My video game console is so cool! A coolness theory-based model for intention to use video game consoles

Determinants of multimodal fake review generation in China's E-commerce platforms

This paper develops a theoretical model of determinants influencing multimodal fake review generation using the theories of signaling, actor-network, motivation, and human-environment interaction hypothesis. Applying survey data from users of China's three leading E-commerce platforms (Taobao, Jingdong, and Pinduoduo), we adopt structural equation modeling, machine learning technique, and Bayesian complex networks analysis to perform factor identification, path analysis, feature factor importance ranking, regime division, and network centrality analysis of full sample, male sample, and female sample to reach the following conclusions: (1) platforms' multimodal recognition and governance capabilities exert significant negative moderating effects on merchants' information behavior, while it shows no apparent moderating effect on users' information behavior; users' emotional venting, perceived value, reward mechanisms, and subjective norms positively influence multimodal fake review generation through perceptual behavior control; (2) feature factors of multimodal fake review generation can be divided into four regimes, i.e., regime 1 includes reward mechanisms and perceived social costs, indicating they are key feature factors of multimodal fake review generation; merchant perception impact is positioned in regime 2, signifying its pivotal role in multimodal fake review generation; regime 3 includes multimodal recognition and governance capabilities, supporting/disparaging merchants, and emotional venting; whereas user perception impact is positioned in regime 4, indicating its weaker influence on multimodal fake review generation; (3) both in full sample, male sample, and female sample, reward mechanisms play a crucial role in multimodal fake review generation; perceived value, hiring review control agency, multimodal recognition and governance capabilities exhibit a high degree of correlation; however, results of network centrality analysis also exhibit heterogeneity between male and female samples, i.e., male sample has different trends in closeness centrality values and betweenness centrality values than female sample. This indicates that determinants influencing multimodal fake review generation are complex and interconnected.

What motivates individuals to share information with governments when adopting health technologies during the COVID-19 pandemic?

BACKGROUND

While digital governance has been adopted by governments around the world to assist in the management of the COVID-19 pandemic, the effectiveness of its implementation relies on the collection and use of personal information. This study examines the willingness of individuals to engage in information-sharing with governments when adopting health technologies during the COVID-19 pandemic.

METHODS

Data were obtained from a cross-sectional survey of 4,800 individuals drawn from 16 cities in China in 2021. Tobit regression models were used to assess the impacts of an array of determinants on an individual's willingness to share information with governments when adopting health technologies.

RESULTS

Individuals who perceived a higher level of helpfulness, risk, expectations from others, weariness toward privacy issues, and were sensitive to positive outcomes were more willing to share information with governments when adopting health technologies during the COVID-19 pandemic. Across all the subgroups, self-efficacy only reduced the willingness to share information with governments for individuals who spent more than seven hours per day online. The negative impacts of being sensitive to negative outcomes on the willingness to share information were only found among females and the less educated group.

CONCLUSIONS

This study revealed the seemingly paradoxical behavior of individuals who perceived high risks of sharing information and a sense of fatigue toward privacy issues yet continued to be willing to share their information with their governments when adopting health technologies during the COVID-19 pandemic. This work highlighted significant differential motivations for sharing information with governments when using health technologies during a pandemic. Tailored policies that resonate with population sub-groups were suggested to be proposed to facilitate crisis management in future situations.

The mechanism analysis on the farmers' motivation of using the quality traceability system based on TAM-ECM model

The quality traceability system for agricultural product plays an important role in tracing the production history or flow of agricultural products in China. At present, the quality traceability system is facing the problem of limited coverage and promotion. Understanding the continuous usage behavior of users can help solve this problem. This study constructed a model integrating TAM-ECM to analyze the mechanisms affecting the continuous usage intention and behavior of users of the quality traceability system, using PLS analysis method and survey data from 197 users of the GanNan navel orange quality traceability system with a usage time of up to two years. The results show that both satisfaction and usage habits directly promote the continuous usage of the quality traceability system. Lowering transaction costs significantly improves perceived value and satisfaction, and has a greater direct impact on satisfaction. Expectation confirmation level has a positive effect on perceived value, and lowering transaction costs also enhances perceived value. Perceived value、transaction cost, and satisfaction are important factors driving the continuous usage of the quality traceability system by users. The research findings provide a reference basis for the government to develop policies to attract users to use the quality traceability system for agricultural product.

