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This study aims to develop a comprehensive measurement that consists of the most significant indicators of team effectiveness. Based on the input-mediator-outcome model, this measurement assesses two main aspects related to team effectiveness: team processes and emergent states (TPES).

The scale was developed and validated following three major steps. First, items were generated through literature reviews and expert reviews. Second, a pretest was conducted to refine the scale among 755 Chinese employees recruited on a survey service platform. Finally, in the evaluation section, 3,950 samples were used for scale validation, including a predictive validity test examining the relationship between TPES and team outcomes among 3,550 participants in paired samples (team members and leaders), from 381 work teams from several private companies in IT industries in China.

The TPES scale measures the team members’ interactions and the team status, consists of five dimensions: goal and role, collaboration, innovation, motivation and cohesiveness, showing acceptable reliability, validity and model fit. The five dimensions show significant positive effects on team performance and members’ satisfaction.

This novel measure of team process and emergent state tailored to IT companies provides a way to comprehensively diagnose a team’s strengths and/or weaknesses, thus suggesting directions for team development.

The literature on unethical pro-organizational behavior (UPOB) has experienced significant growth in the past decade. However, there is limited research on the effects of organizational, team and malleable individual factors on UPOB. It is also necessary to explore its adverse effects for theoretical advancement and to uncover unexplored opportunities. This study aims to systematically examine the existing body of literature on UPOB, providing thorough theoretical, contextual and methodological insights.

Using the preferred reporting items for systematic reviews and meta-analysis technique, the authors identified 43 articles on UPOB from journals included in the ABDC-2019 list. The authors conducted an analysis of the identified articles and categorized them using a modified version of Paul and Rosado-Serrano’s (2019) TCCM framework.

Existing literature primarily focuses on attitudinal and contextual antecedents of UPOB, neglecting individual differences and their consequences. The review suggests that certain desired employee attitudes may also lead to UPOB. In addition, the study highlights underutilization of established behavioral theories, emphasizing the need for a more inclusive theoretical framework. The exploration identifies research gaps, including in multidisciplinary and transdisciplinary studies, aiming to broaden the research scope in this field.

The study highlights the need for a more comprehensive theoretical framework to understand UPOB.

It cautions organizations fostering positive employee attitudes, such as job satisfaction, workplace spirituality and organizational commitment, as these may inadvertently promote UPOB.

Socially, the paper highlights how engaging in UPOB affects the lives of involved employees.

This paper’s originality arises from its methodical review and categorization of prior research on UPOB using a distinctive, multidisciplinary research framework.

The level of cynicism officers experience can directly impact their day-to-day decisions, especially in policing’s currently strained climate. This paper provides an updated systematic review of the predictors, outcomes and conceptualizations of police cynicism within the relevant literature.

A systematic review was conducted utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. Results from the 56 articles published after Langworthy’s 1987 systematic review are included. It was hypothesized that due to the current climate, police cynicism may be more discussed within the literature, especially when examined with the “Ferguson Effect.”

Researchers conceptualize police cynicism in various ways outside the traditional measures. Officers who were more cynical were more likely to maintain tougher law enforcement orientations and may have been more likely to engage in problem behaviors. Lack of internal support was related to organizational cynicism. Support for the Ferguson effect and demographic-related variables (tenure, racial identity and gender) on police cynicism yielded mixed findings despite the field’s inclination of their augmenting impact.

More research needs to be conducted on operationalizing and conceptualizing police cynicism. Researchers should establish standardized measures of police cynicism to help disaggregate results, as well as recruit from multiple agencies to increase generalizability.

This review is the first literature review conducted on police cynicism since Langworthy’s 1987 review. Exploring data post-1987 illustrates contemporary conceptualizations of police cynicism and related significant findings.

The purpose of this study is to review the literature on the adoption determinants of mobile health (M-health) applications for health behavior change following a systematic methodology.

This review systematically identified 134 peer-reviewed studies out of 10,687 from Scopus, Web of Science, PubMed and the Association for Computing Machinery (ACM) published between 2010 and 2021. This review used a thematic analysis to produce the main themes conceptualizing a holistic framework of the investigated M-health application adoption factors.

Despite the exploration of multifaceted adoption determinants and behaviors, the current publications exhibit limitations. The studies not only show a lack of representation of multiple health behaviors and medical conditions but also fail to involve data from low- and middle-developing countries, where M-health application utilization is crucial. Findings revealed that there is a considerable absence of a solid theoretical foundation that unveils a gap in interpreting the adoption factors effectively. Understanding cultural and demographic variances and exploring financial factors and healthcare provider involvement is essential for tailoring M-health application interventions. Continuous assessment of technological factors and evaluation of the actual impact of M-health application usage on behavioral changes and health outcomes will further enhance the effectiveness and adoption of these technologies.

