Continued purchase intention in live-streaming shopping: Roles of expectation confirmation and ongoing trust

Ko, Hsiu-Chia; Ho, Shun-Yuan Article Continued purchase intention in live-streaming shopping: Roles of expectation confirmation and ongoing trust Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Ko, Hsiu-Chia; Ho, Shun-Yuan (2024) : Continued purchase intention in livestreaming shopping: Roles of expectation confirmation and ongoing trust, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-18, https://doi.org/10.1080/23311975.2024.2397563 This Version is available at: https://hdl.handle.net/10419/326539 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Continued purchase intention in live-streaming shopping: Roles of expectation confirmation and ongoing trust Hsiu-Chia Ko & Shun-Yuan Ho To cite this article: Hsiu-Chia Ko & Shun-Yuan Ho (2024) Continued purchase intention in livestreaming shopping: Roles of expectation confirmation and ongoing trust, Cogent Business & Management, 11:1, 2397563, DOI: 10.1080/23311975.2024.2397563 To link to this article: https://doi.org/10.1080/23311975.2024.2397563 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 31 Aug 2024. Submit your article to this journal Article views: 4351 View related articles View Crossmark data Citing articles: 4 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Marketing | research article Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2397563 Continued purchase intention in live-streaming shopping: Roles of expectation confirmation and ongoing trust hsiu-chia ko and shun-Yuan ho Department of information Management, Chaoyang university of technology, taichung, taiwan ABSTRACT consumer demands have recently shifted toward an efficient shopping experience, which is a process that minimizes the time, money, effort, and cognitive load required by consumers to make purchase decisions and complete transactions. this shift has prompted social commerce platforms to invest in innovative business models aimed at maintaining customer loyalty. this study investigated how live-streaming shopping (lss) aligns with changing consumer demands for efficient shopping experiences. We extended the expectation confirmation Model by including utilitarian value and ongoing trust as variables to examine their influence on continued purchase intention. We administered a questionnaire survey to experienced lss users, which yielded 292 valid responses. Partial least squares structural equation modelling was performed on the collected responses to evaluate the proposed research models. the results revealed that postpurchase confirmation of the coviewers’ positive word of mouth significantly increases perceived lss value; this highlights the vital role of real-time social interactions in lss. Moreover, we observed that confirmation of the quality of streamer-provided information can maintain ongoing trust; this demonstrates the importance of streamers’ ability to leverage real-time video streaming effectively. Our analysis of postpurchase expectation confirmation underscored the importance of reliable streaming and ordering systems for ensuring lss satisfaction among consumers. the results also indicated that perceived lss value and ongoing trust significantly enhance consumers’ lss satisfaction, which increases their continued purchase intention. this study contributes to the literature on lss by demonstrating how the efficient characteristics of lss align with evolving consumer demands and behaviours. Introduction social commerce has gained considerable attention in recent years owing to evolving consumer preferences and advancements in technology. the Business research company’s social commerce global Market report 2023 projects that the global market value of social commerce will reach $689.9 billion by 2027, increasing at a compound annual growth rate of 5.1%. social commerce, a branch of e-commerce that combines social interaction and commercial activities, enables consumers to share their shopping experiences and engage in transactions, thus reducing information asymmetry. however, this integration also adds complexity to the shopping process. recent surveys have suggested that consumers are increasingly seeking better deals by visiting higher-value retail channels, frequently visiting price comparison websites, purchasing in bulk, and opting for private-label products (Pwc, 2023). consumers tend to navigate between 7 and 12 information channels, on average, and this complicates their decision-making process. to address this complexity, a trend toward simpler, faster, and more integrated online shopping channels has emerged, and such channels can facilitate the decision-making process and thus meet consumers’ expectations (google, 2022). this trend has led to the rise of innovative social commerce models, such as © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT Hsiu-Chia Ko [email protected] Department of information Management, Chaoyang university of technology, taichung, taiwan https://doi.org/10.1080/23311975.2024.2397563 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY received 28 February 2024 revised 22 July 2024 accepted 22 august 2024 KEYWORDS social commerce; live-streaming shopping (lss); expectation confirmation model (ecM); ongoing trust; continued purchase intention; utilitarian value SUBJECTS consumer Psychology; internet / Digital Marketing / e-Marketing; information & communication technology (ict) 2 h.-c. kO anD s.