Military institutionalization involves internalizing military values, norms and structured behaviours that differ significantly from civilian life, leading to culture shock during the transition. Career experiences in the military imprint behaviours and identities that shape subsequent roles and how retired officers transition to the civilian world. The present study explored how military culture and identity influence military-to-civilian transition difficulty among retired army officers. Fourteen semi-structured interviews were conducted and analysed using interpretative phenomenological analysis, resulting in three overarching themes: Transition and Contrasting Cultures, Transition and the Role of Military Imprinting and Relearning-Connecting Disparate Cultures. The themes are divided into subthemes that shed light on how veterans navigate cultural disparities, experience identity and ego conflict tribulation and highlight factors that facilitate bridging the military-civilian cultural gap. The study's findings underscore the pivotal influence of military culture and the identity forged within military organizations on veterans' transition experiences. These insights carry significant implications for veterans and reintegration programs. It further offers possible suggestions to enhance transition interventions by addressing psychological distress related to reculturation and identity transition to facilitate smoother integration.

Research Question/IssueDo firms that release 10-K reports with lower readability receive a higher number of shareholder proposals on corporate governance?Research Findings/InsightsUsing the passage of the Plain Writing Act of 2010 as an exogenous source for an increase in 10-K readability within a difference-in-differences framework, we find that firms with poor 10-K readability prior to the Act experienced a decline in the likelihood of receiving corporate governance proposals. This effect is primarily concentrated in proposals sponsored by retail investors and is most pronounced in firms with entrenched management, those actively engaging in earnings management through discretionary accruals, and proposals related to executive compensation and disclosure.Theoretical/Academic ImplicationsBy providing evidence on the differential impact of 10-K readability on retail versus institutional investors, we shed light on the varying information processing capabilities and engagement strategies across investor types. Our findings also illuminate the interplay between disclosure readability, managerial entrenchment, and earnings management, offering insights into the mechanisms through which poor 10-K readability influences the initiation of shareholder proposals.Practitioner/Policy ImplicationsOur findings underscore the effectiveness of the Plain Writing Act of 2010 in enhancing transparency and highlight the role of disclosure readability as a tool for shareholder engagement, particularly for retail investors. Further, we show that the Plain Writing Act achieved more than its original goal of promoting clear and transparent communication between the government and the public; it also achieved an unintended outcome by prompting firms to adopt clear writing norms.

Research Question/IssueDo firms that release 10-K reports with lower readability receive a higher number of shareholder proposals on corporate governance?Research Findings/InsightsUsing the passage of the Plain Writing Act of 2010 as an exogenous source for an increase in 10-K readability within a difference-in-differences framework, we find that firms with poor 10-K readability prior to the Act experienced a decline in the likelihood of receiving corporate governance proposals. This effect is primarily concentrated in proposals sponsored by retail investors and is most pronounced in firms with entrenched management, those actively engaging in earnings management through discretionary accruals, and proposals related to executive compensation and disclosure.Theoretical/Academic ImplicationsBy providing evidence on the differential impact of 10-K readability on retail versus institutional investors, we shed light on the varying information processing capabilities and engagement strategies across investor types. Our findings also illuminate the interplay between disclosure readability, managerial entrenchment, and earnings management, offering insights into the mechanisms through which poor 10-K readability influences the initiation of shareholder proposals.Practitioner/Policy ImplicationsOur findings underscore the effectiveness of the Plain Writing Act of 2010 in enhancing transparency and highlight the role of disclosure readability as a tool for shareholder engagement, particularly for retail investors. Further, we show that the Plain Writing Act achieved more than its original goal of promoting clear and transparent communication between the government and the public; it also achieved an unintended outcome by prompting firms to adopt clear writing norms.

