Online reviews play a significant role in consumer purchase decisions on e-commerce platforms. To address information overload in the context of online reviews, factors that drive review helpfulness have received considerable attention from scholars and practitioners. The purpose of this study is to explore the differential effects of discrete emotions (anger, disgust, fear, joy, sadness, and surprise) on perceived review helpfulness, drawing on cognitive appraisal theory of emotion and expectation-confirmation theory. Emotions embedded in 56,157 hotel reviews collected from TripAdvisor.com were extracted based on a transfer learning model to measure emotion variables as an alternative to dictionary-based methods adopted in previous research. We found that anger and fear have positive impacts on review helpfulness, while disgust and joy exert negative impacts. Moreover, hotel star-classification significantly moderates the relationships between several emotions (disgust, fear, and joy) and perceived review helpfulness. Our results extend the understanding of review assessment and have managerial implications for hotel managers and e-commerce vendors.

Nowadays, social media has evolved into a powerful networked ecosystem in which governments and citizens publicly debate economic and political issues. This holds true for the pros and cons of Indonesia's ore nickel export restriction to Europe, which we aim to investigate further in this paper. Using Twitter as a dependable channel for conducting sentiment analysis, we have gathered 7070 tweets data for further processing using two sentiment analysis approaches, namely Support Vector Machine (SVM) and Long Short Term Memory (LSTM). Model construction stage has shown that Bidirectional LSTM performed better than LSTM and SVM kernels, with accuracy of 91%. The LSTM comes second and The SVM Radial Basis Function comes third in terms of best model, with 88% and 83% accuracies, respectively. In terms of sentiments, most Indonesians believe that the nickel ore provision will have a positive impact on the mining industry in Indonesia. However, a small number of Indonesian citizens contradict this policy due to fears of a trade dispute that could potentially harm Indonesia's bilateral relations with the EU. Hence, this study contributes to the advancement of measuring public opinions through big data tools by identifying Bidirectional LSTM as the optimal model for the dataset.

Through Information and Communication Technology (ICT), the growth of music streaming platforms has revolutionized music consumption. "Digital Shadow Work" (DSW) refers to unpaid labor in digital spaces, with some prior research on its aspects. However, a comprehensive understanding is hindered by limitations in existing studies such as a lack of universality and dynamic exploration. To address these gaps and enable a comprehensive investigation into the role of DSW within highly versatile digital applications such as digital streaming platforms, this study employs a grounded theory methodology, a qualitative approach well-suited for exploring the intricacies of DSW among users of Chinese music streaming applications over a two-month period, involving longitudinal interviews with nine participants. The study findings elucidate the dynamic nature of DSW perceptions, which fluctuate across different stages of use and change in intensity over time. This study uncovers mixed attitudes towards DSW tasks, and observes a waning enthusiasm for social features over time, prompting some users to consider switching platforms. This study highlights the importance of thoughtful and user-centric feature development to enhance user satisfaction and the understanding of DSW, providing practical design and enhancement implications for music streaming applications.

Despite the maturity of platforms, only some studies have explored the relationships between the working conditions of platform workers and their organization-like responses to these platforms. Thus, this research utilized the Job Demands-Resources Model (JD-R Model) to analyze the effects of job demands and resources on platform workers' job embracing. The data were collected from 182 food delivery riders in South Korea. This study utilized the PLS technique (partial least squares) to examine the research model. Regarding job demands, this study has found that work overload and physical effort significantly affect burnout. Regarding job resources, the results revealed that service technology support and training significantly affect work engagement. In alignment with the Job Demands-Resources literature, the findings offer tangible proof that burnout has a detrimental impact on job embracing, whereas work engagement has a beneficial effect on job embracing. Our findings indicate that work engagement exerts a more substantial beneficial effect on job embracing, and burnout reduces job embracing. Results also provide novel insights to scholars seeking a comprehensive research model on the impact of on-demand workplace conditions to help platforms attract and retain platform workers.

For nonprofit organizations to effectively conduct social media marketing, which is characterized by viral marketing, it is essential that members engage in voluntary and active social media marketing activities. The purpose of this study was to present and empirically analyze the factors that influence the social media marketing activities of nonprofit organization members. To this end, this paper presents a research model that combines social capital and social cognitive theory based on the theory of reasoned action. To test this research model, valid survey data were collected from 317 members of nonprofit organizations in Korea and analyzed with structural equation modeling. The main results suggest that reciprocity, shared goals, outcome expectations, and self-efficiency affect the social media marketing activity intentions of nonprofit organization members through attitude and subjective norms. These results provide practical guidelines for the revitalization of social media marketing in nonprofit organizations.

E-commerce has grown to be perceived as an integral component of modern customers' lives. Fast innovation enables businesses to implement new business ideas that enhance customers' shopping experiences. The motive is to study the allurement of unrestrained buying behaviors resulting from Live Streaming Commerce in the presence of Emotional imagery. The conceptual model and hypotheses for the study have been framed based on the SOR model. A survey was conducted in north India, where data was collected from 577 consumers experiencing live streaming and analyzed with the help of AMOS and SPSS software. The repulsive behavior scale has been developed by using qualitative research. The findings revealed that there is a significant relationship between the stimulus of livestreaming and unrestrained buying behaviors with the mediating role of emotional imagination. Two crucial mediating factors, pleasure, and arousal (fervent imagination), have successfully predicted experiential shopping behavior. The study has implications for online marketers and policymakers, as marketers can use our developed model to understand consumers' different buying behaviors, and policymakers can select and design specific features for the social presence of live streaming. Integrating three different types of unrestrained buying behavior influenced by live streaming would add to the literature. The study adds value to the literature by developing a scale to measure repulsive behavior after testing and validating with experts.