-Y. hO live-streaming shopping (lss) (radar, 2022), which Mckinsey & company projects will account for 20% of all e-commerce sales by 2026. lss is an innovative form of social commerce that integrates real-time streaming video with e-commerce functionalities (shiu et al., 2023; tashtarian et al., 2023; tian et al., 2023). it has rapidly gained popularity in markets like china and is expanding globally, driven by its ability to create a dynamic and engaging shopping environment. lss platforms typically feature streamers who showcase products, interact with viewers, and facilitate real-time transactions. this method not only reduces the uncertainty associated with online shopping by providing live demonstrations and immediate feedback but also builds a sense of community among viewers who share their opinions and experiences during the broadcast. lss is characterized by real-time word-of-mouth communications from coviewers in live chat rooms, vivid video-type product information from streamers, immediate customer service, and efficient chat-based ordering systems (chen et al., 2022; 2022; li et al., 2021; shiu et al., 2023; tashtarian et al., 2023; tian et al., 2023; Xue et al., 2020; Zhang etal., 2022). During lss broadcasts, streamers provide real-time and vivid product demonstrations, address consumer concerns, and facilitate community interactions and rapid order placement. clearly, lss platforms integrate crucial information necessary for purchase decision-making with a convenient purchasing process and provide products with good value for money, thus enhancing the shopping experience (huo et al., 2023; Xie et al., 2023; Yun et al., 2023). this integrated approach encapsulates the fundamental elements of social commerce (i.e. individuality, conversation, community, and commerce) (huang & Benyoucef, 2013); thus, it streamlines the entire shopping experience and maximizes utilitarian shopping value, thereby aligning with consumers’ demand for a simplified shopping experience. research on lss has indicated that lss products are often similar, which reduces streamer switching costs for consumers (huo etal., 2023). this similarity underscores the importance of maintaining consumers’ continued purchase intention in lss (chong etal., 2023; gu etal., 2023; Wu & huang, 2023). according to the expectation confirmation Model (ecM) (Bhattacherjee, 2001), a consumer’s continued purchase intention in lss is determined by their satisfaction with the lss experience and their perceptions of the value of lss; such satisfaction and value perceptions are influenced by the degree of alignment between prepurchase expectations and postpurchase experiences, which are shaped by the unique characteristics of lss. therefore, understanding how the distinct features of lss meet evolving consumer expectations is crucial for social commerce platforms to increase consumer satisfaction, provide high utilitarian value, and maintain consumers’ continued purchase intention. however, research has yet to apply the ecM to comprehensively examine performance expectations and performance confirmation in lss. in addition to providing utilitarian value, lss cultivates social value through streamer–follower interactions. lss can cultivate social value through the establishment of trust between streamers and followers. nevertheless, relevant studies on trust cultivation in the context of lss have mostly focused on initial trust (chen et al., 2022; Qing & Jin, 2022). Ongoing trust, which is cultivated over time and symbolizes a stable relationship between parties, has a crucial effect on continued purchase intention (ashraf et al., 2020; Zhang, 2020); hence, understanding the influence of ongoing trust in the context of lss is essential. accordingly, further research is warranted to understand how long-term lss consumers develop ongoing trust and understand the effect of their ongoing trust on their continued purchase intention. according to our review of the literature, no lss study has directly investigated how the distinct characteristics of lss meet the evolving expectations of consumers for an efficient shopping experience and how consumers’ postpurchase expectation confirmation affects their subsequent value perceptions and shopping satisfaction. to fill the aforementioned gap, we developed a research model based on the ecM and ongoing trust. We argue that lss effectively aligns with consumers’ expectations for efficient shopping through its unique characteristics, namely word-of-mouth communications, high-quality information, stable live video streaming, convenient order-placement system, and immediate service. this study aimed to investigate how postpurchase expectation confirmation affects consumers’ lss satisfaction, value perceptions, and ongoing trust in streamers; the study also explored the collective influence of lss satisfaction, value perceptions, and ongoing trust in streamers on continued purchase intention. the primary purpose of this research is to understand how the unique characteristics of lss meet these evolving consumer demands and influence continued purchase cOgent Business & ManageMent 3 intention. Our findings offer valuable insights that can enable streamers and retailers to align their service strategies with evolving consumer shopping demands. Literature review Live-streaming shopping (LSS) Research on LSS lss has been extensively adopted by online retailers to enhance their sales performance. nevertheless, research in this field is insufficient (gu etal., 2023; Ma, 2023; Yun etal., 2023). Most studies on lss have focused on the theme of purchase intention (hwang & Youn, 2023; shiu et al., 2023; tian et al., 2023). several studies have highlighted the challenges associated with maintaining customer loyalty in lss. hence, since 2021, researchers have increasingly examined factors influencing continued purchase intention in lss, such as continued usage intention, engagement intention, and stickiness. Most studies on these factors have adopted the stimulus–organism–response model (chong et al., 2023, 2023; ho etal., 2022; Ma, 2023; Wu & huang, 2023; Xie etal., 2023; Yun etal., 2023), with relatively few studies adopting the socio-technical perspective (li et al., 2021; Zhang et al., 2022) and information technology (it) affordance theory (ashraf et al., 2022). the aforementioned studies have reported that the technological capabilities of lss platforms and the charisma of streamers create an immersive shopping environment. Characteristics of LSS several studies have examined the multifaceted nature of lss, emphasizing attributes such as affordability; real-time interaction; audiovisual elements; and authentic, synchronous, and multiparty engagement (shiu etal., 2023; Xue etal., 2020; Zhang etal., 2022). additionally, previous research has primarily investigated the interactivity of lss (Bao & Zhu, 2023; chong et al., 2023; gu et al., 2023; Jiao et al., 2023; li et al., 2021; Ma, 2023; Wang & Oh, 2023; Xie et al., 2023; Yun et al., 2023; Zhang et al., 2022) and the rich content of live videos (gu et al., 