Purpose The significant contribution of women entrepreneurs in the Indian micro, small and medium enterprises (MSMEs) has gained attention recently. The Ministry of MSMEs under government of India has taken several strategic measures to promote women entrepreneurs; however, they are still facing some issues and challenges in running their business due to the ongoing technological disruption and social concerns. Thus, the purpose of this study is to identify and evaluate the key enabling factors and develop a framework to provide a strategic roadmap for the women-owned Indian MSMEs. Design/methodology/approach Key enabling factors are identified through a review of literature and validated based on experts' response. The identified enablers are further modelled using total interpretive structural modelling to establish meaningful contextual relationships. This study also employed MICMAC (cross-impact matrix multiplication applied to classification) analysis for classifying the enablers based on driving and dependence power. Findings This study identified “awareness on government policies”, and “STEP incubation” as the most strategic enablers with the highest driving power, placed at the bottom of the hierarchical model, which needs significant attention. Further, upskilling and reskilling, knowledge management and dynamic capability are the middle-level or linkage enablers that assist in attaining innovation ambidexterity and sustainable business performance. Originality/value This study proposed a hierarchical model with contextual relationships among the identified enablers that may act as a blueprint for the women-owned Indian MSMEs to scale up their business.
Purpose Identity leadership describes the importance for leaders to create and represent a shared identity. In this paper, the pathways from identity leadership to affective commitment were investigated in both collectivistic and individualistic countries. Design/methodology/approach The importance of team identification and the perceived amount of participation in decision-making were investigated in an online cross-sectional study. We recruited 741 participants from 18 different countries. Findings Structural equation modelling showed that in individualistic countries, both team identification and participation in decision-making mediated the association between identity leadership and affective commitment, whereas in collectivistic countries, only team identification acted as a mediator. Originality/value This study adds to the growing literature on identity leadership by investigating its pathways to affective commitment. It also compares two country clusters based on the individualistic/collectivistic dimension. The results suggest that sharing the decision-making power is associated with higher levels of commitment in individualistic cultures, whilst it is not the case in collectivistic cultures.
Purpose This study proposes an optimization framework aimed at enhancing both operational and environmental performance in semi-automated manufacturing processes, addressing a critical gap in existing research. Design/methodology/approach The framework comprises five stages: Identify, Data Collection, Investigate, Optimization and Monitor. It integrates insights from the literature and applies these to a case study in a semi-automated semiconductor plant. Key tools include a Smart Andon Dashboard and a Manufacturing Execution System, as well as the Analytic Hierarchy Process (AHP) for improvement selection. Findings The implementation of the framework resulted in a 32% improvement in operational efficiency and a 70% reduction in machine idle time. These changes significantly reduced energy consumption and improved the plant's carbon footprint. Notably, the study highlights the long-term benefits of optimizing non-bottleneck processes for resource conservation and CO2 emission reduction. Practical implications This research provides a practical methodology for manufacturers to achieve dual objectives of operational efficiency and sustainability. The use of AHP enhances decision-making in the improvement selection process, making it adaptable to various manufacturing environments. Originality/value This study bridges the gap between operational and environmental performance improvements in semi-automated manufacturing. It demonstrates the potential of targeted optimizations to yield substantial benefits for both efficiency and sustainability.
Purpose Manufacturing firms in the textile sector face simultaneous pressure to enhance responsiveness, maintain quality, shorten lead times, and align production with sustainability expectations. In the current Industry 4.0/5.0 environment, flexible manufacturing systems (FMS) are increasingly viewed as strategic enablers of product variety, speed and resource efficiency. However, managers still lack clear evidence on which performance variables deserve priority in textile settings. This study, therefore, identifies, classifies and prioritizes the most influential FMS performance variables for textile manufacturing. Design/methodology/approach A triangulation design was adopted. First, a focused literature review was undertaken to compile candidate FMS performance variables. Second, semi-structured interviews with 17 experts and a questionnaire survey of 135 domain experts were used to validate the variables for the textile context. Third, 19 validated variables were classified into 3 major categories, quality, productivity and flexibility, and prioritized using the best-worst method (BWM), a multi-criteria decision-making approach selected for its lower comparison burden and stronger consistency. Findings The study identifies 19 performance variables that are significant for FMS in textile industries and classifies them into quality, productivity and flexibility categories. The results show that quality-related variables dominate the overall ranking; automation emerges as the most influential quality variable, unit manufacturing cost is the leading productivity variable and the use of automated material-handling devices is the most important flexibility variable. These findings offer an evidence-based basis for sequencing FMS investment and improvement efforts. Research limitations/implications The results are derived from expert judgements and a textile-sector case context and should therefore be interpreted with appropriate contextual caution. Even so, the study offers actionable implications for managers by showing which variables should be prioritized first under limited resources. The findings also suggest that future research should test the framework through larger cross-sector datasets and incorporate broader sustainability and digitalization variables. Originality/value The originality of the study lies not simply in applying BWM, but in contextualizing FMS prioritization for the textile sector at a time when digitalization, sustainability and circular-economy objectives are reshaping manufacturing choices. The paper shows how textile-specific operational constraints influence the relative importance of FMS performance variables in ways that are not fully captured by prior generic manufacturing studies.
