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This study seeks to understand the nexus between intellectual capital and profitability of healthcare firms in India with interaction effects.

Relevant data were extracted from the Centre for Monitoring Indian Economy (CMIE)'s Prowess database for a period of ten years 2009–2018 for a sample of 84 selected firms from the healthcare industry. This study uses value added intellectual coefficient (VAIC) and modified value added intellectual coefficient (MVAIC) as a measure of intellectual capital. Further, the study employs panel regression techniques to explore the relationship between intellectual capital and profitability.

The empirical findings reveal that the intellectual capital coefficient of healthcare firms in India averages 2.7757. It is also observed that a majority of the healthcare firms' intellectual capital coefficient is below the industry average. From the regression analysis, it is evident that the intellectual capital coefficient is positively related to the profitability of healthcare firms in India. As far as the components of intellectual capital coefficient are concerned, the capital employed coefficient (CEC) is the only component driving the profitability of healthcare firms in India. A further introduction of interaction terms improves model explainability and moderates the impact of the predictor variable on the response variable. Furthermore, it is observed that the intellectual capital coefficient of the healthcare industry is immune to changes in political regimes in India.

The findings reveal that intellectual capital is an important driver of corporate performance, thus healthcare firms in developing economies like India need to enhance their intellectual potential. Therefore, corporates and governments in developing economies should stimulate investments in developing intellectual capital for enhanced corporate performance and economic growth. Thus, this study might be used as a reference by policymakers while drafting the future policy for the development of intellectual capital in general and healthcare sector specifically.

This is among the first few studies to explore such an empirical relationship for healthcare firms in India and among the few studies of this kind across the globe. It also makes novel contributions in considering interaction variables and seeking the consistency of results across different political regimes. However, the study examines one nation and one industry; thus, the generalisation of findings requires caution.

The purpose of this paper is to estimate the economic (namely cost, revenue and profit) efficiency and its association with intellectual capital of 37 BSE-listed Indian banks over the period 2005–2018.

This study employs truncated Tobit regression to compute the relationship between intellectual capital and estimated cost, revenue and profit efficiency using Data Envelopment Analysis (DEA) for the 37 BSE-listed Indian banks within the panel data framework.

Estimates suggest that banks’ overall annual average cost, revenue and profit efficiency are 0.4466–0.7519, 0.4825–0.8773 and 0.4905–0.8803, respectively, during the sample period. Further, Tobit regression results indicate that the aggregate intellectual capital (value-added intellectual coefficient or Modified Value-added Intellectual Capital) has a positive but minimal impact on these efficiency parameters at 1 percent significance level for the overall sample as well as public sector banks. Among all the sub-components of intellectual capital, human capital, structural capital and relational capital have a positive and moderate impact on these efficiency measures for the overall sample. Control variables, particularly bank size, are significant drivers of the estimated efficiency of banks.

Findings suggest that banks should invest adequately to enhance their overall intellectual capital to further augment these economic efficiency measures in the long run.

This study computes cost, revenue and profit efficiency of 37 BSE-listed banks based on DEA followed by intellectual capital using the Pulic approach (1998 and 2000) and the Bontis (1998) approach in the first stage. Later, it examines the dynamics between the computed efficiency parameters and intellectual capital using Tobit regression within the panel data framework.

This study aims to compare the fundamental indexation (FI) portfolio vis-à-vis the cap-weighted index (CWI). It also explored the return-generating attributes of the FI portfolios.

This study extracted relevant data from the Centre for Monitoring Indian Economy’s Prowess database from March 1996 to March 2017 from a sample of National Stock Exchange (NSE) 500 companies. The FI portfolios were constructed with First_50 and Next_50 stocks using the latest and five years of trailing average aggregations. Further, the regression technique was used to identify the return-generating attributes of FI portfolios.

It was found that the FI portfolios based on First_50 and Next_50 stocks outperformed the CWI (i.e. NSE_First_50 and NSE_Next_50) in the Indian capital market, and between the two, the FI portfolios based on Next_50 stocks were superior to the FI portfolios based on First_50 stocks. The cross-sectional superiority of FI portfolios is obvious if they are sorted according to four fundamentals, namely, total income, sales, operating cash flows and profit before depreciation interest tax and amortisation. The return-generating process of FI portfolios is well-explained by market premium followed by value premium and investment premium.

This study may enable portfolio managers and investors to measure FI portfolios’ superiority in the Indian capital market and identify the return-generating attributes of FI portfolios so that the loadings can be switched amongst different priced factors for higher yield. Further, this study extends the FI literature, providing evidence from one of the world’s fastest-growing economies.

To the best of the knowledge, this is amongst the first few studies to explore the performance of FI portfolios vis-à-vis CWIs in India, and to use Fama and French (2015) asset pricing models to understand the return-generating attributes of FI portfolios. It is also novel in the sense that it considers the FI portfolios for a longer duration, predating 1997 and coinciding with the inception of CWIs, namely, NSE_First_50 (inception: 1995) and NSE_Next_50 (inception: 1996), reducing the apprehensions of data-snooping biases.

The purpose of this paper is to explore and explain the linkage between intellectual capital (IC) efficiency of banks and their performance.

In total, 39 public and private banks listed in Bombay Stock Exchange from 1999 to 2015 were considered for the study. Panel fixed effects technique is used to draw inferences.

