درخت حوزه‌های تخصصی

This study aims to explain and elaborate a new model for the business environmental hostility in small and medium enterprises in the science and technology park in Kermanshah (Iran). The research method is a mixed method. Content analysis was used in the qualitative part, and a confirmatory factor analysis was used in the quantitative section. In the qualitative section for the environmental hostility dimension of the environmental turbulence, two components of competitive turbulence and regulatory turbulence, as well as seven sub-components were identified and validated. As a consequence, the conceptual model was extracted. This study proposed a conceptual model of business environmental hostility and the researcher-made questionnaire of environmental hostility confirmed with high validity and reliability. The model was verified based on the small and medium-sized enterprises in Kermanshah (Iran), and it is suggested that this new environmental hostility model can be used in practice to improve managing the small and medium-sized enterprises.

Today, the information systems play a critical role in business for each organization. Like other organizations, hospitals use information systems for data collection, data storage, data processing and the like to have long-term and short-term achievements. Despite the very benefits of implementing HIS and its costly implementation, the HIS project sometimes fails. The importance of the HIS failure and preventive practices in this regard have led researchers investigate the causes of failure for information systems in hospitals. In this paper, an FMEA-based model is presented in an intuitionistic fuzzy environment to evaluate the HIS failure factors. For this purpose, Data required to implement the proposed model were collected in 5 hospital, in Kerman (Iran). Based on research studies and survey of hospital academic experts, a total number of 27 failure modes were determined for the implementation HIS. The results of the proposed approach indicated that 8 factors are of paramount importance in terms of HIS failure causes: Individuals' lack of skill/knowledge, lack of integration between system and organizational activities, unrealistic planning, lack of IT management or weak project team (information system), improper software development, lack of managerial skills, misdiagnosis of roles and responsibilities, inconsistency between corporate culture and change requirements (compatibility).

The purpose of this study is to provide a model with a dynamic system method to investigate the factors affecting the technological capabilities enhancement in the high-tech industries of high-speed train of the rail transportation system. For this purpose, after reviewing the literature and conducting several meetings with experts in the rail transportation industry, a conceptual and qualitative structure of the subsystems and how they relate in this area were obtained. Then, in order to identify the effective dynamics in upgrading technological capabilities in high-tech industries in high-speed rail transportation, causal loops were drawn and the model was simulated with VENSIM software. Then, using a simulation model, the technology development process was modeled based on the developed structure. Finally, in order to consider the uncertainty space, based on the expert’s opinions, two scenarios of optimism and pessimism were considered. Based on the obtained results, variables such as having a suitable vision for technology development, recognizing basic technological priorities, ability to use and control technology effectively in main and support processes, ability to learn from one technology to another, ability to identify, evaluate, negotiate and finalize the terms of technology acquisition and support facilities, the ability to identify customers, announce auction prices and negotiate terms of sale, the ability to plan, monitor and control research and development projects and having  a proper system for evaluating technological projects,  have  great impact.

ELECTRE TRI is the most applicable and developed outranking based classification method in the field of MCDA. By including a large number of parameters, it provides a huge amount of information on criteria which enriches decision making process, although calculation of these large number of parameters is very time consuming and difficult task. To tackle this problem, this paper proposes a new method called NSGA-ELECTRE, by which the NSGA-  algorithm learns ELECTRE TRI and elicits its parameters through an evolutionary process. The proposed method contributes to the literature by utilizing a pair of conflicting objective functions including Type I errors and Type II errors instead of using a single criterion named “classification accuracy” which used frequently in the related works. The proposed bi-objective method is applied to six known credit risk datasets. The NRGA model is used as a benchmark for validation. Computational results indicate outstanding performance of the NSGA-ELECTRE method.