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Gamification has been constantly demonstrated as an effective mechanism for employee engagement. However, little is known about how gamification reduces cyberloafing and the mechanism by which it affects cyberloafing in the workplace. This study draws inspiration from self-determination and social bonding theories to explain how game dynamics, namely, personalised challenges, social interactivity and progression status, enhance tacit knowledge sharing behaviour, which, in turn, reduces cyberloafing. In addition, the study also examines the negative moderating effect of fear of failure on the positive relationship between game dynamics and tacit knowledge sharing.

Using a sample of 250 employees from information technology organisations, the study employed a 3-wave study to examine the conditional indirect effects.

The results ascertain that tacit knowledge sharing plays a central role in the relationship between gamification and cyberloafing. Further, game dynamics positively influenced tacit knowledge sharing, which in turn reduced cyberloafing. Especially, social interactivity and progression status greatly reduced cyberloafing behaviour when the fear of failure was low.

This study is one of the initial studies that suggest gamification as a progressive tool to reduce workplace cyberloafing behaviours. It utilises a problematisation approach to analyse and criticise the in-house assumptions regarding cyberloafing prevention measures. Further, the study proposes a conceptual model explaining the link between gamification and cyberloafing through alternate assumptions.

The purpose of this study is to investigate the impact of leaders’ use of motivational language on psychological relatedness and its effect on employee well-being in flexible and remote working conditions.

A survey among 375 full-time working professionals in the US was conducted with varying frequencies of remote work arrangements. For the analysis, we used a series of PROCESS analyses to examine the moderating effect of leaders’ motivational language use on the relationship between participants’ remote work status and relatedness, with employee well-being as the dependent variable.

The findings revealed a significant moderating effect of leaders’ perlocutionary (direction-giving) language use on the relationship between employees’ remote work status and relatedness. Specifically, the relationship between remote work status and relatedness was stronger when the use of perlocutionary (direction-giving) language gradually increased. Such enhanced relatedness, in turn, generated higher satisfaction and psychological well-being. The study shows the strategic advantage of direction-giving language in enhancing relatedness, thereby contributing to higher levels of employee satisfaction and psychological well-being in remote work environments.

The originality of this article lies in its integration of motivational language theory and self-determination theory to explore the well-being of employees within flexible and remote work status. Furthermore, we conceptualize remote work as a continuous variable with different degrees of flexibility, ranging from occasional telecommuting to fully remote work, allowing for a nuanced understanding of how leaders’ use of motivational language interacts with varying levels of remote work arrangements to influence employee well-being.

This paper aims to better understand remote and hybrid employees’ experiences with the interconnection between employee engagement and well-being in relation to the participants’ lived experiences in nontraditional work roles post-pandemic.

To better understand how employees’ experiences with remote work underscore employee engagement and well-being in post-pandemic nontraditional work roles, we conducted 21 semi-structured interviews with remote and hybrid employees in various industries across the United States.

This study has three major findings. First, participants experienced employee engagement and well-being as distinct yet connected, with well-being and engagement simultaneously being positive and negative. Second, employee engagement was driven by the organization, while in some cases, well-being focused more on the individual. Third, participants discussed how their experiences reflected a cyclical connection between engagement and wellbeing.

The findings from this study demonstrate that employee well-being leads to employee engagement. In this way, well-being at the individual level becomes a predecessor or antecedent to employee engagement. Therefore, well-being plays a role in how engaged an employee might be. Participants also offered unique perspectives on engagement and well-being in the workplace, conceptualizing well-being and employee engagement as both micro- and meso-level outcomes.

The purpose of this study is to explore the effectiveness of mixed reality (MR) technology in enhancing library tours for first-year students at a university academic library. It specifically aims to investigate whether MR tours can reduce library anxiety, improve knowledge acquisition and increase motivation when compared to traditional library tours.

