Marketer Generated Content on Social Media: How to Support Corporate Online Distribution

Abstract

Purpose: More and more marketers use social media platforms to create and spread information called Marketer Generated Content (MGC) to inform consumers of products. MGC often embeds product purchase links, thus directing consumers to online distribution channels for online purchases. This study examined the effect of social media MGC on consumers' willingness to buy online in the anchor of consumers' perspective to answer the question of "how social media generated content support corporate online distribution". Research design, data, and methodology: According to the means-end-chain theory, we introduce perceived value and continuous following intention as chain mediators to explain the mechanism of MGC influence on consumers' online purchase intention and consider product type to discuss boundary conditions. Two experiments were designed to test hypothesizes. Results and Conclusion: First, emotional MGC (vs. informational MGC) has lower (higher) perceived utility (hedonic) value. Second, perceived value has a significant mediate role in the effect of MGC on continuous following intention. Third, perceived value and continuous following intention significantly and sequentially mediated the effect of MGC on online purchase intention. Through the sequential mediations of perceived utility value and continuous following intention, Informational MGC of search products significantly increase online purchase intentions. Another parallel sequential mediation, including perceived hedonic, emotional MGC of experience products, partially enhanced online purchase intentions. Finally, this study gives implications for how corporates can use social media MGC to promote product sales online.

1.Introduction

Social media has become an essential channel for companies to communicate with consumers (Goh, Heng, & Lin, 2013). On social media platforms, more and more marketers use social media platforms to create and spread information (e.g., products, achievements, rewards) (Colicev, Malshe, Pauwels, & O'Connor, 2018), which is called Marketer Generated Content (MGC). MGC could inform consumers of products and promote sales online (Cole, Long, Chiagouris, & Gopalakrishna, 2011). MGC often embeds product purchase links, thus directing consumers to online distribution channels (e.g., third-party platforms or standalone sites) for online purchases. Consumerssearch MGC to make informed choices(Wan & Ren, 2017). MGC has become an increasingly important intermediary for communication between companies and consumers.

In previous studies, MGC is typically defined as shared content generated by marketers on official websites or third party social media platforms for different purposes (Kumar, Bezawada, Rishika, Janakiraman, andKannan, 2016; Meire, Hewett, Ballings, Kumar, & Van den Poel, 2019). Previous studies focus on the MGC from different perspectives, such as MGC influencingconsumers’ brand evaluations (John, Emrich, Gupta, & Norton, 2017), consumer sharing, and engagement (Meire et al., 2019; Tellis, MacInnis, Tirunillai, & Zhang, 2019). There isalso some research pertaining to MGC on consumer onlineand offlinepurchase.Mochon, Johnson, Schwartz, andAriely (2017)demonstrated that consumers’ “likes” on social media significantly impact their offline purchasing behavior. By building a prediction model, Song, Huang, Tan, andYu (2019) found that the volume of MGC could directly predict box office revenue. Grewal, Stephen, and Coleman (2019) concluded that posting products on social media framed as identity-relevant can reduce purchase intentions for the same and similar products. Tao, Fang, Luo, andWan (2022)examined the effects of MGC on guests’ online booking intention and revealed that emotion-based description leads to higher guest booking intention than information-based description. These studies have shown that social media MGChasa significant impact on consumer onlineand offlinepurchase intentions butdidnot clearly explain the process of MGC influence on online purchase intention.Marketers expect their generate content onsocial media to engage consumers’ willingness to enter online distribution channels for online purchases. Varieties of content on social media are created and presented to consumers, marketers hoping to get the attention of consumers and achievethe ultimate business goal (Waters, Ghosh, Griggs, & Searson, 2014).

Marketers hope that the process will be sustainable and expect users to ‘follow’ even become ‘fans’. If so, consumers could actively and trustingly accept brand messages from marketers. Suchpositive interactions can help to reinforce consumer loyalty and ultimately increase online sales. Therefore, it is essentialto retain the consumers’ continuous attention to the content generated by the marketers. Previously literature has focused on consumers’ continued intention to use platformsorinformation systems (Bhattacherjee, 2001; Lin, Wu, & Tsai, 2005; Luo & Ye, 2019), paying little attention to how social media generated content elicits consumers' continuous following. In addition, the value of MGC is determined by consumers'perceived value, which impacts website visit intention leading to purchase intention(Martínez-Navarro & Bigné, 2017).Chiu, Wang, Fang, and Huang (2014) have confirmed the relationship between perceived value and continued behavior of the online platform. To answer the question of “how social media generated content support corporate online distribution”, this studyintroducesperceived value and continuous following intention as chain mediators to explain the mechanism of MGC influence on consumer online purchase intention.

