روش های کمی برای سنجش شاخص های کیفیِ بهره وری سازمانی (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
در سال های اخیر، سنجش بهره وری سازمانی به یکی از دغدغه های اصلی محققان و مدیران تبدیل شده است؛ با این حال، تمرکز عمده بر جنبه های فنی و اقتصادی، موجب نادیده گرفتن ابعاد اجتماعی و انسانی بهره وری به عنوان یک مفهوم چندبعدی شده است. این پژوهش با تمرکز بر جنبه های اجتماعی و انسانی بهره وری، با هدف شناسایی و معرفی مناسب ترین روش های کمی، برای سنجش این ابعاد کیفی انجام شده است. در این پژوهش از رویکرد ترکیبی (کیفی-کمی) و منطق استقرایی-قیاسی بهره گرفته شده است. در گام نخست از طریق مرور سیستماتیک، مقالات مرتبط با سنجش بهره وری سازمانی تحلیل شده و با مدل چندسطحی تطبیق داده شدند و با استفاده از تکنیک آنتروپی شانون، وزن شاخص های کمی و کیفی استخراج گردید. در گام های بعدی با استفاده از روش مرور دامنه ای و با بهره گیری از نظر خبرگان در گروه کانونی، مناسب ترین روش های سنجش، برای هر یک از شاخص های کیفی تعیین شد. جامعه آماری در این مرحله از پژوهش خبرگان اجرایی و دانشگاهی با حداقل سه سال سابقه اجرایی و پژوهشی در زمینه مدیریت بهره وری هستند. نتایج نشان می دهد که سهم شاخص های کمی در سنجش بهره وری ۷۰ درصد و سهم شاخص های کیفی ۳۰ درصد است؛ ضمن آن که بسیاری از شاخص های کیفی به دلیل پیچیدگی در اندازه گیری، مورد غفلت قرار گرفته اند. یافته های این تحقیق، ضمن ارائه چارچوب عملیاتی برای سنجش کیفی بهره وری، امکان انتخاب روش سنجش را متناسب با ماهیت شاخص و داده های مورد نیاز فراهم می سازد. از منظر کاربردی، نتایج این پژوهش می تواند مبنایی برای تصمیم گیری مدیران و پژوهشگران در راستای ارزیابی جامع بهره وری باشد.Quantitative Methods for Measuring Qualitative Indicators of Organizational Productivity
In recent years, measuring organizational productivity has become one of the main concerns of researchers and managers; however, the focus on technical and economic aspects has led to the neglect of the social and human dimensions of productivity as a multidimensional concept. This research, focusing on the social and human aspects of productivity, aims to identify and introduce the most appropriate quantitative methods for measuring these qualitative dimensions. This research uses a mixed methodology and inductive-deductive logic. In the first step, by analyzing the theoretical literature, the multilevel model of productivity was considered as the basis of the research, and then, using a systematic review method, articles related to measuring organizational productivity were analyzed and adapted to the multilevel model, and using the Shannon entropy technique, the weights of quantitative and qualitative indicators were extracted. In the next steps, the most appropriate measurement methods for each of the qualitative indicators were determined using the scope review method and the opinions of experts in the focus group. The statistical population in this stage of the research is executive and academic experts with at least three years of executive and research experience in the field of productivity management. The result of this study shows that in literature, the share of quantitative indicators in measuring productivity is about 70 percent and the share of qualitative indicators is 30 percent while many qualitative indicators have been neglected due to the complexity of measurement. The findings of this study, while providing an operational framework for qualitative measurement of productivity, provide the possibility of selecting a measurement method appropriate to the nature of the indicator and the required data. From practical perspective, the results of this study can be a basis for decision-making by managers and researchers in line with comprehensive productivity assessment.
Introduction
Productivity, efficiency, and effectiveness are distinct concepts. Efficiency emphasizes quantitative and short-term changes, and effectiveness emphasizes qualitative and long-term changes (Linna, Pekkola, Ukko & Melkas, 2010). Productivity is also a multidimensional concept with diverse interpretations that include efficiency, effectiveness, assets, profitability, quality, and value creation (Abbasi et al., 2024). In the classical period, productivity was measured based on the ratio between inputs and outputs and had a quantitative and mechanical concept (Abbasi et al., 2024). According to the social systems perspective in the neoclassical era, productivity is not only a technical or economic concept, but also a social phenomenon that is tied to human behavior and intra-organizational interactions (Mackenzie & Bititci, 2023). These new approaches push productivity measurement towards indicators that are qualitative and subjective in nature. However, existing studies focus on economic and quantitative dimensions, and these social and human dimensions have been ignored. Therefore, the purpose of this study is to answer the following questions:
1) Which qualitative indicators have been neglected in the studies?
2) What are the most appropriate measurement methods for each of the quality indicators?
Materials and Methods
This research was conducted in three phases. In the first phase, a systematic review and quantitative analysis was used to classify quantitative and qualitative indicators of productivity and neglected indicators. In the second phase, through a Scoping Review and qualitative analysis, methods for measuring qualitative indicators were examined and the requirements, advantages and disadvantages of each method were determined. In the third phase, the most appropriate measurement methods for each qualitative indicator were determined in a focus group with the consensus of experts in two individual and group phases.
Discussion and Results
The first step involved examining the indicators measured in articles through a systematic review and comparing them with the indicators from Abbasi et al. (2024). The Shannon entropy method was used to calculate the weight of these indicators, revealing that 70% of productivity measurement indicators were quantitative, while only 30% were qualitative. Many qualitative indicators were ignored in the studies.
In the second step, methods for measuring quality indicators were analyzed through a domain review. The literature helped identify the requirements, advantages, and disadvantages of each method. The methods selected were based on their use in studies across various fields and included the LDA method, Shannon entropy, utility measurement, fuzzy Delphi method, multilevel clustering, and several others.
The third step involved surveying experts in a focus group to examine each productivity indicator and determine the most suitable method for each indicator based on expert consensus. The methods chosen considered each indicator's advantages, requirements, and necessary data. For example, to measure cooperation improvement in an organization, neural networks were suggested to evaluate managers' and employees' beliefs about cooperation. The LDA method was proposed for assessing organizational reputation through feedback from new employees and customers. Additionally, a data-driven method was introduced to measure customer loyalty and retention by analyzing repurchase rates and customer returns.
Conclusion
The multilevel model by Abbasi et al. (2024) defines productivity as a process involving both quantitative and qualitative indicators related to inputs, outputs, results, and effects. Ignoring quality indicators overlooks a significant part of productivity assessment. Current literature primarily emphasizes quantitative data, leaving qualitative aspects underexplored due to complexity. The study provides a matrix of qualitative indicators and suitable measurement methods to assist researchers and organizations in effectively measuring productivity. Future research should concentrate on measuring quality indicators across different industries to uncover variations and develop improved solutions.
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