The research, led by Tuyen Kim Thi Dinh and Minh Pham of Ho Chi Minh City Open University together with colleagues from the university’s School of Advanced Study, set out to fill a persistent gap in the literature. Previous studies of livestream commerce had examined individual factors that drive purchase intention, but they paid limited attention to the integrated psychological process by which cognitive evaluations transform into emotional responses and, ultimately, behavioral intentions. To capture that process, the team turned to a classic theoretical structure from environmental and consumer psychology: the Cognition-Affect-Behavior framework, often abbreviated as CAB.

The CAB framework rests on a deceptively simple idea. When people encounter an environment, whether a physical store or a digital space, they first form cognitive appraisals of it, then generate affective or emotional reactions, and finally produce behavioral responses such as approaching, staying, or buying. Applied to livestream commerce, the cognitive layer includes how useful viewers perceive the platform to be, how easy they find it to use, and how strongly they feel transported into the scene, a sensation researchers call perceived telepresence. The affective layer is captured by perceived enjoyment, the pleasure viewers derive simply from watching. The behavioral layer is purchase intention, the self-reported likelihood of buying through the livestream.

To test this model, the researchers surveyed 489 Vietnamese consumers who had watched livestreams and intended to purchase through them. The sample skewed female, was concentrated in Southern Vietnam, and was highly educated, a demographic profile that mirrors the core audience of livestream selling platforms in the region. The team then applied structural equation modeling, a statistical technique that allows researchers to test networks of hypothesized relationships simultaneously, estimating both direct paths between variables and indirect pathways that run through mediating constructs. This approach is particularly well suited to frameworks like CAB, where the theoretical interest lies not just in whether individual factors matter but in how they chain together.

The results were striking in their clarity. Three factors emerged as direct, statistically significant drivers of purchase intention: perceived telepresence, perceived enjoyment, and perceived ease of use. In other words, viewers who felt present in the livestream environment, who found the experience fun, and who found the technology effortless were all more likely to say they would buy. Telepresence deserves particular emphasis. In a livestream, the sensation of being there, watching a host demonstrate a product, answer questions, and respond to comments in real time, appears to do psychological work that static product pages cannot replicate. The study’s authors argue that their findings clarify the role of perceived telepresence in this context, positioning it as a central cognitive ingredient of the livestream experience rather than a peripheral novelty.

Perceived usefulness told a subtler story. It did not exert a direct, statistically meaningful effect on purchase intention on its own. Instead, its influence ran through enjoyment: viewers who found the livestream useful became more engaged and entertained, and that heightened enjoyment in turn lifted their intention to buy. This mediated pathway is a technically important finding because it suggests that usefulness in livestream commerce is not consumed coldly, as a rational calculation of value, but is converted into emotional currency before it shapes behavior. A host who provides genuinely informative product demonstrations does not merely inform the audience; the information makes the show more enjoyable, and the enjoyment sells the product.

Perhaps the most intriguing result concerned expectation confirmation, the degree to which the experience matches what viewers anticipated. Rather than acting as a direct driver, expectation confirmation operated as a moderator, specifically of the relationship between perceived ease of use and purchase intention. In practical terms, this means that the payoff of a smooth, frictionless viewing experience depends on whether the livestream lives up to expectations. When expectations are confirmed, ease of use translates more powerfully into purchase intention; when they are not, even a technically seamless experience may fail to convert viewers into buyers. This moderating role had not been clearly established in prior livestream research, and the authors highlight it as a key contribution of the study.

Methodologically, the research was conducted with careful attention to ethical standards. The study was approved by the Institutional Review Board of Ho Chi Minh City Open University, with approval number 1644/QD-DHM issued on August 4, 2025, and all participants were informed about the purpose and procedures of the study and provided informed consent before taking part. The authors declare no competing interests, and the article is published open access under a Creative Commons license, making the full technical detail of the model available to researchers and practitioners worldwide.

