Electronic Markets最新文献

This paper recaps the issue of online mis- and disinformation as a societal problem, reviews why government roles in defining mis- and disinformation are constitutionally suspect under First Amendment jurisprudence, and builds on a content-neutral theory of disincentivizing the production of mis- and disinformation first proposed by Marshall Van Alstyne. The purpose of this paper is to develop some key implementation details that would be necessary to resolve if the theory is to be adopted operationally.

The interactions between developers and users are a vital driver of complementary innovation and further facilitate the emergence and evolution of digital innovation ecosystems. However, these interactions are somewhat overlooked in research on digital innovation ecosystems. To increase the understanding of their interrelationship, firstly, we explain the foundation of value co-creation between developers and users by introducing service-dominant logic (S-D logic). Then, we analyze the coordination mechanism of their value co-creation using an evolutionary game approach. There are three key findings obtained: First, the underlying logic behind their value co-creation is mutual benefit, and the coordination mechanism elaborates how they select participation strategies by carefully weighing benefits against costs. Second, their engagement behavior is influenced by each other, and their initial choices also determine the evolution outcome of the behavior under certain circumstance. Third, the involvement of developers and users is impacted by similar benefits and costs, such as additional benefits of adopting the active strategy unilaterally, free-riding benefits, additional costs of positive participation, and benefits of ecosystem synergy. However, factors related to platform owners, the loss caused by punishment due to passive participation and the benefits from being featured due to active participation, only affect developers’ strategy choices directly. Finally, we discuss managerial implications of our findings.

Recent developments in the field of artificial intelligence (AI) have enabled new paradigms of machine processing, shifting from data-driven, discriminative AI tasks toward sophisticated, creative tasks through generative AI. Leveraging deep generative models, generative AI is capable of producing novel and realistic content across a broad spectrum (e.g., texts, images, or programming code) for various domains based on basic user prompts. In this article, we offer a comprehensive overview of the fundamentals of generative AI with its underpinning concepts and prospects. We provide a conceptual introduction to relevant terms and techniques, outline the inherent properties that constitute generative AI, and elaborate on the potentials and challenges. We underline the necessity for researchers and practitioners to comprehend the distinctive characteristics of generative artificial intelligence in order to harness its potential while mitigating its risks and to contribute to a principal understanding.

Trust has been identified as inevitable for technology acceptance and might further gain importance as technologies become increasingly complex. However, previous research on trust in online environments lacks a systematic configuration of trust entities in research models; some studies include either trust in technology or trust in provider, others both. In combination with inconsistent results, this leads to a lack of in-depth knowledge about the trust entities’ relationship to each other, to their antecedents, and intention to use. Therefore, this study aims at clarifying these relationships and examining how they vary for different configurations. We performed pairwise meta-analyses to generate summary effects for the individual trust entities and examined four different trust configurations by applying meta-analytic structural equation modeling (MASEM). Our findings advance technology acceptance and trust research and highlight the necessity to carefully configure trust. More specifically, the results from pairwise meta-analysis support a strong relationship between the trust entities that is, however, countered by the effects of antecedents in MASEM. Institution-based trust and reputation are found stronger predictors for trust in provider and familiarity a stronger determinant of trust in technology. Furthermore, the trust entities show comparable paths to intention to use when either trust entity is included in the research model, but when both are integrated, trust in technology is more important than trust in provider.

The popularity of online marketplaces continues to grow worldwide, and with it comes increased attention to the non-economic value they provide. This study aims to analyze the environmental, social, and governance (ESG) reports and news of 13 online marketplace operators and examine their engagement in ESG practices, considering their characteristics and the social environment. By comparing their practices with the generic ESG framework, the study identifies unique factors and approaches specific to ESG practices in online marketplaces, such as constructing industry ecology, contributing to national current affairs and policies, protecting intellectual property, and focusing on information and network security. The study also finds that market transactions, digital innovation, and participant ecology are three key mechanisms that explain the specificity of ESG practices in online marketplaces. The findings of this study offer an ESG framework that can be applied to online marketplaces. It highlights the importance of considering online marketplaces’ social environment and individual characteristics in developing ESG practices. This study provides insights for online marketplace operators to improve their ESG practices and contributes to a growing body of literature on non-economic value creation in online marketplaces.

Artificial intelligence (AI) voice assistants possess significant market potential and offer diverse services through voice interaction. However, the influence of anthropomorphic features on consumers’ mind perception and continued use intention, particularly across various age groups, remains underexplored. To address this research gap, we employ mind perception theory, the stimulus–organism-response model, and cognitive load theory to conduct a research model. Using a sample of 303 survey responses, we evaluate the research model and hypotheses through partial least squares analysis. Findings reveal that these features positively affect alleviating loneliness and enhancing perceived usefulness. Additionally, the alleviation of loneliness and perceived usefulness contribute to consumers’ continued use intention and mediate the relationship between anthropomorphic features and continued use intention. Furthermore, the effect of anthropomorphic features on mind perception varies across age groups. This research enhances understanding of the influence of anthropomorphic features on consumers’ mind perception and continued use intention of AI voice assistants, providing valuable insights for product developers and marketers to enhance the consumer experience.