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This study proposes a methodology based on data source triangulation to measure the “strategic fit” for the automotive supply chain.

At first, the authors measured the responsiveness of the Indian automobile supply chain, encompassing the top ten major automobile manufacturers, using both sentiment and conjoint analysis. Second, the authors used data envelopment analysis to identify the frontiers of their supply chain. The authors also measured the supply chain's efficiency, using the balance sheet. Further, the authors analyzed the “strategic fit” zone and discussed the results.

The results indicate that both the proposed methods yield similar outcomes in terms of strategic fitment.

The study outcomes facilitate measuring the strategic fit, thereby leveraging the resources available to align. The methodology proposed is both easy to use and practice. The methodology eases time and costs by eliminating hiring agencies to appraise the strategic fit. This valuable method to measure strategic fit can be considered feedback for strategic actions. This methodology could also be incorporated possibly as an operative measurement and control tool.

Data triangulation meaningfully enhances the accuracy and reliability of the analyses of strategic fit. Data triangulation leads to actionable insights relevant to top managers and strategic positioning of top managers within a supply chain.

This paper aims to propose a decision-making framework by investigating the impact of perceived risk and computer self-efficacy on the intention to use online stock trading. Furthermore, it demonstrates the mediation effect of attitude and perceived risk as well as the moderating effect of financial literacy.

An integration of two popular models, technology acceptance model (TAM) and theory of planned behaviour (TPB), is used to provide a sound theoretical base and enhance the understanding of investors’ behaviour towards online trading platforms. The proposed hypothesised model was examined using structural equation modelling.

The results obtained from this study indicate that all variables, except subjective norms, had a significant impact on investors’ intention to trade online. Perceived risk was found to be a partial mediator between computer self-efficacy and the intention of investors. Finally, financial literacy was also found as a significant moderator for online trading intention of investors.

This study shows the significance of using the TAM and TPB together to provide a comprehensive understanding of the factors that influence an investor’s behaviour in adopting and using technology for online trading. The hybrid approach of TAM and TPB could be considered for a more nuanced and complete understanding of technology adoption and usage in risky affairs like investment decisions. Again, the significant moderating role of financial literacy provides a lance to look into the scope for improvements in investment decision-makings.

The paper develops an assessment framework for analysing the variables based on the hybrid approach for online trading intention in the context of a developing country.

Business models (BMs) are becoming increasingly crucial for value creation in the healthcare sector. The study explores the conceptualization and application of BM concepts within the healthcare sector and investigates their evolution in emerging economies (EEs) and developed economies (DEs). This study aims to uncover these two contexts' shared characteristics and unique variances through a comparative analysis.

The paper systematically investigates and consolidates the literature on healthcare by employing the antecedents, decisions and outcomes (ADO) framework and finally examines 71 shortlisted articles published between 2003 and 2022.

The recognition of the BM within healthcare is increasing, both in EEs and DEs. EEs prioritize value creation and capture through cost efficiency, while DEs focus on innovation. Key theories employed include a resource-based view, the network theory and the theory of innovation. Case studies are commonly used as a methodology. Further research is needed to explore the decisions and outcomes of BMs.

The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

Beyond comparing and highlighting gaps in BMs between EEs and DEs, benchmarking DE's healthcare business models (HBMs) helps healthcare organizations in EEs align their practices, mitigate risks and establish efficient healthcare systems tailored to their specific contexts. The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

The study analyzes HBMs using an SLR framework perspective and provides practical implications for academicians and practitioners to enhance their decision-making.

The use of social media is becoming increasingly important for entrepreneurial marketing as a way to gain psychological empowerment through female entrepreneurship. The participation of female entrepreneurs on social media has witnessed an increasing trend. The purpose of this study is to understand the impact of social media participation on female entrepreneurs towards digital entrepreneurship intention and their psychological empowerment.

