Input-oriented Data Envelopment Analysis Research Articles

EFFICIENCY MEASUREMENT OF ARTIFICIAL INTELLIGENCE: A RESEARCH ON COMPANIES IN TÜRKİYE

The use of technology is increasing due to Industry 4.0. Both countries and organizations have had to invest in the field of artificial intelligence (AI) to compete with their rivals in global competitive conditions and to adapt to the ever-changing world. An organization or a country needs to evaluate its performance to ensure its sustainability constantly. The Data Envelopment Analysis (DEA) method is widely used in performance evaluation. This study aimed to evaluate Türkiye AI performance for the nine years between 2014 and 2022. In the research, years were included in the analysis as the decision-making unit. Two input and two output variables were used in the analyses. The study was carried out by using the input-oriented CCR DEA model and its super-efficiency model. According to the results of the analysis, efficient/inefficient decision-making units were determined. Several potential improvement suggestions have been put forward for inefficient decision-making units.

Determinants of Sustainable Tourism Efficiency: Evidence from East European Countries

Objective: In the first step, this paper aims to evaluate the tourism efficiency in East European Countries and later to identify factors that can determine the international tourism development in this European Region. Theoretical Framework: This study considers the impact of tourism demand on economic productivity (efficiency), with a particular focus on the role of sustainable tourism in driving economic development. Method: An input-oriented Data Envelopment Analysis model with a constant return to scale was applied for 6 years from 2014 to 2019 focusing on 22 countries in the area (except Kosovo due to lack of data) to estimate efficiency scores for each country. In the second stage, a Tobit Regression Model was used to stress the role of some characteristic factors influencing the efficiency of the international tourism industry. Findings: This study points out the greater attention of public and private institutions toward sustainability tourism (SDG target 8.9) associated with some factors such as environmental pollution (SDG target 3.9), friendly environment urbanization (SDG 11), educational level of the population (SDG 4), and tourism seasonality. Research implications: The study indicates that most East European Countries run inefficiently. Productivity is low, and efficiency needs to be improved in terms of capital- and labor production implementing consistently sustainable development principles.

Cost and efficiency analysis in Iranian primary health centers: a micro costing and data envelopment analysis

BackgroundHealth Complex Model was implemented to provide primary health care services in urban, especially in slum areas. As a pilot at a provincial level, Chamran Health Complex offers healthcare for more than 57,000 residents of Tabriz. Despite the necessity of cost information in healthcare decision-making, there was limited knowledge about the unit cost of services. This study aims to analyze the cost and efficiency of health centers.MethodsActivity-Based Costing method with direct and step-down allocation methods was adopted. We estimated unit costs in a hypothetical scenario according to national standards to quantify the gap between current and standard practice. Input-oriented Data Envelopment Analysis was administered to measure the efficiency of health centers.ResultsThe total cost of the complex was $2,841,897, of which 67% ($1910373) and 33% ($931523) were accounted for direct and indirect costs, respectively. The vaccination center had the lowest ($9), and the occupational health center had the highest average unit cost ($76). The average technical efficiency of the health centers was 0.519, where the HC1 and HC3 showed the best performance.ConclusionThere is remarkable variability in service costs across health centers, which must be addressed in performance management and contracting practices. Although we found a gap between current and standard practice in terms of staff and facilities according to national standards, Chamran Health Complex has an untouched capacity that can be utilized with better planning and without incurring additional costs. It raises the need for revising national standards by the Iran Ministry of Health.

Data Envelopment Analysis (DEA) to Estimate Technical and Scale Efficiencies of Smallholder Pineapple Farmers in Ghana

This study focuses on evaluating the technical and scale efficiencies of smallholder pineapple farmers in Ghana’s Central Region. We surveyed 320 participants selected using random sampling and applied an input-oriented Data Envelopment Analysis (DEA) method to gauge their technical, pure, and scale efficiencies. Our findings indicate that the mean technical efficiency among these farmers is 0.505, with individual scores ranging from 0.079 to 1.000. Notably, 90.82% of the farmers are operating below maximum efficiency levels, suggesting a potential input reduction of up to 49.5% while maintaining current production levels. Relaxing the assumption of constant returns under Variable Returns to Scale (VRS) conditions reveals a notable improvement in technical efficiency, with 10.82% more farmers achieving optimal efficiency levels. Furthermore, our analysis highlights scale inefficiencies, with 67.26% of farmers operating below optimal scale levels. By increasing production by 22.8%, these scale-inefficient farmers could enhance their efficiency and productivity within existing technological frameworks. These findings underscore the importance of collaborative efforts among policymakers, practitioners, and stakeholders within the agricultural value chain to implement interventions such as improving access to technology and innovation for smallholder farmers and making necessary investments in farmer education and training programs to enhance both technical and scale efficiencies in Ghana’s pineapple sector. Such initiatives can drive sustainable growth, improve farmers’ livelihoods, and bolster the sector’s overall competitiveness.