The theoretical significance of the work lies in its extension of the CAB framework into livestream commerce, a domain the framework had rarely been applied to in integrated form. By demonstrating that cognitive appraisals feed emotional responses which then drive behavioral intentions, and by specifying where moderators like expectation confirmation intervene, the study offers a coherent psychological architecture for understanding digital consumer decision-making. It also situates that architecture in an emerging market context, where livestream commerce is growing rapidly and where most existing theory, built largely on Western e-commerce data, has been untested. The Vietnamese sample, dominated by young, educated, female consumers in the country’s south, provides a valuable data point for a global literature that has often overlooked such markets.

The practical implications are equally concrete. For platform designers, the primacy of telepresence and enjoyment suggests that investment should flow toward features that deepen immersion and delight, such as high-quality video, interactive overlays, responsive comment systems, and charismatic host training, rather than toward interface complexity. For sellers and hosts, the mediated role of usefulness implies that informative content is not optional; it is the raw material from which enjoyment is built. And for marketers, the moderating effect of expectation confirmation delivers a cautionary lesson: overpromising in promotional material can backfire, because unmet expectations blunt the conversion power of an otherwise effortless experience. As livestream commerce expands across emerging online markets, the study suggests that the platforms that thrive will be those that understand a simple psychological truth, that people do not buy from streams they merely watch, but from streams that make them feel present, capable, and genuinely entertained.

Subject of Research: Consumer psychology of purchase intention in livestream commerce using the Cognition-Affect-Behavior framework

Article Title: Understanding purchase intention in livestream commerce through the Cognition-Affect-Behavior framework

Article References: Dinh, T. K. T., Doan, B. T. T., Tran, T. B. N., Vo, L. T. N., Pham, D. N., Dinh, T. M. N., & Pham, M. (2026). Understanding purchase intention in livestream commerce through the Cognition-Affect-Behavior framework. Discover Psychology. https://doi.org/10.1007/s44202-026-00911-4

Image Credits: AI Generated

DOI: 10.1007/s44202-026-00911-4

Keywords: livestream commerce, purchase intention, Cognition-Affect-Behavior framework, perceived telepresence, perceived enjoyment, perceived ease of use, expectation confirmation, structural equation modeling, consumer behavior, Vietnam, emerging markets, cyberpsychology

The study, led by Guanghong Xie of Tongji University’s College of Design and Innovation together with colleagues from Wuhan University of Technology and Tongji University, introduces the concept of AI-assisted shopping collaboration. In this arrangement, the human user retains final decision rights and the ability to intervene at any point, while the AI assistant shares the burden of integrating information and carrying out selected task operations. This deliberately limited form of delegation stands apart from agentic AI that can independently plan and pursue goals over extended horizons. By drawing this technological boundary explicitly, the authors aim to give designers and psychologists a clearer shared vocabulary for talking about what these systems do, and what they do not do.

To understand why some users embrace these assistants while others abandon them, the research team turned to two well-established psychological frameworks. The first is self-determination theory, which holds that human motivation flourishes when three basic psychological needs are met: autonomy, the sense that one’s actions are self-chosen; competence, the sense of being effective and capable; and relatedness, the feeling of connection to others. Applied to a shopping assistant, these needs translate into subtle perceptual questions. Does the AI make me feel in control of my own choices? Does it make me feel smarter about my decisions? Does interacting with it feel socially comfortable, almost like talking with a helpful companion?

The second framework is expectation-confirmation theory, a mainstay of information systems research. This model proposes that satisfaction with a technology depends on the gap between what users expected before using it and what they perceive after using it. When performance meets or exceeds prior expectations, confirmation occurs, satisfaction follows, and reuse becomes likely. When performance falls short, disconfirmation sets in and abandonment tends to follow. Combining these two theories, the researchers built an ordered model in which need-related perceptions and perceived trust, together with prior expectations, shape perceived quality and expectation confirmation, which in turn drive satisfaction and ultimately the intention to keep using the assistant.