2023) as primary drivers of lss value and satisfaction. For example, chong etal. (2023) examined the impact of social presence and perceived crowdedness on perceived value within the lss context. they found that a high level of social presence, defined as the degree to which a medium allows users to experience others as being psychologically present, contributes to a sense of community and engagement. this, in turn, enhances the perceived value of the lss experience. similarly, perceived crowdedness, or the feeling that many others are simultaneously participating in the live stream, also elevates perceived value by creating a bandwagon effect that implies popularity and trustworthiness. the study suggests that these factors contribute to the continued usage of live-streaming commerce platforms. as a result, these attributes can be considered strengths of lss; however, when combined, these strengths can turn into weaknesses. For example, lss relies on smooth streaming technology to provide an optimal viewing experience (tashtarian etal., 2023). in addition, chat rooms, which are essential for various interactions (e.g. greetings, expressing wishes, asking questions, sharing word-of-mouth recommendations, and placing orders), may become cluttered, which may affect service quality. Previous research on lss has mostly identified the interactivity of lss and the rich content of live videos as primary drivers of lss value and lss satisfaction, which can increase continued purchase intention. however, the effectiveness of these drivers depends on the consistency between consumers’ expectations of and actual postpurchase experiences regarding the robustness of an lss system and on streamers’ ability to leverage the characteristics of the lss system. notably, few studies have investigated the topic of continued purchase intention in the domains of system and service quality in lss. therefore, to address this gap, we examined the perceptions of experienced lss users regarding word-of-mouth sharing, the quality of information provided by streamers, the system quality of lss, and the service quality offered by streamers. all of these aspects were considered within the context of postpurchase lss experiences. Overall, our investigation was grounded in the ecM and the concept of ongoing trust. in the next section, we provide an overview of the ecM and the concept of ongoing trust. 4 h.-c. kO anD s.-Y. hO Expectation confirmation model (ECM) Overview of ECM Bhattacherjee (2001) developed the ecM to investigate the determinants of continued information system (is) usage. the ecM posits that users form expectations before using an is. after an initial period of use, users develop perceptions regarding system performance. the ecM suggests that users revise or update their expectations regarding an is on the basis of their experiences with the system; hence, users’ experiences are essential for their continued is usage. users evaluate congruence by comparing their postacceptance performance perceptions against their initial expectations, and this process is referred to as ‘confirmation’. in addition to perceived usefulness (a component of postexpectation assessment), the evaluated congruence influences user satisfaction. users who have a higher level of satisfaction with an is are more inclined to continue using the is. Yan et al. (2021) observed that online platforms have a niche set of user needs that they cater to, leading to different user expectations and perceived performance between online platforms. in addition, they argued that relying solely on the perceived usefulness metric, as emphasized in previous technology acceptance models, may not comprehensively capture the numerous benefits offered by different functionalities. therefore, to gain more nuanced insights, they recommended that researchers explore additional dimensions of perceived performance that influence continued usage intention. they also noted that as the objectives of sns usage become multifaceted, the derived benefits also vary. therefore, identifying specific motivations for sns utilization is essential in understanding how diverse usage purposes influence value perceptions. Continued usage intention recent studies on various domains, such as e-commerce (cheng, 2020), wearable devices (Park, 2022), and mobile commerce (abbasi etal., 2022; akel & armağan, 2021; lee etal., 2024; long & suomi, 2022), have adopted ecM to investigate continued usage intention. some studies across different business domains have suggested that information, system, and service quality indicators proposed by Delone and Mclean (2003) reflect different aspects of system performance (cheng, 2020; long & suomi, 2022; Park, 2022). Other studies have developed unique performance metrics tailored to their specific contexts (i.e. mobile commerce) (abbasi etal., 2022). in addition to considering satisfaction and perceived usefulness, some studies have included auxiliary variables in their frameworks to examine the effects of these variables on continued usage intention across various social commerce platforms (long & suomi, 2022; Park, 2022). For example, lee et al. (2024) extended the ecM by integrating it with the theory of network externalities to better explain the factors influencing customers’ continuance intention to use mobile shopping apps. this integration was necessary because the researchers found that the ecM alone could not fully capture all aspects, especially the social influences on Malaysian consumers’ intention to continue using these apps. Foroughi et al. (2023) also extended the ecM by incorporating additional factors such as perceived enjoyment, personal innovativeness, and the attractiveness of alternatives to explore the determinants of travel apps’ continuance usage intention. this extension integrates hedonic, personal, and environmental factors into the original ecM framework, providing a more comprehensive understanding of continuance intention in the context of travel apps. akel and armağan (2021) extended the ecM by adding hedonic and utilitarian benefits as determinants of application continuance intention in location-based applications. they integrated the ecM with the technology acceptance model to examine the role of application aesthetics and