I know my teammates: the role of Group Member Familiarity in Computer-Supported and face-to-face collaborative learning

Considering the importance of group member familiarity in collaborative learning in classroom learning environments, this study examined the impact of group member familiarity on CSCL (computer-supported collaborative learning) in a networked setting. Also, the differences between CSCL in the online environments and FtF (face-to-face) collaborative learning were also compared. An analysis using structural equation modeling revealed that group member familiarity increased teamwork satisfaction, which in turn increased student engagement and perceived knowledge construction. A multi-group analysis revealed that while FtF collaborative learning exhibits higher levels of group member familiarity, teamwork satisfaction, student engagement, and perceived knowledge construction, the mediating effect of teamwork satisfaction was more pronounced in online settings. The study findings provided insights for teachers to improve collaborative learning experiences and adapt different teaching strategies.

A study on the influence of the characteristics of key opinion leaders on consumers’ purchase intention in live streaming commerce: based on dual-systems theory

Drawing on the dual-systems theory (DST), this study proposes a research model that focuses on how the characteristics of the key opinion leader influence consumers’ purchase intention in live streaming commerce (LSC). We collected data through questionnaire surveys from samples of consumers in China (N  = 467), which indicated that key opinion leader’s characteristics (i.e., attractiveness, trustworthiness, expertise) are all positively related to purchase intention. Importantly, the mediator analyses indicated that the effect of systems 1 on the relationship between attractiveness and purchase intention persisted is persistent, regardless of the type of goods purchased by the consumers (utilitarian or hedonic). This finding highlights the importance of attractiveness and reveals unconscious thought (UT) of consumers when shopping in LSC. This study provides a new theoretical perspective (i.e., DST) to identify attractiveness as a determinant of purchase intention in LSC and influence purchase intention by activating systems 1, thus solving the problem of identifying key factors in stimulus–organism–response theory. Besides, our results identify and test UT in LSC. The practical implications of these findings are to provide guidance for the selection of key opinion leaders in LSC and the development of live streaming industry.

Predicting User Satisfaction of Mobile Healthcare Services using Machine Learning

Outbreak of the COVID-19 leads to rapid development of the mobile healthcare services. Given that user satisfaction is of great significance in inducing marketing success in competition markets, this research explores and predicts user satisfaction with mobile healthcare services. Specifically, the current research aimed to design a machine learning model that predicts user satisfaction with healthcare services using big data from Google Play Store reviews and satisfaction ratings. By dealing with the sentimental features in online reviews with five classifiers, the authors find that Logistic regression with term frequency-inverse document frequency (TF-IDF) and XGBoost with Bag of words (BoW) have superior performances in predicting user satisfaction for healthcare services. Based on these results, the authors conclude that such user-generated texts as online reviews can be used to predict user satisfaction, and Logistic regression with TF-IDF and XGBoost with BoW can be prioritized for developing online review analysis platforms for healthcare service providers.

Factors influencing students' adoption intention of brain–computer interfaces in a game-learning context

Purpose

A research framework that explains adoption intention in students with regard to brain–computer interface (BCI) games in the learning context was proposed and empirically examined.

Design/methodology/approach

In this study, an approach integrating the decomposed theory of planned behavior, perceived playfulness, risk and the task–technology fit (TTF) concept was used to assess data collected using a post-experiment questionnaire from a student sample in Taiwan. The research model was tested using the partial least-squares structural equation modeling (PLS-SEM) technique.

Findings

Attitude, subjective norms and TTF were shown to impact intention to play the BCI game significantly, while perceived behavioral control did not show a significant impact. The influence of superiors and peers was found to positively predict subjective norms. With the exception of perceived ease of use, all of the proposed antecedents were found to impact attitude toward BCI games. Technology facilitating conditions and BCI technology characteristics were shown to positively determine perceived behavior control and TTF, respectively. However, the other proposed factors did not significantly influence the latter two dependents.

Originality/value

This research contributes to the nascent literature on BCI games in the context of learning by highlighting the influence of belief-related psychological factors on user acceptance of BCI games. Moreover, this study highlights the important, respective influences of perceived playfulness, risk and TTF on users' perceptions of a game, body monitoring and technology implementation, each of which is known to influence willingness to play.

Factors Affecting Intention of Consumers in Using Face Recognition Payment in Offline Markets: An Acceptance Model for Future Payment Service

Face recognition payment (FRP), an innovative financial technology service, is a recently developed mode of payment service that has garnered attention in the offline market, particularly in China. However, studies examining the adoption of FRP by consumers are scarce. Therefore, this study proposed a causal model built on the Unified Theory of Acceptance and Use of Technology, and key predictors related to the intention of using FRP were identified. The structural equation model-based results obtained from 305 Chinese participants demonstrated that the intention was most affected by relative advantage. In addition, performance expectancy, effort expectancy, social influence, and perceived risk also had a significant impact. However, trust was found to not significantly affect consumers’ intentions, despite it negatively influencing perceived risk. Thus, the results of this study are expected to provide a set of guidelines for companies regarding the implementation of FRP.