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This paper aims to examine the interrelationships between user-centric measures and their impact on the firm’s perceived financial performance as the respondents’ decision-making role changes.

The data was collected jointly with SurveyMonkey, a marketing research company, from marketing professionals working in companies with at least limited experience deploying big data marketing analytics (BDMA) applications. The respondents originated from Canada and the USA, and out of 970 responses in the initial sample, 236 were working in companies with at least limited experience in BDMA deployment. The data analysis used partial least squares structural equation modeling and necessary condition analysis.

All hypotheses except one were accepted. Perceived value for money positively and significantly impacted user satisfaction, positively and significantly impacted perceived financial performance. Also, the decision-making role positively and significantly impacted the perceived value for money and user satisfaction but not the perceived financial performance.

The research contributes to understanding how the decision-maker’s role impacts the perceived user-related performance measures in the BDMA context.

Online games are highly popular Internet applications. Some games enable players to save game progress and accumulate experiences or changes to avatars during gameplay, whereas some other games do not, requiring players to restart from the beginning each time. That is, games differ in avatar accumulability. However, we do not know whether games should be designed to permit avatar accumulability or not and how it affects players’ gameplay experience and therefore game outcomes, indicating gaps. Research addressing these gaps can inform game makers in designing games that effectively strengthen their players’ game loyalty.

We used social identity theory (SIT) to construct a theoretical model. To test this model, three waves of survey data were gathered from the same 778 participants.

These findings uniquely indicate that avatar accumulability fosters avatar identification and increases players’ focused immersion, thus increasing players’ loyalty.

Game providers could include game features that enable players to accumulate their avatars’ in-game skins, levels, items (weapons and equipment) and experience points. This accumulability can help strengthen players’ game loyalty.

Overall, our study extends SIT by adding a new trigger (avatar accumulability) and two novel consequences of avatar identification (image protection and focused immersion) in avatar-related systems (games or gamified systems). The new trigger offers an actionable means to apply SIT, while the novel consequences verify the value of applying SIT to study online games.

This study evaluates parental time investments in adolescents with disabilities relative to their siblings and to nondisabled youth in other families. Parents with several children must allocate time and attention to each, which may not be equal due to the challenges that arise from child disabilities, possibly reinforcing preexisting differences between siblings. In contrast, parents may seek to compensate for health deficiencies by allocating more parental time to a child with disabilities. Using the nationally representative American Time Use Survey (ATUS) (2008–2019) and ordinary least squares (OLS) regression to make across-family comparisons of parental time with disabled children relative to families in which no children had disabilities (N = 18,140), the study further focused on families with a disabled child and used fixed effects regression to evaluate within-family sibling comparisons of parental time investments (N = 648). Results indicate for families with a child with a disability, and parents spend the most one-on-one time with children who have disabilities and less one-on-one time with their other children. One-on-one time with children with disabilities is also higher than one-on-one time in families without children with disabilities. Differences were most pronounced in households in which a child had both cognitive and physical disabilities and in households in which no parent had a bachelor’s degree. Additional parental time for youth with disabilities aligns with theories of compensation and likely promotes development for these youth. Parental time investments may also be a plausible mechanism for explaining some difficulties experienced by siblings of disabled youth.

The present study explored sickness presenteeism as a linchpin connecting prolonged working hours to elevated turnover intention among hospital nurses (mediation). In addition, we examined the joint moderating effects of organizational health care support and supervisor support on the second stage of the “long working hours-presenteeism-turnover” process (three-way moderated mediation).

We conducted a two-wave survey to collect data over a four-month period from hospital nurses in Taiwan. The final sample for analysis had 294 nurses.

We found that presenteeism partially mediated the elevated turnover intention after working long hours four months later. We also found a significant three-way moderation effect of organizational health care support and supervisor support on the positive relationship between presenteeism and turnover intention. Specifically, nurses reported the lowest turnover intention with high levels of both organizational healthcare and supervisor support, the highest turnover intention with both support being low, and the intermediate level of turnover intention when any one of the support was high.

Organizations should build a positive work environment through organizational health care support and supervisor support to retain talents.

Our findings suggested that support resources of different origins (organization-level vs line supervisor) can compensate for one another to protect the employees in demanding work conditions. Our moderated mediation model exploring the psychosocial context of presenteeism has theoretical contributions pivoting on the interplay of resources at different levels in the organization as well as practical implications for presenteeism management.

While the proliferation of chatbots allows companies to connect with their customers in a cost- and time-efficient manner, it is not deniable that they quite often fail expectations and may even pose negative impacts on user experience. The purpose of the study is to empirically explore the negative user experience with chatbots and understand how users respond to service failure caused by chatbots.

This study adopts a qualitative research method and conducts thematic analysis of 23 interview transcripts.

It identifies common areas where chatbots fail user expectations and cause service failure. These include their inability to comprehend and provide information, over-enquiry of personal or sensitive information, fake humanity, poor integration with human agents, and their inability to solve complicated user queries. Negative emotions such as anger, frustration, betrayal and passive defeat were experienced by participants when they interacted with chatbots. We also reveal four coping strategies users employ following a chatbots-induced failure: expressive support seeking, active coping, acceptance and withdrawal.

