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This study investigates organizations' non-adoption intention towards the enterprise metaverse. The innovation resistance theory (IRT) is used as an underpinning theory to examine the impact of various risks on non-adoption intention towards the enterprise metaverse.

A total of 294 responses were collected to examine the proposed hypotheses. A structural equation modelling technique was used to investigate the hypotheses using SPSS AMOS and PROCESS MACRO.

The results of this study reveal that performance, security and psychological risks are significantly associated with non-adoption intention towards enterprise metaverse. Further, distrust significantly meditates the association between performance risk, social risk, technological dependence risk, security risk and psychological risk and non-adoption intention towards enterprise metaverse. Moreover, the results of moderated-mediation hypotheses indicate that the mediating effect of distrust on the association among performance risk, social risk, psychological risk and non-adoption intention towards enterprise metaverse is higher for individuals having high technostress compared to individuals having low technostress.

The study's findings will enrich the metaverse literature. Further, it provides a deeper understanding of enterprise metaverse adoption from a B2B perspective using the underpinnings of IRT. The study helps organizations understand the risks associated with the adoption of the enterprise metaverse.

This study aims to present systematic analysis of research concerning the intersection of human resource management (HRM) and the integration of artificial intelligence (AI) technologies within a digitalized economy further analyzing the trends in research with specific emphasis on utilization of diverse AI technologies within HRM.

This research is based on bibliometric analyses and content analyses. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses review methodology is implemented, using the Scopus database as the primary source which gathered 1,414 articles between 1978 and 2024. This study investigates publishing trends, the most prolific countries, universities, journals, publications and authors in the field. Further, the research trends based on the use of AI in HRM were accomplished through scientific mapping using VOSviewer.

The outcomes demonstrate a rising inclination toward using various AI techniques in HRM which shows increasing influence and growing appeal of the subject. The research uncovers the deployment of diverse technologies, including emerging ones, within the HRM field. It accomplishes this by scrutinizing the connections among various keywords and unearths both contradictions and focal areas of interest within the domain.

The study contributes to the existing body of literature by ascertaining suggestions for further research in the field of HRM integrated with various AI technologies. The integration of these technologies in HR holds a promising and optimistic outlook for the managers, thereby enhancing employee productivity.

Metaverse is one of the decade’s most exciting and transformative technological innovations. While the metaverse holds immense promise, it has potential risks and dark sides. This research aims to investigate and identify the crucial dark dimensions associated with the metaverse platforms.

Employing a qualitative phenomenological methodology, the authors interviewed 45 metaverse users to unravel dark dimensions related to the metaverse. Analyzing the themes extracted from the participants' insights revealed an alignment with the underpinnings of the Technology Threat Avoidance (TTA) theory.

The findings of this study revealed seven major dark dimensions: addiction and dependency, isolation and loneliness, mental health issues, privacy and security, cyberbullying and harassment, digital identity theft and financial exploitation.

The study helps organizations and metaverse platforms understand the crucial dark dimensions of the metaverse. This study concludes by synthesizing prevalent themes and proposing propositions, offering insights for practical application and policy considerations.

This study provides a deeper understanding of the dark side of the metaverse environment from a user perspective using the underpinnings of TTA theory.

Generative artificial intelligence (GAI) can disrupt how consumers search for information on retail products/services online by reducing information overload. However, the risk associated with GAI is high, and its widespread adoption for product/service information search purposes is uncertain. This study examined psychological drivers that impact consumer adoption of GAI platforms for retail information search.

We conducted 31 in-depth, semi-structured interviews with the lead GAI users regarding product/service information search. The data were analysed using a grounded theory paradigm and thematic analysis.

Results show that consumers experience uncertainty about GAI’s functioning. Their trust in GAI impacts the adoption and usage of this technology for information search. GAI provides unique settings to investigate potential additional factors, leveraging UTAUT as a theoretical basis. This study identified three overarching themes – technology characteristics, technology readiness and information characteristics – as possible drivers of adoption.

