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.
Every year tech companies hold glitzy launch events to persuade smartphone consumers to upgrade. However, smartphone users are sticking with their current models for longer periods. Against this backdrop, this study applies the push-pull-mooring (PPM) model to examine the decision-making process behind smartphone upgrades. In the model, obsolescence is treated as a push factor, upgrading benefits and social influence as pull factors, and status quo biases as mooring factors. Additionally, this study incorporates market comparison to explore differences in these factors across regions. Data was collected from 777 smartphone owners in Indonesia, Taiwan, and the United States. The findings indicate that perceived obsolescence, upgrade benefits, and social influence positively influence perceived need and upgrade intention, while status quo bias negatively impacts upgrade intention. Further analysis reveals variations in the impacts of these factors across markets. This study contributes to academia by developing a holistic smartphone upgrading decision-making process model based on PPM framework. It also offers practical guidance for industrial practitioners, shedding light on the reasons why consumers delay upgrading their smartphones.
Although cloud computing is increasing in prominence by allowing outsourced and affordable data processing, it raises severe privacy concerns while transmitting sensitive data to cloud servers. Moreover, sensitive data has significant monetary and reputational worth, and any breach of privacy can result in significant financial and reputational loss. Organizations in the financial, health, criminal, social network, and government sectors have been gathering and processing personal data for profit. However, gathering and sharing individuals' sensitive and confidential information for data mining result in a breach of data privacy. This paper intends to propose a data sanitization and restoration process via a deep learning-based tuned key to ensure cloud security. The data is sanitized by performing the following phases, including data pre-processing, key generation, and key fine-tuning. Data preprocessing includes the extraction of improved statistical features from sensitive data that preserves the originality of the data. Key generation is a subsequent process that takes place in an optimal way via inducing an optimization algorithm, termed as Self Improved Namib Beetle Optimization (SI-NBO) model, to optimize the randomly generated keys under the consideration of parameters like privacy, hiding failure, and preservation ratio. The optimal key is then fine-tuned via the Deep Belief Network (DBN) model to obtain a fine-tuned key. By XORing sensitive data with the key, the sanitized information is obtained. On the other hand, in the restoration process, original data is restored via the same optimal key, which is generated as per the proposed SI-NBO model.
This study examines consumers' intentions to adopt Mobile Payment Systems (MPSs) through the theoretical lens of Innovation Resistance Theory (IRT), emphasizing how situational factors can alter the effects of innovation barriers. Drawing on IRT, the study investigates how key resistance factors (usage, image, value, and risk barriers) affect MPS adoption, and how these relationships are moderated by two contextual influences: the perceived threat of COVID-19 and digital literacy (DL). Data collected from 437 participants in Turkey were analyzed using structural equation modeling. The findings reveal that usage, image, and value barriers significantly hinder MPS adoption intentions, while the risk barrier has no significant effect. Furthermore, both situational moderators play significant roles: the perceived threat of COVID-19 weakens the negative impacts of barriers, encouraging adoption under health-risk conditions, whereas higher DL enhances adoption intentions and buffers the negative influence of value barriers. Beyond the immediate pandemic context, the study's insights hold post-COVID relevance by illustrating how crisis-induced behavioral shifts and increased digital literacy (DL) can sustain long-term engagement with mobile payment technologies. By integrating situational moderators into the IRT framework, this study extends the theory's explanatory power and provides a richer understanding of technology resistance in crisis contexts.
This study aims to delve into a mechanism explaining how individuals' perceptions of AI responsibility are related to their adoption of AI advice based on attribution theory. We propose a concept of AI locus of causality (LOC-AI), which represents an individual's perception of the extent to which AI influences decision-making performance. We built AI-embedded websites for a longitudinal experiment in which participants made decisions on corporate credit ratings and used a panel dataset collected from the experiments. The longitudinal evidence showed that individuals' decision performance influenced trust in AI but not LOC-AI. The results also indicated a positive lagged effect of LOC-AI on the adoption of AI advice but not trust in AI. Overall, we found that individuals were likely to exhibit self-serving biases and to adopt an egocentric and disengagement coping strategy even though they tended to leverage AI advice in their decisions.
The online-to-offline (O2O) mode that integrates online transactions and offline experiences has been applied to the tourism industry. However, few studies have addressed issues regarding hotel rebooking, especially for previous online positive and negative experiences. The purpose of this study is to investigate how customers make switching decisions between online booking sites. The push-pull-mooring (PPM) model is used to connect with the transaction cost theory (TCT). By analyzing 267 questionnaires collected from hotel chains, the findings indicate that perceived value positively affects the intention to rebook through hotel websites. Low website service quality of OTAs positively influences dissatisfaction with OTAs, which in turn positively and negatively influences the intention to rebook through hotel websites or OTAs, respectively. Uncertainty of OTAs positively influences switching costs, which in turn positively influences the intention to rebook through OTAs. The findings provide deeper insights into consumer behavior in the O2O mode and offer marketing practices to two competing services or platforms.
