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Science and technology parks (STPs) have a limited capacity, which can create challenging conditions for applicants. This makes the location selection a multi-criteria decision-making (MCDM) problem to find and apply for the most appropriate STP with the highest accordance with the startup's requirements. This research aims to select the most appropriate STP to locate an international entrepreneurial pharmaceutical startup under uncertainty. Since drugs are generally produced domestically in developing countries such as Iran, the access of pharmaceutical startups to the resources provided by STPs can lead to overcoming competitors and improving the country's health system.

In this research, the factors or attributes effective on startup location were extracted through a two-round Delphi method, which was performed among 15 experts within three groups. Subsequently, the determining factors were used to select the location of a pharmaceutical startup among possible STPs. In this regard, decision-makers were allowed to use different types of numbers to transfer their opinion. Afterward, the heterogeneous weighted aggregated sum product assessment (HWASPAS) method was applied to calculate the score of each alternative and rank them to place the studied startup successfully.

The results indicated that Tehran STP stands in the first place; however, if the decision was made based on single criterion like cost, some other STPs could be preferable, and many managers would lose this choice. Furthermore, the results of the proposed method were close to other popular heterogeneous MCDM approaches.

A heterogeneous WASPAS is developed in this article for the first time to enable international entrepreneurs to imply their opinion with various values and linguistic variables to reduce the emphasis on accurate data in an uncertain environment.

This paper aims to recognise and identify fears of failure in the international entrepreneurship ecosystem and find the effects of these fears on each other to reduce these increasing effects and minimise the mental barriers for potential entrepreneurs. Therefore, this research is beneficial to strengthen international entrepreneurial personality and to reduce the shadows of fears on entrepreneurs’ minds. To address this purpose the international tourism industry has been investigated in the emerging economy of Iran.

This paper is organised based on experts' opinions, and then factors are identified by literature review and are finalised through the Delphi method with a panel of practical and academic experts. These fears are categorised using a questionnaire and factor analysis (FA) technique. The respondents were entrepreneurship students and tourism entrepreneurs. Subsequently, the effect and importance of fears of failure are scored and ranked by some experts. These fears were analysed through decision-making trial and evaluation laboratory (DEMATEL) method. The results presented the effects of fears of failure in the international entrepreneurial in the tourism industry and the relationship amongst them.

The first two fears are respectively “fears of future” which are caused by the unclear situation; and “fear of losing credit” that is due to the importance of “personal relationships” in Iranians culture. As pointed out by results, “fear of future” is the most effective group of factors that may lead to other fears too. This fear seems important in the international entrepreneurial tourism industry because of future ambiguity.

This study indicates the relations between groups of fears and offers some practical and applicable solutions to reduce these effects. To the best knowledge of the authors, analysing the relationship between fears amongst the entrepreneurs has not been investigated previously.

The purpose of this paper is to extend an approach to solve linear programming problems with grey data and variables, based on a developed multi‐objective programming approach.

The proposed approach to generally solve the grey linear programming problems is based on the notion of order relation between interval grey numbers. This notion is applied to cascade the grey objective function to a bi‐objective problem based on the objective function of the original problem. The same approach is taken to transform grey constraints to a set of corresponding linear constraints. Finally, the obtained multi‐objective model can be solved by any existing methods in the literature.

One of the shortcomings of previous approaches to solve grey linear programming problems was that they required the grey coefficients of objective function to be both side negative or positive. The approach proposed here does not have such a requirement and guarantees the feasibility of solutions.

A different approach is developed in the paper that can be used to solve grey linear programming problems in general form. The method relaxes the limitation of existing approaches.

The purpose of this paper is to extend a methodology for solving multi‐objective linear programming (MOLP) problems, when the objective functions and constraints coefficients are stated as interval numbers.

The approach proposed in this paper for the considered problem is based on the maximization of the sum of membership degrees which are defined for each objective of multi objective problem. These membership degrees are constructed based on the deviation from optimal solutions of individual objectives. Then, the final model based on membership degrees is itself an interval linear programming which can be solved by current methods.

The efficiency of the solutions obtained by the proposed method is proved. It is shown that the obtained solution by the proposed method for an interval multi objective problem is Pareto optimal.

The proposed method can be used in modeling and analyzing of uncertain systems which are modeled in the context of multi objective problems and in which required information is ill defined.

The paper proposed a novel and well‐defined algorithm to solve the considered problem.

Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous.

To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters.

Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts.

The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained.

The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.