It has been exactly a decade since the term Industry 4.0 was coined. Researchers have been fascinated with this term and have since explored different areas of Industry 4.0. This study uses bibliometric tools to conduct an in-depth, year-wise analysis of Industry 4.0 literature in operations management. A detailed year-wise keywords cluster analysis highlights the evolution of the intellectual structure of Industry 4.0. Two new aspects namely the morphological study of keywords and the year-wise new keywords analysis are included in the keyword analysis. The analysis identifies the most significant studies, influential contributors (authors), and the countries that have majorly contributed to the body of knowledge of Industry 4.0. Since the research on Industry 4.0 is relatively new and attracts the heightened focus of scholars worldwide, the timely in-depth bibliometric review provided in this study will help future scholars explore multiple research directions in Industry 4.0.

While migration is a largely discussed form of labor mobility, internal return migration of workers has not received much attention. Individuals return to their native or previous place either for reasons related to work, distress, or family. Not only are they returning to a place that is familiar to them in terms of language, culture, and society, but also, they may have acquired some skills before returning. Therefore, labor market outcomes for return migrants are likely to differ from those of nonmigrants, who are otherwise similar to each other in most aspects. Against this context, the paper compares the earnings of internal return migrants with nonmigrants in India using the Periodic Labor Force Survey 2020-21. To this end, the study performs ordinary least square estimation by sector (rural and urban), gender, age cohorts, and employment activity status (self-employed, regular salaried, casual labor). As part of robustness analysis, it also uses the propensity score matching method. Both the exercises suggest that internal return migrants, on average, experience an earnings penalty in rural areas but a premium in urban areas. Also, a regular salaried internal return migrant experiences an earnings premium irrespective of the location (rural or urban). The earning pattern for men largely imitates the overall pattern. However, the earnings differential between female internal return migrants and nonmigrants is not significant as most of them migrate because of reasons other than work, like marriage. The findings of this study have significant implications, as what individuals do and how much they earn upon their return have direct consequences on the employment and income distribution of any region.

While migration is a largely discussed form of labor mobility, internal return migration of workers has not received much attention. Individuals return to their native or previous place either for reasons related to work, distress, or family. Not only are they returning to a place that is familiar to them in terms of language, culture, and society, but also, they may have acquired some skills before returning. Therefore, labor market outcomes for return migrants are likely to differ from those of nonmigrants, who are otherwise similar to each other in most aspects. Against this context, the paper compares the earnings of internal return migrants with nonmigrants in India using the Periodic Labor Force Survey 2020-21. To this end, the study performs ordinary least square estimation by sector (rural and urban), gender, age cohorts, and employment activity status (self-employed, regular salaried, casual labor). As part of robustness analysis, it also uses the propensity score matching method. Both the exercises suggest that internal return migrants, on average, experience an earnings penalty in rural areas but a premium in urban areas. Also, a regular salaried internal return migrant experiences an earnings premium irrespective of the location (rural or urban). The earning pattern for men largely imitates the overall pattern. However, the earnings differential between female internal return migrants and nonmigrants is not significant as most of them migrate because of reasons other than work, like marriage. The findings of this study have significant implications, as what individuals do and how much they earn upon their return have direct consequences on the employment and income distribution of any region.

While migration is a largely discussed form of labor mobility, internal return migration of workers has not received much attention. Individuals return to their native or previous place either for reasons related to work, distress, or family. Not only are they returning to a place that is familiar to them in terms of language, culture, and society, but also, they may have acquired some skills before returning. Therefore, labor market outcomes for return migrants are likely to differ from those of nonmigrants, who are otherwise similar to each other in most aspects. Against this context, the paper compares the earnings of internal return migrants with nonmigrants in India using the Periodic Labor Force Survey 2020-21. To this end, the study performs ordinary least square estimation by sector (rural and urban), gender, age cohorts, and employment activity status (self-employed, regular salaried, casual labor). As part of robustness analysis, it also uses the propensity score matching method. Both the exercises suggest that internal return migrants, on average, experience an earnings penalty in rural areas but a premium in urban areas. Also, a regular salaried internal return migrant experiences an earnings premium irrespective of the location (rural or urban). The earning pattern for men largely imitates the overall pattern. However, the earnings differential between female internal return migrants and nonmigrants is not significant as most of them migrate because of reasons other than work, like marriage. The findings of this study have significant implications, as what individuals do and how much they earn upon their return have direct consequences on the employment and income distribution of any region.

