Triggering sustainable firm performance, supply chain competitive advantage, and green innovation through lean, green, and agile supply chain practices

Effects of digital transformation and environmental resource integration capability on medical equipment suppliers' green innovation performance

In today's digital age, the effort of medical enterprises towards green innovation has gained traction in academic and business circles. However, the current research system for medical equipment suppliers lacks a systematic study on how digital transformation can enhance the outcomes of green innovation. This research aims to develop a theoretical framework for digital transformation, environmental resource integration capability, managerial environmental concern and green innovation performance with respect to the resource-based view and conducting empirical analysis using survey data from medical equipment supply enterprises. Our findings reveal that digital transformation has a significant effect on the promotion of green innovation performance through environmental resource integration capability. Moreover, the managerial environmental concern moderates above mediation effect. These findings not only provide compelling insights into the impact of digital transformation on green innovation performance but also have important implications for sustainable development and cleaner production relevant academic research and policy-making.

Unlocking sustainable supply chain performance through dynamic data analytics: a multiple mediation model of sustainable innovation and supply chain resilience

This article provides a theoretical framework for comprehending the connections between dynamic data analytics capability (DDAC), innovation capabilities (IC), supply chain resilience (RES), and sustainable supply chain performance (SSCP). Since this is the first empirical investigation of the sequential mediation effect between DDAC and SSCP through IC and RES, it fills a critical need in the supply chain literature. A quantitative methodology was used, involving a survey questionnaire distributed to 259 large Pakistani manufacturing firms. We used PLS-SEM to test for the expected associations. Findings show that using DDAC has a beneficial effect on both innovative and resilient capabilities, which in turn leads to better SSCP. The research illuminates the sequential mediating roles of product, process, and resilience, underlining the need of combining data-driven innovation with resilience in order to achieve sustainable supply chain performance. These results provide useful guidance for businesses that want to boost their sustainability results by taking a more all-encompassing approach to data-driven innovation and resilience.

Identification and ranking of enablers to green technology adoption for manufacturing firms using an ISM-MICMAC approach

The debate about the adoption of green technologies (GTs) is trending due to increasing environmental concerns across the globe. In the context of the manufacturing industry, the research about enablers to GT adoption while using the ISM-MICMAC approach is scarce. Hence, the empirical analysis of GT enablers is being done in this study using a novel ISM-MICMAC method. Firstly, the research framework is developed by using the ISM-MICMAC methodology. Then, enablers to GTs were extracted from existing studies and validated by experts. Results showed that the most significant enabler to GT adoption is providing incentives for green manufacturers (1) in the ISM model. So, manufacturing firms must take initiatives for GTs to mitigate the negative environmental impact of industrialization without losing their earnings. This research brings substantial empirical scholarship to understand GT enablers and their contribution in GT enabler incorporation in the manufacturing industry of developing economies.

Big data analytics capabilities for reinforcing green production and sustainable firm performance: the moderating role of corporate reputation and supply chain innovativeness

The literature review lacks empirical studies on the role of big data analytics (BDA) and green technological innovation capabilities (GTICs) in promoting the sustainable performance of the manufacturing industry. The primary objective of this study is to examine the role of BDA-technology capability, GTIC, and environmental orientation toward green production and sustainable firm performance. Moreover, this research paper investigates the mediating role of green production and green competitive advantage and the moderating role of corporate reputation and supply chain innovativeness. Primary data was collected from Pakistani manufacturing firms through the survey method. Structural equation modeling was applied to measure and verify the relationship of proposed hypotheses. Empirical findings show BDA technology capability, GTIC, and environmental orientation positively contribute to green production. Moreover, green production helps achieve a green competitive advantage, and green competitive advantage positively influences sustainable firm performance. Furthermore, mediating role of green production and green competitive advantage and moderating role of corporate reputation and supply chain innovativeness was also confirmed. This study contributes by developing a comprehensive model showing the relationship between organizational capabilities, BDA technology capability, GTIC, and sustainable firm performance by considering potential mediators and moderators. Thus, this research suggests enhancing green production and sustainable firm performance through adopting BDA technology capability and GTIC by Pakistani manufacturing firms.

