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Abstract

The online knowledge-sharing market in China has been rapidly growing, with increasing user demand for paid knowledge products. Like in other e-commerce contexts, users must make product evaluations under conditions of information asymmetry. In the age of social media, engagement metrics can be a particularly rich source of product information for users. However, there has been little research on how engagement metrics influence user decision making in online knowledge market. As such, mainly drawing on the Social Impact Theory, this study conducted a 2 (engagement metrics: high vs. low) × 2 (source expertise: high vs. low) between-subject factorial design experiment to explore the impact of engagement metrics on user purchase intention for online knowledge products. Participants consisted of 151 college students who completed measures on purchase intention, trust, demographics, and other individual variables. Results revealed that only when source expertise is high do higher engagement metrics lead to higher consumer trust, in turn resulting in higher purchase intention. Differentiating from findings on the impact of engagement metrics in other online contexts, this study highlights the importance of source expertise for influencing user purchase intention in the knowledge-sharing market.

Keywords

engagement metrics source expertise knowledge-sharing market online knowledge products purchase decision-making

Introduction

The practice of paid content has been emerging on various content platforms, such as online news, music streaming, audiobooks, etc. In China especially, there has been a recent surge in the knowledge-sharing market with many application developers adding paid content or services (Sharing Economy Research Center of National Information Center, 2017). These content products are online knowledge provided by content creators from various fields, with users paying ahead to consume their content. These knowledge products are mainly found on mobile radio APPs (e.g., Ximalaya) and Q&A platforms (e.g., Zhihu). In terms of format, these platforms are similar to that of Spotify’s podcast section or Quora in the United States, however in addition to entertainment, they also or primarily provide content centered on self-improvement and educational purposes (Li et al., 2018). In terms of content, the knowledge products on these platforms are more like LinkedIn’s e-learning courses, providing users with in-depth, professional knowledge such as legal language in English, self-financing, and computer programming.

The online knowledge industry in China has witnessed impressive growth since 2016. According to iiMedia Research (2020a), a prestigious Chinese research institution in the mobile internet industry, the market generated RMB 26.5 billion in revenue in 2016 and exceeded RMB 392 in 2020 with an annual growth rate of about 40%. Most of the paid knowledge consumers in China are under 30 years old, mainly living in first- and second-tier cities, and usually earn less than 10,000 yuan a month (iiMedia Research, 2020a). This generation of consumers grew up in the digital age, and most of them have a habit of online shopping (Graessley et al., 2019; Hollowell et al., 2019; Mummalaneni & Meng, 2009). Engagement metrics, which serve as a source of social impact, have become commonplace amongst websites and applications and have proven to be an influential factor in people’s online purchase decisions (Coulter & Roggeveen, 2012). As a typical content product sold online, online knowledge products are often displayed with engagement metrics like the number of likes, which may play an important role in people’s purchase decision-making process.

However, despite the increasing academic attention paid to the online knowledge industry, the extant literature on this topic is relatively scarce, particularly those pertaining to user purchase intention or behavior (Yan et al., 2021; Zhu & Zhang, 2021). Even amongst the limited published literature that explores factors influencing people’s purchase intention toward online knowledge products, most studies focus on factors related to product features, including content quality and prices (Yan et al., 2021; Zhu & Zhang, 2021). Fairly little attention has been paid to environment-related factors such as engagement metrics, which are quite relevant yet external to the product itself. Furthermore, though engagement metrics have been consistently found to be a significant predictor of user purchase decision-making, these previous studies were mostly conducted in the context of material products (Coulter & Roggeveen, 2012; Rahman et al., 2017; Zhang et al., 2014) or free content consumption (such as online news) (Metzger et al., 2010; Sundar & Nass, 2001; Winter et al., 2016). To be able to generalize these findings, it is therefore also necessary to extend our research endeavors to the new context of online knowledge products. These products share some commonalities with the traditional material products found in e-commerce and free online content, yet are distinctive in their combination of content consumption and pay-to-consume nature.

Moreover, the users of online knowledge products have a higher need for practical skills and knowledge. A report found that 52.3% of users purchase online knowledge products for professional learning (iiMedia Research, 2020b). However, the industry currently employs a celebrity-centered marketing strategy, in which celebrities, particularly movies stars and singers, are often invited as content providers of online knowledge products. The idea behind this strategy is to boost and accelerate the engagement metrics of online knowledge products. Considering the user motivations behind the consumption of online knowledge products, this casts doubt on the effectiveness of such a strategy, especially regarding the sustainable development of the online knowledge industry. In fact, an industrial report showed that the re-purchase rate of online knowledge products in 2018 was only 30% (iiMedia Research, 2018). Thus, to better understand the impact of engagement metrics on user decision-making in the purchasing of online knowledge products, we incorporated source expertise, an indicator of content quality, into our study as well.