Efficiency Analysis of Life Insurance Companies in Indonesia and Malaysia

This study focuses on measuring the performance of life insurance companies in Indonesia and Malaysia. Input oriented Data Envelopment Analysis (DEA) uses panel data to extract the efficiency score of 5 life insurance companies from Indonesia and 5 life insurance companies from Malaysia for the year of 2015-2019. DEA-based Malmquist Total Factor Productivity (TFP) Index used to measure the efficiency change along the study period. This study utilizes asset and operating expenses as input variables, as for output variables, this study utilizes premium and investment income. Based on an efficiency score computed by DEA, most of the life insurance companies in both countries perform efficiently during the study period. However, life insurance companies in Indonesia still perform more efficiently than Malaysian life insurance companies. The results of the Malmquist TFP Index for life insurance companies in both countries show that there is 2.5% improvement in technical efficiency change, 1.5% deterioration in technology efficiency change, 0.6% improvement in pure efficiency change, 1.9% improvement on scale efficiency change, and 0.9% increase in TFP change. It shows that the main reason for TFP increased is due to improvement of technical efficiency change.

Input allocative efficiencies for operation and maintenance of rural piped water supply systems in highland areas of Vietnam

Strengthening the functioning of existing rural piped water supply systems is a critical strategy for ensuring household water security, particularly in water-scarce contexts. Improving operation and maintenance (O&M) of the systems is an important area of focus, commonly plagued by poor reliability and functionality over time. From an economic perspective, there is an opportunity to optimise O&M input efficiencies as a foundation for improved management. This paper presented challenges and opportunities to optimise O&M input efficiencies based on an analysis of water supply systems in Vietnam’s highland areas characterised by mountainous terrain and water scarcity. The analysis focused on state-based agencies for O&M given their mandate for restoring the inefficient systems and identified input norms for guidance on how to optimise O&M activities. We applied an input-oriented data envelopment analysis (DEA) model under constant returns to scale assumption to estimate technical, economic and allocative efficiencies. The results identified efficiency levels of 90%, 30% and 33% respectively. The study suggests a 10% reduction in general input amounts and identified efficient input target values reveal potential reduction rates for technical labour (12%), electricity (12%), as well as the technical and economic norms of technical labour (0.86 person- day∙(100 m3)–1 water sold) and electricity (0.53 kWh∙m–3 water sold). The policy implications for O&M state-based agencies include the adoption of input-based contracting mechanisms, while the government is encouraged to approve water tariffs and provide compensation based on input items to promote water service supply as a public good in water- scarce and challenging areas.

Efficiency Analysis of Traditional and Takaful Insurance Firms in Egypt: A Two-Stage Efficiency Model

The purpose of this study is to measure and analyze the efficiency of insurers (traditional, takaful) in Egypt, and to examine the determinants (Insurer-specific factors) that appear to affect the efficiency of insurers. The study uses a two-stage efficiency model: (1) input-oriented Data Envelopment Analysis (DEA), a non-parametric model to measure the efficiency of 17 non-life insurers (12 traditional, 5 takaful) in Egypt during the period from 2012 to 2021, and (2) a panel regression model to examine the determinants (Insurer-specific factors) of efficiency in the traditional and takaful insurance industry in Egypt. This study found that on average, the technical efficiency (TE) level of non-life insurers (traditional and takaful) was 79%, and there is still a need for 21% efficiency to improve the efficiency in the industry, moreover, takaful insurers achieved a better efficiency level than traditional insurers. On the other hand, the findings also showed that return on equity, size, type of operations, and age are positively related to insurers' efficiency, while reinsurance, claims (risks), market share, leverage, and investments are negatively related to insurers' efficiency. This study provides indicators relevant to insurance management (traditional and takaful) that will help them better utilize their resource base and increase their operational efficiency; to our knowledge, this paper is the first empirical evidence for traditional and takaful insurers together to measure efficiency and examine its determinants in Egypt.