Our study extends our current understanding of human-chatbot interactions and provides significant managerial implications. It highlights the importance for organizations to re-consider the role of their chatbots in user interactions and balance the use of human and chatbots in the service context, particularly in customer service interactions that involve resolving complex issues or handling non-routinized tasks.

This paper aims to understand the characteristics of current misinformation detection studies, including the datasets used by researchers, the computational models or algorithms being developed or applied, and the performance of misinformation detection models or algorithms.

We first identified articles from the Scopus database with inclusion and exclusion criteria. Then a coding scheme was derived from the articles based on research questions. Next, datasets, models, and performance were coded. The paper concluded with answers to research questions and future research directions.

From 115 relevant articles published during 2019–2023 on misinformation detection. We found that most studies used previously existing datasets. Twitter (now X) has been the most widely used source for collecting social media misinformation data. The ten most frequently used datasets are identified. Most studies (96.1%) developed or applied machine learning, especially deep learning models. The most advanced current misinformation detection models could achieve pretty high performance. For example, among 104 studies reporting performance with accuracy, 44.2% achieved an accuracy of 0.95 or higher, and 24.0% achieved 0.90–0.94 on accuracy.

Our study only reviewed English articles from 2019–2023 that are included in the Scopus database. Articles that are not included in the Scopus database are not reviewed.

The high performance of misinformation detection indicates that social media should be able to detect most misinformation if they are willing to do it. However, no system or algorithm could achieve 100% misinformation on performance. Due to the complexity of misinformation, users of social media still need to improve their capabilities of evaluating information on the Internet.

This study provides evidence to policymakers that social media platforms have the capability of detecting most misinformation posted. These platforms are responsible for alerting to suspicious postings with misinformation.

This study identifies datasets, computer models, and performance of models from current misinformation detection research. The findings will help social media companies, computer scientists, and information system designers improve their misinformation detection systems. It will also help students in information science and computer science to study the latest models and algorithms. Information professionals may work with computer scientists to improve datasets used for misinformation detection.

Video game streaming (VGS) is emerging as an extremely popular, highly interactive, inordinately subscribed and very dynamic form of digital media. Incorporated environmental elements, gratifications and user pre-existing attitudes in VGS, this paper presents the development of an extended model of uses and gratification theory (EUGT) for predicting users' behavior in novel technological context.

The proposed model was empirically tested in VGS context due to its popularity, interactivity and relevance. Data collected from 308 VGS users and structural equation modeling (SEM) was employed to assess the hypotheses. Multi-model comparison technique was used to assess the explanatory power of EUGT.

The findings confirmed three significant types elements in determining VGS viewers' engagement, including gratifications (e.g. involvement), environmental cues (e.g. medium appeal) and user predispositions (e.g. pre-existing attitudes). The results revealed that emerging technologies provide potential opportunities for new motives and gratifications, and highlighted the significant of pre-existing attitudes as a mediator in the gratification-uses link.

This study is one of its kind in tackling the criticism on UGT of considering media users too rational or active. The study achieved this objective by considering environmental impacts on user behavior which is largely ignored in recent UGT studies. Also, by incorporating users pre-existing attitudes into UGT framework, this study conceptualized and empirically verified the higher explanatory power of EUGT through a novel multi-modal approach in VGS. Compared to other rival models, EUGS provides a more robust explanation of users' behavior. The findings contribute to the literature of UGT, VGS and users' engagement.

This study proposes the novel construct of digital transformation anxiety and investigates its effect, which is mediated by absorptive capacity and dynamic capability, on digital innovation performance.

This study conducted a questionnaire survey among Taiwanese manufacturing and service companies to verify the research framework. A total of 130 valid responses were collected and analyzed using partial least squares structural equation modeling (PLS-SEM) and bootstrapping to test direct and mediation effects, respectively.

Digital transformation anxiety negatively affects absorptive capacity and dynamic capability, whereas absorptive capacity and dynamic capability positively affect digital innovation performance. Dynamic capability more strongly mediates the association between digital transformation anxiety and digital innovation performance than absorptive capacity. Additionally, digital transformation anxiety does not negatively affect digital innovation performance. Finally, manufacturing companies had significantly higher levels of digital transformation anxiety than service companies.

This study proposes the novel construct of digital transformation anxiety to address a gap in the literature. Digital transformation anxiety leads companies to adopt unnecessarily conservative practices, preventing them from flexibly responding to technological advances. This insight highlights the negative effect of such anxiety on absorptive capacity and dynamic capability, extending the application of path dependency theory to companies. The findings underscore the value of enhancing dynamic capability and reallocating resources to foster digital innovation. The study identified and explored the concept of digital transformation anxiety and extended the perspective of dynamic capability to include digital transformation and digital innovation.

The current findings indicate that digital transformation anxiety does not substantially affect digital innovation performance in Taiwanese companies. Consequently, Taiwanese companies should focus on developing their absorptive capacity and dynamic capability to enhance digital innovation.

The study proposes the novel construct of digital transformation anxiety and explores its effect on business units. It presents a pioneering framework derived from path dependence theory and the perspective of dynamic capability.