Organizational stakeholders are often burdened with the amount of information coming their way through various means of organizational communication. Information overload has been studied, and methodologies have been proposed to optimize the amount of information to which each user is exposed to be consistent with their processing capacity, mostly through filtering approaches. Our focus is on investigating the impact of communication network topologies that maximize the overall network value. We propose a conceptual framework, followed by a mathematical simulation model, of a small network of uniform users with limited information processing capacities, exchanging messages that decay over time at a uniform, organization-wide rate. Exogenous concepts in our framework are the amount of information generated in an organizational network which is then presented to individual stakeholders, the stakeholder's ability to process information and the organization-wide information decay factor. Within the context of our proposed framework, we investigate the ability of different network topologies to facilitate the dissemination of organizational information. The level of interconnectedness of the network is expressed via different graph metrics with the minimum inbound degree being the most critical indicator of network success within the context of our benchmark organizational model across a variety of scenarios representing different levels of information decay and the stakeholders' ability to consistently contribute information of value to other stakeholders. Our results suggest that organizations should strive for levels of interconnectedness in their communication networks that are consistent with the stakeholders' information processing capacity.
Live-streaming shopping has transformed traditional e-commerce by adding a social element, allowing consumers to interact with sellers and virtually experience products. This study examines impulsive buying behavior in the context of live-streaming shopping from the perspectives of consumers and sellers in Indonesia. Using the Stimulus-Organism-Response (S-O-R) model, the study compares the impact of a social presence on impulsive buying behavior, considering the consumer trust and flow state on e-commerce and social network platforms due to the different characteristics between these platforms. An online survey was performed utilizing a quota sampling method to ensure balanced samples of customers of live-streaming shopping from each platform. The 346 valid responses were analyzed using partial least squares structural equation modeling (PLS-SEM) to maximize the predictive power of the structural model and multigroup analysis (MGA) to compare live-streaming shopping platforms The findings indicate varied effects of a social presence on impulsive buying behavior through consumer trust and flow state. Interestingly, there was only one significant difference of the impact of social presence on consumer trust and flow state based on the live-streaming platforms. This research enhances our understanding of the relationship between a social presence and impulsive buying behavior in live-streaming shopping, providing practical insights for practitioners to optimize the impact of a social presence in driving impulsive buying. It offers valuable knowledge for researchers and industry professionals in the online shopping industry.
Influence Maximization (IM) in online social networks is a well-known complex optimization problem, having significant applications in viral marketing and E-commerce. This paper proposes a novel meta-heuristic driven solution methodology inspired from the shuffled searching behavior of Flamingos for the IM problem under "K" seeding budget constraint. The proposed approach intelligently amalgamates varied structural heuristics of networks such as communities, closeness and betweenness during iterations of optimization to heuristically prune the search space and accelerate convergence. The expected diffusion value function is also incorporated in our proposed approach using the Independent Cascade (IC) model to quantify reliable estimates of influence spreads. A detailed experimentation is conducted for constrained parameter "K" (seed set size) ranging seed set values in the range 10-50. An exhaustive comparative analysis of the results generated by our proposed approach with seven state-of-the-art IM approaches on five real-world social networks in the size ranges small (few hundred) to large (in thousands) is performed. The comparative analysis provides strong evidence for the significantly improved magnitude of spread estimates by the seeds generated by our proposed approach in contrast to many state-of-the-art IM solution methodologies proving its efficacy.
Despite several studies on social media (SM) use in the workplace, our understanding of their impact on managerial capabilities and job performance is still unclear. Further, most studies have considered SM use to be a single, isolated technology use rather than an engagement with an ensemble of applications. This paper helps improve our understanding of how multiple social media use indirectly enhances job performance by enhancing managerial social capital and individual absorptive capacity. This study adopts a hypothetico-deductive approach and data from 208 working professionals are collected to test the proposed research model. Structural equation modeling (PLS-SEM) and bootstrapping methods are used to analyze the direct and mediation effects respectively. The structural equation modeling results show that neither public SM (PSM) use nor enterprise SM (ESM) use directly impacts job performance. Rather, managerial social capital and individual absorptive capacity mediate the relationship between SM use and job performance. Common-method bias and non-response bias are potential limitations, but both pre- and post-data collection methods were used to minimize these concerns. This study contributes to academic literature on the business value of SM, use of multiple SM in the workplace, and the impact of SM use on managerial capabilities such as individual absorptive capacity. This study also has practical implications for managers and finds that instead of banning SM applications in the workplace, appropriate policies can ensure that managers can use such tools to enhance networking and social capital.
