Data Envelopment Analysis Research Articles

The HADRIAN novel human–machine interface prototype for automated driving: safety and impact assessment

AbstractThe current paper was performed within the HADRIAN project and focuses on exploring the effects of innovative Human–Machine Interface (HMI) prototypes on safety, driving performance, and driver perceptions. Employing driving simulator experiments and questionnaires, this study investigates whether HADRIAN innovative HMI enhances safety and receives positive evaluations from drivers. Specifically, the research centers on a driving simulator experiment that evaluates novel HMI prototypes designed to improve automated driving at SAE Levels 2 or 3. To facilitate HMI assessment, a tailored safety and impact assessment methodology was developed using unique Key Performance Indicators (KPIs). To benchmark and generate a total score for the HADRIAN HMI, data envelopment analysis was deployed based on the aforementioned KPIs. The findings shed light on the influence of HADRIAN HMI innovations on safety and perceived impact when compared to a baseline “state-of-the-art” HMI. Subsequently, a comprehensive discussion unfolds, highlighting the key KPIs that contributed significantly to the safety and perceived impact scores. This method and its outcomes can serve as a valuable resource for other HMI stakeholders, enabling them to employ similar human-centered assessment methodologies to assess the safety and perceived impact of potential HMI configurations.

Weight Restriction Approach in a Two-stage Network Structure: A DEA-based Approach

The standard data envelopment analysis model aimed to maximize the efficiency scores using the most favourable input/output weights. In two-stage network structures, the presence of intermediate undesirable measures leads to a distinct domain of weight restriction study. In the presence of undesirable intermediate measures and equipped with non-cooperative game theory, this study developes a joint weight restriction approach to derive a common set of weights. Concurrently, the overall efficiency of the system can be evaluated. Numerical instances of 34 OECD countries in 2012 have been presented to elucidate the details and applicability of the proposed method.

Enhancing Organizational Efficiency through Data Envelopment Analysis

Öz Bugünün rekabetçi iş ortamında, organizasyonlar rekabet avantajlarını korumak için verimliliği ve üretkenliği maksimize etmeye çalışmaktadır. Veri Zarflama Analizi (DEA), çeşitli endüstrilerde karar birimi performansını ve verimliliğini değerlendirmek için güçlü bir araç olarak ortaya çıkmıştır. Bu makale, DEA'nın organizasyonel verimliliği artırmadaki uygulamalarını kapsamlı bir şekilde ele almaktadır.Makalenin ilk bölümü, DEA kavramını ve temel prensiplerini tanıtarak, karar birimlerinin girdi-çıktı ilişkilerini karşılaştırarak göreceli verimliliğini değerlendirebilme yeteneğini vurgular. CCR ve BCC modelleri de dahil olmak üzere çeşitli DEA modelleri, matematiksel formülasyonlarıyla birlikte detaylı bir şekilde ele alınmaktadır.Ardışık bölümler, DEA'nın pratik uygulanmasına derinlemesine bir bakış sunarak, bir verimlilik analizi yapmanın içerdiği temel aşamaları anlatır. Bu aşamalar arasında birim seçimi, girdi-çıktı tanımlaması, veri toplama, verimlilik ölçümü ve sonuç yorumlama bulunmaktadır. DEA sonuçlarının doğruluğunu sağlamak için veri kalitesi ve güvenilirliğinin önemi üzerinde özel vurgu yapılmaktadır.Ayrıca, makale, organizasyonlar içinde bir karar destek aracı olarak DEA'nın benimsenmesinin faydalarını araştırmaktadır. Etkisizliklerin belirlenmesinden kaynak tahsisine ve stratejik planlamaya rehberlik etmeye kadar, DEA, karar alıcılar için bir dizi avantaj sunmaktadır. Makale ayrıca, DEA'nın sürekli iyileştirme kültürünü teşvik etmedeki rolünü ve endüstri standartlarına karşı benchmarking yapılmasını vurgular.Sonuç olarak, bu makale, organizasyonel verimliliği artırmada DEA'nın önemini vurgulamakta ve pratik uygulamasına dair içgörüler sunmaktadır. DEA'yı stratejik bir yönetim aracı olarak kullanarak, organizasyonlar işletmelerini optimize edebilir, performans iyileştirmeleri sağlayabilir ve bugünün dinamik iş ortamında rekabet avantajını koruyabilirler.

