Earticle

In the past, individuals took years to adopt a technology meaningfully. However, the rapid increase in ChatGPT users shows a new trend where individuals actively participate in adopting new technology. I collected responses from 420 individuals in two countries and used PLS-SEM to evaluate the impact of UTAUT2 constructs on an individual's behavioral attitude on adopting ChatGPT and their actual intention to adopt it. This study finds that an individual's performance expectancy, satisfaction from using ChatGPT, and the influence of their social circle positively affect their attitude toward adopting it. However, their habit of using it, price value, facilitating conditions, and effort expectancy do not significantly impact their attitude to adopt it. The results from the cross-cultural analysis show that when it comes to using ChatGPT, individuals from different cultures demonstrate notable differences in attitudes resulting from their satisfaction and habit of using the technology.

With the advancements and widespread adoption of social media platforms, electronic word-of-mouth (eWOM) has become increasingly diverse, significantly impacting consumer decisions through its varied information dissemination. Compared to textual reviews, video reviews on the social medias are becoming more crucial in consumers' shopping decision-making process. This is attributed to video reviews offering consumers more comprehensive information through visual cues and dynamic product explanations. While the perceived helpfulness of information presented in video reviews can reflect viewers' evaluations of review quality, there is a notable absence of research examining the factors influencing the assessment of review helpfulness in videos. To bridge this gap, we investigated the impact of YouTube channel characteristics and video duration on the helpfulness of video reviews for electronic products. Additionally, this study applied speech-to-text conversion and text mining techniques to extract and analyze emotional factors. The findings of the model analysis revealed that a greater number of subscribers to a channel corresponds to increased helpfulness of video reviews. Furthermore, the helpfulness of video reviews tends to rise when the expressed emotions are more negative. This study also confirmed a non-linear relationship between video duration and review helpfulness, concluding that videos of an appropriate duration are most beneficial to consumers. This study not only adapted and validated the antecedents of helpfulness from textual reviews to video reviews but also expanded research on the review helpfulness within the realm of social media. Drawing on the analytical outcomes, practical insights were provided for the generation and management of eWOM and video reviews on social media platforms. Especially, the results provide constructive for individuals and platforms involved in generating and presenting more helpful content for product reviews.

Often, a worker’s occupational domain and educational qualifications mismatch, leading to a discrepancy with their actual skill set. This can lead to either underperformance or overperformance, as the competencies required by the company may differ from those possessed by potential employees. This research aims to develop a system for matching user profiles and job vacancies called the Job Matching System. The system can help individuals find jobs that suit their educational background and skills. By collecting large amounts of data from the Jobstreet.co.id website, the system automatically identifies job opportunities in four job categories, which are data analyst, digital marketing, developer, and machine learning. We used the Latent Dirichlet Allocation approach to analyze the gathered data and identify potential topics within large data sets. Based on this information, the database correlates educational background with appropriate job classifications. The "Job Matching" website has a built-in decision-making tool. Prior knowledge of the worker’s background is required to aid in the matching procedure and facilitate the alignment of competencies with the worker’s preferred industry throughout the matching process.

This study explored factors influencing user acceptance of virtual reality (VR) technology. In this study, we proposed a comprehensive research model combining the technology acceptance model (TAM) extension with the stimulus–organism–response (SOR) framework. The integration of TAM and SOR, along with the exploration of three distinct sensory inputs - vividness, presence, and interactivity - shaping user intentions in the VR context, makes a substantial contribution to the existing literature. Assessed through perceptions of usefulness, ease of use, and enjoyment, our findings revealed that perceived enjoyment was positively affected by vividness and presence. Perceived usefulness was exclusively influenced by presence factors, whereas perceived ease of use was solely affected by interactivity. This study’s results confirmed the direct positive influence of perceived usefulness, ease of use, and enjoyment on the intention to adopt VR. However, the mediating effect of perceived ease of use was supported only by perceived usefulness. Our findings offer valuable insights for both researchers and industry professionals, aiding the development of more effective VR systems through an enhanced understanding of users’ willingness to adopt VR technology.