Social networks are highly effective in mobilizing individuals toward collective action. However, such endeavors to rally large groups risk escalating into violent actions, eroding social unity and provoking societal unrest. The information consumption patterns vary considerably across different platforms through which it is disseminated, with platform-specific characteristics significantly impacting how users interact and disseminate information. Nevertheless, this distinctive characteristic has received minimal attention in the literature. Considering these differences, we seek to understand the mechanisms underlying information dissemination on social networks in the context of mob violence and protests. We conducted a comparative analysis of communication on the Reddit and YouTube networks about the riots in France in 2023. By utilizing social network analysis, we clarify the differences in information dissemination across these networks and contribute to information systems literature on social media actions with theoretical and methodological insights into the dissemination of information about riots.

Purpose - This study aims to provide a comprehensive bibliometric analysis of cyberloafing by identifying key trends, distinctive works and prominent contributors in the area. Design/methodology/approach - The authors conducted a bibliometric analysis of 398 documents (between 2001 and 2024) indexed in Scopus. The Biblioshiny through R studio and VOS Viewer was used to aid the investigation. Findings - The study used bibliometric techniques like citation and keyword analysis to analyse existing literature, identify influential publications, key terms and emerging research trends. Additionally, bibliographic coupling enabled the classification of learning outcomes (LO) into five clusters, and Theory-Context-Characteristics-Methodology (TCCM) provides a structured approach to identifying future research directions by categorising them into Theory, Context, Characteristics and Methodology to ensure comprehensive and systematic academic exploration. Practical implications - The research seeks to identify gaps, emerging themes, and the intellectual trajectory of cyberloafing, offering a valuable resource for researchers and policymakers to understand its antecedents, attitudes and implications for organisations. Originality/value - This study provides a comprehensive novel bibliometric analysis of cyberloafing research from 2001 to 2024, highlighting thematic mapping and cluster analysis and using TCCM for knowledge advancement and future research pathways.

Strategy execution remains a critical challenge for organizations worldwide, with up to 67% of well-formulated strategies failing due to poor implementation. This study addresses the lack of comprehensive strategy execution frameworks by developing and validating the CADAP (capability, agility, design, alignment and people) strategy execution framework. Through a systematic bibliometric analysis of 40 years of literature, we identified eight influential frameworks and conducted research on their limitations. Employing a rigorous combination of inductive and deductive approaches, along with gap analysis, we conceptualized a new framework-CADAP strategy execution framework, consisting of five dimensions: capability, agility, design, alignment and people. We substantiated our framework using natural language processing on 534 articles from four top-tier journals and validated it through a case study of a global bank divestiture. Results demonstrate that CADAP provides a holistic approach to strategy execution, with organizations effectively implementing it, showing improved adaptability and performance in complex environments. Overall, this study contributes to management literature by offering an empirically grounded framework for strategy execution, providing a roadmap for organizations to navigate modern business complexities and foster resilience in the global marketplace. Finally, the limitations and possible future research directions are discussed.