This study uses a quasi-experimental research design, comparing two groups of first-year students (n = 96). One group (n = 48) experiences a MR library tour using the HoloLens 2 device, while the other group (n = 48) participates in a traditional library tour led by library staff. The participants’ library anxiety, knowledge acquisition and motivation are measured using relevant questionnaires before and after the tours.

The findings indicate that while both MR and traditional tours are effective in imparting knowledge, there is no statistically significant difference in overall knowledge acquisition. However, the MR tour significantly enhances students’ perceived competence, interest and effort, providing higher engagement and motivation. Traditional tours, on the other hand, are more effective in reducing library anxiety, particularly in relation to interactions with librarians.

This study highlights the potential of MR technology to enhance library tours by balancing immersive experiences with ease of use. MR bridges the gap between virtual reality’s immersion and the accessibility, lower cost of content development and simplicity of application usage. It aligns with trends in academic libraries by offering high engagement without common health issues like motion sickness. However, while MR applications can be developed at a relatively lower cost, the high cost of MR equipment remains a limitation for institutions. Despite this, MR presents a promising solution for improving student engagement and learning, with the potential to become more accessible as hardware costs decrease.

Two key concepts in organizational behaviour research, employee engagement and organizational commitment are examined in this bibliometric analysis.

The Preferred Reporting Items for Systematic reviews and Meta-Analyses framework is used for the compilation of the papers, and the VOSviewer application with the SCOPUS database is used for bibliometric analysis. 387 authors wrote a total of 138 articles.

Through the examination of an extensive collection of scholarly articles, this research pinpoints significant patterns, noteworthy writers and recurring themes in the literature. The study helps to create better organizational practices by providing a roadmap for future research as well as a mapping of the current condition of the field.

The study is an original piece of work by the author with no conflict of interest with any party, person or organization.

Accurate diagnosis of brain tumors is crucial for effective treatment and improved patient outcomes. Magnetic resonance imaging (MRI) is a common method for detecting brain malignancies, but interpreting MRI data can be challenging and time-consuming for healthcare professionals.

An innovative method is presented that combines deep learning (DL) models with natural language processing (NLP) from ChatGPT to enhance the accuracy of brain tumor detection in MRI scans. The method generates textual descriptions of brain tumor regions, providing clinicians with valuable insights into tumor characteristics for informed decision-making and personalized treatment planning.

The evaluation of this approach demonstrates promising outcomes, achieving a notable Dice coefficient score of 0.93 for tumor segmentation, outperforming current state-of-the-art methods. Human validation of the generated descriptions confirms their precision and conciseness.

While the method showcased advancements in accuracy and understandability, ongoing research is essential for refining the model and addressing limitations in segmenting smaller or atypical tumors.

These results emphasized the potential of this innovative method in advancing neuroimaging practices and contributing to the effective detection and management of brain tumors.

This study aims to capture children’s interpretation of holistic well-being within Irish primary schools and add to the development of a comprehensive systems-informed positive education model.

This study utilized visual participatory research methods, including PhotoVoice and one-on-one interviews, to assess children’s (n = 16) well-being, guided by Von Unger’s comprehensive seven-step framework. Data analysis was anchored within grounded theory, beginning with data collection, initial coding, focused coding and culminating in identifying themes and subthemes. Data were interpreted using the mosaic approach by integrating visual and verbal data.

This analysis uncovered three primary themes that affect student well-being: relationships, space and physical environment and learning and curriculum, each with detailed subthemes. For instance, student–teacher relationships, peer relationships, safety, learning spaces, the creative curriculum including arts and music and the experiential richness of outdoor learning are crucial to students' educational growth and well-being. These aspects are seen as interconnected, shaping a holistic educational experience beyond academic learning to encompass students’ comprehensive well-being. The students' narratives demonstrated that learning is not merely an academic exercise but a vital component of their well-being.

This study significantly departs from traditional educational research by advocating for a dynamic, action-oriented understanding of “well-being.” It challenges the static, possessive interpretations of well-being and introduces the concept of well-being as a fluid and ever-evolving process. This reconceptualization positions well-being as a complex construct, influenced by an intricate web of relationships, spanning human and non-human interactions, organizational and environmental structures, personal desires, behavioral practices and broader societal and cultural frameworks.