The empirical work involved 300 users of AI shopping assistants, surveyed in a cross-sectional design that received ethics approval from the Institutional Review Board of Tongji University and was conducted in accordance with the Declaration of Helsinki. What distinguishes the analysis is its methodological ambition. Rather than relying on a single statistical technique, the team triangulated three complementary approaches: partial least squares structural equation modeling to test the structural associations among constructs, artificial neural networks to assess the predictive importance of each factor, and necessary condition analysis to determine whether any factors act as prerequisites rather than mere contributors.

The results carry several surprises with real design implications. Among the three basic psychological need-related perceptions, perceived relatedness emerged as the most prominent. In other words, the feeling of social connection, of the assistant being something more than a cold tool, was the strongest of the three motivational levers in the framework. This finding resonates with a growing body of evidence that people respond socially to conversational interfaces, and it suggests that the warm, dialogue-like character of generative shopping assistants is not a cosmetic feature but a psychological foundation of continued use. Designers who strip conversational sociality out of these tools may inadvertently be removing the very quality that keeps users engaged.

Prior expectations also proved remarkably influential. They showed strong associations with, and high predictive importance for, the two proximal evaluations in the model: perceived quality and expectation confirmation. This underscores a double-edged dynamic familiar from service research. High expectations can set a demanding bar, but they also frame the entire evaluative experience; users who arrive with vivid, concrete expectations judge quality and confirmation through that lens. For companies deploying AI shopping assistants, this means that marketing promises and onboarding experiences are not merely promotional materials. They actively calibrate the psychological benchmark against which every subsequent interaction will be measured.

The necessary condition analysis added a third layer of insight. Perceived quality and expectation confirmation did not merely correlate with satisfaction; they emerged as necessary conditions for high satisfaction. Without a minimum threshold of perceived quality and expectation confirmation, high levels of satisfaction appear to be unattainable regardless of how well other factors perform. This is a stricter and more actionable standard than correlation alone. It tells platform designers that investments in response accuracy, relevance, and reliable task execution are non-negotiable prerequisites, and that no amount of social charm or motivational framing can compensate for a fundamentally underwhelming core experience.

The triangulation of three analytical methods is itself part of the study’s contribution. Structural equation modeling reveals whether theoretical pathways hold, neural networks reveal which inputs matter most for prediction, and necessary condition analysis reveals which inputs gate the outcome entirely. When all three converge, as they did for prior expectations, perceived quality, and expectation confirmation, the evidence is considerably stronger than any single method could provide. The authors argue that this integrated psychological and multi-method perspective offers a template for studying not just shopping assistants but human-AI collaboration in general, particularly in domains where users delegate limited tasks while keeping ultimate control.

As agentic AI systems grow more capable and platforms race to deploy assistants that can browse, compare, and even purchase autonomously, this research offers a timely corrective. The findings suggest that the path to durable adoption runs not through maximal autonomy but through careful collaboration design: preserving the user’s sense of agency, nurturing a feeling of social connection, managing expectations honestly, and delivering a core experience that is unquestionably good. The study, funded by the Shanghai Philosophy and Social Science Planning Project and partner programs, was published in Current Psychology, with data available from the corresponding author on reasonable request. For an industry betting its future on AI-mediated commerce, the message is clear: the psychology of collaboration, not the raw power of the model, may decide who wins the next generation of shopping.

Subject of Research: Psychological factors driving user satisfaction and reuse intention in AI-assisted shopping collaboration

Article Title: AI-Assisted shopping collaboration design: Basic Psychological need-related perceptions, expectation evaluation, and reuse intention

Article References: Xie, G., Bao, Q., Wang, Z., Shen, X., & You, F. (2026). AI-Assisted shopping collaboration design: Basic Psychological need-related perceptions, expectation evaluation, and reuse intention. Current Psychology, 45(17), Article 1488. https://doi.org/10.1007/s12144-026-10054-3

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10054-3

Keywords: AI shopping assistant, AI-assisted shopping collaboration, self-determination theory, expectation confirmation, reuse intention, generative AI, human-AI collaboration, consumer behavior, perceived relatedness, structural equation modeling, artificial neural networks, necessary condition analysis