Purpose Although an extensive body of research treats corporate governance (CG) and sustainable performance (SP) separately, less attention has been paid to the assessment of SP practices and its interactions with CG in the mining industry. Design/methodology/approach Drawing on a sample of mining companies listed on India's National Stock Exchange's top 200 listed companies over a period of 11 years, this study first builds a SP index through content analysis of sustainability reports providing detailed descriptive analysis of SP. This is followed by panel data regression analysis through the GMM method to examine how CG mechanisms affect SP. Findings SP in the mining industry has increased over time, although the information on the indicators remains inconsistent. Empirical analysis further suggests that board characteristics such as size and diversity do not lead to enhanced SP. Practical implications The findings help identify the importance of certain CG attributes in India and other major emerging economies, where similar studies can be undertaken to determine the role of CG in enhancing responsible production of sensitive sectors such as mining. Social implications This study highlights inconsistent sustainability reporting, which can undermine transparency and accountability. It emphasizes the need for independent and diverse boards to drive sustainable development in this sector. Originality/value The SP index developed in this study provides important insights into the mining industry's sustainability reporting and contributes to the performance measurement and management literature by offering a replicable, sector-specific tool to evaluate and examine SP and its association with CG attributes.
Purpose This study attempts to evaluate the financial performance of ten selected Indian commercial banks from 2018 to 2023 and compares the rankings produced by prominent distance-based multi-criteria decision-making (MCDM) methods. Design/methodology/approach This study employs the CRiteria Importance Through Intercriteria Correlation (CRITIC) method to determine the weights of the indicators under the CAMELS framework. The Comprehensive Distance-Based Ranking (COBRA) method is then used to evaluate and rank the selected. The robustness of the findings is assessed by comparing the results with four distance-based MCDM methods, i.e., the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Evaluation Based on Distance from Average Solution (EDAS), Combinative Distance-Based Assessment (CODAS) and the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS). Finally, the Friedman test, Kendall's coefficient of concordance (W) and the Bonferroni-adjusted Wilcoxon post hoc test are applied to statistically validate the results. Findings HDFC Bank Ltd. and Kotak Mahindra Bank Ltd., among the top performers during 2018–2022, experienced a notable decline in 2023, whereas Axis Bank Ltd. improved steadily. Friedman and Bonferroni-adjusted Wilcoxon tests revealed significant differences between COBRA and other distance-based methods. COBRA generated the most consistent rankings, while TOPSIS and EDAS were more sensitive in distinguishing alternatives. CRITIC weights indicated temporal fluctuations in CAMELS indicators, with profitability, asset quality and employee productivity emerging as important performance drivers. These findings emphasize the need for adaptive performance management systems that reflect evolving priorities and emerging areas requiring managerial attention. Research limitations/implications Although the analysis aimed to follow the guidelines and principles related to MCDM decision analysis and theories involved in bank performance evaluation, this study has some limitations. It is limited to six consecutive years, from 2018 to 2023. The study could be expanded, and the impact of different mergers and acquisitions on the Indian banking sector's performance can be measured using the proposed approach. Practical implications The findings provide policy implications for regulators, such as the Reserve Bank of India. The movement of weights and rank shifts identified over time emphasize the necessity of consistent adjustment in supervisory priorities, especially for asset quality, provisioning for NPAs and employee efficiency. Second, the study also emphasizes the necessity of including both profitability ratios and risk sensitivity measures in policy-making for the establishment of systemic resilience. Social implications For practitioners such as bank managers and analysts, this study offers a replicable decision-support model that is quantitative in rigor yet flexible. The COBRA model, as validated through robustness analysis combined with other MCDM methods, can be utilized as a strategic benchmarking tool. Investors can utilize the ranking for decision-making purposes, while auditors and credit rating agencies can consider using this method to complement their risk assessment processes. In all, the research not only fills a methodological lacuna in bank performance measurement literature but also offers a useful toolkit for tracking and enhancing banking efficiency in an ever-changing economic environment. Originality/value The study contributes to MCDM and performance management research by comparing distance-based MCDM methods and validating an integrated CRITIC–COBRA approach within the CAMELS framework for banking performance evaluation.