In short, the goal of this present study is to investigate user purchase decisions toward online knowledge products from the perspective of engagement metrics with the guidance of the Social Impact Theory. From an academic point of view, this study and its findings fill a gap in the current social impact literature by extending it into the new context of pay-to-use content. In addition, it deepens our understanding of the underlying mechanism of user purchase decisions by introducing the user trust toward content providers as the mediator and source expertise of online knowledge products as the moderator. Practically speaking, this study can also provide practitioners with meaningful insights on how to understand user consumption and better manage products for sustainable development in the digital age. The paper is organized as follows: first, we review relevant theories and prior empirical studies, developing the conceptual model. Then, we test our conceptual model with experiment data. In conclusion, we discuss the theoretical and practical implications of this study.

Literature Review

Theoretical Framework

This study is guided by the Social Impact Theory (SIT), which is a useful theoretical framework to understand how the social environment impacts people’s attitude changes and decisions (Chang et al., 2018). This theory has been applied in various contexts, such as changes in political attitude on social media (Chang et al., 2018) and consumers’ attitude changes in retail contexts (Argo et al., 2005). According to Latané (1981), social impact refers to any influence on individual feelings, thoughts, or behavior created from the real, implied, or imagined presence or actions of others. Furthermore, Latané (1981) posited that social impact was the result of social forces, which included the number of sources exerting the impact and the strength of the source of impact.

To a great extent, engagement metrics function as a source of social impact, corresponding to the quantitative dimension in the SIT. On the surface, engagement metrics are just numbers, but they also signal other people’s presence. Therefore, it is quite understandable that these numbers have an effect on people, given the fact that as social beings, humans are inclined to be influenced by others (Bandura, 2001). When many individuals endorse a certain opinion, people tend to perceive it as an indicator of popularity and thus are more likely to follow the crowd by “jumping on the bandwagon” (Marsh, 1985). Drawing on the SIT, Miller and Cryss Brunner (2008) examined the social impact in technologically-mediated communication and confirmed its salience on influencing user decision-making despite the lack of non-verbal cues that occurred in face-to-face communication.

According to the SIT, in addition to the quantitative dimension, the strength of the source of impact, referring to the authority of a person or a group, is also an important component of social impact. Consistent with the SIT, the MAIN (Modality, Agency, Interactivity, and Navigability) model (Sundar, 2008) proposed that there are multiple cues in today’s online environment. In addition to bandwagon cues, cues for expertise are frequently displayed as well. These different cues and their interactions may play important roles in shaping people’s subsequent perception and decision-making. Therefore, drawing upon the SIT, we intended to examine the impact of bandwagon cues on users’ trust and their purchase intention, as well as the interplay effect of bandwagon cues and expertise cues in the context of online knowledge products.

Engagement Metrics and Purchase Intention

Although different terms such as social media metrics, popularity metrics, and social endorsement are used in various studies, researchers have converged on a similar understanding that people treat the publicly displayed counts of likes, comments, and shares embedded in social media as indicators of social proof when processing online information or making decisions (J. Kim, 2021). For the sake of simplicity, the term “engagement metrics” is used to refer to the typical “cues” that represent the intensity of consumer engagement in an organization’s offering and/or organizational activities (Vivek et al., 2012), specifically the quantitative metrics produced by others’ engagement such as the number of followers and their likes, shares, and comments in online environments.

Consistent with the SIT, previous studies examining individuals’ consumption of online content (particularly online news) provide extensive support for engagement metrics as a source of social impact on user perception and decision-making. For instance, Metzger et al. (2010) found that readers paid attention to social endorsements and assessed the credibility of online news based on aggregated recommendations from others. Winter et al. (2016) showed that readers were more likely to read news stories with a high number of recommendations than those with a low number of recommendations. Additionally, an earlier study conducted by Sundar and Nass (2001) specifically examined the impact of engagement metrics in online news consumption, and the results clearly lent support to the tenets of the SIT.

These research results are also in line with bandwagon cues, a concept proposed by Sundar (2008) based on the heuristic-systematic model (HSM). As a dual information processing model, the HSM provides us with an insightful discussion on how individuals process information and later form decisions (Eagly & Chaiken, 1993). It postulates information recipients process information using systematic or heuristic strategies, and these two information processing processes are not necessarily mutually exclusive. In the systematic mode, individuals spend significant effort on evaluating arguments and assessing their validity for decision making, while the heuristic mode posits that individuals spend little effort and rely on more accessible informational cues (e.g., engagement metrics) to reach their conclusions (Chaiken, 1980). According to the least effort principle, heuristic information processing is the default strategy because people are economically-minded processors who prefer to invest less cognitive effort and will spend more effort only when they have to (Bohner et al., 1995). Therefore, people may automatically apply heuristic cues rather than systematic information process more quickly, reflecting the importance of heuristic cues such as bandwagon cues in shaping people’s subsequent decision-making.

