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The purpose of this paper is to propose an optimal technology selection (OTS) method considering technology alternatives' required input levels of resource, to help companies select an optimal technology.

The proposed method clustered technology alternatives according to their required input levels of resource. After that, in each cluster, the proposed method used data envelopment analysis‐assurance region (DEA‐AR) and analytic hierarchy process (AHP) rating method to evaluate the efficiencies and priorities of the technology alternatives, respectively. Finally, combined scores of the technology alternatives were calculated. A company can choose a proper technology cluster, and then select the technology alternative with the highest combined score within the selected cluster as the optimal technology.

The results showed that the OTS method cannot only select suitable technology which accords with a company's actual input capabilities, but also provide a more accurate selection result.

Traditionally, technologies are evaluated without considering the technologies' required input levels, and simply ranked for selection. However, there are differences between a company's actual resource levels and a selected technology's required input levels of resource. This study proposes an integrated method to evaluate technology systematically and provides a more reasonable selection process for selecting optimal technology.

Agility is the ability of an organization to adjust its supply chain tactics and operations to respond quickly against altering business environments such as fluctuating demand pattern, supply chain disruption and global competition. An agile organization must possess a promising capability of swiftly responding to dynamic conditions while being cost-effective without compromising the efficiency. Such high-performance adaptability necessitates the role of supply chain managers to maximize the agility of the supply chain through the efficient use of input resources. Therefore, the purpose of this study to reveal a new decision support tool that would allow the key decision-makers to maximize the agility of the supply chain while deploying the input resources more effectively.

In present study, an integrated approach of popular analytic hierarchy process (AHP) and goal programming (GP) has been adopted as a potential solution methodology. AHP has been implemented to allocate the local and global weights to decision variables, whereas GP incorporates the AHP weights into the desired model.

It was found that the proposed decision support tool restricts the value of the decision variables for maximizing the agility and optimizing the usage of input resources. The results obtained from the model validate the objective of achieving targeted agility level within the available resource limitations.

The decision support tool developed in the proposed study offers a systematic and effectively simple approach to supply chain managers with a goal of identifying the degree of focus under each decision variable in the respective manufacturing organizations.

A novel decision support tool has been developed known as an agility control system), which helps the decision-maker to achieve the required agility in the supply chain by controlling the decision variables.

This study analyses the effects of firm efficiency on firm performance and how controlling shareholders moderate the link between the two variables.

This study employs data envelopment analysis to estimate firm efficiency and the panel regression method to assess the hypothesised relationships among 1,295 firm-year observations of publicly listed firms in Malaysia from 2015 to 2019.

The results indicate that firm efficiency (technical efficiency, pure technical efficiency and scale efficiency) has mixed relationships with firm performance (return on assets, market-to-book ratio and operating cash flows), all of which are being moderated by controlling shareholdings.

This study highlights the importance of assessing firm efficiency as the key success factor for improving firm performance. Industrial managers should manage efficiently their resources or operating costs in achieving their corporate financial goals. Moreover, this study notes the presence of controlling shareholders, who can be either self-interested or company goal aligned.

This study suggests becoming efficient in transforming inputs into outputs is a prerequisite before investigating accrual-based and cash-based firm performance measures, and the presence of controlling shareholders matters in these regards.

The purpose of this paper is to develop an artificial neural network (ANN) based prediction model via integration with data envelopment analysis (DEA) to provide the means of predicting incremental performance goals. The findings confirm the usefulness of the herein developed prediction approach, based on the results of analyses of time series data from the smartphone industry.

A two-stage hybrid model was developed, incorporating sequential measurement and prediction capability. In the first stage, a Chames, Cooper, and Rhodes DEA model is the preprocessor, generating efficiency scores (ES) of decision-making units (DMUs). In the second or follow-on stage, the ANN prediction module utilizes knowledge variables and ES to predict the change in performance needed for a desired level of improvement.

This combined approach effectively captured the information contained in the industry’s turbulent characteristics, and subsequently demonstrated an adaptive prediction capability. The back propagating neural network successfully predicted the incremental performance targets of DMUs, which translated the desired improvement levels into actionable performance goals, e.g., revenue and operating income.

This paper presents an incremental prediction approach that supports better practice benchmarking. This study differentiates itself from previous research by introducing an adaptive prediction method which generates relevant quantity outputs based upon desired improvement levels. The proposed modeling approach integrates performance measurement with a prediction framework and advances benchmarking practices to enable better performance prediction.

Employs data envelopment analysis (DEA) to evaluate the relative performance of 23 university libraries in Taipei City and County. The estimated results show that 11 university libraries are relatively efficient. The results also show that nine out of these 11 have a relatively good academic research function. Only Ming‐chuan Management College and Shih‐chien Design College are attributed lower research capabilities. Marine and Oceanic University, Yang‐ming University and Ming‐chuan Management College are rated the top three libraries and enjoy the highest levels of efficiency in the sample. Shows that the resource utilization of these university libraries functions well. Finds that the inefficient libraries manage their acquisition expenditures and book circulation poorly.

The purpose of this study is to evaluate the operational efficiency of 23 four‐ and five‐plum international tourist hotels (ITHs) in Taipei in 2006, paying attention to efficiency improvement using a non‐radial data envelopment analysis (DEA) model.

