiveness of DCM on OPI through social media by using Structural Equation Modeling (SEM). The confirmatory factor analysis (CFA) is adopted to test the developed conceptual model. The perceptions of Hong Kong citizens, active social media users, on DCM are captured from the questionnaires. The analysis is concentrated on DCM on Instagram, a popular online social media platform in Hong Kong. Section “Literature Review and Hypothesis” presents the theoretical background and a hypothesis model of the research. Section “Methodology” presents the research methodology. CFA is performed after the hypothesis model has been developed. The perception of Hong Kong citizens, who are active social media users, on DCM is captured by questionnaire. The results and discussion of the effectiveness of DCM are presented in Section “Data Analysis and Results,” respectively. The survey results provide theoretical and managerial implications in Section “Discussion.” Conclusion, limitations, and future research are discussed in the below section.

Literature Review and Hypothesis

Digital marketing is the component of marketing that utilizes the Internet and online-based digital technologies to promote products and services, such as desktop computers and mobile phones. Digital marketing campaigns have become prevalent as the number of digital platforms and e-commerce platforms increase, and as people discover that online shopping is more convenient and time-efficient. It employs combinations of search engine optimization (SEO), search engine marketing, content marketing, influencer marketing, data-driven marketing, e-commerce marketing, social media marketing (SMM), direct email marketing, and advertising ( Wang and McCarthy, 2020 ; Bowden and Mirzaei, 2021 ; Mathew and Soliman, 2021 ; Yaghtin et al., 2021 ).

Social Media, built on Web 2.0 technology, allows users to share, discuss, and exchange content. It is open, accessible, and content-based so users can access the content on either technology, time, geographical, ability, or identity. Users can share content instantaneously and access the audience anytime and anywhere. There is an increasing number of online sellers advertising and selling their products on social media. The above strategy is called SMM. There is nearly no additional cost to e-commerce, and sellers will face a minimal entry barrier. Sellers can also understand their customers through direct conversation and interaction, such as discovering and sharing product information and delivering valuable opinions. Therefore, the sellers can target the customers who have an enormous willingness to buy and suggest appropriate products to them, and finally, customers can make purchase decisions.

Customer engagement can be cultivated by participating in commercial activities, marketing campaigns, and interaction, including viewing, liking, commenting, and sharing the content ( Amblee and Bui, 2011 ). Positive electronic word-of-mouth (eWOM) can be created when the posts have numerous likes with encouraging comments. Potential customers will have a more favorable attitude and confidence toward the product. It can raise trust in online sellers and boost purchase intention ( Hajli, 2014 ).

Instagram, a popular social media platform in Hong Kong, is mainly a visual-based photo and video-sharing social networking platform. By sharing products’ information, online sellers can attract potential consumers and drive consumer engagement through the photography-based function of Instagram ( Bergström and Bäckman, 2013 ). A simple and most crucial rule to gain advantage in the Instagram algorithm is to generate quality content and deliver it to users. People have found that when an account with more than 5,000 followers creates 5–6 posts every day, Instagram will deliver its posts to other users who have not followed the account. Moreover, a brand can gain an advantage with UGC, which is the content developed or created by general users. For example, “like,” share, and comment can increase attention and browse traffic, tagged posts can be found in the brand’s profile, and users can generate more quality content. With UGC, which manifestly aligns with increasing trends, Instagram empowers consumers to determine media content, rather than paid experts, to be primarily distributed on the Internet ( Holliman, 2014 ; Hollebeek and Macky, 2019 ).

The content farm employs freelancers, including bloggers and part-time writers, to produce content on trending topics, resulting in a high search and browse traffic to the websites ( Bakker, 2012 ). Being online, however, means the presence of duplicators, as free content can be assessed, copied, and republished by others effortlessly. This situation is now occurring on Instagram as well. Some users possess several accounts related to different hot areas to raise their income by attracting various audiences. However, they cannot manage every account well by posting 5–6 quality photos every day. So, they purchase photos with a caption from a part-time photo designer. Usually, the pictures in this transaction are low quality, useless, or even copied from other accounts. Low-quality content cannot build purchase intention, even if it hits the trend and favor of the audience. Nevertheless, content marketing can deliver valuable information ( Bakker, 2012 ).

One of the traditional digital marketing campaigns is paid advertisement, which includes pop-up and embedded ads in a website and search engine, as well as intersection commercials before and during videos. On Instagram, paid advertisements will appear as intrusive advertisements on Instagram stories and on the home page. As mentioned in the research of Li et al. (2002) , intrusiveness occurs when commercials disturb the ongoing entertaining activities of the user. Forced and intrusive businesses will injure consumer perceived value, and the consumer may even respond negatively. Diversely, the perceived intrusiveness level of an advertisement will be decreased when the user finds the content is valuable and consistent with the websites or editorials ( Ying et al., 2009 ). Paid advertisements finally decrease purchase intention ( Goodrich et al., 2015 ).

This section will be described from the digital marketing to SMM and DCM, as DCM is in the subset of digital marketing, and adopting DCM on Instagram is one of the SMM methods. The attributes and advantages of DCM are also captured from the literature. Some popular content marketing frameworks are included to illustrate how to develop an effective DCM and why DCM can have those benefits.

Digital Content Marketing

Digital Content Marketing refers to the act of conducting all marketing-related activities through the Internet, including advertising, purchasing process, customer service, and delivery service ( Koiso-Kanttila, 2004 ; Ahmad et al., 2016 ; Naidoo and Hollebeek, 2016 ). Holliman (2014) proposed that inbound marketing is more efficient and effective in costing, spreading, extending the customer boundary, and co-creating value. DCM, which delivers valuable and interactive content to potential customers, is a technique to support inbound marketing.

Instead of putting significant effort and resources into outreaching leads, DCM focuses on creating excellent content that can provide a long-tail effect ( Ahmad et al., 2016 ; Hollebeek and Macky, 2019 ). People describe DCM as an art of communicating with the customer, but without directly selling a product ( Koiso-Kanttila, 2004 ; Ahmad et al., 2016 ; Hollebeek and Macky, 2019 ). By creating great content and e-conversation, a company can build a relationship with its existing customers, acquire new customers, retain customer loyalty, and build a reliable brand name. Moreover, the company can cultivate sales activity through customer engagement, loyalty, and relationship in the long run ( Holliman, 2014 ). Killing content has the features of all branded content, random content, and the content the customer wants to know. Therefore, it is interestingly relevant to the customer engaging and syndicating. DCM is able to share valuable and free content related to the brand or the field. Moreover, DCM can attract and convert audiences to customers and repeat consumers ( Le, 2013 ). People come to read, see, learn, and experience; therefore, the company usually tells its unique and meaningful stories to grasp and retain customers’ attention, which also comes within DCM’s scope and as a particular content form of company images ( Holliman, 2014 ). Pulizzi and Barrett (2009) explained that companies are the experts in their business fields, and they can capture the most reliable and latest content resources. Therefore, a lead interested in the content may be willing to search or investigate certain content. Expert content can draw the attention of potential customers, who can understand the value of the content and product provided ( Stone and Woodcock, 2014 ).

By creating great content and customer engagement, a company can build brand awareness, acquire new customers, retain customer loyalty, and finally achieve repeat sales ( Holliman, 2014 ; Hollebeek and Macky, 2019 ). The customers have a more favorable attitude and confidence toward the product and the brand, as they are the hot trend between peers. It can raise the trust of online sellers, and the OPI can be boosted ( Hajli, 2014 ). The brand, adopting DCM strategies, is discovered by customers when they demand the relevant content or product, thereby revealing a more significant consumer-engaged attitude. It is different from intrusive advertisements, which interrupts current activity while delivering a sales message ( Holliman, 2014 ; Hollebeek and Macky, 2019 ). Through direct conversation and continuous interaction, sellers can engage their customers who are willing to buy. Sellers can also suggest appropriate products to customers to make purchase decisions collaboratively ( Chen et al., 2011 ). Moreover, quality content helps maintain customer loyalty with two-way conversation ( Koiso-Kanttila, 2004 ; Ahmad et al., 2016 ; Naidoo and Hollebeek, 2016 ).

As shown in Figure 1 , adopting DCM on social media is a part of Social Network Marketing (SNM), and companies not only grasp the opportunity, the emerging trends of SNM, to cultivate long-term OPI, but also boost immediate OPI by delivering better content ( Madsen and Slåtten, 2015 ; Ahmad et al., 2016 ; Wertalik, 2017 ; Mason et al., 2021b ; Abbas et al., 2022 ). The current literature has been found to describe the attributes and advantages of DCM, and numerous empirical studies have been conducted to find the influences of SNM on OPI ( Yang et al., 2016 ; Bolat and O’Sullivan, 2017 ). Research on the impact of Social Media Content Marketing (SMCM) on brand health indicated that SMCM plays a vital role in brand health since it acts as the inter-connection for a potential customer to grasp the brand’s information ( Canhoto et al., 2015 ; Ahmad et al., 2016 ; Kareem et al., 2016 ; Mason et al., 2021a ). However, no study has investigated the effectiveness of DCM on both immediate and long-term OPI simultaneously. Conducting DCM on social media is an SNM strategy, but not every marketing in social media can be concluded as DCM. Thus, the influences of DCM via social media may not be fully equal to SNM, which may be affected by more attributes ( Dessart, 2017 ; Valos et al., 2017 ; Tafesse and Wien, 2018 ). For example, the familiarity and the perceived value of a product. This study is an empirical investigation to evaluate the effectiveness of DCM on both immediate and long-term OPI. A conceptual model based on the extant literature is developed to evaluate the DCM’s efficacy with SEM.

Figure 1 . A conceptual framework of digital marketing.

Immediate Purchase Intension

In the research investigated by Chinomona et al. (2013) , the significant positive impact of perceived product quality on perceived product value was investigated. The customer’s perceived value of the product can affect OPI. The researchers proved that the interviewees perceived the product as valuable when they recognized that the product had good quality and excellent attributes. The perceived product quality is about cognitive familiarity based on impressions, advertisements, and comments from others on the product. Therefore, OPI can reduce the perceived risk and increase transaction intentions ( Chinomona et al., 2013 ). Hollebeek and Macky (2019) believed that content marketing is customer-oriented, as it aims to offer suitable solutions to customers with persuasive argumentation and helps customers to recognize the outstanding advantage of the product before they buy it, but not force them to buy ( Koiso-Kanttila, 2004 ; Rowley, 2008 ; Holliman, 2014 ). The valuable content can comfort customers by telling them how the product/service meets their demands and what they can gain from it.

H1 : DCM under MR environment on social media is positively related to the perceived value on the product/service.

Familiarizing with the perceived value can build trust in the product, and a higher level of trust can unlock customer’ OPI. It has been proven that the product’s perceived value can reinforce the immediate OPI through trust, reducing consumer perceived risk ( Chinomona et al., 2013 ). Therefore, the perceived value of OPI can be immediately obtained, when customers receive the engaging content that entices people to take some kind of action. In addition, branded content, that combining both advertising and entertainment into one marketing communication content, could link to organization brand.

H2 : Perceived value on the product/service is positively related to the immediate OPI.

Long-Term Purchase Intention

Social Network Marketing adopts social media as a platform for brands to interact with their customers to develop further purchaser relationships, which helps to maintain loyalty and repeat purchase ( Meyer-Waarden and Benavent, 2006 ; Wu et al., 2008 ; Papagiannidis et al., 2013 ; Malthouse et al., 2016 ; Nabec et al., 2016 ). Adopting DCM on social media, the seller can deliver quality content in different media types (text/audio/photo/video) on the post, story, and profile ( Ahmad et al., 2016 ). The bargaining power in the market has shifted from sellers to buyers through the capability of the Internet, which significantly leverages the consumer’s voice ( Mohamad et al., 2018 ). Companies can no longer make a unilateral decision regarding the price, quality, and after-sales service, and are being pushed to participate in conversations with customers to understand their needs and cultivate a close relationship through customer engagement. Therefore, excellent customer engagement can be cultivated by adopting DCM on social media, as SNM has been proven to have a significant influence on customer engagement ( Areeba et al., 2017 ; Mohamad et al., 2018 ; Rozina et al., 2019 ; Hartiwi et al., 2020 ).

Brand trust indicates that consumers feel comfortable and are willing to make OPI, even in a situation of uncertainty ( Laroche et al., 2012 ). Enduring involvement with the product has been demonstrated to positively influence brand trust ( Erik, 2019 ). The enduring content allows customers to get familiarized with the product and the brand communities. Customers, therefore, cultivate more brand trust as they have perceived less risk and reduced uncertainty ( Laroche et al., 2012 ). Especially for new leads, initial trust is formed through the brand impression by the available information of the product and brand communities, which is the critical element that DCM will deliver ( Stouthuysen et al., 2018 ).