Mobile technologies have enabled marketers to target consumers anywhere and anytime. However, as consumers react and respond differently depending on what situation they are in, there is an apparent need to determine when, where, and what kind of advertisement is most relevant to the consumer. This paper proposes a holistic approach to examine the response of consumers when faced with two types of contextual factors (environmental/spatial and social contexts) through the lens of the Mobile Advertising Effectiveness Framework. We focus on the contextual effects of perceived distance from the offline store and the effect of popularity cue indication. A scenario-based survey is conducted to investigate the effects of perceived distance and popularity cue on the users' attitudes, and ultimately on their response intentions, upon receipt of mobile ads. Results of the study confirm the hypotheses: first, mobile ads sent when users perceive the physical store to be in close proximity tend to evoke more positive attitudes and elicit better responses compared to when users perceive the store to be farther away. Additionally, ad messages indicating high popularity were found to be more appealing than those with low popularity. These empirical results underscore the pivotal role of context, encompassing both spatial context (proximity to offline stores) and social context (popularity cues), in shaping consumer attitudes and response intentions in mobile advertising. The findings of the study offer theoretical insights that underline the significance of holistic context-based approaches that in turn, marketers may use to design more effective mobile ad campaigns that may elicit better responses from consumers.

Contact-tracing applications have significantly contributed to mitigating the spread of coronavirus disease 2019 (COVID-19), yet the extensive use of these location-based applications raises serious privacy concerns. Drawing on the Information-Motivation-Behavioral (IMB) skills model, our study investigated factors that influence users' protective behaviors toward location privacy, elucidating the privacy paradox and the mediating role of implementation intention. Through an online survey conducted in China with 311 participants, we found that privacy concerns and privacy awareness positively affected the use of mobile location service settings, with privacy concerns mediating the relationship between privacy awareness and the intention to protect privacy. Furthermore, our study demonstrated the privacy paradox, revealing the pivotal mediating role of implementation intentions in bridging the gap between users' intentions and their actual behaviors. This study offers new perspectives on the privacy paradox, particularly through the lens of implementation intention, and provides valuable insights for motivating greater use of contact-tracing applications. It offers both theoretical and practical guidance for stakeholders to address privacy concerns during global pandemics like COVID-19, thereby encouraging a more widespread and responsible engagement with technology in public health.

More than 1 million automobile insurance repairs occur per year globally, and the related repair costs add up to astronomical amounts. Insurance companies and repair shops are spending a great deal of money on manpower every year to claim reasonable insurance repair costs. For this reason, promptly predicting insurance claims for vehicles in accidents can help reduce social costs related to auto insurance. Several recent studies have been conducted in auto insurance repair prediction using variables such as photos of vehicle damage. We propose a new model that reflects auto insurance repair characteristics to predict auto insurance repair claims through an association rule method that combines gradient descent and location information. This method searches for the appropriate number of rules by applying the gradient descent method to results generated by association rules and eventually extracting main rules with a distance filter that reflects automobile part location information to find items suitable for insurance repair claims. According to our results, predictive performance could be improved by applying the rule set extracted by the proposed method. Therefore, a model combining the gradient descent method and a location-based association rule method is suitable for predicting auto insurance repair claims.

This paper delves into the comparative analysis of human interactions with AI agents based on the presence or absence of a facial representation, combined with the presence or absence of paralanguage voice elements. The "CASA (Computer-Are-Social-Actors)" paradigm posits that people perceive computers as social actors, not tools, unconsciously applying human norms and behaviors to computers. Paralanguages are speech voice elements such as pitch, tone, stress, pause, duration, speed that help to convey what a speaker is trying to communicate. The focus is on understanding how these elements collectively contribute to the generation of flow, intimacy, trust, and interactional enjoyment within the user experience. Subsequently, this study uses PLS analysis to explore the connections among all variables within the research framework. This paper has academic and practical implications.

Recently, active research has been conducted on the metaverse as a new education platform. However, only a few studies analyze the specific characteristics of this platform from potential users' perspectives. Therefore, based on literature reviews and expert surveys on education, this study specifies the attributes and levels to be considered in developing metaverse education platforms. An online survey was conducted among university students in South Korea, and conjoint analysis was performed to propose the conditions for education platforms optimized for university education. The results revealed that 85% of respondents were willing to use metaverse education platforms, and preferred virtual classrooms that enable indirect experience in a web-based personal computer environment. In particular, the respondents showed a high preference for the education platforms that were available at $5 per month and used newly created three-dimensional avatar characters of themselves. This study is significant since its results have strategic implications for expanding the metaverse's use as a new educational space.

A growing body of research explores how users of online communities navigate through large-scale platforms to find the information they seek. This study builds on the theories of homophily, structural embeddedness, and social exchange to investigate how interest homophily and existing communication structures serve as mechanisms driving information searches and the subsequent formation of communication networks in these communities. Specifically, we analyze comment-on-post tie formation using network data from "Today's House," the largest online user community specializing in interior design in Korea. Employing the LR-QAP method, a permutation-based hypothesis testing algorithm for social network data, our research identifies that network tie formation is driven by both homophilous information searches based on instrumental and hedonic interests, as well as by structurally induced searches such as preferential attachment, reciprocity, and transitivity. In addition, we investigate the contingent effects of communication structure on homophilous tie formation. Our findings suggest that while network-wide structural characteristics enhance homophilous tie formation based on instrumental interests, local network processes leverage homophily based on hedonic interests. We conclude by discussing the theoretical implications of the differential influence of participation motivations on information search patterns and the practical implications for the design of online communities.