A combination of factors, such as supportive government policies, rapid development of internet technology, and the unprecedented impact of the epidemic, has significantly accelerated the adoption and expansion of the live streaming + e-commerce business model. These developments create new opportunities for businesses to engage with consumers in real-time, leading numerous suppliers, manufacturers, and retailers to incorporate live streaming operations into their sales and marketing strategies. However, the influence of Key Opinion Leaders (KOLs) within this context varies depending on the leadership structure in place. This variation, in turn, influences crucial decisions regarding investments in KOLs and the allocation of associated costs, shaping the overall effectiveness and profitability of the live streaming e-commerce model. This paper develops a live streaming e-commerce model involving a supplier/manufacturer and a retailer, examining KOL-driven decisions across various channel leadership structures. Furthermore, a revenue-sharing mechanism is introduced to facilitate supply chain coordination. The key findings are as follows: (1) Demand and profits positively correlated with the KOL effect and negatively correlated with consumer rationality; (2) The manufacturer's profit under its own leadership consistently surpasses that under retailer leadership, while the retailer's profit, depending on leadership, is influenced by the cost scale of the KOL effect; (3) The highest overall profit in a centralized live streaming e-commerce business model also leads to the maximization of social welfare, and a revenue-sharing contract effectively coordinates the supply chain, resulting in a "win-win" outcome for both the supplier/manufacturer and the retailer.
Gig economy platforms provide flexible work options that lower labor market entry barriers and create additional income opportunities for workers. As more workers engage in platform-based employment, concerns have emerged about whether these platforms draw labor away from traditional sectors. This study investigates the impact of gig economy platforms on employment in manufacturing industries. By exploiting the staggered entry of food delivery platforms across Chinese cities as a natural experiment and by drawing on firm-level employment data from the Chinese State Administration of Taxation, we find that the entry of these platforms significantly increased manufacturing employment. Our mechanism analysis shows that these platforms act as a labor reservoir that absorbs workers during economic disruptions and facilitates their return to manufacturing once conditions stabilize. In addition to this buffering role, gig economy platforms also promote labor migration by providing short-term income opportunities that enhance cities' attractiveness to migrant workers. Heterogeneity analysis reveals that these positive effects are more pronounced in private firms, enterprises with limited investment in employee training, and regions with higher average wage levels. Extending the analysis, we further show that these platforms contribute to long-term improvements in the skill of the urban workforce. These findings offer new insights into the relationship between platform-based employment and traditional sectors and carry important implications for labor policy and workforce management.
Amid Vietnam's rapid digital transformation and national emphasis on AI under its National Strategy on AI (2021-2030), this study explores the factors influencing employees' adoption of AI technologies in Vietnam's emerging high-tech industry. Applying the Decomposed Theory of Planned Behavior (DTPB) as the theoretical framework, this research investigates how contextual, motivational, and organizational factors shape AI adoption behavior in a fast-growing Southeast Asian economy. Leveraging data from 308 valid responses, the study evaluates the impact of self-efficacy, resource- and technical-facilitating conditions, expected external rewards, expected reciprocity, self-worth awareness, and organizational climate on employees' attitudes, perceived behavioral control (PBC), and subjective norms toward AI adoption. Using SEM, the results demonstrate that attitude, subjective norms, and PBC significantly influence employees' intentions to adopt AI technologies, with subjective norms emerging as the most influential driver in this context. The findings highlight the importance of Vietnam's organizational climate and government-led innovation policies in fostering AI adoption within high-pressure, innovation-driven environments such as the high-tech sector. This study contributes to the AI adoption literature by extending the DTPB and providing context-specific theoretical and managerial insights for emerging markets, offering practical recommendations for organizations to enhance AI adoption through targeted incentives, supportive climates, and robust infrastructure investments.
Opportunistic product returns by customers have become a significant concern for e-commerce businesses, significantly impacting their profits and increasing logistical complexity. This study aims to understand how different cognitive routes influence customers' opportunistic product return behavior on e-commerce platforms. This research employs a sequential mixed-methodology approach. Initially, a qualitative study (Phase I) was conducted with 21 respondents to identify the various reasons that drive and resist customers from performing opportunistic product returns. A quantitative study (phase II) was conducted to test various hypotheses proposed under the behavioral reasoning theory framework using the PLS-SEM method. The qualitative study results showed that customers engage in opportunistic returns for three reasons: variety seeking, financial benefits, and impulsive purchase, whereas factors such as social group influence and complexity of the return procedure discourage customers from participating in opportunistic returns. Furthermore, the quantitative study determined the significance of customers' values, reasons, attitudes, and intentions in their decision-making process for performing opportunistic returns. Unlike other studies, the current study investigates the effects of both "reasons for" and "reasons against" opportunistic returns on customers, examining the factors that encourage and deter them from performing such returns in e-commerce within a single, holistic model. The study findings will aid e-commerce businesses in developing technology that can address emerging issues of opportunistic customer return behavior.