However, most of the present extant literature is limited to free online content, with fairly little attention given to paid online content (Zhao et al., 2018). Thus, despite the important role of engagement metrics, which have been proven to play a role in user perception and use of free online content, the relationship between engagement metrics and purchase intention of pay-to-use online content largely remains unexplored. Therefore, research needs to be extended from the free online content context to the pay-to-use context. To address this question, studies on the e-commerce of physical material products may provide us with some insightful views. For instance, B. Lu et al. (2016) found that engagement metrics were related to user purchase intention toward physical products (e.g., clothing and computers) from e-commerce sellers. It is reasonable to argue that when a user encounters an online product with high engagement metrics, he/she might speculate that there are many other users who believe this product is worth buying. That is, higher engagement metrics is more likely to nudge users to form a positive impression and in turn make a decision to purchase.

In light of the above discussion, we also posit that engagement metrics are an important source of social impact or form of bandwagon cues for users to refer to when they consume online knowledge products and make purchase decisions.

H1: The higher the engagement metrics of an online knowledge product is, the higher user purchase intention will be.

The Mediating Role of Trust

Trust is a psychological state comprising of the intent to accept vulnerability based on the positive expectations of another’s intentions or a willingness to rely on exchange partners (Ganesan, 1994; Singh & Sirdeshmukh, 2000). As an important psychological construct, trust has been studied across various disciplines, including social psychology, sociology, economics, and marketing (Bratu, 2019; Doney & Cannon, 1997; Mircică, 2020; Popescu & Ciurlău, 2019). Considering the vital role content providers play in both the production and promotion of online knowledge products (Li & Hu, 2020), our study operationalized user trust as a set of users’ beliefs about the trustworthiness of online content providers with main references to H.-W. Kim et al. (2012).

McKnight et al. (2002) extended the concept of trust into the e-commerce context, on which the current study focuses. In the e-commerce environment, users may feel threatened by potential risks such as intangible providers, which in turn make them reluctant to complete e-commerce transactions (Kuisma et al., 2007). According to the Signaling Theory, the transaction of online knowledge products exists in an environment of information asymmetry, where the risk for opportunistic behavior on the seller’s side is high. Users may suspect the content provider’s ability to fulfill their end of the transaction, while recognizing that they do not have the capacity to detect potential deceptions. Due to the inadequate information, users will refer to signals of engagement metrics to speculate on the ability of the content provider. Based on this theory, we can speculate that when engagement metrics of a knowledge product is high, it is easier for users to trust the provider’s ability to provide knowledge they need. Such a rationale is aligned with B. Lu et al. (2016), whose study found that engagement metrics had a positive effect on consumer trust toward the online seller. Similarly, the study by Lee and Sundar (2013) also confirmed the positive relationship between engagement metrics and users’ trust toward content providers. Therefore, we proposed that the higher the engagement metrics, the higher the trust a consumer will have.

At the same time, trust has consistently proved to be a significant predictor of purchase intention in the context of e-commerce (Hong & Cha, 2013; Y. Lu et al., 2010). For example, based on survey data, Hsin Chang and Wen Chen (2008) revealed that trust toward the online retailer positively influences online purchase intention. Hong (2015) found that the more customers perceived an e-tailor to be trustworthy, the more likely they would buy from that e-tailor. Pappas (2016) also concluded that customer trust toward a Web-vendor had a positive impact on their intent to purchase. These findings are not surprising as trust is the basis of trading and is much more important in the online context, given products or services are often provided by unfamiliar merchants online (Hong & Cha, 2013). More trust means the consumers are less worried about the possible risk they might face (e.g., get a low-quality online knowledge product), thus in turn are more likely to purchase the product. Therefore, this study reasons that the more users think a content provider is trustworthy, the more likely they believe the knowledge produced by the provider is what they need, and thus generate higher purchase intention, leading us to our second hypothesis:

H2: The higher the engagement metrics are, the higher user trust toward the content provider will be, which in turn leads to higher user purchase intention.

The Moderating Role of Source Expertise

A concept similar to authoritativeness and competence, source expertise is usually defined as “the extent to which a communicator is perceived to be a source of valid assertations,” which can be derived from high levels of knowledge, ability, and skills (Erdogan, 1999; Go et al., 2014). Based on this definition, we define source expertise as the content provider having skills or knowledge obtained from formal training in a specific field. Since the conduction of an early study of Hovland et al. (1953), source expertise has been consistently found to be a significant factor influencing people’s perceptions of source credibility. Specific to the context of content consumption, past studies have found that a content provider’s expertise has a significant positive influence on trust (M.-S. Kim & Ahn, 2007; Wood et al., 2008) and purchase intention (Wen et al., 2009).