perceived enjoyment (hedonic benefits) and application quality and application utility (utilitarian benefits) on user satisfaction and continuance intention. in summary, expanding the ecM framework to include additional context-related factors can capture a wider range of influences on user continuance intention. this expansion not only strengthens the explanatory power of the ecM but also provides valuable insights for designing and managing applications across various domains. cOgent Business & ManageMent 5 Ongoing trust Initial trust and ongoing trust Online shopping research has predominantly focused on the concept of initial trust. initial trust in the context of e-banking, for instance, involves accepting the risk of using electronic financial services without prior experience or valid information about their ability fulfil one’s needs (kimiagari & Baei, 2022). this form of trust is characterized by an individual’s inclination to rely on another party, trusting that this party will act in the individual’s best interest while avoiding deception and upholding their commitments (Osakwe et al., 2022). although individuals may gather extensive information from a website regarding a seller, product, or service, the establishment of initial trust in the absence of real transactions is speculative; this thus engenders a challenge in establishing a stable foundation of trust (Zhang, 2020). lee and choi (2011) described ongoing trust as a positive belief in another party’s reliability and integrity, nurtured through actual interactions and historical observations. in e-commerce, consumers refine their initial trust with each online shopping experience, thereby leading to the development of ongoing trust. this trust, which is rooted in genuine experiences, facilitates the formation of a reliable relationship. Ongoing trust enables a comprehensive understanding of the seller and thus mitigates social uncertainties and fears; consequently, this trust promotes continued purchase intention (ashraf et al., 2020; Zhang, 2020). Establishment of ongoing trust Doney and cannon (1997) determined that trust in business partnerships is cultivated through five evaluation processes: calculation, prediction, capability, intentionality, and transference. Building upon this framework, kim et al. (2004) examined the processes that encourage consumers to revisit a website and build trust with an online store. in their study, calculation is defined as the evaluation of an online store based on its reputation among consumers. Prediction involves assessing the online store’s future behaviour based on customer satisfaction. capability is about evaluating the store’s ability to fulfil its obligations. intentionality refers to understanding the motivations behind the online store’s actions. Finally, transference involves adopting other consumers’ opinions about the website’s reputation as one’s own evaluation. kim etal. (2004) emphasized that consumers accumulate experiences on platforms with each transaction or interaction they have. these experiences serve as a benchmark for evaluating website quality, information quality, and service quality. kim et al. also argued that even for consumers who repeatedly visit or make purchases from the same website, word of mouth remains an essential reference point for determining whether they continue to trust the website. consequently, consumers adjust their trust levels after each interaction on the basis of word of mouth, their satisfaction level, and quality assessments. this trust subsequently influences the consumers’ continued engagement intention for the website. studies, particularly those in domains such as online banking (hoehle et al., 2012) and recommendation systems (ashraf etal., 2020), have extended the ecM by considering ongoing trust as a determinant of continued usage intention. For example, acknowledging the unique characteristics of travel products and their platforms, Zhang (2020) combined the ecM with trust transfer theory. this integration expanded the scope of expectation confirmation from a unidimensional concept to a multifaceted construct that includes dimensions such as product quality and service quality. Research model and hypothesis development Research model Drawing from the ecM and the concept of ongoing trust, we propose that in lss, consumers form or adjust their performance expectations based on their confirmation of an efficient shopping experience after engaging with lss. When consumers’ expectations align with their actual shopping experiences, their satisfaction increases. such alignment enhances perceived postpurchase value and cultivates ongoing trust in the relevant streamer. Overall, the utilitarian value and social value of lss influence a viewer’s intention to continue buying products sold by a streamer. table 1 presents the relationships between 6 h.-c. kO anD s.-Y. hO lss characteristics, postpurchase performance expectations, performance confirmation, perceived value, and ongoing trust. Figure 1 illustrates the proposed research model. Effects of performance expectation and confirmation on perceived value, ongoing trust, and satisfaction in LSS Positive word of mouth about a streamer from coviewers Positive word of mouth is generated when consumers voluntarily engage in discussions, provide recommendations, or disseminate information, without any underlying commercial motive, regarding a product that they like (kim & son, 2009). consumer endorsements have a strong influence on prospective customers. traditional e-commerce platforms, which do not allow real-time multiparty interactions, require consumers to navigate through multiple websites to access word-of-mouth information, which complicates their shopping experience. By contrast, in lss, the real-time positive word of mouth shared by viewers in a chat Table 1. Relationship between Lss characteristics, postpurchase performance expectations, performance confirmations, perceived value, and ongoing trust (Source: our analytical results). Performance expectations from Lss characteristics Definition of performance confirmations Perceived value ongoing trust Positive word of mouth about streamers from coviewers: • Coviewers frequently sharing positive experiences and testimonials in the chat room