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The purpose of this paper is to provide the maintenance personnel with a methodology for modeling and estimating the reliability of critical machine systems using the historical data of their inter‐failure times.

The failure patterns of five different machine systems were modeled with NHPP‐log linear process and HPP belonging to stochastic point process for predicting their reliability in future time frames. Besides the classical approach, Bayesian approach was also used involving Jeffreys's invariant non‐informative independent priors to derive the posterior densities of the model parameters of NHPP‐LLP and HPP with a view to estimating the reliability of the machine systems in future time intervals.

For at least three machine systems, Bayesian approach gave lower reliability estimates and a larger number of (expected) failures than those obtained by the classical approach. Again, Bayesian estimates of the probability that “ROCOF (rate of occurrence of failures) would exceed its upper threshold limit” in future time frames were uniformly higher for these machine systems than those obtained with the classical approach.

This study indicated that, the Bayesian approach would give more realistic estimates of reliability (in future time frames) of the machine systems, which had dependent inter‐failure times. Such information would be helpful to the maintenance team for deciding on appropriate maintenance strategy.

With the help of Bayesian approach, the posterior densities of the model parameters were found analytically by considering Jeffreys's invariant non‐informative independent prior. The case study would serve to motivate the maintenance teams to model the failure patterns of the repairable systems making use of the historical data on inter‐failure times and estimating their reliability in future time frames.

This study aims to evaluate the influence of corporate governance index (CGI), ownership concentration (OC) and other features on the dividends of listed Indian pharmaceutical companies. The other features included are leverage, excess return over cost of equity and stock-market return. This study thus helps to provide more insights on the dividend distribution issues for a shareholder in the challenging and demanding pharma industry, especially when stakes are high.

The data for all 26 pharmaceutical companies which form part of the NSE NIFTY-500 index for six years (2014–2019) is procured using Centre for Monitoring Indian Economy’s (CMIEs) Prowess database. An eight-pointer scale (unweighted scale) is used to develop the CGI. For OC, this paper considers the proportion of promoters’ shareholding, domestic institutional investors’ shareholding and foreign owners’ shareholding. Both static and dynamic panel data models are used to evaluate the effect of CGI and OC on dividends.

The panel data analysis depicts that CGI significantly positively influences the dividends of pharmaceutical companies in India. Thus, the authors find support for La Porta et al.’s outcome agency model. The results also reveal that only promoters’ holdings are significantly inversely related to dividends out of the three OC variables used for this study. This discussion implies that family-run pharmaceutical companies in India tend to retain profits instead of distributing dividends.

This study provides two direct insights for policymakers and stakeholders. First, because this study shows that CGI significantly positively influences dividends, corporate governance (CG) is an essential factor for determining dividends. Second, because the results also reveal that OC in the hands of promoters hurts dividends, it implies that the higher the promoter holding, lesser is the dividend distributed by the company. Both these results can be used as a quantitative tool by investors to assess Indian pharmaceutical companies better. However, a similar study could be directed to assess the impact of CGI and OC on dividends of other industries. Moreover, additional variables of CG and OC can also be evaluated in further detail. There is also a need to empirically validate the impact of CG and OC on a company’s performance.

The results are robust and reveal that variation in CGI does impact dividend policy. This aids in confirming that CG is a crucial aspect influencing dividends. The findings also add to the increasing studies across the globe evaluating the influence of CG and OC on dividends.