Research has explored generated content on various product typesand influences consumer response. Primarily from user-generated content (UGC)perspective, research has examined the impact of user-generated online reviews and product typeinteractions on consumer decision making (Weathers, Swain, & Grover, 2015)and satisfaction (Lim, Al-Aali, & Heinrichs, 2015); user-generated content on social media and product type interactions on brand beliefs (Micu, Sciandra, & Micu, 2019)and informationseeking (Cheong, 2021). In terms of research on MGC, Tao et al. (2022)examined the role of travel types on the effectiveness of MGC and demonstrated heterogeneity in the effectiveness of MGC strategies for hedonic and utilitarian travel types. Matching brand communication content to product type leads to more positive results for brandsales (Kronrod, Grinstein, & Wathieu, 2012; Sela, Wheeler, & Sarial-Abi, 2012), but it is unclear how MGC impacts on product sales vary by type in general. Therefore, we examined the interaction effect between MGC and product type, verified the effect of MGC strategy on purchase intention in different conditions, and clarified the boundary for the impact of MGC on the corporate online sales. The following two research questions will be examined in this study.

RQ1. How marketer-generated content influences consumers’ continuous following intention and online purchase intention

RQ2. Do the effects of these influence processes vary across products type, and if so, how?

Firstly, to answer research questions, we classified MGC into two types of content (informational MGC and emotional MGC) based on Meire et al. (2019)and detected the different categoriesof MGC how to influence consumers’ responses, including perceived value, continuousfollowing, and online purchase intention.Secondly, according to the means-end chain theory, perceived value is the final motivation directly influencing consumers’ behavior (Gutman, 1997). Consumers receive two main types of value from online information, utility value and hedonic value(Kim & Han, 2011). Empirical research confirms the relationship between consumers’ perceived value (utility and hedonic value) and continued use intention(Luo & Ye, 2019), as well as the relationship between perceived value and online purchasing behaviors (Chiu et al., 2014). Therefore, we employed perceived (utility and hedonic) values with continuous following intention as a serial mediator to explain the mechanism of MGC influence on consumers’ online purchase intention. Thirdly, to examine social MGC varies in terms of product category, we classified products into search products and experience products (Nelson, 1974), and examined how MGC interaction with products type influencesconsumer’ online purchase intention.

The rest of thestudyis organized as follows. The second section presents the literature review and develops the research hypotheses. Then, the experimental design process and empirical analysis results of this paper are introduced in the fourth section. Finally, we discussed the main research conclusions and implications, then considered limitations and future research directions.

2. Literature Review and Hypotheses Development

2.1.MGC and Perceived Value

Customers continue to follow the MGC driven by different motivations. According to the Means-end chain, there are three levels of cognitive abstraction, attributes, consequences, and values, where higher levels of abstraction represent deeper levels of consumer motivation. In such a hierarchy, value can be considered as the utility abstraction that motivates consumers to engage in continuous behavior, i.e., consumer behavior is value-driven. Furthermore, the perceived value drive for continued behavior has been confirmed by research(Chiu et al., 2014; Luo & Ye, 2019). Therefore, it is reasonable to consider perceived value as a motivation to explain customers’ continuous behavior.

Customers perceived hedonic value and utility value (Kim & Han, 2011). Utility value refers to “the functional, goal, and rational oriented purpose of using a product or service, while hedonic value suggests the multisensory, novelty, fantastic, and emotive aspects of the shopping experience” (Kivetz & Zheng, 2017). As defining characteristics of MGC (Meire et al., 2019), Informational MGC includes informing customers about the product, relevant events, and conditions, or providing information about the company in general; Emotional MGC is evoking sensory emotions, messages high in arousal, containing calls to action and persuasive content, or entertaining content.