An Exploration of Prediction Performance Based on Projection Pursuit Regression in Conjunction with Data Envelopment Analysis: A Comparison with Artificial Neural Networks and Support Vector Regression

Data envelopment analysis (DEA) is a leading approach in performance analysis and discovering newer benchmarks, and the traditional DEA models cannot forecast the future efficiency of decision-making units (DMUs). Machine learning, such as the artificial neural networks (ANNs), support vector machine/regression (SVM/SVR), projection pursuit regression (PPR), etc., have been viewed as beneficial for managers in predicting system behaviors. PPR is especially suitable for small and non-normal distribution samples, the usual cases in DEA analysis. This paper integrates DEA and PPR to cover the shortcomings we faced while using DEA and DEA-BPNN, DEA-SVR, etc. This study explores the advantages of combining these complementary methods into an integrated performance measurement and prediction model. Firstly, the DEA approach is used to evaluate and rank the efficiency of DMUs. Secondly, we establish two DEA-PPR combined models to describe the DEA efficiency scores (also called the production function) and the DEA-efficient frontier function. The first combined model’s input variables are input–output indicators in the DEA model, and the output variable is the DEA efficiency. In the second model, its input variables are input or output indicators in the DEA model, and the output variable is the optimal input indicator for input-oriented DEA or the output indicator for output-oriented DEA. We conducted positive research on two examples with actual data and virtual small, medium-sized, and large samples. Compared with the DEA-BPNN and DEA-SVR models, the results show that the DEA-PPR combined model has more vital global optimization ability, better convergence, higher accuracy, and a simple topology. The DEA-PPR model can obtain robust results for both small and large cases. The DEA-BPNN and DEA-SVR models cannot obtain robust results for small and medium-sized samples due to overfitting. For large samples, the DEA-PPR model outperforms DEA-BPNN, DEA-SVR, etc. The DEA-PPR combined model possesses better suitability, applicability, and reliability than the DEA-BPNN model, the DEA-SVR model, etc.

Impact of COVID-19 on the Efficiency of Indian IT Companies

The purpose of this study is to investigate the impact of the COVID-19 epidemic on Indian information technology enterprises using data from the 10 largest IT firms listed on the Nifty IT index. Data were sourced from the PROWESS IQ database for 2017–2022. Input-oriented data envelopment analysis (DEA) using CCR and BCC models was applied by taking gross revenue as an output and labour, costs of fuel, materials, power, administrative and general costs as input variables. The study finds a positive although not statistically significant difference in the efficiency of Nifty IT companies during COVID-19 compared to pre-pandemic levels. Additionally, firms experienced higher scale efficiency compared to production efficiency growth throughout 2020–2022. Around 70% of Nifty IT firms faced a decreased efficiency in input utilization during the COVID-19 outbreak. Further, 50% of Nifty IT firms operated under increasing returns to scale and required expansion of business in 2021–2022. The dimensions of returns to scale exhibited stability in the long run. Companies with increasing returns to scale had a 7% lower average technical efficiency than firms with decreasing returns to scale. Large firms demonstrated better scale efficiency. The results of the study can be used by managers to increase the efficiency of firms.

Effect of adoption of organic farming on technical efficiency of olive-growing farms: empirical evidence from West Bank of Palestine

Organic farming is one of the methods that increases the value added of agricultural products in a sustainable way. This paper examines how the adoption of organic farming has impacted the technical efficiency of Palestinian olive-growing farms in the West Bank. Using cross-sectional data of olive farms in the Jenin governorate, we employ an input-oriented data envelopment analysis framework to compute radial and input use efficiencies. Considering heterogeneity in technology between organic and conventional farming, a metafrontier with a directional distance function approach was applied. As self-selection bias may exist due to the decision to adopt organic farming, we apply the endogenous switching regression method to reduce bias caused by unobserved heterogeneity. Results suggest that organic farms in Jenin are not less efficient than conventional farms. Their organic farming method improves input use efficiency with respect to labor and cost relative to conventional farming. While organic farming is commonly considered to be less efficient and more costly, our findings from Jenin imply that it is, in fact, a more efficient method. We suggest that promoting organic olive farming could offer an effective strategy for small farms to add value, despite the severe geopolitical constraints of farming in the West Bank.

