The transition to a circular economy is increasingly recognized as being vital for the sustainable development of industries. Indeed, the emergence of new green and digital technologies, such as Internet of Things, artificial intelligence, precision agriculture and renewable energy solutions, drives businesses to undergo multiple transformations. With the adaption of their business models and the enhancement of customer orientation, these technologies facilitate the creation of new business solutions and services that promote business circularity. Several factors have been identified in the emerging literature on the circular economy that could potentially impact the operationalization of the circular economy. It is, however, still unclear how these factors interact to result in the implementation of circularity. Hence, the purpose of this study is to explore the various configurations of enabling conditions that lead to successful circular economy operationalization. By using a fuzzy set qualitative comparative analysis (fsQCA), we examine the impact of different configurations of “digitalization”, “servitization”, “business model innovation”, “green practices”, and “customer participation” conditions on achieving circular economy. Based on survey data obtained from Dutch AgriTech companies, multiple paths to successful circular economy adoption are observed. The findings underscore the complexity of achieving circular economy, demonstrating that no single strategy is sufficient. Instead, a combination of several factors must synergize in order to ensure sustained circularity. Therefore, this study contributes to both the literature on digital servitization and circular economy, while providing insight to practitioners and policy makers seeking to promote circularity.

The purpose of this study is to investigate how large manufacturing firms can orchestrate their ecosystem for the successful co-creation of data-driven digital services and solutions as a way to achieve sustainable industry benefits. An exploratory single-case study approach has been adopted for this study, which included 23 in-depth interviews conducted with informants from seven Swedish and international companies involved in a transportation ecosystem. Based on the analysis, this study has developed a multi-level framework for ecosystem orchestration that contains three main operational levels: individual, organization, and ecosystem, with an integrated sustainable industry benefits section as the outcome of this process. By providing a detailed account of ecosystem orchestration, this study contributes to the successful co-creation of digital services and solutions. In addition, insights from this research can be used as a continuous evaluation and improvement tool for managers to orchestrate their ecosystem for digital servitization.

Manufacturing companies are introducing innovative ways to facilitate the sustainable transition of their customers’ operations. The emerging literature on digital servitization proposes numerous factors, such as the use of advanced artificial intelligence analytics, orientation toward outcomes, and aligning ecosystem partnerships, which can potentially influence the sustainable performance of industrial customers. However, there is currently a lack of understanding regarding how these factors interact to result in sustainable outcomes. Hence, this study seeks to shed light on these complex relationships by identifying viable smart solution strategy configurations for achieving customer sustainable performance. Drawing on a dataset of 180 Swedish manufacturing firms, this study uses a configurational comparative method—namely, fuzzy-set qualitative comparative analysis—to identify the impact of different configurations of “AI-driven optimization,” “outcome orientation,” “value co-creation,” and “ecosystem orchestration” conditions on the realization of customer sustainable performance. This study has identified five smart solution strategies that empower manufacturers to realize sustainable performance for their customers. Among the five configurational strategies identified, the first configurational strategy appears to be the most prominent, as it is based on an outcome-based approach in which the firm uses its technological expertise and its ecosystem partnerships to take over customer operations. Thus, this study contributes to the ongoing discussion in digital servitization on its enabling role for industrial sustainability practices.

Purpose: The purpose of this study is to understand how digital servitization enables the process of internationalization for industrial SMEs.
Design/methodology/approach: The present study has adopted an exploratory multiple case study, conducting a total of 40 interviews in a two-phased approach to data collection. The first phase consisted of exploratory interviews with twenty-six industrial SMEs connected to the manufacturing industry in Sweden and Finland. Then, six SMEs were selected in the second phase to participate in additional in-depth interviews.
Findings: First, this study identifies three gradual enabling phases concerned with the digital service maturity of SMEs consisting of digital awareness, digital service innovation and digital service mass customization. Second, the three interdependent phases of ecosystem knowledge synergy, ecosystem integration and ecosystem value co-creation were identified to improve ecosystem involvement. Finally, a process framework has been developed for SME internationalization consisting of a digital servitization innovation strategy, a digital servitization ecosystem strategy and a digital servitization scaling strategy.
Originality/value: The present research contributes to how digital servitization enables SME internationalization by demonstrating how the development of digital service offerings and ecosystem partnerships supports the internationalization process. This research extends the literature by proposing a process framework for the digital-servitization-enabled internationalization of SMEs. This process perspective provides a richer explanation of the complex interplay between servitization, digitalization and ecosystems choices, which supports the expansion into international markets.