According to SIT, it is necessary to take source expertise, an indicator of the strength of the source of impact, into consideration when exploring people’s ensuing decision-making. In the same vein, source expertise is another important kind of heuristic cue to which people turn for help to make quick judgments (Sundar, 2008). Therefore, we are curious about whether the impact of engagement metrics—the bandwagon cues on which the present study is focused—on user trust and purchase intention of online knowledge products will be moderated by source cues. In the last decade, more and more academic attention has been given to the interaction effects of heuristic cues, given the fact that multiple cues are always appearing in online platforms at the same time. To the best of the authors’ knowledge, there are mainly two hypotheses proposed to explain the conjoined roles of bandwagon cues and source cues in shaping the subsequent outcomes: a source primary effect and a cue-cumulation effect.

By loosely adapting the least effort principle and sufficient principle of the HSM of information processing (Neuwirth et al., 2002), Sundar et al. (2007) proposed a source primary effect based on the notion that online users (like all media consumers) are miserly when it comes to using their cognitive resources. That is, users will usually prefer heuristic over systematic processing, and at the same time, they probably process only one heuristic cue rather than all cues if it is enough for them to base a relatively risk-free judgment call on. Sundar and colleagues’ empirical study confirmed this assumption, finding that bandwagon cues mattered only when source credibility was low (Sundar et al., 2007). Another study by Chung (2017) also indicates that engagement metrics play a more important role when source credibility is low.

In contrast, a cue-cumulation effect provides us another theoretical possibility that two or more positive heuristic cues would lead to better perception than a single one, which is consistent with the rationale behind the additivity hypothesis of information dual-process models in the social psychological literature (Chaiken & Trope, 1999). A study conducted by Go et al. (2014) revealed a significant two-way interaction effect between engagement metrics and expertise on perceived credibility, suggesting a positive cumulative effect of the two variables. More recently, Lin et al. (2016) found a cue-cumulative effect in social media platforms. That is, the co-presence of more cues enhances the positive effect of perceived source credibility in tweets.

The previous inconsistent findings suggest a need to further understand the interactive effect of different heuristic cues on user perception and decision-making. Moreover, previous studies have been mostly conducted in the context of material products or free content consumption. Given that there are no studies that have examined the interaction effect of such cues in the context of pay-to-use online knowledge products, this investigation provides us a new scenario in which to gain a more comprehensive understanding of the interplay of these two cues in the digital age. As a result, we raise the following research questions:

RQ1: How, if at all, does source expertise moderate the effect of engagement metrics on user trust toward the content provider?

RQ2: How, if at all, does source expertise moderate the effect of engagement metrics on purchase intention?

Based on the above hypothesis and research question, the final conceptual model purposed for the current research is as Figure 1 shows.

Figure 1.

Conceptual model proposed.

Method

Participants

The study adhered to the tenets of the Declaration of Helsinki, and the protocol was approved by the authors’ Institutional Review Board (No. B2019003S). Before recruiting participants, this study used G*power to calculate the minimum sample size. The results showed that 114 participants are needed to achieve a medium effect size of 0.15 and a minimum power of 0.8 (Faul et al., 2007) in multiple regression with nine predictors (two independent variables, one interaction term, one mediating variable, and five covariates).

This laboratory experiment was conducted among college students from two universities in Shanghai, China. One is well-known for its science and technology majors, with the majority of students in this university studying in the fields of engineering and natural science. Therefore, to eliminate the potential confounding effects related to academic discipline, we also recruited students from another university ranked highly in humanities and social sciences. The recruitment poster was distributed through social media among students in these two universities. The posters indicated that the participants must be those who have no previous experience using online knowledge products. The qualified participants were invited to our lab at their most convenient time during April 2019. After obtaining consent forms from the participants, they were presented with an introduction to online knowledge products. Then, the participants were randomly exposed to one of four stimuli and filled in the questionnaire measure. During the experiment, participants could withdraw freely. Finally, the students were debriefed and offered a gel pen as a reward. The whole experiment lasted around 20 min.

A total of 151 students completed the experiment. Out of the 151 participants, 80 (53.0%) were female and 71 (47.0%) were male, 69 (45.7%) were junior undergraduates (45.7%), 40 (26.5%) were senior undergraduates, and 42 (27.8%) were graduate students. In terms of major distribution, 91 (60.3%) students majored in science and engineering, followed by 60 (39.7%) majoring in the humanities and social sciences. As for disposable monthly income, 28 (18.5%) participants reported lower than 1,000 RMB yuan, 41 (27.2%) between 1,001 and 1,500 RMB yuan, 35 (23.3%) between 1,501 and 2,000 RMB yuan, and 47 (31.1%) more than 2,000 RMB yuan. The mean score of involvement is 3.93 (SD = 1.23).