regarding shopping with a streamer who is commonly watched by the group during a broadcast Positive word-of-mouth confirmation: • the degree to which the reference value of word of mouth, as shared by coviewers, matches viewer expectations Word-of-mouth value: • savings in time and effort that would otherwise be spent searching for word-of-mouth information across various websites during the prepurchase stage Calculation/transference/ prediction: • utilizing reference information about a streamer’s reputation to calculate their credibility • Predicting a streamer’s future behavior depending on their reputation • a trust-building process that essentially involves transferring trust on the basis of existing reputational data Quality of information offered by the streamer: • Real-time video demonstrations of product use accompanied by the streamer’s expert insights and commentary • Provision of price comparison details by the streamer, enabling the viewers to make informed purchase decisions information quality confirmation: • the degree to which the reference value of information disseminated by a streamer matches viewer expectations information efficiency value: • savings in time and effort related to evaluating product attributes and comparing prices across multiple websites during the prepurchase stage • Procuring high-value products, thus achieving monetary savings ability/intention: • evaluating a streamer’s professional competence in showcasing and discussing products • Determining the degree to which a streamer aligns with the audience’s requirements and interests, thereby gauging their genuine intention to serve the audience system quality in Lss: • seamless live video streaming, ensuring a glitch-free viewing experience • efficient and user-friendly Lss order placement system, facilitating the shopping experience system quality confirmation: • the degree to which an Lss system meets viewer expectations in terms of seamless viewing and order placement ordering process value: • savings in time and effort typically associated with complex product ordering processes during the purchase stage Capability: • evaluating a streamer’s proficiency in handling and processing product orders service quality of the streamer: • Prompt real-time responses by the streamer or their support team, focusing on resolving problems, guiding viewers through the shopping process, and simulating the provision of in-person shopping assistance • Direct customer service provision through sns private messaging or phone calls to address postpurchase inquiries and concerns service quality confirmation: • the degree to which the service quality offered by the streamer aligns with viewer expectations streamer service value: • savings in time and effort across all shopping stages (before, during, and after purchase) resulting from minimized back-and-forth communications and waiting times • streamlined processes for handling and responding to customer inquiries Capability/intent combined: • gauging a streamer’s professional competence in order processing • understanding a streamer’s authenticity and good intentions through the way they address audience inquiries cOgent Business & ManageMent 7 room provides fresh insights into the streamer (hwang & Youn, 2023). lss reduces the time that consumers spend on various websites, and this thus enhances consumers’ perceived value of and satisfaction with lss (Jebarajakirthy & shankar, 2021). in addition, real-time testimonials serve as indicators of a streamer’s reputation and provide viewers with information regarding the streamer’s credibility and predictions regarding future transaction-related actions. after making a purchase, viewers evaluate the positive word of mouth shared in the chat room. if this word of mouth aligns with their expectations, their perceived lss value, lss satisfaction, and ongoing trust in the streamer increase. accordingly, we proposed the following hypothesis: h1: Positive word of mouth regarding a streamer confirmation positively affects consumers’ (a) perceived lss value, (b) ongoing trust in the streamer, and (c) lss satisfaction. Effects of information quality, system quality, and service quality in lss, information quality can be defined as viewers’ perception of the usefulness, reliability, and completeness of the information provided by a streamer (Xu et al., 2020). Busalim et al. (2021) defined system quality in social commerce as customers’ perception of the technical and functional capabilities of a social commerce website, particularly its availability and accessibility. studies have evaluated website service quality in terms of dimensions such as responsiveness, assurance, empathy, reliability, problem-solving ability, and after-sales service (Busalim et al., 2021; Molinillo et al., 2021). Many studies have demonstrated the positive effects of information quality, system quality, and service quality on customer satisfaction across various contexts (Wang et al., 2021). these dimensions enhance consumers’ perceived value (Molinillo et al., 2021) and trust (Wang et al., 2021). For instance, Zhang (2020) confirmed that after consumers purchase a travel product, their satisfaction with and ongoing trust in the relevant platform and tourist destination increases if their experiences meet or exceed their expectations regarding product quality, service quality, and convenience. in this study, we extended the concept of postpurchase performance confirmation in lss to cover information quality, system quality, and service quality (table 1). after purchasing a product, consumers compare the product’s performance with their expectations in terms of the aforementioned quality dimensions. if the actual product performance meets or exceeds consumers’ expectations, the consumers’ perceived lss value and lss satisfaction increase. such confirmation also increases viewers’ confidence in a streamer’s credibility, expertise, and benevolent intent, thereby reinforcing their ongoing trust in the streamer. accordingly, we proposed the following hypotheses: h2: information quality confirmation positively affects consumers’ (a) perceived lss value, (b) ongoing trust in a streamer, and (c) lss satisfaction. h3: system quality confirmation positively affects consumers’ (a) perceived lss value, (b) ongoing trust in a streamer, and (c) lss satisfaction. h4: service quality confirmation positively affects consumers’ (a) perceived lss value, (b) ongoing trust in a streamer, and (c) lss satisfaction. Figure 1. Research model. 