Informational content provides price, brand, and transactional information also describes features and prices related to the product or service that enhance the consumer’s perception of utility value. Conversely, emotional content uses emotional content to persuade, often using humor, passion, nostalgia, and warm emotional appeals. When emotional content is presented, consumers feel enjoyable and relaxed, enhancing their perception of hedonic value. We propose the following hypothesis:

H1: Compared to informational MGC, emotional MGC has lower (higher) perceived utility (hedonic) value.

2.2. MGC and ContinuousFollowing Intention

In the view of the means-end-chain theory, perceived value is the final motivation that affects individuals’ consuming behavior (Gutman, 1997). In the literature on the continuous use of information systems, empirical studies have demonstrated that utilitarian values (Jasperson, Carter, & Zmud, 2005) and hedonic values determine users’ continuance intentions (Lin et al., 2005).

MGC reviewers can be viewed as consumers of generated content in the social media context. Marketers provide content that satisfies their intrinsic needs, thus generating value. Consumers are willing to invest time and effort in long-term attention. Specifically, the informational MGC influences consumers’ perceptions of a productor brand, which increases the perceived utility value. As the cognitive needs are continuously met, consumers continuously desire to follow the marketer.

On the other hand, the emotional MGC entertains and pleasures the followers, increasing their perceived hedonic value. Many studies documented that the stimulus of a website impactsconsumers’ states of affective, eventually sharping behavioral responses(Ha & Lennon, 2010).The perceived hedonic value strengthens the emotional connection between the two parties, making it possible to form a long-term bond. Based on the aforementioned literature, thefollowing hypothesis was driven:

H2: Perceived value mediates the effect of MGC on the continuous following intention. (a)Informational MGC, via increasing perceived utility value, boosts consumers’ intention to follow consistently; (b) Emotional MGC boosts consumers’ intention to follow consistently via increasing perceived hedonic value.

2.3.MGC and Online Purchase Intention

MGC plays a significant role in influencing purchasing behavior (Grewal et al., 2019). From the perspective of MGC content, informative content of MGC positively correlated with consumer online engagement(e.g., forwards, comments and likes)(_{Lee, Hosanagar, & Nair, 2014}). Both Informational MGC and Emotional MGC positively affected guests’ booking intentions on the P2P accommodation platform(Tao et al., 2022). Informative MGC provides consumers with objective information that contains rational claims and makes them clearly understand the product’s characteristics. It helps consumers reduce the time of searching for information and the cost of decision making, thereby increasing the perceived utility value and thus stimulating consumers’ online purchase intentions (Goh et al., 2013). Emotional MGC evokes positive emotions (e.g., warmth, comfort) using many positive words to satisfy consumers emotionally(Hirschman & Holbrook, 1982). The emotional information meets consumers’ emotional demands, leading to an increase in consumers’ perceived value and promoting the emotional response of consumers, which helps to attract their attention to MGC. When consumers generate these significant emotional responses, it results in future purchases (Arif, Aslam, & Siddiqui, 2020).

In conclusion, Informational MGC and Emotional MGC satisfy consumers’ intrinsic needs for substantial value, producing a positive and pleasurable experience. Furthermore, the perception of value enhances the consumer’s perception of the marketer, leading to the retention of the following behavior, translating into purchasing potential.The consumer will make the final purchase based on the online channel provided by the marketer (for example, a link to a third-party platform). In other words, MGC leads to heightened perceived value and enhanced continuous follow intention, which results in positive online purchaseintention. Therefore, we presume:

H3: Perceived value and continuous following intention serve as serial mediators in the effect of MGC on online purchase intention.

2.4. Moderation of Product Category

2.4.1. Moderation Effect of Product category

We classify products into search and experience products according to how consumers perceive the quality of the product before experiencing it (Nelson, 1974). Because consumers’ process information differs for search and experience products(Huang, Lurie, & Mitra, 2009), the product type moderates the relationship between MGC and consumers’ perception of online product reviews (Weathers et al., 2015). Search products are standardized products, and consumers judge product quality through informative content. The more comprehensive information consumers have about the objective attributes for these products, the lower their information asymmetry. The informative content provided by marketers, such as features, usage, and parameters, meets consumers’ needs for product information, effectively improving consumers’ information asymmetry (Shareef, Dwivedi, Kumar, & Kumar, 2017), helping to improve consumers’ perception of utility value.

