1. Introduction
The influence of social media on the daily lives of individuals is undergoing substantial transformation. In response to the increasing consumer desire for convenient and immediate online purchasing experiences, firms have begun to recognize and exploit the e-commerce capabilities of these platforms [1]. The utilization of social media platforms allows these consumers to engage in purchasing activities without the need to go away from the platform, hence facilitating a streamlined and expedited customer experience. The confluence of e-commerce and social media has unavoidably given rise to the emergence of social commerce [2]. Therefore, social commerce refers to an online commercial platform that utilizes social media and Web 2.0 technologies to facilitate social interaction and user-generated content [3]. A report shows the global revenue generated via social commerce reached 724 billion U.S. dollars in 2022 and is projected to exceed six trillion U.S. dollars by 2030 [1]. Additionally, it is noteworthy that Asian countries exhibit a notable inclination towards social buying. In 2022, China achieved one of the highest social media shopping rates globally, standing at 84% [2]. In the meanwhile, livestreaming commerce, often known as a subset of e-commerce or social commerce [4], garnered significant consumer attention in the Asia Pacific area starting in 2021 [5]. Notably, the biggest difference is between livestreaming e-commerce and livestreaming social commerce. Users’ shopping needs vary on Taobao, the largest livestreaming e-commerce platform worldwide [6], and TikTok, mainland China’s dominant social media platform, especially for short-form video content and livestreaming [7]. When livestreaming platforms are implemented, they have various consequences since TikTok is an entertainment short video application and Taobao is an online shopping application [8]. To put it another way, viewers of Taobao livestreaming seek content that is more specialized and focused in order to comprehend a certain product, see product demos, and engage with the anchor. Taobao consumers often have active, not passive, purchasing demands. Users spend a longer average amount of time on TikTok because they want to be amused, which leads them to purchase rapidly and impulsively in response to livestreaming settings and amusing material [9,10].
The use of live streaming as a means to enhance sales performance in China has been embraced by several vendors operating on social commerce platforms, owing to its growing popularity [11]. The livestreaming mode has changed traditional social commerce in several ways. First, traditional social commerce focuses on text and static photos for product information, but livestreaming platforms offer streamers in-the-moment video demonstrations, which improve product understanding; second, through the bullet screen, livestreaming shopping enables prompt client inquiries, with streamers answering in real-time; lastly, the dynamic engagement of livestream purchasing decreases concerns about supplier legitimacy, lowering perceived risks associated with online purchase [11,12]. Therefore, whether in online retail platforms or social media platforms, livestreaming commerce has emerged as a widely adopted shopping channel among Chinese consumers [13].
This particular kind of livestreaming enterprise capitalizes on the substantial fan base it has [14], and the active development of livestreaming shopping calls attention to empirical research. A majority of studies explored the antecedents that impact purchasing intention in the livestreaming shopping environment from various theoretical perspectives, such as IT affordance [11], the elaboration likelihood model [15], signaling theory [16], dual-process theory [17], the SOR framework [18], flow theory [19], social telepresence [20], and perceived value theory [21]. It is critical to comprehend the variables that affect customers’ factual behaviors in a livestreaming commerce environment as the number of transactions taking place increases. There were several studies focused on consumers’ impulse behavior in the livestreaming shopping context [19,20,21]. However, the study of the livestreaming shopping behaviors in the context of social commerce is still limited. In addition, prior research suggested that internet shopping behavior is often sensible [22], due to the increased uncertainty and risks consumers encounter in the online shopping environment [23]. Researchers discovered that social commerce via live streaming offers a natural interface for not just human-to-human contact [24], but also human–computer interaction [25]. In addition, people aged 18 to over 40 years old made up the majority of livestream shoppers in China [26]. The intricacy of today’s livestreaming commerce environment prompts academics to revisit the fundamentals of online buying behavior. Therefore, it is important to explore what factors influence customers’ decision-making processes in the context of livestreaming social commerce. In addition, social commerce encounters substantial challenges, encompassing concerns related to privacy, trust, and ethical considerations [27]. Hence, it is imperative to investigate how perceived risk in the social commerce context affects online consumers’ attitudes and actual purchasing behaviors.
By focusing on the consumers’ actual purchasing behavior, this pioneering research aims to provide a complete model for understanding customers’ decision making processes within the setting of livestreaming social commerce, drawing upon the framework of Technology Acceptance Model 3 (TAM 3). The present study contributes a valuable addition to the existing literature on TAM 3, as well as enhancing the theoretical analysis of consumer actual buying behavior. Furthermore, this study holds the potential to foster sustainability by offering valuable insights into the development of marketing and promotional strategies that can enhance sales performance for merchants. Such strategies, when effectively implemented, can contribute to the broader objective of bolstering economic growth within the Chinese market. This research is structured as follows. In the next section, the authors will provide a brief review of the literature related to TAM3. Following this, hypotheses will be formulated with support from the relevant literature. Subsequently, the methodology employed to test these hypotheses will be presented. After reporting the results from the data analysis, the authors will engage in a discussion of the findings, offer insights into practical and research implications, and finally, present their conclusions.
2. Literature Review and Hypotheses Development
2.1. Technology Acceptance Model 3
The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2, [28]) and TAM 3 are widely applied in the field of information systems and technology adoption. The UTAUT2 is a comprehensive theoretical model used to understand and explain the adoption and use of technology by individuals [28]. However, the primary focus of this article is not on technology adoption but on understanding customer behavior in the context of livestreaming social commerce. Thus, TAM3 is a more general framework that can be adapted to various contexts beyond technology.
TAM [29] is designed to anticipate the individual adoption and usage of new IT device, which asserts that people’s behavioral intention to use an IT device is driven by its perceived utility and perceived ease of use. Venkatesh and Davis [30] have extended TAM to TAM2 by introducing determinants of perceived usefulness, such as subjective norm, image, job relevance, output quality, result demonstrability, and perceived ease of use, along with the inclusion of two moderators: experience and voluntariness. Venkatesh and Bala [31] developed TAM3, which encompasses four distinct categories: individual variations, system features, social impact, and enabling factors. These categories function as factors that contribute to the perception of usefulness and ease of use. This study also aims to understand the impacts of individual differences; therefore, this study was based on TAM3 [31] as the framework to understand the comprehensive nature of consumers’ livestreaming purchasing behavior through a social media application.