Assessment of Economic Efficiency and Its Determents for Mixed Crop Livestock Production under Dryland Agriculture System in the Western Zone of Tamil Nadu, India

In order to accomplish economic, social, and environmental objectives, farm diversification is a significant feature in India’s agricultural systems and rural development. It is crucial to examine factors impacting farm productivity, such as socio, structural, and Circular Economy (CE) factors, in order to ensure the sustainability of varied farms. A decision to follow a farm circularity approach is required to increase agricultural production, return on investment, and achieve sustainability, so the current study was designed to move forward in identifying farm efficiency and their influencing drivers in production systems for the successful execution of circularity in future farms. The objective of this paper is to find the existing operational efficiency of farms, variables that impact a farm’s efficiency, the disparities in farm efficiency, and variables dominating farm efficiency. The efficiency behaviours of Indian farms operating in 2020–2022 were studied and regressed against the factors influencing farm performance. A two-step technique and survey data were employed. To assess variations in the relevance of drivers, an input-oriented Data Envelopment Analysis was employed; in the second stage, efficiency ratings were used as a dependent variable in a Tobit regression analysis. Explanatory variables for enhancing farm performance included social, structural, and Circular Economy (CE) drivers. The age of the farmer, total farm area, and dryland area as socio-structural variables, and reduce, recycle, and redesign as CE variables, have proven to affect the farm’s efficiencies significantly. Managerial implications in terms of technical, allocative, cost efficiency, and their drivers have been drawn from study findings. CE has been confirmed to be the strategy over which the farmer has complete control and has been found to have a more significant impact on farm efficiency. Social structural determents are second in terms of their impact. Farmers in the region have cushy access to inputs; however, they are not utilized to their maximum effect. Therefore, the study indicates that inefficient dryland farmers could increase their production by concentrating on a circular economy strategy.

Data envelopment analysis to evaluate the efficiency of tobacco treatment programs in the NCI Moonshot Cancer Center Cessation Initiative

BackgroundThe Cancer Center Cessation Initiative (C3I) is a National Cancer Institute (NCI) Cancer Moonshot Program that supports NCI-designated cancer centers developing tobacco treatment programs for oncology patients who smoke. C3I-funded centers implement evidence-based programs that offer various smoking cessation treatment components (e.g., counseling, Quitline referrals, access to medications). While evaluation of implementation outcomes in C3I is guided by evaluation of reach and effectiveness (via RE-AIM), little is known about technical efficiency—i.e., how inputs (e.g., program costs, staff time) influence implementation outcomes (e.g., reach, effectiveness). This study demonstrates the application of data envelopment analysis (DEA) as an implementation science tool to evaluate technical efficiency of C3I programs and advance prioritization of implementation resources.MethodsDEA is a linear programming technique widely used in economics and engineering for assessing relative performance of production units. Using data from 16 C3I-funded centers reported in 2020, we applied input-oriented DEA to model technical efficiency (i.e., proportion of observed outcomes to benchmarked outcomes for given input levels). The primary models used the constant returns-to-scale specification and featured cost-per-participant, total full-time equivalent (FTE) effort, and tobacco treatment specialist effort as model inputs and reach and effectiveness (quit rates) as outcomes.ResultsIn the DEA model featuring cost-per-participant (input) and reach/effectiveness (outcomes), average constant returns-to-scale technical efficiency was 25.66 (SD = 24.56). When stratified by program characteristics, technical efficiency was higher among programs in cohort 1 (M = 29.15, SD = 28.65, n = 11) vs. cohort 2 (M = 17.99, SD = 10.16, n = 5), with point-of-care (M = 33.90, SD = 28.63, n = 9) vs. no point-of-care services (M = 15.59, SD = 14.31, n = 7), larger (M = 33.63, SD = 30.38, n = 8) vs. smaller center size (M = 17.70, SD = 15.00, n = 8), and higher (M = 29.65, SD = 30.99, n = 8) vs. lower smoking prevalence (M = 21.67, SD = 17.21, n = 8).ConclusionMost C3I programs assessed were technically inefficient relative to the most efficient center benchmark and may be improved by optimizing the use of inputs (e.g., cost-per-participant) relative to program outcomes (e.g., reach, effectiveness). This study demonstrates the appropriateness and feasibility of using DEA to evaluate the relative performance of evidence-based programs.

