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This study aims to elaborate on the antecedents of muzakki (zakat payers) paying zakat (Islamic philanthropy) through institutions with social media arrangements, while the drivers of social media engagement are used in the conceptual model with trust and intention.

Overall, the final sample of 230 respondents was obtained through the database of official zakat management institutions. Regarding analytical tools, this study combines the partial least square structural equation modelling and necessary condition analysis approaches to explore research findings.

The findings show that firm-generated information and trust play an important role directly and indirectly. At the same time, other constructions, such as social factors and user-based factors, provide variations in necessary conditions to increase the muzakki’s intention to channel their zakat through institutions.

Zakat institutions must focus on improving social media-based services by integrating important information, such as credibility and transparency, with muzakki’s preferences. Additionally, zakat information on social media must be attractively packaged and contain facilities that muzakki can use in communicating, such as; their opinions, suggestions and input. The findings, in general, underscore the attachment between muzakki and zakat institutions through social media, which can significantly impact the positive environment of zakat institutions.

To the author’s knowledge, this study is pioneering in conceptualizing and testing a theoretical model linking drivers of social media engagement, trust and intention to pay zakat through the institution, particularly in the levels of necessity.

We build on the transactional model of stress and coping and the appraisal theory of emotions to theorize how users cognitively and emotionally cope with IT addiction-induced stress, distinguish between the roles of guilt and shame in shaping the coping responses and their effects on one’s psychological well-being.

We test our theory via two complementary empirical studies in the context of social networking sites (SNS). Study 1 (n = 462) adopts a variable-centered approach using structural equation modeling to validate the research model. Study 2 (n = 409) uses Latent Profile Analysis to identify a typology of SNS users based on Study 1’s findings.

This paper provides a model of guilt-vs shame-driven cognitive-emotional coping with IT addiction and its effects on users’ psychological well-being. It also offers a typology of SNS users on this basis.

This paper sheds light on guilt-vs shame-driven coping with IT addiction and its consequences on users’ psychological well-being and identifies distinct classes of users based on their coping choices and their consequences.

The increasing popularity of music streaming services also increases the need to customize the services for each user to attract and retain customers. Most of the music streaming services will not have explicit ratings for songs; they will have only implicit feedback data, i.e user listening history. For efficient music recommendation, the preferences of the users have to be infered, which is a challenging task.

Preferences of the users can be identified from the users' listening history. In this paper, a hybrid music recommendation system is proposed that infers features from user's implicit feedback and uses the hybrid of content-based and collaborative filtering method to recommend songs. A Content Boosted K-Nearest Neighbours (CBKNN) filtering technique was proposed, which used the users' listening history, popularity of songs, song features, and songs of similar interested users for recommending songs. The song features are taken as content features. Song Frequency–Inverse Popularity Frequency (SF-IPF) metric is proposed to find the similarity among the neighbours in collaborative filtering. Million Song Dataset and Echo Nest Taste Profile Subset are used as data sets.

The proposed CBKNN technique with SF-IPF similarity measure to identify similar interest neighbours performs better than other machine learning techniques like linear regression, decision trees, random forest, support vector machines, XGboost and Adaboost. The performance of proposed SF-IPF was tested with other similarity metrics like Pearson and Cosine similarity measures, in which SF-IPF results in better performance.

This method was devised to infer the user preferences from the implicit feedback data and it is converted as rating preferences. The importance of adding content features with collaborative information is analysed in hybrid filtering. A new similarity metric SF-IPF is formulated to identify the similarity between the users in collaborative filtering.

Research on online user privacy shows that empirical evidence on how privacy literacy relates to users' information privacy empowerment is missing. To fill this gap, this paper investigated the respective influence of two primary dimensions of online privacy literacy – namely declarative and procedural knowledge – on online users' information privacy empowerment.

An empirical analysis is conducted using a dataset collected in Europe. This survey was conducted in 2019 among 27,524 representative respondents of the European population.

The main results show that users' procedural knowledge is positively linked to users' privacy empowerment. The relationship between users' declarative knowledge and users' privacy empowerment is partially supported. While greater awareness about firms and organizations practices in terms of data collections and further uses conditions was found to be significantly associated with increased users' privacy empowerment, unpredictably, results revealed that the awareness about the GDPR and user’s privacy empowerment are negatively associated. The empirical findings reveal also that greater online privacy literacy is associated with heightened users' information privacy empowerment.

