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Small and medium-sized enterprises (SMEs) have contributed substantially to the global entrepreneurial environment and economic growth, particularly within emerging economies. However, the success rate of these enterprises is relatively low in most developing countries, particularly India. Therefore, this study aims to contribute towards creating a sustainable model for enhancing entrepreneurial performance by examining the impact of entrepreneur digital literacy, entrepreneurial attitude and innovative work behaviour on their sustainable performance. It also explores the role of innovative work behaviour as a mediator in the relationship between entrepreneurial digital literacy, attitude and SMEs’ sustainable performance.

Data was collected from a sample of 329 entrepreneurs registered with the Department for Promotion of Industry and Internal Trade portal in India using snowball random sampling and was analysed via the structural equation modelling technique.

The findings reveal that entrepreneurial digital literacy and attitude play a robust and critical role in supporting innovation and SMEs’ long-term success, respectively. Entrepreneurial digital literacy equips individuals with the skills to navigate digital tools effectively, fostering innovation within SMEs and enabling them to adapt to evolving market demands. Similarly, a positive entrepreneurial attitude, characterised by a proactive and risk-taking mindset, is essential for driving SMEs’ long-term success by facilitating growth and adaptation in competitive business environments. Moreover, the relationship between entrepreneurial digital literacy, attitude and sustainable performance was found to be partially mediated by innovative work behaviour. Innovative work behaviour, including creativity, proactivity and risk-taking, is a bridge, translating digital literacy and entrepreneurial mindset into tangible outcomes that contribute to sustainable entrepreneurial performance and resilience in dynamic market conditions.

The described research work offers a valuable contribution to the field by integrating digital literacy and entrepreneurial attitude within the context of entrepreneurship. Exploring how innovative work behaviour mediates the relationship between digital literacy, entrepreneurial attitude and sustainable entrepreneurial performance contributes to a deeper understanding of the mechanisms driving success in today’s digital economy.

The current study intends to synthesise the prior studies on employability skills among work-ready professionals in higher education through bibliometric and network visualisation tools. This study also identifies the gaps in the existing literature that still need to be filled and maps the course for future investigation.

The current study examined research papers on employability skills among work-ready professionals in higher education to evaluate global patterns and the top authors, institutions, journals, and nations contributing to this field. The authors analysed eight hundred eighty-five papers from the Scopus database between 1982 and 2023 for the study. Open-source bibliometric tools like Biblioshiny were used in the study to examine the body of literature and to spot new directions for future research.

The study’s findings indicate that since 2008, researchers, academics, and policymakers have paid more attention to employability skills among work-ready professionals, and 2019 is the year seeing the highest no. of publications. Across the globe, 2077 authors contributed to the articles, papers, and journals published in the particular domain. Regarding location, the United Kingdom is the world’s most productive nation, with 342 articles produced. Similarly, Australia, India, and Malaysia are the leading nations that have contributed to the field. Both keywords significantly contribute to scientific knowledge.

The gaps in this study will serve as a reference point for researchers conducting future studies in this field. Additionally, the result of this analysis offers a roadmap for higher education to enhance graduate employability by embedding necessary skills into them.

There has not been a bibliometric analysis concerning employability skills among Work-ready professionals in the existing literature; hence, this article is innovative. This study is the first attempt at integrating the concept. The study also gives an overview of potential directions for future research in this discipline.

Zero-failure reliability testing aims at demonstrating whether the product has achieved the desired reliability target with zero failure and high confidence level at a given time. Incorporating accelerated degradation testing in zero-failure reliability demonstration test (RDT) facilitates early failure in high reliability items developed within short period of time to be able to survive in fiercely competitive market. The paper aims to discuss these issues.

The triangular cyclic stress uses one test chamber thus saving experimental cost. The parameters in model are estimated using maximum likelihood methods. The optimum plan consists in finding out optimum number of cycles, optimum specimens, optimum stress change point(s) and optimum stress rates.

The optimum plan consists in finding out optimum number of cycles, optimum specimens, optimum stress change point(s) and optimum stress rates by minimizing asymptotic variance of estimate of quantile of the lifetime distribution at use condition subject to the constraint that total testing or experimental cost does not exceed a pre-specified budget. Confidence intervals of the design parameters have been obtained and sensitivity analysis carried out. The results of sensitivity analysis show that the plan is robust to small deviations from the true values of baseline parameters.

For some highly reliable products, even accelerated life testing yields little failure data of units in a feasible amount of time. In such cases accelerated degradation testing is carried out, wherein the failure termed as soft failure is defined in terms of performance characteristic of the product exceeding its critical (threshold) value.