Purpose The study is aimed at developing a comprehensive performance measurement system (PMS) specific to the Industry 5.0-enabled circular supply chains (I5.0-enabled CSCs) to meet the requirement of having frameworks that are human-centric, sustainable, and technologically innovative. Design/methodology/approach A hybrid model that applies a modified balanced scorecard (BSC) with advanced multi-criteria decision-making (MCDM) methods, including the spherical fuzzy Bayesian best-worst method (SF-BBWM) and the spherical fuzzy combinative distance-based assessment method (SF-CODAS) to identify, prioritize and evaluate 30 performance measures of I5.0-enabled CSCs. Empirical validation was conducted through case studies of four Indian textile manufacturing firms. Findings The findings demonstrate that the most significant dimensions of successful adoption of I5.0-enabled CSC are operational efficiency and technological advancement. Key performance measures such as reduction of operational waste and deployment of cyber-physical systems ranked highest. Research limitations/implications From a performance management perspective, the study contributes theoretically by demonstrating how PMS can develop from linear Industry 4.0-based models to include Industry 5.0 values of resilience, cooperation between humans and technology and sustainability. The validation of the model is limited to the Indian textile industry, and it may be applied to other industries, other geographic regions and other MCDM methods in the future. Practical implications The proposed PMS offers a structured and scalable tool that can be applied by managers and policymakers in evaluating and improving CSC performance according to I5.0 objectives to present a viable recommendation for sustainable and ethical operations. Originality/value This study is among the first to propose and empirically validate a PMS tailored to I5.0-enabled CSCs, advancing performance management literature by extending the BSC logic to incorporate circularity, digital-physical integration and human-centric principles within a unified measurement framework.
Purpose Informed by social exchange and resource-based view theories, this study proposes and tests a model of the association between different types of leadership (transformational (TFL), transactional (TL) and servant leadership (SL)), quality management practices (QMPs), organizational culture, and organizational performance (OP) of drug rehabilitation centers in the Kingdom of Saudi Arabia (KSA). Design/methodology/approach Data were collected from 420 medical staff members at six governmental drug rehabilitation centers located in different regions of KSA. Findings The results showed that TFL and SL styles were positively associated with both QMPs and OP, whereas TL had no significant effect. QMPs positively mediated the relationship between leadership style and OP. Additionally, organizational culture influenced both OP and QMPs; however, it had no effect on leadership styles. Practical implications The findings of this study have management implications that can help healthcare organizations in KSA, including drug rehabilitation centers, adopt the most effective leadership style to improve QMPs and OP. Originality/value This study is among the first to examine the combined effects of TFL, TL, and SL on OP while assessing the mediating role of QMPs. It contributes to the leadership and performance management literature by demonstrating how QMPs translate leadership into improved performance and extends social exchange and resource-based perspectives within the context of drug rehabilitation centers in KSA.
Purpose-This study aims to understand how loneliness at work affects employees' task performance and their involvement with their jobs. Since supervisors often play a central role in shaping employees' experiences, the study also looks at the effect of having shared values with one's supervisor on the proposed relationships at low levels of value congruence. By doing so, the study goes beyond the usual focus on peer relationships and brings attention to the importance of supervisor-employee value alignment in creating a sense of belonging at work. Design/methodology/approach-Employing a quantitative design using PROCESS MACRO, the theoretical model was tested using data collected from 500 mid-level IT managers in two phases. Findings-The results revealed that workplace loneliness significantly undermines both task performance and job involvement among employees. Importantly, the analysis showed that person-supervisor value congruence moderated these relationships. When value congruence was low, the detrimental effects of loneliness were amplified, indicating that a lack of alignment with supervisors makes employees more vulnerable to the emotional costs of loneliness at work. Originality/value-The findings provide an early and novice perspective on loneliness beyond peer-to-peer relationship quality and reflect on the significance of value congruence with their supervisors. Moreover, the proposed model empirically establishes that lonely employees indulge more in negative information than positive by establishing that while positive value congruence does not affect any outcome, missing value congruence worsens the job outcomes.
