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The rapid global changes we are witnessing pose a pressing challenge that necessitates reevaluating conventional supply chain practices. Consequently, the integration of digital technologies into supply chain operations, often referred to as digital supply chain (DSC), has emerged as a strategic shift that aims to empower organizations to proactively seize new opportunities rather than being caught off guard by unforeseen disruptions arising from economic volatility, global pandemics and regional conflicts. Thus, this study embraces a knowledge-centric approach to explore the direct and indirect impact of knowledge management, innovation and learning capabilities on DSC adoption in an emerging economy context. Furthermore, it aims to shed light on the moderating role of environmental dynamism in this intricate interplay.

Employing a cross-sectional survey, the research data were collected from 354 managers representing Egyptian manufacturing and service firms utilizing a structured questionnaire. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM).

The results unveiled that knowledge management capability (KMC) has the highest path coefficient value among all endogenous variables. It accounts for a significant portion of the variance in innovation and learning capabilities, which play pivotal roles in adopting DSC. Notably, learning capability appears to exert a more powerful influence on DSC adoption than innovation capability through direct and mediating effects. Furthermore, the findings underscore the moderating effect of environmental dynamism on the relationship between learning capability and DSC adoption. However, this moderating role is not observed in the link between innovation capability and DSC adoption.

There is a growing trend among firms to adopt DSC in response to significant environmental shifts. This study offers valuable insights for managers and policymakers, providing them with a deeper understanding of the DSC adoption process. The study’s findings assist in identifying crucial factors that boost DSC adoption and offer guidance on successfully leveraging digital technologies for managing supply chain practices. Moreover, the study offers stimulating directions for future DSC research directions.

The study contributes to the existing literature by expanding our understanding of the adoption of DSC by utilizing knowledge, innovation and learning capabilities within the context of emerging economies.

This study aims to explore the influence of innovation in entrepreneurial activities and job creation, examine the influence of innovation in entrepreneurial activities on jobs creation and investigate the moderating influence of entrepreneurial characteristics between entrepreneurial activity, innovation and job creation.

The data was collected from Oman Global Entrepreneurship Monitor data 2020. Sample of 2,000 individuals was involved in entrepreneurial activities. SPSS and PLS software were used to test the structural model and research hypothesis.

The results revealed that innovation affects the level of created jobs. Entrepreneurial activity has a significant influence on innovation, jobs created and an indirect effect on jobs created through the mediating influence of innovation. All direct and indirect relationships of entrepreneurial activity and innovation with the expected jobs are insignificant. The findings demonstrate that demographics moderate the relationship between entrepreneurial activity and innovation.

The study outcomes will be useful for policymakers to understand the innovation types in entrepreneurial activities and whether these enterprises can create future jobs.

The findings will help entrepreneurial ecosystems and managers to strengthen their business ventures using innovative business models.

This research will provide decision-makers with mechanism on how better business environment will benefit stakeholders.

While this topic is widely investigated in developed countries, little is known about the impact of innovation on entrepreneurial activities in creating jobs in developing countries. This research contributes to entrepreneurship innovation and institutional theory literature by understanding the role of the entrepreneurship conditions framework that support the jobs creation.

This paper aims at measuring the factors affecting early-stage entrepreneurial activity by opportunity vs necessity motives in India using theory of planned behaviour.

The study is based on the Adult Population Survey (APS) of Global Entrepreneurship Monitor (GEM), covering 4,165 respondents in 2018. The data has been analysed using descriptive statistics, chi-square test, analysis of variance and logistics regression. The theory of planned behaviour has been used to identify the determinants of early-stage entrepreneurial activity by opportunity vs necessity motives.

About 13.1% of the respondents reported early-stage entrepreneurial activity, of which opportunity motives were reported by 6.5% respondents, necessity motives by 5.4% respondents and the remaining 1.2% respondents reported other motives. Further, the mean difference in early-stage entrepreneurial activities by motives shows the domination of opportunity-driven entrepreneurial activities. Finally, marginal effects of all determining variables and three components of the theory of planned behaviour, i.e. attitude towards entrepreneurship (ATE), perceived subjective norm (PSN) and perceived behavioural control (PBC), have been estimated on opportunity vs necessity motives of early-stage entrepreneurial activities.

