2-tuple Linguistic Decision-making with Consistency Adjustment Strategy and Data Envelopment Analysis

This paper evaluates the operating efficiency of accounting firms from five perspectives of human resource allocations by sampling a total of 42 large accounting firms listed in the 2013 Survey Report on Accounting Firms compiled by the Financial Supervisory Commission. The paper adopts the three analytical models—CCR (Charnes, Cooper, and Rhode model), BCC (Banker, Charnes, and Cooper model), and Super SBM (a slack-based measure of super-efficiency)—in the data envelopment analysis (DEA) framework. The four revenue streams—auditing publicly listed companies, tax advisory services, management consulting, and business registration and others—are defined as outputs in the empirical study. On the other hand, human resource metrics are referred to as Inputs. These metrics are either financial (wages) or non-financial (job positions, education, age, business services, and CPA licenses). According to the research findings, 22 out of the 42 sampled accounting firms are operationally efficient, while seven are inefficient when measured using the five constructs of human resource inputs. In terms of the overall operating efficiency, the average efficiency values in the CCR model are the highest when measured with education, age, and practice as the three constructs of human resources. In the Super SBM model, the average efficiency values are the highest when evaluated with practice, education, and age. The efficient accounting firms are mostly measured using the human resource constructs of education (34 firms), age (32 firms), and job positions (30 firms). For both efficient and inefficient firms, tax advisory services and auditing publicly listed companies are the two major sources of revenue and competitive advantage. Meanwhile, the management consulting business still requires extra effort. This paper suggests that accounting firms should spend more on wages as an input in order to enhance employees' loyalty and boost their willingness to work. The non-financial human resource metrics for efficient firms are partners, master's degrees, 25–34 years old, and auditing services; employees without CPA licenses are, in fact, the major contributor to operating efficiency. Managers are advised to assign important tasks to these employees and provide them with generous remunerations and benefits so as to retain these high-caliber professionals and motivate them to create revenues for different businesses. The non-financial human resource metrics for inefficient accounting firms are associates, university degrees, 25–34 years old, and auditing services; employees with CPA licenses are the major contributor to operating efficiency. Thus, managers should fully utilize this human resource and empower them to go the extra mile in boosting revenue streams and improving service quality. Finally, neither the change in input attributes nor the application of different DEA models alters the outcome of performance evaluations. Moreover, this paper expects to provide a template for allocating human resources and synchronizing accounting firms' operating strategies. 

Operating efficiency of accounting firms based on different perspectives of human resource structures

Performance evaluation of the practical application of cleaner production management system: A case study of steel enterprise

In the wake of the losses of human lives and disruption to the world economy caused by the spread of the COVID-19 pandemic, it has become imperative to assess the effectiveness of containment strategies adopted by countries. The success of any containment strategy of achieving low mortality and high recovery rate depends on the efficient utilization of available but limited resources, such as number of hospital beds and healthcare workers. While the spreading pattern of the pandemic has been researched heavily, there is limited research that comprehensively focuses on the efficient utilization of available resources to achieve the desired aims of low mortality and high recovery. In order to close this research gap, we employ a two-stage network data envelopment analysis (DEA) to identify the inefficiency in the process and resolve the resource constraints by considering medical and non-medical (administrative) interventions as two serial stages. The number of infected people is treated as the intermediate product, which is an undesirable output of the first stage and subsequently enters the second stage as an input. This network DEA model successfully addresses the conflict between the two stages over the handling of infected people and assesses the vulnerabilities of the countries against the transmission rates of the disease in the respective countries. Thus, the objective of this study is to develop a well-coordinated plan for different government agencies to jointly mitigate the risk under constrained resources. The findings reveal that almost 60% of the Organization for Economic Cooperation and Development (OECD) countries have used their resources suboptimally and are producing, on average, almost half the amount of the maximum possible outputs. As a sizeable amount of inefficiency can be explained by varying economic and demographic factors, such as health expenditure and the proportion of the aged population, the efficiency evaluation has been revisited with adjustments for unfavorable externalities. The analysis and its implications can help policymakers formulate optimal resource plans and identify potential areas for improvement. 

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Examining operational efficiency with prudent risks of Covid-19: a contextual DEA analysis with an undesirable intermediate measure

Multiple agricultural productions were a way of life for Ethiopian farmers. However, it was known for low productivity due to improper resource allocation. Hence, the farm household is under food insecurity and earned a low annual income. To overcome these challenges, the present study used hierarchical-based cluster data envelopment analysis by collecting data from 152 sample households through structured questionnaires. The finding suggested that the farm households in the study area were characterized by the low level of technical efficiency in multiple agricultural productions, implying that most farmers were unable to keep up with the current production frontier and technologies. The study's key result is that hierarchical-based cluster data envelopment analysis is more efficient than traditional data envelopment analysis. Furthermore, farmers in the study area are technically inefficient. From the determinants of technical efficiency in multiple agriculture, access to credit and the fertility of farmland have a positive impact on technical efficiency, whereas the age of the household and distance from infrastructures have a negative impact. Based on the significant determinants of efficiency, the present work recommends government agencies and agricultural development planners must improve farmers' knowledge towards soil fertility management practices through the construction of soil bunds, tree planting, grass planting, fencing, and the use of natural fertilizer; expansion of microfinance to rural area; and construction of the road for the market facility in the study area. Additionally, changing farmers' knowledge towards the uses of integration of manure products from livestock as fertilizer inputs for crop production and residues of crops as livestock consumption were paramount important.

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Multiple Agricultural Production Efficiency in Horro District of Horro Guduru Wollega Zone, Western Ethiopia, Using Hierarchical-Based Cluster Data Envelopment Analysis

Environmental performance evaluation of European farms by assessing polluting factors in joint production

Data envelopment analysis (DEA) is used for the performance evaluation of a set of decision making units (DMUs). Such performance scores are necessary for taking managerial decisions like allocation of resources, improvement plans for the poor performers, and maintaining high efficiency of the leaders. In classical DEA, it is assumed that the DMUs are operating in a similar environment. But in practice, this assumption is normally broken as DMUs operate in a varied environment due to several uncontrollable factors like socio-economic differences, competitiveness in the region and location. In order to address this issue, categorical DEA was proposed for the construction of peer groups by creating crisp categories based on the level of competitiveness. However, such categorizations suffer from indeterminate factors, for example, human judgment and biases, linguistic ambiguity and vagueness. In this paper, we propose a more realistic DEA approach which is capable of handling categories defined in natural languages or with vague boundaries and generates efficiency as triangular fuzzy number. The analysis indicates that if a higher degree of fuzziness is allowed while defining the boundaries of the reference set, it results in (1) a compromise with the accuracy, signified by the spread of the fuzzy efficiency, (2) degradation of the quality, signified by the centre of the fuzzy efficiency, of the decision. Finally, the applicability of this approach has been demonstrated using public library data for different regions in Tokyo city. The sensitivity of the optimal decisions to the changes in fuzzy parameters has also been investigated.

Utsav Pandey

Papers

Environmental performance evaluation of European farms by assessing polluting factors in joint production

Data envelopment analysis in hierarchical category structure with fuzzy boundaries