A non‐radial DEA model is proposed to improve the efficiency of inefficient hotels focusing on the output side by allowing for non‐proportional reductions in each positive input or augmentations in each positive output. The model considers four inputs (total number of rooms, total number of employees, food and beverage (F&B) capacity, and total operating cost) and three outputs (guest room revenues, F&B revenues, and other revenues); variable returns to scale are assumed.

The empirical results show that the radial and non‐radial DEA models provide not only different performance measurements for the corresponding hotels but also different benchmarks from different angles for improving efficiency.

Hoteliers or other service providers should find this alternative DEA model helpful and more flexible in re‐examining their resource utilization and possibly reshuffling their resource pool.

Unlike traditional DEA studies, which focus on measuring efficiency while ignoring differences among input or output variables, this study pays attention to efficiency improvement by considering changes in output with non‐proportional augmentations to improve efficiency.

The main contribution of this paper is empirical in nature. We use data envelopment analysis to evaluate the relative efficiency of 34 commercial banks in Taiwan. Fifteen banks are identified as efficient ones and they are divided into four sub‐groups. Conversely, 19 banks are attributed as inefficient ones and the slack analysis are followed. The inefficient banks can effectively promote resource utilization efficiency by better handling their labour and capital operating efficiency and enlarging bank investment function. In addition, we compare the data envelopment analysis results to the financial ratios and show that a consistent effect cannot be obtained. This is to say that we cannot derive which bank has a higher performance from financial ratio analysis only.

Due to the rapid development and innovation in the Internet-based technology, conventional banks are under pressure and have to compete with Internet-based finance. This has made banks adopt measures to improve operational efficiency and reduce input and increase output.

The authors had proposed a two-stage fairness concern efficiency model based on the classical theory of data envelopment analysis (DEA) and performed an empirical study to measure agricultural loan efficiency in the 20 major Chinese banks.

The findings of the empirical analysis are as follows: (1) peer-induced fairness concern has no impact on deposit efficiency in a centralized bank supply chain; (2) The China Merchants Bank (CMB) has the third lowest deposit efficiency; (3) monotonicity of loan efficiency with input allocation depends on a bank's ownership structure; (4) efficiency ranks are strongly affected by the fairness concern; (5) most Chinese banks show a low agricultural loan efficiency.

This paper contributes to the literature in several ways. First, to the best of the authors’ knowledge, this is the first attempt to analyze agricultural loan efficiency for a bank supply chain system with the fairness concern. This work reveals the hidden factor that restricts loan efficiency of Chinese banks. Second, the proposed fairness concern two-stage DEA model has shown good ability for full ranking. It can provide a new perspective to the classical DEA literature for ranking decision-making units (DMUs). Third, the authors have demonstrated empirical bank efficiency for the 20 major Chinese banks.

Market shaping research predominantly focusses on the activities of the market shaper, rather than the equally important roles of other market actors. Market shapers may enhance resource density and value creation within markets, yet such influences cannot exhaustively explain how markets get shaped. Other market actors also must and do exert effort in the value co-creation processes; this study aims to explore the effects of reducing their efforts, as a mechanism to facilitate market shaping.

This conceptual paper uses a theory adaptation approach to link value co-creation with market shaping and effort. It offers a conceptual framework and five propositions that outline the role of effort reduction in the value co-creation process to achieve market shaping.

The proposed conceptual framework indicates how enhanced resource density, resulting from the firm’s market shaping activities and reduced effort lead to enhanced value creation for market actors. Effort reduction can be achieved by reducing either the level of resource input required or the activities required to access, transform and combine resources to co-create value. Potential resource flows then may benefit the market shaper.

This research contributes to emergent market shaping literature by offering effort reduction as a viable tactic. Specifically, it broadens the scope of consideration of effort in value co-creation, and it advances understanding of resource density as a focal market shaping construct. The resultant framework offers a foundation for future market shaping research.

Microfinance institutions (MFIs) play a significant role in society to help low-income consumers that liaise with sustainable development goals. Therefore, the purpose of this paper is to examine the effects of two economic freedom components, namely, regulatory efficiency on business freedom and monetary freedom; and market openness on investment freedom and financial freedom. Their influence on the efficiency of MFIs in both social and financial ways is examined.

This study collected a total of 88 MFIs from Thailand and the Philippines for the years 2011 to 2017. The data envelopment analysis approach has been used to measure the MFIs’ efficiency level. Then, the ordinary least squares and generalised least square estimation methods serve to analyse the effects of economic freedom and other determinants on efficiency.

The results show that overall MFIs operate at an encouraging level. However, they were managerially inefficient when exploiting resources to achieve both social and financial efficiency. Therefore, MFIs should focus more on managerial operations to improve the level of efficiency. Results from panel regression analysis showed a mixed outcome for the relationship between economic freedom and MFIs’ efficiency both financially and socially. This suggested that different freedoms will result in different outcomes and significantly influence MFIs’ financial and social efficiency.

Regulatory efficiency and market openness are the vital aspects of economic freedom components that may significantly influence MFI’s performance specifically on social and financial efficiency. This study fills the research gap by examining the relationship between economic freedom components and specific MFIs’ social and financial efficiency, to ensure MFIs work to achieve sustainable development goals.