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This paper aims to propose a two-period model in which an original equipment manufacturer (OEM) decides the remanufacturability level of products in product design and unit patent licensing fee at the first period, and a third-party remanufacturer (3PR) that has been licensed by the OEM enters the remanufacturing market to compete with the OEM at the second period.

This paper analyzes the OEM's optimal decisions of remanufacturability level in the product design and unit patent licensing fee at the first period, as well as the OEM's and the 3PR's optimal decisions of selling prices at the second period, under two scenarios that the remanufacturing is constrained or unconstrained by the collected quantity available at the end of the first period, by making use of game theory.

The study finds that the OEM will choose high remanufacturability in product design only when the unit cost saving of remanufacturing or unit production cost of new products exceed certain thresholds.

The study is the first attempt to simultaneously integrate product design and patent licensing in remanufacturing. It provides useful insights for OEM managers who face competition from 3PRs and may use their product design strategies to deter 3PRs and may protect patent of products by levying patent licensing fees from 3PRs.

Considerable evidence suggests that although they overlap, entrepreneurial and employee stressors have different causal antecedents and outcomes. However, limited empirical data explain how entrepreneurial traits, work and life drive entrepreneurial stressors and create entrepreneurial strain (commonly called entrepreneurial stress). Drawing on the challenge-hindrance framework (CHF), this paper hypothesises the causal effect of hindrance stressors on entrepreneurial strain. Furthermore, the study posits that entrepreneurial stressors and the resultant strain affect entrepreneurial behaviour.

The study adopts an SEM-based machine-learning approach. Cross-lagged path models using SEM are used to analyse the data and train the machine-learning algorithm for cross-validation and generalisation. The sample consists of 415 entrepreneurs from three countries: India, Oman and United Arab Emirates. The entrepreneurs completed two self-report surveys over 12 months.

The results show that hindrances to personal and professional goal achievement, demand-capability gap and contradictions between aspiration and reality, primarily due to unique resource constraints, characterise entrepreneurial stressors leading to entrepreneurial strain. The study further asserts that entrepreneurial strain is a significant predictor of entrepreneurial behaviour, significantly affecting innovativeness behaviour. Finally, the finding suggests that psychological capital moderates the adverse impact of stressors on entrepreneurial strain over time.

This study contributes to the CHF by demonstrating the value of hindrance stressors in studying entrepreneurial strain and providing new insights into entrepreneurial coping. It argues that entrepreneurs cope effectively against hindrance stressors by utilising psychological capital. Furthermore, the study provides more evidence about the causal, reversed and reciprocal relationships between stressors and entrepreneurial strain through a cross-lagged analysis. This study is one of the first to evaluate the impact of entrepreneurial strain on entrepreneurial behaviour. Using a machine-learning approach is a new possibility for using machine learning for SEM and entrepreneurial strain.