H3 : DCM under MR environment on social media is positively related to customer engagement.

H4 : DCM under MR environment on social media is positively affecting brand trust.

Enduring conversation and excellent customer engagement can increase familiarity between the seller and customer. As mentioned, the seller, or the content provider, is the expert in the specific area related to the content. Excellent content can build trust between the seller and customer, as the customer will perceive it worthwhile and reliable if they can absorb valuable knowledge during online shopping. Ahmad et al. (2016) found that sites will lose their customer’s interest if they only deliver simple responses or quick answers to their customer’s inquiries. Differing from that, social media sites, which provide unique content with plentiful customer engagement, can gain more customer attention and trust.

Either cognitive, emotional, or behavioral customer engagement acts as the primary effect (first-tier) of DCM, and intra-interaction, respectively, fosters the brand-related sense-marketing, citizenship behavior, and identification (second-tier) through different customer engagement. The third tier consequence of DCM is trust, either on credibility or benevolence and brand attitude. DCM will finally affect consumer-based brand equity and firm-based brand equity ( Hollebeek and Macky, 2019 ).

Functional motive can also integrate with the hedonic motive (for example, entertaining and interesting content) to drive behavioral engagement, which means customers are willing to spend time, effort, and energy interacting with the brand. Moreover, functional motive integrates with authenticity motive (integrity and credibility content) and can cultivate cognitive engagement, and authenticity motive combines with hedonic involvement to achieve emotional engagement. Besides spending effort on interaction with the brand, people are triggered into brand identification and sense of belonging and further achieve trust on either credibility or benevolence ( Hollebeek and Macky, 2019 ).

H5 : Customer engagement is positively related to brand trust.

Besides attracting more leads, detailed and trusted content can obtain higher customer retention ( Pulizzi and Barrett, 2009 ), which means the seller will be the priority choice. Moreover, social media have been proven to play a significant role in OPI, as trust can be cultivated and accumulated through quality product/service, customer engagement, and trust ( Mohamad et al., 2018 ). Areeba et al. (2017) has pointed out that customer engagement becomes a primary concern for the online retailer as the accumulated emotional ties between customers and companies help to convince their consumers to make the right buying decision ( Rose and Samouel, 2009 ; Hollebeek, 2011 ; Leckie et al., 2016 ; Dessart, 2017 ).

Hollebeek and Macky (2019) illustrated three incentives that can drive customers to make purchase decisions through interaction with DCM communications. For the functional motive, customers are willing to seek valuable information. Regarding the hedonic motive, customers found that they can entertain themselves, relax, and absorb the knowledge if they are enjoying the content. Regarding the authenticity motive, the ultimate desires of consumers are achieved through the brand-related connection, integrity, credibility, and customer relationship from DCM ( Hollebeek and Macky, 2019 ). Rather than persuading potential customers to purchase the product directly, DCM is designed to develop and reinforce consumer engagement, awareness, trust, and the relationship between both parties ( Ahmad et al., 2016 ; Hollebeek and Macky, 2019 ). Therefore, DCM can increase long-term sales and lead to repeat sales by accumulating relationships and trust. Figure 2 shows the conceptual model of DCM.

Figure 2 . Conceptual model: digital content marketing (DCM).

H6 : Customer engagement is positively related to long-term OPI.

H7 : Brand trust is positively related to long-term OPI.

Methodology

A survey was conducted to obtain the opinion and perception of the effect of DCM under an MR-based training platform environment among Hong Kong residents. The effect of DCM on OPI was analyzed by SEM. Around 1000 questionnaires were distributed to the participants through Google Form from November 2021 to January 2022. Only the digital surveying method has been adopted in the research due to the outbreak of COVID-2019.

Mobile MR-apps enhance retail visits, including online shopping adoptability, by providing multiple product demonstration capabilities ( Alcañiz et al., 2019 ; Wedel et al., 2020 ; Castillo and Bigne, 2021 ). The questionnaire presented the DCM simulating the online purchase intention through Instagram’s social media. The DCM is designed for multiple scenarios, including online travel agencies, fashion, beauty, and electronic products, shown in an MR on mobile devices. The research had two sections. In the first part, the participants used the mobile device to conduct three template scenarios, including online travel agencies, fashion, beauty, and electronic products with Instagram. Those contents are designed under the MR-based mobile environment. All the participants also conducted MR-based digital content through social media as examples for simulating the effects of the real-world DCM through social media. The second part consisted of an online questionnaire that assessed six constructs based on literature.

There are five reasons for choosing Instagram rather than another social media and digital platform. First, the user base of Instagram has been observed a significant growth trend, which is more evident than others. Second, the target groups of online retail in Hong Kong are mainly teenagers, young people, and middle-aged people, which entirely match the majority of user groups of Instagram. Third, as social media marketing simultaneously, DCM on Instagram can seize its benefits, for instance, customer engagement, user-generated content, and electronic word-of-mouth. Moreover, Instagram has many functions for corporations to communicate with audiences, and the graphics-based peculiarities allow them to deliver engaging and valuable content to their fans efficiently. Last but not least, there are many e-commerce and boutiques on Instagram. Sellers can market their customer and audiences simultaneously. Therefore, the search is to determine the effectiveness of DCM on Instagram. The experimental subjects are active users of social media in Hong Kong.

The questionnaire was designed based on Confirmatory Factor Analysis and a multi-item measurement scale. A seven-point Likert-type scale, where one indicates “strongly disagree” and seven interprets “strongly agree,” was adopted to evaluate the perception on different dimensions related to DCM ( Park et al., 2004 ; Leong et al., 2015 ). The questionnaire items are summarized and modified based on the literature whereas the hypothesis settings are. Hence, the questionnaire included six constructs, measured on various scales adapted from previous studies.

The online survey was designed for Hong Kong citizens who use social media frequently. The participants were voluntary anonymous, and the results were confidential. The survey was first developed in English based on the literature and previous studies and translated into Chinese by a bilingual researcher. Three questions have been asked to indicate the Chinese and English proficiency level and any language-related difficulties.

Two screening questions developed that the potential participants were regular social media users and online shopping customers. Only participants who were regular social media users and online shopping customers were considered. Five incomplete responses answered by people who do not use any social media were found, nor non-online shopping users, and 25 invalid responses failed to answer Neutral in either one or both verification questions. There were 374 questionnaires in total qualified for the data analysis in this study. The model fit indices are affected by the sample size significantly. Therefore, Boomsma and Hoogland (2001) recommended that CFA have more than 400 samples and at least a sample size of N > 200 ( Lee et al., 2018a ).

Data Analysis and Results

The SEM was analyzed with IBM SPSS Statistics 25 and IBM SPSS AMOS 25, ensuring reliability, confidentiality, and significance level. As a method for the covariance-based SEM, the AMOS provides more flexibility for data requirements. The benefit of AMOS-SEM is that it offers a parameter estimation model assessment and is fit for use in reflective indicators and parameter estimation modeling. Hence, AMOS-SEM performs well even when the sample size is large compared to, e.g., PLS-SEM.

Respondents’ Characteristics

Respondents’ characteristics are reported in Table 1 . The gender distribution of the qualified surveys is 178 Male (47.59%) and 196 (52.40%) Female. Nearly half of the participants fell into the 19–24 age group, followed by the 25–34 age group (35.56%) and the 35–44 age group (8.56%). Around 357 out of 374 participants had or were pursuing an Associate Degree/Higher Diploma or Bachelor’s Degree or above. The respondents’ characteristics also include the times of online shopping in the past 12 months, using social media habits, adopting different social media platforms, and reasons for online shopping. More than 75% of the respondents use social media for more than 2 h per day. Facebook, Instagram, and YouTube are the top priority social media usage in Hong Kong, of which nearly 90% of the respondents have been using. More than 75% of respondents have been shopping online during the past 12 months. The significant reasons for shopping online are convenience and a wide selection of choices. Therefore, the questionnaire results can show the opinions of significant online consumers in Hong Kong.

Table 1 . Respondents’ characteristics.

Measurement Model

Given that the results were similar, only the sample results as a whole are presented. Hair (2010) suggested convergent validity and the measurement reliability of data should be assessed by Standardized factor loading K of each measurement items, Cronbach’s alpha α , Composite reliability (CR), and Average Variance Extracted (AVE). Convergent validity measures of constructs that theoretically are related to each other are, in fact, observed to be related to each other. The value criteria, which indicates that the data are reliable and valid, are shown as follows: K is excellent when greater than 0.7, good between 0.5–0.7; α should be greater than 0.7 ( Freeze and Raschke, 2007 ; Corrêa et al., 2020 ). Bagozzi and Yi (2011) suggested C.R. should be higher than the acceptable levels of 0.700; AVE should be greater than 0.500, or 0.400 in the cases of exploratory research ( Corrêa et al., 2020 ). The majority of factors show sufficient internal consistency. Most of the measurement items’ K were above 0.7, and at least over 0.6. The α of the constructs were above 0.7, and ranged between 0.761 and 0.863. The C.R. varied between 0.717 and 0.833 and AVE loaded between 0.499 and 0.621. Therefore, the majority of measurements had significant internal consistency, and a few measures had relatively low reliability but also supported the convergent validity. Table 2 summarizes the confirmatory factor analysis, which includes K , α , C.R., and AVE on the constructs and measurement items. Table 2 also lists the questionnaire items which are based on a certain of literatures. Discriminant validity was tested using item cross-loadings, which indicates that a construct should share more variance with its indicators than with other constructs ( Castillo and Bigne, 2021 ) shown in Table 3 . The value of the correlations was significant value of p < 0.01, except for brand trust and customer engagement.

Table 2 . The measurement model (convergent validity).

Table 3 . Discriminant validity (correlations between constructs).

These two formulas calculate the C.R. and AVE:

e = residual/error

To examine the common method bias, Podsakoff et al. (2003) proposed and summarized for the confirmatory factor analysis was estimated, restricting all the indicators in the model to load on a single factor. Table 4 shows the model absolute fit measures. The Goodness-of-fit index (GFI) is adequate when larger than 0.9, and a perfect fit with the value near 1.0 ( Bentler, 1990 ). GFI scores in the range of 0.8–0.9 represent a good fit as they are quite affected by the sample size ( Doll et al., 1994 ). Adjusted Goodness-of-fit index (AGFI) is further analysis from GFI considering the degree of freedom which is adequate when larger than 0.9 ( Bentler, 1982 ). Standardized root means square residual (SRMR) scores less than 0.05 represent a reasonable ( Jöreskog and Sörbom, 1989 ). Root Mean Square Error of Approximation (RMSEA) is recommended to be equal to/below 0.08 ( Hair, 2010 ). Table 5 shows the model comparison fit measures. Normed fit index (NFI) values range between 0 and 1, and the higher value indicates a better fit ( Ullman, 2001 ). NFI should be greater than 0.95, which is reasonable. Bentler (1990) and Schumacker and Lomax (2004) proposed that the value of NFI over 0.8 is acceptable, as it will be under loaded when analyzing with the small sample size. The non-normed fit index (NNFI) or The Tucker-Lewis Index (TLI) should be greater than 0.9 ( Bentler and Bonett, 1980 ; Hoyle, 1995 ). Relative fix index (RFI) is the extension from NFI and should be greater than 0.9 ( Bentler and Bonett, 1980 ). The comparative fit index (CFI) is similar to NFI but considers penalties. The value is typically greater than 0.9 ( Bentler and Bonett, 1980 ). Table 6 shows the model parsimonious fit measures. Hair (2010) mentioned that X 2 distribution should be less than 3 but greater than 1 would be the best scenario. Parsimonious goodness-fit-index (PGFI) and Parsimonious normed fit index (PNFI) should be greater than 0.5 ( Bentler and Bonett, 1980 ). The results showed that the computed fit indices provided strong support for the hypothesis (GFI = 0.901; AGFI = 0.912; SRMR = 0.042; RMSEA = 0.031; NFI = 0.907; NNFI = 0.905; RFI = 0.911; CFI = 0.921; X 2 /df = 1.777; PGFI = 0.676; and PNFI = 0.741.).

Table 4 . Model absolute fit measures.

Table 5 . Model comparison fit measures.

Table 6 . Model parsimonious fit measures.