Effective management of healthcare waste (HCW) imposes a great challenge to all countries. Specially in the developing countries, it is often mixed with municipal waste, adversely affecting the health and safety of the medical personnel, general public and environment. Healthcare waste management (HCWM) basically deals with segregation, collection and storage, routing and transportation, treatment and safe disposal of HCW, while obeying some national legislation. In every stage of HCWM, there are several alternative choices/strategies to be evaluated against a set of conflicting criteria. Numerous multi-criteria decision-making (MCDM) methods have appeared to resolve the issue. This article reviews 101 articles available in Scopus and other scholarly databases on applications of MCDM techniques in solving HCWM problems. Those articles are classified into six groups: (a) selection of the most effective HCW treatment technology, (b) identification of the best HCW disposal site, (c) assessment of the best-performing healthcare unit adopting ideal HCWM strategies, (d) selection of third party logistics providers, (e) identification of HCWM barriers and (f) evaluation of specific HCWM plans. It is observed that the past researchers have mostly preferred to apply MCDM tools for solving HCW treatment technology selection problems, whereas analytic hierarchy process, decision-making trial and evaluation laboratory and best-worst method and fuzzy set theory have been the mostly favoured MCDM tool, criteria weight measurement techniques and uncertainty model, respectively. The outcomes of this article would help the healthcare personnel/policymakers in unveiling the current status of HCWM research, exploring extant research gaps and challenges and providing future directions leading to sustainable environment.

PurposeThis paper identifies consumer acceptance criteria of artificial intelligence (AI)-enabled products and services in the business. We first investigate the existing three models. They are the technology acceptance model (TAM), the unified theory of acceptance and use of technology (UTAUT) and the consumer acceptance of technology (CAT). We then discuss the applicability of these three models for AI-enabled products and services. Finally, we outline the shortcomings of the models and propose an AI-enabled product and service acceptance model (AIEPSAM). We also validate the proposed AIEPSAM model with empirical results using primary survey data.Design/methodology/approachTo understand the customer's point of view on AI applications in products and services, we identify some critical factors and present a conceptual framework of consumers' acceptance criteria based on existing literature, prior research and prominent technology management theories. Then, the study broadens the horizon beyond established principles associated with technology acceptance to accommodate AI-specific factors/variables like data privacy, explainability and apparent opacity of algorithms. In this paper, we propose an AIEPSAM and validate that model with primary survey data.FindingsWe argue that although TAM, UTAUT and CAT models are generally applicable to explain consumers' attitudes towards technology, these models alone are insufficient to encompass the entire spectrum of AI-related issues that must not be ignored. The proposed model, namely AIEPSAM, accommodates the limitations of the existing models and modifies the CAT model to make it suitable for the acceptance of AI technology.Originality/valueWe attempt to articulate the consumer acceptance criteria of AI-enabled products and services and discover useful insights, leading to the critical examination of TAM, UTAUT and CAT models and formulating AIEPSAM with validation through primary survey data. This study is not to criticize the TAM and other technology acceptance models but to incorporate AI-specific factors into those models. Through this study, we propose the required modifications in the existing technology acceptance models considering the AI-specific additional factors. The AIEPSAM will assist companies in building AI-enabled products and services and better understanding the technology emergence (TE) and technology opportunities (TO).

This paper presents a stochastic MILP model for the fair distribution of profits in a supply chain under ripple effect with risk-neutral primary suppliers and a robust manufacturer. The robustness is understood as the mean-risk fairness that aims at equitably efficient business-as-usual and worst-case performance of the manufacturer. The objective is to equitably optimise conditional profit-at-risk and expected profit of the manufacturer as well as expected profits of all primary suppliers. In addition, the backup suppliers are considered andrecovery supply portfolios optimised for each disruption scenario. To coordinate production across the entire supply chain, a collaborative partnership is applied enforcing the manufacturer's expected service level to be not less than the expected service level of each primary supplier. The findings indicated that if the manufacturer aims at equitably efficient maximisation of average and worst-case profit under collaborative partnership, the associated expected profits of all primary suppliers may also converge to their respective collaborative maxima, and the more reliable is supply chain environment, the closer to their maxima are the supplier's profits. The findings also demonstrated that the fair distribution of supply chain profits under collaborative partnership simultaneously enforces coordinated production of parts by primary suppliers and products by the manufacturer.abstract & iquest;Please edit the abstract down to no more than 200 words.