Manufacturing firms are increasingly seeking to capture the potential of digitalization by transforming towards digital servitization. Yet, most manufacturers struggle to realize the value through digital servitization because it requires a sustained focus on forming ecosystem partnerships. Digital servitization research has long recognized the importance of ecosystem tranformation but much of the existing discussion on this interlink is fragmented and understudied. Therefore, this study’s purpose is to investigate how manufacturing firms engaged in digital servitization transform their ecosystems. To this end, we have examined the triggers, firm-level enablers, ecosystem phases and activities, and effects of ecosystem transformation in digital servitization. We provide a comprehensive review of the phases of ecosystem transformation including ecosystem formation, orchestration, and expansion as well as their associated activities. These findings have been consolidated into an integrative framework for ecosystem transformation and, based on this analysis, suggestions for future research are provided for digital servitization scholars.

Purpose: Nowadays, the risk assessment and reliability engineering of various production processes have become an inevitable necessity. Because if these risks are not going to be evaluated and no solution is going to be taken for their prevention, managing them would be really hard and costly in case of their occurrence. The importance of this issue is much higher in producing healthcare products due to their quality's direct impact on the health of individuals and society.Design/methodology/approach: One of the most common approaches of risk assessment is the failure mode and effects analysis (FMEA), which is facing some limitations in practice. In this research, a new generalized multi-attribute failure mode analysis approach has been proposed by utilizing the best–worst method and linguistic 2-tuple representation in order to evaluate the production process of hemodialysis solution in a case of Tehran, Iran.Findings: According to the results, entry of waste to the mixing tanker, impurity of raw materials and ingredients and fracture of the mixer screw have been identified as the most important potential failures. At last, the results of this research have been compared with the previous studies.Originality/value: Some reinforcement attributes have been added to the traditional FMEA attributes in order to improve the results. Also, the problems of identical weights for attributes, inaccuracy in experts' opinions and the uncertainties in prioritizing the potential failures were improved. Furthermore, in addition to the need for less comparative data, the proposed approach is more accurate and comprehensive in its results.

Energy planning is one of the most important issues affecting the behaviors and policies of different countries at national and international levels. Concerns over the depletion of non-renewable energy sources and the pollution caused by their consumption have led most countries to focus on renewable energy sources. In this regard, governments are trying to develop policies for extensive use of their renewable energy sources. The precise formulation of such policies requires the development of quantitative approaches to assess the potential of the country in this field. Because selecting inappropriate approaches for evaluating the energy resources leads to wrong decisions in planning and policy-making for the whole energy sector and can cause irrecoverable damages to the country. Since energy issues are multi-dimensional in their nature, the development and selection of the appropriate decision-making approach among the available methods have become a critical issue in this field. In order to overcome this problem, this study proposed a hybrid approach by combining data envelopment analysis (DEA) and fuzzy best–worst method (FBWM) for the prioritization of renewable energy sources (RESs) in Iran. For this purpose, it considered five technical, economic, environmental, social, and political sustainability dimensions into account. The obtained results indicated that the solar, hydroelectric, wind, biomass, and geothermal energy sources are respectively the most efficient RESs in Iran. Also, a three-stage sensitivity analysis approach has proved the consistency of DEA-FBWM approach over the other decision-making methods. Consequently, the proposed approach can be used as a reliable method in the energy policy-making process.

Nowadays, quality promotion of healthcare services has become one of the most challenging goals of healthcare systems all over the world. Also, one of the most important factors affecting the implementation of healthcare policies in any country is managing and controlling knowledge and learning of healthcare systems. In this study, a model has been proposed for healthcare quality improvement which investigates the relationship between knowledge-based practices (knowledge management and organisational learning) and healthcare policies (patient safety culture and clinical governance) and their impact on healthcare quality in three training hospitals of Tehran, Iran. The results and findings have confirmed the strength of the proposed framework. The novelty of this research lies in the inclusion of knowledge-based practices while implemented on patient safety culture as a basis for the dominance of hospitals' clinical governance in order to improve the healthcare quality which leads to healthier families, healthier lifestyles and healthier societies.