Experience products are non-standardized products that consumers need to personally experience in order to acquire complete product information (Susan & David, 2010). Consumers need to communicate and interact with the seller to compensate for the lack of experience, thus reducing information asymmetry (Rod & Saunders, 2009). For experience products, marketers who provide too much informational content can be perceived as a for-profit, whereas emotional content creates an emotional or subjective impression of the intangible aspects to describe the product (Sciulli & Bebko, 2006). Marketers combine emotional content (or entertaining content) with the product. Consumers form subjective judgments of the product and intuitive perceptions of the marketer(Benlian, Titah, & Hess, 2012).Consumers perceive pleasantness (or playfulness) in browsing, enhancing the perceived hedonic value. We propose:

H4: Poduct category moderates the impact of MGC on perceived value. (a)For search products, the effect of Informational MGC (vs. Emotional MGC) on perceived utility value is greater. (b) For experience products, the effect of Emotional MGC (vs. Informational MGC) on perceived hedonic value is greater.

2.4.2. Moderation of Product Category on Online Purchase Intention

When consumers intend to buy products, they have different needs for product information sources and information content, and different MGC types satisfy consumers’ intrinsic needs, processing different perceptions of utility value and hedonic value (H4). Consumers’ perceived (utility and hedonic) value parallel mediates the impact of MGC on continuous following intention (H2). Perceived value and continuous following intention serve as serial mediators in the effect of MGC on online purchase intention(H3). We infer that the impact of informational and emotional MGC on purchase intentions differs across product types via series mediation. For search products, informational MGC (vs. emotional MGC) influences consumers’ perceptions of product quality(Wang, 2013), forms quality evaluations, enhances perceptions of utility value, and generates trust in the marketer’s competence (Özpolat, Gao, Jank, & Viswanathan, 2013). It makes consumers willing to follow the marketer, translating into an intention to buy through online distribution channels provided by marketers. For experiential products, emotional MGC (vs. informational MGC) uses emotional appeal to attract consumers, excite and arouse consumers (Kofi & McLean, 2018). When emotional appeal satisfies a consumer’s emotional needs, it enhances their value perception, builds an emotional connection with the marketer, and willingness to continue following. According to the emotional transfer model of persuasion (Chang & Tuan Pham, 2013), consumers transfer the same emotions to product attributes and make purchase behavioral reasoning decisions. Therefore, we propose.

H5: Prceived value and continuous following intention sequentially mediated the interaction effect of MGC and product type on online purchase intention.

The conceptual framework is shown in Figure 1.

Figure 1. Conceptual Framework

3. Study 1

Study 1 had several goals. First, we examined differences in the perceived value generated by respondents’ processing of MGC across information paths (H1). Second, we examined the mediating role of perceived utility and hedonic value in the effect of MGC on continuous follow intention (H2) and the series mediation of perceived value and continuous follow intention (H3).

3.1. Pretest: About the Stimulus

In Study 1, we chose the multi-functional backpack as an experimental stimulus. Featuring a multi-functional backpack by a fictitious brand, “HAMM.” We adopted fictitious brand names to control pre-existing brand associations with well-established brands. We searched twenty pieces (ten informational and ten emotional) of marketers generated content about backpacks on social media. Based on the definition of MGC characteristics (Meire et al., 2019), we generated two versions of the MGC for the “HAMM” brand backpack, containing one informational and one emotional in each version. After elaborating the definition dimensions of MGC informational and emotional content, three PhDs majoring in marketing were asked to judge and choose one version. Finally, one version was selected as the stimulus for the experiment. The marketer’s social media accounts were then given the virtual name “Jennie.”

3.2. Method and Procedure

3.2.1. Participants

One hundred and forty-six undergraduates from a Chinese university participated in the scenario experiment (M = 21.04, S.D. = .92, 71.3% female) and were randomly assigned to one of two conditions: informational-content- present or emotional-content-present. Due to failing the attention check, 12 participants were eliminated. Therefore, one hundred and thirty-four samples (M = 21.83, S.D. = .90, 74.6% female) were used in statistics analysis.

In the first part of the experiment, we conducted a supposedly unrelated study (called multi-functional backpack advertising test). First, all participants were asked to read the following information: “Imagine that you are planning to buy a backpack online. You are browsing your WeChat. The following material is a backpack message you saw posted by a marketer during your browsing process”. Then those in the informational-content-present condition were shown the Informational MGC. In contrast, participants in the emotional-content-present condition were shown emotional MGC. Following reading the generated content, all participants completed a self-reporting task.

