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Easily employable quantitative supply chain complexity (SCC) measures considering the significant dimensions of complexity as well as the drivers that represent those dimensions are limited in the literature. The purpose of this paper is to propose an integrated interpretive structural modeling (ISM) and a graph-theoretic approach to quantify SCC by a single numerical index considering the interdependence and the inheritance of the SCC drivers.

In total, 18 SCC drivers identified from the literature are clustered according to the significant dimensions of complexity. The interdependencies established through ISM and inheritance values of SCC drivers are mapped into a Variable Permanent Matrix (VPM). The permanent function of this VPM is then computed and the resulting single numerical index is the measure of SCC.

A scale is proposed by computing the minimum and maximum threshold values of SCC with the help of expert opinions of the Indian automotive industry. The complexity of commercial and passenger vehicle sectors within the automotive industry is measured and compared using the proposed scale. From the results, it is identified that the number of suppliers, increase in spare-parts due to shortened product life-cycle and demand uncertainties increase the SCC of the passenger vehicle sector, while number of parts, products and processes, variety of products and process and unreliability of suppliers increase the complexity of the commercial vehicle sector. The result indicates that various SCC drivers have a different impact on determining the SCC level of these two sectors.

The authors propose an integrated method that can be readily applied to measure and quantify SCC considering the significant dimensions of complexity as well as the interdependence and the inheritance of the SCC drivers that contribute to those dimensions. This index further helps to compare the complexity of the supply chain which varies between industries.

Knowledge sharing is increasingly important in today’s information age and extant literature considers knowledge hoarding as an undesirable form of knowledge-withholding behavior. As knowledge hoarding is a generic, nonintentional behavior, specific attitudes and organizational processes are unlikely to curb it. Hence, the study postulates that reflection, awareness and group identification are necessary to combat innate tendencies toward knowledge hoarding. To test these hypotheses, this study aims to explore the role of mindfulness and relational systems in reducing employees’ knowledge hoarding by increasing their meaning-making through work.

The study results are based on a cross-sectional survey of 203 employees in India working for different organizations. Standardized scales were used for capturing data, and partial least squares structural equation modeling was used for analysis.

Mindfulness and team cohesion were positively related to an increase in meaning-making through work. Supervisor support improved perceptions of team cohesion. However, contrary to expectations, team cohesion and meaning-making through work were positively, rather than negatively, related to knowledge hoarding.

The cross-sectional nature of the study prevents strong inference of causal relationships. Future studies may use a longitudinal design to test the relationships.

It highlights the role of meditation sessions and supervisory support in improving employees’ perceptions of meaning-making through work. It exhorts managers to systematically assess the impact and societal perceptions regarding knowledge hoarding rather than automatically assume a negative attitude.

To the best of the authors’ knowledge, this is the first study to investigate the impact of mindfulness, team cohesiveness and meaning-making through work on employees’ knowledge hoarding behaviors. The study results suggest that knowledge hoarding may be perceived positively in certain cultures. It highlights the inconsistencies in the conceptualization and operationalization of knowledge hoarding and suggests the need for better construct delineation and empirical studies related to knowledge hoarding.

Workplace deviant behaviors (WDBs) have a significant negative impact on firms. Present study explores the role of employees’ perception of firms’ internal corporate social responsibility (internal CSR) in reducing their intention to engage in WDB. Social exchange theory (SET) and job demand-resource (JD-R) model form the conceptual underpinning of the study.

Hypotheses were developed based on a comprehensive literature review and tested on employees working in various public and private sector organizations in India. AMOS and SPSS PROCESS macro were used to test the conceptual model.

Employees’ perception of firms’ internal CSR reduced their intention to engage in WDB. Occupational strain was confirmed as a mediator in the above mentioned relationship. Further, the study also establishes internal CSR as an antecedent to increased perceptions of procedural justice.

Managers may leverage internal CSR communication as a tool to minimize WDB at the workplace. Moreover, it may also be used to reduce occupational strain and strengthen the perceptions of fairness among employees.