What Drives the Top Management Team's Sentiment and Perception of Future Risk and Uncertainty? A Selling-Capability Based View

ABSTRACT Purpose This study postulates that a firm’s level of value appropriation capability (viz. selling capability) plays a pivotal role in influencing a firm’s top management team’s (TMT) sentiment and its perception of future risk and uncertainty. Further, the current study posits that the nexus between firm-level selling capability and the TMT’s sentiment and future risk perception is contingent upon the firm’s industry growth. Design/methodology/approach This research employs a textual analysis-based measure of TMT’s sentiment and perception of future risk and uncertainty and data envelopment analysis (DEA)-based measure of firm-level selling capability, using a sample of 243 USA-based firms during the period 2014 to 2020 and employing an endogeneity-robust econometric modeling technique. Findings The current study demonstrates that selling capability positively affects a firm’s TMT’s sentiment and negatively impacts the TMT’s perception of future risk and uncertainty. Furthermore, the association between selling capability and TMT’s sentiment and future risk perception are accentuated by industry growth. Research limitations/implications Future studies are warranted to unearth how the association between the focal variables changes depending on the effect of other firm-specific internal boundary conditions. Practical implications The results demonstrate sales and marketing managers should develop efficient sales organization and management techniques to bolster the overall firm-level selling capability. Managers should focus on enhancing their firm-level selling capability to maximize the positive impact on the TMT’s sentiment. Originality/value Although studies have shown the effect of managerial sentiment on firm behavior, there is no empirical evidence, to our knowledge, as to what drives TMT’s sentiment and perception of risk and uncertainty. The present treatise shows that a firm’s selling capability and industry growth affect the TMT’s sentiment and the perception of future risk and uncertainty.

Evaluation of Logistics Development Under the Visual Field of Low-Carbon Environmental Protection Based on Hierarchical Methods

The development of the modern logistics industry in China is relatively backward, and the logistics industry has always shown characteristics of high emissions, high energy consumption, and high pollution. The low-carbon transformation and development of the logistics industry has become a prominent issue. At present, research on low-carbon logistics mainly focuses on its definition and characteristics. The research on its influencing factors is mostly qualitative analysis, and there is relatively little research on performance evaluation. This article establishes a performance evaluation index system from the perspective of low-carbon environmental protection, including information technology level, warehousing, transportation, packaging, etc., and constructs a comprehensive evaluation model that combines an analytic hierarchy process with data envelopment analysis for low-carbon logistics performance evaluation. By reviewing the collected data and taking five logistics enterprises in China as examples, the feasibility of low-carbon logistics performance evaluation indicators and models was verified.

Digital economy and its effect on total factor productivity in services: evidence from China

ABSTRACT The emergence of the digital economy has provided a new development model and driving force for social and productive activities. The quest to accelerate the transformation and upgrading of the service economy through the digital economy has garnered significant attention in society and academia. In light of this context, this study employs data envelopment analysis (DEA) to measure total factor productivity (TFP) in the service sector, utilizing provincial panel data from China for the period 2007–2019. The empirical results indicate that the digital economy significantly contributes to the growth of TFP in the service sector, and specifically, the channels of its impact include alleviating resource mismatch and promoting industrial structure upgrading. Furthermore, the impact of the digital economy on services TFP varies by region and stage of development of the digital economy. Moreover, based on the informational characteristics of the digital economy, the digital economy not only affects the local service TFP, but also generates a spatial spillover effect on the service TFP of neighbouring regions. This study provides a new empirical evidence to examine the productivity improvement of the digital economy in the service sector and provides important policy implications for achieving high-quality development of the service sector.

A Comparative Performance Analysis of Türkiye's Health System within OECD Countries

A country's health system stands as one of its most critical infrastructures, significantly influencing the well-being of its citizens. In line with the World Health Organization's (WHO) widely accepted definition, a country's health system involves all organizations, people, and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization., 2007). As indicated in this definition, a health system carries characteristics of complex systems posing significant challenges in its management and maintenance. The complex nature of the health system also challenges the country's policymakers to improve the outcomes of the health system. Examining specific key performance indicators of the health system can assist policymakers in gaining deeper insights into its internal capacities and identifying areas for improvement. This paper provides a comparative performance analysis of Türkiye’s health system compared to other Organization for Economic Co-operation and Development (OECD) countries using selected key performance indicators of the health system's capacity and resources. The methodology involves a comprehensive review of existing literature, data collection and analysis, and performance evaluation using the Data Envelopment Analysis (DEA) technique. In this performance analysis, health systems capacity-related indicators, such as health expenditure, number of doctors, number of nurses, and number of hospital beds, are used as input variables, and life expectancy at birth and citizens’ satisfaction with the healthcare system are output variables. The analysis intends to identify best practices and potential areas for improvement within Türkiye’s health system by benchmarking insights from other OECD countries.

