Earticle

As the use of smart devices has become more widespread, monetary gifting through social network services (SNSs) has increasingly taken place in SNS group contexts. Despite the growing prevalence of such practices, the determinants of monetary gifting behavior in SNS groups remain underexplored. Drawing on social exchange theory, this study develops a research model that explains monetary gifting as a group-based exchange shaped by both SNS platform characteristics and group contexts. This study empirically validated the proposed model using both subjective and objective data gathered from an SNS platform across multiple time points. The findings show that benefit-related factors—reciprocity of SNS group monetary gifting, online social companionship, online social contribution, and convenience of SNS group monetary gifting—as well as a cost-related factor, social anxiety of SNS group monetary gifting, influence the frequency of monetary gifting in SNS groups through individuals’ assessments of perceived worth. This study contributes to the literature by providing a theoretically grounded explanation of monetary gifting behavior in SNS group contexts and by highlighting the combined role of SNS platform characteristics and group-based interaction dynamics. The findings also offer practical insights for SNS providers seeking to promote monetary gifting within group-based social interactions.

The rapid evolution of artificial intelligence, particularly Generative AI, has widened the gap between industrial practice and Information Systems research. To address this issue, this study investigates the industry-academia speed gap and proposes future research agendas to bridge this divide. We conceptualize this gap as a structural temporal asynchrony driven by distinct knowledge production mechanisms. Methodologically, we employ a computationally assisted Systematic Literature Review (SLR) that integrates Latent Dirichlet Allocation and embedding- based semantic similarity analysis to compare discourse patterns across industry news and IS academic papers. Our analysis reveals that industry and academia interpret identical phenomena through different lenses, even within shared thematic areas. Furthermore, we identify unique topic clusters that emerge exclusively within each domain. Based on these findings, we propose an expanded AI research framework by extending Padmanabhan et al. (2022) and present a research agenda directing scholars toward underrepresented yet industrially significant areas. This approach facilitates more timely and substantial contributions to the IS field while providing rigorous insights to assist in establishing corporate mid-to-long-term strategies.

Rapid urban digitalization has intensified the demand for integrated governance systems in developing cities, where fragmented bureaucratic structures and limited interoperability often constrain the effectiveness of smart city initiatives. This study examines how seven interoperability attributes–design process, government process alignment, compatibility with policies and regulations, user interaction, change management, service consumption, and governance contribution–shape smart city governance effectiveness, using Yogyakarta, Indonesia, as a critical case. A mixed-method approach was employed, combining a survey of 300 local government officials analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) with semi-structured interviews conducted to contextualize and validate the quantitative findings. The results indicate that design process exerts the strongest positive influence on smart city governance (β = 0.740, p < 0.001), followed by service consumption (β = 0.180, p < 0.001), and user interaction (β = 0.120, p < 0.05). Government process alignment shows a statistically significant but negative effect (β = −0.110, p < 0.05), while compatibility with policies and regulations exhibits a weak yet positive influence. In contrast, change management and governance contributions do not demonstrate statistically significant direct effects. These findings underscore that interoperability functions as a multidimensional governance capability rather than a purely technical requirement, emphasizing the centrality of process-centric integration and active service utilization in advancing smart city governance. This study contributes a replicable analytical framework and highlights the need for future research to incorporate longitudinal designs and emerging governance dimensions, such as cybersecurity and ethical data governance.

This study examines how information presentation strategies―specifically online review provision and price framing―affect user experience and reuse intention in an LLM-based hotel recommender chatbot. To investigate this, we developed a hybrid chatbot integrating a large language model (LLM), Text-to-SQL, and Retrieval-Augmented Generation (RAG), and conducted a 2 × 2 between-subjects experiment based on real user interactions. Data were collected from 160 participants who interacted with the chatbot and subsequently completed a survey. The results indicate that both online review provision and downward price framing significantly enhance perceived recommender chatbot quality, which subsequently increases user satisfaction and reuse intention, whereas their interaction effect is not significant. In addition, exploratory factor analysis revealed that the multidimensional structure of chatbot quality converged into a single higher-order factor. Mediation analysis further showed that the effects of information presentation strategies on behavioral outcomes were mediated by perceived chatbot quality and satisfaction. These findings suggest that, in conversational recommender environments, information presentation strategies influence user behavior primarily through holistic quality evaluations rather than through direct effects on satisfaction.

This research aims to analyze the human resource executives’ implementation intention of workforce analytics and its outcome in the Indian hotel industry by extending the technology, organization, and environment model (TOE). To identify the reasons for workforce analytics adoption in the hotel industry, the researchers have adopted a research design in which qualitative and quantitative techniques were used. A total of 250 samples from the hotels located in Bengaluru, Chennai, Kochi, and Hyderabad. This study employed interviews, and the collected data were analyzed using multivariate techniques. The findings ascertained that complexity, relative advantages, compatibility, competitive pressure, training, and management support are the predictors of workforce analytics adoption intention. Regulatory Support does not influence workforce analytics adoption intention. The association between relative advantage and adoption intention weakens in the presence of data privacy concerns. This study offers practitioners and executives in the hotel sector unique practical insights into the antecedents of workforce analytics adoption. This study makes a theoretical contribution by expanding the TOE model to give higher explanatory power using technology, organization, and environment constructs to investigate employee intentions to use workforce analytics.