This research aims to examine the influence of transformational leadership on employee efficiency. The research also examines the role of knowledge sharing as a mediator between transformational leadership and employee efficiency.

The research is based on the survey conducted among 200 employees of logistics firms. Exploratory Factor analysis (EFA) and Confirmatory Factor Analysis (CFA) approaches are used for the evaluation.

The study found that transformational leadership has positive and significant influence on employee efficiency. The research also demonstrates that after introducing knowledge sharing, it fully mediated the influence of transformational leadership on employee efficiency. The study suggests that, if leaders share their knowledge and expertise among the team, employees have a propensity to be highly effective and efficient than without knowledge sharing.

Blue collar staff and unskilled labors of the firms are not included in the study. So, the study is limited to white collar staff only which can further be expanded by considering other ground staff. Also few or no such researches have been conducted in logistics firms, particularly in Indian logistics firms. So, the result of this study can be used as reference to explore the area. This study can be replicated in the logistics firms of other regions also.

The finding of the study will help the top management of the organizations to formulate strategies to enhance its senior-subordinate relationship through knowledge sharing. The study also suggests that regular dissemination of knowledge among the team improves the efficiency of the team members and hence the performance of the organization.

This research examines the degree to which knowledge sharing acts as a mediator between transformational leadership and employee efficiency, which has not been found in previous studies.

The purpose of this paper is to understand antecedents of app-based shopping in an Indian context. The paper has used unified theory of acceptance and use of technology (UTAUT) 2 model for examining the impact of various constructs on behavioral intention and usage behavior of smart phone users toward the mobile shopping apps.

The constructs were tested and validated by means of a structured questionnaire which was administered on a sample of 350 mobile app shoppers in Delhi. AMOS 20 was used to analyze the collected data.

The study revealed that hedonic and habit are the strongest predictors of users’ behavioral intention to use mobile apps for shopping. Respondents are also influenced by the deals that are being offered by the marketers. The research also suggests that facilitating conditions help in usage of mobile apps for shopping.

Managerial implications simplifying the interface which would encourage the less technologically advanced individuals to use mobile apps. Hedonic element of shopping through mobile apps should also be enhanced.

This study contributes to the research on intentions and usage behavior of consumer technologies by adopting UTAUT 2 model to explain the intentions and usage behavior toward mobile apps for shopping. The paper also measured the role of deals in influencing the consumers.

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.

Acceptance sampling plans are designed to decide about acceptance or rejection of a lot of products on the basis of sample drawn from it. Accelerating the life test helps in obtaining information about the lifetimes of high reliability products quickly. The purpose of this paper is to formulate an optimum time censored acceptance sampling plan based on ramp-stress accelerated life test (ALT) for items having log-logistic life distribution. The log-logistic life distribution has been found appropriate for highly reliable components such as power system components and insulating materials.

The inverse power relationship has been used to model stress-life relationship. It is meant for analyzing data for which the accelerated stress is nonthermal in nature, and frequently used as an accelerating stress for products such as capacitors, transformers, and insulators. The method of maximum likelihood is used for estimating design parameters. The optimal test plan is obtained by minimizing variance of test-statistic that decides on acceptability or rejectibility of lot. The optimal test plan finds optimal sample size, stress rates, sample proportion allocated to each stress and lot acceptability constant such that producer’s risk and consumer’s risk is satisfied.

Asymptotic variance plays a pivotal role in determining the sample size required for a sampling plan for deciding the acceptance/rejection of a lot. The sample size is minimized by optimally designing a ramp-stress ALT so that the asymptotic variance is minimized.

The model suggested is of use to quality control and reliability engineers dealing with highly reliable items.

To reduce the electricity consumption in our homes, a first step is to make the user aware of it. Reading a meter once in a month is not enough, instead, it requires real-time meter reading. Smart electricity meter (SEM) is capable of providing a quick and exact meter reading in real-time at regular time intervals. SEM generates a considerable amount of household electricity consumption data in an incremental manner. However, such data has embedded load patterns and hidden information to extract and learn consumer behavior. The extracted load patterns from data clustering should be updated because consumer behaviors may be changed over time. The purpose of this study is to update the new clustering results based on the old data rather than to re-cluster all of the data from scratch.

This paper proposes an incremental clustering with nearness factor (ICNF) algorithm to update load patterns without overall daily load curve clustering.

Extensive experiments are implemented on real-world SEM data of Irish Social Science Data Archive (Ireland) data set. The results are evaluated by both accuracy measures and clustering validity indices, which indicate that proposed method is useful for using the enormous amount of smart meter data to understand customers’ electricity consumption behaviors.

ICNF can provide an efficient response for electricity consumption patterns analysis to end consumers via SEMs.