The admiration of e-commerce businesses has been climbing fast, and e-commerce businesses are competing with each other to increase their market share. Here, the chatbot is playing an important role for e-commerce to communicate with customers. The purpose of this paper is to examine the theory of planned behavior (TPB) to explain the way the constructs of TPB impact customers' intentions to continue using chatbots (ICUC), which also impacts the number of loyal customers in e-commerce. This study used primary data from 510 participants residing in the USA who have prior experience interacting with chatbots. The data were analyzed using partial least squares structural equation modeling (PLS-SEM) to examine the relationship between TPB contracts and behavioral intention. The findings indicated that all three TPB constructs had a significant impact on the customer's ICUC (p < .01), with the model explaining 48.3% of the variance in ICUC (R-2 = 0.483). ICUC also impacts loyal customers in e-commerce. Moreover, knowledge about AI-based chatbots and electronic word of mouth (e-WOM) have significant impacts on TPB constructs and ICUC. The theoretical contribution of this study has shown the use of the TPB model in AI-based chatbots and the way it is linked to customer loyalty in e-commerce. In addition, this study suggested to the e-commerce managers that they can use AI-based chatbots to communicate with customers in order to gain a large number of loyal customers.
During COVID-19, home-based businesses have become popular, and more Generation Z (Gen-Z) has set up online Instagram businesses. This research investigates the factors motivating Gen-Z to set up their online business on Instagram and how they examine the pandemic condition and manage it via marketing promotion. Semi-structured interviews were conducted on Zoom with successful Gen-Z part-time Instagram fashion shop owners and analyzed with theme-based qualitative analysis. Results showed that participants were achievement-oriented, risk-taking, unafraid of failing, emphasizing progress, and targeting future opportunities. In addition to the positive impacts of the COVID-19 pandemic on their online business, they used Instagram to engage with their community because they understood their followers' wants and were good at using various Instagram features to develop long-lasting and loyal connections. They also focused on the future, were active self-learners and entrepreneurs, and created value from their surroundings. Despite literature on business on Instagram, how Gen-Z influences society, how COVID-19 changes people's lives, and how social media impacts people's decision-making, there has been a research gap connecting all these aspects for what spurred Gen-Z to set up a business on Instagram and their actions during COVID-19.
Amidst the dynamic evolution of digital platforms, governance mechanisms play a pivotal role in shaping their operations and impact. This paper presents a comprehensive literature review on digital platform governance, offering insights into its multifaceted dimensions and contemporary research trends. Through an extensive examination of existing scholarship, this review synthesizes key findings, theoretical frameworks, and empirical methodologies employed in studying digital platform governance. Furthermore, it delineates emerging research outlooks and identifies critical gaps for future investigation. By delving into diverse aspects such as regulatory frameworks, user policies, content moderation, and platform ecosystem dynamics, this paper contributes to a nuanced understanding of digital platform governance. Ultimately, it serves as a roadmap for scholars and practitioners seeking to navigate the complex terrain of digital platform governance and chart new avenues for research and innovation.
Central bank digital currency (CBDC) serves as a transformative instrument for advancing digital strategies within enterprises, increasingly becoming an integral component of organizational digital initiatives. Nevertheless, the current adoption rate of CBDC among firms remains notably low, highlighting the significant practical importance of examining the factors that influence the intention to utilize CBDC from the corporate perspective. This article focuses on CBDC as the subject of investigation and, utilizing the Technology-Organization-Environment (TOE) framework in conjunction with managerial capabilities, develops a theoretical model to understand the intention to adopt CBDC. Furthermore, the Partial Least Squares Structural Equation Modeling (PLS-SEM) method is employed to empirically analyze the determinants influencing firms' intentions to adopt the e-CNY, using data collected from pilot cities involved in China's CBDC initiative. The findings reveal that the adoption of CBDC issued by central banks is primarily driven by organizational factors, such as organizational readiness and top-management support, while environmental factors, including vendor partnerships and market uncertainty, exert a secondary influence. Technical considerations appear to have minimal impact on the intention to adopt CBDC, with complexity identified as the sole factor negatively associated with this intention. Although managerial attributes do not directly affect a firm's propensity to use CBDC, both technical and organizational elements can exert indirect influence. This study provides valuable insights to assist firms in making informed decisions and strategically allocating resources in the context of utilizing central bank digital currency.
With the development of digital technology, electronic commerce has become an important part of the current economy and brings consumers significant benefits. However, many consumers have lower levels of willingness to shop online. Because consumers' self-efficacy has a positive effect on their willingness to shop online, it is important to investigate the origins of self-efficacy. This study proposed that consumer competency that indicated by knowledge, attitude, and skill is a significant predictor of self-efficacy and that the relationship between them is moderated by consumer metacognition. Two studies with samples of 379 college students and 494 individuals from the general population revealed that (1) self-efficacy positively predicted consumers' online consumption willingness; (2) consumers' knowledge/skill about online shopping negatively correlated with their self-efficacy, i.e. a competent consumer is diffident; (3) consumers' knowledge/skill negatively predicted their self-efficacy and consumption willingness; and (4) metacognition moderated the relationships between their knowledge/skill and self-efficacy. This study revealed an excessive difference among consumers in online shopping and extended the application scope of theories of metacognition, which may provide a new research direction for future studies. Based on these findings, improving consumers' competency and metacognition may result in a win-win situation between consumers and online retailers by improving the sustainability of the relationship between them.
