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Degradation measurement of some products requires destructive inspection; that is, the degradation of each unit can be observed only once. For example, observation on the mechanical strength of interconnection bonds or on the dielectric strength of insulators requires destruction of the unit. Testing high-reliability items under normal operating conditions yields a small amount of degradation in a reasonable length of time. To overcome this problem, the items are tested at higher than normal stress level – an approach called an accelerated destructive degradation test (ADDT). The present paper deals with formulation of constant-stress ADDT (CSADDT) plan with the test specimens subject to stress induced by temperature and voltage.

The stress–life relationship between temperature and voltage is described using Zhurkov–Arrhenius model. The fractional factorial experiment has been used to determine optimal number of stress combinations. The product's degradation path follows Wiener process. The model parameters are estimated using method of maximum likelihood. The optimum plan consists in finding out optimum allocations at each inspection time corresponding to each stress combination by using variance optimality criterion.

The method developed has been explained using a numerical example wherein point estimates and confidence intervals for the model parameters have been obtained and likelihood ratio test has been used to test for the presence of interaction effect. It has been found that both the temperature and the interaction between temperature and voltage influence the quantile lifetime of the product. Sensitivity analysis is also carried out.

Most of the work in the literature on the design of ADDT plans focusses on only a single stress factor. An interaction exists among two or more stress factors if the effect of one factor on a response depends on the levels of other factors. In this paper, an optimal CSADDT plan is studied with one main effect and one interaction effect. The method developed can help engineers study the effect of elevated temperature and its interaction with another stress factor, say, voltage on quantile lifetime of a high-reliability unit likely to last for several years.

This paper aims to focus on solving highly constrained redundancy optimization problems in binary complex systems.

The proposed algorithm searches a possibly improved solution in the k‐neighborhood (k≥2) of the current best feasible solution, by adding one unit in a selected subsystem and eliminating one from some other subsystem(s).

The algorithm is tested on complex system structures from the literature by solving a set of problems (with both linear and non‐linear constraints), with given and randomly generated data. It is observed that, compared with the other existing heuristics, there is much overall improvement in various performance measures.

The proposed algorithm is a better alternative and can be easily and efficiently applied to numerous real life systems such as computer and communication systems, telecommunication networks, automobile, nuclear and defense systems etc., giving optimal/near‐optimal solutions.

Researchers in reliability optimization have placed emphasis on heuristic approaches. The paper presents a new heuristic algorithm for solving the constrained redundancy optimization problems in complex binary systems.

Conceiving reliable systems is a strategic issue for any industrial society for its economical and technical development. This paper aims to focus on solving highly constrained redundancy optimization problems in complex systems.

Genetic algorithms (GAs), one of the metaheuristic techniques, have been used and a dynamic adaptive penalty strategy is proposed, which makes use of feedback obtained during the search along with a dynamic distance metric and helps the algorithm to search efficiently for final, optimal or near optimal solution.

The effectiveness of the adaptive penalty function is studied and shown graphically on the solution quality as well as the speed of evolution convergence for several highly constrained problems. The investigations show that this approach can be powerful and robust for problems with large search space, even of size 1017, and difficult‐to‐satisfy constraints.

The results obtained in this paper would be applicable on designing highly reliable systems meeting the requirement of today's society. Moreover, an important advantage of applying GA is that it generates several good solutions (mostly optimal or near optimal) providing a lot of flexibility to decision makers. As such, the paper would be of interest and importance to the system designers, reliability practitioners, as well as to the researchers in academia, business and industry. The paper would have wide applications in the fields of electronics design, telecommunications, computer systems, power systems etc.

Genetic algorithms have been recently used in combinatorial optimization approaches to reliable design, mainly for series‐parallel systems. This paper presents a GA for parallel redundancy optimization problem in complex systems.

The purpose of this article is to understand how women develop entrepreneurship as a career identity through women's various life stages. Using a life story approach, the authors study the formation of Indian businesswomen's entrepreneurial identity in businesswomen's unique socio-cultural context.

The study drew upon 15 semi-structured interviews with practicing women entrepreneurs using a qualitative methodology. Gioia methodology was used to systematically analyze the data for theory building.

The narratives of the Indian women entrepreneurs indicate that Indian women's entrepreneurial identity was a developmental process influenced by various episodes in different life stages-childhood, adolescence, marriage and motherhood. Life episodes influenced the creation and enactment of this entrepreneurial identity, which led to the emergence of entrepreneurship as a career choice.

The study's retrospective design may have raised concerns involving memory recall. The open-ended questions gave the participants the freedom to recount the life episodes that influenced the participants the most and may have partly mitigated this concern.

Prior studies have focused on specific life stages of women entrepreneurs, without taking a holistic life-story view, thereby missing out on how career identity is formed as a result of life episodes. Using the developmental psychology approach, the authors provide a nuanced and holistic lens to understanding women's entrepreneurship.

