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The purpose of this paper is to identify motivational factors for using wearable healthcare devices and examine the process by which these factors are integrated with the technology acceptance model (TAM) and contribute to the adoption of the devices.

An online survey assessed the proposed motivational factors for the adoption of wearable healthcare devices. Confirmatory factor analysis and structural equation modeling were conducted on collected data (n=877) to demonstrate the reliability and validity of the measurement and structural model.

Perceived control and interactivity of wearable healthcare devices as well as users’ innovative tendencies are positively associated with usage intention, while perceived cost has no significant effects on user intention to use the devices. The results also supported the explanatory strength and predictability of TAM.

Although the promising role of wearable devices in healthcare industries has gained much consumer attention, limited empirical investigations have been conducted on explicating how user attitude and usage intention are shaped regarding the devices. This study serves as one of the first attempts to empirically examine the adoption process, with implications for the future usage of wearable technology in the healthcare context.

With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions.

The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used.

The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%.

This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.

The purpose of this paper is to identify the motivational factors that help shape user perceptions of and attitudes toward car-sharing services and develop a research model that integrates these factors with the technology acceptance model to explicate car sharing’s adoption pattern.

An online survey was administered to examine the role of proposed motivational factors for the adoption of wearable healthcare devices. Confirmatory factor analysis and structural equation modeling were conducted on collected data (n=638) to demonstrate the reliability and validity of the measurement and structural model.

Perceived reliability, compatibility, and enjoyment of car-sharing services as well as users’ innovative tendencies are positively associated with usage intention. However, users’ privacy concern and perceived cost of using the services are found to have no significant effects on the adoption of the services.

While the recent advent of mobile communication devices and services has increased access to social sharing-based platform services such as car sharing, this study provides a research framework that helps to understand how various psychological factors contribute to the adoption of a social-sharing service.

Based on rapid improvements in telecommunications and wireless networks with extensive educational contents, numerous studies have been conducted to improve our educational success/attainment/environment. With this trend, the purpose of this paper is to investigate users’ perceptions of teaching assistant (TA) robots and the possible motivations that impact the users’ intention to use (IU) the robots.

In light of the rapid development of and attempts at understanding interactions with social robots, including TA robots, this study uses structural equation modeling and confirmatory factor analysis.

The results indicated that perceived usefulness was the most crucial factor determining the users’ IU for TA robots. In addition, the relationships of the original technology acceptance model were confirmed. The study findings demonstrated the crucial importance of perceived enjoyment and service quality.

Although the role of TA robots has gained user attention, few investigations have been conducted to explain how IU is formed. The current study can thus act as the foundation for exploring the acceptance process in the context of TA robots.

Although the notable and significant role of building information modeling (BIM) technologies in construction industries has gained user attention, only few studies have been examined on the user adoption of the technologies. The purpose of this paper is to introduce an acceptance model for BIM technologies and investigate how external factors which were extracted by in-depth interviews promote the adoption of such technologies.

An on-line survey was conducted by two South Korean survey agencies to test the acceptance model for BIM technologies. Then, the structural equation modeling (SEM) and confirmatory factor analysis (CFA) methods were used.

The results of the SEM and CFA methods from on-site construction employees (n=818) in Korea collected by the online survey indicate that compatibility and organizational support play a core role in positively and significantly affecting both perceived ease of use and usefulness, and that the connections introduced by the origin technology acceptance model are mainly confirmed.

Using the findings of the results, both implications and notable limitations are presented. Moreover, practical developers, as well as academic researchers can employ the results when they attempt to conduct future research.

Recently, analyses of the characteristics of viral content in the social media field have attracted considerable attention. However, the influence of instant videos has grown significantly, and most social media platforms have begun to introduce them.

The authors conducted a series of independent-samples t-tests using a large-scale data set collected from the YouTube Shorts platform to identify the characteristics of popular instant videos and discussions surrounding them. The authors further analyzed how they differ from other viral content.

The results indicate that viewers leave varied variety of comments based on the topic of conversation in the community, rather than on the video itself. Furthermore, video producers and viewers attempt to reach a consensus in a straightforward and intuitive manner. All analyzed texts contained appropriate attitudes and tendencies according to their roles on the platform.

This study aimed to discover and understand the video and conversational characteristics of popular instant videos, which differ from the existing widely known viral content.

Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews.

The authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision).

The accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses.

The findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services.

With the rapidly improving communication technologies, a growing number of people communicate with each other in online environments. In particular, social networking services (SNSs) are one of the widely used and common places that enable active communication among users. To understand what drives successful online conversations in SNSs, this study aims to explore the roles of posts and first comments in successful online conversations.

To address the purpose, the data of news-related channels in Reddit were collected and analyzed.

The study found that successful conversations tend to have the post and first comment with high scores. Also, the first comments in successful online conversations tend to be easier than those in other conversations.

The results reveal that successful online conversations can be generated not only with empathic posts but also with touching or attractive first comments. In other words, users are likely to participate in an online conversation that starts with an empathic post and first comment. Moreover, both practical and theoretical implications are presented.

Due to rapid increases in theoretical progress, the paper investigated user perceptions toward tele-presence systems with possible and antecedent motivations that affect attitude and intention to use. The paper aims to discuss these issues.

The paper conducted an internet survey. Responses from 1,620 participants were collected and investigated to identify motivations and possible factors.

The results demonstrate that attitude has the most powerful effect on intention to use. In addition, social presence and perceived usefulness have significant effects on the intention to use. The results also demonstrate the crucial roles of perceived adaptivity and system quality on attitude. The factors examined in the study may be core features of user acceptance toward tele-presence systems with significant implications for improving and creating better and friendlier tele-presence systems for users.

This paper is of value to researchers designing and improving tele-operation and tele-presence services in the society.