The effect of crop insurance in Ecuadorian rice farming: a technical efficiency approach

PurposeThe aim of this study is to estimate the technical efficiency of the massive and economically important crop of rice in Ecuador, and then conduct a comparison between groups of farmers with and without insurance.Design/methodology/approachThe authors use an input-oriented data envelopment analysis approach (DEA) to estimate technical efficiency scores. The DEA is combined with the double bootstrap approach in Simar and Wilson (2007) to study factors that may affect technical efficiency. This method overcomes the traditional two-stage DEA approach frequently used in the efficiency literature. The authors thus research the role of insurance on rice efficiency production using this technique and sizeable field-level survey data from 376 rice farmers distributed in five provinces during the 2019 winter cycle in Ecuador.FindingsMost uninsured rice farmers operate with increasing returns to scale, which means that farms improve their resource use efficiency by increasing their size. However, since scale efficiencies are relatively high, it appears that inefficiencies are explained by inadequate input use. Also, the authors find evidence that insured farmers have a negative relationship with technical efficiency in rice production. In other results, when exploring the influence of additional variables on efficiency, the authors find that parameters related to transplanting, high education, farm size and some locations are positive and statistically significant.Social implicationsThe results of this work are relevant for policymakers interested in evaluating technology performance, risk management instruments and farm efficiency in an industry in a developing country such as rice production in Ecuador.Originality/valueThis paper is the first attempt to estimate farm-level technical efficiency employing the double bootstrap approach to assess the efficiency and its determinants of Ecuadorian rice producers.

Data Envelopment Analysis for the Efficiency of Higher Education Departments at Sepuluh Nopember Institute of Technology, Indonesia

<p style="text-align: justify;">The quality of higher education is vital for a country’s future, not only in terms of transferring knowledge to younger generation but also for supporting economic development. This paper applies data envelopment analysis (DEA) to evaluate the relative efficiency of 38 academic departments at Sepuluh Nopember Institute of Technology, Surabaya, Indonesia. The input factors are the number of lecturers, the number of staff and budget allocations, whereas the output is the performance achievement level. The empirical analysis incorporates two traditional DEA models: the Charnes, Cooper and Rhodes (CCR) and the Banker, Charnes and Cooper (BCC) models with input orientation. The results indicate that the CCR model considers five departments efficient while the BCC model considers ten departments efficient, five of which are those considered efficient by the CCR model. It may seem counterintuitive that a department with an output performance achievement below 100% is deemed efficient, and vice versa. However, the underlying principle of efficiency under input-oriented DEA model is resource utilization. Finally, we provide recommendations for the departments with low efficiency scores to improve their performance.</p>

Estimating the potato farming efficiency: A comparative study between stochastic frontier analysis and data envelopment analysis.

The government of Bangladesh has been trying to encourage potato consumption to reduce pressure on rice consumption and earn foreign currency along with ensuring zero hunger that helps to achieve the Sustainable Development Goal. It is necessary to use farmers' resources and current technology more efficiently to meet the demand. Therefore, the authors aimed to evaluate the farm-level efficiency of potato farming in Bangladesh. The Cobb-Douglas Stochastic Frontier Analysis (SFA) and the input-oriented Data Envelopment Analysis (DEA) methods are used to compute farm-level technical, allocative, and economic efficiencies and inefficiency of potato farming. The primary data were collected through interviews of 300 potato farmers from Munshigonj, Rangpur, Dinajpur, and Joypurhat districts of Bangladesh. The findings revealed that the efficiency score of the SFA model is higher than the DEA model, which implies that the SFA frontier fits better than the DEA frontier. In the case of DEA, variable returns to scale (VRS) technical efficiency (TE) enveloped data more closely than constant returns to scale (CRS) TE. Results of efficiency suggest significant economic, technical, and allocative inefficiencies in potato farming and there is a scope to increase potato production levels through efficiency improvement. Inefficiency analysis shows that infrastructure and socio-economic factors jointly influence potato production variability. The authors suggest for using the SFA to find efficiencies in the agriculture sector. To achieve efficiency in potato production, the government needs to pay attention for improving the allocative and economic efficiencies along with emphasizing to choose the appropriate technology and efficient use of resources for the scale of operation.

Covid-19 Dönemi Konaklama Tesisi Etkinlik ve Verimlilikleri: Kapadokya/Türkiye Örneği

Performance of the accommodation facilities should be measured and monitored periodically for sustainability in many dimensions which have potential to affect profitability directly. In this study, efficiencies of 88 hotels from Cappadocia/Türkiye are measured via data envelopment analysis. Four inputs and five outputs are considered to measure efficiencies and effectiveness in 2020 during the COVID-19 period. The data are collected via phone calls, hotels’ web pages and Tripadvisor in 2020. 83% of these hotels (73 hotels) are small-sized hotels which have equal or less than 20 rooms. Constant return to scale, variable return to scale and scale efficiencies of hotels are exposed via input-oriented data envelopment analysis. The findings show that facilities which have higher scale had lower classical efficiency. Additionally, it is observed that one of the main reasons for the inefficiency is revenue per available room. In terms of effectiveness, hotels working with travel agencies had a lower performance.

