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The purpose of this paper is to estimate the multicomponent reliability by assuming the unit-Gompertz (UG) distribution. Both stress and strength are assumed to have an UG distribution with common scale parameter.

The reliability of a multicomponent stress–strength system is obtained by the maximum likelihood (MLE) and Bayesian method of estimation. Bayes estimates of system reliability are obtained by using Lindley’s approximation and Metropolis–Hastings (M–H) algorithm methods when all the parameters are unknown. The highest posterior density credible interval is obtained by using M–H algorithm method. Besides, uniformly minimum variance unbiased estimator and exact Bayes estimates of system reliability have been obtained when the common scale parameter is known and the results are compared for both small and large samples.

Based on the simulation results, the authors observe that Bayes method provides better estimation results as compared to MLE. Proposed asymptotic and HPD intervals show satisfactory coverage probabilities. However, average length of HPD intervals tends to remain shorter than the corresponding asymptotic interval. Overall the authors have observed that better estimates of the reliability may be achieved when the common scale parameter is known.

Most of the lifetime distributions used in reliability analysis, such as exponential, Lindley, gamma, lognormal, Weibull and Chen, only exhibit constant, monotonically increasing, decreasing and bathtub-shaped hazard rates. However, in many applications in reliability and survival analysis, the most realistic hazard rates are upside-down bathtub and bathtub-shaped, which are found in the unit-Gompertz distribution. Furthermore, when reliability is measured as percentage or ratio, it is important to have models defined on the unit interval in order to have plausible results. Therefore, the authors have studied the multicomponent stress–strength reliability under the unit-Gompertz distribution by comparing the MLEs, Bayes estimators and UMVUEs.

Inferences for multicomponent reliability is derived for a family of inverted exponentiated densities having common scale and different shape parameters.

Different estimates for multicomponent reliability is derived from frequentist viewpoint. Two bootstrap confidence intervals of this parametric function are also constructed.

Form a Monte-Carlo simulation study, the authors find that estimates obtained from maximum product spacing and Right-tail Anderson–Darling procedures provide better point and interval estimates of the reliability. Also the maximum likelihood estimate competes good with these estimates.

In literature several distributions are introduced and studied in lifetime analysis. Among others, exponentiated distributions have found wide applications in such studies. In this regard the authors obtain various frequentist estimates for the multicomponent reliability by considering inverted exponentiated distributions.

The purpose of this article is to derive inference for multicomponent reliability where stress-strength variables follow unit generalized Rayleigh (GR) distributions with common scale parameter.

The authors derive inference for the unknown parametric function using classical and Bayesian approaches. In sequel, (weighted) least square (LS) and maximum product of spacing methods are used to estimate the reliability. Bootstrapping is also considered for this purpose. Bayesian inference is derived under gamma prior distributions. In consequence credible intervals are constructed. For the known common scale, unbiased estimator is obtained and is compared with the corresponding exact Bayes estimate.

Different point and interval estimators of the reliability are examined using Monte Carlo simulations for different sample sizes. In summary, the authors observe that Bayes estimators obtained using gamma prior distributions perform well compared to the other studied estimators. The average length (AL) of highest posterior density (HPD) interval remains shorter than other proposed intervals. Further coverage probabilities of all the intervals are reasonably satisfactory. A data analysis is also presented in support of studied estimation methods. It is noted that proposed methods work good for the considered estimation problem.

In the literature various probability distributions which are often analyzed in life test studies are mostly unbounded in nature, that is, their support of positive probabilities lie in infinite interval. This class of distributions includes generalized exponential, Burr family, gamma, lognormal and Weibull models, among others. In many situations the authors need to analyze data which lie in bounded interval like average height of individual, survival time from a disease, income per-capita etc. Thus use of probability models with support on finite intervals becomes inevitable. The authors have investigated stress-strength reliability based on unit GR distribution. Useful comments are obtained based on the numerical study.

Burr distribution has been proved to be a useful failure model. It can assume different shapes which allow it to be a good fit for various lifetimes data. Hybrid censoring is an important way of generating lifetimes data. The purpose of this paper is to estimate an unknown parameter of the Burr type XII distribution when data are hybrid censored.

The problem is dealt with through both the classical and Bayesian point of view. Specifically, the methods of estimation used to tackle the problem are maximum likelihood estimation method and Bayesian method. Empirical Bayesian approach is also considered. The performance of all estimates is compared through their mean square error values. The paper employs Monte Carlo simulation to evaluate the mean square error values of all estimates.

The key findings of the paper are that the Bayesian estimates are superior to the maximum likelihood estimates (MLE).

This work has practical importance. Indeed, the proposed methods are applied to real life data.

The paper is original and is quite applicable in lifetimes data analysis.

Previous studies that have attempted to link TQM and employees’ satisfaction are either theoretical without empirical evidence or had limited outcome in scope as they link only few elements of TQM with employees’ job satisfaction and commitment. This study is warranted due to the paucity of insights into the impact of soft strategies on determining job satisfaction and commitment. Despite the considerable body of TQM literature that has evolved to examine the relationship between TQM and employees’ job satisfaction in various countries as well as industries there is no existing literature that recognizes the soft aspects of TQM within the context of the Indian manufacturing industry. The paper aims to discuss these issues.

On the basis of the proposed hypotheses a conceptual model was proposed and tested. A questionnaire survey was employed for data collection. The participants were 450 shop floor employees of three Indian manufacturing organizations.