Purpose This study aims to assess the sustainable development goals (SDG) performance of 13 OPEC countries by 2023, using a comprehensive framework of 14 indicators covering social, environmental and economic pillars. Design/methodology/approach The paper employs gray relational analysis (GRA) and COmplex PRoportional ASsessment (COPRAS), recognized multi-criteria decision-making (MCDM) methods to reveal 13 OPEC countries' SDGs performances. Findings The results indicate that the countries with the best performance are the UAE and Saudi Arabia (ranking in the top tier in both models), while those with the lowest performance are Congo and Angola. A critical tension was identified between environmental cost and socioeconomic capacity, where high-income nations often face high environmental pressure. Research limitations/implications Comparing OPEC countries not only among themselves but also against OECD nations or non-oil-producing developing economies in similar income brackets would offer deeper insights into the specific impact of resource wealth on global sustainability performance. Practical implications High-capacity nations (UAE, Saudi Arabia) must prioritize strategic decoupling of economic growth from environmental degradation through renewable energy and carbon-capture innovations. Low-capacity nations (Nigeria, Congo, Angola) should focus on institutionalizing “SDG floors,” specifically targeting primary education and healthcare to mitigate extreme social deficits. Originality/value This study fills a significant gap in the literature by providing a holistic assessment of SDGs performance for OPEC countries, integrating the social, environmental and economic pillars. Unlike many existing studies that focus on single dimensions (such as energy or economy), this research explores the complex tension between “socio-economic capacity and environmental pressure” within hydrocarbon-dependent economies.
Purpose Agility has become essential in manufacturing with the rise of digital transformation and increasing global uncertainty. Yet, turning strategic aims into concrete actions for implementing and improving agility remains underexplored. This study examines how agility can be operationalised in manufacturing. Design/methodology/approach Grounded in dynamic capabilities and contingency theory, the study identifies and classifies agility enablers and examines their relationship with agility improvement processes through a survey of 206 manufacturing practitioners, analysed using clustering and decision trees. Findings Analysis of agility enablers revealed two practitioner groups: agility promoters and agility supporters. Sector, product development projects, and production strategy shaped agility positioning, while respondent characteristics influenced agility perceptions. The study culminates in a roadmap linking enablers and improvement processes to guide agility implementation. Practical implications The study provides a roadmap to support and prioritise agility improvement projects and initiatives in manufacturing. Originality/value The paper integrates theoretical and empirical insights, offering an updated view of agility enablers in the digital transformation context and a structured approach for their implementation. The roadmap clarifies how agility enablers can be translated into structured improvement processes, supporting performance management in manufacturing organisations.
Purpose This study investigates the influence of people, plan and process (3Ps) related antecedents on the extent of strategy implementation success and its subsequent impact on organizational performance. This study further investigates the moderating role of dynamic capabilities in the relationship between those antecedents and strategy implementation success. Design/methodology/approach The study follows a deductive approach and collected data from middle and senior managerial employees through a questionnaire survey. A total of 282 respondents participated in the study. The statistical software programs IBM SPSS 29, IBM SPSS AMOS 29, PROCESS Macros and Microsoft Excel were used to analyze the data and draw the statistical inferences. Findings The findings reveal that people and plan-related antecedents significantly influence strategy implementation success in organizational contexts. The influence of process related antecedents is also statistically insignificant. This study further reveals the moderating role of dynamic capabilities in the relationships between 3Ps related antecedents and strategy implementation success. The findings contribute to both the strategic management and dynamic capabilities literature and provide managerial guidelines for future strategic decision-making processes. Originality/value Research on strategy implementation is still negligible, and empirical evidence of a holistic approach is rare. This study could be considered one of the pioneering studies to conduct specific holistic or integrated model- or framework-based research to assess the influence of specifically categorized antecedents on strategy implementation success.