From a strategic management perspective, comparison of graduate/undergraduate education performances of research universities and efficiency determinants: The case of Turkey

This study determined undergraduate and graduate education efficiency scores using the 2020 data from 20 state research universities in Turkey. The study used input-oriented data envelopment analysis to compare undergraduate and graduate education efficiencies. In the study, the efficiency of the research universities in the prioritized field(s) was compared with the efficiency in the field(s) in which they operate intensively. It also includes suggestions on increasing their effectiveness in the prioritized field(s). In addition, the Tobit regression model, which is a regression model for limited dependent variables, was used to determine the determinants of efficiency scores. The findings show the undergraduate and graduate education performances of research universities comparatively. In addition, based on the results obtained from the Tobit regression model, suggestions were made to increase graduate performance. Five factors (the number of graduate students per faculty member, the number of undergraduate students per academic staff, the number of graduates/undergraduates in the number of students and graduations, and the number of faculty members per program) have a significant effect on graduate performance. Therefore, it is important in terms of strategic management that research universities should be restructured by considering these factors or that they should be considered in plans. The study offers an alternative perspective to performance management in both education and management.

Investigating the prospect of cleaner production in informal enterprises: A scientific assessment of environmental burdens and economic efficiency

The present study aims to assess the prospects for cleaner production (CP) and sustainable development (SD) of informally operated small manufacturing enterprises, which are frequently blamed for uncontrolled waste disposal and causing pollution to the environment. The economic efficiency level of these firms has been explored to this end, and the metallic pollution loads in the surrounding environment have been scientifically analyzed to investigate the nexus between these two. DEA (Data Envelopment Analysis)-Tobit analysis has been employed, and a pollution load index (PLI) of heavy metal pollution comprising two environmental compartments (soil and water) has been constructed based on the concentration level of metalloid pollutants in the samples collected from the surrounding areas of the studied informal firms in Bangladesh. The study disproves CP practice in majority of the informal firms in Bangladesh by observing a positive relationship between firm-level efficiency and pollution load sourced from their production activities. Afterwards, this study estimates the eco-efficiency level of firms by considering pollution load as an undesirable output and minimizing its impact in an input-oriented DEA model. Applying the eco-efficiency scores in censored Tobit regression analysis, the outcome endorses the prospect of CP for informally operated enterprises in Bangladesh. However, the CP prospect can only materialize if and only if firms are provided with adequate technical, financial, and strategic support for achieving eco-efficiency in their production. The informal and marginal nature of the studied firms restricts them from getting access to the facilities and support services needed for implementing CP and moving towards sustainable manufacturing. Therefore, this study recommends green practices in informal manufacturing and limiting the informal firms by bringing them gradually under the coverage of formalization, which is in line with the achievement of the targets mentioned in Sustainable Development Goal 8.

Estimation of technical efficiency of chemical-free farming using data envelopment analysis and machine learning: evidence from India

PurposeThis study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.Design/methodology/approachThe study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.FindingsThe results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.Research limitations/implicationsThe study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.Practical implicationsGiven the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.Social implicationsAlthough many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.Originality/valueNo prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

Integrating Assessment of Characterization, Sustainability and Efficiency for the Production of Rainbow Trout (Oncorhynchus mykiss): A Case Study in the Amazonas Region of Peru

In this research, aspects of sustainability and efficiency were evaluated to provide information to decision makers. First, 39 rainbow trout farms were characterized, sustainability indices were determined for 36 production units using Sarandon’s methodology, and technical efficiency was evaluated using input-oriented Data Envelopment Analysis (DEA). The production units studied were grouped into three clusters, and the most determining variables were associated with total annual production. In addition, a medium-level general index was obtained with a total of 60 sub-indicators, divided into four social indicators (with 10 sub-indicators), four economic indicators (with 34 sub-indicators) and three environmental indicators (totaling 16 sub-indicators). Of 33 production units evaluated, 14 were identified as efficient; the Amazonas region’s trout farmers were found to operate at 83.87% technical efficiency on average. All resources showed room for improvement and thus can be further adjusted. The most underutilized resources were land (area), feed and seed (fry), which could be reduced to increase technical efficiency. In conclusion, the trout farming units in northeastern Peru are differentiated into three groups by production volume and operate at a medium level of sustainability, with most at levels of technical inefficiency.