Very limited research is available on internal CSR and WDB. Through this study authors contribute to the nascent literature by affirming the negative relationship between internal CSR and WDB using the SET and JD-R model.

Artificial intelligence (AI)-based human capital management (HCM) software solutions represent a potentially effective way to leverage and streamline a bank’s human resources. However, despite the attractiveness of AI-based HCM solutions to improve banks’ effectiveness, to the best of the authors’ knowledge, there are no current studies that identify critical success factors (CSFs) for adopting AI-based HCM in the banking sector. This study aims to fill this gap by investigating CSFs for adopting AI-based HCM software solutions in the banking sector.

Full consistency method methodology and technology–organization–environment, economic and human framework are used for categorizing and ranking CSFs.

The study identifies the technological and environmental dimensions as the most and least important dimensions for AI-based HCM adoption in banks. Among specific CSFs, compatible technology facilities, sufficient privacy and security and relative advantages of technology over competing technologies were identified as the most important. Implementation of AI-based HCM solutions requires significant outlays of resources, both human and financial, for banks.

The study provides bank administrators a set of objective parameters and criterion to evaluate the feasibility of adopting a particular AI-based HCM solution in banks.

The automotive industry appears to overcome much of its obstacles, despite the constant struggle facing COVID-19. The pandemic has resulted in significant improvements in the habits and conduct of consumers. There is an increased preference for personal mobility. In this dynamic environment with unexpected changes and high market rivalry, automotive supply chains focus more on executing responsive strategies with minimum costs. This paper aims to identify and model the drivers to the responsiveness of automotive supply chain.

Seventeen drivers for supply chain responsiveness have been identified from the extensive literature, expert interview. An integrated methodology of fuzzy decision-making trial and evaluation laboratory–interpretive structural modelling (DEMATEL–ISM) is developed to establish the interrelationship between the drivers. The cause–effect relationship between the drivers was obtained through fuzzy DEMATEL technique, and a hierarchical structure of the drivers was developed using the ISM technique.

The result of the integrated methodology revealed that strategic decision-making of management, accurate forecasting of demand, advanced manufacturing system in the organisation and data integration tools are the critical drivers.

This study has conceptual and analytical limitations. In this study, a limited number of drivers are examined for supply chain responsiveness. Further research may examine the role of other key performance indicators in the broad field of responsiveness in the automotive supply chain or other industry sectors. Future study can uncover the interrelationships and relative relevance of indicators using advanced multi-criteria decision-making methodologies.

The authors proposed an integrated methodology that will be benefitted to the supply chain practitioners and automotive manufacturers to develop management strategies to improve responsiveness. This study further helps to compare the responsiveness of the supply chain between various automotive manufacturers.

The purpose of this paper is to identify various factors influencing additive manufacturing (AM) implementation from operational performance in the Indian manufacturing sector and to establish the hierarchical relationship among them.

The methodology includes three phases, namely, identification of factors through systematic literature review (SLR), interviews with experts to capture industry perspective of AM implementation factors and to develop the hierarchical model and classify it by deriving the interrelationship between the factors using interpretive structural modeling (ISM), followed with the fuzzy Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis.

This research has identified 14 key factors that influence the successful AM implementation in the Indian manufacturing sector. Based on the analysis, top management commitment is an essential factor with high driving power, which exaggerates other factors. Factors, namely, manufacturing flexibility, operational excellence and firm competitiveness are placed at the top level of the model, which indicates that they have less driving power and organizations need to focus on those factors after implementing the bottom-level factors.

Additional factors may be considered, which are important for AM implementation from different industry contexts. The variations from different industry contexts and geographical locations can foster the theoretical robustness of the model.

The proposed ISM model sets the directions for business managers in planning the operational strategies for addressing AM implementation issues in the Indian manufacturing sector. Also, competitive strategies may be framed by organizations based on the driving and dependence power of AM implementation factors.

This paper contributes by identification of AM implementation factors based on in-depth literature review as per SLR methodology and validation of these factors from a variety of industries and developing hierarchical model by integrative ISM-MICMAC approach.