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This conceptual, multi-voiced paper aims to collectively explore and theorize family entrepreneuring, which is a research stream dedicated to investigating the emergence and becoming of entrepreneurial phenomena in business families and family firms.

Because of the novelty of this research stream, the authors asked 20 scholars in entrepreneurship and family business to reflect on topics, methods and issues that should be addressed to move this field forward.

Authors highlight key challenges and point to new research directions for understanding family entrepreneuring in relation to issues such as agency, processualism and context.

This study offers a compilation of multiple perspectives and leverage recent developments in the fields of entrepreneurship and family business to advance research on family entrepreneuring.

This study presents evidence concerning the effects of affective and cognitive rhetoric on the underpricing of firms at the time of their initial public offering. It is suggested that firms that use less affective, and more cognitively oriented discourse in their IPO prospectus will experience better underpricing outcomes. We examine these assertions using a sample of young high-tech IPO firms where investors rely on prospectuses as accurate and informative firm communications. Results from a robust five-year time span observe initial support for the hypothesized effects. Moreover, the signaling of a higher degree of entrepreneurial orientation in the firm prospectus is found to worsen the negative effects of affective discourse

This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located.

The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms.

According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO.

In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered.

The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs.

This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.

The research is based on a critically analyzed literature review focused on the unanticipated outcomes, trade-offs and tensions of sustainable operations and supply chain management (OSCM), including the articles selected for this special issue.

The authors introduce the key concepts, issues and theoretical foundations of this special issue on “The hidden side of sustainable operations and supply chain management (OSCM): Unanticipated outcomes, trade-offs and tensions”. The authors explore these issues within this context, and how they may hinder the authors' transition to more sustainable practices.

The authors present an overview of unanticipated outcomes, trade-offs, tensions and influencing factors from the literature, and identify how such problems may emerge. The model addresses these problems by highlighting the crucial effect of the underlying state of knowledge on sustainable OSCM decision-making.

The authors limited the literature review to journals that ranked 2 and above as defined by the Chartered Association of Business Schools Academic Journal Guide. The main implication for research is a call to focus attention on unanticipated outcomes as a starting point rather than only an afterthought. For practitioners, good intentions such as sustainability initiatives need careful consideration for potential unanticipated outcomes.

The study provides the first critical review of unanticipated outcomes, trade-offs and tensions in the sustainable OSCM discourse. While the literature review (including papers in this special issue) significantly contributes toward describing these issues, it is still unclear how such problems emerge. The model developed in this paper addresses this gap by highlighting the crucial effect of the underlying state of knowledge concerned with sustainable OSCM decision-making.

In this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used in this study.

It can be seen that support vector machine (SVM) is used with particle swarm optimization (PSO) where PSO is used as a fast and accurate classification to search the problem-solving space and finally the results are compared with the neural network performance.

Based on the result, the authors can say that both new models are trustworthy in 6 days, however, SVM-PSO is better than basic research. The hit rate of SVM-PSO is 77.5%, but the hit rate of neural networks (basic research) is 74.2.

In this research, two approaches (raw-based and signal-based) have been developed to generate input data for the model: raw-based and signal-based. For comparison, the hit rate is considered the percentage of correct predictions for 16 days.