Xinqiang Chen

The absence of sensory information in online food consumption hinders consumers’ direct taste, olfactory, and tactile experiences prior to purchase. Consequently, visual information becomes a crucial compensatory cue for forming judgments about food sensory attributes. This study proposes an analytical framework centered on visual compensation to investigate how sensory information richness, visual realism, and visual vividness are associated with consumer purchase intention through gustatory mental imagery, texture mental simulation, and taste expectation. Utilizing survey data from 1226 online food consumers, this research employs PLS-SEM, IPMA, and fsQCA for empirical testing. The results indicate that sensory information richness is positively associated with consumers’ perception of visual realism and vividness, which in turn are positively associated with gustatory mental imagery and texture mental simulation. Gustatory mental imagery and texture mental simulation are subsequently associated with taste expectation, while texture mental simulation is also directly associated with purchase intention. IPMA further reveals that taste expectation, characterized by high importance yet relatively low performance, represents a potential priority for improvement, whereas texture mental simulation demonstrates both high importance and high performance. fsQCA findings illustrate that high PI can be achieved through various combinations of visual perception and psychological processing conditions. Moreover, the configurations associated with High PI differ from those associated with Not High PI. This study develops an integrative account of how visual perception, internal sensory representations, and taste expectations are linked to purchase judgments in online food consumption. It also expands the understanding of visual compensation mechanisms in digital food consumption from both structural and configurational perspectives.

Short-form video platforms have become deeply embedded in everyday digital life, where algorithmic recommendation enhances user experience but also raises concerns about invasiveness and manipulativeness. Drawing on the Cognitive–Affective–Conative (CAC) framework, this study examines the relationships between users’ cognitive evaluations of recommendation algorithms and continuance intention through algorithm dependence and algorithm aversion, while also identifying configurational pathways associated with high continuance intention. Based on 547 valid responses from short-form video users in China, we measured personalization, diversity, invasiveness, manipulativeness, algorithm dependence, algorithm aversion, and continuance intention. PLS-SEM was used to test direct and mediating effects, and fsQCA was employed to identify sufficient configurations associated with high continuance intention. The results show that personalization and diversity positively predict continuance intention and exert significant indirect effects through algorithm dependence, whereas manipulativeness negatively predicts continuance intention; invasiveness and algorithm aversion show no significant direct effects. The fsQCA results identify three dependence-maintenance pathways—experience-reinforced dependence, risk-buffered dependence, and risk-tolerant dependence—with algorithm dependence serving as a core condition across all four sufficient configurations. Notably, algorithm dependence and algorithm aversion coexist in two risk-tolerant configurations associated with high continuance intention. By distinguishing algorithm dependence from algorithm aversion, this study refines the affective dimension of the CAC framework, clarifies their differentiated roles in continuance intention, and provides configurational insights for responsible recommendation-system governance.

Near-expired food has received increasing attention in recent years as an important way to reduce food waste and promote sustainable consumption. However, although consumers recognize its economic value and environmental significance, they still have concerns about its quality and potential risks. Drawing on social cognitive theory and social exchange theory, this study adopts a benefit–risk trade-off perspective to examine how personal and environmental factors influence purchase intention toward near-expired food through perceived benefits and perceived risks. Based on 547 valid questionnaires collected from Chinese consumers, this study employs PLS-SEM, multi-group analysis (MGA), and fuzzy-set qualitative comparative analysis (fsQCA) for empirical testing. The results show that personal norm and price discount significantly increase perceived benefits, whereas social image concern and product uncertainty significantly increase perceived risks. Perceived benefits have a significant positive effect on purchase intention, whereas perceived risks have a significant negative effect. The MGA results further show that purchase experience and income level lead to significant differences in consumers’ decision paths. The fsQCA results indicate that both high and non-high purchase intention can be formed through multiple distinct but equivalent paths. High purchase intention mainly follows two patterns, benefit-driven and cognitive trade-off. Non-high purchase intention is mainly characterized by benefit deficiency and risk interference. The findings provide implications for the marketing and risk management of near-expired food.

As education undergoes digital transformation, ChatGPT-4 has emerged as one of the most visible tools of generative artificial intelligence. While widely discussed, its impact on student satisfaction and learning outcomes in higher education remains underexplored. This study investigates the factors that shape art and design students’ satisfaction when using ChatGPT to support coursework. Unlike previous research focusing on ChatGPT adoption behavior, this study extends the Information Systems Success Model (ISSM) to the context of art and design education. Drawing on 435 valid survey responses, we employed a mixed-methods approach. Partial Least Squares Structural Equation Modeling (PLS-SEM) was first applied to examine how system quality, compatibility, personal innovativeness, and perceived usefulness influence satisfaction directly and through mediating mechanisms. To complement this, fuzzy-set Qualitative Comparative Analysis (fsQCA) was used to identify multiple combinations of conditions that lead to high satisfaction. The findings show that compatibility, perceived usefulness, and personal innovativeness significantly enhance satisfaction, with path coefficients of 0.378, 0.342, and 0.155, respectively. Importance–Performance Map Analysis (IPMA) further highlights personal innovativeness and system quality as critical drivers. By providing both theoretical and practical insights, this study contributes to the growing body of research on generative AI in art and design education and informs the design of courses and digital learning tools.

