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Consumer demand for e-commerce services has skyrocketed due to the introduction of social distancing standards and lockdown measures that countries have taken to combat the pandemic. There has been a notable surge in the popularity of on-demand delivery services, with a significant influx of new users turning to the e-platform for assistance. This research paper tries to identify the enablers of E-commerce Utility and Service Quality and establish a connection using total interpretive structural modelling (TISM). Enablers are the building blocks for providing customers with an enhanced and more consistent service experience contributing to service quality. The enablers and the linkages thus established hold valuable insights for e-commerce marketers, aiding them in effectively reaching their customers, and achieving desired growth outcomes. The TISM- based model and the MICMAC analysis identified two barriers; website design and personalization as the decisive attributes of e-commerce service quality, possessing strong driving power and weak dependence. Furthermore, the factors of reliability, responsiveness, information, and ease of use form the linkage zone, indicating that any action taken on these factors would not only influence other factors but also have a reciprocal effect on them.

Data plays an ever greater part in the tourism industry. While the platform-based sharing of open public data, private-sector intermediary platforms, and the use of social media to understand consumer trends are already well recognized, more potential for innovation exists in sharing private data among organizations in Smart Tourism Destinations. Research into the factors enabling and hindering coopetition in this kind of data sharing platforms is still in the nascent stage of development. Our case study of Sapporo, a major Japanese city endeavouring to create itself as a Smart Tourism Destination, sheds light on the initial approaches to involve organizations to such a data sharing agreement. Founding on seven interviews with ten participants of Sapporo Smart City project organization (SARD), we derived enablers and impediments that promote coopetition in data sharing as part of Smart Tourism Destination development. We also present practical recommendations and future research opportunities for such initiatives.

Intradisciplinary research refers to research that integrates ideas often associated with different research domains in a discipline. Such cross-fertilization leverages abundant diversity present in the IS discipline to tackle increasingly complex IS problems and grand challenges. Despite its importance and recent attention, a concerted, sustained effort toward intradisciplinary research is lagging. A fundamental issue we see is a lack of an elaborate IS research map that effectively shows similarities and differences among research domains and demonstrates types of ideas that may travel and integrate into different domains. We thus aim to propose an elaborate IS research map and compile research elements that can be tried and combined across research domains. To do so, we utilize two IS classics (i.e., IT views and IS phenomena), identify their complementarity, and interweave the two disparate ways of portraying the IS research field. The resultant view-phenomenon (VP) matrix specifies research domains based on two consistent, comprehensive criteria and helps researchers discern similarities and differences among research domains more effectively. The VP matrix also sheds light on a variety of research elements that can flow across research domains. The VP matrix along with the research elements together facilitate intradisciplinary efforts and, more broadly, help the IS discipline to prosper. The VP matrix is particularly helpful for doctoral students and young scholars.

In the wake of the COVID-19 pandemic, unsubstantiated information concerning vaccines and the coronavirus has proliferated on various social media platforms. Consequently, we have considered viable actions to mitigate the impact of such unverified content, enabling individuals to use social media platforms more effectively and minimize any ensuing confusion. Recent measures in this area have included YouTube’s practice of labeling vaccine or corona videos as authoritative when emanating from reputable organizations and Twitter’s practice of flagging vaccine-related content as potentially misleading or taken out of context. This study seeks to explore how such contrasting labeling practices influence users’ believability and engagement differentially, while also examining the moderating impact of regulatory focus. The results indicate that authoritative labeling positively influenced users’ believability and engagement, whereas misleading labeling adversely affected users’ believability and engagement. Additionally, our findings revealed that authoritative labeling has a stronger impact on promotion-focused individuals, while misleading labeling has a more pronounced effect on prevention- focused individuals. Our findings offer insights into how social media platforms can design and present information to their users, taking into account their regulatory focus.

Internet-only banks (IOB) refer to financial institutions which provide services entirely through online digital platforms without physical branches. Although IOBs have been around for over 26 years, there is still room for investigating what factors motivate customers to use them continuously and extensively. Therefore, this study aims to examine what factors lead to customers’ intention of continued use and cross-buying on IOBs by extending the Post-Acceptance Model of IS Continuance (PAMISC). The result shows that perceived economic benefit and ongoing trust have significant relationships with the intention of continued use and that ongoing trust has significant direct and mediating (via intention of continued use) relationships with the intention of cross-buying. Also, personal innovativeness positively moderates the relationship between ongoing trust and the intention of cross-buying. This study sheds light on the literature on branchless financial services and PAMISC. IOB practitioners should revisit the effectiveness of customers’ economic benefits to establish ongoing trust with customers.