This review is one of the first comprehensive reviews of determinants of M-health application adoption targeting health behavior change for the general public and patients.

The study aims to understand the influence of supplier development (SD) activities on supplier satisfaction through the lens of social capital theory (SCT) and to understand the mediating role of the buyer–supplier relationship (BSR) in improving supplier satisfaction (SS).

The research is based on survey of 110 key informants belonging to 50 medium to small supplier companies in the Indian automotive sector. We employed the PLS variance-based modelling technique for the data analysis.

The investigation resulted in a comprehensive framework for SD activities influencing SS. Further findings recognize a positive influence of SD activities such as payment terms and BSR, which are components of structural social capital on the SS. SD activities such as quality management and delivery, which are indicators of relational capital, affect SS through the mediation of BSR.

The study confirms the role of BSR in SS. The deliberations can help the managers of buyer and supplier firms and researchers to classify and strategize SD activities to improve performance and BSR to become preferred customers through SS.

The microfinancing sector is infamous for being prone to high credit risks due to loan defaults by its poor borrowers. Conversely, the sector is also criticized for creating debt traps for the poor. The dual nature of these peculiar problems in microfinancing causes the market failure phenomenon. Therefore, the current study explores whether public policy intervention is required to address market failure.

The study undertakes a critical review of existing literature, the news, the policy documents and other publicly available information to shape the viewpoints in this study. Constructive criticism is used to build arguments to arrive at a conceptual framework that depicts how public policy should interact with markets to address the peculiar problems of the microfinancing sector.

The findings indicate that market failure in microfinancing is real and pressing. Therefore, public policy is invited, though in its limited form. While the policy intervention may help the formal microfinancing arena by regulating the interest rates, the policy administration in the informal sector is likely to fail. Therefore, the policy should attempt to create an environment of inclusiveness. Policies that rely on coercion are not recommended. In the long run, subsidies via policy intervention are discouraged. Instead, the policy should motivate the microfinancing sector to become self-reliant.

The study is one of its kind to provide perspectives on specific market failures and policy interventions in microfinancing, particularly in economies where formal and informal sectors coexist and are equally crucial.

The purpose of this study is to provide an overview of artificial intelligence (AI) in the talent management sphere. The study seeks to contribute to the body of knowledge with respect to human resource management and AI by conducting a literature review on the integration of AI in talent management, synthesising existing approaches and frameworks, as well as emphasising potential benefits.

The study adopts desk research, computational literature review (CLR) and uses topic modelling [with bidirectional encoder representations from transformers (BERTopic)] to throw light on the diffusion of AI in talent management.

The study’s main finding is that the area of AI in talent management is on the verge of gradual development and is in tandem with the growth of AI. We deduced that there is a link between talent management practices (planning, recruitment, compensation and rewards, performance management, employee empowerment, employee engagement and organisational culture) and AI. Though there are some known fears with regards to using the innovation, the benefits outweigh the demerits.

The current study has some limitations. The scope and size of the sample are the primary limitations of this study. No form of qualitative analytics was used in this study; as a result, the information obtained was limited. The study provides a snapshot of AI in talent management and contributes to the lack of literature in the joint fields. Also, the study provides practitioners and experts an overview of where to target investments and resources if need be.

The originality of this study comes from the combination of CLR methods and the use topic modelling with BERTopic which has not been used by previous reviews. In addition, the salient machine learning algorithms are identified in the study, which other studies have not identified.

The purpose of this study is to extract factors enabling the digital car sharing enterprises' supply chain resilience (SCR), to interpret different factor prioritizations in terms of industry representatives’ assessments and specialties, and to discuss the results by applying and comparing different ranking techniques.

To achieve the purpose, the factors were identified via an in-depth systematic literature review, and next, these factors were examined by industry representatives to gather the decision matrices, then analytic hierarchy process (AHP) and measuring attractiveness by a categorical based evaluation technique (MACBETH) were applied separately to model the decision problem, and finally the findings were interpreted with different participants’ perspectives.

The findings revealed that the AHP and MACBETH provide nearly identical rankings in terms of main factors by implying the significance of the triple bottom line of sustainability. Therefore, the economic, social, and environmental dimensions of sustainability should be accomplished to obtain a resilient digital car sharing enterprise supply chain. In addition, readiness and agility are the other important factors affecting the enterprises’ resilience, and finally, although digitalization seemed to be the least important one, its sub-factor emerged at the top of the ranking list.

Up to the authors’ knowledge, this is the first study in the literature that focuses on the SCR of car sharing companies, a particular type of digital enterprise, and uses AHP and MACBETH to examine the important factors that might affect the SCR of these companies. Practitioners should take the findings of both methods into account when evaluating the results and determine the short- and long-term strategies accordingly.

This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies.

This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI.

By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI.

Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.