2.2. Hypotheses Development
2.2.1. The Relationships among Perceived Risk, Trust and Purchasing Behavior
Perceived risk is often viewed as the inherent likelihood of experiencing negative consequences while engaging in online buying activities, including both the element of ambiguity and the possible severity of outcomes [32]. E-commerce has a higher propensity to engender ambiguity, particularly in areas such as online transactions, shipping and return policies, hence contributing to the perception of risk, in comparison to those of conventional commerce [33]. Perceived risk is taken into account in TAM 3 as an external variable that depicts how consumers perceive possible risks and uncertainties related to adopting a technology or platform [34], such as social commerce, and in turn, leads users to evaluate the trustworthiness of the platform.
Previous studies have shown that a high perceived risk level seems to be inversely associated with consumer confidence in online activities [35,36]. Nevertheless, clients are more likely to establish confidence in a provider if the perceived level of risk remains within a certain range [33]. Customers carefully choose online merchants that they deem to be sufficiently dependable in order to reduce their perceived risk [32]. Hansen et al. [37] state that the influence of perceived risks and consumer trust in online reviews significantly shapes consumer attitudes and behaviors concerning these reviews, with risk-taking directly impacting the intention to trust. This argument has been confirmed by a study by Sun [38], that found that perceived risk positively impacts consumers’ trust in livestreaming shopping in China. Furthermore, the positive relationship between perceived risk has been confirmed in online purchase intentions [39,40]. There is sufficient evidence to support a strong association between behavioral intention and usage behavior [41,42]. Therefore, the following hypotheses are presented:
In the livestreaming social commerce context, perceived risk positively impacts trust.
In the livestreaming social commerce context, perceived risk positively impacts consumers’ purchasing behavior.
Trust plays a crucial role in influencing the behavioral intentions and actual use of users under the Technology Acceptance Model (TAM), especially in online activities, such as e-payment [43] and e-learning [44]. Trust has been acknowledged as a vital element in the online environment and has been extensively examined within the realm of e-commerce [45]. Trust is commonly defined as a personal evaluation made by an individual on the reliability, integrity, dependability, and competence of another individual or organization [46]. Consumers are presently extensively using internet technology for duties pertaining to sensitive information [28]. Trust is a crucial and well-recognized notion that plays a significant role in shaping behavioral intention and use behavior across many domains, particularly in the context of electronic environments. While previous studies have shown the significant influence of trust on purchasing behavioral intentions [38,47,48] and purchasing behavior [38,49,50], it remains imperative to consider its relevance within the context of livestreaming social commerce. Therefore, the hypothesis below is presented:
In the livestreaming social commerce context, trust positively impacts consumers’ purchasing behavior.
2.2.2. The Relationships among Innovativeness, Deal Proneness and Purchasing Behavior
Within the context of TAM3, individual difference factors include personality characteristics and demographics such as gender and age, which possess the potential to impact an individual’s views of perceived utility and perceived ease of use [31]. Highly inventive customers are innately curious, value creative discovery, and are thus more inclined to use new goods and services [51]. Individual innovativeness, therefore, is a trait of individual users and may affect how they embrace and utilize technology, which is in line with TAM 3′s enhanced emphasis on individual differences. Although some studies have investigated the influence of consumer innovativeness on usage intentions or behavior [52,53], to gain additional empirical evidence and propose a more comprehensive framework, it is necessary to involve this factor in the current study.
Deal proneness, another user characteristic or preference, is a psychological idea that affects actions connected to value awareness and coupon responsiveness [54]. Consumers who are prone to making deals think about the psychological benefits of doing so and may not care about the implications for their finances [55]. A previous study found that higher innovative consumer tendencies are anticipated to partly impact their propensity to redeem bargains [56]. This finding also has been confirmed by Ghosh [57], who claims that consumers’ innovativeness positively impacts their deal proneness. In addition, Martínez-López et a. [58] mentioned that the availability of tools for price comparison encourages consumers to make purchases online rather than via more conventional channels. Therefore, online retailers use several promotions to entice clients to engage in activities or make purchases, and even online marketplaces create a variety of festivals to encourage consumption [59,60]. Therefore, this study presented the hypotheses below:
In the livestreaming social commerce context, innovativeness positively impacts deal proneness.
In the livestreaming social commerce context, innovativeness positively impacts consumers’ purchasing behavior.
In the livestreaming social commerce context, deal proneness positively impacts consumers’ purchasing behavior.
3. Research Methodology
3.1. Sample
The target audience for this research was mainland Chinese customers who have livestreaming purchasing experiences on social media platforms such as TikTok. To ensure the accuracy of the possible answers, one pre-screening question asked respondents whether they had experienced livestreaming shopping using a social media application for more than a year. If the respondents chose the “no” option, the survey was instantly ended.
A self-administered survey with two components was created to gather data. Age, gender, and prior livestreaming purchasing through a social media app were among the demographic data in the first section; in the second section, seventeen questions on a 7-point Likert scale, from “1 = strongly disagree” to “7 = strongly agree,” were used to assess the respondents’ psychological characteristics. The scales of previous studies were selected as follows: five items were adapted for trust from Zhou et al. [42] and Xu [61], three items were adapted for perceived risk from Xu [61], three items were adopted for innovativeness from Escobar-Rodríguez and Carvajal-Trujillo [62], three items were adapted for deal proneness from Tak and Panwar [55], and three items were adapted for consumers’ purchasing behavior from Zhou et al. [42]. All items are listed in Appendix A.
3.2. Data Collection
There were 27,661 COVID-19 infections in mainland China as of 1 December 2022 [63]; therefore, randomly reaching target respondents in various cities across China posed a considerable difficulty. Faugier and Sargeant [64] mentioned that the snowball sampling technique is considered the most viable strategy in situations when easily available sample frames are not accessible. In addition, data collection was conducted via an online questionnaire hosted on wjx.com. The first sample was initially gathered via snowball sampling from the author’s personal contacts on the Chinese mainland. Following that, they were urged to tell their acquaintances about the link. By the end of December 2022, there were 675 valid responses collected. The sample size used in this research meets Taro Yamane formula’s statistical standards [65], which asks for at least 399 valid data points when there are 464 million livestreaming users by the end of December 2021 [2]. Therefore, the study followed the standards specified by Taro Yamane for sample representation. The demographic statistics are presented in Table 1. According to Table 1, the preponderance of female respondents (61.3%) in this study does not introduce bias but rather reflects the prevailing trend in the Chinese context. That is, in contrast to male shoppers, female consumers exhibit a higher inclination to engage in online shopping events and embrace emerging functionalities such as social commerce and livestreaming commerce [66]. This indicates that the dataset of this study reflects the gender characteristics observed in the population.
