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This hypothetical case study aims to revisit the classical model given by Henri Fayol whereby he put forward a set of 14 principles to guide managers in decision-making across organizations. The case study showcases the dilemma in which the top manager of an automobile company finds himself when some of the very basic principles – on which the whole discipline of management is founded – are ignored. It will also serve as an aid for faculty members in B-Schools to teach students the significance of basic management principles postulated many years back which stand relevant even in contemporary times.

This case study is based on a hypothetical scenario in the corporate world. Different incidents in a fictitious automobile manufacturing firm are presented and the corresponding principles given by Henri Fayol are inferred.

This case study highlights that decision-making gets complicated if fundamental principles of management are not complied with. The decision taken during each and every situation which has been discussed in this case study is contrary to the correct course of action as propounded by Fayol. Modern-day managers must acknowledge the relevance and importance of these principles for achieving success in business.

This case study underscores that even in this volatile business environment where most of the management practices are technology-driven, we cannot disregard the most elementary rules of management. The managers working at different levels in the organizational hierarchy may be guided to make the right decisions in situations similar to the ones described.

Innovation is the way of life and we see various innovative techniques and methods being introduced in our daily life. This study aims to focus on digital innovation in the wealth management domain. This study examines the effect of usage of robo-advisory services in investment decision-making and behavioural biases, i.e. overconfidence and loss aversion. Such studies are more pronounced in developed countries and little has been studied about investor behaviour in association with advisory services in developing countries such as India.

Overconfidence and loss-aversion biases, investment decision-making and advisory services questions are measured using a five-point Likert scale. The number of respondents was 172 investors. A purposive sampling is used for gathering responses from investors. Structural equation modeling model was run using AMOS 22 version software package.

The authors found that behavioural biases positively and significantly influence the irrationalities of investment decision-making. The findings of this study also provide empirical evidence that the usage of robo-advisory services, by individual investors, is still incapable of mitigating behavioural biases, such as overconfidence bias and loss-aversion bias.

The sample size of this study could be a limiting factor. This study is limited only to two biases, while other behavioural biases affect the investment decision-making of the investors, which can be considered for future research along with the impact of robo-advisory services in different socio-cultural backgrounds.

This study will assist fintech start-ups, banks, architecture of robo advisors, product owners and wealth management service providers improvise their products, platforms and offerings of these automated advisory services. This could help individual investors to mitigate their behavioural biases in investment decision-making.

This study is useful to society as the awareness of robo-advisory services is very less, at present, and there is a need to increase the usage of these services to extend the benefit of this to the lower stratum of society. These services would be useful to all investors who find it difficult to afford financial advisors and help them mitigate their behavioural biases for investment decision-making.

This study is the first of its type that establishes the linkage between behavioural biases, digital innovation in fintech, i.e. robo-advisory services and individual investor’s investment decision-making in individual investor of the Indian stock market.

The purpose of this research is to identify and study the contextual relationships of the significant lean enablers in the Indian coal mining industry using the application of interpretive structural modeling (ISM), matrice d' impacts croisés-multiplication appliquée á un classement (MICMAC) and structural equation modeling (SEM).

In this study, a conceptual model based on ISM was developed forming a hierarchy and contextual relationships of significant enablers for lean implementation in the Indian coal mining industry using a literature review and eliciting expert opinion, which is followed by MICMAC for grouping of enablers and questionnaire survey to validate the ISM based conceptual model using SEM.

The study modeled and analyzed ten significant enablers of lean implementation in the Indian coal mining industry. The findings suggest that the most important lean enablers in the Indian coal mining industry are employee empowerment, employee motivation and commitment, consistent financial performance measurement and management support.

Judgmental sampling was used for selecting the respondents for conducting the questionnaire survey in this research work as there are a few numbers of coal mines implementing lean principles in India. Although the study was not restricted to a particular part of India with the sample representing the heterogeneous population, the study represents more data from the coal mines in eastern India.

The model on lean enablers would help the researchers, decision-makers and practitioners to anticipate potential lean enablers in the Indian coal mines and rank the enablers for improved and efficient usage of the available resources creating value to customers with lean and to sustenance academic research on lean.

Studies on lean enablers in the mining sector are scarce in the literature, and this study is a novel contribution of exploring lean enablers in the Indian coal mining industry using an integrated approach of ISM–MICMAC and SEM.

This study aims to explore the relationship between chief executive officer (CEO) power and stock price crash risk in India. Furthermore, it seeks to analyse how insider trades may moderate the impact of CEO power on stock price crash risk.

A study of 236 companies from the S&P BSE 500 Index (2014–2023) have been analysed through pooled ordinary least square (OLS) regression in the baseline analysis. To enhance the results' reliability, robustness checks include alternative methodologies, such as panel data regression with fixed-effects, binary logistic regression and Bayesian regression. Additional control variables and alternative crash risk measure have also been utilised. To address potential endogeneity, instrumental variable techniques such as two-stage least squares (IV-2SLS) and difference-in-difference (DiD) methodologies are utilised.

Stakeholder theory is supported by results revealing that CEO power proxies like CEO duality, status and directorship reduce one-year ahead stock price crash risk and vice versa. Insider trades are found to moderate the link between select dimensions of CEO power and stock price crash risk. These findings persist after addressing potential endogeneity concerns, and the results remain consistent across alternative methodologies and variable inclusions.

This study significantly advances research on stock price crash risk, especially in emerging economies like India. The implications of these findings are crucial for investors aiming to mitigate crash risk, for corporations seeking enhanced governance measures and for policymakers considering the economic and welfare consequences associated with this phenomenon.