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Mobile health (mHealth) technologies, in particular, have been sought after and advocated as a means of dealing with the pandemic situation. Despite the obvious advantages of mHealth, which include monitoring and exchanging health information via mobile applications, mHealth adoption has yet to take off exponentially. Expanding on the unified theory of acceptance and use of technology (UTAUT) model, this study aims to better comprehend consumers’ receptivity to mHealth even after the pandemic has subsided.

Through purposive sampling, data were collected from a sample of 345 mobile phone users and analysed using partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) capture both linear and nonlinear relationships.

Effort expectancy, performance expectancy, social influence, pandemic fear and trustworthiness positively influenced mHealth adoption intention, with the model demonstrating high predictive power from both the PLSpredict and ANN assessments.

The importance–performance map analysis (IPMA) results showed that social influence had great importance for mHealth uptake, but demonstrated low performance.

Referrals are an alternative that policymakers and mHealth service providers should think about to increase uptake. Overall, this study provides theoretical and practical insights that contribute to the advancement of digital healthcare, aligning with the pursuit of Sustainable Development Goal 3 (SDG 3) (good health and well-being).

This study has clarified both linear and nonlinear relationships among the factors influencing intentions to adopt mHealth. The findings from both PLS and ANN were juxtaposed, demonstrating consistent findings.

Sustainable development concern, coupled with changes in the talent landscape, has led to a heightened focus on green human resource management (GHRM). Drawing on attribution theory and conservation of resources theory, this study examined GHRM, leader–member exchange (LMX) and core self-evaluations (CSE) in relation to work engagement together with human resource management (HRM) performance attributions as a mediator.

Partial least squares structural equation modelling (PLS-SEM) was used to analyse the data collected from 110 respondents working in ISO 14001 certified organisations in Malaysia.

Results revealed that GHRM and LMX were positively related to HRM performance attributions that were intended to improve employee performance. However, CSE was not found to be related to HRM performance attributions. Consequently, HRM performance attributions were positively related to work engagement. Furthermore, GHRM and LMX had positive indirect effects on work engagement through HRM performance attributions as a mediator.

Since the data collected were from Malaysia only, it limits the generalisability of the results to other regions.

The findings suggest that organisations should adopt GHRM and train its leaders to forge stronger social bonds with their subordinates to elicit higher work engagement by positively influencing employee attributions on the motives of HRM practices.

This study contributes to the acknowledged gap on GHRM and HRM attributions by examining the non-green employee outcomes of GHRM and the antecedents of HRM performance attributions. This study also contributes by integrating attribution theory with conservation of resources theory to provide the mediation mechanism in linking GHRM and LMX towards higher work engagement through HRM performance attributions as a mediator; thus empirically illustrating the resource gain spirals.

The recent COVID-19 pandemic caused a severe economic downturn. Employees working in these organisations face employment uncertainty. The pandemic disrupted their daily routines, and it added a layer of complexity to the already resource-constrained environment. During these times, employees would conserve their resources to maintain competitiveness, one of which is knowledge hiding. While economic activities are resuming, the appearance of new variants could mean the transition towards endemicity could be put on hold. Hence, there is a need to rethink the behaviour of employees as they would have elevated levels of anxiety towards resuming daily work activities. Therefore, this study aims to address the question of understanding employees’ perspectives toward knowledge sharing and knowledge hiding.

Drawing on the conservation of resources theory, social learning theory and the social exchange theory (SET), a conceptual framework involving ethical leadership was developed to examine if knowledge hiding or knowledge sharing behaviour is a resource for employees during these times. The partial least squares method of structural equation modelling was used to analyse results from 271 white-collar employees from Singapore.

The results show that ethical leadership encourages knowledge sharing but does not reduce knowledge hiding. At the same time, knowledge hiding, not knowledge sharing, improves one’s perception of work performance. Additionally, psychological safety is the key construct that reduces knowledge hiding and encourages sharing behaviour.

Overall, this study extends the theories, demonstrating that, first and foremost, knowledge hiding is a form of resource that provides employees with an added advantage in work performance during the endemic. At the same time, we provide a new perspective that ethical leaders’ demonstration of integrity, honesty and altruism alone is insufficient to encourage knowledge sharing or reduce knowledge hiding. It must lead to a psychologically safe environment.

