دانلود مقاله برآورد عوامل موفقیت مدیریت ریسک سازمانی پویا در شرکت‌های کوچک و متوسط با تصمیم‌گیری یکپارچه
خلاصه
1. مقدمه
2. مدیریت ریسک سازمانی در SMEs
3. رویکرد q-ROF-انتروپی-RS-ARAS پیشنهادی
4. جمع آوری داده ها و نتایج پیاده سازی
5. بررسی های مقایسه ای و حساسیت
6. نتیجه گیری
بیانیه مشارکت نویسنده CRediT
در دسترس بودن داده ها
منابع
Abstract
1. Introduction
2. Enterprise risk management in SMEs
3. Proposed q-ROF-entropy-RS-ARAS approach
4. Data collection and implementation results
5. Comparative and sensitivity investigations
6. Conclusions
CRediT authorship contribution statement
Data availability
References
چکیده
برای موفقیت، یک شرکت اساساً باید میزان مناسبی از ریسک را بپذیرد. بنابراین، اهمیت بسیار زیاد مدیریت ریسک، بسیاری از محققین را به تمرکز بر نحوه اجرای موثرترین سیستم های مدیریت ریسک سازمانی (ERM) جلب کرده است. به طور کلی، شرکت های کارآفرین کوچکتر باید به طور غیر رسمی با این چالش کنار بیایند. اکثر شرکت های تولیدی کوچک و متوسط (SMEs) شرکت های خانوادگی هستند. در نتیجه، پویایی خانواده تا حد زیادی بر نحوه تجارت چنین شرکت هایی تأثیر می گذارد. مجموعه فازی ارتوپیر q-Rung (q-ROFS) یک پنجره گسترده برای استخراج اولویت تصمیم گیرندگان (DMs) فراهم می کند. با الهام از مزایای q-ROFS، در مقاله حاضر، یک چارچوب تصمیم گیری جدید، ارزیابی نسبت جمعی آنتروپی q-ROF-rank (RS)-Additive Ratio (ARAS)، به نام q-ROF-انتروپی-RS رویکرد -ARAS توسعه یافته است. رویکرد q-ROF-انتروپی-RS برای محاسبه وزن عوامل موفقیت بحرانی (CSFs) برای ERM پویا در SMEها و مدل q-ROF-ARAS برای ارزیابی اولویت‌های سازمانی استفاده می‌شود. یک مطالعه موردی تجربی برای ارزیابی CSFها برای مدیریت ریسک سازمانی پویا در SMEها انجام شده است. علاوه بر این، مقایسه و بررسی حساسیت برای نشان دادن برتری چارچوب توسعه‌یافته انجام می‌شود.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
To succeed, a firm essentially needs to take the right amount of risk. Thus, the great significance of risk management has attracted many researchers to focus on how to implement enterprise risk management (ERM) systems most effectively. Generally, smaller entrepreneurial firms have to cope with this challenge more informally. Most manufacturing small and medium-sized enterprises (SMEs) are family companies; as a result, family dynamics greatly impact how such companies do business. The q-rung orthopair fuzzy set (q-ROFS) provides a wide window for the preference elicitation of decision-makers (DMs). Inspired by the advantages of the q-ROFS, in the current paper, a novel decision framework, the q-ROF-entropy-rank sum (RS)-additive ratio assessment (ARAS), called the “q-ROF-entropy-RS-ARAS” approach, is developed. The q-ROF-entropy-RS approach is applied to compute the weights of critical success factors (CSFs) for dynamic ERM in SMEs, and the q-ROF-ARAS model is used to assess enterprise preferences. An empirical case study is conducted to evaluate the CSFs for dynamic enterprise risk management in SMEs. Additionally, the comparison and sensitivity investigation are carried out to show the superiority of the developed framework.
Introduction
In the current world, which is becoming increasingly digital, companies face several challenges in establishing and/or sustaining a competitive advantage in the market (Ganju et al., 2016; Rocha et al., 2016). Compared with conventional firms, responsive firms operate differently. Such companies are normally acquainted with dynamic, exponential, and disruptive thinking; through these experiences, they are made ready to adopt exponential growth (Ismail et al., 2014). How a company is structured and operates demonstrates its “Enterprise Risk Management (ERM)” system. As a result, to effectively manage risks, there is a need for constant alignment between the enterprise and its risk function (Knight, 1921), which could be achieved by incorporating ERM into the firm’s decision-support processes. ERM, as a decision support tool, could be effectively used by every company (with a focus on organizational processes) to manage the risks that may arise during business operations in the real environment (ISO, 2018). ERM is defined as a “process that combines the organization’s entire risk management activities in one integrated, holistic framework to achieve a comprehensive corporate perspective” (ISO, 2018). Currently, many firms use different ERM frameworks. Two popular risk frameworks are the ISO31000 and the “Committee of Sponsoring Organizations of the Treadway Commission (COSO)”. The COSO helps firms enhance the quality of their model in the management of risks well to satisfy the demand of an evolving business environment. By adopting the COSO, companies would be capable of further understanding the risks that affect the outcome of their business strategies and goals. ISO3100 is presently used as a best practice in risk management; it also incorporates the best practices of the COSO (ISO, 2018).
Conclusions
Business communities and scholars working in this domain have become increasingly interested in ERM. Some previously-conducted studies have reported that despite the fact that ERM has received positive attention, it has not been extensively implemented yet. The literature still lacks research into the determinants of ERM and ERM CSFs. Most past studies have concentrated on issues related to large enterprises and ignored SMEs. Accordingly, the current study proposes an inclusive framework for the CSFs of ERM adoption in manufacturing SMEs. To analyze, rank, and evaluate the CSFs for dynamic ERM in SMEs, this study introduced an integrated q-ROF-entropy-RS-ARAS. To rank critical success factors for dynamic ERM in SMEs, the q-ROF-entropy-RS is utilized for determining the integrated weight values. Afterward, the q-ROF-ARAS is used to prioritize different enterprises with the CSFs for dynamic ERM in SMEs in this process. To show the usefulness of the presented method, a case study is discussed on the critical success factors identified to be effective in applying dynamic ERM in SMEs. The outcome of the assessment shows that the enterprise-IV (O4) with a utility degree of 0.7675 is the optimal choice for evaluating the CSFs for dynamic ERM in SMEs. To validation of the results of this study, a comparison is discussed between the performance of the presented approach and that of extant models. Finally, sensitivity analysis and comparison with some extant models have been presented to validate the robustness and stability of the obtained outcomes.