کاوشهای مدیریت بازرگانی (Aug 2021)
Identifying, explaining, and ranking the factors affecting the social responsibility of iron ore mining companies in Yazd Province
Abstract
Introduction: The growth and development of various institutions and organizations as well as a rising trend in competitions among these entities have made them reflect on their organizational interests to survive in the business world. Due to the rising importance of our global interdependence, some concepts like corporate social responsibility (CSR) have a significant role in this dynamic and flourishing industry composed of lodging, transportation, and so on. Accordingly; any small decisions or actions occurring in one of these centers, regardless of their direct effects on the desired sectors, can gradually have direct and indirect, hidden and obvious as well as tangible and intangible impacts on every sector in the society and, consequently, lead to a series of actions and reactions at all levels. Studies have shown that social responsibility is one of the best tools for gaining public legitimacy and competitive advantage. According to this, social responsibility means the responsibility or commitment of a person or organization to social concepts such as individuals or the physical environment around them. Thus, the purpose of this study is to identify, explain and rank the factors affecting the corporate social responsibility (CSR) of the iron ore mining companies in Yazd Province. Methodology: A combination of qualitative and quantitative methods was employed in this study. The research procedure started with studying the theoretical foundations to identify the factors affecting the CSR ranking of the iron ore mining companies in Yazd Province, Iran. Accordingly, attempts were made to review the most important factors and indicators affecting the CSR ranking by the explanation of some selected experts and through the study and review of the resources available in this area, including the existing models and theories in this field. The Corporate Social Responsibility (CSR) indicators and dimensions were extracted using the content analysis of the interviews with the expert groups from a total number of nine iron ore mining companies in Yazd Province, Iran. Then, the dimensions were analyzed using interpretive structural modeling (ISM). The relations among the indicators were also determined via a Fuzzy Cognitive Map, and, subsequently, they were ranked through an FC Mapper. In the end, the intensity of the impact of the indicators on one another was calculated through the Mic Mac technique. Results and Discussion: At the first step of the study, a qualitative research methodology was implemented through meetings and interviews with the selected experts of the iron ore mining companies of Yazd Province. The concepts associated with CSR were explained, the key statements based on the identifiers (codes) were registered, and they were consequently introduced as open codes. Regarding the content analysis, after the review of the open codes, the statements with overlapping concepts and meanings were incorporated, and then the dimensions were identified as the axial codes. After that, the content analysis was performed of the interviews with the expert groups from a total number of nine iron ore mining companies. The analyzed dimensions included safety and health as well as legal, ethical, environmental, philanthropic, and economic aspects. The dimensions were analyzed by interpretive structural modeling (ISM) at five levels. At the first level, there was the economic dimension. The second level was for the environmental and philanthropic dimensions. At the third level, there was the ethical dimension. The fourth level belonged to safety and health, and the fifth level was the legal dimension. So, the relations among the indicators were determined via a fuzzy cognitive map. Creating an FCM model requires inputs that come from the experience and knowledge of experts in the field. In FCM models, the accumulated experiences of individuals with update knowledge in the field, for which the model was drawn, are integrated, and cause-and-effect relationships are established among the factors that make up the system. Subsequently, they were ranked through an FC Mapper. The factors ranked the highest were "striving to reduce harm to the environment", "striving to reduce toxic and greenhouse gases", "increasing employee satisfaction and motivation", "developing and promoting community knowledge and culture" and "providing real-time information about the work and the disadvantages resulting from it”. In the end, the severity of the effects of the variables on one another was also calculated through MicMac. Accordingly, the indicators of “Supporting voluntary involvement of employees in social activities”, “Efforts to reduce toxic and greenhouse gases”, and “Transparency in signing contracts with contractors” were considered as effective ones. Also, the dichotomous indicators with very high capacity to become key actors within the system included “Respect for contractual obligations” and “Increase in employee satisfaction and motivation”. The indicators influenced were “Attention to employees’ working conditions”, “Provision of factual information about work and its resulting damage,” “Efforts to reduce damage to the environment,” “Development and promotion of community knowledge and culture” as well as “Attention to customer satisfaction”. The remaining indicators were placed in the independent group. Conclusion: This research can be beneficial to governments, researchers and organizations because it helps them to understand the indicators based on corporate social responsibility so as to be persuaded to fulfill their commitment to the society.
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