This article proposes a framework to elucidate the structural dynamics of the generative AI ecosystem. It also outlines the practical application of this proposed framework through illustrative policies, with a specific emphasis on the development of the Korean generative AI ecosystem and its implications of platform strategies at AI platform-squared. We propose a comprehensive classification scheme within generative AI ecosystems, including app builders, technology partners, app stores, foundational AI models operating as operating systems, cloud services, and chip manufacturers. The market competitiveness for both app builders and technology partners will be highly contingent on their ability to effectively navigate the customer decision journey (CDJ) while offering localized services that fill the gaps left by foundational models. The strategically important platform of platforms in the generative AI ecosystem (i.e., AI platform-squared) is constituted by app stores, foundational AIs as operating systems, and cloud services. A few companies, primarily in the U.S. and China, are projected to dominate this AI platform squared, and consequently, they are likely to become the primary targets of non-market strategies by diverse governments and communities. Korea still has chances in AI platform-squared, but the window of opportunities is narrowing. A cautious approach is necessary when considering potential regulations for domestic large AI models and platforms. Hastily importing foreign regulatory frameworks and non-market strategies, such as those from Europe, could overlook the essential hierarchical structure that our framework underscores. Our study suggests a clear strategic pathway for Korea to emerge as a generative AI powerhouse. As one of the few countries boasting significant companies within the foundational AI models (which need to collaborate with each other) and chip manufacturing sectors, it is vital for Korea to leverage its unique position and strategically penetrate the platform-squared segment-app stores, operating systems, and cloud services. Given the potential network effects and winner-takes-all dynamics in AI platform-squared, this endeavor is of immediate urgency. To facilitate this transition, it is recommended that the government implement promotional policies that strategically nurture these AI platform-squared, rather than restrict them through regulations and stakeholder pressures.

This paper proposes the presence and importance of cultural differences to address user engagement in worldwide social media platforms. Based on Hofstede’s cultural dimensions, this paper addresses their new meanings in the context of user engagement in social media. Our propositions address two research questions: (1) how do cultural dimensions, displayed on social media platforms, differ across national cultures?; (2) what different preferences the social media platforms have in terms of which cultural dimensions promote or suppress user engagement? User engagement in social media platforms is explained by the cultural differences in terms of the four cultural dimensions: individualism vs. collectivism, uncertainty avoidance, power distance, and masculinity vs. femininity. Implications are also discussed for research and practice.

This research aims to use machine learning technology in human resource management to predict employees’ work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers’ work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study’s outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

Most studies on recommender systems to evaluate recommendation performances focus on offline evaluation methods utilizing past customer transaction records. However, evaluating recommendation performance through real-world stimulation becomes challenging. Moreover, such methods cannot evaluate the duration of the recommendation effect. This study measures the personalized recommendation (stimulus) effect when the product recommendation to customers leads to actual purchases and evaluates the duration of the stimulus personalized recommendation effect leading to purchases. The results revealed a 4.58% improvement in recommendation performance in the online environment compared with that in the offline environment. Furthermore, there is little difference in recommendation performance in offline experiments by period, whereas the recommendation performance declines with time in online experiments.

A few years before, MOOCs appeared and developed at a rapid pace. Various MOOC methods, theories, strategies, elements, and techniques were used to improve distance education. In their development, their weaknesses like dropout, low participation, low completion rate, low engagement, and others have emerged that are addressed by recent studies including that of gamified and adaptive gamified MOOCs. This article presents the most important theories, strategies, and elements used in gamification and their usefulness and contribution to MOOCs, with the ultimate goal of proving rich information to researchers, application designers, and practitioners of gamified and adaptive gamified MOOCs.

The amount of data a related to a person is so substantial that it appears that a digital version of them can be built thereon. They are usually handled as personal information, and the attempts made to understand personal information have led to bundling and unbundling of various data, yielding numerous fragmented categories of personal information. Therefore, we attempt to construct a generalizable lens for a deeper understanding of person-related data. We develop a theoretical framework that provides a fundamental method to understand these data as an entity of a digitally represented person based on literature review as well as the concepts of inforg and infosphere. The proposed framework suggests person-related data consist of three informational inforg dimensions that can preserve the archetype of a person, form, content, and interaction. Subsequently, the framework is examined and tested through several analyses in two different contexts: social media and online shopping mall. This framework demonstrates the suggested dimensions are interrelated with certain patterns, the prominent dimension can determine the data characteristics, and the dimensional composition of data types can imply the characteristics of the digitally represented person in certain contexts.

When a competent employee leaves an organization, the technical skills and know-how possessed by that employee also disappear, which may lead to various problems, such as a decrease in organizational morale and technology leakage. To address such problems, it is important to increase employees’ job satisfaction. Due to the advancement of both information and communication technology and social media, many former and current employees share information regarding companies in which they have worked or for which they currently work via job portal websites. In this study, a web crawl was used to collect reviews and job satisfaction ratings written by all and incumbent employees working in nine industries from Job Planet, a Korean job portal site. According to this analysis, regardless of the industry in question, organizational culture, welfare support, work system, growth capability and relationships had significant positive effects on job satisfaction, while time and attendance management, performance management, and organizational flexibility had significant negative effects on job satisfaction. With respect to the path difference between former and current employees, time and attendance management and organizational flexibility have greater negative effects on job satisfaction for current employees than for former employees. On the other hand, organizational culture, work system, and relationships had greater positive effects for current employees than for former employees.

This study aims to assess the impact of chatbot usability and demographics on continuous intention to use across different sectors. The research employed Braun’s Bot Usability Scale (BUS11) to measure chatbot usability, focusing on accessibility, quality, conversation quality, privacy risk, and response time. A total of 187 participants completed a survey as part of this study. Variance-based SEM was utilized to examine relationships and test hypotheses. This study contributes to the ongoing discourse on chatbot adoption and user behaviour. It enhances the understanding of chatbot usability, highlighting the role of age in continued intention to use chatbots. The findings suggest that different age groups may possess specific preferences and expectations regarding chatbot usability. These differing preferences can influence their intention to continue using this technology. The study reveals that chatbot usability significantly impacts continuous intention to use and that age moderates the relationship between perceived conversation quality, information, privacy, security, and continuous intention to use. Based on the study’s results, it is recommended that chatbot designers enhance usability to promote long-term adoption and usage.