Evaluating the research and development (R&D) efficiency of the Chinese construction industry

PurposeThe construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.Design/methodology/approachBased on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.FindingsResults suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.Originality/valueThis study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.

Research on the path of technological innovation and resource allocation optimization of state-owned enterprises from the perspective of ecological environment and biomechanics

In the evolving global landscape, competition among nations has increasingly centered on the allocation of innovation resources, which are crucial for enterprises to implement development strategies under supply-side reforms. The efficiency and rationality of resource allocation directly affect the innovation capacity and operational performance of enterprises. This paper examines the relationship between technological innovation and resource allocation in state-owned enterprises through the lens of the ecological environment, employing data from multiple sources and utilizing the Data Envelopment Analysis (DEA) model to assess resource allocation efficiency. The study reveals that the proportion of R&D personnel invested in enterprises in China increased significantly from 46.35% to 73.38%, while the proportion in research institutions and universities declined to 11.48% and 11.36%, respectively. These shifts underscore the growing dominance of state-owned enterprises in driving technological innovation. The findings highlight that aligning technological innovation mechanisms with ecological sustainability not only enhances innovation capabilities and competitiveness but also accelerates industrial development and contributes to the sustainable growth of the national economy.

Evaluating directional scale elasticity in the RAM model with undesirable outputs: an application in the horticultural industry

This paper advances the concept of scale elasticity by examining the impact of directional input changes on both desirable and undesirable outputs. We integrate two disposability concepts into a unified measure within the Range Adjusted Measure (RAM) model, enabling the computation of both directional scale elasticity and directional scale damage. This approach allows for a comprehensive analysis of scale effects in the presence of undesirable outputs. Grounded in Data Envelopment Analysis (DEA), our research enhances the understanding of managing undesirable outputs while accounting for directional scale changes, offering significant implications for both performance improvement and environmental sustainability. The paper includes theoretical foundations, numerical examples to illustrate the methodologies, and an empirical application in the Iranian horticulture industry, demonstrating the effectiveness and adaptability of our approach.

A Regional Efficiency Assessment of Long-Term Care Services in Taiwan

Taiwan is currently an aging society and will be a super-aging society in the near future. The purpose of this research is to use two models of data envelopment analysis (DEA)—the slacks-based measurement (SBM) model and the dynamic slacks-based measurement (DSBM) model—to analyze the efficiency of long-term care (LTC) in Taiwan. This analysis aims to explore the current situation of LTC in Taiwan and provide policy recommendations for LTC. The computation empirical result on the LTC efficiency score presents that the DSBM model exhibits higher efficiency than the SBM model after considering the carry-over variable in the former model. The result from the SBM model indicates that Taiwan’s outlying islands display the worst LTC efficiency, but this result does not appear in the DSBM model. Lastly, these two models both indicate that the number of elderly people being serviced in institutions exhibits higher efficiency and lower slack than those serviced in homes in 2017 and 2018. This paper concludes that the DEA approach is a viable method for examining the performance of the LTC services system as Taiwan approaches a super-aged society.

Evaluation of Capital Utilization Efficiency in Agricultural Listed Firms Based on a Three-Stage Data Envelopment Analysis