The proposed model was evaluated, and the estimated path coefficient and p -value are presented in Figure 3 . Table 7 summarizes the hypothesis results of each measure. According to the result, Hypotheses H1, H2, H3, H5, and H7 are accepted, while H4 and H6 are rejected in the proposed model. DCM in social media is strictly related to both the perceived value of the product/service H1: β = 0.97, p < 0.01 and customer engagement H3: β = 0.89, p < 0.01. The perceived value of the product/service stipulated a significant positive relationship with immediate OPI (H2: β = 0.87, p < 0.01). Customer engagement indicated a strictly positive relationship with brand trust H5: β = 0.59, p < 0.01. And brand trust significantly affects long-term OPI H7: β = 0.66, p < 0.01. Although the result does not point to a direct positive relationship between DCM and brand trust, exceptional customer engagement can reinforce brand trust. The result illustrates that customer engagement has no significant direct effect on long-term OPI, while customer engagement still affects OPI through increasing brand trust. Table 8 shows the mediating effects which standardized indirect effects of mediators. As a result, perceived value partially mediated the relationship between DCM and immediate OPI. Brand trust has partially mediated the relationship between customer engagement and the long-term OPI.

Figure 3 . Structural Equation Model (SEM) result. ***< 0.01.

Table 7 . Summary of the hypothesis testing results.

Table 8 . The mediation impact.

The findings confirm the assumption that good use of MR-based DCM could bring positive effect on both long-term and immediate OPI through mediating factors.

Immediate OPI

Digital Content Marketing delivers practical, engaging, and correct content to its leads. Therefore, potential customers who have been receiving enough details about the product/service are willing to search for more details about the product. They can capture the characteristics and quality of the product, and it can obtain a high perceived value from the DCM under the MR environment marketing description (H1). People claim that they can realize whether the product/service can fulfill their demand, and whether the product/service is worth the price. One of the most critical online shopping intentions is the product’s quality and features. Consequently, more perceived value by customers will bring more behavioral OPI (H2) as they have an excellent perception of the product and perceive less risk of online shopping. Therefore, the immediate OPI can be cultivated, consistent with the SEM results from Chinomona et al. (2013) .

Long-Term OPI

Besides achieving immediate OPI (in terms of product/service) by delivering helpful content to the leads, DCM under MR environment can cultivate trust and customer loyalty by customer engagement, finally affecting long-term OPI positively. DCM under MR environment in social media can seize the benefits, for instance, customer engagement (H3), user-generated content, and electronic word-of-mouth. The respondents acknowledge that DCM in social media is interactive and can promote positive customer experiences. In this study, a strict relationship between DCM in social media with customer engagement was found, which is in line with the previous studies conducted by Areeba et al. (2017) , Mohamad et al. (2018) , Rozina et al. (2019) , and Hartiwi et al. (2020) .

Digital Content Marketing on social media was found to have no strictly positive effect on brand trust, while it affects customer engagement, which can finally boost brand trust (H5). Companies engage their leads with continuous interaction, and this presence helps them in times of trouble. In addition, leads will be provided with customized service and offered suitable and better product choices, as firms are more familiar with their leads and are able to recognize their desires. Thus, companies are recommended to develop brand trust through excellent customer experience and other users’ positive actions (likes or shares), as customers are perceived less risk and uncertainty ( Laroche et al., 2012 ; Erik, 2019 ). The significant result between customer engagement and brand trust is in line with the conceptual framework developed by Hollebeek and Macky (2019) and the observation of Ahmad et al. (2016) . As predicted, brand trust has a positive relationship with long-term OPI (H7), which is aligned with the previous study ( Hwang and Zhang, 2018 ; Mohamad et al., 2018 ; Hollebeek and Macky, 2019 ). The participants claimed that they would have more OPI if the seller was reliable or gained positive comments from other users.

The absence of a direct positive relationship between DCM under MR environment and brand trust can be explained by the conceptual model of Hollebeek and Macky (2019) . Under the model, customer engagement is the first-tier consequence of DCM, while brand trust is the third-tier consequence. A progressive relationship exists between DCM, customer engagement, and brand trust. Therefore, DCM can enforce brand trust, mediated by customer engagement. Although the result does not indicate the significant relationship between customer engagement and long-term OPI, which has been proved in various relevant studies, customer engagement accumulated brand trust and positively affected long-term OPI. The potential customer-generated per $1,000 spent by content marketing or paid search campaign was compared in the research of Le (2013) . A paid search campaign can grasp the advantages as the company paid for the leads in the first one and a half years. However, leads from paid search campaigns are constant, but content marketing will have more rapid growth in the future because of the accumulated trust and loyalty. Content marketing can produce three times more than the paid search campaign in the last month of the third year ( Le, 2013 ). The long-tail effect of DCM under an MR-based training platform will surprise everyone, as it requires time to acquire trust between both parties and occurs rampant growth. Therefore, there is no significant direct effect between customer engagement and long-term OPI, but a strict positive relationship between brand trust and long-term OPI exists. Thus, SMEs should not give up developing DCM, even if they cannot observe powerful results initially.

Theoretical and Managerial Implications

It is no doubt that paid advertisements can reach many digital users who access the Internet through search engines, websites, social media advertisements, and video commercials on YouTube. However, they are intrusive and hard-selling and may result in annoying and negative impressions from leads, as they disturb the endless entertainment of the leads. Therefore, the viewers usually ignore the paid advertisements and close the paid advertisement page; some people even pay for the external blocker or subscribe to premium membership to avoid them ( Truong and Simmons, 2010 ). Thus, paid advertisements are an expensive investment and lack effectiveness in recent years. With MR-enabled DCM, even SMEs can achieve extraordinary sales performance from their marketing campaigns and access their targeted customers with great content through two-way communication.

Social media networks are the most popular way people can grasp information. Launching the DCM in social media can present selling messages to their targeted customers effectively and avoid the issues of the traditional paid advertisements, for instance, intrusive marketing and misleading ads. With the feature of MR, customers may get to know more about the characteristics of a product or a service. The process itself also stimulates customers’ engagement with a brand. Li et al. (2002) pointed out that reducing intrusiveness has a significant positive impact on advertising effectiveness and customer engagement. E-commerce can capture customers’ preferences and massive data during the conversation and provide customized products ( Pulizzi and Barrett, 2009 ). The longer the investment period of DCM, the more the substantial long-tail effect can be acquired ( Le, 2013 ). Therefore, the Return on Investment goes up if the companies apply successful DCM, as they no longer need to spend on useless advertisements and related rent ( Truong and Simmons, 2010 ).

Digitalization is a worldwide trend. Digital users in Hong Kong spend nearly 2.5 h on their mobile devices, an hour longer than they do on the TV. Last but not least, each person in Hong Kong had 2.3 devices and was enjoying 129.5 MB/s connection speed on average in 2017. 5G network technology has been launched, the access speed of digital content can be shortened to instant, and people can access various content more readily ( Li et al., 2020 , 2021a ). With better bandwidth and lower latency, the MR scenarios that customers can experience would be attractive in further detail. All these figures showed that digital marketing in Hong Kong has tremendous potential to grow.

According to the marketing expenditure in Hong Kong, traditional advertising has been replaced by digital marketing since 2012 ( Wong and Wei, 2018 ). The spending on the online advertisement has increased from 9% in 2012 to 32% in 2019. The total digital advertising value is now around 5.5 billion HKD ( STATISTA, 2020 ). In the same period, TV advertising, which used to have the most market share, fell to 14% in 2019. The expected budget on digital marketing would reach 34% and be more than double TV ad spending by 2021. Digital marketing involves many varieties. Following the Hong Kong Digital Marketing Statistics, leads discover an unfamiliar brand through search engines the most (35%), followed by eWOM (29%), social media ads (24%), and recommendations on social media (21%; Kemp, 2019 ). To provide the best experience over the Internet, companies should put more resources into developing digital content marketing under the MR environment. This provides valuable and engaging content to raise immediate purchase intention and build trust and long-term purchase intention. Regarding the data analysis of SEM, the effectiveness of DCM on purchase intention in terms of both immediate effect and long-tail effect were proved either through familiarity with the product/service or customer engagement. The recommendations will focus on the three most popular industries in e-commerce: Fashion and Beauty, Airline and Travel, and Electronic Products. According to the study on online retail, more than 90% of respondents, who are frequent online shoppers, sometimes or always purchase clothes online, and over 50 and 35% of respondents buy books/toys and air ticket/travel online, respectively, from the Hong Kong Consumer Council Report.

Other frequently online purchase sectors are clothing and beauty, as the products are quickly replaced by trending items in the fast fashion industry. People are confident enough to purchase branded clothing even if they cannot physically inspect or try the items. They believe branded goods have passed quality assurance and they are comforted by the fact that they can exchange unwanted items. However, customers have low confidence toward unknown brands, which usually are SMEs. Regarding the reasons for never and rarely online shopping, around 50% of respondents claimed a lack of confidence in online shopping because they have had bad experiences before and could not physically inspect the product. A questionnaire has done by the consumer council shows that 22% of interviewees are afraid of online shopping and no confidence in the product quality. Lack of confidence will cause the purchase intention of the potential customer to collapse. However, online paid ads can reach many audiences but cannot cultivate their trust in the brand. Moreover, the companies should continuously invest a relatively large amount in promotion, as they have to pay for the marketing rent. Therefore, DCM on Instagram is a better approach for SMEs to achieve promotion goals with an affordable budget.

More and more people look for flight tickets, hotel booking, and travel tours through Online Travel Agent (OTA) rather than visit the physical travel agency. OTAs provide services 24/7 from anywhere, and users can compare the prices with several OTAs simultaneously rather than visit different physical stores. Expedia, Trip.com , Trivago, and Skyscanner are examples of famous OTAs. It is no longer attractive to promote tours only through paid advertisements on the search engine. Intrepid Travel is a travel agency, which mainly offers small groups, big adventures, and responsible travel. They have currently adopted DCM, showcasing aspirational travel images posted on Instagram and Facebook taken by real travelers, Intrepid Travel, is interspersing with its content. It also allows real travelers to share their experiences, which helps the company connect more with its core audience. Last but not least, Intrepid Travel shows its enthusiasm for travel by replying to comments, which can draw the connections with the viewers as both of them have share the same passion on the adventurous travel.

Moreover, the DCM approach offers solutions for companies to reach the target audience precisely, which means the companies can reach their ideal customers through social media. Although approaching a smaller group of leads, DCM allows sellers to focus on targeted customers, easily perceive the product’s value and have greater OPI. With the DCM assisted with MR, sellers have more valuable data collected by sufficient customer engagement to improve marketing insights. For example, the number of “likes” indicates how many people are interested in a product, and their comments may involve inquiries and attitudes to the product. Thus, the sellers can strengthen their marketing tactics according to online data. In addition, future fabrications can be adjusted following the trend and the preference of potential customers. Deeper interaction with the ideal customers can improve the behavior brand attitude and result in repeat purchases ( Hollebeek and Macky, 2019 ). In particular, MR-based DCM has enormous potential to grasp a significant market share in the Hong Kong digital advertising market.

Conclusion, Limitations, and Future Research

Regarding the result of the study, both the immediate and long-term OPI has been proved. The immediate impact comes from the perceived value toward the product or service described exhaustively in the DCM under the MR-based training platform environment. Furthermore, customer engagement can cultivate brand trust and enlarge the long-term OPI due to behavioral loyalty. The effectiveness of DCM under the MR environment has been introduced segmentally. However, it may take time to see the long-tail effect of DCM under the MR-based training platform, as the companies have to accumulate leads by continuously providing unique content. An effective marketing tactic for SMEs, DCM, a section of social media marketing, is suggested to take a significant component, supported by the paid advertising on either search engines or social media. MR can be further used and extended to enhance the customers’ experience and satisfaction. Online shopping in Hong Kong is most common among young and middle-aged adults and highly educated people, perfectly fitting the respondents’ characteristics. Therefore, the results can indicate the preferences and opinions on DCM for the above group of residents. However, online shopping market and e-commerce are proliferating, and people in other age groups and education levels may also be willing to accept and adopt the digital method of purchasing. The result will no longer be sufficient to represent all online shoppers. The findings fill the gaps in the literature by providing empirical evidence for OPI boosted by DCM via social media. Therefore, future research can be extended to broader respondents, who may have different responses and preferences on DCM. Future research could extend customer engagement and trust constructs with other individual difference variables and extend to the mediating effect on the antecedents. MR’s adaptability and effectiveness to different marketing channels could be further considered. The technology acceptance model and the theory of planned behavior model could be further analyzed for new model development. The multi-group analysis considering different countries could be considered. Consumer behavior under the MR-based platform for DCM could be a new construct to analyze further and consider.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

CL, OC, and KK contributed to conceptualization. OC, YC, and KK performed data curation. YC and KK carried out formal analysis, performed investigation, and contributed to project administration. CL contributed to funding acquisition. OC, PT, and KK provided methodology. CL, OC, YC, and KK provided resources. PT and KK provided software. OC performed supervision. XZ, PT, and KK carried out validation. CL, OC, YC, XZ, PT, SL, HN, and KK helped with visualization. OC, YC, PT, and KK performed writing—original draft. CL, OC, YC, PT, and KK performed writing—review and editing. All authors have read and agreed to the published version of the manuscript. All the authors contributed to the article and approved the submitted version.