Purpose The author identified the substantial knowledge gaps (KGs) by peer-reviewing important research documents in the field of applying trapezoidal fuzzy sets transfer functions to evaluate green supplier performance (GSP) and multi-criteria decision-making (MCDM) statistical methodologies. The identified KGs were validated using an extensive literature survey. As an objective, the author devised a sophisticated GS-MLH evaluation model, experienced advanced techniques based on TFs to evaluate the green supply chain performance of supplier industries. Design/methodology/approach The authors developed an MCDM robust integrated approach (RIA), combining the hybrid with MOOSRA using dominance theory to address the supplier evaluation problem. To quote the least fuzzy data from experts against multi-level hierarchical (GS-MLH) model and transform the fuzzy data into crisp values, the authors developed an MCDM mathematical equation, which evaluates the ARs for first-level architectures using fuzzy linguistic assessment for second-level GSC architectures. Findings This approach calculates the performance score of GS alternatives as a percentage. Ultimately, the research findings are supported by an empirical case study conducted in the automobile parts manufacturing business, providing evidence for the practical applicability of the research. Originality/value The author discovered a lack of advanced assessment models for monitoring the performance of GS using the GS-MLH model, as First KG, is accommodated. (2). As the second KG noted that previous research could not provide any proof of using MCDM mathematical, statistical equations to calculate crisp appropriateness ratings (ARs) for first-level designs using linguistic input provided by experts for second-level GSC architectures, this is fulfilled. (3) As the third KG, the authors determined that no robust statistical approach in MCDM can accurately measure the performance of Generalized Systems (GSs) in terms of percentage, except for crisp values based on dominance theory, which is addressed.
Purpose This study aims to develop a research model to analyze the relationship between digital transformation and lean thinking principles in the higher education sector. Specifically, this work seeks to explore the drivers, barriers and lean outcomes of digital transformation in public higher education institutions (HEIs) that implement digital initiatives to improve their operation. Design/methodology/approach A survey study was conducted using a structured questionnaire administered to the administrative staff of a HEI located in southern Brazil, yielding a final valid sample of 387 respondents. The main data analysis techniques employed included exploratory and confirmatory factor analysis, discriminant validity and structural equation modeling. Findings The results highlight technological capability as a key driver of digital transformation, while the lack of political and senior management support and the resistance to innovation and change among employees are identified as barriers to digital transformation in HEIs. Furthermore, lean outcomes associated with digital transformation include reduction of errors and delays, continuous improvement, enhanced work performance and improved customer value (experience). Practical implications This research offers a conceptual model which was empirically validated in a HEI to illustrate the association of digital transformation and lean thinking principles. Our findings can be leveraged by HEIs' top management interested in the adoption of digital technologies to attain lean outcomes in their institutions and by policy makers to foster further technological development in the educational sector. Originality/value We narrow the relation of two prominent perspectives in the context of higher education. Our study shows that digital transformation is positively associated with lean thinking principles and directly contributes to modernizing administrative processes by reducing errors and waiting time, by enhancing organizational performance and by improving customers' value.
Purpose This study aims to examine the influence of Clear and Measurable Goals (CMG), Incentives (ICV), and Performance Feedback (PFB) on the effectiveness of Performance Management Systems (EPMS) and Performance of Public Sector Organizations (PPO) in Indonesia. Additionally, it explores the mediating role of PMS effectiveness in this relationship. Design/methodology/approach The research uses a quantitative approach, collecting primary data through a survey distributed to 221 Heads of Departments within local government agencies (SKPD) in East Java Province, Indonesia. To compare the relationship between PMS effectiveness in SKPDs with high and low levels of customer pressure, a multigroup analysis is applied using a recently proposed evaluation procedure designed for PLS-SEM. Findings The study reveals that ICV significantly and positively affect PPO. In contrast, CMG and PFB do not show significant direct effects. However, EPMS mediates the relationship between CMG, ICV, and PFB with PPO. The findings highlight EPMS as a critical mechanism linking CMG, ICV, and PFB to PPO, providing both theoretical refinement for PMS research and actionable insights for strengthening performance governance in Indonesian public organizations. Originality/value This study offers novel insights into performance management literature by providing empirical evidence from the Indonesian public sector and by integrating goal-setting theory, agency theory, and institutional theory to explain how performance management practices translate into organizational performance through the EPMS. This study introduces the mediating effect of PMS effectiveness, offering a deeper understanding of how CMG, ICV, and PFB influence organizational performance in the public sector.