Rural revitalization is intricately tied to national prosperity, with listed companies in agriculture, forestry, animal husbandry, and fisheries playing pivotal roles in driving this transformative agenda. However, these sectors face critical challenges, notably resource constraints and environmental pressures, which underscore the necessity of evaluating capital utilization efficiency. This study employs a combined three-stage DEA model and Malmquist index to analyze capital utilization efficiency across 85 listed companies within China’s agriculture, forestry, animal husbandry, and fishery industries over the period 2018–2022. The primary objectives are to quantify capital utilization efficiency, identify pathways for industry optimization, advance ecological agricultural sustainability, and offer valuable insights to investors and policymakers. Key findings include: (1) When grouped by sub-sector and adjusted to account for external environmental factors and random disturbances, the initial comprehensive capital utilization efficiency in agricultural companies was found to be significantly overestimated. Subsequent third-stage adjustments revealed decreases in average comprehensive efficiency, technical efficiency, and scale efficiency by 29.79%, 3.03%, and 27.37%, respectively, largely driven by a marked decline in scale efficiency, ultimately diminishing overall efficiency. (2) The capital utilization efficiency in agricultural trading firms remains suboptimal, with scale efficiency posing a critical limitation. High dependency on government subsidies and excess specialized personnel further constrain efficiency improvements. (3) Dynamic analysis using the DEA-Malmquist model indicates low total factor productivity in the agricultural sub-sector, primarily due to inefficiencies in capital management and suboptimal scale allocation. These findings underscore the need for targeted strategies to enhance resource allocation and management, bolster talent development and financial management frameworks, and drive technological research and development, innovation, and efficient capital allocation across the agriculture, forestry, animal husbandry, and fishery sectors.

Regular Variable Returns to Scale Production Frontier and Efficiency Measurement

Breakthrough in Measuring Efficiency with Respect to Nonconvex Technology The variable returns to scale (VRS) frontier, commonly used in data envelopment analysis, has a convex technology set and follows a specific “regular VRS” structure in economics. This structure demonstrates increasing, constant, and then, decreasing returns to scale, and it is the standard in data envelopment analysis. When the convexity assumption is relaxed, modeling regular VRS becomes challenging, and no satisfactory solution currently exists for multioutput production. In “Regular Variable Returns to Scale Production Frontier and Efficiency Measurement,” Li, Tsang, Lee, and He introduce a new framework for analyzing regular variable returns to scale and propose an innovative empirical production frontier. This new frontier can more accurately measure technical efficiency without the convexity assumption. The implications of this research are extensive, impacting fields like manufacturing, agriculture, healthcare, banking, etc., with crucial findings for informed decision making and effective policy implementation.

Analysis of Sustainable Efficiency of Freight Transport in Major European Economies: An Integrated Multi-Region Input-Output and DEA Approach

This paper integrates the multi-region input-output model (MRIO) and data envelopment analysis (DEA) methods to analyze the freight transport efficiency in Europe. Social, economic, and environmental influences were combined into a sustainable efficiency rating of the freight transport sector of Germany, France, Italy, Spain, and the Netherlands. First, the freight transport sector's carbon footprint (CFP) was quantified using the MRIO model. The lifecycle-based CFP emissions of freight transport activities were assessed using a dataset from 2000 to 2018. Nineteen stochastic model-based MRIO lifecycle assessments were built for each country. Secondly, sixty instances of DEA models were created using a linear program for each mode in the selected countries. Thirdly, the sustainable efficiency scores were determined for each freight transport mode in each country over four periods: 2000–2004, 2005–2009, 2010–2014, and 2015–2018. The results illustrate that the sustainable efficiency score of inland, water, and air transport modes ranged from 0.38 to 1.

A novel ranking method in data envelopment analysis: a real case on Chinese banking industry

Purpose This study aims to address the lack of discrimination between fully efficient decision-making units in nonparametric efficiency analysis models by introducing a new ranking technique that incorporates contextual variables. Design/methodology/approach The proposed method combines Data Envelopment Analysis (DEA) and Ordinary Least Squares (OLS). First, DEA evaluates the partial efficiency of each unit, considering all inputs and only one output. Next, OLS removes the influence of contextual variables on the partial efficiencies. Finally, a ranking criterion based on modified partial efficiencies is formulated. The method is applied to data from 100 Chinese banks, including state-owned, commercial and industrial institutions, for the year 2020. Findings The ranking results show that the top six positions are assigned to highly esteemed banks in China, demonstrating strong alignment with real-world performance. The method provides a comprehensive ranking of all units, including nonextreme efficient ones, without excluding any. It resolves infeasibility issues that arise during the ranking of efficient units and ensures uniqueness in efficiency scores, leading to a more reliable and robust ranking process. Contextual variables exerted a greater influence on the first partial efficiency compared to the second. Notably, Total Capital Adequacy (TCA) significantly impact bank efficiency. Originality/value This study introduces a novel ranking method that effectively integrates contextual variables into DEA-based efficiency analysis, addressing limitations of existing methods. The practical application to Chinese banks demonstrates its utility and relevance.