Green effectual orientations to shape environmental performance through green innovation and environmental management initiatives under the influence of CSR commitment

This study elaborates the concept of green effectuation orientations (GEOs) to enhance the environmental performance of entrepreneurial SMEs based in the Jiangsu province of China. For this purpose, we collected the data from 328 employees of entrepreneurial SMEs via a survey and analyzed it through SEM. We evaluated the GEO concept on mindset and action-based views to operationalize the construct with all subdimensions fully. Our study illustrates some unexplored domains of entrepreneurship research and comes up with the findings that (GEOs) are positively correlated with environmental performance (EP), green innovation performance (GIP), and environmental management initiatives (EMI). Additionally, the findings of this research also unveil that EMI and GIP pave the way for EP, as both mediate the relationship between GEO and EP. Last but not the least, this research study also explained the effective mechanism through which CSR commitment as a moderator can affect the relationship between GEO and EP. The findings of this study expand the spheres of effectuation theory and come up with several exciting theoretical and managerial implications. Besides, it is the first of its kind to demonstrate that GEO could enhance GIP, EMI, and EP.

Sustainable supply chain management and green technologies: a bibliometric review of literature

To attain ecological sustainability and transition to sustainable supply chain management (SSCM), effective technological innovation (TI) and solid waste management (SWM), as likely impending resources, are essential components. From 2000 through 2021, a detailed map of SSCMs in the context of TI and systematic history will be created, highlighting the most significant research themes and trends, primary features, development, and possibly relevant areas for future study. Due to utilizing bibliometric analysis, text mining, and content analytics methodologies, the following concerns were addressed: (1) How has SSCM research progressed over time in the TI domain? (2) Which SSCM research areas and trends receive the most attention in the TI domain? Additionally, (3) what are the research directions for SSCM in the context of TI? As a result, bibliometric networks were developed and examined using 983 journal articles from the Scopus database to highlight the substantial body of literature. As a result, SSCM has been divided into five crucial study themes: (i) transition to TI, (ii) SSCM in closed-loop supply chains, (iii) municipal solid waste management (MSWM), (iv) environmental consequences and life-cycle evaluation, and (v) policymakers and practitioners in SSCM can use the SSCM research landscape and its primary highlight patterns to guide and add in the TI. Considering SSCM research as a way to reduce waste, future study directions are also suggested.

Robust design of a green-responsive closed-loop supply chain network for the ventilator device

This study aims to investigate the closed-loop supply chain network design problem considering the environmental and responsiveness features. For this purpose, a multi-objective mathematical model is suggested that minimizes the carbon emissions and the total costs and maximizes the responsiveness of the system. Due to the dynamic space of the business environment, uncertainty is an integral part of the supply chain problem. Therefore, this research applies the robust possibilistic programming method to cope with uncertainty. Afterwards, since the research problem has a high level of the complexity, a hybrid solution approach based on a heuristic method and the meta-goal programming method is developed to solve the research problem in a reasonable time. Then, due to the importance of the ventilator device during the recent pandemic (COVID-19), this study considers this product as a case study. The main contribution of the current study is to design a green-responsive closed-loop supply chain network under uncertainty using a multi-objective robust possibilistic programming model, for the first time in the literature, especially in the medical devices industry. On the other side, the other contribution of this study is to develop an efficient hybrid solution method. The achieved results demonstrate the efficiency of the offered model and the developed hybrid method. Eventually, by carrying out sensitivity analysis, the impact of some of the critical parameters on the model is investigated. Based on the obtained results, an increase in the demand sizes leads to increasing the environmental damages and the total costs while reducing the responsiveness level. On the other side, an increase in the rate of return leads to an increase in all of the objective functions. Also, the achieved results show that when the capacity parameter is increased, the total costs are decreased, but the responsiveness and environmental impacts are increased.