Design and Procedure

The study employed a 2 (engagement metrics: high vs. low) × 2 (source expertise: high vs. low) between-subject factorial design. Upon providing their consent, the participants were randomly assigned to one of four stimulus conditions with different levels of engagement metrics and source expertise. The four stimulus conditions were (1) high engagement metrics and high source expertise (n = 41); (2) low engagement metrics and high source expertise (n = 39); (3) high engagement metrics and low source expertise (n = 36); (4) low engagement metrics and low source expertise (n = 35). Chi-squared test showed there was no difference among the four groups in terms of gender ( $χ^{2} = 2.875, df = 3, p = 0.411$ ), grade ( $χ^{2} = 9.565, df = 6, p = 0.411$ ), major ( $χ^{2} = 0.692, df = 3, p = 0.875$ ), and disposable monthly income ( $χ^{2} = 6.588, df = 9, p = 0.680$ ). The results of ANOVA showed that the mean score of involvement is not significantly different among the four groups (F = 0.072, p = .975). Thus, the randomization of this experiment was successful.

After viewing the stimulus material, which is detailed in Section 3.3, participants were asked to report their perceived popularity of the online knowledge product, perceived expertise of the content provider, involvement with the knowledge product, trust toward the content provider, purchase intention of the knowledge product, as well as demographic information. Finally, respondents were debriefed on the purpose of the study and thanked for their participation.

Stimulus Material

Unlike on various MOOC platforms (Wan et al., 2020) and other online course providers, the majority of online knowledge products are provided in audio rather than video form, as users often listen to them on their commute to work or school. This is reflective of one of the key features of these predominantly app-based knowledge products in China: their convenience, which enables young netizens to capitalize on their fragmented time between work and home. A popular form of such audio e-learning is audiobook guides, similar to audiobooks, but in addition to reading the narrator also provides their insights on the content, allowing users to accelerate and concentrate the learning process.

We chose online audio guides for books as the stimulus material in the present study. First, it is easily accessed in almost all major knowledge-sharing platforms and has become one of the most popular types of pay-to-use online knowledge products currently in China. Second, compared with other types such as online audio courses, online audiobooks guide for books are series-based, and a single episode usually lasts around 30 min, which enables participants to finish the complete product experience within the laboratory scenario. The layout of the stimulus material was primarily designed based on audiobook guides from Ximalaya, Zhihu, and igetget—the three most popular online paid knowledge platforms in China—whereas all the information about the book, such as the title and the author, were fabricated in order to avoid instances where participants might have existing impressions of an actual book.

Engagement metrics in this study were manipulated by varying the number of likes, namely the number of other consumers interested in the online knowledge product. The reasons for selecting the number of likes as the engagement metrics were twofold. Firstly, the number of “likes” is a frequently-seen metric that appears in almost every online media platform and is easily recognized. Secondly, compared to other metrics such as the number of comments, which is more complicated due to its containing positive, negative, and neutral content, the varying number of likes directly represents the degree of positive assessment. Participants in the condition of high engagement metrics were told there were 1,834 consumers who liked the knowledge product, whereas participants in the condition of low engagement metrics were told that only three consumers liked the knowledge product. Source expertise was manipulated by giving different labels to the content provider to display their different levels of expertise. Since the book topic we chose for the stimuli was on psychological well-being and self-control, we labeled the content provider as “a PhD in psychology” and “a content provider for the platform” for the high-expertise condition. Meanwhile, for the low-expertise condition, the content provider was only given the label “a content provider for the platform.”Figure 2 only displays two conditions (high engagement metrics and high source expertise, low engagement metrics and low source expertise).

Figure 2.

Stimuli displayed: (a) high engagement metrics and high source expertise and (b) low engagement metrics and low source expertise.

Measures

The studied constructs were measured by the following scales. Unless indicated, all scales used in this study were anchored on a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree).

The dependent variable is user purchase intention toward the online knowledge product. The measurement items used for this construct were adapted from Dodds et al. (1991), including “My willingness to pay for the knowledge product is high,”“The probability that I would consider buying the knowledge product is high,” and “The likelihood of purchasing this knowledge product is high.” Internal consistency of the measurement was verified with $Cronbach " s α = 0.975$ , far exceeding the 0.70 thresholds.

The measurement of user trust toward the content provider consisted of two items, which were adapted from H.-W. Kim et al. (2012). The two items were “The content provider is trustworthy” and “The content provider is capable of doing his/her job” respectively. The internal consistency of this construct was also excellent ( $Cronbach " s α = 0.921$ ).

In addition to demographic variables including the college students’ gender, major, grade and disposable monthly income, involvement with the online knowledge product was also considered as an extra covariate. Since the content topic was the same for all participants, it was not warranted that every participant would be interested in the subject, which may impact user perception and purchase intention. Therefore, user involvement was taken into consideration as another variable in our study. The measurement items were adapted from Holzwarth et al. (2006), which included: “The knowledge product is relevant for me,”“The knowledge product is important for me,”“I’m interested in the knowledge product” ( $Cronbach " s α = 0.931$ ).