An integrative study on the green cultural industry and its determinants in Jiangsu province, China under the cultural revitalization initiative: a global perspective

IntroductionsThe cultural industry is pivotal in promoting sustainable economic development. This study aims to evaluate the economic efficiency of the cultural industry in Jiangsu Province by establishing a Data Envelopment Analysis (DEA) model and analyzing influencing factors using the Tobit model, all within the broader context of China’s cultural industry.MethodsA DEA model was developed to assess the economic efficiency of the cultural industry across Chinese provinces, allowing for a comparative analysis of performance. The Tobit regression model was utilized to investigate factors influencing these efficiency outcomes, with a particular emphasis on inter-provincial comparisons to understand the position and challenges faced by Jiangsu’s cultural industry.ResultsDespite Jiangsu’s cultural industry ranking among the largest in scale nationally, its economic efficiency is only moderate, consistently experiencing diminishing returns to scale. The study identifies low scale efficiency, small enterprise size, and suboptimal urbanization processes within the province as the main issues.DiscussionThe inefficiencies highlighted by the DEA model suggest a misalignment between the scale of operations and the economic outputs in Jiangsu’s cultural industry. Urbanization emerges as a crucial factor, with current practices not sufficiently supporting the growth potential of the cultural sector.ConclusionBased on these findings, the study proposes targeted policy recommendations for Jiangsu, including avoiding blind scale expansion, adjusting industrial structures, encouraging enterprise consolidation and optimization, and identifying new growth areas to better support the cultural industry’s development and contribute to sustainable economic progress.

Environmental Efficiency of Municipalities in the Araguaia River Valley in the State of Mato Grosso, Brazil: an Approach Via Data Envelopment Analysis

Objective: The objective of this study is to evaluate the environmental efficiency of municipalities located in the Araguaia River Valley, in the State of Mato Grosso, Brazil, focusing on the relationship between economic development and sustainability, aiming to identify practices that promote the conservation of biodiversity and the reduction of environmental impacts. Theoretical Framework: The text addresses concepts of sustainable development and sustainability, highlighting important distinctions between them. It highlights the relevance of urban planning, in association with the Sustainable Development Goals, as a tool for including environmental factors in promoting environmental quality and the well-being of communities. In this sense, the rational use of natural resources appears to be a preponderant and essential factor in ensuring sustainability and promoting balanced development. Method: This is a qualitative and quantitative approach. The data were obtained through the Brazilian Institute of Geography and Statistics website for the Gross Domestic Product variable and on the Terrabrasilis website for the deforestation variable, involving 25 municipalities in Mato Grosso, in the Araguaia river basin. Based on the data, the R software was used to process the information, using a mathematical model based on Data Envelopment Analysis. Results and Discussion: The results showed a low level of environmental efficiency in the VRS model, with average values of 0.25762 (2017), 0.21655 (2018), 0.36978 (2019), 0.214396 (2020) and 0.28803 (2021). A minority of municipalities were highlighted in environmental efficiency, indicating that they are located on the DEA production frontier, pointing to a balance between environmental protection and economic growth. The success of the municipalities that stood out demonstrates the importance of planning, social mobilization and commitment, it is possible to reverse or minimize the scenario of environmental degradation. Research Implications: The study points to the need for public policies aimed at improving the environmental efficiencies of municipalities that showed lower performance, especially minimizing deforestation. Originality/Value: The originality of the study focuses on the importance of studies aimed at measuring environmental efficiency in the municipalities that make up the cerrado, highlighting the importance of preserving and conserving this biome.

Application of mathematical models on efficiency evaluation and intervention of medical institutions in China.