Purpose This study develops a context-specific digital transformation (DT) maturity model and phased implementation roadmap tailored for developing countries, using Iran and MAPNA Group as the case. By applying the TOE framework, it overcomes the limitations of existing technology-centric models designed primarily for advanced economies. The core contribution to performance management is identifying performance-critical DT enablers and clarifying their causal effects on productivity and organizational outcomes under severe resource, institutional and infrastructural constraints typical of developing contexts.Design/methodology/approach A novel hybrid MCDM methodology integrating Fuzzy Delphi Method (FDM), DEMATEL-ISM and best-worst method (BWM) within a unified framework was employed. Expert judgments from senior managers and engineers at MAPNA Group, Iran, provided empirical data. Causal relationships, hierarchical structures and priority weights were derived through systematic matrix calculations and consistency checks, ensuring methodological rigor and practical relevance.Findings Organizational culture and leadership emerged as the strongest causal drivers of successful DT. Key independent enablers include leadership commitment, employee ICT competencies, and data-driven culture, whereas smart supply chains and integrated digital planning are highly dependent performance outcomes. The resulting roadmap emphasizes that organizational readiness and low-cost digital initiatives (e.g. cloud solutions) must precede capital-intensive Industry 4.0 technologies in resource-constrained and institutionally complex environments.Originality/value Most DT maturity models have been developed for advanced economies and fail to address the technological, organizational and environmental barriers prevalent in developing countries. This study delivers the first empirically validated, TOE-based maturity model and prioritized roadmap explicitly designed for such contexts. Its key contribution to performance management is providing a practical decision-support tool that identifies high-impact enablers, reveals causal performance drivers, and offers an actionable roadmap, enabling managers to systematically improve productivity, efficiency, resilience and Industry 4.0 readiness in resource-constrained settings.
Purpose The objective of the study is threefold: first, to examine the impact of management quality on employees' task non-performance; second, to explore the mediating role of counterproductive behaviors in this main relationship; and finally, to assess the moderating role of work-related stress in the effect of management quality on these deviant behaviors. Design/methodology/approach The sample consists of 604 employees from a retail company in Morocco. Structural Equation Modeling (SEM) was conducted to identify the relationships between management quality, counterproductive work behaviors, work-related stress, and task non-performance. Findings The results indicated that management quality was negatively correlated with counterproductive behavior and task non-performance. Employees' counterproductive behaviors mediate the relationship between management quality and task non-performance and also serve as predictors of the latter. The negative relationship between management quality and counterproductive behavior is stronger when employees experience work-related stress, highlighting the moderating role of stress in this dynamic. Research limitations/implications First, the generalization of the results was limited, as the characteristics of participants in the retail sector are not identical to those in other industries. Second, applying a quantitative approach to a theory traditionally used in qualitative research risks losing certain contextual nuances, and does not capture the richness of underlying human and organizational interactions. Third, although the six levers of management quality were measured independently, they were integrated as second-order variables in a comprehensive model. Finally, the study incorporated only two dimensions of individual work performance. It would be interesting to measure contextual performance and adaptive performance. Practical implications Companies need to optimize management quality to enhance individual work performance by training managers to foster a healthy work environment. Furthermore, the moderating role of stress highlights the importance for companies to implement mechanisms for measuring and monitoring employee stress through regular surveys, individual interviews, and other assessment tools. Regarding the mediating role of counterproductive behaviors, companies should adopt a proactive approach by identifying early warning signs of such behaviors and taking timely action. This includes implementing mentoring programs, providing psychological support, establishing anonymous feedback mechanisms, and creating regular discussion spaces where employees can freely express their concerns. Originality/value Our study offers an innovative perspective on the effects of management on employee behavior. Its originality lies in its integrative approach, which combines management quality, workplace stress, and counterproductive behaviors to explain task performance within a single model. It stands out from previous research, which addresses these concepts separately or in a binary manner, through its adoption of a systemic perspective that illustrates the mediating role of counterproductive behaviors and the moderating effect of workplace stress.