With intense competition, online retailers face the critical challenge of customer retention amidst the propensity for switching brands. Thus, understanding the factors influencing customer switching behavior in this domain is paramount for sustaining market share and fostering brand loyalty. This study aims to delve into the intricacies of customer switching behavior, specifically in the context of baby care products. By employing survey method and convenience sampling technique, 384 responses were collected and analyzed using PLS-SEM method and mediation analysis. By investigating the mediating role of e-satisfaction between positive and negative switching barriers and e-loyalty, this research seeks to provide valuable insights into the underlying mechanisms shaping consumer preferences and loyalty in the landscape of e-commerce. Identifying and comprehending these factors can empower online retailers to tailor effective marketing strategies, enhance customer satisfaction, and cultivate enduring relationships with their consumers.

Moving beyond existing assumptions that regard personal information as fixed and practices that focus solely on its collection and use, advances in data processing methods have enabled the construction of digital entities using digitally captured personal data. From the perspective of the datafication of privacy, we attempt to identify individuals’ digital data across different contexts and examine how data processing levels and the identifiability of a person are associated. To this end, we collected users’ perceptions of personal data in social media and online shopping contexts and examined the relationships between data processing levels―measured by human- machine-generated and changeability characteristics―and the identifiability of personal data. Based on an exploratory examination of various data types in two contexts, we suggest new personal information categories that embrace the notion of data processing.

We explore how consumers' willingness to share their information with companies, and how much information companies should obtain to design their products, based on the attributes of value that become increasingly evident over time in the escalating use of personal information and the uncertainty of product quality. We discuss product launch strategies for smart devices influenced by the future value through consumer learning. In a dual program strategy, the scope of data sharing with relatively weak third parties increases, and if the benefits to consumers from future certainty increase, the price difference between the two products decreases. We show that if the future rewards from certainty by consumer learning is above a certain level, it is advantageous for the company to use a dual program strategy, whereas if it is below the level, a single program strategy is dominant. We also find that if the network effects are strong, a dual program is profitable, and if the network effects are weak, the operational strategy depends on the level of certainty rewards.

AI assistants are designed to closely interact with their users to help them complete various everyday tasks. To explain the continued use of AI assistants, we develop a research model that extends the investment model by Rusbult (1980), and empirically test it with data collected from an online survey of 172 users. The findings indicate that both satisfaction, which leads a user to become dedicated to a relationship with an AI assistant, and investment size, which constrains a user to a relationship with an incumbent AI assistant, significantly influence a user’s commitment to an AI assistant. The commitment in turn enhances a user’s continued use of an AI Assistant. We also find that perceived usefulness, rapport, and perceived social presence are key antecedents of satisfaction, while brand relationship and setup costs are key antecedents of investment size.

The use of mobile instant messaging (MIM) systems has increased recently. There are various MIM applications in use. Various factors influence the choice of MIM systems by different user groups. Based on the Unified Technology Acceptance and Use Theory (UTAUT), this study aims to investigate the factors that affect public personnel preference and the use of MIM systems in Türkiye. In this context, an empirical study was performed to examine the attitudes of public personnel working in Türkiye toward the MIM systems, which are preferred in their social and professional life. The data was collected from 441 public personnel and analyzed using Structural Equation Modeling. The results show that the most significant factors affecting behavioral intention are performance expectancy and privacy and security expectancy factors. The effect of effort expectancy and social influence on behavioral intention is influenced by gender. Also, the effect of privacy and security expectancy on behavioral intention is affected by experience and age. This research is important in drawing attention to data privacy and security in MIM systems. Also, although the use of MIM systems developed within their organizations or domestically is mandatory for public personnel, it has been determined that the usage rates of these systems are low. This should be considered by public administrators and companies developing MIM systems.