3.3. Statistical Analysis
The statistical method employed for data analysis was partial least squares structural equation modeling (PLS-SEM). PLS-SEM was selected due to its suitability for non-normally distributed data and its applicability to exploratory research geared towards prediction and theory development [67]. In this study, PLS-SEM was deemed appropriate as the research aimed to construct a comprehensive model for understanding customer decision-making processes within the livestreaming social commerce context, drawing from the framework of TAM 3. It is worth noting that the data exhibited skewness and kurtosis values within the ranges of −0.583 to −1.020 and −0.286 to 0.708, respectively, indicating that not all variables conformed to a normal distribution. PLS-SEM analysis was performed in SmartPLS 4.0 [68].
4. Results
4.1. Analysis of Measurement Model
Before the structural model’s evaluation, all conditions must be met by testing the measurement model [67], which is equivalent to confirmatory factor analysis (CFA) in covariance-based structural equation modeling (CB-SEM, [69]). Since only reflective constructs were used in this research, it is necessary to compute the factor loadings, Cronbach’s alpha (CA), composite reliability (CR), average variance extracted (AVE), and discriminant validity [70].
First and foremost, to determine an indicator’s dependability, factor loadings were computed, as shown in Table 2, and the factor loadings needed to be above 0.708 to be significant at the 0.05 level [67]. In the meantime, cross-loadings were assessed, and the results showed that each indication loaded more strongly for its specific build than it did for any other construct [71].
Table 3 shows the results of an analysis using CA and CR to evaluate the construct’s internal consistency reliability. The results demonstrate that all metrics are higher than the necessary threshold of 0.70 [67]. Additionally, AVE was used to test convergent validity, and all results were above the standard threshold of 0.5 [67]. Lastly, discriminant validity was evaluated using two techniques. The Fornell–Larcker criterion [72] was used as the first technique, and Table 3 shows that AVEs fulfill the criteria for discriminant validity since their square root is greater than their correlation coefficient with other constructs. Because the original Fornell–Larcker PLS technique may exaggerate indicator loadings and underestimate structural model relationships [73], the second technique was used to assess the discriminant validity using the Heterotrait–Monotrait (HTMT) ratio of correlation, a higher-boundary criterion. Given that each concept was independent of the others, the results of the HTMT ratio test were all lower than a threshold of 0.85 [67], indicating that the discriminant validity was good.
4.2. Multicollinearity and Common Method Bias Assessment (CMB)
To examine multicollinearity, full variance inflation factor (VIF) statistics were used. The results reveal a range of full VIFs with latent variables from 1.42 to 1.93, which are not greater than 3. In essence, this demonstrates that multicollinearity was not a concern [67]. The possibility of common method bias in this research was also examined using the partial correlation procedure [74]. The partial correlation approach tests whether the zero-order and partial correlations are statistically consistent by using a marker variable that is conceptually unrelated to at least one of the conceptual model’s core components. Since the TAM model is not conceptually connected to respondents’ livestreaming purchasing experiences via social media applications, this marker variable was employed in the present investigation. Following the suggestion in Ringle et al. [75], the correlation matrix demonstrated that neither the dependent variable, consumer purchasing behavior, nor any of the other four constructs were significantly connected to the marker variable. The outcome of the partial correlation process revealed that none of the research correlations had undergone substantial modifications, indicating that common method bias was not a big issue in this study [74].
4.3. Structural Model Analysis
PLS-SEM analysis was performed to assess H1 to H18. The statistics of the latent variables were tested using the PLS algorithm with 300 iterations, and the significance was assessed using bootstrapping (5000 times) [67,76]. The outcomes indicate the relationship between the various constructs, as determined by examining the significance of the path coefficient, the R2, the Q2, and the effect size (f2). According to Figure 1, the findings show that the current model’s ability to explain customers’ purchase decisions for livestreamed commerce is poor, but its predictive power is medium (R2 = 0.35; Q2 = 0.31). In addition, regarding the outcomes, it can be stated that the main predictors of consumers’ purchasing behavior of livestreaming shopping through a social media platform in order of significance are as follows: trust (β = 0.37, p < 0.00, f2 = 0.16), deal proneness (β = 0.20, p < 0.00, f2 = 0.04) and innovativeness (β = 0.13, p < 0.00, f2 = 0.02). Lastly, the results from the model assessment indicate that perceived risk and trust were positively and significantly associated (β = 0.39, p < 0.00, f2 = 0.18); moreover, perceived risk positively impacts consumers’ purchasing behavior but not significantly (β = 0.07, p = 0.13, f2 = 0.01). Additionally, innovativeness was found to significantly and positively impact deal proneness (β = 0.42, p < 0.00, f2 = 0.22).
4.4. Mediating Effects
The mediating functions of trust and deal proneness have been explored in order to more precisely analyze the mediating influence between components in the theoretical framework. After bootstrap estimation (5000 times), mediating effects were investigated based on the regulation carried out and procedures applied by Zhou et al. [42]. That is, total effects, indirect effects and direct effects were used to explore the mediating effects between the components. In the beginning, a basic need for the meaningful presence of mediating effects is the importance of total effects and indirect effects. Additionally, if the direct effects are also important, the mediator is considered a “partial mediator”; otherwise, it is seen as a “full mediator.” Table 4 presents that both trust and deal proneness have complimentary partial mediating effects in the proposed model.
5. Discussion
In the context of livestreaming social commerce, this research offered a more thorough model based on the framework of TAM 3 [31] to comprehend customer purchase behavior in mainland China. Using 675 valid responses, a PLS-SEM analysis supported the majority of the hypotheses proposed. First and foremost, the analysis presented that consumer-perceived risk positively influences their trust in livestreaming social commerce. This finding is consistent with that pf previous studies by Hong [33] and Ling et al. [39]; however, it contrasts the findings of Kamalul et al. [35] and Park et al. [36]. That is, consumers tend to rely on trust as a coping mechanism in the face of perceived uncertainties and risks. It aligns with the notion of Jiang et al. [32] that in a circumstance of high perceived risks, consumers tend to scrutinize product-related information on social media platforms more diligently in order to minimize potential losses.
Furthermore, the finding reveals a positive relationship between trust and consumer actual purchasing behavior, which is in line with the findings of previous studies [49,50].This indicates that consumers place a premium on the trustworthiness of the platform or technology they are using for their purchasing decisions. They are more likely to engage in online transactions when they perceive the platform as reliable, secure, and credible. This finding aligns with the core tenets of TAM3, which highlights the importance of trust as a key construct. It confirms that trust is not just a byproduct but a fundamental driver of technology acceptance and usage.
Lastly, this study found that consumers’ innovativeness significantly and positively impacts their deal proneness, which is in line with a previous study in [57]. It implies that individuals characterized by a higher degree of innovativeness are more inclined to explore and embrace novel offerings in the market. This predictive relationship underscores the potential for innovative individuals to be early adopters, setting trends and influencing market dynamics.