This cross-sectional study integrates the Stimulus–Organism–Response framework with uses and gratification theory to understand and determine a theoretical framework in understanding the importance of social media for female entrepreneurship in the contemporary digital era. To ensure internal consistency of the latent constructs, this study determines Cronbach’s alpha for all the variables. Further, exploratory factor analysis is performed to ensure the unidimensionality of the latent constructs. Structural equation modelling is performed to test the theoretical framework.

Data analysis confirms the significant effect of social media participations on female entrepreneurs towards their digital entrepreneurship intention which further affects their psychological empowerment.

The practical contributions of this study highlight the importance of female entrepreneurship which is essential for attaining self-reliance by reducing the socio-economic barriers. Further, female entrepreneurs’ participation in social media communities enhances the levels of empowerment.

This paper aims to understand the two-way interaction between corporate social responsibility (CSR) and the attractiveness of organization to job seekers (AOJS).

A system dynamics model is developed in the form of a causal loop diagram (CLD) that explains the CSR-AOJS interaction dynamically. To test the credibility of the developed model, the survey data are used to validate the causal relationships in the CLD.

This study found that developing an effective strategy or tool by capturing various essential CSR elements can attract potential job seekers.

The developed model is relevant to policymakers, decision-makers and managers when strategizing the CSR plan to attract potential job seekers.

In the recent post-pandemic era, the globe has been anxious for the sustainable disposal of healthcare waste to protect public health, protect the environment and enhance future preparedness. Developing countries, in particular, have struggled to dispose of healthcare waste (HCW) to eradicate the hazardous effects of medical waste generated during and after the deadly COVID-19 pandemic. Hence the purpose of the research paper is to develop a hybrid decision-making framework to identify various barriers for sustainable disposal of healthcare waste use of Grey-Decision Making Trial and Evaluation Laboratory (G-DEMATEL) and Analytical Network Process (ANP).

A hybrid framework of Grey-Decision Making Trial and Evaluation Laboratory (G-DEMATEL) and Analytical Network Process (ANP) has been used to rank barriers and sub-barriers in the disposal of healthcare waste.

The study’s findings suggest that lack of segregation practices, absence of green procurement policy, obsolete technologies and resistance to adopting change management are the topmost causal barriers influencing the remaining barriers. Lack of commitment among healthcare administrations, lack of standard performance measures and resistance to adopting change appear to be the topmost crucial barriers.

The study’s finding enables all stakeholders to prioritize the barriers systematically for better performance and save resources during the process. The policymakers can use the results to design a clear regulatory framework.

The literature has highlighted the factors and their association with the disposal of healthcare waste mainly in isolation. The results are validated against the Grey-Analytical Hierarchy Process (G-AHP) to ensure the robustness of the proposed framework. This paper is one of the preliminary attempts to propose a framework of the interrelationships of the factors that have a direct role in survival for management education.

This paper aims to analyse trends and determinants of NPAs in India's banks. It has empirically examined the bank-specific determinants of NPAs.

An FE panel estimation of a sample of 44 banks was carried out for the post-crisis time period, from 2010 to 2020 to identify the bank-specific determinants of NPAs. The sample of 44 banks includes 20 PSBs, 19 private banks and 5 foreign banks. Separate FE estimation was also carried out to identify the drivers of NPAs in PSBs.

The determinant of NPAs during the post-crisis period suggests that faulty earning management and deterioration in loan quality have resulted in high NPAs in India's banks. The result is similar for PSBs as well.

The findings of the study suggest that the banks, especially the Public Sector Banks (PSBs) need to revisit their earning management strategies to maximise income and improve their loan quality in order to reduce the incidence of loan failure.

The paper contributes by empirically analysing the determinants of NPAs during the recent decade, between 2010 and 2020. Separate estimations have been carried out to understand whether the drivers of NPAs differ in the case of PSBs.

The purpose of this study is to introduce a new type of sensor which uses microwave metamaterials and direct-coupled split-ring resonators (DC-SRRs) to measure the dielectric properties of solid materials in real time. The sensor uses a transmission line with a bridge-type structure to measure the differential frequency, which can be used to calculate the dielectric constant of the material being tested. The study aims to establish an empirical relationship between the dielectric properties of the material and the frequency measurements obtained from the sensor.