Table 1 shows marginal means, 95% confidence intervals of the means, and standard deviations of the means of the key measured variables in our study (e.g., trust as the mediator and purchase intention as the dependent variable) for each treatment group and the overall sample.

Table 1.

Cell Means of Key Measured Variables by Treatment.

	N	Trust		Purchase intention
	N	M [95% CI]	SD	M [95% CI]	SD
High engagement metrics and high source expertise	41	4.659 [4.321, 4.996]	1.069	2.992 [2.533, 3.451]	1.454
Low engagement metrics and high source expertise	39	4.051 [3.721, 4.381]	1.018	2.812 [2.386, 3.238]	1.315
High engagement metrics and low source expertise	36	3.931 [3.510, 4.351]	1.243	2.991 [2.520, 3.461]	1.390
Low engagement metrics and low source expertise	35	3.929 [3.551, 4.306]	1.099	3.171 [2.689, 3.654]	1.403
Overall	151	4.159 [3.976, 4.342]	1.140	2.987 [2.764, 3.210]	1.384

Data Analysis and Results

Manipulation Check

To confirm that the experimental manipulations performed as intended, participants were first given four questions to assess whether they correctly recognized the engagement metrics and source expertise when evaluating the online knowledge product. To be specific, the participants were asked to indicate how popular the knowledge product was (“The knowledge product is popular,”“There are lots of people interested in the knowledge product,” $Cronbach " s α = 0.898$ ) and how much of an expert they perceived the content provider to be (“This content provider is expertise in psychology,”“This content provider has lots of knowledge in psychology,” $Cronbach " s α = 0.935$ ).

The manipulation check was successful across the variables of engagement metrics and source expertise. Participants who were exposed to the stimuli with high engagement metrics perceived the knowledge product to be significantly more popular than those who received the stimuli with low engagement metrics ( $M_{high engagement metrics} = 4.071, SD = 1.197; M_{low engagement metrics} = 3.682, SD = 1.167; t (149) = 2.022, p = 0.045$ ).In the same vein, those who were exposed to the content provider with the label “a PhD in psychology” perceived him/her to have significantly higher expertise ( $M_{high source expertise} = 4.981, SD = 1.170; M_{low source expertise} = 4.113, SD = 1.138; t (149) = 4.612, p < 0.001$ ).

Moderated Mediating Analysis

The bootstrapping approach was adopted in the present study since it does not impose an assumption of normality, thereby providing more accurate confidence intervals than alternative methods for assessing mediation effects (Cheung & Lau, 2007). Specifically, we performed the conditional process analysis using Model 8 in PROCESS SPSS macro (Hayes & Andrew, 2012) to test the moderated mediation model, with engagement metrics as the independent variable, trust as the mediator, purchase intention as the dependent variable, and source expert as the moderator of the relationship between engagement metrics and trust. Before conducting the data analysis, the experimental treatment of engagement metrics was coded as one dummy variable (0 = low engagement metrics, 1 = high engagement metrics), and source expertise was coded as another dummy variable (0 = low source expertise, 1 = high source expertise). In addition, following the suggestions of Rosenthal and Cummings (2021) on considering covariates in addition to experimental effects in data analysis, this study also employed involvement and demographic factors (e.g., gender, major, grade, and income) in the model examination to address other interested predictors of consumer trust and purchase intention on online knowledge product. Since most of these variables could not be manipulated, including them as covariates can help us better understand the impacts of engagement metrics, source expertise, and their interaction term on the trust and purchase intention of online knowledge products.

Firstly, providing support for the moderated mediation process model, the interaction of the two variables (engagement metrics × source expertise) had a significant impact on trust (mediator) at a .05 significance level ( $β = 0.565, p = 0.047, C I = [0.007, 1.123]$ ). In turn, trust significantly predicted purchase intention ( $β = 0.425, p < 0.001, C I = [0.223, 0.628]$ ). The interaction of engagement metrics and source expertise was not a significant predictor of purchase intention after controlling for the mediator trust ( $β = 0.001, p = 0.998, C I = [- 0.690, 0.692]$ ). The interaction effects on trust and purchase intention are displayed in Figure 3. As for the effects of covariates, the results showed that none of the demographic variables were significantly associated with trust and purchase intention, while involvement significantly predicted both trust and purchase intention (see Table 2).

Figure 3.

Interaction effects on trust and purchase intention: (a) interaction effect on trust and (b) interaction effect on purchase intention.

Table 2.

Regression Analysis.