6. Theoretical and Practical Implication
The results from this study provide theoretical contributions to the existing literature on TAM 3, as well as enhancing the theoretical analysis of consumer actual purchasing behavior by providing a comprehensive model. First and foremost, by applying TAM 3 to a new business scenario, livestreaming social commerce, this study explores consumers’ actual purchasing behavior by integrating consumers’ perceived risk, trust, innovativeness and deal proneness, enriching and extending the literature. Second, this research adjusted the measurement items for constructs in the context of livestreaming social commerce, except for innovativeness, and subsequently validated the connections between these constructs. Finally, the finding provides additional empirical support for the notion that perceived risk positively impacts consumers’ trust in the context of livestreaming social commerce.
The main findings of this study also offer practical implications for business and platform providers, as well as marketers. First of all, businesses and platform providers may emphasize trust by investing in strategies to enhance trustworthiness, such as transparent policies, secure payment systems, and authentic user reviews, which can have a direct and positive impact on consumers’ purchasing behaviors. Furthermore, they may consider taking an opportunity to foster trust by openly addressing perceived risks and implementing risk mitigation strategies. Furthermore, understanding the dynamics of how perceived risk can enhance trust may enable the development of more targeted trust-building initiatives within technology adoption contexts, ultimately influencing consumer behavior positively. In addition, firms and marketers may find value in targeting innovative consumers, tailoring their strategies to resonate with the preferences and openness to innovation displayed by these individuals, for instance by designing marketing campaigns and launching new products. Lastly, legislative policymakers are encouraged to formulate and enact legislation and regulations tailored to the protection of online consumers’ privacy and financial well-being.
7. Conclusions
Livestreaming social commerce has been a vital means to enhance sales performance in China, while previous studies on consumers’ actual purchasing behavior on livestreaming social commerce platforms are limited. The study effectively addresses existing gaps in investigating consumers’ actual online purchasing behavior by investigating the influence of perceived risk, trust, consumers’ innovativeness, and deal proneness. Based on the snowball sampling method, an online questionnaire was conducted in mainland China in December 2022. The findings show that consumers’ trust, innovativeness, and deal proneness are significantly related to their actual purchasing behavior in the context of livestreaming social commerce. Furthermore, this research provides additional empirical support for the notion that perceived risk positively impacts consumers’ trust in the context of livestreaming social commerce. Through this research, the authors offered new knowledge that can help online shoppers, businesses, platform providers, and marketers to focus on understanding and adapting to livestreaming social commerce for effective marketing strategies, thereby enhancing their competitiveness and sales performance in emerging markets such as China. Drawing from the results, the authors have presented a number of practical implications for merchants, platform providers and policymakers, including consumer segmentation, and online consumers’ privacy protection.
8. Limitations and Recommendations for Future Trends
It is important to acknowledge the contributions of this work while considering some limitations. The use of a snowball sampling technique in data collection resulted in a limitation in generalizing the survey findings to the broader population. In future research endeavors, it may be advantageous to use a probability-based approach for data collection, since this methodology has the potential to provide more generalizability. Conversely, the data were acquired via a self-administered questionnaire survey, a method susceptible to the effect of respondents’ subjective bias. In future research endeavors, it is conceivable that scholars may use a diverse range of resources in order to enhance the precision and reliability of response measurements. Lastly, the predominant inclusion of respondents under the age of 30 years old in this study poses a limitation, potentially introducing bias and hindering the generalizability of findings. Future studies should aim for a more diverse and representative sample, spanning a broader respondent demographic.
Conceptualization, X.S. and J.-É.P.; methodology, X.S.; formal analysis, X.S.; investigation, Y.M. and S.D.; resources, S.D.; writing—original draft preparation, X.S. and Y.M.; writing—review and editing, X.S. and J.-É.P.; project administration, X.S. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of National Institute of Development Administration (reference no. ECNIDA 2022-0107).
Informed consent was obtained from all subjects involved in the study.
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical reasons.
The authors declare no conflict of interest.
Footnotes
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Demographic characteristics of the respondents.
| Demographic Factors | Descriptive Statistics | |
|---|---|---|
| Gender | Male | 261 persons (38.7%) |
| Female | 414 persons (61.3%) | |
| Age | Below 18 years old | 102 persons (15.1%) |
| 18–25 years old | 214 persons (31.7%) | |
| 26–30 years old | 105 persons (15.6%) | |
| 31–40 years old | 125 persons (18.5%) | |
| 41–50 years old | 59 persons (8.7%) | |
| 51–60 years old | 41 persons (6.1%) | |
| Above 60 years old | 29 persons (4.3%) | |
| Experience | 1–3 years | 428 persons (63.4%) |
| 4–6 years | 204 persons (30.2%) | |
| over 7 years | 43 persons (6.4%) | |
Factor loadings and cross loadings.
| Items | Deal Proneness | Innovativeness | Perceived Risk | Trust | Purchasing |
|---|---|---|---|---|---|
| DP1 | 0.97 (277.24) | 0.43 | 0.44 | 0.43 | 0.44 |
| DP2 | 0.94 (148.36) | 0.38 | 0.41 | 0.39 | 0.40 |
| DP3 | 0.94 (173.86) | 0.39 | 0.38 | 0.36 | 0.41 |
| INT1 | 0.42 | 0.95 (165.60) | 0.35 | 0.37 | 0.35 |
| INT2 | 0.40 | 0.93 (154.69) | 0.30 | 0.37 | 0.38 |
| INT3 | 0.38 | 0.94 (140.89) | 0.29 | 0.34 | 0.34 |
| PR1 | 0.43 | 0.34 | 0.97 (220.118) | 0.40 | 0.35 |
| PR2 | 0.42 | 0.32 | 0.95 (178.29) | 0.35 | 0.30 |
| PR3 | 0.38 | 0.30 | 0.94 (134.25) | 0.35 | 0.30 |
| TR1 | 0.40 | 0.36 | 0.37 | 0.96 (200.91) | 0.52 |
| TR2 | 0.37 | 0.36 | 0.34 | 0.92 (149.68) | 0.49 |
| TR3 | 0.40 | 0.34 | 0.38 | 0.92 (136.58) | 0.50 |
| TR4 | 0.38 | 0.35 | 0.37 | 0.90 (109.35) | 0.45 |
| TR5 | 0.35 | 0.37 | 0.32 | 0.91 (118.197) | 0.47 |
| PB1 | 0.41 | 0.39 | 0.33 | 0.53 | 0.95 (171.58) |
| PB2 | 0.41 | 0.32 | 0.31 | 0.48 | 0.94 (147.26) |
| PB3 | 0.42 | 0.36 | 0.32 | 0.49 | 0.94 (182.07) |
Note. DP = deal proneness, INT = innovativeness, PR = perceived risk, TR = trust, PB = purchasing behavior. The bold values are the factor loadings, and the values in the () are T-values.