14 h.-c. kO anD s.-Y. hO intellectual content. Both hsiu-chia ko and shun-Yuan ho reviewed and approved the final version of the manuscript for publication. additionally, both authors agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Disclosure statement no potential conflict of interest was reported by the author(s). Funding this work was supported by the national science and technology council, republic of china under grant nstc 112-2410-h-324 -002 -MY2 About the authors Hsiu-Chia Ko holds a PhD degree from the national sun-Yat-sen university, taiwan. she is currently an associate Professor of information Management at the chaoyang university of technology, taiwan. her research interests include social media, social commerce, online communities, knowledge management, and electronic commerce. she has published articles in cyberpsychology, Behaviour, and social networking, electronic commerce research and applications, internet research, Online information review, Journal of internet commerce, Behaviour & information technology, and the international Journal of technology Marketing. Shun-Yuan Ho is currently a doctoral candidate in the information Management Department of chaoyang university of technology. he graduated with a master’s degree from the information Master’s Program at Jingyi university in 2010. his research interests include enterprise resource planning, information security, technology acceptance, and supply chain. ORCID hsiu-chia ko http://orcid.org/0000-0003-3102-4825 Data availability statement the participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available. References radar. (2022). 2022 influencer marketing trends report. abbasi, g. a., sandran, t., ganesan, Y., & iranmanesh, M. (2022). go cashless! determinants of continuance intention to use e-wallet apps: a hybrid approach using Pls-seM and fsQca. Technology in Society, 68, 101937. https://doi. org/10.1016/j.techsoc.2022.101937 akel, g., & armağan, e. (2021). hedonic and utilitarian benefits as determinants of the application continuance intention in location-based applications: the mediating role of satisfaction. Multimedia Tools and Applications, 80(5), 7103–7124. https://doi.org/10.1007/s11042-020-10094-2 ali, a., & Bhasin, J. (2019). understanding customer repurchase intention in e-commerce: role of perceived price, delivery quality, and perceived value. Jindal Journal of Business Research, 8(2), 142–157. https://doi.org/10.1177/2278682119850275 ashraf, M, ahmad, J, chani, M. i, khan, M i, Yasin, M a, sajjad, M, abbas, M W, Orangzaib. (2022). Determinants of livestream shopping reuse intention in social commerce: role of trusting belief and it affordance. Journal of Hunan University Natural Sciences, 49(4), 1–11. https://doi.org/10.55463/issn.1674-2974.49.4.1 ashraf, M., ahmad, J., sharif, W., raza, a. a., salman shabbir, M., abbas, M., & thurasamy, r. (2020). the role of continuous trust in usage of online product recommendations. Online Information Review, 44(4), 745–766. https://doi. org/10.1108/Oir-05-2018-0156 Bao, Z., & Zhu, Y. (2023). understanding customers’ stickiness of live streaming commerce platforms: an empirical study based on modified e-commerce system success model. Asia Pacific Journal of Marketing and Logistics, 35(3), 775–793. https://doi.org/10.1108/aPJMl-09-2021-0707 Bhattacherjee, a. (2001). understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921 cOgent Business & ManageMent 15 Busalim, a. h., ghabban, F., & hussin, a. r. c. (2021). customer engagement behaviour on social commerce platforms: an empirical study. Technology in Society, 64, 101437. https://doi.org/10.1016/j.techsoc.2020.101437 chalomba, n., Duh helen, i., & gujral, M. (2019). generation Y’s brand satisfaction, continuance intention and loyalty to branded mobile apps. Management Dynamics: Journal of the Southern African Institute for Management Scientists, 28(3), 30–43. https://doi.org/10.10520/eJc-171977974e chandrruangphen, e., assarut, n., & sinthupinyo, s. (2022). the effects of live streaming attributes on consumer trust and shopping intentions for fashion clothing. Cogent Business & Management, 9(1), 2034238. https://doi.org/10.10 80/23311975.2022.2034238 chen, c.-D., Zhao, Q., & Wang, J.-l. (2022). how livestreaming increases product sales: role of trust transfer and elaboration likelihood model. Behaviour & Information Technology, 41(3), 558–573. https://doi.org/10.1080/014492 9X.2020.1827457 chen, h., chen, h., & tian, X. (2022). the dual-process model of product information and habit in influencing consumers’ purchase intention: the role of live streaming features. Electronic Commerce Research and Applications, 53, 101150. https://doi.org/10.1016/j.elerap.2022.101150 cheng, Y.-M. (2020). Quality antecedents and performance outcome of cloud-based hospital information system continuance intention. Journal of Enterprise Information Management, 33(3), 654–683. https://doi.org/10.1108/JeiM-04-2019-0107 chong, h. X., hashim, a. h., Osman, s., lau, J. l., & aw, e. c.-X. (2023). the future of e-commerce? understanding livestreaming commerce continuance usage. International Journal of Retail & Distribution Management, 51(1), 1–20. https://doi.org/10.1108/iJrDM-01-2022-0007 Delone, W. h., & Mclean, e. r. (2003). the Delone and Mclean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748 Doney, P. M., & cannon, J. P. (1997). an examination of the nature of trust in buyer-seller relationships. Journal of Marketing, 61(2), 35–51. https://doi.org/10.2307/1251829 Foroughi, B., sitthisirinan, s., iranmanesh, M., nikbin, D., & ghobakhloo, M. (2023). Determinants of travel apps continuance usage intention: extension of technology continuance theory. Current Issues in Tourism, 27(4), 1–17. https://doi.org/10.1080/13683500.2023.2169109 google. (2022). 