The results have shown that six out of the nine soft aspects of TQM played a role in determining job satisfaction and commitment. The results have also shown that the predictors of both job satisfaction and commitment were the same except for the strength of prediction. The proposed model showed an acceptable fit.

This is the first study to examine the impact of soft aspects of TQM in determining job satisfaction and commitment in the Indian manufacturing organizations.

This study aims to investigate the adoption intention of artificial intelligence (AI) in family businesses through the perspectives of digital entrepreneurship and entrepreneurship orientation.

The study examines contributing factors explaining the adoption intention of AI in the context of family businesses. The developed research model is examined and validated using structural equation modelling based on 631 respondents' data. Purposeful sampling is used to collect the respondents' data.

The proposed model included two endogenous (i.e. business innovativeness and adoption intention) and six exogenous variables (i.e. affordances, culture and flexible design, entrepreneurial orientation, generativity, openness and technology orientation) through ten direct paths and three indirect paths. The results depicted the significant influence of all the exogenous variables on the endogenous variable reflecting support of all the hypotheses. The business innovativeness partially mediates the relationships of culture and flexible design, entrepreneurial orientation and technology orientation with adoption intention. Further, the results demonstrated a model variance of 24.6% for business innovativeness and 64.2% for adoption intention of artificial intelligence in the family business.

The study contributes to theoretical developments in entrepreneurship and family business research and AI's theoretical progress, especially to digital entrepreneurship.

Theoretically, it contributes to the literature of entrepreneurship, particularly digital entrepreneurship. Additionally, the research model adds to the role of entrepreneurial orientation and digital entrepreneurship in the emerging family entrepreneurship literature. Considering the scarcity of research in this field, the empirically validated model explaining critical antecedents of AI adoption intention in the family business is a foundation for discussion, critique and future research.

The paper aims to provide insights into the influence of COVID-19 on employee engagement and ergonomics in the banking sector. The purpose of this study is understood from the perspective of impact of the pandemic on banking employees.

Data for the study are collected from the employees working in the banking sector of India. The study applied the partial least squares (PLS) method of analysis to understand the relationship between employee engagement and ergonomics in the banking sector.

The findings of the study suggest results with regards to change in the perception of the employees in the bank and its influence on the work ergonomics due to pandemic. The findings indicate that banks need to develop measures and strategies for improving employee engagement programs and work ergonomics at banks.

The study is confined to the banking employees working in a specific region. Therefore, future research could focus more on the influence of Covid-19 on the organizational culture of the banking system and provide insight into this direction of research.

This study provides directions for human resource management for developing effective practices for improving the performance of the employees in the banking sector.

This study offers support to the banking sector by providing insights into how it can improve the working environment and, thereby, enhance working in the banking sector.

This is the study that attempts to provide insights into how ergonomics is important for working in the banking sector, especially during a pandemic. The findings provide important implications for the banking sector and improve work ergonomics.

The Internet is used as a tool to seek health information by individuals. Mental health concerns are the high prevalence of the novel coronavirus disease 2019 (COVID-19) and preventive steps are required to curb the illness. Therefore, to gain more insight into health concerns, it is now a common practice to seek health information on the Internet. This study propose an integrated theoretical model to explore the relationship between COVID-19 protocols and perceived online trust with online health information seeking intention (OHISI) and a moderating effect of perceived severity and perceived urgency.

Data are collected from 325 athletes in the category of individual and team sports through an online survey in a Likert-scale questionnaire. The analysis is performed with a quantitative methodology.

The study reveals the bright side of online health information (OHI), which brings athletes together and has played out with virtual happy hours, meetings and events. The bright side of OHI reflects social, cultural, technological and economic benefits. An OHI chatbot offers bright personalised side information to the individual seeker, which is more convenient and efficient than human capabilities.

The pivotal contribution is the integrated theoretical framework that is derived from multidisciplinary literature to capture the complexity of OHI. Also, it conceptualises the constructs in the context of OHI and COVID-19.

Laser land leveling (LLL) is a climate-smart technology that improves water use efficiency and reduces risk in crop cultivation due to weather variability. Hence, this technology is useful for cultivating water-intensive crops in a sustainable way. Given this background, the state government of Karnataka initiated to promote LLL in drought-prone districts and selected Raichur district for implementation. Moreover, farmers in this district had observed drought situation during monsoon paddy growing season in 2018. Therefore, this study attempts to investigate the importance of LLL technology for paddy cultivation under drought conditions.

A primary survey with 604 farmer households had been conducted in Raichur in 2018. Among them, 50% are adopters of LLL who have been selected purposively and rest 50% are non-adopters who have grown paddy in the adjacent or nearest plot of the laser-leveled plot. The adoption and causal impact of LLL has been estimated using propensity score matching, coarsened exact matching and endogenous switching regression methods.

The result reveals a positive and significant impact of LLL on paddy yield and net returns to the farmers. The results indicate an increment of 12 and 16% in rice yield and net income, respectively, for LLL adopters in comparison to the non-adopters of LLL.

The major limitation of the study is that it does not adopt the method of experimental study due to certain limitations; hence, the authors employed a quasi-experimental method to look at the possible impact of adoption of LL.

There have been various agronomic studies focusing on the ex-ante assessment of the LLL. This study is an ex-post assessment of the technology on the crop yield and farmers' income in a dry semi-arid region of India, which, according to the authors, is the first in this approach.