Dependent variable	Model 1				Model 2
	Trust				Purchase intention
	Coff.	p Value	LLCI	ULCI	Coff.	p Value	LLCI	ULCI
Independent variables
Engagement metrics	0.037	.855	−0.368	0.442	−0.125	.618	−0.619	0.370
Source expertise	0.179	.373	−0.217	0.576	−0.303	.219	−0.789	0.182
Engagement metrics × Source expertise	0.565	.047	0.007	1.123	0.001	.998	−0.690	0.692
Trust					0.425	.000	0.223	0.628
Covariates
Involvement	0.581	.000	0.468	0.693	0.458	.000	0.278	0.639
Gender	−0.042	.773	−0.330	0.246	−0.161	.368	−0.513	0.191
Major	−0.101	.498	−0.393	0.192	0.051	.780	−0.307	0.408
Grade	0.009	.835	−0.077	0.095	0.065	.224	−0.040	0.170
Disposable monthly income	0.065	.262	−0.049	0.178	−0.040	.573	−0.179	0.099
Constant	1.410	.001	0.675	2.144	−0.353	.460	−1.293	0.588
Model fit
R	.691				.690
R ²	.478				.477
F	16.242***				14.266***

***

p < .001.

Secondly, the significance of both the conditional direct and indirect effect of engagement metrics on purchase intention was examined across each level of source expertise with estimated standard errors and 95% confidence intervals (CIs). No direct effect was found in either high ( $C I = [- 0.619, 0.370]$ ) or low ( $C I = [- 0.606, 0.358]$ ) source expertise conditions, for the 95% CI at both conditions straddled zero (Table 3). The index of moderated mediation effect (effect = 0.240, SE (boot) = 0.125, 95% CI (boot) = [0. 030, 0.537]) indicates that the mediation effect of source expertise on the relationship between engagement metrics and trust was conditional. A closer look at Table 3 reveals that the indirect effect was significant in the condition of high source expertise condition but not in that of low source expertise. Specifically, when source expertise is high, a significantly positive indirect effect exists ( $C I = [0.110, 0.483]$ ), indicating that trust mediated the relationship between engagement metric and purchase intention. However, when source expertise is low, the indirect effect is not statistically significant ( $C I = [- 0.147, 0.207]$ ), which suggests that trust does not function as a mediator in the proposed relationship.

Table 3.

Conditional Direct Effect.

Direct effect	Source expertise	Effect	SE	LLCI	ULCI
	0 (low)	−0.125	0.250	−0.619	0.370
	1 (high)	−0.124	0.244	−0.606	0.358
Indirect effect	Source expertise	Effect	Boot SE	Boot LLCI	Boot ULCI
	0 (low)	0.016	0.090	−0.147	0.207
	1 (high)	0.256	0.090	0.110	0.483

Note. Conditional indirect effects are based on 5,000 bootstrap samples.

Discussion

Distinct from the published literature on user willingness to pay for online knowledge products, which has mainly focused on product-related factors, this study focused on a factor that has been largely unexamined till this point—engagement metrics as a source of social impact or type of bandwagon cues. Based on the experiment, we found that engagement metrics had a conditional impact on users’ purchase intention via their trust toward content providers. Specifically, only when source expertise is higher do higher engagement metrics lead to higher consumer trust, in turn resulting in higher purchase intention. This study and its findings have both theoretical and practical implications.

Theoretical Implications

First, this study extends current social impact research into a new context—the paid consumption of online knowledge products—and sheds light on the conditional impact of engagement metrics on user decision-making. In line with the SIT and bandwagon effect, a rich body of research has shown the importance of social impact or peer pressure in understanding people’s decision-making (Coulter & Roggeveen, 2012; Metzger et al., 2010; Sundar & Nass, 2001; Winter et al., 2016; Zhang et al., 2014; Zhao et al., 2018). Even though the importance of engagement metrics in predicting user willingness to pay for online knowledge products was still supported, its impact was shown to be conditional in our study. Such a finding adds new insight to the existing body of knowledge on the impact of social influence in the digital age. To be specific, it highlights that the effect of engagement metrics functioning as a source of social impact or type of bandwagon cues is highly contextual; thus, we need to consider it in a more nuanced way. As mentioned above, user purchase of online knowledge products is usually driven by motivations of professional learning and self-development rather than that of entertainment or time-killing (iiMedia Research, 2020a). In other words, people consume online knowledge products for utilitarian rather than hedonic purposes. Hence, source expertise, a variable that well reflects the quality online knowledge products, holds a strong influence over how people evaluate the products and later form their purchase decision. To a great extent, whether and how engagement metrics have an impact on users’ willingness to pay for online knowledge products depends on their perception of the source’s expertise.

