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With the emergence of big data analytics and the importance of analytics-driven decisions, the travel industry is swiftly jumping on and adopting the bandwagon. However, research in this domain is limited. Accordingly, the present research seeks to understand how big data analytics capabilities (BDAC) add value to tourism supply chains (TSCs) and can dynamic capabilities (DC) improve the triple bottom line.

Data from 218 valid responses were collected from different Indian tourism industry units. Confirmatory factor analysis (CFA) was applied to confirm the constructs, followed by partial least squares structural equation modelling (PLS-SEM) to check the mediating effect of DC on TSCs performance.

The findings show that BDAC significantly influence the performance of TSCs and that DC plays a critical role in strengthening the impact of BDAC on TSCs' economic performance. These results corroborate that DC plays a key moderating role.

This study contributes significantly to the tourism sector in India, where tourism is a key contributor to the country's gross domestic product. Theoretically, this study contributes to the resource-based view (RBV) and practically encourages professionals in the tourism sector to promote the use of BDAC to enhance the performance of TSCs.

The originality of the study is that it has tried to comprehend the moderating role of dynamic capabilities which impact BDAC to improve TSC performance.

This study presents evidence of the role of performance measurement and management (PMM) in sustainable supply chain governance. This study tests a model hypothesizing whether it is the PMM itself or the mediating effect of supply chain governance that is essential for both business and sustainability performance.

This study builds on a survey of 274 SMEs in Finland.

The findings indicate that PMM does not directly contribute to SMEs’ business or sustainability performance. Supply chain governance mediates the relationship between PMM and business performance. Business performance also enhances sustainability.

These findings can guide managers in managing company relationships with customers and suppliers. The mediating role of supply chain governance highlights the potential of PMM to enhance performance. Without supply chain governance, the PMM, while efficient in traditional business practices, may lose its effectiveness because of the pressure to advance sustainability values within firm operations.

The role of PMM in enhancing supply chain sustainability is frequently overlooked in the existing research, necessitating an empirical evaluation of PMM’s impact on supply chain sustainability. This study addresses this gap by focusing on the SME context, where the pressure to adopt sustainable practices is increasing, yet SMEs employ PMM less frequently than larger firms.

This paper aims to examine the influence of big data (BD) on human resource management (HRM). It defines how these data can be a useful tool in the decision-making process of companies’ human resources to obtain a sustainable competitive advantage.

This paper emphasizes the need to develop a holistic approach to emphasize these relations. Starting from these observations, the document proposes empirical research employing Eurostat data to test the benefits of BD in HRM decisions that optimize the relationship between training, productivity, and well-being.

The findings estimate HRM decisions and their impact in a broader macroeconomic and microeconomic perspective.

BD research is emerging as a crucial discipline in human resources. To overcome this problem, the paper develops an analysis of the literature on cleaner production and sustainability context; it creates a conceptual framework to clarify whether the existing studies consider the growing intensity of BD on human resources.

This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI).

Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model.

This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP.

This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain.

This study aims to measure the integrated impact of big data analytics and artificial intelligence (BDA-AI) adoption by using the ChatGPT generative AI online platform as a BDA-AI tool on the operational and environmental performance.

This study considers Taiwanese professionals who engage with ChatGPT; the sample consists of 388 online users.

This study’s main finding is that the considered antecedents – including technological, organizational and environmental contexts, tangible resources and workforce skills – are significantly associated with BDA-AI adoption. Notably, BDA-AI adoption exhibits a significant relationship with operational performance, environmental performance and environmental process integration. Moreover, environmental process integration is significantly correlated with environmental performance. Lastly, operational performance is significantly correlated with environmental performance.

This study contributes to the heavily lacking but developing literature on the antecedents and consequences of BDA-AI adoption. Its theoretical foundation consists of the technological-organizational-environmental model, Roger’s diffusion of innovation theory and resource-based view theory.

Reverse logistics (RL) has become integral in modern supply chains, with many companies investing in circular economy (CE), a recuperative and effective industrial economy. The traditional linear model triggered many negative environmental consequences such as climate change, ocean pollution, loss of biodiversity and land degradation. The development of RL strategies that support the transition between RL to CE is crucial. The purpose of this paper is to connect RL with CE in the context of Industry 4.0 and develop a hierarchal structure to explore the relationship between RL and CE critical success factors in the context of Industry 4.0.

This study used both qualitative and quantitative approach. Literature review in collaboration with the Delphi method is used to identify and validate critical success factors. Then, the ISM-based model and MICMAC method were used to determine the relationship between CE and RL success factors and its driving and dependence power.

This study result shows that waste reduction, skilled employees and expert's involvement and top management commitment and support will provide guidelines and paths for implementing CE and RL, leading to the competitiveness of a firm.