Reliabilities and correlation of constructs.
| Constructs | CA | CR | AVE | Correlation of Constructs and Heterotrait–Monotrait (HTMT) Ratio | ||||
|---|---|---|---|---|---|---|---|---|
| Deal Proneness | Innovativeness | Perceived Risk | Trust | Usage Behavior | ||||
| Deal Proneness | 0.95 | 0.97 | 0.90 | 0.95 | ||||
| Innovativeness | 0.94 | 0.96 | 0.88 | 0.42 (0.45) | 0.94 | |||
| Perceived Risk | 0.95 | 0.97 | 0.91 | 0.43 (0.46) | 0.33 (0.35) | 0.95 | ||
| Trust | 0.96 | 0.97 | 0.85 | 0.42 (0.44) | 0.38 (0.41) | 0.39 (0.40) | 0.92 | |
| Purchasing Behavior | 0.94 | 0.96 | 0.89 | 0.44 (0.46) | 0.38 (0.40) | 0.34 (0.36) | 0.53 (0.56) | 0.94 |
Note. Square root of AVE is presented diagonally; the value within () is the value of the HTMT ratio.
Mediating effects on the structural model paths.
| Path | Effects | Estimate | Bootstrap 5000 Times | Percentile | Conclusion | |||
|---|---|---|---|---|---|---|---|---|
| S.E | T-Statistics | p-Value | Low | Upper | ||||
| Perceived Risk → Trust → Purchasing Behavior | Direct |
0.39 *** | 0.04 | 9.10 | 0.00 | 0.30 | 0.47 | Complimentary Partial Meditation |
| Indirect Effects | 0.14 *** | 0.02 | 5.98 | 0.00 | 0.10 | 0.19 | ||
| Total |
0.21 *** | 0.05 | 4.53 | 0.00 | 0.12 | 0.30 | ||
| Innovativeness → Deal Proneness → Purchasing Behavior | Direct |
0.13 *** | 0.04 | 2.89 | 0.00 | 0.04 | 0.21 | Complimentary Partial Meditation |
| Indirect Effects | 0.09 *** | 0.02 | 3.72 | 0.00 | 0.04 | 0.13 | ||
| Total Effects | 0.21 *** | 0.04 | 4.87 | 0.00 | 0.13 | 0.30 | ||
Note. *** p < 0.001.
Appendix A
Measurement scales
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Consumers’ Purchasing Behavior
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Livestreaming social commerce is my first choice when I need to buy something.
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I will follow the anchor of the livestreaming social commerce platforms.
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I will recommend to my friends to use livestreaming social commerce platforms.
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Trust
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I think livestreaming social commerce is trustworthy.
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I trust the quality of goods purchased on livestreaming social commerce platforms.
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The livestreaming social commerce platform has a good after-sales service system.
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The law can fully protect my interest in livestreaming social commerce.
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I believe that livestreaming social commerce forms can protect my privacy and safety.
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Perceived Risk
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I am worried that commodities provided by livestreaming social commerce platforms do not match the actual situation.
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I am worried that the quality of products provided by livestreaming social commerce platforms is not good.
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I am worried that personal information will be leaked by livestreaming social commerce platforms.
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Consumers’ Innovativeness
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If I heard about new information technology, I would look for ways to experiment with it.
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Among my peers, I am usually the first to explore new information technologies.
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I like to experiment with new information technologies.
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Deal Proneness
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Redeeming coupons and/or taking advantage of promotional deals on livestreaming social commerce makes me feel good.
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I am more likely to buy brands or patronize service firms that have promotional deals on livestreaming social commerce platforms.
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Beyond the money I save, redeeming coupons and taking advantage of promotional deals on livestreaming social commerce platforms give me a sense of joy.
References
1. Yltävä, L. Social Commerce—Statistics & Facts. Available online: https://www.statista.com/topics/8757/social-commerce/#topicOverview (accessed on 13 September 2023).
2. Ma, Y. Social Commerce in China—Statistics & Facts. Available online: https://www.statista.com/topics/6718/social-commerce-in-china/#editorsPicks (accessed on 13 September 2023).
3. Cheng, X.; Gu, Y.; Shen, J. An Integrated View of Particularized Trust in Social Commerce: An Empirical Investigation. Int. J. Inf. Manag.; 2019; 45, pp. 1-12. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2018.10.014]
4. Cai, J.; Wohn, D.Y.; Mittal, A.; Sureshbabu, D. Utilitarian and Hedonic Motivations for Live Streaming Shopping. Proceedings of the 2018 ACM International Conference on Interactive Experiences for TV and Online Video; Seoul, Republic of Korea, 26–28 June 2018; ACM: Seoul, Republic of Korea, 2018; pp. 81-88.
5. Chevalier, S. Global Consumer Appetite for Livestream Commerce 2021, by Region. Available online: https://www.statista.com/statistics/1275093/global-consumer-appetite-for-livestream-commerce/ (accessed on 13 September 2023).
6. Xiao, Q.; Wan, S.; Zhang, X.; Siponen, M.; Qu, L.; Li, X. How Consumers’ Perceptions Differ towards the Design Features of Mobile Live Streaming Shopping Platform: A Mixed-Method Investigation of Respondents from Taobao Live. J. Retail. Consum. Serv.; 2022; 69, 103098. [DOI: https://dx.doi.org/10.1016/j.jretconser.2022.103098]
7. iiMedia Research. Research Report on Competition in China’s Short Video Head Market (2020–2021); iiMedia Research: Guangzhou, China, 2021.
8. Sun, Y.; Bao, Z. Live Streaming Commerce: A Compulsive Buying Perspective. Manag. Decis.; 2023; 61, pp. 3278-3294. [DOI: https://dx.doi.org/10.1108/MD-10-2022-1461]
9. Nugraha, Y.D.; Suliyanto,; Permana, R.M.T.; Azib,; Hadiarti, D. Impulsive Purchase Behaviour of Z Generation of Muslim Women on TikTok Shop: The Application of S-O-R Framework. J. Islam. Mark.; 2023; ahead-of-print [DOI: https://dx.doi.org/10.1108/JIMA-06-2022-0182]
10. Yang, Q.; Lee, Y.-C. What Drives the Digital Customer Experience and Customer Loyalty in Mobile Short-Form Video Shopping? Evidence from Douyin (TikTok). Sustainability; 2022; 14, 10890. [DOI: https://dx.doi.org/10.3390/su141710890]
11. Sun, Y.; Shao, X.; Li, X.; Guo, Y.; Nie, K. How Live Streaming Influences Purchase Intentions in Social Commerce: An IT Affordance Perspective. Electron. Commer. Res. Appl.; 2019; 37, 100886. [DOI: https://dx.doi.org/10.1016/j.elerap.2019.100886]
12. Wongkitrungrueng, A.; Assarut, N. The Role of Live Streaming in Building Consumer Trust and Engagement with Social Commerce Sellers. J. Bus. Res.; 2020; 117, pp. 543-556. [DOI: https://dx.doi.org/10.1016/j.jbusres.2018.08.032]
13. Ma, Y. Market Value of Live Commerce in China 2018–2023. Available online: https://www.statista.com/statistics/1127635/china-market-size-of-live-commerce/ (accessed on 30 September 2023).