The research was supported in part by the School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China and in part by the Division of Business and Hospitality Management, College of Professional and Continuing Education, The Hong Kong Polytechnic University, Hong Kong SAR, China. The work described in this paper was partially supported by the grant from the Research Grants Council of the Hong Kong Special Administrative Region, China and Hong Kong Metropolitan University (Project No. R7016, Reference code: 2020/3003).

Conflict of Interest

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

Publisher’s Note

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

Abbas, A. F., Jusoh, A., Mas’od, A., Alsharif, A. H., and Ali, J. (2022). Bibliometrix analysis of information sharing in social media. Cogent Bus. Manag. 9:2016556. doi: 10.1080/23311975.2021.2016556

CrossRef Full Text | Google Scholar

Ahmad, N. S., Musa, R., and Harun, M. H. M. (2016). The impact of social media content marketing (SMCM) towards brand health. Procedia Econ. Finance 37, 331–336. doi: 10.1016/S2212-5671(16)30133-2

Alcañiz, M., Bigné, E., and Guixeres, J. (2019). Virtual reality in marketing: a framework, review, and research agenda. Front. Psychol. 10:1530. doi: 10.3389/fpsyg.2019.01530

PubMed Abstract | CrossRef Full Text | Google Scholar

Ali, M. A., Ting, D. H., Ahmad-Ur-Rahman, M., Ali, S., Shear, F., and Mazhar, M. (2021). Effect of online reviews and crowd cues on restaurant choice of customer: moderating role of gender and perceived crowding. Front. Psychol. 12:780863. doi: 10.3389/fpsyg.2021.780863

Amblee, N., and Bui, T. (2011). Harnessing the influence of social proof in online shopping: the effect of electronic word of mouth on sales of digital microproducts. Int. J. Electron. Commer. 16, 91–114. doi: 10.2753/JEC1086-4415160205

Areeba, T., Mudassir, H., and Talha, H. (2017). The impact of social network marketing on consumer purchase intention in Pakistan: consumer engagement as a mediator. Asian J. Bus. Account. 10, 167–199.

Google Scholar

Azer, J., and Alexander, M. (2020). Negative customer engagement behaviour: the interplay of intensity and valence in online networks. J. Mark. Manag. 36, 361–383. doi: 10.1080/0267257X.2020.1735488

Bagozzi, R. P., and Yi, Y. (2011). Specification, evaluation, and interpretation of structural equation models. J. Acad. Mark. Sci. 40, 8–34. doi: 10.1007/s11747-011-0278-x

Bakker, P. (2012). Aggregation, content farms and huffinization. J. Pract. 6, 627–637. doi: 10.1080/17512786.2012.667266

Barreto, A. M. (2013). Do users look at banner ads on Facebook?: an international journal. J. Res. Interact. Mark. 7, 119–139. doi: 10.1108/JRIM-Mar-2012-0013

Bentler, P. M. (1982). Confirmatory factor analysis via noniterative estimation: a fast, inexpensive method. J. Mark. Res. 19, 417–424. doi: 10.1177/002224378201900403

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychol. Bull. 107, 238–246. doi: 10.1037/0033-2909.107.2.238

Bentler, P. M., and Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychol. Bull. 88, 588–606. doi: 10.1037/0033-2909.88.3.588

Bergström, T., and Bäckman, L. (2013). Marketing and PR in Social Media: How the Utilization of Instagram Builds and Maintains Customer Relationships [Bachelors’ Thesis, Stockholms University].

Bolat, E., and O’sullivan, H. (2017). Radicalising the marketing of higher education: learning from student-generated social media data. J. Mark. Manag. 33, 742–763. doi: 10.1080/0267257X.2017.1328458

Boomsma, A., and Hoogland, J. J. (2001). “The robustness of LISREL modeling revisited,” in Structural Equation Models: Present and Future. A Festschrift in Honor of Karl Jöreskog. Vol. 2 . eds. R. Cudeck, S. du Toit and D. So ̈rbom (Chicago, IL: Scientific Software International). 139–168.

Bowden, J., and Mirzaei, A. (2021). Consumer engagement within retail communication channels: an examination of online brand communities and digital content marketing initiatives. Eur. J. Mark. 55, 1411–1439. doi: 10.1108/EJM-01-2018-0007

Canhoto, A. I., Vom Lehn, D., Kerrigan, F., Yalkin, C., Braun, M., and Steinmetz, N. (2015). Fall and redemption: monitoring and engaging in social media conversations during a crisis. Cogent Bus. Manag. 2:1084978. doi: 10.1080/23311975.2015.1084978

Castillo, M. J., and Bigne, E. (2021). A model of adoption of AR-based self-service technologies: a two country comparison. Int. J. Retail Distrib. Manag. 49, 875–898. doi: 10.1108/IJRDM-09-2020-0380

Chen, Y., Fay, S., and Wang, Q. (2011). The role of Marketing in social media: how online consumer reviews evolve. J. Interact. Mark. 25, 85–94. doi: 10.1016/j.intmar.2011.01.003

Chinomona, R., Okoumba, L., and Pooe, D. (2013). The impact of product quality on perceived value, trust and students’ intention to purchase electronic gadgets. Mediterr. J. Soc. Sci. 4:462. doi: 10.5901/mjss.2013.v4n14p463

Consumer Council (2016). Online Retail—A Study on Hong Kong Consumer Attitudes, Business Practices and Legal Protection. ed. Consumer Council (HKSAR, China: Consumer Council).

Corrêa, S. C. H., Soares, J. L., Christino, J. M. M., Gosling, M. D. S., and Gonçalves, C. A. (2020). The influence of YouTubers on followers’ use intention. J. Res. Interact. Mark. 14, 173–194. doi: 10.1108/JRIM-09-2019-0154

Dawes, J., and Nenycz-Thiel, M. (2014). Comparing retailer purchase patterns and brand metrics for in-store and online grocery purchasing. J. Mark. Manag. 30, 364–382. doi: 10.1080/0267257X.2013.813576

Dessart, L. (2017). Social media engagement: a model of antecedents and relational outcomes. J. Mark. Manag. 33, 375–399. doi: 10.1080/0267257X.2017.1302975

Doll, W. J., Xia, W., and Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Q. 18, 453–461.

Erik, E. V. (2019). Effects of enduring involvement and perceived content vividness on digital engagement. J. Res. Interact. Mark. 14, 1–16. doi: 10.1108/JRIM-05-2018-0071

Fan, J., Zheng, P., and Li, S. (2022). Vision-based holistic scene understanding towards proactive human–robot collaboration. Robot. Comput. Integr. Manuf. 75:102304. doi: 10.1016/j.rcim.2021.102304

Flavián, C., Ibáñez-Sánchez, S., and Orús, C. (2019). The impact of virtual, augmented and mixed reality technologies on the customer experience. J. Bus. Res. 100, 547–560. doi: 10.1016/j.jbusres.2018.10.050

Freeze, R., and Raschke, R. L. (2007). “An Assessment of Formative and Reflective Constructs in IS Research”, in ECIS 2007 Proceedings , 1481–1492.

Goodrich, K., Schiller, S. Z., and Galletta, D. (2015). Consumer reactions to intrusiveness of online-video advertisements. J. Advert. Res. 55, 37–50. doi: 10.2501/JAR-55-1-037-050

Grant, R., Clarke, R. J., and Kyriazis, E. (2007). A review of factors affecting online consumer search behaviour from an information value perspective. J. Mark. Manag. 23, 519–533. doi: 10.1362/026725707X212801

Hair, J. F. (2010). Multivariate Data Analysis. Upper Saddle River, NJ: Prentice Hall.

Hajli, M. (2014). A study of the impact of social media on consumers. Int. J. Mark. Res. 56, 387–404. doi: 10.2501/IJMR-2014-025

Hartiwi, P., Ridho, B., and Yuniarty, (2020). Student purchase intention in higher education sector: the role of social network marketing and student engagement. Manag. Sci. Lett. 10, 103–110.

Hollebeek, L. D. (2011). Demystifying customer brand engagement: exploring the loyalty nexus. J. Mark. Manag. 27, 785–807. doi: 10.1080/0267257X.2010.500132

Hollebeek, L. D., Conduit, J., and Brodie, R. J. (2016). Strategic drivers, anticipated and unanticipated outcomes of customer engagement. J. Mark. Manag. 32, 393–398. doi: 10.1080/0267257X.2016.1144360

Hollebeek, L. D., and Macky, K. (2019). Digital content marketing’s role in fostering consumer engagement, trust, and value: framework, fundamental propositions, and implications. J. Interact. Mark. 45, 27–41. doi: 10.1016/j.intmar.2018.07.003

Holliman, G. (2014). Business to business digital content marketing: marketers’ perceptions of best practice. J. Res. Interact. Mark. 8, 269–293. doi: 10.1108/JRIM-02-2014-0013

Hoyle, R. H. (1995). Structural Equation Modeling: Concepts, Issues, and Applications. Thousand Oaks, California: Sage Publications.

Hwang, K., and Zhang, Q. (2018). Influence of parasocial relationship between digital celebrities and their followers on followers’ purchase and electronic word-of-mouth intentions, and persuasion knowledge. Comput. Hum. Behav. 87, 155–173. doi: 10.1016/j.chb.2018.05.029

Jacoby, J., Nelson, M. C., and Hoyer, W. D. (1982). Corrective advertising and affirmative disclosure statements: their potential for confusing and misleading the consumer. J. Mark. 46, 61–72.

Jamil, K., Dunnan, L., Gul, R. F., Shehzad, M. U., Gillani, S. H. M., and Awan, F. H. (2022). Role of social media marketing activities in influencing customer intentions: a perspective of a new emerging era. Front. Psychol. 12:808525. doi: 10.3389/fpsyg.2021.808525

Jöreskog, K. G., and Sörbom, D. (1989). LISREL 7: A Guide to the Program and Applications. Spss Inc., 2nd Chicago.

Kareem, J. A. H., Rashid, W. N., Abdulla, D. F., and Mahmood, O. K. (2016). Social media and consumer awareness toward manufactured food. Cogent Bus. Manag. 3:1266786. doi: 10.1080/23311975.2016.1266786

Kemp, S. (2019). Digital 2019: Hong Kong. Datareportal.

Keung, K. L., Chan, Y. Y., Ng, K. K. H., Mak, S. L., Li, C. H., Qin, Y., et al. (2022a). Edge intelligence and agnostic robotic paradigm in resource synchronisation and sharing in flexible robotic and facility control system. Adv. Eng. Inform. 52:101530. doi: 10.1016/j.aei.2022.101530

Keung, K. L., Lee, C. K. M., and Ji, P. (2021). Data-driven order correlation pattern and storage location assignment in robotic mobile fulfillment and process automation system. Adv. Eng. Inform. 50:101369. doi: 10.1016/j.aei.2021.101369

Keung, K. L., Lee, C. K. M., and Ji, P. (2022b). Industrial internet of things-driven storage location assignment and order picking in a resource synchronization and sharing-based robotic mobile fulfillment system. Adv. Eng. Inform. 52:101540. doi: 10.1016/j.aei.2022.101540

Keung, K. L., Lee, C. K. M., Ji, P., and Ng, K. K. H. (2020). Cloud-based cyber-physical robotic mobile fulfillment systems: a case study of collision avoidance. IEEE Access 8, 89318–89336. doi: 10.1109/ACCESS.2020.2992475

Keung, K. L., Lee, C. K. M., Ng, K. K. H., and Yeung, C. K. (2018). “Smart city application and analysis: real-time urban drainage monitoring by IoT sensors: a case study of Hong Kong,” in 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) , December 16–19, 2018; 521–525.

Khan, F., and Siddiqui, D. K. (2013). The importance of digital marketing. An exploratory study to find the perception and effectiveness of digital marketing amongst the marketing professionals in Pakistan. J. Inf. Syst. Operat. Manag. 7, 221–228.