While few advanced studies made systematic efforts to measure changes occurred on websites since the GDPR enforcement, it remains unclear, however, how individuals perceive, understand and apply the GDPR rights/guarantees and their likelihood to strengthen users' information privacy control. Therefore, this paper contributes empirically to understanding how online users' privacy literacy shaped by both users' declarative and procedural knowledge is likely to affect users' information privacy empowerment. The study empirically investigates the effectiveness of the GDPR in raising users' information privacy empowerment from user-based perspective. Results stress the importance of greater transparency of data tracking and processing decisions made by online businesses and services to strengthen users' control over information privacy. Study findings also put emphasis on the crucial need for more educational efforts to raise users' awareness about the GDPR rights/guarantees related to data protection. Empirical findings also show that users who are more likely to adopt self-protective approaches to reinforce personal data privacy are more likely to perceive greater control over personal data. A broad implication of this finding for practitioners and E-businesses stresses the need for empowering users with adequate privacy protection tools to ensure more confidential transactions.

Increasing incidents of privacy invasion on social networking sites (SNS) are intensifying the concerns among stakeholders about the misuse of personal data. However, there seems to be limited research on exploring the impact of specific privacy concerns on users’ intention to engage in various privacy protection behaviors. This study aims to examine the role of social privacy concerns, institutional privacy concerns and privacy self-efficacy as antecedents of privacy protection–related control activities intention among young adults active on SNS.

Data collected from 284 young adults active on SNS was analyzed through partial least squares structural equation modeling using Smart PLS.

The results indicate that institutional privacy concerns, social privacy concerns and privacy self-efficacy positively influence the control activities intention of SNS users. The extent of privacy self-efficacy and privacy protection-related control activities intention differs among users based on gender.

This study is limited to a population of young adults in the age group of 18–25 years.

The findings of this study form the basis for specific recommendations addressing the different types of privacy concerns experienced by social media users, promoting responsible privacy control behaviors on online platforms and discouraging the possible misuse of information by third parties.

This study validates a theoretical framework that can contribute to future investigations concerning the use of SNS. The study findings form the basis for a set of practical recommendations for policymakers, SNS platforms and users.

The Internet of Things (IoT) platform empowers the digital transformation of the manufacturing industry by providing information technology services. Simultaneously, it enters the market by offering smart products to consumers. In light of different service fee scenarios, this article explores the optimal decision-making for the platform. It investigates the pricing models and entry decisions of IoT platforms.

In this study, we have formulated a game-theoretic model to scrutinize the influence of the IoT platform ventured into the smart device market on the pre-existing suppliers operating under subscription-based and usage-based pricing agreements.

Our outcome shows that introducing an IoT platform’s smart device has a differential effect on manufacturers depending on their contract type. Notably, our research indicates that introducing the platform’s own smart device within the subscription-based model does not negatively impact the profitability of incumbent manufacturers, so long as there is a noticeable discrepancy in the quality of the smart devices. However, our findings within the usage-based model demonstrate that despite the variance in smart device quality differentiation, the platform’s resolution to launch their device and impose their pricing agreements adversely affects established manufacturers. Additionally, we obtain valuable Intel regarding the platform’s entry strategies and contractual inclinations. We demonstrate that the platform is incentivized to present its smart device when reasonable entry costs remain. Furthermore, the platform prefers subscription-based contracts when the subscription fee is relatively high in non-platform entry and entry cases.

These findings hold significant practical implications for firms operating in an IoT-based supply chain.

This paper aims to illustrate how service ecosystem governance may provide a suitable ground to pursue holistic resilience to “wicked” socio-economic and ecological problems, for enhancing “place-based” sustainable performance outcomes through an organizational, interorganizational and context setting.

This work suggests the use of “place-based” collaborative ecosystem platforms driven by a dynamic performance governance approach as a setting where facilitated performance dialogue is carried out among networked stakeholders. This fosters a holistic view of performance sustainability where intangibles, inertial, cultural and behavioral factors play a key role in policy analysis.

The paper illustrates how different research streams framing stakeholder relationships under a business, hybrid organization and public sector perspective converge toward the “service ecosystem” construct, as a common field for sustainable “place-based” value creation. This performance governance perspective frames accountability for achieving sustainable outcomes through interconnected viewpoints, i.e. (1) time (short vs long-term), (2) subject (single organization, “theme-focused” service ecosystem and “place-based” service ecosystem) and (3) field (socio-economic, cultural and ecological).