14. Cao, J.; Li, J.; Wang, Y.; Ai, M. The Impact of Self-Efficacy and Perceived Value on Customer Engagement under Live Streaming Commerce Environment. Secur. Commun. Netw.; 2022; 2022, e2904447. [DOI: https://dx.doi.org/10.1155/2022/2904447]
15. Gao, X.; Xu, X.-Y.; Tayyab, S.M.U.; Li, Q. How the Live Streaming Commerce Viewers Process the Persuasive Message: An ELM Perspective and the Moderating Effect of Mindfulness. Electron. Commer. Res. Appl.; 2021; 49, 101087. [DOI: https://dx.doi.org/10.1016/j.elerap.2021.101087]
16. Lu, B.; Chen, Z. Live Streaming Commerce and Consumers’ Purchase Intention: An Uncertainty Reduction Perspective. Inf. Manag.; 2021; 58, 103509. [DOI: https://dx.doi.org/10.1016/j.im.2021.103509]
17. Chen, H.; Chen, H.; Tian, X. The Dual-Process Model of Product Information and Habit in Influencing Consumers’ Purchase Intention: The Role of Live Streaming Features. Electron. Commer. Res. Appl.; 2022; 53, 101150. [DOI: https://dx.doi.org/10.1016/j.elerap.2022.101150]
18. Kang, K.; Lu, J.; Guo, L.; Li, W. The Dynamic Effect of Interactivity on Customer Engagement Behavior through Tie Strength: Evidence from Live Streaming Commerce Platforms. Int. J. Inf. Manag.; 2021; 56, 102251. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2020.102251]
19. Guan, Z.; Hou, F.; Li, B.; Phang, C.W.; Chong, A.Y.-L. What Influences the Purchase of Virtual Gifts in Live Streaming in China? A Cultural Context-Sensitive Model. Inf. Syst. J.; 2022; 32, pp. 653-689. [DOI: https://dx.doi.org/10.1111/isj.12367]
20. Ma, X.; Zou, X.; Lv, J. Why Do Consumers Hesitate to Purchase in Live Streaming? A Perspective of Interaction between Participants. Electron. Commer. Res. Appl.; 2022; 55, 101193. [DOI: https://dx.doi.org/10.1016/j.elerap.2022.101193]
21. Yu, F.; Zheng, R. The Effects of Perceived Luxury Value on Customer Engagement and Purchase Intention in Live Streaming Shopping. Asia Pac. J. Mark. Logist.; 2021; 34, pp. 1303-1323. [DOI: https://dx.doi.org/10.1108/APJML-08-2021-0564]
22. Aragoncillo, L.; Orus, C. Impulse Buying Behaviour: An Online-Offline Comparative and the Impact of Social Media. Span. J. Mark.—ESIC; 2018; 22, pp. 42-62. [DOI: https://dx.doi.org/10.1108/SJME-03-2018-007]
23. Zhang, X.; Cheng, X.; Huang, X. “Oh, My God, Buy It!” Investigating Impulse Buying Behavior in Live Streaming Commerce. Int. J. Hum.-Comput. Interact.; 2023; 39, pp. 2436-2449. [DOI: https://dx.doi.org/10.1080/10447318.2022.2076773]
24. Zhao, Y.; Bacao, F. How Does Gender Moderate Customer Intention of Shopping via Live-Streaming Apps during the COVID-19 Pandemic Lockdown Period?. Int. J. Environ. Res. Public Health; 2021; 18, 13004. [DOI: https://dx.doi.org/10.3390/ijerph182413004]
25. Hu, L.; Zhang, B.; Zhang, P.; Qi, J.; Cao, J.; Gao, D.; Zhao, H.; Feng, X.; Wang, Q.; Zhuo, L. et al. A Virtual Character Generation and Animation System for E-Commerce Live Streaming. Proceedings of the 29th ACM International Conference on Multimedia; Virtual, China, 20–24 October 2021; pp. 1202-1211.
26. Ma, Y. Usage Rate of Live Commerce among Consumers in China as of February 2022, by Age Group. Available online: https://www.statista.com/statistics/1339472/china-live-commerce-penetration-rate-by-age/ (accessed on 30 September 2023).
27. Mutambik, I.; Lee, J.; Almuqrin, A.; Zhang, J.Z.; Baihan, M.; Alkhanifer, A. Privacy Concerns in Social Commerce: The Impact of Gender. Sustainability; 2023; 15, 12771. [DOI: https://dx.doi.org/10.3390/su151712771]
28. Venkatesh, V.; Thong, J.Y.L.; Xu, X. Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. J. Assoc. Inf. Syst.; 2016; 17, pp. 328-376. [DOI: https://dx.doi.org/10.17705/1jais.00428]
29. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q.; 1989; 13, pp. 319-340. [DOI: https://dx.doi.org/10.2307/249008]
30. Venkatesh, V.; Davis, F.D. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manag. Sci.; 2000; 46, pp. 186-204. [DOI: https://dx.doi.org/10.1287/mnsc.46.2.186.11926]
31. Venkatesh, V.; Bala, H. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decis. Sci.; 2008; 39, pp. 273-315. [DOI: https://dx.doi.org/10.1111/j.1540-5915.2008.00192.x]
32. Jiang, G.; Liu, F.; Liu, W.; Liu, S.; Chen, Y.; Xu, D. Effects of Information Quality on Information Adoption on Social Media Review Platforms: Moderating Role of Perceived Risk. Data Sci. Manag.; 2021; 1, pp. 13-22. [DOI: https://dx.doi.org/10.1016/j.dsm.2021.02.004]
33. Hong, I.B. Understanding the Consumer’s Online Merchant Selection Process: The Roles of Product Involvement, Perceived Risk, and Trust Expectation. Int. J. Inf. Manag.; 2015; 35, pp. 322-336. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2015.01.003]
34. Lee, M.-C. Factors Influencing the Adoption of Internet Banking: An Integration of TAM and TPB with Perceived Risk and Perceived Benefit. Electron. Commer. Res. Appl.; 2009; 8, pp. 130-141. [DOI: https://dx.doi.org/10.1016/j.elerap.2008.11.006]
35. Kamalul Ariffin, S.; Mohan, T.; Goh, Y.-N. Influence of Consumers’ Perceived Risk on Consumers’ Online Purchase Intention. J. Res. Interact. Mark.; 2018; 12, pp. 309-327. [DOI: https://dx.doi.org/10.1108/JRIM-11-2017-0100]
36. Park, J.; Amendah, E.; Lee, Y.; Hyun, H. M-Payment Service: Interplay of Perceived Risk, Benefit, and Trust in Service Adoption. Hum. Factors Ergon. Manuf. Serv. Ind.; 2019; 29, pp. 31-43. [DOI: https://dx.doi.org/10.1002/hfm.20750]
37. Hansen, J.M.; Saridakis, G.; Benson, V. Risk, Trust, and the Interaction of Perceived Ease of Use and Behavioral Control in Predicting Consumers’ Use of Social Media for Transactions. Comput. Hum. Behav.; 2018; 80, pp. 197-206. [DOI: https://dx.doi.org/10.1016/j.chb.2017.11.010]
38. Sun, X. Consumer Intention and Usage Behavior of Live-Streaming Shopping: An Extension of the Unified Theory of Acceptance and Use of Technology. J. Behav. Sci.; 2022; 17, pp. 106-124.