Second, our finding regarding the interaction effect of multiple cues is contrary to source primacy effect but supports the cue-cumulation effect. Prior studies focusing on online news consumption repeatedly found that bandwagon cues had little impact on user evaluation and decision-making when the news story was from a highly credible news organization (Metzger et al., 2010; Sundar & Nass, 2001; Winter et al., 2016). Such source primary effect is consistent with the HSM, which posits people are cognitive misers who prefer to invest less cognitive effort and will spend much effort only when they have to (Bohner et al., 1995). The discrepancy between our findings and the previous research results could be well explained by the different research objects. Compared to the consumption of free news stories, people usually become pickier when they need to pay money for online knowledge products. Consequently, individuals first turn to source expertise to make an initial evaluation. If the source expertise is not high, they will have a quick judgment that the content provider is not trustworthy, regardless of whether the engagement metrics are high or low. Only if the source expertise is high will they then have a look at engagement metrics to make a better decision. An online knowledge product offered by a source with high expertise and recommended by a large number of users is perceived to be more worthy of purchasing than their counterparts with other combinations of source expertise and engagement metrics (e.g., high source expertise with low engagement metrics, low source expertise with high engagement metrics, and low source expertise with low engagement metrics). Such an interaction effect implies a cue-cumulative effect of multiple cues (Lin et al., 2016), and provides an insightful look into the ongoing debate on the interplay effect of concurrent different heuristic cues.

Furthermore, the results suggest consumer trust toward the online knowledge provider plays a significant mediating role between engagement metrics and purchase intention. This finding helps to illustrate how engagement metrics influence purchase intention in online knowledge marketing. What’s worth noting is that such a mediating role is conditional. Particularly, the mediating effect fails when source expertise is low. This conditional mediating effect further underscores the importance of source expertise in this unique market. It also highlights the need to consider boundary conditions when exploring the mechanism underlying the effects of engagement metrics in future research.

Practical Implications

Apart from theoretical contributions, the findings of the current study also offer practical implications for managing online knowledge platforms and products in the future. For instance, online knowledge platforms need to continually improve both their product exposure among potential consumers and the expertise level of their content providers. In the early development stage of online knowledge platforms in China, celebrities, particularly movie stars and singers, are often invited as content providers for online knowledge products to attract users. The idea behind this strategy is to help online knowledge products gain high engagement metrics as soon as possible. The marketers believe that high engagement metrics will improve the sales of online knowledge products. However, according to our findings, this strategy may not be that effective in the long run unless the expertise of the content provider is ensured as well. Therefore, in order to make better use of social impact to improve individuals’ purchase intention of online knowledge products, the platforms should devote their resources to improving their source expertise at the same time. In addition, our regression analysis shows that user involvement with the content has a statistically significant impact on their trust and purchase intention, suggesting that finding and forwarding relevant online knowledge products for users is another important measure for platforms to consider in the future.

Limitations and Future Research

The present study is not without its limitations. First, our study’s generalizability is limited due to the use of student samples. Though students are the majority of potential users of online knowledge platforms, there may still remain some gaps between college students and working professionals that might affect the perception and adoption of the online knowledge products under discussion. Second, we manipulated engagement metrics only using the number of likes by others. Future studies can improve the manipulation of engagement metrics with the combination of the number of likes, shares, and comments, which tend to appear simultaneously on digital platforms in today’s online environment. Different metrics might have different main impacts as well as potential interaction effects. Finally, the analysis of this study was confined to a selected type of online knowledge product—audiobook guides. Going forward, further academic attention needs to be paid to other types of online knowledge products to validate the generalizability of these research results.

Conclusion

Drawing upon the SIT, this study adopted a between-subject experiment to examine the social impact in the case study of user’ purchase decision-making toward online knowledge products. We found that the impact of engagement metrics on user purchase intention via trust is contingent upon the moderator of source expertise. Only when source expertise is higher, do higher engagement metrics lead to higher consumer trust, in turn resulting in higher purchase intention. This study provides a more comprehensive understanding of social impacts in the digital age for academia while also offering practical implications for the industry professional to more effectively sustain the development of online knowledge products.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Institute of College Student Development, Shanghai Jiao Tong University (Grant No. DFYLL-2020081).

Ethics Statement

The protocol was approved by Institutional Review Board at School of Media and Communication, Shanghai Jiao Tong University (No. B2019003S). The authors promise that the study adhered to the tenets of the Declaration of Helsinki.

ORCID iDs

Wu Li

Pengya Ai

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More Than Just Numbers: How Engagement Metrics Influence User Intention to Pay for Online Knowledge Products

Abstract

Keywords

Introduction

Literature Review

Theoretical Framework

Engagement Metrics and Purchase Intention

The Mediating Role of Trust

The Moderating Role of Source Expertise

Method

Participants

Design and Procedure

Stimulus Material

Measures

Data Analysis and Results

Manipulation Check

Moderated Mediating Analysis

Discussion

Theoretical Implications

Practical Implications

Limitations and Future Research

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

Ethics Statement

ORCID iDs

References