3.2.2. Measures

Following the generated-content-present manipulation, participants indicated their perceived utility and hedonic value, which served as mediation variables.

As the generated-content-present manipulation check, all participants then rated how much simulating the generated content involved had felt informational and emotional. Informational was measured using a three-item scale (α = .92) (“I would learn a lot about the backpack and related brand” “Information obtained about the product would be useful.” “I think information about the backpack obtained would be helpful, ” adopted from Escalas &Stern (2003) and Lee &Hong (2016). Emotional was measured on three items (“After seeing this content of the product, I had intense feelings, ” “The content about the backpack emotionally attracted me, ” “The emotional aspects lead me to like this content, ” α = .93) from Logan, Dix, Bright, and Gangadharbatla (2012); Pavlou, Liang, and Xue (2007)

Participants reported their perceived utility value using a scale adapted from Kim and Han (2011); Zhang, Guo, Hu, and Liu (2017) (“The content is helpful for me.” ; “The content is useful for me.” ; “The content is practical for me.”; ranging from 1 = “strongly disagree” to 5 = “strongly agree”). The perceived hedonic value uses a scale from Pura (2005) (“I feel pleased and relaxed in browsing the content.”; “I gain joy and happiness in browsing the content.”; “I enjoyed being immersed in browsing the content.”). We then combined items to generate perceived utility value (α = .95) and hedonic value (α = .96). This study assessed continuous following intention (α = .89) adapted from Bhattacherjee (2001)and Chiu et al. (2014)(“I intend to continue to follow this marketer’s circle of friends.” “I would like to stop following this marketer’s circle of friends if I can.” (reverse coded)).

Three items scale of purchase intentions (α = .93) employed adopting from Grewal, Monroe, & Krishnan (1998). Three items are (1) “I would purchase this new backpack.”; (2) “I would consider buying this new backpack.”; (3) “The probability that I would consider buying this new backpack is high.”

All measurements employed a five-point Likert scale ranging from 1 = “strongly disagree” to 5 = “strongly agree.” In the final section of the experiment, all respondents reported the demographicinformation.

3.3. Results

3.3.1. Manipulation checks

We coded in the informational-content-present condition (Informational MGC) as 0, and emotional condition (EmotionalMGC)as1.A one-way ANOVAwith perceived informational as the dependent variable (F(1, 132) =27.53, p <0.01) showed that participants in the informational- content-present conditions(M =3.76, S.D.=0.89) perceived informational significantly higher than participants in the emotional-content-presentcondition (M =2.93, S.D.=.92). In contrast, with perceived emotional as the dependent variable (F(1, 132) = 3.54, p-value = .06) showed that participants in the emotional-content-presentconditions(M = 3.01, S.D.=0 .97) perceived emotional marginallyhigher than participants in the informational-content-present condition (M = 2.71, S.D. = .86).The results proved that the manipulation of the generated content was successful.

3.3.2. Perceived Utility Value and Hedonic Value

ANOVA was conducted to test the effect of MGC on perceived utilityvalueandhedonicvalue. Theresults reveal a significant main effect of MGC on perceived utilityvalue (F(1, 132) = 8.52, p-value = .004 < .05) and hedonic value(F(1, 132) =12.73, p-value =.001< .05).In addition, comparing in the informational-content-present condition (M_{perceived utility value}=3.31, S.D. =.83; M_{perceived hedonicvalue}= 2.41, S.D.=.91, respectively), participants reportedlower perceived utility value(M =2.88, S.D. =.91; t(134) =2.92, p-value =.004< .05, d =.438)and higherperceived hedonic value(M =2.99, S.D. =.98; t(134) =−3.57, p-value =.001, d = −.582) in the emotional-content-present condition, s_{upporting H1.}

3.3.3. Mediation Analyses

We conducted mediation analyses with Hayes (2017)’s PROCESS (model 4; 5, 000 bootstrapped samples) to test the effect of MGC(informationalMGC=0, emotionalMGC= 1) oncontinuance intentions, using perceived utilityvalue and perceived hedonicvalue as mediatorsrespectively.The positive effect of informational MGC on continuance intentions is mediated by perceived utilityvalue(indirect effect b =−.135, S.E. =.07; 95% confidence interval [CI] = [-0.31, -0.03]). This result can be interpreted as informational MGC(vs. emotional MGC), which boosts consumers' intention to follow consistently via increasing perceived utility value. Similarly, the positive effect of emotionalMGCon continuance intentions is mediated by perceived hedonicvalue (indirect effect b =0.23, S.E. =.08; 95% confidence interval [CI] =[0.09, 0.39]). The results show thatemotional MGC, via increasing perceived hedonic value, boosts consumers’intention to follow consistently.H2 is supported.