BACKGROUND: The efficiency of medical services directly impacts the economic burden of healthcare, making it crucial to analyze the input-output efficiency of various types of medical institutions. However, while hospitals had been extensively analyzed for their efficiency, other types of medical institutions had received limited attention in this regard. METHODS: In this study, we employed data envelopment analysis (DEA) methods based on time series and internal benchmarks to autonomously assess the efficiency of 18 distinct categories of healthcare facilities in China over the past decade. The verification was conducted through the utilization of the critical incident technique (CIT). Additionally, we utilized the Delphi process (AHP) method to evaluate suppliers of medical consumables, implemented a multi-population genetic algorithm for managing these consumethod and analytic hierarchymables efficiently, and applied stakeholder theory to manage medical personnel efficiency. RESULTS: Our findings indicated that medical institutions capable of providing clinical services exhibited higher levels of efficiency compared to those unable to do so. Multiple indicators suggested redundancy within these institutions. Notably, comprehensive benefit evaluation revealed that clinical laboratory had performed poorly over the past decade. We selected an inefficient medical institution for intervention in reagent management and the work efficiency of medical staff. After implementing the Delphi method and multi-population genetic algorithm for consumable replenishment, the reagent cost was reduced by 40%, 39% and 31% respectively in each of the three experimental groups, compared to the control group. By applying stakeholder theory and process reengineering methods, we were able to shorten quality control management time for medical staff in the experimental group by 41min per day, reduce clinical service time by 25min per day, and extend rest time by 70min per day, while the quality indicators were all meeting the targets. CONCLUSION: By employing various mathematical models as described above, we were able to reduce costs associated with medical consumables and enhance medical personnel work efficiency without compromising quality objectives.

A Lagrangian relaxation approach for algae-based biofuel supply chain network design under uncertainty and pricing issue.

Biofuel has gained significant attention as a potential source to meet fuel demands instead of fossil fuel. The price of biofuel and alternative fuel have a considerable impact on biofuel demand. Thus, it is important to design a biofuel supply chain network that incorporates the biofuel price into an elastic demand. More precisely, a variable demand, including customer importance level (to the environment), biofuel price, and substituted fuel price, is considered in this research. Furthermore, this research presents a bi-objective mixed-integer quadratic formulation that aims to maximize the total profit of the supply chain and carbon absorption in harvesting areas. The problem is solved by the ε-constraint and lagrangian relaxation methods due to its complexity. Moreover, substituted fuel price uncertainty is addressed by two-stage stochastic programming. Finally, a real case study utilizing the data envelopment analysis approach is applied to assess the efficiency and currency of the addressed model. Several consequences are illustrated in the case study, such as rich areas for exporting algae, suggesting hub locations for biofuel production, etc.

The impact of FinTech/Blockchain Adoption on Corporate ESG and DEI Performance

Monitoring a company's efficiency is one of its primary responsibilities. There are many approaches in our contemporary society that either use IT or the conventional technique. Methods for measuring efficiency fall into three primary categories: parametric, nonparametric, and ratio indicators. We prioritize a firm's inputs and outputs when choosing metrics to measure efficiency. Establishing objectives and goals in entrepreneurship necessitates a thorough comprehension, appreciation, and knowledge of sustainability, and assessing the economic growth quality of a corporation is an essential task for theoretical and empirical sustainability assessment. When measuring the efficiency of entrepreneurship in terms of achieving desired values of macroeconomic indicators (e.g., sustainable economic growth objectives), data envelopment analysis (DEA), a widely used technique in efficiency analysis, has taken into account the economic, environmental, and social impact of entrepreneurship as the three dimensions of sustainability. The objective of this paper is to test the influence of FinTech/Blockchain adoption on corporate ESG and DEI performances using a novel DEA approach for sustainable development assessment. It highlights the significance of using a scalable technique for ESG efficiency study and gives scholars a more thorough viewpoint on the subject. In a collection of 50 enterprises, a DEA model was utilized for the analysis. For sustainable performance assessment using the proposed DEA technique, we defined as inputs six financial metrics, and as outputs 11 ESG/Blockchain adoption, and four DEI quality metrics to measure the firm’s efficiency. The annual business data was gathered between 2017 and 2023. In all country situations we discovered that, when DEI initiatives mediate, there is a strong correlation between ESG corporate performance and the quality of economic growth (particularly in the innovation and integrity blockchain adoption performance success metrics). Our study provides additional in-depth details on the FinTech/blockchain adoption environment in comparison to the findings of previous researchers. The sustainable entrepreneurship performance (a latent variable regarded as a dependent target factor) is calculated using eight (8) factors as observed variables, which is the first to consider the dynamics of ESG/BCA and DEI quality metrics as DEA outputs. The study also examines the mediating role of DEI corporate initiatives. By conducting an empirical investigation, the suggested scalable framework makes it evident which company is more efficient in moving toward sustainability, assisting corporate management in increasing the effectiveness of economic growth.