Currently, social network (SN) analysis is focused on the discovery of activity and social relationship patterns. Usually, these relationships are not easily and completely observed. Therefore, it is relevant to discover substructures and potential behavior patterns in SN. Recently, formal concept analysis (FCA) has been applied for this purpose. FCA is a concept analysis theory that identifies concept structures within a data set. The representation of SN patterns through implication rules based on FCA enables the identification of relevant substructures that cannot be easily identified. The authors’ approach considers a minimum and irreducible set of implication rules (stem base) to represent the complete set of data (activity in the network). Applying this to an SN is of interest because it can represent all the relationships using a reduced form. So, the purpose of this paper is to represent social networks through the steam base.

The authors’ approach permits to analyze two-mode networks by transforming access activities of SN into a formal context. From this context, it can be extracted to a minimal set of implications applying the NextClosure algorithm, which is based on the closed sets theory that provides to extract a complete, minimal and non-redundant set of implications. Based on the minimal set, the authors analyzed the relationships between premises and their respective conclusions to find basic user behaviors.

The experiments pointed out that implications, represented as a complex network, enable the identification and visualization of minimal substructures, which could not be found in two-mode network representation. The results also indicated that relations among premises and conclusions represent navigation behavior of SN functionalities. This approach enables to analyze the following behaviors: conservative, transitive, main functionalities and access time. The results also demonstrated that the relations between premises and conclusions represented the navigation behavior based on the functionalities of SN. The authors applied their approach for an SN for a relationship to explore the minimal access patterns of navigation.

The authors present an FCA-based approach to obtain the minimal set of implications capable of representing the minimum structure of the users’ behavior in an SN. The paper defines and analyzes three types of rules that form the sets of implications. These types of rules define substructures of the network, the capacity of generation users’ behaviors, transitive behavior and conservative capacity when the temporal aspect is considered.

The global non-attainment of the Sustainable Development Goal (SDG) 5 indicates the issue of rising gender inequality. Educated women shying away from the labor force is worsening it. The labor market dynamics might shape the female labor force participation (FLFP). The present study recommends a policy framework by analyzing this dynamism across 125 countries over 1990–2020.

The Two-step System Generalized Method of Moments is used to address endogeneity bias. Dynamism in policy environment is captured by relaxing the Ceteris Paribus condition in the empirical model.

Results show that the moderation of labor market factors has increased with the attainment of Secondary and Tertiary Education. Results also highlight that these factors promote FLFP through prospective opportunities but also hinder female participation through employer’s discrimination despite educational attainment.

Studies have examined the role of education on FLFP. However, prior research has not investigated the role of labor market factors in influencing the impact of education on FLFP. The consideration of these factors will help in addressing the global policy lacuna by recommending a policy framework for enhancing FLFP through internalization of the externalities exerted by the labor market factors, and thereby, help the countries attaining the SDG 5 objectives.

In the contemporary world, where sustainability at higher education is at the forefront, the ever-changing business ecosystem nurtures a new drift towards economic, environmental and social performance. This study aims to measure the impact of green human resource management (GHRM) practices on sustainability in the higher education of a developing country context through a theoretical aspect of Resource-Based View (RBV).

This study adopted a quantitative approach to propose and test a model based on predictors of sustainability. The survey approach received 190 responses from employees (faculty and non-faculty members) working as a full-time in the 40 higher education institutions (HEIs) of Pakistan. The structural and measurement model was calculated using SmartPLS.

The results show that “green training and development” (GTD) and “top management commitment towards greening workforce (TMCGW)” have a positive relationship with sustainability while green recruitment and selection (GRS) was not supported by sustainability. The mediating effect of TMCGW plays a crucial role between GTD and sustainability. Also, this study contributes through the moderating interaction effect of Gender between GTD and sustainability. Overall the GHRM practices promote employee green behavior and sustainability.

The proposed research model in the current study is a substantial gap in the literature and exploring this connection requires new theoretical frameworks. To bridge this literature gap, this study examined the role of GHRM on sustainability at micro-level (employee perspective) through a theoretical aspect of RBV in the developing country higher education context of Pakistan. Importantly, this study enhances the understanding of the emerging global wave of green mobility and highlights the impact of GHRM practices on sustainability through perception of academic professionals.