We conducted a series mediation analyses with Hayes (2017)’sPROCESS (model 6; 5, 000 bootstrapped samples) to test the effect of MGC on purchase intentions, using perceived utility value (perceived hedonic value) and continuance intentions as chain mediators. The results show that the mediating chain effect of perceived utility value (perceived hedonic value) and continuance intention is significant (b=−.04; b=.06, 95% confidence interval excluding zero). The direct effect of MGC on purchase intention is b_{perceived utility value}=. 19, b_{perceived hedonic value}= .09) with 95% confidence interval both excluding zero (including zero). It indicates that perceived utility value (perceived hedonic value)and continuanceintention have a partial (complete) serial mediating effect in the effect of MGC on purchase intention (see Table 1). It is proved that the positive effect of informational MGC(emotionalMGC) on purchase intention is mediated sequentially through perceived utility value (perceived hedonic value) and continuance intentions. Theseresults confirm H3.

3.4. Discussion

We find empirical support for H1, emotional MGC(vs. informational MGC)produceslower perceived utility value and higher perceived hedonic value. In other words, informational MGC elicits greater perceived utility value but lower perceived hedonic value. The results also confirm the mediating roles of perceived utility and hedonic values (H2).Finally, we conclude that MGC leads to heightened perceived value and enhanced continuous follow intention, which results in positive online purchase intention (H3). Lee, Bae, and Koo (2011)argue that the attributes of search goods are relatively objective and easy to compare, and consumers can quickly form relatively homogeneous evaluations of search goods; the attributes of experience goods are difficult to compare evaluated. When consumers are confronted with different product types, they have different paths to process information and produce different value perceptions. Therefore, we will examine the moderating role of product type in the effect of MGC on perceived value and continuance intention in Study 2.

Table 1. Mediating Effects of Perceived Value and Continuous Following Intention

4.Study 2

In Study 2, we examined the effect of MGC interactions with product type on perceived (utility and hedonic) value(H₄)and onlinepurchase intention (H5).

4.1. Method and Procedure

We chose moisturizing cream as an experienced product and batterycharger as a search product. Like Study 1, stimulus materials of informational MGC and emotional MGC were generated separately forboth products. A total of 191 undergraduates from two Chinese universities participants (M=19.32, S.D. =1.45; 83% female) were assigned to a 2 (MGC: informationalMGCvs. emotional MGC) × 2 (product type: search product vs. experience product) between-subjects design. After reading the stimulus material, participants in each of the four scenarios completed a self-reporting scale which was identical to Study 1.We created indices of perceived utility value (α =.96), perceived hedonic value(α =.96), continuous follow intention (α = .90), and purchase intention (α = .95) performed the analyses.

4.2. Results

4.2.1. Manipulation Checks

A 2× 2analysis of variance (ANOVA) onperceived informational (α= .94)manipulation check revealed that participants reported higher perceived informational in the informational-content-present conditions (M= 3.62, S.D. = .10) versus the emotional-content-present(M = 2.79, S.D. = .09)condition (F(1, 189) = 35.130, p-value < .01)). In contrast, with perceived emotional (α = .92) as the dependent variable(F(1, 189)= 3.77, p-value= .05))showed that participants in the emotional-content-presentconditions (M =2.98, S.D.= .11) perceived emotional marginally higher than participants in the informational-content-present condition (M=2.68, S.D.=.10).The results proved that the manipulation of the generated content was successful.