Koiso-Kanttila, N. (2004). Digital content marketing: a literature synthesis. J. Mark. Manag. 20, 45–65. doi: 10.1362/026725704773041122

Kumar, S. P., and Gunaseelan, D. (2016). Misleading advertisements and issues. Int. J. Manag. 7, 475–483.

Laroche, M., Habibi, M. R., Richard, M.-O., and Sankaranarayanan, R. (2012). The effects of social media based brand communities on brand community markers, value creation practices, brand trust and brand loyalty. Comput. Hum. Behav. 28, 1755–1767. doi: 10.1016/j.chb.2012.04.016

Le, D. (2013). Content Marketing. [Bachelors’ Thesis, Haaga-Helia University of Applied Sciences].

Leckie, C., Nyadzayo, M. W., and Johnson, L. W. (2016). Antecedents of consumer brand engagement and brand loyalty. J. Mark. Manag. 32, 558–578. doi: 10.1080/0267257X.2015.1131735

Lee, C. K. M., Lv, Y., Ng, K. K. H., Ho, W., and Choy, K. L. (2018b). Design and application of internet of things-based warehouse management system for smart logistics. Int. J. Prod. Res. 56, 2753–2768. doi: 10.1080/00207543.2017.1394592

Lee, C. K., Ng, K. K. H., Chan, H. K., Choy, K. L., Tai, W., and Choi, L. (2018a). A multi-group analysis of social media engagement and loyalty constructs between full-service and low-cost carriers in Hong Kong. J. Air Transp. Manag. 73, 46–57. doi: 10.1016/j.jairtraman.2018.08.009

Leong, L.-Y., Hew, T.-S., Lee, V.-H., and Ooi, K.-B. (2015). An SEM–artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline. Expert Syst. Appl. 42, 6620–6634. doi: 10.1016/j.eswa.2015.04.043

Li, H., Edwards, S. M., and Lee, J.-H. (2002). Measuring the intrusiveness of advertisements: scale development and validation. J. Advert. 31, 37–47. doi: 10.1080/00913367.2002.10673665

Li, S., Fan, J., Zheng, P., and Wang, L. (2021b). Transfer learning-enabled action recognition for human-robot collaborative assembly. Procedia CIRP 104, 1795–1800. doi: 10.1016/j.procir.2021.11.303

Li, Q., Feng, J., Guo, J., Wang, Z., Li, P., Liu, H., et al. (2020). Effects of the multisensory rehabilitation product for home-based hand training after stroke on cortical activation by using NIRS methods. Neurosci. Lett. 717:134682. doi: 10.1016/j.neulet.2019.134682

Li, Q., Ng, K. K. H., Fan, Z., Yuan, X., Liu, H., and Bu, L. (2021a). A human-centered approach based on functional near-infrared spectroscopy for adaptive decision-making in the air traffic control environment: a case study. Adv. Eng. Inform. 49:101325. doi: 10.1016/j.aei.2021.101325

Li, S., Zheng, P., Fan, J., and Wang, L. (2021c). Towards proactive human robot collaborative assembly: a multimodal transfer learning-enabled action prediction approach. IEEE Trans. Ind. Electron. 69, 8579–8588. doi: 10.1109/TIE.2021.3105977

Li, S., Zheng, P., and Zheng, L. (2021d). An AR-assisted deep learning-based approach for automatic inspection of aviation connectors. IEEE Trans. Ind. Informat. 17, 1721–1731. doi: 10.1109/TII.2020.3000870

Liu, C., Cao, S., Tse, W., and Xu, X. (2017). Augmented reality-assisted intelligent window for cyber-physical machine tools. J. Manuf. Syst. 44, 280–286. doi: 10.1016/j.jmsy.2017.04.008

Liu, Y., Liu, X., Wang, M., and Wen, D. (2021). How to catch customers’ attention? A study on the effectiveness of brand social media strategies in digital customer engagement. Front. Psychol. 12:800766. doi: 10.3389/fpsyg.2021.800766

Liu, B., Zhang, Y., Zhang, G., and Zheng, P. (2019). Edge-cloud orchestration driven industrial smart product-service systems solution design based on CPS and IIoT. Adv. Eng. Inform. 42:100984. doi: 10.1016/j.aei.2019.100984

Madsen, D. Ø., and Slåtten, K. (2015). Social media and management fashions. Cogent Bus. Manag. 2:1122256. doi: 10.1080/23311975.2015.1122256

Malthouse, E. C., Calder, B. J., Kim, S. J., and Vandenbosch, M. (2016). Evidence that user-generated content that produces engagement increases purchase behaviours. J. Mark. Manag. 32, 427–444. doi: 10.1080/0267257X.2016.1148066

Marbach, J., Lages, C. R., and Nunan, D. (2016). Who are you and what do you value? Investigating the role of personality traits and customer-perceived value in online customer engagement. J. Mark. Manag. 32, 502–525. doi: 10.1080/0267257X.2015.1128472

Mason, A. N., Brown, M., Mason, K., and Narcum, J. (2021a). Pandemic effects on social media marketing behaviors in India. Cogent Bus. Manag. 8:1943243. doi: 10.1080/23311975.2021.1943243

Mason, A. N., Narcum, J., and Mason, K. (2021b). Social media marketing gains importance after COVID-19. Cogent Bus. Manag. 8:1870797. doi: 10.1080/23311975.2020.1870797

Mathew, V., and Soliman, M. (2021). Does digital content marketing affect tourism consumer behavior? An extension of technology acceptance model. J. Consum. Behav. 20, 61–75. doi: 10.1002/cb.1854

Meyer-Waarden, L., and Benavent, C. (2006). The impact of loyalty programmes on repeat purchase behaviour. J. Mark. Manag. 22, 61–88. doi: 10.1362/026725706776022308

Mohamad, M., Zawawi, Z. A., and Hanafi, W. N. W. (2018). The influences of social network marketing on student purchase intention in the digital era: the mediating role of consumer engagement. Glob. Bus. Manag. Res. 10, 938–947.

Nabec, L., Pras, B., and Laurent, G. (2016). Temporary brand–retailer alliance model: the routes to purchase intentions for selective brands and mass retailers. J. Mark. Manag. 32, 595–627. doi: 10.1080/0267257X.2015.1111923

Naidoo, V., and Hollebeek, L. D. (2016). Higher education brand alliances: investigating consumers’ dual-degree purchase intentions. J. Bus. Res. 69, 3113–3121. doi: 10.1016/j.jbusres.2016.01.027

Papagiannidis, S., Pantano, E., and Bourlakis, M. (2013). Modelling the determinants of a simulated experience in a virtual retail store and users’ product purchasing intentions. J. Mark. Manag. 29, 1462–1492. doi: 10.1080/0267257X.2013.821150

Park, J.-W., Robertson, R., and Wu, C.-L. (2004). The effect of airline service quality on passengers’ behavioural intentions: a Korean case study. J. Air Transp. Manag. 10, 435–439. doi: 10.1016/j.jairtraman.2004.06.001

Podsakoff, P. M., Mackenzie, S. B., Lee, J.-Y., and Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903. doi: 10.1037/0021-9010.88.5.879

Pulizzi, J., and Barrett, N. (2009). Get Content, Get Customers: Turn Prospects into Buyers with Content Marketing. New York: McGraw-Hill.

Rose, S., and Samouel, P. (2009). Internal psychological versus external market-driven determinants of the amount of consumer information search amongst online shoppers. J. Mark. Manag. 25, 171–190. doi: 10.1362/026725709X410089

Rowley, J. (2008). Understanding digital content marketing. J. Mark. Manag. 24, 517–540. doi: 10.1362/026725708X325977

Rozina, I., Syeda, K., Maheen, A., and Atif, A. (2019). The impact of social network marketing on consumer purchase intention in Pakistan: a study on female apparel. Manag. Sci. Lett. 9, 1093–1104. doi: 10.5267/j.msl.2019.3.015

Schumacker, R. E., and Lomax, R. G. (2004). A Beginner’s Guide to Structural Equation Modeling. 2nd Edn. Mahwah, N.J.: Lawrence Erlbaum Associates.

Sharma, R. R., and Chander, S. (2011). What’s wrong with misleading advertising?—an empirical investigation. Asia Pac. Bus. Rev. 7, 191–205. doi: 10.1177/097324701100700116

Shiyong, Z., Jiaying, L., Haijian, W., Dukhaykh, S., Lei, W., Biqing, L., et al. (2022). Do product characteristics affect customers’ participation in virtual brand communities? An empirical study. Front. Psychol. 12:792706. doi: 10.3389/fpsyg.2021.792706

Siddique, J., Shamim, A., Nawaz, M., Faye, I., and Rehman, M. (2021). Co-creation or co-destruction: a perspective of online customer engagement valence. Front. Psychol. 11:591753. doi: 10.3389/fpsyg.2020.591753

STATISTA (2020). Digital Advertising Report 2019. STATISTA.

Stone, M. D., and Woodcock, N. D. (2014). Interactive, direct and digital marketing: a future that depends on better use of business intelligence. J. Res. Interact. Mark. 8, 4–17. doi: 10.1108/JRIM-07-2013-0046

Stouthuysen, K., Teunis, I., Reusen, E., and Slabbinck, H. (2018). Initial trust and intentions to buy: the effect of vendor-specific guarantees, customer reviews and the role of online shopping experience. Electron. Commer. Res. Appl. 27, 23–38. doi: 10.1016/j.elerap.2017.11.002

Tafesse, W., and Wien, A. (2018). Implementing social media marketing strategically: an empirical assessment. J. Mark. Manag. 34, 732–749. doi: 10.1080/0267257X.2018.1482365

Thomson, E. S., and Laing, A. W. (2003). “The net generation”: children and young people, the internet and online shopping. J. Mark. Manag. 19, 491–512. doi: 10.1362/026725703321663764

Truong, Y., and Simmons, G. (2010). Perceived intrusiveness in digital advertising: strategic marketing implications. J. Strateg. Mark. 18, 239–256. doi: 10.1080/09652540903511308

Tudoran, A. A. (2019). Why do internet consumers block ads? New evidence from consumer opinion mining and sentiment analysis. Internet Res. 29, 144–166. doi: 10.1108/IntR-06-2017-0221

Ullman, J. (2001). Structural Equation Modeling—Using Multivariate Statistics. Needham Heights: Allyn & Bacon.

Valos, M. J., Maplestone, V. L., Polonsky, M. J., and Ewing, M. (2017). Integrating social media within an integrated marketing communication decision-making framework. J. Mark. Manag. 33, 1522–1558. doi: 10.1080/0267257X.2017.1410211

Wang, P., and Mccarthy, B. (2020). What do people “like” on Facebook? Content marketing strategies used by retail bank brands in Australia and Singapore. Australas. Mark. J. 29, 155–176. doi: 10.1016/j.ausmj.2020.04.008

Wang, S., Ye, Y., Ning, B., Cheah, J.-H., and Lim, X.-J. (2022). Why do some consumers still prefer in-store shopping? An exploration of online shopping cart abandonment behavior. Front. Psychol. 12:829696. doi: 10.3389/fpsyg.2021.829696

Wedel, M., Bigné, E., and Zhang, J. (2020). Virtual and augmented reality: advancing research in consumer marketing. Int. J. Res. Mark. 37, 443–465. doi: 10.1016/j.ijresmar.2020.04.004

Wertalik, D. (2017). Social media and building a connected college. Cogent Bus. Manag. 4:1320836. doi: 10.1080/23311975.2017.1320836

Wong, E., and Wei, Y. (2018). Customer online shopping experience data analytics. Int. J. Retail Distrib. Manag. 46, 406–420. doi: 10.1108/IJRDM-06-2017-0130

Wu, W.-P., Chan, T. S., and Lau, H. H. (2008). Does consumers’ personal reciprocity affect future purchase intentions? J. Mark. Manag. 24, 345–360. doi: 10.1362/026725708X306130

Xia, L., Zheng, P., Huang, X., and Liu, C. (2021). A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization. J. Intell. Manufact. doi: 10.1007/s10845-021-01784-1 [Epub ahead of print]

Xu, Y., Chen, Z., Peng, M. Y.-P., and Anser, M. K. (2020). Enhancing consumer online purchase intention through gamification in China: perspective of cognitive evaluation theory. Front. Psychol. 11:581200. doi: 10.3389/fpsyg.2020.581200

Yaghtin, S., Safarzadeh, H., and Karimizand, M. (2021). B2B digital content marketing in uncertain situations: a systematic review. J. Bus. Ind. Mark. doi: 10.1108/JBIM-03-2021-0174 [Epub ahead of print]

Yang, S., Lin, S., Carlson, J. R., and Ross, W. T. (2016). Brand engagement on social media: will firms’ social media efforts influence search engine advertising effectiveness? J. Mark. Manag. 32, 526–557. doi: 10.1080/0267257X.2016.1143863

Ying, L., Korneliussen, T., and Grønhaug, K. (2009). The effect of ad value, ad placement and ad execution on the perceived intrusiveness of web advertisements. Int. J. Advert. 28, 623–638. doi: 10.2501/S0265048709200795

Zhang, X., Zheng, P., Peng, T., He, Q., Lee, C. K. M., and Tang, R. (2022). Promoting employee health in smart office: a survey. Adv. Eng. Inform. 51:101518. doi: 10.1016/j.aei.2021.101518

Zheng, P., Xia, L., Li, C., Li, X., and Liu, B. (2021). Towards self-X cognitive manufacturing network: an industrial knowledge graph-based multi-agent reinforcement learning approach. J. Manuf. Syst. 61, 16–26. doi: 10.1016/j.jmsy.2021.08.002

Keywords: digital content marketing, social media marketing, marketing, customer engagement, structural equation modeling, customer loyalty, mixed reality

Citation: Li CH, Chan OLK, Chow YT, Zhang X, Tong PS, Li SP, Ng HY and Keung KL (2022) Evaluating the Effectiveness of Digital Content Marketing Under Mixed Reality Training Platform on the Online Purchase Intention. Front. Psychol . 13:881019. doi: 10.3389/fpsyg.2022.881019

Received: 22 February 2022; Accepted: 29 March 2022; Published: 30 June 2022.