The correctness of predictions rendered by an AI/ML model is key to its acceptability. To foster researchers' and practitioners' confidence in the model, it is necessary to render an intuitive understanding of the workings of a model. In this work, we attempt to explain a model's working by providing some insights into the quality of data. While doing this, it is essential to consider that revealing the training data to the users is not feasible for logistical and security reasons. However, sharing some interpretable parameters of the training data and correlating them with the model's performance can be helpful in this regard. To this end, we propose a new measure based on Euclidean Minimum Spanning Tree (EMST) for quantifying the intrinsic separation (or overlaps) between the data classes. For experiments, we use datasets from diverse domains such as finance, medical, and marketing. We use state-of-the-art measure known as Davies Bouldin Index (DBI) to validate our approach on four different datasets from aforementioned domains. The experimental results of this study establish the viability of the proposed approach in explaining the working and efficiency of a classifier. Firstly, the proposed measure of class- overlap quantification has shown a better correlation with the classification performance as compared to DBI scores. Secondly, the results on multi-class datasets demonstrate that the proposed measure can be used to determine the feature importance so as to learn a better classification model.

Purpose-Like all other service industries, evaluation of service quality in health-care units is a complex decision-making task involving multiple stakeholder groups with varying interest, conflicting qualitative criteria and competing health-care units. The past researchers have already attempted to solve this problem while integrating different uncertainty models with various multi-criteria decision-making (MCDM) tools. This paper aims to propose application of an MCDM method for evaluating service quality of health-care units in uncertain environment. Design/methodology/approach-This paper presents application of an integrated approach combining grey numbers with combined compromise solution (G-CoCoSo) method for appraising service quality of six Urban Primary Health Centers (UPHCs) in Kolkata, India, based on the opinions of three different stakeholder groups (health-care service recipients, medical officers and health-care administrators) against six subjective criteria (tangibles, responsiveness, service, assurance, empathy and hygiene). A sensitivity analysis is also performed to investigate the effect of varying values of lambda on the ranking performance of G-CoCoSo method. Findings-Based on the collective judgments of the three stakeholder groups expressed in grey numbers, tangibles is identified as the most important criterion, followed by responsiveness. On the other hand, assurance criterion has the least importance. The G-CoCoSo method singles out H3 as the best UPHC, followed by H1. On the contrary, H5 appears as the worst performing UPHC. The results of sensitivity analysis prove that this integrated method is insensitive to changing values of lambda. Similarly, a comparative study against other grey integrated state-of-the-art MCDM methods validates its solution accuracy. Originality/value-To the best of the authors' knowledge, G-CoCoSo is used for the first time in this paper to solve a health-care service quality evaluation problem demonstrating satisfactory results. It would assist both the health-care professionals and patients in identifying the relative strengths and weaknesses of each of the UPHCs under consideration.

We introduce a linear programming-based approach for hyperparameter tuning of machine learning models. The approach finetunes continuous hyperparameters and model parameters through a linear program, enhancing model generalization in the vicinity of an initial model. The proposed method converts hyperparameter optimization into a bilevel program and identifies a descent direction to improve validation loss. The results demonstrate improvements in most cases across regression, machine learning, and deep learning tasks, with test performance enhancements ranging from 0.3% to 28.1%.

Additive manufacturing (AM) is a disruptive technology that enables the fabrication of intricate geometries layer-by-layer by discretizing the given geometry into multiple slices. Overhangs are regions of these slices where the surface projection exceeds the underlying horizontal support. AM techniques, like material extrusion (MEX), require explicit support structures, which are added to ensure proper printability and dimensional stability. Although supports provide part balancing to avoid material sagging, they should be minimised as they increase the overall material usage, print time and associated costs. Limited studies have been done on the self-supporting capacity of thin-walled AM structures. This research presents a novel analytical model based on the beam bending principle to determine the material's limit to self-sustain overhangs. The model determines this limit in terms of an overhang angle (from the vertical) using part geometry, process parameters and material properties. It is found that the overhang angle has an inverse square root relation with an apparent number of layers, which can be linearly approximated as a function of the number of layers. The model is further extended to incorporate buckling effects in the extruder fibres. Analytical results showed that overhangs as high as 75o are possible without any external supports, as against the conventional 45 degrees limit. The presented model can alleviate the AM process by increasing the printing efficiency and reducing material wastage.