Purpose - This study identifies key criteria for strengthening resilience and sustainability in retail through a human-centric approach to Warehousing 5.0 (WH5.0). It analyzes how this model improves operational efficiency, environmental impact, and adaptability. To this end, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. Design/methodology/approach - A hybrid approach integrating the best - worst method (BWM), interpretive structural modelling (ISM), and cross-impact matrix multiplication applied to classification (MICMAC) analysis was utilized to examine critical adoption criteria for human-centric WH5.0. The BWM prioritizes the relative importance of the criteria, ISM reveals the hierarchical structures and interrelationships among them, and MICMAC classifies the criteria based on their driving and dependence power. Findings - This study demonstrates that integrating human-centric WH5.0, supply chain resilience (SCRes), and sustainable operations (SO) into retail logistics improves operational adaptability and sustainability. By prioritizing key criteria, strategies are identified for strengthening resilient and responsible warehouse operations. This work drives the long-term robustness of the retail supply chain (RSC). Practical implications - The proposed framework enables decision-makers to gain deeper insights into how different adoption criteria influence warehouse performance in RSC. It provides a comprehensive, performance-oriented evaluation that simultaneously considers operational efficiency, resilience, environmental sustainability, and social performance, supporting informed decision-making in the implementation of human-centric WH5.0. Originality/value - The traditional RSC is increasingly being challenged by volatility and sustainability pressures. This study contributes by conceptualizing warehouse performance in retail as a human-centric and resilience-driven process, integrating WH5.0, SCRes, and SO into a unified performance management framework.
Purpose This study evaluates the transformational efficiency of the California State University (CSU) system by examining how financial and human resources are converted into academic and long-term socioeconomic outcomes. It addresses challenges in performance assessment within multi-campus public university systems characterized by institutional heterogeneity and resource constraints. Design/methodology/approach An output-oriented Data Envelopment Analysis (DEA) model with assurance-region constraints is applied to twenty-three CSU campuses. To enhance reliability in a small-sample setting, the analysis integrates bootstrap resampling, stability-radius diagnostics, and a semi-parametric Corrected Ordinary Least Squares (COLS) frontier for cross-method validation. A second-stage Tobit model examines institutional and socioeconomic determinants. Findings Efficiency varies substantially across campuses despite shared governance, indicating that performance is shaped by structural and contextual differences. Instructional spending is positively associated with efficiency, while a higher proportion of economically disadvantaged students is negatively related to measured performance. Scale effects contribute to disparities. Robustness diagnostics show that most efficiency classifications are stable, although some frontier positions are sensitive to perturbation. Practical implications The results support peer-based benchmarking, targeted resource prioritization, and equity-sensitive performance evaluation. DEA-derived targets provide actionable guidance for campus-level improvement, while robustness diagnostics encourage cautious interpretation of rankings in accountability-driven public systems. Originality/value The study advances higher education performance measurement by integrating deterministic and semi-parametric frontier methods with robustness diagnostics in a unified framework. It extends DEA applications by incorporating long-term outcomes and cross-method validation, and reframes efficiency as a context-sensitive construct shaped by equity, scale and institutional heterogeneity.
Purpose The research investigates the role of big data analytics (BDA) in enhancing sustainability reporting quality and improving corporate financial performance in Jordan's manufacturing sector. By integrating BDA into environmental, social and governance (ESG) reporting frameworks, the study highlights how digital technologies strengthen corporate accountability, improve data transparency, and support compliance with global sustainability standards. Design/methodology/approach This study employs a mixed-methods approach, combining survey data from 224 top managers in Jordanian manufacturing firms with partial least squares structural equation modeling (PLS-SEM). The analysis explores the relationships between BDA capabilities, sustainability reporting quality (SRQ) and corporate financial performance (CFP). Findings The results confirm that BDA significantly improves SRQ by increasing data accuracy, transparency and timeliness, thereby positively influencing CFP. Integrating BDA into ESG reporting optimizes sustainability disclosures, reduces greenwashing risks, fosters investor confidence and strengthens regulatory compliance. SRQ also plays a mediating role, amplifying the impact of BDA on financial performance. Overall, the study underscores the transformative potential of BDA in closing the credibility gap in sustainability reporting and aligning corporate disclosures with international ESG frameworks. Originality/value This research provides empirical evidence on the role of BDA in transforming sustainability reporting and corporate performance in a developing economy. It demonstrates how data-driven ESG practices bridge information asymmetries, enhance regulatory alignment and drive sustainability strategies in Jordan's manufacturing sector.