Reviewed by:

Copyright © 2022 Li, Chan, Chow, Zhang, Tong, Li, Ng and Keung. 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: O. L. K. Chan, [email protected] ; K. L. Keung, [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.

Conference on Sustainability and Cutting-Edge Business Technologies

SICB 2023: Cutting-Edge Business Technologies in the Big Data Era pp 367–379 Cite as

The Impact of Predictive Analytics and AI on Digital Marketing Strategy and ROI

Mohammad Ali Al Khaldy 3 ,
Basim Abbas Ali Al-Obaydi 4 &
Alaa Jamil al Shari 4
Conference paper
First Online: 27 August 2023

399 Accesses

Part of the book series: Studies in Big Data ((SBD,volume 136))

The study explores the effects of artificial intelligence (AI) and predictive analytics on digital marketing strategy and return on investment (ROI). It reviews relevant literature on the use of AI and predictive analytics in digital marketing, focusing on email marketing, customization, social media marketing optimization, and consumer segmentation. The research methodology is described, including data collection and analysis procedures. The findings highlight the significant impact of predictive analytics and AI on digital marketing strategy and ROI, with businesses that effectively utilize these technologies reporting higher engagement, conversions, and revenue growth. The study acknowledges limitations and proposes future research directions of successful AI and predictive analytics integration in digital marketing campaigns. It concludes with recommendations for businesses to invest in predictive analytics and AI to remain competitive in the dynamic digital landscape.

Digital Marketing
Predictive Analytics
Personalization
Artificial Intelligence

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Gangurde, R., Kumar, B., Gore, S.: Building prediction model using market basket analysis. Int. J. Innov. Res. Comput. Commun. Eng. 5 (2), 1302–1309 (2017)

Google Scholar

Bawack, R.E., Fosso Wamba, S., Carillo, K.: Artificial intelligence in practice: implications for IS research (2019)

Nair, K., Gupta, R.: Application of AI technology in modern digital marketing environment. World J. Entrepreneurship Manage. Sustain. Dev. 17 (3), 318–328 (2021)

Surendro, K.: Predictive analytics for predicting customer behavior. In: 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT). IEEE (2019)

Yüksel, A., Yüksel, F.: Market segmentation based on tourists’ dining preferences. J. Hosp. Tour. Res. 26 (4), 315–331 (2002)

Article Google Scholar

Kumar, V., et al.: Understanding the role of artificial intelligence in personalized engagement marketing. Calif. Manage. Rev. 61 (4), 135–155 (2019)

Article MathSciNet Google Scholar

Stavinova, E., Bochenina, K., Chunaev, P.: Predictability classes for forecasting clients behavior by transactional data. In: Computational Science–ICCS 2021: 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part III. (2021). Springer. https://doi.org/10.1007/978-3-030-77967-2_16

Quarteroni, S.: Natural language processing for industry: ELCA’s experience. Informatik-Spektrum 41 (2), 105–112 (2018)

Broby, D.: The use of predictive analytics in finance. J. Finance Data Sci. 8 , 145–161 (2022)

Olson, C., Levy, J.: Transforming marketing with artificial intelligence. Appl. Mark. Analy. 3 (4), 291–297 (2018)

Camilleri, M.A.: The use of data-driven technologies for customer-centric marketing. Int. J. Big Data Manage. 1 (1), 50–63 (2020)

Pereira, A.M., et al.: Customer models for artificial intelligence-based decision support in fashion online retail supply chains. Decis. Support Syst. 158 , 113795 (2022)

Kietzmann, J., Paschen, J., Treen, E.: Artificial intelligence in advertising: how marketers can leverage artificial intelligence along the consumer journey. J. Advert. Res. 58 (3), 263–267 (2018)

Ziafat, H., Shakeri, M.: Using data mining techniques in customer segmentation. J. Eng. Res. Appl. 4 (9), 70–79 (2014)

Bleier, A., De Keyser, A., Verleye, K.: Customer engagement through personalization and customization. Custom. Engagement Mark. 75–94 (2018)

Xiong, Y., et al.: Recognize complex events from static images by fusing deep channels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1600–1609 (2015)

Roca, S., et al. Virtual assistant prototype for managing medication using messaging platforms. In: XV Mediterranean Conference on Medical and Biological Engineering and Computing–MEDICON 2019: Proceedings of MEDICON 2019, 26–28 September 2019, Coimbra, Portugal (2020). Springer. https://doi.org/10.1007/978-3-030-31635-8_116

Ngowi, A.R.: The effectiveness of internet advertising on Consumer behaviour: The case of Moshi cooperative university students. Mzumbe University (2015)

Uban, A.S., Chulvi, B., Rosso, P.: Understanding patterns of anorexia manifestations in social media data with deep learning. In: Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access (2021)

Yang, J., Leskovec, A.: Patterns of temporal variation in online media. In: Proceedings of the fourth ACM International Conference on Web Search and Data Mining (2011)

Agarwal, S., Agarwal, B., Gupta, R.: Chatbots and virtual assistants: a bibliometric analysis. Libr. Hi Tech 40 (4), 1013–1030 (2022)

Van Esch, P., Stewart Black, J.: Artificial intelligence (AI): revolutionizing digital marketing. Australas. Mark. J. 29 (3), 199–203 (2021)

Jarek, K., Mazurek, G.: Marketing and artificial intelligence. Central Europ. Bus. Rev. 8 (2) (2019)

Roggeveen, A.L., Sethuraman, R.: Customer-interfacing retail technologies in 2020 & beyond: an integrative framework and research directions. J. Retail. 96 (3), 229–309 (2020)

Chintalapati, S., Pandey, S.K.: Artificial intelligence in marketing: a systematic literature review. Int. J. Mark. Res. 64 (1), 38–68 (2022)

Chan-Olmsted, S.M.: A review of artificial intelligence adoptions in the media industry. Int. J. Media Manag. 21 (3–4), 193–215 (2019)

Nygård, R.: AI-Assisted Lead Scoring (2019)

Mari, A.: Voice Commerce: Understanding shopping-related voice assistants and their effect on brands (2019)

Haleem, A., et al.: Artificial intelligence (AI) applications for marketing: a literature-based study. Int. J. Intell. Netw. (2022)

Brito, P.Q., et al.: Customer segmentation in a large database of an online customized fashion business. Robot. Comput.-Integr. Manufact. 36 , 93–100 (2015)

Sharaf Addin, E.H., et al.: Customer mobile behavioral segmentation and analysis in telecom using machine learning. Appl. Artif. Intell. 36 (1), 2009223 (2022)

Chiu, C.-Y., et al.: An intelligent market segmentation system using k-means and particle swarm optimization. Expert Syst. Appl. 36 (3), 4558–4565 (2009)

Ray, A., Mathew, R.: Review of cloud-based natural language processing services and tools for chatbots. In: Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI-2018), pp. 156–162. Springer (2020). https://doi.org/10.1007/978-3-030-24643-3_18

Prajwal, S., et al.: Universal semantic web assistant based on sequence to sequence model and natural language understanding. In: 2019 9th International Conference on Advances in Computing and Communication (ICACC). IEEE (2019)

Bahja, M.: Natural language processing applications in business. E-Bus.-High. Educ. Intell. Appl. (2020)

Liao, S.-H., Hsu, S.-Y.: Big data analytics for investigating Taiwan Line sticker social media marketing. Asia Pac. J. Mark. Logist. 32 (2), 589–606 (2020)

Yang, J., et al.: Social media data analytics for business decision making system to competitive analysis. Inf. Process. Manage. 59 (1), 102751 (2022)

Arasu, B.S., Seelan, B.J.B., Thamaraiselvan, N.: A machine learning-based approach to enhancing social media marketing. Comput. Electr. Eng. 86 , 106723 (2020)

Campbell, C., Sands, S., Ferraro, C., Tsao, H.Y.J., Mavrommatis, A.: From data to action: how marketers can leverage AI. Bus. Horiz. 63 (2), 227–243 (2020)

Khodabandehlou, S.: Designing an e-commerce recommender system based on collaborative filtering using a data mining approach. Int. J. Bus. Inf. Syst. 31 (4), 455–478 (2019)

Kim, B.D., Kim, S.O.: A new recommender system to combine content-based and collaborative filtering systems. J. Datab. Mark. Custom. Strat. Manage. 8 , 244–252 (2001)

Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V.K., Soman, K.P.: Stock price prediction using LSTM, RNN and CNN-sliding window model. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, pp. 1643–1647 (2017)

Bounsaythip, C., Rinta-Runsala, E.: Overview of data mining for customer behavior modeling. VTT Inf. Technol. Res. Rep. Version. 29 (1), 1–53 (2001)

Olson, D.L., Chae, B.K.: Direct marketing decision support through predictive customer response modeling. Decis. Support Syst. 54 (1), 443–451 (2012)

Hawarah, L., Ploix, S., Jacomino, M.: User behavior prediction in energy consumption in housing using Bayesian networks. In: Artificial Intelligence and Soft Computing: 10th International Conference, ICAISC 2010. Springer Berlin Heidelberg.: Zakopane, Poland, pp. 372–379 (2010). https://doi.org/10.1007/978-3-642-13208-7_47

Chanpariyavatevong, K., Wipulanusat, W., Champahom, T., Jomnonkwao, S., Chonsalasin, D., Ratanavaraha, V.: Predicting airline customer loyalty by integrating structural equation modeling and Bayesian networks. Sustainability 13 (13) (2021)

Lemos, A.M.F.: In optimizing multi-channel use in digital marketing campaigns (2015)

Oentaryo, R., et al.: Detecting click fraud in online advertising: a data mining approach. J. Mach. Learn. Res. 15 (1), 99–140 (2014)

MathSciNet Google Scholar

Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315 (5814), 972–976 (2007)

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Al Khaldy, M.A., Al-Obaydi, B.A.A., al Shari, A.J. (2023). The Impact of Predictive Analytics and AI on Digital Marketing Strategy and ROI. In: Yaseen, S.G. (eds) Cutting-Edge Business Technologies in the Big Data Era. SICB 2023. Studies in Big Data, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-031-42455-7_31

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For many, being a social media influencer has transformed from a side gig into a multimillion dollar business. Creators now have more say in the sponsored content they create for brands as well as the earnings they receive. And as creators continue to prove themselves as influential brand advocates, marketers are tapping these influencers for content beyond social feeds, including TV, out-of-home, and other digital media.

Influencers have also proven resilient amid economic uncertainty and an increasingly crowded space. Influencer marketing spend rose roughly 3.5 times faster in 2023 than social ad spending, according to EMARKETER’s July 2023 forecast.

influencer marketing social ad spend change

In this guide, we explore the current state of influencer marketing, and why marketers, social platforms, and media companies should adjust their strategies as the power of the creator economy grows.

Want to learn more about influencer marketing and other marketing trends? Sign up for the EMARKETER Daily newsletter.