This study develops a system-centered framework for explaining users’ intention to adopt virtual reality (VR) by examining the balance between immersive experiential features and device-related physical constraints. Moving beyond traditional technology acceptance approaches, we conceptualize sensory attributes (vividness, presence, and interactivity) as experiential affordances and device attributes (physical discomfort and motion sickness) as embodied constraints. Based on a laboratory experiment using the Meta Quest 2 head-mounted display and a post-experience survey, the results show that presence and interactivity have strong positive effects on adoption intention, while vividness has a marginally positive effect. Motion sickness shows a significant negative effect on adoption intention, whereas physical discomfort does not exhibit a statistically significant impact within the short-term exposure setting. The moderating effects of device attributes are not statistically significant, although marginal negative patterns suggest that embodied constraints may weaken experiential benefits under longer or more demanding usage conditions. These findings provide practical implications for both researchers and practitioners seeking to improve VR experiences and encourage broader adoption.

In spite of a vast amount of extant research on recommender systems, two important questions did not receive much attention: 1. Should the missing ratings be estimated based on the mechanism of missingness ? and 2. Are the existing findings appropriate for businesses with relatively medium-sized inventories of up to 20,000 items ? Our study addresses these questions by experimenting with ten publicly available datasets. This unique 3 x 3 x 5 study explores three missingness mechanisms, three levels of missingness and five methods. With an empirics-first methodology, the study reveals the limitations of two fairly popular methods. It also offers a few striking results which should be of interest to businesses with medium-sized inventories.

This study elucidates the evolutionary trajectory of Digital Transformation (DX) in the South Korean sports industry over 15 years (2011–2025). Applying text mining and Latent Dirichlet Allocation (LDA) topic modeling to 137,958 news articles collected via BIG Kinds, with topic number (K) selection validated through Coherence Score (Cv) and Log Perplexity, the analysis identifies four distinct evolutionary phases grounded in the Technology-Organization-Environment (TOE) framework, Diffusion of Innovations (DOI) theory, and Socio-Technical Systems (STS) theory: (1) Mobile Transition (2011–2015), marked by the mainstreaming of mobile sports consumption and portal-based broadcasting; (2) Technology Verification (2016–2019), characterized by 5G and immersive media demonstrations at the PyeongChang Olympics; (3) Virtual Expansion (2020–2022), wherein COVID-19 accelerated OTT adoption, Metaverse ecosystems, and NFT-based digital assets; and (4) Industrial Advancement (2023–2025), defined by AI institutionalization ― exemplified by the KBO’s Automated Ball-Strike System ― and transition to a subscription economy. The findings contribute to IS research by challenging the linearity of the DOI S-curve model, establishing media discourse as a social mechanism of technology diffusion, and proposing a generalizable DX phase framework for offline-centric industries. Practical implications address hybrid fan experience design, data governance, and public access policy.

The rapid growth of the OTT industry has intensified competition among OTT services, driving improvements in consumer experience through diverse service features. While OTT platforms focus on original content to remain competitive, they have made limited efforts to enhance consumers’ satisfaction from a comprehensive service perspective. This study aims to assess the quality of OTT service attributes and identify which attributes contribute most to consumer satisfaction. Utilizing the Kano model and the Service Diversity Index, this study evaluates and categorizes key attributes of OTT services. A Kano questionnaire survey was conducted among 427 participants in South Korea to assess consumer satisfaction across six major OTT platforms: Netflix, Disney+, TVING, Wavve, WATCHA, and Coupang Play. The results reveal that the embedded e-commerce features, particularly the product list, are perceived as highly attractive, significantly enhancing consumer satisfaction. The study contributes to research on service diversity in digital platforms and provides practical insights for OTT providers, emphasizing the importance of integrated service strategies.

This study examines the critical factors shaping e-government adaptation at the rural government level in Indonesia, focusing on the implementation of the Open Rural Information System (OpenSID) in Kampar District, Riau Province. Although many rural governments have formally adopted digital platforms, sustained use remains uneven, indicating that platform availability does not automatically lead to effective adaptation. Using a qualitative exploratory design, this study draws on secondary data, OpenSID monitoring data, official documents, and interviews with 13 informants representing policy-level actors, rural government officials, and e-government operators. The data were analyzed through thematic analysis assisted by NVivo 12 Plus, using open coding, axial coding, and cross-stage analysis across the stages of initiation, planning, development, implementation, and evaluation. The findings show that rural e-government adaptation is shaped by four interrelated factors: technical, governmental, social, and innovation-diffusion factors. Technical factors emerged as the most recurrent concern, particularly issues related to internet connectivity, ICT infrastructure, system usability, technical support, and operator capacity. However, the study also finds that technical readiness alone is insufficient. Sustained adaptation requires continuous governmental support, social acceptance, and innovation characteristics that fit local administrative routines. The study contributes to e-government literature by shifting the focus from adoption as an initial decision to adaptation as a staged and multidimensional process of sustained use, institutionalization, and continuous adjustment in rural governance.