Perceived value is a key factor shaping consumer purchase decisions. In the field of functional snack consumption, generational differences in value perception dimensions significantly influence decision-making processes, creating both challenges and opportunities for targeted marketing. Drawing on perceived value theory, this study develops a model examining the impact of perceived value on consumption intention for functional snacks. A questionnaire survey was conducted among Chinese Generation Y and Generation Z consumers, and the data were analyzed using PLS-SEM and fuzzy-set Qualitative Comparative Analysis (fsQCA). The results indicate that self-oriented values (functional and hedonic) exert a significant positive effect on other-oriented values (symbolic and social), and both categories of values positively affect consumption intention. Regarding generational moderation, the effects of functional and hedonic values on purchase intention do not differ significantly across generations; however, symbolic value has a stronger influence on Generation Z, while social value plays a more prominent role for Generation Y. Importance–Performance Map Analysis (IPMA) results further reveal differences in the relative importance and performance of value perception between the two groups. Configuration analysis shows that compared with Generation Y, Generation Z exhibits a higher threshold for strong consumption intention, a lower threshold for weak consumption intention, and greater sensitivity to value deficiency. These findings provide practical insights for functional snack companies to address generational differences and optimize marketing strategies.

China’s live streaming boom has led to mukbang live streaming as a unique food marketing tool. Hosts interact with viewers by tasting and showcasing diverse cuisines in real time. This form of mukbang live streaming has recently been utilized to promote and sell green agri-food products. However, in-depth research into how mukbang live streaming affects the purchase intention for green agri-food products and the underlying mechanisms remains scant. We developed a theoretical model based on stimuli–organism–response theory to explore the impact mechanism. Data was collected via a survey of 455 users from agriculture-related live streaming platforms and analyzed using structural equation modeling with partial least squares. The study found that professional recommendation, audiovisual experience, and social interaction enhance consumers’ perceived utilitarian value; green advocacy, audiovisual experience, and social interaction positively affect consumers’ perceived social value. Both perceived utilitarian value and perceived social value positively affect the intention to purchase green agri-food products. Additionally, we used importance-performance map analysis to compare the model’s effects with latent variable averages, revealing each factor’s importance and performance. The findings offer new insights and recommendations for agri-food marketing strategies, particularly in enhancing consumer behaviors towards green agri-food products, aiding suppliers and mukbang live streaming platforms in more effectively promoting these products.

While digitalization offers new opportunities for small- and medium-sized enterprises (SMEs), it also introduces the phenomenon of the “digitalization paradox”. This paper develops a theoretical model comprising digitalization, digital technology–business alignment, external social capital, and SMEs’ performance, rooted in strategic alignment theory (SAT) and social capital theory (SCT). The necessary data for the study were obtained by distributing questionnaires to 352 small and medium-sized enterprises engaged in digital practices in China, and hierarchical regression analysis was employed to investigate the impact of digitalization on the performance of SMEs and its boundaries of influence. The results indicate an inverted U-shaped relationship between digitalization and SME performance, with both digital technology–business alignment and external social capital serving as positive moderators. Specifically, digital technology–business alignment and external social capital both enhance the positive impact of digitalization on the performance of SMEs and mitigate its negative effects. The findings enhance comprehension of the “digitalization paradox” and offer new insights and solutions for SMEs to navigate the opportunities and challenges of digitalization.

Amidst the digital economy surge, live streaming e-commerce of agricultural products has significantly boosted agricultural prosperity. Investigating farmers’ behavioral intentions toward adopting live streaming e-commerce holds critical importance for fostering agricultural healthy and swift growth. Utilizing the Technology Acceptance Model (TAM) as a foundation, this study incorporates three additional variables—government support, platform support, and social learning—to devise a theoretical model. It takes the agriculture-related live streaming e-commerce platform as an example, with 424 Chinese farmers as the sample, to quantitatively assess the factors that impact the intentions to adopt live streaming e-commerce behaviors. The findings indicate that, firstly, the TAM is applicable to the assessment of farmers’ intentions to adopt live streaming e-commerce. Secondly, government support positively impacts perceived usefulness, social learning enhances perceived ease of use, and platform support positively impacts both perceived ease of use and usefulness. Lastly, the technology acceptance extension model applicability varies among farmer groups: government support influence on perceived ease of use is more significant among traditional farmers, social learning impact on perceived ease of use is higher in farmers with higher education levels, and platform support effect on perceived usefulness is stronger among farmers experienced in e-commerce. Therefore, differentiated promotion strategies by the government are necessary, and e-commerce platforms should leverage their technology to offer efficient services and encourage farmer education. A multi-party collaboration model involving the government, platforms, and farmers is essential to collectively foster the healthy development of rural live streaming e-commerce.

With the advent of the era of big data, the application of big data analytics in entrepreneurial activities has become increasingly prevalent. However, research on the relationship between big data analytic capabilities and entrepreneurial activities is still in its infancy, and the mechanism by which the two interact remains unclear. Drawing on resource-based theory and entrepreneurial process theory, this research examines the impact mechanism of big data analytic capabilities on the growth performance of start-up enterprises and explores the mediating role of entrepreneurial opportunity recognition and entrepreneurial opportunity exploitation. Empirical analysis reveals that big data analytic capabilities have a significant positive impact on the growth performance of start-up enterprises; entrepreneurial opportunity exploitation plays a mediating role in the relationship between big data analytic capabilities and the growth performance of start-up enterprises, but entrepreneurial opportunity recognition does not show a significant mediating effect between the two; and entrepreneurial opportunity recognition and entrepreneurial opportunity exploitation play a chain-mediated role in the relationship between big data analytic capabilities and the growth performance of start-up enterprises. These research findings enrich the study of digital entrepreneurship and provide valuable references for the entrepreneurial practice of start-up enterprises.