New digital technologies are reshaping the business landscape and accounting work. This paper aims to investigate how incorporating more data and new data analytics (DA) tools impacts the role and use of judgment in financial due diligence (FDD).

The paper reports findings from a field study at a Big Four accounting firm in Sweden (“DealCo”). The primary data includes semi-structured interviews, observations and other meetings. Theoretically, it draws on Dewey’s The Logic of Judgments of Practise and Logic: The Theory of Inquiry and distinguishes between theoretical (what is probably true) and practical judgment (what to do).

In DealCo’s FDD practice, using more data and new DA tools meant that the realm of possibility had expanded significantly. To manage the newfound abundance and to use DA effectively, DealCo’s advisors invoked practical and theoretical judgments in different stages and areas of the data-driven FDD. The paper identifies four critical uses of judgment: Setting priorities and exercising restraint (practical judgment) and forming hypotheses and doing sense checks (theoretical judgment). In these capacities, practical judgment and theoretical judgment were essential in transforming raw data into actionable insights and, in effect, an indeterminate situation into a determinate one.

The study foregrounds the practical dimension of knowledge production for decision-making and contributes to a better understanding of the role, use and importance of accounting professionals’ judgment in a data-driven world.

Several challenges and issues are involved in successfully managing and improving the port logistics system (PLS) performance. Ports still face issues, including insufficient cargo handling equipment or equipment sharing during loading and unloading, which requires manual container inspection and delays clearance. This research aims to enhance the port logistics performance at one of India's cargo-handling ports. This paper seeks to identify various situations, actors, processes, learnings, actions and performance metrics particular to India's major container handling port.

The study objectives are accomplished using the Situation-Actor-Process–Learning-Action-Performance (SAP-LAP) framework, Fuzzy Analytic Hierarchy Process (FAHP) and Interpretive Ranking Process (IRP). The FAHP prioritises or ranks actions in a fuzzy environment. The ranking obtained by the FAHP method is assessed using the IRP approach.

This study examined action criteria and sub-attributes that define a PLS's effective implementation regarding handling containers in India. The results illustrate that strategic action must be prioritized first, followed by infrastructural and operational development, Technology upgradation and new methods and Training and Development initiatives.

This research provides a logical framework for evaluating the importance of various actions throughout the decision-making process. It would assist managers and practitioners in interpreting the impact of critical actions on performance and improving the operation of PLS by constructing resilient and adaptable solutions.

The study integrates methodologies like the IRP, SAP-LAP and FAHP. It focuses on various actions for an effective port logistics implementation system. The findings of this study allow decision-makers to understand interpretative reasoning by performing pairwise comparisons among the factors.

In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must ensure that their star performers believe that company’s reward and recognition (R&R) system is fair and equal. This study aims to use an explainable machine learning (eXML) model to develop a prediction algorithm for employee satisfaction with the fairness of R&R systems.

The current study uses state-of-the-art machine learning models such as Naive Bayes, Decision Tree C5.0, Random Forest and support vector machine-RBF to predict employee satisfaction towards fairness in R&R. The primary data used in the study has been collected from the employees of a large public sector undertaking from an emerging economy. This study also proposes a novel improved Naïve Bayes (INB) algorithm, the efficiency of which is compared with the state-of-the-art algorithms.

It is seen that the proposed INB model outperforms the state-of-the-art algorithms in many scenarios. Further, the proposed model and feature interaction are explained using the explainable machine learning (XML) concept. In addition, this study incorporates text mining techniques to corroborate the results from XML and suggests that “Transparency”, “Recognition”, “Unbiasedness”, “Appreciation” and “Timeliness in reward” are the most important features that impact employee satisfaction.

To the best of the authors’ knowledge, this is one of the first studies to use INB algorithm and mixed method research (text mining along with machine learning algorithms) for the prediction of employee satisfaction with respect to the R&R system.