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The purpose of this paper is to analyze the residual stress relaxation in laser shock‐peened and shot‐peened IN100 subject to thermal exposure.

Shot peening (SP) is a commonly used surface treatment that imparts compressive residual stress into the surface of components. The shallow depth of compressive residual stresses, and the extensive plastic deformation associated with SP, has been overcome by modern approaches such as laser shock peening (LSP). LSP surface treatment produces compressive residual stress magnitudes that are similar to SP that extend four to five times deeper, and with less plastic deformation. Retention of compressive surface residual stresses is necessary to retard initiation and growth of fatigue cracks under elevated temperature loading conditions.

Results indicated that the LSP processing retains a higher percentage of the initial residual stress profile over that of SP.

The retained residual stresses after thermal exposure of these surface treatment processes can be incorporated into a life prediction methodology that takes credit for beneficial compressive surface residual stresses to delay initiation and retard fatigue crack growth.

This study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and premium rates in insurance companies. The proposed method aims to improve default risk predictions and assist with client segmentation in the banking system.

This research introduces the group method of data handling (GMDH) technique and a diversified classifier ensemble based on GMDH (dce-GMDH) for predicting default risk. The data set comprises information from 30,000 credit card clients of a large bank in Taiwan, with the output variable being a dummy variable distinguishing between default risk (0) and non-default risk (1), whereas the input variables comprise 23 distinct features characterizing each customer.

The results of this study show promising outcomes, highlighting the usefulness of the proposed technique for bancassurance and client segmentation. Remarkably, the dce-GMDH model consistently outperforms the conventional GMDH model, demonstrating its superiority in predicting default risk based on various error criteria.

This study presents a unique approach to predicting default risk in bancassurance by using the GMDH and dce-GMDH neural network models. The proposed method offers a valuable contribution to the field by showcasing improved accuracy and enhanced applicability within the banking sector, offering valuable insights and potential avenues for further exploration.

This study aims to contribute to the body of scientific knowledge about teaching and promoting social entrepreneurship in higher education institutions (HEIs) based on a measurement before and after concluding an educational experience.

It tests hypotheses to draw conclusions from analyzing the pre- and post-test results of three study cases with different training experiences, to know the characteristics of the 304 participants.

The study indicated that incorporating transversal social entrepreneurship projects in various courses resulted in students feeling more capable regarding their social entrepreneurship potential.

The study presents the analysis of social entrepreneur training in three different curricular study cases. The information obtained adds value to social entrepreneurship education research that takes social entrepreneurship beyond business schools.

The purpose of this study is to analyze the post-harvest supply chain enablers (PHSCEs) for vertical integration to enhance rural employability, farmer profitability and rural produce marketability (i.e. market prospects) in the post-harvest supply chain (PHSC). The impact of vertical integration is also explored for various commercial produces.

A structural equation modeling (SEM) of PHSCEs for vertical integration was developed to enhance market prospects, rural employability and farmer profitability. The impact of business-to-business (B2B) and business-to-customer market prospects are explored in various dimensions for stakeholders such as farmers, manufacturers (processors), distributors and retailers. The fuzzy technique for order of preference by similarity to ideal solution (F-TOPSIS) was used to prioritize these PHSCEs to improve market prospects and rural employability.

The PHSCEs are clustered into three groups, namely, initiatives at the strategic frontier, initiatives at the tactical frontier and concerns for rural employability via vertical integration using exploratory factor analysis, confirmatory factor analysis and SEM to prove the null hypothesis. With F-TOPSIS results, the availability of warehousing was found to be the most crucial enabler when observing the PHSCEs from the initiatives’ perspective. The technology adaptability and availability, institute for training and research and information infrastructure and information visibility were found to be the key PHSCEs when observed from PHSC stakeholders’ perspectives.

The implementation of this study will improve the rural produce marketability, rural employability, B2B marketing (i.e. effective distribution) and subsequent value chains with the practice of vertical integration for fresh produce at the rural level.

The outcomes of this study have a key role in developing the rural regions and improving rural livelihoods via value addition. The awareness of commercial cultivation and value addition in rural areas needs to be improved. This will help farmers to earn better revenues with improved market prospects in comparison to the revenues obtained from the cultivation of staple/conventional crops.

In an era of cold chains and food processing, this study aims to disseminate awareness about value addition for commercial and fresh produces at the rural level. The implication of this study will improve rural produce marketability, rural employability and farmer profitability at the rural level with the level of vertical integration.

The authors’ aim is to investigate if entrepreneurial orientation (EO) has a role in interpreting an external crisis, such as COVID-19, as an opportunity for investing in digital transformation.

The authors undertook multiple case study research on the digital transformation of seven “Made in Italy” SMEs and proposed a conceptual framework that sees an external stimulus (e.g. the pandemic) as a driver of the digitalisation, filtered by the entrepreneurial orientation of SMEs.

The authors’ results show how EO deals with the changing environment and helps address the market opportunities related to digitalisation.

This study contributes to the existing literature on EO and digital transformation of SMEs, creating a new model for forthcoming studies on this topic. In addition, interesting insights are offered on the role of EO to promote the introduction of digital transformation in SMEs operating in the “Made in Italy” sectors.

This study shows the role of EO as a reactive characteristic during a crisis. Thus, the authors’ suggestion to SMEs is to develop their EO as a part of the strategic orientation. In addition, this work encourages policymakers to invest in the promotion of specific interventions aimed at supporting entrepreneurs in enhancing their capacity to effectively manage digital transformation.

Identifying the most important triggers of digitalisation in times of crisis remains an underexplored area of research. Thus, this study adds value to both digital transformation and entrepreneurial orientation topics.