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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 examine the relationship between accounting conservatism and pledging of shares by controlling shareholders of a firm to obtain a loan. The pledging of shares by the controlling shareholders of a firm results in alterations to the payoff and risk structure for these shareholders. Since accounting numbers have valuation implications, pledging of shares by a controlling shareholder has an impact on accounting policy choices made by the firm. The purpose of this paper is to examine the impact of controlling shareholder share pledging to obtain a loan on a specific accounting policy choice, namely, conservatism.

The paper uses a large data set from India comprising 14,786 firm years consisting of 1,570 firms belonging to 58 industries for a period of 11 years (2009–2019). The authors use ordinary least square regression with robust standard errors. The authors conduct robustness checks and the results are consistent across alternative statistical methodologies and alternative measures of the primary dependent and independent variables.

The primary results show that pledging of shares by the controlling shareholders results in higher conditional conservatism and lower unconditional conservatism. Further analysis reveals that the relationship is stronger when the controlling shareholder holds a majority ownership in the firm. Additionally, the results show that for business group affiliated firms, which are unique to developing countries, both the conditional and the unconditional conservatism are incrementally lower when the controlling shareholder pledges the shares. For family firms with a family member as CEO, the conditional conservatism is incrementally higher and the unconditional conservatism is incrementally lower. Finally, the authors show that the results hold when the pledge intensity variable is measured with a one-year lag and finally, the authors show that conditional conservatism is incrementally higher in the year of the increase in the pledge and the year after, but there is no such incremental impact on unconditional conservatism.

The research is limited to the listed firms in India. Since majority of the listed firms are controlled by families and the family firms around the world are heterogeneous the findings of the research may not be applicable to other countries.

The study has implications for policy-making and monitoring of the pledging by the controlling shareholders. It also helps the investors in making investment decisions with respect to family firms in India.

The study is unique as it focuses on the relationship between pledging of shares by the controlling shareholders and its impact on accounting conservatism. To the best of the authors’ knowledge, this is the first research integrating these two aspects.

The main aim of this study is to explore the relationship between information system flexibility and dynamic capabilities to build sustainable and net zero supply chains under the influence of environmental dynamism.

We have formulated a self-administered survey, with 359 participants contributing responses. Prior to delving into foundational assumptions, such as homoscedasticity and normality, a nonresponse bias analysis was executed. The integrity of the data, in terms of reliability and construct validity, was gauged using confirmatory factor analysis. Subsequent regression outputs corroborated all the proposed assumptions, fortifying the extant scholarly literature.

The empirical findings of this research underscore a positive correlation between Information system flexibility, dynamic capabilities and a net zero supply chain, especially in the context of environmental dynamism. Data sourced from the cement manufacturing sector support these observations. We also found that environmental dynamism moderates the relationship between data analytics capability and sustainable supply chain flexibility but does not moderate the relationship between Resource flexibility and sustainable supply chain flexibility. Additionally, this research strengthens the foundational principles of the dynamic capability theory.

The conceptual framework elucidates the interplay between information system flexibility, dynamic capabilities, and sustainable supply chain flexibility, emphasizing their collective contribution towards achieving sustainable chain net zero, introducing environmental dynamics as a moderating variable that augments the scholarly discourse with a nuanced layer of analytical depth.

Employee burnout is increasingly coming under attention due to its negative impact on employee well-being and organisational effectiveness. This study, a systematic review, aims to evaluate the role of servant leadership and its mediators in preventing and mitigating against burnout experiences in organisations.

A preferred reporting items for systematic review and meta-analyses (PRISMA) was conducted using three databases, Academic search Complete, Embase and Scopus, in addition to bibliography searches. Articles were included if they reported on primary data, in English from inception to 2023. The mixed methods critical appraisal tool was used to assess the quality of articles, and a narrative synthesis was used to report results.

The search strategy yielded 4,045 articles, of which (N = 17), with total sample size of (N = 10,444) are included. Findings suggest that servant leadership is predictive of burnout, and that several mediators impact this relationship. Most studies were conducted in health care (n = 8) and banking (n = 3), and while the quality of the studies was mostly high (64%), the methods used were mainly descriptive and cross-sectional, which limits the extent to which causality can be inferred. A theory of change is provided based on the findings from this review and integrated with the extant literature on servant leadership theory, and can be used by organisations to support the policy, training and practice of servant leadership to reduce burnout.

Servant leadership is predictive of burnout; however, further research needs to be undertaken in this important emerging area.

To develop a fuzzy causal model of enterprise flexibility dimensions in a case study of Indian pharmaceutical industry.

The eight dimensions of enterprise flexibility were identified based on literature review. Fermatean fuzzy decision-making trail and evaluation laboratory (FF-DEMATEL) technique is applied to develop the cause-and-effect interrelationship model among various enterprise flexibility dimensions.

The information technology flexibility, supply chain flexibility, technical flexibility and marketing flexibility are found to be causing/influencing other flexibilities and contributing to overall enterprise flexibilities. Therefore, more attention needs to be paid to develop and sustain them for competitive advantage.

Fermatean fuzzy sets offer more flexibility and more accurate handling complex uncertain group decision making. FF-DEMATEL is a more accurate method to develop inter-dependencies and causal model than ISM, TISM. Ratings from the limited number of decision experts (DEs) from few pharmaceutical firms were done. Future study should take bigger sample of firms and more number of DEs to generalize the findings.

The model will help managers in pharmaceutical industry to prioritize the dimensions of enterprise flexibility to achieve agility, responsiveness, resilience and competitive advantage.

To the best knowledge of the authors, causal modeling enterprise flexibility dimensions using FF-DEMATEL has been studied for the first time in a developing economy context.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).