In the proposed design, the opposite arm of the bridge transmission line is loaded by DC-SRRs, and the distance between DC-SRRs is optimized to minimize the mutual coupling between them. The DC-SRRs are loaded with the material under test (MUT) to perform differential permittivity sensing. When identical MUT is placed on both resonators, a single transmission zero (notch) is obtained, but non-identical MUTs exhibit two split notches. For the design of differential sensors and comparators based on symmetry disruption, frequency splitting is highly useful.

The proposed structure is demonstrated using electromagnetic simulation, and a prototype of the proposed sensor is fabricated and experimentally validated to prove the differential sensing principle. Here, the sensor is analyzed for sensitivity by using different MUTs with relative permittivity ranges from 1.006 to 10 and with a fixed dimension of 9 mm × 10 mm ×1.2 mm. It shows a very good average frequency deviation per unit change in permittivity of the MUTs, which is around 743 MHz, and it also exhibits a very high average relative sensitivity and quality factor of around 11.5% and 323, respectively.

The proposed sensor can be used for differential characterization of permittivity and also as a comparator to test the purity of solid dielectric samples. This sensor most importantly strengthens robustness to environmental conditions that cause cross-sensitivity or miscalibration. The accuracy of the measurement is enhanced as compared to conventional single- and double-notch metamaterial-based sensors.

Past studies have proved the effect of justice on performance. However, the findings across studies have been inconsistent and there remains a substantial unexplained variance between the constructs. Therefore, justice researchers urge for further exploration of the underlying mechanism of the relationship by introducing influencers. This study responds to such call of the researchers by testing the influence of organizational identification (OID) on the justice-performance relationship in the context of Indian Public Sector Units (PSUs).

The study is grounded in the positivism philosophy. The research hypotheses were tested using cross-sectional data gathered using a pre-tested questionnaire. In this study, the authors have used covariance-based structural equation modeling commercial software (AMOS 24.0).

OID did not mediate the relationship between justice facets and performance. Rather, informational justice, followed by procedural and distributive justice had direct positive influences on performance. OID, however, moderated the procedural justice-performance such that the relationship strengthened at higher levels of OID and the interpersonal justice-performance relationship such that the relationship dampened at higher levels of OID. Interestingly, the relationship between interpersonal justice and performance was insignificant in the absence of the moderator, showcasing the strongest case of moderation.

The paper explores OID as an influencer to justice-performance relationship in Indian PSU context for the very first time, thereby highlighting unique finings. It contributes to the further understanding of the unexplained variance in the justice-performance relationship.

This study thoroughly examines the relationship between environmental, social and governance (ESG) scores and their subcategories with the investment decisions made by foreign institutional investors (FII). These subcategories include resource use, emission reduction and innovation under the environmental pillar, workforce, human rights, community and product responsibility under the social pillar and management, shareholders and CSR strategy under the governance pillar.

A machine learning technique known as “topic modeling” is used to analyse the current literature on ESG. To investigate the correlation between ESG scores and their subcategories with the investment decisions made by FII and to address concerns regarding multicollinearity and overfitting, a penalty-based regression model is employed.

The findings indicate that FIIs invest in firms with higher emission reduction and innovation scores under the environmental indicator. Additionally, firms with high human rights, community and product responsibility scores under the social indicator category have a positive relationship with FII investors. All subcategories of governance indicators, such as corporate social responsibility (CSR), strategy, shareholders and management scores, also positively impact FII investment. Of the three indicators, i.e. ESG, non-promoter FIIs give maximum weightage to governance indicators.

Since ESG is a contemporary topic, the findings on the relationship between different categories of ESG on FII investment will support managers in their FII investment. Also, the study will help the government frame policy decisions on ESG.

Previous studies have explored the impact of the overall ESG indicators on FII investments, but they have not specifically studied the influence of sub-indicators within these categories on investment decisions. By addressing this gap, the study enhances stakeholder theory by identifying and prioritizing the various subcategories of ESG indicators that impact FII investment decisions.