Consumers seek exhaustive and reliable information for purchase decisions. Due to the abundance of online information, they experience information overload. GAI platforms reduce information overload by providing synthesized and customized product/service search results. However, its reliability, trustworthiness and accuracy have been questioned. The functioning of GAI is opaque; the popular technology adoption model such as UTAUT is general and is unlikely to explain in totality the adoption and usage of GAI. Hence, this research provides the adoption drivers for this unique technology context. It identifies the determinants/antecedents of relevant UTAUT variables and develops an integrated conceptual model explaining GAI adoption for retail information search.

This study examines the impact of five dimensions of decent work on the faculty engagement in Pakistan’s higher education institutions. Furthermore, it examines the moderating influence of intrinsic religiosity on the associations above.

This research employed a cross-sectional approach to collect data from 542 faculty members working with higher education institutions through electronic and in-person questionnaire administration.

The findings indicate that safe interpersonal working conditions, opportunities for free time and rest, adequate compensation, and the availability of healthcare services significantly positively impact the level of work engagement among faculty members. Additionally, the research revealed that intrinsic religiosity reinforces the previously established significant associations.

The research acknowledges specific constraints that could impact the applicability of its findings, including the utilization of a cross-sectional methodology, the dependence on self-reported information, and the possibility of sample biases. Subsequent investigations may rectify these constraints to provide a more all-encompassing comprehension of the subject matter.

The findings possess practical significance for the management of HEIs in cultivating an ideal working atmosphere for faculty members. Ensuring adequate compensation, secure working environments and healthcare accessibility is underscored to enhance faculty engagement. Furthermore, acknowledging the significance of intrinsic religiosity can improve faculty engagement.

This research contributes to the current body of literature by investigating the complex relationship among decent work, faculty engagement, and intrinsic religiosity in the specific context of higher education institutions in Pakistan.

Artificial intelligence (AI) is a powerful and promising technology that can foster the performance, and competitiveness of micro, small and medium enterprises (MSMEs). However, the adoption of AI among MSMEs is still low and slow, especially in developing countries like Jordan. This study aims to explore the elements that influence the intention to adopt AI among MSMEs in Jordan and examines the roles of firm innovativeness and government support within the context.

The study develops a conceptual framework based on the integration of the technology acceptance model, the resource-based view, the uncertainty reduction theory and the communication privacy management. Using partial least squares structural equation modeling – through AMOS and R studio – and the importance–performance map analysis techniques, the responses of 471 MSME founders were analyzed.

The findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier. AI autonomy positively influences both firm innovativeness and AI adoption intention. Firm innovativeness mediates the relationship between AI autonomy and AI adoption intention, and government support moderates the relationship between facilitating conditions and AI adoption intention.

The findings provide valuable insights for policy formulation and strategy development aimed at promoting AI adoption among MSMEs. They highlight the need to address perceived risks and enhance facilitating conditions and underscore the potential of AI autonomy and firm innovativeness as drivers of AI adoption. The study also emphasizes the role of government support in fostering a conducive environment for AI adoption.

As in many emerging nations, the AI adoption research for MSMEs in Jordan (which constitute 99.5% of businesses), is under-researched. In addition, the study adds value to the entrepreneurship literature and integrates four theories to explore other significant factors such as firm innovativeness and AI autonomy.

Drawing on conservation of resources, social cognitive and self-verification theories, the current study endeavors to extend our comprehension of the mechanisms linking Islamic work ethics (IWE) to employee ethical behavior. More specifically, the current study investigates the potential impact of IWE on employees’ ethical behavior through the serial mediating roles of moral identity and felt obligation.

By utilizing two-wave data collected from 513 employee-co-worker dyads in the education sector in Turkey, we employed AMOS to conduct a confirmatory analysis and the PROCESS macro for SPSS to test the hypothesized relationships.