The findings provide managerial insight, particularly useful to third-party logistics companies' managers who are looking to implement RL and CE, to help prioritize where to invest company resources to generate prime difference. Furthermore, this study also identified Industry 4.0 technologies, which would tackle top identified critical success factors within the hierarchical model such as block chain and digital platforms.

This paper contributes to the literature by exploring the connection between RL and CE in the context of Industry 4.0 that determines the critical success factors enabling sustainable inter-firm collaboration.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

In the digital age, emerging technologies have affected every industry. Information and communications technology and digital technologies have transformed traditional supply chains into smart and more resilient ones, enabling effective management of challenges. Given the importance of the two topics, namely sustainable supply chain management and Industry 4.0 in supply chain management, on the one hand, and the dearth of theoretical research performed in this area on the other, this study aims to propose a conceptual model on a sustainable digital supply chain management in manufacturing companies.

This study utilized a qualitative approach. First, an in-depth review of the relevant literature was done. Then, following a multi-grounded theory methodology, relevant data were gathered by reviewing 92 papers and conducting nine semi-structured interviews with industry experts. These data were analyzed using the MAXQDA software.

A total of 41 concepts, ten sub-components and three main components (dimensions) were extracted, and the proposed conceptual model was presented. Finally, based on this conceptual model, three propositions were suggested.

Considering that the present study was performed in the context of Iranian manufacturing companies, caution should be exercised in relation to the generalizability of the obtained results. Also, due to the problems in the digital technology infrastructure and the limited use of these technologies by manufacturing companies (emphasized by the interviewees), this study focused on the theoretical dimension of using digital technologies by these companies.

The proposed comprehensive model can help academicians as well as practitioners to focus better and explore the variables and constructs of the model, paving the way toward successful implementation of digital technologies in the manufacturing supply chain.

To the best knowledge of the authors, this study is among the first of its kind which presents a holistic and comprehensive digital supply chain model aimed at guiding companies to consider sustainability from all the main dimensions and their relevant indicators.

In recent years, counterproductive knowledge behavior (CKB) and its types have received increasing interest in knowledge management as the degree of knowledge sharing and innovation in enterprises continues to increase. A rapidly growing number of studies have shed light on the important antecedents and consequences of employees’ CKB. However, the various labels, conceptualizations and operationalizations of CKB have fragmented this body of research. This study aims to systematically integrate the effects of the six types of organizational characteristics on CKB and further draws more general conclusions based on the results of previous studies.

Based on a survey of 103 effect values responsible for 52 CKB samples, the authors use the ABC theory to explore the effects of the six types of organizational characteristics on CKB. Moderator analysis were performed to resolve inconsistencies in empirical studies and understand the contexts under which CKB has the strongest or weakest effect.

The results showed that task interdependence and a positive organizational atmosphere, in general, negatively affect employees’ CKB in the moderation analysis. In contrast, workplace discomfort, negative organizational atmosphere, internal competition and time pressure positively and partly affect employees’ CKB. The direction and magnitude of these effects were affected by emotional factors, knowledge personnel types and sample sources. Discussing the theoretical, methodological and practical implications of these findings can offer a guiding framework for future research.

Better control of employees’ CKB is not achieved by adjusting organizational characteristics alone but by combining personal characteristics and mood changes with it to balance organizational characteristics and CKB. Furthermore, the large-sample joint study integrated the conceptual definition of CKB. The multivariate data study provided more reliable conclusions and a solid theoretical foundation for CKB research areas.

This study aims to examine various factors influencing the inclination of students toward Green Entrepreneurial Intention (GEI), including University Support (USP), Family Support (FSP), Religiosity (REL), Commitment to Environment (CEN) and Green Entrepreneurial Motivation (GEM), as well as Attitude towards Green Entrepreneurship (AGM).

Data were collected through an online survey of Muslim students at Indonesian Islamic universities. A five-point Likert scale was used in the online questionnaire, with 419 processed data. Partial least squares structural equation modeling was used to analyze the data and test the relationship between the variables.

The results showed that AGM, CEN and REL impacted GEM. AGM was influenced by FSP but not by USP while GEI was significantly influenced by AGM, FSP and USP.

The limitation of the study is the composition of the sample, consisting solely of Islamic university students. Another limitation is the variables used. Future studies should analyze other factors, such as role models, green knowledge or family background.

This study provided fresh perspectives by empirically establishing a framework for assessing GEI, considering REL variables, an unexplored area conceptually. Practically, it helped to advance sustainable entrepreneurship education, particularly in Islamic universities. Accordingly, it provided several practical contributions for universities to develop curricula that better support green entrepreneurship among students.

This study represented the first investigation into the influence of REL on GEI, specifically among university students. Furthermore, Stimuli, Organism and Response theory was used as a foundation for the development of the diverse variables under investigation.