Results of the study provide evidence of positive association between IC and performance of banks; however, only human capital and structural capital have shown instances of significant positive linkage with banks performance. The results also indicate that the IC efficiency of private sector banks is better than public sector banks in India.

This study may enable Indian banking firms to measure their IC efficiency and develop policies to promote and improve upon their intellectual potential to enhance banks performance.

It is a novel study in Indian context that considers interaction variables in extending the prior understanding of the role of IC in enhancing banks performance, which may build sustainable advantage for banks in emerging economies like India.

Being a developing nation with huge opportunity of growth prospects the assessment of valuation models becomes important to have a more realistic value estimate. The purpose of this paper is to empirically examine the comparative accuracy and explanatory performance of discounted cash flow (DCF) and residual income model (RIM) valuation models for the Indian chemical industry and come up with a composite valuation model.

To achieve the objective of the study the authors first determine the intrinsic values using both the models. Comparisons of the models are based on prediction errors and the explanatory performance of market value on value estimates. The study uses panel regression to forecast estimates of earnings and measure explanatory performance. The authors examine the ability of the value estimates to explain cross-sectional variation in the observed market values. The study also uses GMM method for deriving robust estimators. Variables for the study are collected from the CMIE’s prowess data base (release 4). The authors consider all 1,075 BSE listed chemical companies for the purpose of the study. The study uses annual data points starting from 31 March 2002 to 31 March 2011.

The comparative framework shows that both Residual Income model and Composite Valuation model are superior to Discounted cash flow model and are equally likely. But since composite value estimates considers all bonafide informations of individual models, the estimates of Composite Valuation model becomes more reliable.

The study only compares and combines the two most widely used valuation models around the world. Future studies can be conducted using the third widely used valuation models, i.e. multiples and see the level of accuracy of individuals as well as the composite model.

As a concern very few research has been conducted in this area in India. This paper provides practitioners with a snapshot of the applicability of DCF and RIM valuation models. And also shows how a composite value estimate can improve accuracy.

The purpose of this study is to examine the serial mediating mechanism between self-leadership and employee creativity through organizational commitment and work engagement. Drawing on the self-determination theory and broaden and build theory, this study investigates the indirect effect of self-leadership on employee creativity through organizational commitment and work engagement.

The relationships were investigated using PROCESS macro for SPSS. Data were collected from 324 employees working in the Indian automobile industry. Structural equation modelling was used to evaluate the model fit of the measurement model.

The results of the study revealed that self-leadership impacts employee creativity. Further, the findings showed that both organizational commitment and work engagement individually mediate the relationship between self-leadership and employee creativity. The key finding of this research was the partial serial mediation of organizational commitment and work engagement in the relationship between self-leadership and employee creativity.

This is one of the primary studies that examined the serial mediating effect of organizational commitment and work engagement in the relationship between self-leadership and employee creativity. This study contributes to the existing literature on self-leadership and employee creativity by evincing the mediating mechanism of organizational commitment and work engagement.

Introduction: Cost competitiveness, customer focus, and sustainability compliance are essential for new-age firms to survive and succeed in the VUCA market environment. This study examines how automobile corporations have improved cost competitiveness, productivity, and product quality.

Purpose: This study examines the importance of cost competitiveness, customer focus, and sustainability compliance for the long-term survival of organisations in VUCA markets, looking at the practical efforts made by automobile corporations to enhance cost competitiveness, productivity, and quality.

Methodology: The study utilises a comprehensive analysis of the strategies and initiatives implemented by the selected automobile companies. It involves a review of relevant literature, case studies, financial data analysis, and interviews with key industry experts, providing a holistic understanding of the actions taken by these organisations to achieve their goals.

Findings: The study reveals that cost competitiveness, customer focus, and sustainability compliance are critical factors for the long-term survival and success of organisations in the automotive industry. The analysed automobile companies have undertaken practical efforts to improve cost competitiveness, enhance productivity, and ensure high-quality products, enabling them to navigate the challenges and maintain a competitive edge.

Significance: The findings of this study contribute to a deeper understanding of the importance of cost competitiveness, customer focus, and sustainability compliance in the automotive industry. It highlights the need for organisations to constantly monitor both qualitative and quantitative profit to avoid complacency and ensure long-term efficiency. The study’s insights are relevant to businesses operating in other sectors, as they face similar challenges in the VUCA market environment.

Drawing on social exchange theory (SET) and proactive motivation model, this study aims to examine the relationship between workplace dignity and employees’ tacit knowledge sharing (TKS) and assess the mediating role of psychological safety and organizational identification in this relationship.

Data are collected in the three waves from 307 first-line supervisors and professionals of high- and medium-high-tech manufacturing organizations of Pakistan. Partial least squares structural equation modelling technique is applied using SmartPLS 4 software to test hypothesized relationships.

Results reveal that workplace dignity is directly and positively related to TKS and psychological safety and organizational identification mediate this relationship.

This study highlights the importance of workplace dignity as a vital determinant of TKS. Findings of this research underscore the need for enactment of humanistic and employee-oriented organizational policies and practices that signal workplace dignity which can result in increased psychological safety and enhanced organizational identification leading towards higher TKS.

This research proffers novel understanding of the nexus between an embryonic socio-emotional element of workplace context, namely, workplace dignity and TKS. This study not only advances knowledge management literature from dignity perspective but also contributes to SET and proactive motivation model.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.

Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.

It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.

This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.