This paper contributes theoretically and practically to the existing body of knowledge by predicting the factors affecting opportunity vs necessity motives of early-stage entrepreneurial activities by applying the theory of planned behaviour. Considering the current focus of the government on promoting entrepreneurship, this piece of research can be valuable in adopting a motive-based approach in implementing entrepreneurial initiatives.

This paper provides unique insights into developing a policy framework for promoting new ventures based on the perceived motives of the entrepreneurs.

The main focus is to provide a non-similar solution for the magnetohydrodynamic (MHD) flow of Casson fluid over a curved stretching surface through the novel technique of the artificial intelligence (AI)-based Lavenberg–Marquardt scheme of an artificial neural network (ANN). The effects of joule heating, viscous dissipation and non-linear thermal radiation are discussed in relation to the thermal behavior of Casson fluid.

The non-linear coupled boundary layer equations are transformed into a non-linear dimensionless Partial Differential Equation (PDE) by using a non-similar transformation. The local non-similar technique is utilized to truncate the non-similar dimensionless system up to 2nd order, which is treated as coupled ordinary differential equations (ODEs). The coupled system of ODEs is solved numerically via bvp4c. The data sets are constructed numerically and then implemented by the ANN.

The results indicate that the non-linear radiation parameter increases the fluid temperature. The Casson parameter reduces the fluid velocity as well as the temperature. The mean squared error (MSE), regression plot, error histogram, error analysis of skin friction, and local Nusselt number are presented. Furthermore, the regression values of skin friction and local Nusselt number are obtained as 0.99993 and 0.99997, respectively. The ANN predicted values of skin friction and the local Nusselt number show stability and convergence with high accuracy.

AI-based ANNs have not been applied to non-similar solutions of curved stretching surfaces with Casson fluid model, with viscous dissipation. Moreover, the authors of this study employed Levenberg–Marquardt supervised learning to investigate the non-similar solution of the MHD Casson fluid model over a curved stretching surface with non-linear thermal radiation and joule heating. The governing boundary layer equations are transformed into a non-linear, dimensionless PDE by using a non-similar transformation. The local non-similar technique is utilized to truncate the non-similar dimensionless system up to 2nd order, which is treated as coupled ODEs. The coupled system of ODEs is solved numerically via bvp4c. The data sets are constructed numerically and then implemented by the ANN.

This paper is the third part of a research effort that aims to identify how various crises affected basic business processes using Institut Européen d'Administration des Affaires (INSEAD’s) industrial excellence framework (IEF) in large organizations.

This research compares three data sets from the same four companies against quality management benchmarks, with a span of more than two decades: (1) data collected in 2005 operating under a financially thriving corporate environment; (2) data collected in 2015, operating under a strong financial crisis and (3) data collected in 2022, operating under the serious COVID-19 crisis.

The authors identify the characteristics that helped them navigate successfully through almost two crises in a row. These companies successfully navigated crises by applying assessment and continuous improvement processes, forging close relationships with their suppliers and training their people systematically.

The findings can be adopted by practitioners who wish to navigate their organizations during serious crises. Researchers and academics may also find this study interesting for managing quality during a pandemic.

There are not many studies that investigate how the pandemic influenced the quality management practices in organizations in Greece in sequential crises. This paper brings insights into the enduring advantages of adopting a quality framework.

This study aims to investigate the impact of destination accessibility on pro-tourism behaviour directly and indirectly through environmental impacts and/or affective destination image from residents’ point of view. It also investigates the moderated mediation effect of residency length in the indirect relationships between destination accessibility and pro-tourism behaviour through the negative environmental impacts or affective destination image.

A quantitative method was applied. More specifically, the survey approach was found to be the most effective method of data collection for this research. In total, 351 questionnaires were collected through an online survey from people who currently live in Alexandria. The questionnaire was translated into Arabic while adhering to back-translation guidelines. SPSS and AMOS software were used to analyse the collected data.

The results showed that residents’ destination accessibility affects both destination image and destination environment but does not affect the pro-tourism behaviour. It indirectly affects the pro-tourism behaviour through only affective destination image. Residency length plays a moderation role in this indirect relationship.

The current study can help policymakers, governments and tourism institutions to make decisions. Policymakers can use these results to adopt a comprehensive policy that encompasses different interested parties in tourism, in order to ensure that tourism development efforts will succeed. The government can also join hands with tourism institutions to mitigate the negative impacts of tourism in order to balance the negative and positive impacts of tourism.

The study contributes to the existing literature by highlighting the importance of destination accessibility to support tourism behaviours in domestic tourism.