What is the creator economy?

The creator economy , also called the influencer economy, is the interconnected ecosystem of creators, audiences, digital platforms, marketers, and agencies and/or vendors. The creator economy, as defined by EMARKETER’s Creator Economy Explainer report , enables creators to generate revenues in the form of money, goods, or services through their content, usually delivered via social media platforms. Meanwhile, marketers can partner with creators to build awareness for their brands.

Because interaction among stakeholders is constantly evolving, revenue data is sparse. A March 2023 estimate from Citigroup values the creator economy at $65.2 billion in 2023. By 2024, that figure is expected to reach $74.0 billion.

What is influencer marketing?

Influencer marketing involves a celebrity, content creator, subject matter expert, or public figure endorsing a brand’s product or service to drive social proof.

The market is rapidly expanding as influencer types have increased, with influencers filling every conceivable niche and sub-niche interest.

Here are the types of influencers, based on follower count and focuses:

Mega-influencers: Often celebrity influencers, those accounts with more than 1 million followers
Macro-influencers: Influencers with a follower count of 100,000 to 999,999
Mid-tier influencers: Influencers with a follower count of 20,000 to 99,999
Micro-influencers: Followers ranging between 5,000 to 19,999
Nano-influencers: A community of 1,000 to 4,999 followers
Kidfluencers: Gen Alphas with social followings that often review toys and games
Gaming influencers: Esport and video game players who discuss game strategy and livestream while in-game
Virtual influencers: Computer-generated avatars like Noonoouri, a fashion model and digital-only popstar

The pandemic sped up changes in influencer marketing that were already underway, such as the trend toward “unfiltered” or less-scripted content, the rise of TikTok, and the popularity of “everyday influencers” with genuine and relatable personalities like Elyse Myers.

Industries like financial services that hadn’t invested in influencer marketing earlier are also learning to navigate the space. And as ecommerce and social media converge, influencers will become increasingly vital intermediaries, helping to connect brands with consumers on social media.

Creators vs. influencers: What’s the difference?

Creators create content. Influencers are creators if the content they develop can sway the purchase decisions of a population regardless of whether they are being paid to promote a product. The term refers to a wide group of people from celebrities to loyal customers. Creators are influencers if their content affects purchase decisions.

Who is a creator?

Everyone has the potential to be a content creator thanks to how the term is defined. Adobe estimates there could be up to 303 million creators worldwide. For context, that figure is close to the US population at the start of 2023, per the US Census Bureau.

Most content creators worldwide are under 41, but there are creators of all ages, including Gen Xers and baby boomers, per Adobe.

Creators over 60 years old, dubbed “granfluencers,” have found popularity on social media due to their cross-generational appeal.

For example, Nonna Pia, a TikTok account with 4 million followers, features a grandmother who cooks classic Italian dishes while her grandson narrates. Similarly, the Instagram account “Excuse My Grandma” follows a grandmother-granddaughter duo as they discuss generational differences in dating, fashion, and more.

Creator categories

Although there is an account for every interest and target audience—from foraging to chiropractic medicine—lifestyle, fashion, and beauty are among the top creator categories.

While broad, these top categories overlap with many other industries, including travel, health, entertainment, food and drink, and art. Cross-category appeal is ideal for brand marketers looking to expand their reach to find like-minded audience demographics.

Areas that have seen recent growth in creator focus include:

Collegiate sports

A 2021 NCAA policy change allows college athletes to be compensated for their name, image, and likeness. College athletes already command large audiences and regularly create content on social media, per August 2022 data from Curastory.

B2B professionals

Industry professionals are increasingly building audiences by sharing B2B content online. For example, a “ LinkedInfluencer ” is an influencer on B2B social platform LinkedIn.

Nearly 1 in 4 people in the US are on LinkedIn, according to EMARKETER’s May 2023 forecast, as both the creator economy and B2B ad spending grow. As buyers get younger and B2B marketing becomes increasingly digital, authenticity and experience will matter more in marketing.

Financial services

In the financial sector, “ finfluencers ” use their social media platform to share videos that cover personal experiences, tips, and advice about investing, budgeting, financial trends, and the economy.

Finfluencers are capitalizing on a widespread lack of financial literacy. However, the quality of the content is controversial because some creators do not have a professional background in finance. Despite concerns, 60% of investors ages 18 to 35 use social media as a source of investment information, according to a Finra Investor Education Foundation report.

The generational response

Younger shoppers are more likely to use social media . About 129.5 million US Gen Zers and millennials will use social media in 2024, per a May 2023 EMARKETER forecast.

Younger shoppers are also more likely to follow and buy from accounts run by influencers. A Q2 2022 Klarna survey found that over 40% of Gen Zers and millennials worldwide follow influencers , compared with one-quarter of Gen Xers and less than 10% of baby boomers.

Millennials

Often considered a millennial concept, influencer culture is embraced by this extremely active social media user base. Close to 70 million US millennials will use social media next year, per EMARKETER’s May 2023 forecast.

Millennials look to trusted influencers for product recommendations, reviews, and as a discovery point for new brands.

They’re also willing to purchase what they see being promoted by their favorite influencers on social media. Over half (54.1%) of US social network users ages 25 to 34 will make a purchase on a social platform in 2024, per September 2023 EMARKETER data.

In 2024, 60.7 million US Gen Zers will use social media, according to EMARKETER’s May 2023 forecast. And while that growth is expected to continue through 2027, Gen Z will remain slightly behind millennial users.

Gen Zers, however, are not as sold on following influencers as their older peers. Less than half (48.6%) use social media to view creator/influencer content, according to a July 2023 EMARKETER survey’s findings.

Where influencers do have an outsize influence over Gen Z is women’s beauty and wellness. Close to 80% of Gen Z women rank creators as their most trusted source for beauty recommendations, a survey conducted by LTK found.

Those recommendations are also turning into online and offline sales. Eighty-three percent of Gen Z women shop for creators’ product suggestions online, and 82% shop for those items in-store.

Influencing by “de-influencing”

While the majority of influencers promote what to buy, some influencers are turning to platforms like TikTok to tell their followers what not to buy. As of January 2024, #deinfluencing videos on TikTok have roughly 1.3 billion views, up considerably from 208 million in February 2023.

Seen as a method to combat overconsumption in a tight economy, deinfluencing videos often offer economical alternatives to expensive products or discuss the pitfalls of fast-fashion shopping hauls.

Creators also use de-influencing videos to share critical commentary to distance themselves from brand controversies and post honest reviews of products that don’t meet expectations. Doing so can help reinforce creator trust through authenticity and transparency.

Influencer marketing spend

In 2024, advertisers will spend $5.89 billion on influencer marketing, a 14.7% increase YoY, according to EMARKETER’s July 2023 forecast, which excludes paid media.

US influencer marketing spend grew more than three times faster than social ad spending in 2023, and it will remain ahead through 2025, according to a July 2023 EMARKETER forecast.

Social platforms are reliant on creators, not the other way around, as creators’ options extend and include owned channels like blogs, podcasts, and newsletters.

A number of well-known creators, such as YouTuber MrBeast and podcaster Alex Cooper, have launched their own audio and video networks.

Although not every creator has a strong enough brand or following to create a successful media business , the growing success of these ventures should signal to brand marketers and entertainment companies that creators can offer much more than an outlet to generate hype or hawk goods.

Where influencers post sponsored content

Creators utilize a number of social media platforms.

To be impactful, influencer content should be engaging, entertaining, and educational, and marketers should work with credible creators who have built trust with their communities.

US marketers will allocate over $1 billion to sponsored content on each of the top four influencer marketing platforms in 2024, according to EMARKETER’s July 2023 forecast. When it comes to influencer monetization , Instagram posts were the top format that creators worldwide were paid by brands to create in December 2022, per a Later and Mavrck December 2022 survey. Here’s a look at the top social media marketing platforms for influencers.

US influencer marketing spend by platform

Instagram remains the top platform for sponsored content, per a June 2023 report from Mavrck. About 98% of US creators share brand content via Instagram feed posts, Instagram Stories, and Instagram Reels, while 69.1% go live on the platform.

As of June 2023, more creators worldwide report being paid to create Reels (98.0%) compared with TikTok videos (89.6%).

Creator monetization: In May 2023, Meta began testing a new payout model for Ads on Reels. The monetization program pays creators based on the performance of the Reel, rather than the earnings of the Reel ad, per TechCrunch.

US sponsored content post types shared by creators

As TikTok’s popularity rises, brand opportunities for paid content is likely to increase as well.

Influencer marketing spending growth on TikTok has grown 27.8%, compared with 12.7% on Instagram, according to EMARKETER’s July 2023 forecast.

In 2024, over half of marketers (54.0%) will use TikTok specifically for influencer marketing, with $1.25 billion in US influencer marketing spend going to the platform, per the same EMARKETER forecast.

Creator monetization: If an influencer promotes a brand’s product on TikTok Shop, the social app’s ecommerce tool, they can earn a commission through product sales. For both brands and influencers, TikTok Shop holds a lot of promise and room for growth. In 2024, we expect 40.7 million TikTok users to make a purchase on the app.

YouTube, like Instagram and TikTok, has a solid hold on influencer marketing. YouTube is the top platform for US adults to follow influencers, according to March 2023 CivicScience data.

EMARKETER forecasts that in 2024, US marketers will spend $1.07 billion on influencer content for YouTube.

Similar to Instagram Reels, YouTube has been emphasizing Shorts as a cost-effective option for marketers.

Livestreaming is also leveraged by YouTube to connect influencers—and, ultimately, the brands they partner with—to followers. One-fourth (25.0%) of internet users say they watch creator- or influencer-led livestreams on YouTube, making it the most popular livestreaming app, ahead of TikTok (18.7%), Facebook (17.4%), and Instagram (14.0%), per an April 2023 survey by The Influencer Marketing Factory.

Creator monetization: YouTube attracts and retains creators in a number of ways. The platform offers a way to connect creator, artist, and brand stores to their YouTube channel, enabling users to more easily find and purchase featured products. YouTube also has affiliate shopping capabilities for creators interested in revenue opportunities.

Other platforms with influencer marketing potential include Facebook, Twitch, and to a lesser extent, Snapchat and X, the social media company formerly known as Twitter.

Despite its waning popularity among users, especially youths, Facebook is still expected to see $1.00 billion in influencer marketing spending next year, per an EMARKETER forecast.

Popular with the esports gaming community, livestreaming app Twitch recently launched a number of features for creators. In July 2023, Twitch announced its Discovery Feed, made up of livestream clips and ad features that help creators share their content from other platforms such as YouTube, TikTok, and Reddit.

Snap has struggled with ad monetization and commands a fraction of influencer marketing spend compared with larger platforms. In 2023, Snap crossed $40 million in influencer marketing spending, and is expected to see growth of 3.4% in 2024.

In October 2023, Snap released Creator Collab Campaigns, a suite of tools to facilitate brand-creator partnerships on the platform. Previously, Snap’s primary focus was helping creators monetize directly on the platform through programs like ad revenue sharing.

X (formerly Twitter)

X saw total US ad revenues decline by 54.9% in 2023 after Elon Musk’s 2022 purchase, according to EMARKETER’s October 2023 forecast, which means it is not an attractive platform for influencer marketing.

The aforementioned June 2023 Mavrck survey found that 45.9% of US creators shared promoted tweets, while a December 2022 Later and Mavrck survey found that 8.3% of creators worldwide have been paid by a brand to post on X.

Influencer marketing strategy

An influencer marketing strategy allows a brand to further its reach and tap new audiences, but narrowing down the right creator to work with requires an understanding of the landscape and what type of partnership will best serve the brand’s objectives.

Influencer marketing hubs: As influencer marketing took off, agencies dedicated to influencers sprang up to help brands manage the opportunity and vice versa.

Acting as a directory, influencer marketing hubs organize creator profiles by follower count per social platform, audience demographics, location, services offered, the price of partnership, and their interests (e.g., fashion, travel, home improvement, etc.), to take the guesswork out of selecting an influencer to partner with.

In addition to facilitating brand-creator relationships and identifying new talent, influencer hubs create campaign narratives, determine KPIs, and amplify influencer-led social campaigns, among other responsibilities.

For creators, working with an influencer marketing agency can be beneficial to scale and manage their own business. As influencer campaigns become more complex, an agency can manage payment, negotiate contracts, handle data analytics and reporting, and oversee other business functions.

Influencer marketing campaigns: An influencer marketing campaign is one that leverages the influence the creator has over their followers.