The results provide evidence for our hypothesized model. Our results indicate that employees with higher levels of IWE are more likely to exhibit higher levels of ethical behavior. Our results also reveal that IWE has a significant and positive impact on employees’ moral identity and sense of obligation, which in turn enhances their ethical behavior.

By integrating multiple theories, the current research addresses a dearth in the literature and provides a nomological network from Islamic work ethics to ethical employee behavior through the serial mediating role of moral identity and felt obligation. This study adds value to the literature on human resource management and work ethics by examining how IWE affects the attitudes and behaviors of employees in both the public and private sectors. Accordingly, organizations can strengthen their workforce’s moral identities and instill a sense of obligation to behave morally by incorporating workplace ethics into HRM processes.

The purpose of this study is to examine how users’ intention to adopt online banking is influenced by perceived trust (PT), awareness (AWE) and social influence (SI) and to assess whether customer relationship management (CRM) moderates the impact of these factors on adoption intentions.

Data were collected from 565 respondents in Pakistan using a stratified sampling technique. The analysis was conducted using Partial Least Squares Structural Equation Modeling with SmartPLS-4 to examine the proposed relationships.

This study’s findings reveal that PT, AWE and SI do not directly influence users’ intention to adopt online banking. Trust impacts intention through perceived ease of use, while AWE and SI affect intention via both perceived usefulness and ease of use. CRM negatively moderates SI but positively moderates the effects of PT and AWE on users’ intention.

This study explores the novel role of CRM as a moderator, offering fresh insights into how CRM enhances the impact of PT, AWE and SI on online banking adoption.

The significance of financial literacy (FL) in deciding how to allocate one’s investment capital has recently attracted much attention from various market participants and stakeholders. The study examines how FL affects individual investors' investment decisions (ID) in emerging markets. Additionally, the study investigates the potential mediating effects of attitude (ATT) and overconfidence bias (OCB) on the association between FL and ID.

The study employed a structured questionnaire to collect data from 311 individual investors in India, using both convenience and snowball sampling methods. The collected data were analysed using Partial Least Square Structural Equation Modelling (PLS-SEM) and processed through SMART PLS 4.0 software to test the study’s hypotheses.

FL alone may not greatly affect ID, but the study enhances understanding of investor behaviour by examining how ATT and OCB mediate the link between FL and ID. The findings imply that FL, combined with positive ATT and overconfidence, empowers individual investors with the knowledge and skills for appropriate decision-making.

This research would benefit financial institutions, financial experts, and individual investors in India since it enables them to evaluate the causes and biases affecting their IDs and manage their portfolios accordingly. Policymakers should develop appropriate FL programs for investors to make informed decisions to achieve financial well-being.

The paper is exceptional in its approach as it delves into the mediating function of ATT and OCB in the intricate association between FL and ID. This innovative approach sets it apart from other studies in the field, making it a unique contribution to literature.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0370

The study explores new aspects of financial investment management with technological involvement, providing detailed knowledge for future research. It identifies gaps in the literature and summarizes key research topics, utilizing a precise data collection framework.

The study is structured using systematic and bibliometric analysis with the antecedents, decisions, outcome-theories, context, and methods (ADO-TCM) framework. Data from Scopus and Web of Science were filtered based on Q1, Q2, social sciences citation index (SSCI) and Australian Business Deans Council (ABDC) criteria, resulting in 128 articles majorly emphasizing the last ten years. The “R” package facilitated bibliometric analysis, starting with data cleaning and import into Biblioshiny for effective results interpretation.

The study found that artificial intelligence detects and mitigates biases in investment decisions through rigorous pattern analysis, including social and ethical biases. The ADO-TCM framework revealed emerging theories, such as robo-advisory theory, offering new directions in behavioral finance for researchers and practitioners. The top authors and articles highlighted existing work in financial management.

The study’s originality is highlighted by its use of unique frameworks for data collection (SPAR-4-SLR) and interpretation (ADO-TCM).