2022 taiwan smart consumption key report: sharing good things value business. retrieved July 30, 2022, from https://services.google.com/fh/files/misc/2022_google_taiwan_commerce_whitepaper.pdf gu, Y., cheng, X., & shen, J. (2023). Design shopping as an experience: exploring the effect of the live-streaming shopping characteristics on consumers’ participation intention and memorable experience. Information & Management, 60(5), 103810. https://doi.org/10.1016/j.im.2023.103810 hair, J. F., hult, g. t. M., ringle, c. M., sarstedt, M., Danks, n. P., & ray, s. (2021). Mediation analysis. Partial Least Squares Structural Equation Modelling (PLS-SEM) Using R: A Workbook, 4, Classroom Companion: Business, 139–153. cham: springer. https://doi.org/10.1007/978-3-030-80519-7_7 hair, J. F., risher, J. J., sarstedt, M., & ringle, c. M. (2019a). When to use and how to report the results of Pls-seM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/eBr-11-2018-0203 hair, J. F., sarstedt, M., & ringle, c. M. (2019b). rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584. https://doi.org/10.1108/eJM-10-2018-0665 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. (2014). common beliefs and reality about Pls: comments on rönkkö and evermann (2013). Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928 ho, c.-i., liu, Y., & chen, M.-c. (2022). antecedents and consequences of consumers’ attitudes toward live streaming shopping: an application of the stimulus–organism–response paradigm. Cogent Business & Management, 9(1), 2145673. https://doi.org/10.1080/23311975.2022.2145673 hoehle, h., huff, s., & goode, s. (2012). the role of continuous trust in information systems continuance. Journal of Computer Information Systems, 52(4), 1–9. https://doi.org/10.1080/08874417.2012.11645571 huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: a close look at design features. Electronic Commerce Research and Applications, 12(4), 246–259. https://doi.org/10.1016/j.elerap.2012.12.003 huo, c., Wang, X., sadiq, M. W., & Pang, M. (2023). exploring factors affecting consumer’s impulse buying behavior in live-streaming shopping: an interactive research based upon sOr model. SAGE Open, 13(2), 215824402311726. https://doi.org/10.1177/21582440231172678 hwang, J., & Youn, s.-Y. (2023). From brick-and-mortar to livestream shopping: Product information acquisition from the uncertainty reduction perspective. Fashion and Textiles, 10(1), 7. https://doi.org/10.1186/s40691-022-00327-3 Jebarajakirthy, c., & shankar, a. (2021). impact of online convenience on mobile banking adoption intention: a moderated mediation approach. Journal of Retailing and Consumer Services, 58, 102323. https://doi.org/10.1016/j.jretconser.2020.102323 Jiao, Y., sarigöllü, e., lou, l., & huang, B. (2023). how streamers foster consumer stickiness in live streaming sales. Journal of Theoretical and Applied Electronic Commerce Research, 18(3), 1196–1216. https://doi.org/10.3390/jtaer18030061 kim, h.-W., Xu, Y., & koh, J, (2004). a comparison of online trust building factors between potential customers and repeat customers. Journal of the Association for Information Systems, 5(10), 392–420. https://doi.org/10.17705/ 1jais.00056 16 h.-c. kO anD s.-Y. hO kim, s. s., & son, J.-Y. (2009). Out of dedication or constraint? a dual model of post-adoption phenomena and its empirical test in the context of online services. MIS Quarterly, 33(1), 49–70. https://doi.org/10.2307/20650278 kimiagari, s., & Baei, F. (2022). Promoting e-banking actual usage: Mix of technology acceptance model and technology-organisation-environment framework. Enterprise Information Systems, 16(8–9), 1894356. https://doi.org/ 10.1080/17517575.2021.1894356 kock, n., & lynn, g,. (2012). lateral collinearity and misleading results in variance-based seM: an illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580. https://doi.org/10.17705/ 1jais.00302 lee, J.-n., & choi, B. (2011). effects of initial and ongoing trust in it outsourcing: a bilateral perspective. Information & Management, 48(2-3), 96–105. https://doi.org/10.1016/j.im.2011.02.001 lee, W. O., huam, h. t., & raja Yusof, r. n. (2024). Determinants of mobile shopping apps continuance intention: a review and proposed conceptual model. Journal of Entrepreneurship and Business, 12(1), 77–90. https://doi. org/10.17687/jeb.v12i1.1193 li, Y., li, X., & cai, J. (2021). how attachment affects user stickiness on live streaming platforms: a socio-technical approach perspective. Journal of Retailing and Consumer Services, 60, 102478. https://doi.org/10.1016/j.jretconser.2021.102478 long, t., & suomi, r. (2022). continuance intention of theme park apps: an extended expectation confirmation theory perspective. WHICEB 2022 Proceedings, 16. https://aisel.aisnet.org/whiceb2022/16 Ma, Y. (2021). elucidating determinants of customer satisfaction with live-stream shopping: an extension of the information systems success model. Telematics and Informatics, 65, 101707. https://doi.org/10.1016/j.tele.2021.101707 Ma, Y. (2023). effects of interactivity affordance on user stickiness in livestream shopping: identification and gratification as mediators. Heliyon, 9(1), e12917. https://doi.org/10.1016/j.heliyon.2023.e12917 Ming, J., Jianqiu, Z., Bilal, M., akram, u., & Fan, M. (2021). how social presence influences impulse buying behavior in live streaming commerce? the role of s-O-r theory. International Journal of Web Information Systems, 17(4), 300–320. https://doi.org/10.1108/iJWis-02-2021-0012 Molinillo, s., aguilar-illescas, r., anaya-sánchez, r., & liébana-cabanillas, F. (2021). social commerce website design, perceived value and loyalty behavior intentions: the moderating roles of gender, age and frequency of use. Journal of Retailing and Consumer Services, 63, 102404. https://doi.org/10.1016/j.jretconser.2020.102404 Osakwe, c. n., Okeke, t. c., & kwarteng, M. a. (2022). trust building in mobile money and its outcomes. European Business Review, 34(2), 244–262. https://doi.org/10.1108/eBr-09-2020-0221 Park, e. (2022). user acceptance of smart wearable devices: an expectation-confirmation model approach. Telematics and Informatics, 47, 101318. https://doi.org/10.1016/j.tele.2019.101318 Park, h. J., & lin, l. M. (2020). the effects of match-ups on the consumer attitudes toward internet celebrities and their live streaming contents in the context of product endorsement. Journal of Retailing and Consumer Services, 52, 101934. https://doi.org/10.1016/j.jretconser.2019.101934 Pwc. (2023). February 2023 global consumer insights pulse survey. retrieved from https://www.pwc.com/gx/en/ industries/consumer-markets/consumer-insights-survey-feb-2023.html Qing, c., & Jin, s. (2022). What drives consumer purchasing intention in live streaming e-commerce? Frontiers in Psychology, 13, 938726. https://doi.org/10.3389/fpsyg.2022.938726 shiu, J. Y., liao, s. t., & tzeng, s.