39. Ling, K.; Daud, D.; Piew, T.; Keoy, K.H.; Hassan, P. Perceived Risk, Perceived Technology, Online Trust for the Online Purchase Intention in Malaysia. Int. J. Bus. Manag.; 2011; 6, pp. 167-182. [DOI: https://dx.doi.org/10.5539/ijbm.v6n6p167]
40. Qalati, S.A.; Vela, E.G.; Li, W.; Dakhan, S.A.; Hong Thuy, T.T.; Merani, S.H. Effects of Perceived Service Quality, Website Quality, and Reputation on Purchase Intention: The Mediating and Moderating Roles of Trust and Perceived Risk in Online Shopping. Cogent Bus. Manag.; 2021; 8, 1869363. [DOI: https://dx.doi.org/10.1080/23311975.2020.1869363]
41. Mosquera, A.; Juaneda-Ayensa, E.; Olarte-Pascual, C.; Pelegrín-Borondo, J. Key Factors for In-Store Smartphone Use in an Omnichannel Experience: Millennials vs. Nonmillennials. Complexity; 2018; 2018, e1057356. [DOI: https://dx.doi.org/10.1155/2018/1057356]
42. Zhou, M.; Huang, J.; Wu, K.; Huang, X.; Kong, N.; Campy, K.S. Characterizing Chinese Consumers’ Intention to Use Live e-Commerce Shopping. Technol. Soc.; 2021; 67, 101767. [DOI: https://dx.doi.org/10.1016/j.techsoc.2021.101767]
43. Salloum, S.A.; Al-Emran, M. Factors Affecting the Adoption of E-Payment Systems by University Students: Extending the TAM with Trust. Int. J. Electron. Bus.; 2018; 14, pp. 371-390. [DOI: https://dx.doi.org/10.1504/IJEB.2018.098130]
44. Saleh, S.S.; Nat, M.; Aqel, M. Sustainable Adoption of E-Learning from the TAM Perspective. Sustainability; 2022; 14, 3690. [DOI: https://dx.doi.org/10.3390/su14063690]
45. Zhao, J.; Fang, S.; Jin, P. Modeling and Quantifying User Acceptance of Personalized Business Modes Based on TAM, Trust and Attitude. Sustainability; 2018; 10, 356. [DOI: https://dx.doi.org/10.3390/su10020356]
46. Alalwan, A.A.; Dwivedi, Y.K.; Rana, N.P. Factors Influencing Adoption of Mobile Banking by Jordanian Bank Customers: Extending UTAUT2 with Trust. Int. J. Inf. Manag.; 2017; 37, pp. 99-110. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2017.01.002]
47. Carfora, V.; Cavallo, C.; Caso, D.; Del Giudice, T.; De Devitiis, B.; Viscecchia, R.; Nardone, G.; Cicia, G. Explaining Consumer Purchase Behavior for Organic Milk: Including Trust and Green Self-Identity within the Theory of Planned Behavior. Food Qual. Prefer.; 2019; 76, pp. 1-9. [DOI: https://dx.doi.org/10.1016/j.foodqual.2019.03.006]
48. Yu, W.; Han, X.; Ding, L.; He, M. Organic Food Corporate Image and Customer Co-Developing Behavior: The Mediating Role of Consumer Trust and Purchase Intention. J. Retail. Consum. Serv.; 2021; 59, 102377. [DOI: https://dx.doi.org/10.1016/j.jretconser.2020.102377]
49. Oghazi, P.; Karlsson, S.; Hellström, D.; Hjort, K. Online Purchase Return Policy Leniency and Purchase Decision: Mediating Role of Consumer Trust. J. Retail. Consum. Serv.; 2018; 41, pp. 190-200. [DOI: https://dx.doi.org/10.1016/j.jretconser.2017.12.007]
50. Lin, X.; Wang, X.; Hajli, N. Building E-Commerce Satisfaction and Boosting Sales: The Role of Social Commerce Trust and Its Antecedents. Int. J. Electron. Commer.; 2019; 23, pp. 328-363. [DOI: https://dx.doi.org/10.1080/10864415.2019.1619907]
51. Choo, H.J.; Sim, S.Y.; Lee, H.K.; Kim, H.B. The Effect of Consumers’ Involvement and Innovativeness on the Utilization of Fashion Wardrobe. Int. J. Consum. Stud.; 2014; 38, pp. 175-182. [DOI: https://dx.doi.org/10.1111/ijcs.12078]
52. Hwang, J.; Lee, J.-S.; Kim, H. Perceived Innovativeness of Drone Food Delivery Services and Its Impacts on Attitude and Behavioral Intentions: The Moderating Role of Gender and Age. Int. J. Hosp. Manag.; 2019; 81, pp. 94-103. [DOI: https://dx.doi.org/10.1016/j.ijhm.2019.03.002]
53. Zwain, A.A.A. Technological Innovativeness and Information Quality as Neoteric Predictors of Users’ Acceptance of Learning Management System: An Expansion of UTAUT2. Interact. Technol. Smart Educ.; 2019; 16, pp. 239-254. [DOI: https://dx.doi.org/10.1108/ITSE-09-2018-0065]
54. Lichtenstein, D.R.; Netemeyer, R.G.; Burton, S. Distinguishing Coupon Proneness from Value Consciousness: An Acquisition-Transaction Utility Theory Perspective. J. Mark.; 1990; 54, pp. 54-67. [DOI: https://dx.doi.org/10.1177/002224299005400305]
55. Tak, P.; Panwar, S. Using UTAUT 2 Model to Predict Mobile App Based Shopping: Evidences from India. J. Indian Bus. Res.; 2017; 9, pp. 248-264. [DOI: https://dx.doi.org/10.1108/JIBR-11-2016-0132]
56. Ailawadi, K.L.; Neslin, S.A.; Gedenk, K. Pursuing the Value-Conscious Consumer: Store Brands versus National Brand Promotions. J. Mark.; 2001; 65, pp. 71-89. [DOI: https://dx.doi.org/10.1509/jmkg.65.1.71.18132]
