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This study aims to map scientific publications, intellectual structure and research trends in the sustainable business model (SBM) field. Specifically, it attempts to (1) identify the fundamental contributions of research in this area of knowledge; and (2) determine the research lines that constitute the most prominent intellectual structure. We leverage these insights to formulate and propose a future research agenda for SBM.

The authors made recourse to the bibliometric, co-citation and cluster analysis techniques. To evaluate potential patterns among articles, we analysed how articles are jointly cited. We further applied hierarchical cluster analysis to the articles and used co-citation analysis to group the interrelated articles into distinct sets.

The results enable the identification and classification of the prevailing theoretical foci in the domain of SBM: (1) SBM implementation; (2) SBM challenges; (3) institutional SBM; (4) circular SBM; and (5) emerging SBM.

This study identifies, explores, analyses and summarises the main theoretical approaches and themes surrounding SBM research to date, contributing to deepening the literature by identifying the priority areas concerning sustainable business models and encouraging future research of an internationally excellent standard.

This study aims to investigate environmental efficiency based on energy change by using energy-related or nonenergy-related variables by reckoning with months and years as decision-making units (DMUs) for a hospital under radial and nonradial models.

The non-oriented slack-based measures (SBM)-data envelopment analysis (DEA) model considering desirable and undesirable outputs has been embraced in this study, where its obtained results were compared with the results of other DEA models are output-oriented SBM-DEA and Banker, Charnes, & Cooper-DEA. For this purpose, this research has used a data set covering the 2012–2018 period for a reference hospital, which includes energy-related and nonenergy-related variables.

The results demonstrate that environmental efficiency based on energy reached the highest level in the winter months, whereas the summer months have the lowest efficiency values arising from the increasing electricity consumption due to high cooling needs. According to results of the non-oriented SBM model, the month with the highest efficiency in all periods is January with a 0.936 average efficiency score, the lowest month is August with a 0.406 value.

This paper differs from other studies related to energy and environmental efficiencies in the literature with some aspects. First, to the best of the authors’ knowledge, this study is the first one that takes into account time periods (months and years) as (DMUs for a single organization. Second, this study investigates environmental nonefficiencies, which are derived from energy uses and factors affecting energy use.

Waste management is one of the vital objectives for the EU since it has a substantial effect on the environment. European Commission expects annual waste creation on Earth to increase by 70% by 2050. European Commission also estimates that efficient waste management might boost the EU economy's gross domestic product (GDP) by 0.5% by 2030. Hence, it is essential to conduct research including both efficiency and influencing factors analysis for effective waste management. First, we employ both slack-based measure (SBM) and super-SBM data envelopment analysis approaches to investigate the waste management efficiency of the EU region and distinguish between efficient countries. The countries with small areas such as Luxembourg and Ireland have demonstrated super efficiency. Second, we maintain our empirical research with ordinary least square analysis to explore the determinants of waste management. We also conclude that population density, GDP per capita, and tourism rise the amount of waste generated in the EU region.

To provide an overview of the similarity‐based modeling (SBM) technology and review its application to condition monitoring of rotating equipment using features calculated from vibration sensor signals.

Concentrates on the practical capabilities and underlying technology of SBM. Examines the effectiveness of it as an approach to detect and diagnose faults in an electric motor‐driven shaft during variable speed operating conditions.

The SBM is a non‐parametric pattern recognition technology developed by SmartSignal that is applied generally to multivariate condition monitoring problems. A vibration sensor is monitored by first transforming the digitized time domain sensor signal into relevant features over time. These features are monitored continuously in real time to detect any discernable differences from normality. The deviations in turn, produce fault signatures in time‐feature space that aid in fault diagnosis.

Gives information on an approach that employs a multivariate similarity‐based modeling technique to characterize the expected behavior of vibration signal features which enables the detection of incipient faults in rotating machinery.

This paper uses various Data Envelopment Analysis (SBM-DEA) approaches to study the efficiency of major airlines in Asia-Pacific region. To evaluate the operation efficiency of fourteen major airlines in Asia-Pacific region from 2003-2011, Available Seat Kilometers(ASK), Available Ton Kilometers(ATK), the number of employees are used as input factors, Revenue Passenger Kilometers(RPK), Revenue Ton Kilometers(RTK), the amount of Sales are used as output factors.

The non-radial SBM-DEA (Slacks-based Measure of Efficiency) model was able to provide a more comprehensive efficiency of combining economic performance and regional difference. And it was also able to capture slack values in input excess and output shortage.

The results demonstrate that Korea and Japan airlines are operated efficiently and could be regarded as the benchmarking airlines. On the other hand, most of the China and ASEAN airlines are deemed to be inefficient. Also analyzing slacks may be more suitable way for the evaluation or suggestion of an improvement scheme for the inefficient airlines. The excess of labor is the major cause of the airlines’ inefficiency.