Several studies have explored the antecedents of automobile purchase intention (API) of customers across different geographical regions of the world. These have, however, come up with contradictory findings about the polarities of effects of some antecedents, as well as about some of their differences across countries. We were motivated by these contradictions and differences to carry out a meta-analysis on API. In the study, we considered articles from peer-reviewed databases such as Google Scholar, Web of Science, Scopus, ScienceDirect, and Wiley. Our study and analysis of the full texts of 136 articles yielded 64 antecedents. The specific polarity of effect (positive or negative) on API is established for 59 of these antecedents, whereas the same remained undetermined for the rest five. We identified these as research gaps, the areas yet to be convincingly explored. Furthermore, based on the absence of publication bias and the 95% CI of impact not crossing the line of null effect, we enlisted 20 antecedents spread across the different stages of the automobile purchase decision process, which we propose to add to the theory of consumer behavior as the automobile consumer behavior (TACB). Our study can help managers to better understand consumer behavior in terms of the discovered antecedents, especially those having a higher impact at a particular stage of the process, and can result in better business decisions.

Background:Cyberbullying continues to take its toll on social media users, as dark traited individuals (narcissism, psychopathy, machiavellianism) continue to leverage the evolving modes of technology-driven social engagement for the exploitation of others.Purpose:In a similar direction, this study theoretically discusses and meta-analytically examines the strength and nature of the relationship between the Dark Triad and cyberbullying.Method:The study synthesises the relationship between dark triad and cyberbullying spread across close to two decades, from 2005 to 2023, and tests the moderating effect of age, gender, and cyberbullying scale reliability.Result:It was observed that all dark traits had a significant positive relationship with cyberbullying, with psychopathy being the strongest predictor, followed by Machiavellianism and narcissism. Further, the estimates of psychopathy and narcissism displayed low to moderate heterogeneity, which was primarily explained by age and gender.Conclusion:We discuss three probable competing explanations for the dark triad - cyberbullying relationship, namely, low empathy, low agreeableness, and low honesty-humility and advance three important research questions geared towards classification of an ever-expanding repertoire of anti-social online behaviours, rationalisation of definitions and measures used to measure cyberbullying, and distinguishing the effect of the dark core and dark traits effects on cyberbullying.

Background:Cyberbullying continues to take its toll on social media users, as dark traited individuals (narcissism, psychopathy, machiavellianism) continue to leverage the evolving modes of technology-driven social engagement for the exploitation of others.Purpose:In a similar direction, this study theoretically discusses and meta-analytically examines the strength and nature of the relationship between the Dark Triad and cyberbullying.Method:The study synthesises the relationship between dark triad and cyberbullying spread across close to two decades, from 2005 to 2023, and tests the moderating effect of age, gender, and cyberbullying scale reliability.Result:It was observed that all dark traits had a significant positive relationship with cyberbullying, with psychopathy being the strongest predictor, followed by Machiavellianism and narcissism. Further, the estimates of psychopathy and narcissism displayed low to moderate heterogeneity, which was primarily explained by age and gender.Conclusion:We discuss three probable competing explanations for the dark triad - cyberbullying relationship, namely, low empathy, low agreeableness, and low honesty-humility and advance three important research questions geared towards classification of an ever-expanding repertoire of anti-social online behaviours, rationalisation of definitions and measures used to measure cyberbullying, and distinguishing the effect of the dark core and dark traits effects on cyberbullying.