57. Ghosh Dastidar, S. An Investigation of Consumers’ Exploratory Tendencies as Motivators of Their Responsive Behaviour to Deals. IIMB Manag. Rev.; 2016; 28, pp. 72-87. [DOI: https://dx.doi.org/10.1016/j.iimb.2016.04.003]
58. Martínez-López, F.J.; Pla-García, C.; Gázquez-Abad, J.C.; Rodríguez-Ardura, I. Utilitarian Motivations in Online Consumption: Dimensional Structure and Scales. Electron. Commer. Res. Appl.; 2014; 13, pp. 188-204. [DOI: https://dx.doi.org/10.1016/j.elerap.2014.02.002]
59. Chen, C.; Li, X. The Effect of Online Shopping Festival Promotion Strategies on Consumer Participation Intention. Ind. Manag. Data Syst.; 2020; 120, pp. 2375-2395. [DOI: https://dx.doi.org/10.1108/IMDS-11-2019-0628]
60. Vakeel, K.A.; Sivakumar, K.; Jayasimha, K.R.; Dey, S. Service Failures after Online Flash Sales: Role of Deal Proneness, Attribution, and Emotion. J. Serv. Manag.; 2018; 29, pp. 253-276. [DOI: https://dx.doi.org/10.1108/JOSM-08-2017-0203]
61. Xu, R. UGC Lei Zhihui Lvyou Fuwu Pingtai Shiyong Xingwei Tan Jiu—Jiyu UTAUT Moxing. Res. Econ. Manag.; 2021; 42, pp. 93-105.
62. Escobar-Rodríguez, T.; Carvajal-Trujillo, E. Online Purchasing Tickets for Low Cost Carriers: An Application of the Unified Theory of Acceptance and Use of Technology (UTAUT) Model. Tour. Manag.; 2014; 43, pp. 70-88. [DOI: https://dx.doi.org/10.1016/j.tourman.2014.01.017]
63. National Health Commission of the People’s Republic of China. Update on the COVID-19 Infection as of June 29; National Health Commission of the People’s Republic of China: Beijing, China, 2022.
64. Faugier, J.; Sargeant, M. Sampling Hard to Reach Populations. J. Adv. Nurs.; 1997; 26, pp. 790-797. [DOI: https://dx.doi.org/10.1046/j.1365-2648.1997.00371.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/9354993]
65. Yamane, T. Elementry Sampling Theory; 1st ed. Prentice Hall: Hoboken, NJ, USA, 1967.
66. Ma, Y. Frequency of Online Purchases in China as of June 2022, by Gender. Available online: https://www.statista.com/statistics/1127873/china-online-shopping-frequency-by-gender/ (accessed on 28 October 2023).
67. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to Use and How to Report the Results of PLS-SEM. Eur. Bus. Rev.; 2019; 31, pp. 2-24. [DOI: https://dx.doi.org/10.1108/EBR-11-2018-0203]
68. Ringle, C.M.; Wende, S.; Becker, J.-M. SmartPLS 4. Oststeinbek: SmartPLS. Available online: https://www.smartpls.com (accessed on 12 September 2023).
69. Dash, G.; Paul, J. CB-SEM vs PLS-SEM Methods for Research in Social Sciences and Technology Forecasting. Technol. Forecast. Soc. Chang.; 2021; 173, 121092. [DOI: https://dx.doi.org/10.1016/j.techfore.2021.121092]
70. Thatcher, J.B.; Perrewé, P.L. An Empirical Examination of Individual Traits as Antecedents to Computer Anxiety and Computer Self-Efficacy. MIS Q.; 2002; 26, pp. 381-396. [DOI: https://dx.doi.org/10.2307/4132314]
71. Urbach, N.; Ahlemann, F. Structural Equation Modeling in Information Systems Research Using Partial Least Squares. J. Inf. Technol. Theory Appl. (JITTA); 2010; 11, 2.
72. Fornell, C.; Larcker, D.F. Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. J. Mark. Res.; 1981; 18, pp. 382-388. [DOI: https://dx.doi.org/10.1177/002224378101800313]
73. Henseler, J.; Dijkstra, T.K.; Sarstedt, M.; Ringle, C.M.; Diamantopoulos, A.; Straub, D.W.; Ketchen, D.J.; Hair, J.F.; Hult, G.T.M.; Calantone, R.J. Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013). Organ. Res. Methods; 2014; 17, pp. 182-209. [DOI: https://dx.doi.org/10.1177/1094428114526928]
74. Lindell, M.K.; Whitney, D.J. Accounting for Common Method Variance in Cross-Sectional Research Designs. J. Appl. Psychol.; 2001; 86, pp. 114-121. [DOI: https://dx.doi.org/10.1037/0021-9010.86.1.114] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11302223]
75. Simmering, M.J.; Fuller, C.M.; Richardson, H.A.; Ocal, Y.; Atinc, G.M. Marker Variable Choice, Reporting, and Interpretation in the Detection of Common Method Variance: A Review and Demonstration. Organ. Res. Methods; 2015; 18, pp. 473-511. [DOI: https://dx.doi.org/10.1177/1094428114560023]
76. Ringle, C.M.; Sarstedt, M.; Straub, D.W. Editor’s Comments: A Critical Look at the Use of PLS-SEM in “MIS Quarterly”. MIS Q.; 2012; 36, pp. iii-xiv. [DOI: https://dx.doi.org/10.2307/41410402]
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; Jean-Éric Pelet 2
; Dai, Shiying 3 ; Ma, Yi 4 1 International College, National Institute of Development Administration, Bangkok 10240, Thailand;
2 Department of Information Systems, IAE d’Amiens, 80000 Amiens, France;
3 Graduate School, Yunnan Minzu University, Kunming 650504, China
4 College of Educational Innovation, Saint John’s University, Bangkok 10900, Thailand;