This work has an interdisciplinary track. It recommends feedback and “stock-and-flow” modeling to enhance framing counterintuitive patterns of behavior of dynamic complex socio-economic, cultural and ecological subsystems within “place-based” collaborative ecosystem platforms. Combining an inside-out with an outside-in view triggers sustainable outcome-based dynamic performance governance through an organizational, interorganizational and context setting.

This paper proposes that the trade-off between medical benefits and privacy concerns among mHealth users extends to their disclosure intentions, manifested as individuals simultaneously holding intentions to tend to disclose in the near future and to reduce disclosure in the distant future. Consequently, this paper aims to explore the privacy decision-making process of mHealth users from the perspective of a dual trade-off.

This paper constructs the model using the privacy calculus theory and the antecedent-privacy concern-outcome framework. It employs the construal level theory to evaluate the impact of privacy calculus on two types of disclosure intentions. The study empirically tests the model using a data sample of 386 mHealth users.

The results indicate that perceived benefits positively affect both near-future and distant-future disclosure intentions. In contrast, perceived risks just negatively affect distant-future disclosure intention. Additionally, perceived benefits, near-future and distant-future disclosure intentions positively affect disclosure behavior. The findings also reveal that privacy management perception positively affects perceived benefits. Personalized services and privacy invasion experience positively affect perceived benefits and risks, while trust negatively affects perceived risks.

This paper considers the trade-off in the privacy calculus phase as the first trade-off. On this basis, this trade-off will extend to the disclosure intention. The individuals’ two times of trade-offs between privacy concerns and medical benefits constitute the dual trade-off perspective. This paper first uses this perspective to explore the privacy decision-making process of mHealth users. This paper employs the construal level theory to effectively evaluate the impact of privacy calculus on both disclosure intentions in mHealth, extending the theory’s applicability. Moreover, we introduce antecedents of privacy calculus from the perspectives of platform, society, and individuals, enhancing the study’s realism. The research findings provide a basis for mHealth platforms to better cater to users’ privacy needs.

This study aims to examine the effects of multidimensional factors of platform features, group effects and emotional attitudes on social media users’ privacy disclosure intention.

This study collected the data from 426 respondents through an online questionnaire survey and conducted two approaches of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) for theoretical hypothesis testing and configuration analysis of the data.

The results show that social media platform features (rewards of information disclosure, personalized service quality and data transparency), group effects (group similarity, group information interaction and network externality), individual emotional attitudes (trust and privacy concern) and control variable (gender) have a significant impact on privacy disclosure intention, as well as trust and privacy concern play mediating roles. Additionally, the fsQCA method reveals five causal configurations that explain high privacy disclosure intentions. Furthermore, the study reveals that male users pay more attention to platform features, while female users are more inclined to group effects.

This study attempts to construct a comprehensive model to examine the factors that affect users' intention to disclose their privacy on social media platforms. Drawing on the cognition-affect-conation model and multidimensional development theory, the model integrates multidimensional factors of platform features, group effects, trust and privacy concern to complement existing theoretical frameworks and privacy disclosure literature. By understanding the complex dynamics behind privacy disclosure, this study helps platform providers and policymakers develop effective strategies to ensure the vitality and momentum of the social media ecosystem.

Knowledge transfer which refers to the communication of knowledge from a source so that it is learned and applied by a recipient has long been a challenge for knowledge management. The purpose of this study is to understand influencing factors of transactive memory system (TMS) and knowledge transfer.

Drawing on the theories of communication visibility, social distance and flow, this study develops a research model. Then, data are collected from users of the social media mobile App. Partial least squares-structural equation modeling (PLS-SEM) is employed to analyze data.

TMS is a valid second-order construct in the social media mobile app context, which is more reflected by credibility. Meanwhile, communication visibility and social distance each have positive effects on TMS which further has a positive effect on knowledge transfer. Flow has a positive effect on knowledge transfer.

Developers of the mobile App should carefully consider the role of information and communication technology (ICT) in supporting TMS and knowledge transfer. They should consider recommendation algorithm so that the benefit of communication visibility can be retained. They should design the feature to classify users based on similarity so as to stimulate users' feeling of close social distance. They should keep on improving features based on users' holistic experience.

This study incorporates the perspectives of communication visibility, social distance and flow to understand TMS and knowledge transfer, presenting a new lens for research.