4.2.2. The Moderating Effect of Product Type on Perceived Value

To examine the moderating effect of product type on perceived utility (and hedonic) value, we tested a moderate model (Hayes, 2017, Model 1), with MGC as the independent variable; perceived utility as the dependent variable, first; product type as moderate variable, and bootstrapping procedure using 5, 000 resamples with replacement. The test results show that a main effect of MGC (b = −1.28, t= −6.88, p-value < .001), with no significant effect of product type (b = −.16, t = −.82, p-value = .41 > .05) and a significant interaction effect (b = .73, t = 2.73, p-value =.007 < .05). It suggests that product type has a moderating effect on the relationship between MGC and perceived utility value (see Figure 2). The effect of MGC on perceived utility value was significantly higher for search products (b = −1.28, t = −6.88, p-value < 0.001) than for experience products (b = −.56, t = −2.93, p-value = .003 < 0.05). Since the informational MGC was coded as 0 and the emotional MGC was 1, for search products, a negative regression coefficient with a larger absolute value (b = −1.28) indicates a greater effect of informational MGC on perceived utility valuecompared to emotional MGC. The results showed that the effect of informational MGC (vs. emotional MGC) on perceived utility value is greater for search products than for experience products, supporting H4a.

Similarly, with perceived hedonic value as the dependent variable, the interaction effect between MGC and product type is no significant at the 95% confidence interval (CI= [−.05, .77]), but at 90% (CI= [.02, .70]). In order to show the difference effect MGC on perceived hedonic value between product condition, we used 90% confidence interval to show this result. At the 90% confidence interval, the MGC main effect (b =.50, t = 3.44, p-value < .001) and interaction effect (b = .36, t = 1.75, p-value = 0.08) were significant, but the product type main effect was not (b = .07, t = .45, p-value =.63). The results show the effect of MGC on perceived hedonic value was significantly higher for experience products (b = .86, S.E. = .15, 90% CI = [.61, 1.10]) than for search products (b = .50, S.E. = .14, 90% CI = [.25, .73]) (see Figure 3). In experience products condition, regression coefficient (b = .86) indicate that emotional MGC (vs. informational MGC) have higher perceived hedonic value. In the summary, effect of emotional MGC (vs. informational MGC) on perceived hedonic value is greater for experience products than for search products, partial supporting H4b.

Figure 2. The Moderating Effect of Product Type

Figure 3. The Moderating Effect of Product Type

Table 2 .Moderated Mediation Test Results

4.2.3. Moderating Effect of Product Type on Serial Mediation

To examine whether differences in perceived value and continuous follow intention guided the interactive effect of MGC and product type on online purchase intention, we followed Hayes’s (2017) Model 83 bootstrapping procedure using 5, 000 resamples with replacement. First, including perceived utility value as mediator 1, continuous follow intention as mediator 2, results showed that the index of moderated mediation was significant (b = .30, S.E. = .12, 95% CI= [.08, .57], excluding zero). As shown in Table 2, MGC →UV→CI→PI path, in the search product condition, perceived utility value and continuous follow intention serially mediated effect is significant (b = −.54, S.E. = .11, 95% CI= [ −.79, −.34]). In the experience product condition, serially mediated the effect is significant (b = −.23, S.E. = .09, 95% CI= [−.43, −.07]). The difference in the chain mediation effect between search and experience products is .30 (95% CI= [.08, .57], excluding zero), which indicates a significant difference between the two chain mediation effects. It shows that product type has a moderating effect on the chain mediating effect of perceived utility value and continuous follow intention between MGC and purchase intention. The chain mediation effect was stronger in the search product condition than the experience product.

For another parallel mediating variable, we change perceived hedonic value as mediator 1 (see Table 2, MGC→HV→CI→PI path), product type as the index of moderated mediation was no significant at the 95% confidence interval (CI = [−.02, .36]), but at 90% (CI = [.01, .32], excluding zero). In order to show the difference serially mediated effect between product condition, we used 90% confidence interval in the following discussion. The results showed that the index of moderated mediation was significant (b = .15, S.E.= .10, 90% CI= [.01, .32]). In both search and experience product condition, serially mediated the effect are significant (b = .21, S.E = .07, 90% CI= [.11, .33]; b =. 36, S.E. = .10, 90% CI = [.22, .54]), and the series mediation difference is also significantly (b = .15, S.E. = .10, 90% CI= [.01, .32]). The chain mediating effect is stronger for experience products than search products.