The purpose of this paper is to determine the relative efficiencies of public sector hospitals in Uttarakhand, India.

The study use data of public hospitals collected from Directorate of Medical Health and Family Welfare, Government of Uttarakhand, Dehradun, India for the year 2011. The cross-sectional data analyses are carried out by applying data envelopment analysis (DEA) based slack based model.

The analysis found that out of total 36 hospitals only ten hospitals are relatively overall technical efficient. The average overall technical efficiency 54.10 per cent indicates that an average hospital has the scope of producing the outputs with the inputs 45.90 per cent lesser than their existing levels. The slack analysis results show that on average 12.57 per cent of beds, 13.16 per cent of doctors, 14.04 per cent of paramedical staff can be reduced and 17.53 per cent of out-door patients, 66.55 per cent of in-door patients, 208.23 per cent of major surgeries, 110.73 per cent of minor surgeries can be expanded if all the inefficient hospitals operate at the level of efficient hospitals.

The present study is undertaken to measure the relative efficiencies of public sector hospitals in Uttarakhand. There is dearth of studies being done on Indian healthcare sector and this study will help to utilize healthcare resources efficiently for formulating policy implications for public hospitals in Uttarakhand. For the robustness of DEA results, Jackknifing analysis is also conducted.

The purpose of this study is to measure the technical efficiency of 20 Algerian insurance companies from 2016 to 2020, by using slacks-based measure (slacks-based measure [SBM] data envelopment analysis [DEA]) model. This research aims to provide a comprehensive assessment of this companies' efficiency, taking into account both the desirable and undesirable outputs.

The study uses a nonoriented, SBM model with the assumption of constant returns to scale to estimate the technical efficiency of commercial insurance companies over a five-year period. The inputs used are labor expense, agent expense and investments, while the outputs included are gross premiums and investment income as desirable outputs and gross claims as undesirable output.

Among 20 insurance companies evaluated, only 5 companies consistently achieved technical efficiency during the study period (Caisse Nationale de Mutualité Agricole [CNMA], MACIR, CARDIF, MUTUALISTE and AGLIC); so they represent the best practices in the Algerian insurance sector, with overall average of the technical efficiency is 81%. However, the reference sets analysis showed that CNMA and AGLIC had high robustness. Also, the results demonstrate the impact of ignoring the undesirable outputs on the accuracy of the assessment.

The sample of the study consists of the active insurance companies in Algeria, based on the Annual Insurance Reports of Algeria; there are 20 companies as shown in the table. The data are taken from the annual reports of insurance companies during the 2020 period, issued by the Algerian Ministry of Finance.

The challenge for insurance company is how to find a balance between reducing claims paid and simultaneously improving the quality of insurance services. In fact, it is observed that studies evaluating their efficiency ignore claims in the analysis process. Therefore, the study highlights the importance of considering undesirable outputs within the DEA framework; this allows for a more accurate assessment of the company's performance and helps in improvement. Furthermore, although the insurance sector plays a crucial role, it has not received enough research attention compared to other financial sectors, especially in Arab and developing countries.

The literature on efficiency assessment in the insurance companies shows a lack of addressing undesirable outputs (such as claims) within the DEA framework; so this study aims at bridging this research gap. Also, the study provides an overview of the efficiency of Algerian insurance companies.

This study aims to analyse the components of sustainable business models (SBMs) in the dairy industry, in relation to firm-relevant organisational features (size, ownership structure and production process) and through the lenses of the business model framework and the sustainable value exchange matrix (SVEM). This contribution proposes a taxonomy of emerging SBMs and sustainable value creation in the dairy industry.

This research makes use of a multiple case study approach, with cases selected in collaboration with industry experts. The selected firms are highly committed to sustainability transition. Results are drawn from qualitative data obtained from in-depth interviews and secondary sources. The interpretation phases, initially based on open coding, have been enriched by applying the components of business models (BMs) frameworks and the SVEM, and the analyses have been enhanced through an additional interpretative workshop with experts.

The authors related the BMs characteristics of some typical dairy firms transitioning to sustainability, using SBM components and taxonomies emerging in the literature, based on the formalisation of sustainability practices, the scope of operations, and the degree of integration of the three dimensions of sustainable value. These findings led to the discovery of three types of SBM in this dairy industry, referred to as “Milky Ways”.

This paper contributes to the scant literature on sustainability in dairy firms, highlighting the different paths followed by small and medium-sized enterprises (SMEs), cooperatives and large companies in remoulding their business models towards sustainability and thus achieving sustainable value creation.