-Y. (2023). how does online streaming reform e-commerce? an empirical assessment of immersive experience and social interaction in china. Humanities and Social Sciences Communications, 10(1), 1–8. https://doi.org/10.1057/s41599-023-01731-w social commerce global Market report (2023). the Business research company. retrieved December 30, 2023, from https://www.thebusinessresearchcompany.com/report/social-commerce-global-market-report tashtarian, F., Bentaleb, a., erfanian, a., hellwagner, h., timmerer, c., & Zimmermann, r. (2023). hxl3: Optimized delivery architecture for httP low-latency live streaming. IEEE Transactions on Multimedia, 25, 2585–2600. https:// doi.org/10.1109/tMM.2022.3148587 tian, B., chen, J., Zhang, J., Wang, W., & Zhang, l. (2023). antecedents and consequences of streamer trust in livestreaming commerce. Behavioral Sciences, 13(4), 308. https://doi.org/10.3390/bs13040308 Wang, J., & Oh, J. i. (2023). Factors influencing consumers’ continuous purchase intentions on tiktok: an examination from the uses and gratifications (u&g) theory perspective. Sustainability, 15(13), 10028. https://doi.org/10.3390/ su151310028 Wang, J., shen, X., huang, X., & liu, Y. (2021). influencing factors of the continuous usage intention of consumers of online food delivery platform based on an information system success model. Frontiers in Psychology, 12(3267), 716796. https://doi.org/10.3389/fpsyg.2021.716796 Wongkitrungrueng, a., & assarut, n. (2020). the role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117(1), 543–556. https://doi.org/10.1016/j.jbusres.2018.08.032 Wu, Y., & huang, h. (2023). influence of perceived value on consumers’ continuous purchase intention in live-streaming e-commerce—mediated by consumer trust. Sustainability, 15(5), 4432. https://doi.org/10.3390/su15054432 Xie, J., Yoon, n., & choo, h. J. (2023). how online shopping festival atmosphere promotes consumer participation in china. Fashion and Textiles, 10(1), 1–19. https://doi.org/10.1186/s40691-022-00325-5 cOgent Business & ManageMent 17 Xu, X., Wu, J.-h., & li, Q. (2020). What drives consumer shopping behavior in live streaming commerce? Journal of Electronic Commerce Research, 21(3), 144–167. Xue, J., liang, X., Xie, t., & Wang, h. (2020). see now, act now: how to interact with customers to enhance social commerce engagement? Information & Management, 57(6), 103324. https://doi.org/10.1016/j.im.2020.103324 Yan, M., Filieri, r., & gorton, M. (2021). continuance intention of online technologies: a systematic literature review. International Journal of Information Management, 58, 102315. https://doi.org/10.1016/j.ijinfomgt.2021.102315 Yun, J., lee, D., cottingham, M., & hyun, h. (2023). new generation commerce: the rise of live commerce (l-commerce). Journal of Retailing and Consumer Services, 74, 103394. https://doi.org/10.1016/j.jretconser.2023.103394 Zeithaml, V. a. (1988). consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.2307/1251446 Zhang, M., liu, Y., Wang, Y., & Zhao, l. (2022). how to retain customers: understanding the role of trust in live streaming commerce with a socio-technical perspective. Computers in Human Behavior, 127, 107052. https://doi. org/10.1016/j.chb.2021.107052 Zhang, X. (2020). Ongoing trust and tourism O2O platform continuance: a two-trustee involved model with moderating variable. SAGE Open, 10(2), 215824402092065. https://doi.org/10.1177/2158244020920659 Appendix: Questionnaire items Positive word of mouth about streamer confirmation (kim et al., 2004; Zhang, 2020): coviewers’ positive word-of-mouth recommendations about this streamer align with my expectations. coviewers’ favorable reviews and recommendations about this streamer align with my expectations. coviewers’ positive portrayal of this streamer aligns with my expectations. information quality confirmation (kim et al., 2004; Zhang, 2020): the product information provided by the streamer is relevant to my needs, as i expected. easy to understand and aligns with my expectations. reliable, as i expected. useful and aligns with my expectations. valuable and aligns with my expectations. system quality confirmation (kim et al., 2004; Zhang, 2020): the lss system used by the streamer is easy to use, as i expected. can rapidly load all the text, graphics, and audio and video content, as i expected. can be easily used to place an order, as i expected. has a user-friendly interface, as i expected. enables me to ask questions and receive timely responses, as i expected. service quality confirmation (Molinillo et al., 2021; Zhang, 2020): the streamer and their sales team answer my questions and meet my expectations. provide warmer services than i expected. provide me with prompt services in case of any problems. provide the transaction security and privacy protection make me feel safe. pay attention to their audience. understand the needs of their audience. Ongoing trust in streamers (Zhang, 2020): according to my previous experience of purchasing products from this streamer, i believe that they are trustworthy. are reliable. are capable of providing products and services. 18 h.-c. kO anD s.-Y. hO care about the needs of their audience. keep their promises and commitments. lss value perception (Molinillo et al., 2021): lss gives me good deals for the money that i pay. lss is worth the effort that i put in purchasing products from this streamer. lss adds value in spite of the nonnegligible risks involved in buying products from this streamer. Overall, purchasing products from this streamer through lss is beneficial for me. lss satisfaction (ashraf et al., 2020): Overall, shopping with this streamer makes me feel satisfied. pleased. contented. delighted. continued purchase intention (chen et al., 2022): in the near future, i will very likely purchase products from this streamer again. choose this streamer as a shopping channel again. continue purchasing products sold by this streamer.