The result shows that for search products (vs. experience products), the serial mediating role of perceived utility value and continuous following intention is stronger; for experience products (vs. search products), the chain mediating role of perceived hedonic value and continuous following intention is more robust. Since the informational MGC was coded as 0 and the emotional MGC was 1, for search products, a negative regression coefficient with a larger absolute value (b = −.53)indicates a greater indirect effect of informational MGC (vs. emotional MGC) on online purchase intention. In the experience products condition, regression coefficient (b = .36) indicates that emotional MGC (vs. informational MGC) has a greater indirect effect on online purchase intention. Thus, we conclude that Informational MGC of search products will generate higher online purchase intentions through sequential mediations (95% CI) of perceived utility value and continuous following intention; Emotional MGC of experience products will generate higher online purchase intentions through sequential mediations (90% CI) of perceived hedonic value and continuous following intention, supporting H5.

4.3. Discussion

Study 2 indicates that product category moderates the impact of MGC on perceived value (H4). For example, informational MGC (vs. emotional MGC) on perceived utility value is greater; for experience products, emotional MGC (vs. informational MGC) on perceived hedonic value is greater.

We demonstrated that the interaction effect of MGC and product type on online purchase intentionwassequentially mediated by perceived value and continuous following intention(H5). The moderated mediation effect was significant in the perceived utility value path and partially significant in the hedonic value path. For search products, informational MGC (vs. emotional MGC) yields higher perceived utility value, facilitating continued following intentions and leading to higher online purchase intentions. The partially significant results show that emotional MGC (vs. Informational MGC) produces online purchase intention through perceived hedonic value and the continuous following intention for experiential products.

5.General Discussion

5.1. Conclusions

Across these studies, we demonstrate how MGC on social media distinctly affects consumer purchases intention online, deeply recognize the importance of MGC to corporate online sales.According to the means-end chain (MEC) theory, we assess the effect of MGC on perceived utility and perceived hedonic value; significant mediating effect of perceived utility and hedonic value on continuous following intention, which the present studies of MGC paid little attention. Consistent with Grewal et al. (2019), MGC significantly impacts online purchase intention. We further discuss the mechanism of MGC that influences online purchase intention through a significant chain mediator. On the other hand, from the perspective of product type, we analyze the boundary conditions under which MGC impacts purchase intention. Finally, it is concluded that through the sequential mediations of perceived utility value and continuous following intention, informational MGC of search products significantly increase purchase intentions; through another parallel sequential mediation of perceived hedonic and continuous following intention, emotional MGC of experience products also enhanced purchase, but partially significant.

5.2. Implication

Thisstudy makes several theoretical contributions. First, we explained how social media MGC can influence corporate online distribution from the consumers’ perspective. Through experiments, this study investigated how different content of MGC can have a significant impact on consumers' online buy products. Second, to explain how MGC influences online purchase intentions, we included continuous following intention which the present studies of MGC paid little attention. Third, we found the interaction effect between MGC and product type on online purchase intentions, which will produce the effect of online distribution of the corporate different products. It extended MGC study of the boundaries of products sold online.

There are several managerial implications in this research. First, with the rapid growth of e-commerce, corporates increasingly use social media to introduce their products and lead consumers to online distribution channels to buy. This study answered the question of how social media MGC can support corporate online distribution. Corporate marketers by posting content appeal to consumers, gain higher willingness to continue following and generate higher willingness to buy online, thereby increasing the effectiveness of corporate online sales. Second, as the interaction between product type and MGC has a significant impact on consumers’ willingness to buy online, marketers can adapt social media MGC to suit the product type when corporate sell different products. Specifically, informational content can be added for search products and emotional content for experience products. In this way, marketers can boost corporate online sales.

5.3. Limitations and Future Research

We noted that this study has several limitations and provides future research opportunities. The limitations of this study are as follows: first, due to the difficulty in obtaining data, the impact of the generated content on online distribution was only from a consumer perspective examined the impact of MGC onpurchase intentiononline. Future research could explore the effect of MGC on corporate online distribution by combining behavioraldata on social media with sales data from distribution platforms.

Second, although the effects and mechanisms of generated content and product type on online purchase intention have been explored, other factors (distribution price, distribution platform preference) have not been explored. Future research could explore the mechanisms by which these factors influence MGC on consumers' online purchase behavior, as well as the impact on corporates’ online distribution effectiveness.