This paper aims to examine the relationship between workplace toxicity and psychological capital (PsyCap). It also investigates the moderating role of gratitude in the toxicity–PsyCap link.

The study is based on explorative-cum-descriptive research design. The sample comprises 411 employees engaged in banking, insurance, IT, automobile and oil and gas companies. The collected data is explored for reliability, validity, multicollinearity and common method variance estimates. Also, the relationship between workplace toxicity and PsyCap and the moderating effect of gratitude are examined using structural equation modelling.

The findings report a negative association between toxicity and PsyCap. Also, the study concludes a significant moderating effect of gratitude. The study recommends the institutionalisation of a gratitude-based organisation to reduce the impact of workplace bullying and uncivil behaviour.

The study is based on primary data and one of the few studies that explore psychological capital as a dependent variable, which is influenced by toxic behaviours at work.

The study examined the relationship between workplace toxicity and turnover intentions among Indian healthcare employees. It also explored the role of gratitude as a moderator in the workplace toxicity–turnover intentions relationship.

The study is based on a cross-sectional research design. The sample comprises 315 employees from the Indian healthcare sector. Approximately, 400 employees are approached both through email and office visits. Responses were received from 336 participants, and 21 incomplete questionnaires were discarded. The relationships between four variables of workplace toxicity and turnover intentions are examined using correlation and hierarchical regression. The moderation effect of gratitude is studied using the PROCESS macro in SPSS 21.

The results revealed that workplace toxicity could explain 45.8% variations in employees' turnover intentions. It also reported significant negative regression coefficients between all four dimensions of workplace toxicity and turnover intentions. It suggested that toxic health organizations may promote turnover intentions among healthcare employees. Also, findings recommended a significant moderating effect of gratitude amid the relationships of four dimensions of workplace toxicity and turnover intentions.

Hospital administrators must ensure that health professionals have the necessary support to remain effective in the field by providing a conducive working environment emerging from sound human resource practices that promote respect, collegial relationships, teamwork and collaboration. The present research demonstrates gratitude as one such factor that could act as a catalyst within the workplace. Practitioners could achieve a healthy work environment by developing complementary relief measures that build organizational capacities and improve its culture while sponsoring programs for individual employees that instill positivity through awareness of gratitude in everyday life.

This study offered a comprehensive understanding of workplace toxicity by investigating its four dimensions. Also, it is one of the pioneer studies that evaluate the role of gratitude in restricting workplace toxicity-induced turnover intentions.

The purpose of this paper is threefold: first, to investigate the role of big data and predictive analytics (BDPA) and social capital on the performance of humanitarian supply chains (HSCs); second, to explore the different performance measurement frameworks and develop a conceptual model for an HSC context that can be used by humanitarian organizations; and third, to provide insights for future research direction.

After a detailed review of relevant literature, grounded in resource-based view and social capital theory, the paper proposes a conceptual model that depicts the influence of BDPA and social capital on the performance of an HSC.

The study deliberates that BDPA as a capability improves the effectiveness of humanitarian missions to achieve its goals. It uncovers the fact that social capital binds people, organization or a country to form a network and has a critical role in the form of monetary or non-monetary support in disaster management. Further, it argues that social capital combined with BDPA capability can result in a better HSC performance.

The proposed model integrating BDPA and social capital for HSC performance is conceptual and it needs to be empirically validated.

Organizations and practitioners may use this framework by mobilizing social capital, BDPA to enhance their abilities to help victims of calamities.

Findings from study can help improve coordination among different stakeholders in HSC, effectiveness of humanitarian operations, which means lives saved and faster reconstruction process after disaster. Second, by implementing performance measurements framework recommended by study, donors and other stakeholders will get much desired transparency at each stage of HSCs.

The findings contribute to the missing link of social capital and BDPA to the existing performance of HSC literature, finally leading to a better HSC performance.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

Blockchain technology was once only associated with the financial industry, but it is now being used in a variety of industries, including education. Researchers all over the world take a keen interest in studying the various applications of blockchain technology for the last 4–5 years. The current study is a review of previously published studies on blockchain technology’s applicability in the sector of education. The systematic review was used to conduct the qualitative analysis using the PRISMA Framework (Preferred Reporting Items for Systematic Review and Meta-Analysis). For this comprehensive literature review analysis, 99 publications were chosen in the final stage of selection. Bibliometric analysis is employed to analyse the collected data. Authorship analysis, co-authorship analysis, keyword co-occurrences, and important applications of blockchain in education are the primary parts in which the literature’s findings are organised. Important directions are given for researchers and academicians involved in blockchain-related research who may use the bibliometric analysis of the present study as a reference.