When in a paid partnership with a brand, the influencer campaign’s objective is to increase awareness, engagement, and, ultimately, sales. In comparison, an unsponsored campaign, while achieving the same objectives to various degrees, can be seen as more trustworthy and authentic by the influencer’s community.

Influencer campaigns fall into two categories:

User-generated content (UGC) is organic content shared by a social media influencer to promote a brand or product without direct input from the featured brand. The influencer is not paid for UGC posts, and is often not as polished as content created with brand involvement. UGC posts range from product reviews, recommendations, tutorials, and personal experiences with a given good or service.
Influencer-generated content is a collaboration between a creator and partner brand. These campaigns follow a brand’s creative guidelines—including tone of voice, talking points, aesthetics, and frequency of posts—to produce content fitting for that creator’s audience. As more influencers become trusted partners, brands have loosened the reins, allowing talent to tap into their own creativity for campaigns. Like UGC, influencer-led campaigns are more authentic and engaging, and may prove more effective for the brand involved.

Influencer posts: When paid for by a brand partner, an influencer post is a type of native advertising. When unpaid with no brand involvement, the post is considered UGC.

Most commonly seen on social media platforms, formats include Instagram photos with captions, short-form videos on TikTok, and long-form video content posted to YouTube. Influencers can also post written content on blogging platforms like Substack. Influencers will often promote their posts across various platforms to increase reach, engagement, and effectiveness.

While format is an important aspect of influencer marketing, the post’s creative should be at the forefront. According to EMARKETER’s Influencer Monetization 2023 report, the “three E’s” of influencer marketing should be remembered when creating posts.

“Regardless of the format, each piece of content should be engaging, entertaining, and educational to drive the most impact,” according to EMARKETER analyst Jasmine Enberg. Brands should also trust creators’ input on creative and format decisions because they know what will resonate best with their audience, Enberg continued.

Product launches: Influencers are an ideal way for a brand to launch new products.

When introducing a new product, brands can expand their reach by working with influencers with a similar following as the brand’s target audience. A kitchenware brand, for instance, may work with a popular food influencer to introduce a new range of pots to show off the products, its attributes, and how to buy.

As influencers become brands in their own right, many have launched their own product lines.

Thanks to an engaged and well-known audience, some influencers are able to develop products that align with the interests of the community they’ve built across their social media footprint.

For example, beauty vlogger and influencer Huda Kattan launched her own line of false eyelashes in 2013 after her community expressed an interest. Kattan then expanded the line into a full range of cosmetics. Huda Beauty, named after Kattan’s YouTube channel and blog, is now sold D2C and at Sephora.

Creators with large followings have launched food and beverage products, fitness programs, clothing lines, restaurants, and more to capitalize on their popularity and the value and willingness of their followers to support their businesses. Venturing into their own products also helps creators diversify their revenues and avoid alienating their audiences with too much sponsored content.

Collaboration: Similar to leveraging an influencer’s help to launch a new product, brands also partner with their stable of creators for product collaborations.

Often developed as a one-off or limited-edition product, brand-influencer collaborations can be a great source of product innovation.

In March 2023, Chipotle added two new limited-edition menu items to its quesadilla lineup, the “Keithadilla” and the “Fajita Quesadilla Hack,” which were developed and popularized by TikTok creators Keith Lee and Aleix Frost.

For some influencers, a brand collaboration may be a jumping-off point to developing their own product lines if the collaborative effort was deemed successful.

Common influencer marketing KPIs

These metrics are a good way for brands to measure their return on investment for influencer marketing campaigns:

Facebook engagement rates (subscribers only): Engagement rate is defined as measurable interactions on social media posts, including likes, comments, favorites, retweets, shares, replies, and reactions, and is calculated based on all these interactions divided by total follower count.
Facebook posts per week
Instagram affiliate engagement rate : The percentage of an affiliate influencer’s audience that interacts with an affiliate influencer’s campaign or post on Instagram; this includes likes, comments, and shares.
Instagram affiliate impression per follower rate : The number of affiliate-generated views that a specific post or piece of content received over a specific period of time on Instagram.
Instagram affiliate reach : The percentage of followers and viewers from an affiliate influencer’s audience that is exposed to the affiliate influencer’s Instagram.
Instagram affiliate view rate : The percentage of an affiliate influencer’s audience that views an affiliate influencers’ campaign or post on Instagram.
Instagram engagement rates : Engagement rate is defined as measurable interactions on social media posts, including likes, comments, favorites, retweets, shares, replies, and reactions, and is calculated based on all these interactions divided by total follower count.
Instagram posts per week
Social affiliate clicks : The clicks generated by social influencer efforts, including generating traffic or leads (through affiliate links) to the company’s website.
Social affiliate conversion rate : The percentage of clicks generated by social influencer efforts, including generating traffic or leads (through affiliate links) to the company’s website that also result in completed orders or purchases.
Social affiliate engagement rate : The percentage of an affiliate influencer’s audience that interacts with an affiliate influencer’s campaign or post on a social media platform; this includes likes, comments, and shares.
Social affiliate orders : The orders generated by social influencer efforts, including generating traffic or leads (through affiliate links) to the company’s website.
Social affiliate sales : The sales or revenues generated by social influencer efforts, including generating traffic or leads (through affiliate links) to the company’s website.
Social affiliate view rate : The percentage of an affiliate influencer’s audience that views an affiliate influencer’s campaign or post on a social media platform.
TikTok affiliate engagement rate : The percentage of an affiliate influencer’s audience that interacts with an affiliate influencer’s campaign or post on TikTok; this includes likes, comments, and shares.
TikTok affiliate view rate : The percentage of an affiliate influencer’s audience that views an affiliate influencer’s campaign or post on TikTok.
X (formerly Twitter) tweets per week

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This study has described various forms of digital marketing, effectiveness of it and the impact it has on firm's sales. The examined sample consists of one hundred fifty firms and fifty ...
(PDF) DIGITAL MARKETING
This research paper describes a framework for research in digital marketing that highlights the strategies in the marketing process as well as the effectiveness of the marketing process and impact ...
(PDF) Digital Marketing Strategies and the Impact on Customer
This systematic literature review emphasizes different contemporary marketing strategies such as e-WOM, e-mail, search engine optimization, social media marketing, and corporate blogging. Among ...
Digital marketing: A framework, review and research agenda
Our objectives for this paper are three-fold. First, we develop and describe a framework for research in digital marketing that highlights the touchpoints in the marketing process as well as in the marketing strategy process where digital technologies are having and/or will have a significant impact. Next, we organize the developments and ...
A Scoping Review of the Effect of Content Marketing on Online Consumer
While not a mature field yet, the body of knowledge of content marketing has grown over the last 12 years since the first scholarly paper about content marketing by Rowley (2008).Although some confusion about content marketing remains, more recent studies across different disciplines have focused on how content marketing influences online consumer behavior and the mechanisms used to achieve ...
Setting the future of digital and social media marketing research
This section synthesizes the existing literature focusing on digital and social media marketing and discusses each theme listed in Table 1 from a review of the extant literature. Studies included in this section were identified using the Scopus database by using the following combination of keywords "Social media", "digital marketing" and "social media marketing".
Impact of digital marketing
The paper is original in terms of reviewing the literature published on the topic, "impact of digital marketing", between years 2012 to 2020. As the world has been forced to go digital due to COVID-19 outbreak, it has become all the more significant to take an account of developments in the field of "digital marketing".
A Framework for Digital Marketing Research: Investigating the Four
SUBMIT PAPER. Journal of Interactive Marketing. ... Create email alert. Restricted access. Research article. First published online February 15, 2022. A Framework for Digital Marketing Research: Investigating the Four Cultural Eras of Digital Marketing ... An Empirical Investigation of Web Page Effectiveness European Journal of Marketing 32 7/8 ...
The Gold Rush of Digital Marketing: Assessing Prospects of Building
To conclude, more research needs to be done about the impact of a business location on the effectiveness of brand awareness strategies, and future research should focus on understanding global differences since digital marketing is advancing at diverse speeds and in unique ways across the world.
Digital transformation and marketing: a systematic and thematic
This article provides a systematic review of the extensive and fragmented literature focused on Digital Transformation (DT) and marketing by identifying the main themes and perspectives (i.e., employees, customers, and business processes) studied by previous research. By mapping the DT literature in the area of marketing, 117 articles, published between 2014 and 2020, have been identified ...
Using Data Sciences in Digital Marketing: Framework, methods, and
Numerous studies demonstrated that a key way to increase the effectiveness of DM strategies is the application of DS techniques in this industry (Braverman, ... The remainder of this paper is structured as follows. The following section presents the theoretical framework of DS. ... Main topics of research in Digital Marketing using Data Sciences.
Digitalization and its impact on contemporary marketing strategies and
Despite particular research conducted on the issues related to digital marketing and marketing analytics, additional attention is needed to study the revolution and potentially disruptive nature of these domains (Petrescu and Krishen 2021, 2022).Considering the substantial impact of digital marketing and marketing analytics in the current competitive and demanding business landscape, the ...
The effect of digital marketing transformation trends on consumers
Research methods and scales will be presented in detail in section 4 of the paper. Research results will be presented in section 5. And an in-depth discussion in the 6 sections. Finally, the conclusion with a brief assessment of the limitations of the study as well as suggestions for future research. ... Develop an effective digital marketing ...
How digital technologies reshape marketing: evidence from a ...
The paper is structured as follows. ... and the assessment of the effectiveness of digital marketing. The issue of human resources and capabilities remains a particularly prominent topic, as there is a widening gap between the accelerating complexity of markets and technologies and the evolution of firms' digital marketing capabilities ...
(PDF) Effectiveness of Digital Marketing in the Challenging Age: An
Additionally the differences between traditional marketing and digital marketing in this paper are presented. This study has described various forms of digital marketing, effectiveness of it and the impact it has on firm's sales. The examined sample consists of one hundred fifty firms and fifty executives which have been randomly selected to ...
Effectiveness of Online Marketing Tools: A Case Study
Different tools and techniques are used to influence the purchasing decision of consumers. This case study on online marketing, research through survey and analysis of data received from respondents is still in its embryonic stage, and it is conducted to find the effectiveness of tools and techniques—online chat assistance, email ...
PDF Effectiveness of Digital Marketing in the Challenging Age
How effective is digital marketing in the current era? This paper from Research Leap explores the impact of digital marketing on consumers and firms' sales, and compares it with traditional marketing methods. Read the full paper to learn more about the various forms of digital marketing and their effectiveness.
Trends and patterns in digital marketing research: bibliometric
The research's aim is to investigate trends and patterns in the area of digital marketing research from 1979 to June 2020 through a bibliometric analysis technique. ... method to examine the effectiveness and growth of digital marketing title publications. The main objective of this study is to recognize and analyze top-cited research papers ...
PDF Effectiveness of Digital Marketing Stratigies
of digital marketing and the remaining 28.20% is explained by other factor. Great er engagement is explained by 51.60% of digital marketing and the remaining 48.40% is explained by other factor. Clear Information about the products or services is explained by 62.90% of digital marketing and the remai ning 37.10% is explained by other factor.
(PDF) A study on effectiveness of digital marketing for ...
A study on effectiveness of digital marketing for Small Business Units. February 2022. IJARCCE 11 (2) Authors: Dr. Hiren Harsora. Anil Sharma. St. Kabir Institute Of Professional Studies.
Frontiers
Immediate Purchase Intension. In the research investigated by Chinomona et al. (2013), the significant positive impact of perceived product quality on perceived product value was investigated.The customer's perceived value of the product can affect OPI. The researchers proved that the interviewees perceived the product as valuable when they recognized that the product had good quality and ...
The Impact of Predictive Analytics and AI on Digital Marketing Strategy
Future research could overcome these constraints by utilizing larger, more varied samples and including objective metrics for the effectiveness of digital marketing. This study also concentrated on the short-term effects of AI and predictive analytics on digital marketing strategy and ROI.
Influencer Marketing: Audiences, Platforms, Strategies, & KPIs
The creator economy, also called the influencer economy, is the interconnected ecosystem of creators, audiences, digital platforms, marketers, and agencies and/or vendors. The creator economy, as defined by EMARKETER's Creator Economy Explainer report, enables creators to generate revenues in the form of money, goods, or services through ...
STUDY OF THE EFFECTIVENESS OF DIGITAL MARKETING
Effectiveness of Digital Marketing in Education: An Insight into Consumer Perceptions. March 2015 Indian Journal of Science and Technology. Yatin Jog. Tripti Dhote. Gesu Shrivastava. The media ...