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In the online marketing context, virtual reality (VR) has been used to display products and shopping environment, which effectively enhances the consumer experience. VR technology can not only recreate scenes similar to the real world (virtual authenticity, VA) but also create fictitious and desirable scenes that do not exist in the real world (virtual ideality, VI). The differences between VA and VI in influencing consumers' responses have not been fully understood. In addition, social signals have appeared in VR apps. However, the role of social signals in a VR context has rarely been studied. To fill the gaps in the literature, this study investigates the roles of VA and VI in shaping consumers' responses, as well as whether and how their effectiveness in shaping consumers' responses is influenced by social signals.

A VR real estate service platform was selected as the experimental platform, and two experiments were conducted to test the hypotheses. The ANOVAs and regressions were used for data analysis.

Results show that VA leads to a higher level of perceived diagnosticity than VI, whereas VI leads to a higher level of inspiration than VA; perceived diagnosticity and inspiration positively affect visit intention. Furthermore, the relationship between consumers' perceived diagnosticity, inspiration and visit intention is moderated by the presence of social signals.

The study revealed the differences between VA and VI in shaping consumers' responses, as well as the effect of social signals in VR environment, which provide a new perspective for future VR research in the context of interactive marketing.

Leveraging information technology (IT) to improve the treatment and support of patients is a widely studied topic in healthcare. For chronic diseases, such as diabetes, the use of information technology is even more important since its effect extends from a clinic environment to patients’ daily life. The purpose of this paper is to investigate the impacts of one widely adopted information technology, the mobile phone, on diabetes treatment, specifically on the complicated process of patients’ health, emotions and compliance.

We leverage a unique longitudinal dataset on diabetes patients’ health status in rural areas of China to study the problem. We also cross-link the dataset with mobile carrier data to further differentiate mobile phone use to phone calls and network use. To address the endogeneity concerns, we apply PSM and a series of instrument variables.

We identify clear evidence that mobile phone use can significantly improve patients’ emotions and compliance, where the effect is generally larger on patients in worse health conditions. While mobile phone calls clearly benefit diabetes patients, we do notice that mobile phone network use has a negative moderating effect with patients’ health condition on improving compliance.

This study not only enriches our theoretical understanding of the role of mobile phones in diabetes management, it also shows the economic benefit of promoting patients’ use of mobile phones, which should be considered by medical care providers and medical policymakers.

Course recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.

The study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.

The main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.

The findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.

This study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.

Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups.

The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed.

Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment.

The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts.

The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient.

The proposed methods have potential value to improve scientific collaboration and research innovation.

The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.

The purpose of this paper is to investigate the influence of social commerce constructs (SCCs), social support and relationship quality on social commerce intentions, which lead to use behaviour of social networking sites for social commerce.

Data were collected from 343 users of social networking sites in Pakistan. The data analysis was conducted using PLS-SEM.

The results show that SCCs have empowered consumers through the existence of virtual groups, ratings and reviews and recommendations and referrals, thereby having a significant impact on social commerce intentions. The relationship quality with social networking sites, measured through commitment, satisfaction and trust, also proved to be a leading forecaster of social commerce intentions. The impact of social support could not positively influence the relationship quality with the social networking site. However, social support influences the social commerce intentions significantly.

Future research should enrich model with some moderating variables and data may be collected from actual online shoppers only.

This study provides valuable insights to retailers to formulate their social commerce strategies as per decision factors results to have maximum engagement of consumers in social commerce.

The study proposes the unique model for finding the social commerce intentions and use behaviour using social support theory, relationship marketing theory and information systems literature.

China is primarily a nonreligious country with less than 10% of people following Buddhism, Taoism, Islam, Catholicism, or other religions. Two major communication paths, the land and sea Silk Roads, directly affected the distribution and development of Muslim tourism and attractions. The combination of Islam with local custom and culture is a unique feature in China, and contributes to its development as a form of ethnic rather than religious tourism. As a result, research in China focuses on ethnic product development, minority sports and anthropological tourism, themed events, and intangible cultural heritage.

This study investigates the use of gamification in promoting the silver generation's adoption of mobile payment technology through the gamified cultural practice of gifting red packets. It considers the effectiveness of using gamification in a cultural context to promote technology acceptance among older adults. This crossover between digital technology and cultural traditions brings unique gaming elements to the adoption of technology.

Drawing upon technology acceptance Model (TAM) and prospect theory, a research model is evaluated using structural equation modeling. Data were collected via survey from elderly consumers who are current users of WeChat but are yet to use its mobile payment functions.

The results reveal the perceived effectiveness of gamification is determined by the perceived enjoyment of the game and contributes to users' attitude development, directly and through its perceived usefulness. Perceived risks were identified as a barrier to converting positive attitude into adoption intention.

The findings contribute to the conceptualization and understanding of the effectiveness of gamification in technology adoption, specifically among the silver generation.

In contrast with previous gamification studies on gamified experience, this study introduces a new conceptualization of the perceived effectiveness of gamification and its measurement. This study validates game engagement as being effective in encouraging seniors to adopt a technology. In an era of an aging population where digitization is a norm, improving the digital literacy and digital inclusion of elders by encouraging them to adopt technology is essential to developing a more accessible and inclusive social environment.

Consumers nowadays are no longer bystanders in the process of production but are proactive collaborators with the power to co-create value with brands. This study aims to explore the impact of social commerce on the co-creation process of brand value in a social commerce setting.

A questionnaire survey was conducted online to gather 300 eligible responses. The data were empirically validated using the partial least squares structural equation modelling (PLS-SEM) method.

The results indicated that brand engagement (BEN) is vital to brand co-creation (BCC) in social commerce, which could be driven by social-hedonic value (SHV) and social information sharing (SIS).

This study stresses the influence of consumer autonomy in the process of BCC by probing the role of SIS. Moreover, by considering the prevailing trend in social media, this study offers a nuanced perspective on the values of social commerce from the viewpoint of SHV.

This study may serve as a useful guide for practitioners to improve their digital outreach strategy on social commerce to forge stronger relationships, encourage further engagements and promote value co-creation within their brand community.

This examines the effect of relationship quality (RQU) and BEN on BCC through a relational viewpoint.

This study investigates the relationship between social commerce adoption determinants, perceived trust and purchase behaviour among pastry product consumers.

The research data were collected using a five-point Likert from 409 pastry consumers through an online survey. Partial least square-structural equation modelling (PLS-SEM) was utilised to test the study model and hypotheses.

The study findings indicate that pastry consumers perceived social commerce as effortless, engaging, enjoyable, can be trusted, simple to use and time-saving, motivating them to consume pastry products via social commerce platforms.

This study provides insights and implications for social commerce researchers and marketers related explicitly to perishable products.

This study is one of the few studies that predict and explain the consumer acceptance of social commerce in the pastry products realm. As only a handful of research has endeavoured to research social commerce, this study findings contribute to the conceptualisation of technology acceptance theory by understanding the determinants of social commerce adoption among consumers.