Green innovation, digitization, and sustainability have garnered significant attention in recent years due to their potential to transform organizations. This study examines the relationship between green innovation and sustainable performance and the role of green digital learning orientation and big data analytics in that relationship. Grounded in the resource-based view, this investigation analyses the direct effect of green innovation on sustainable performance, the moderating role of green digital learning orientation in that relationship, and the three-way interaction effect of green innovation, green learning orientation, and big data analytics on sustainable performance. The study collected 567 valid questionnaires from managers in manufacturing and service businesses in Iraq. The findings indicate that green innovation is positively related to sustainable performance and this relationship is stronger when green digital learning orientation is high. Additionally, the study found that big data analytics has a significant three-way interaction effect on sustainable performance. Overall, this study emphasizes the importance of green innovation, green digital learning orientation, big data analytics, and sustainable performance. The contributions of this work can be valuable to startups and traditional organizations seeking to explore or seize opportunities in green innovation and digitization.

Purpose: This paper introduces MedMaintBot, an AI chatbot designed to support biomedical technicians and non-expert users like nurses. The study explores the impact of integrating such an AI chatbot into Total Productive Maintenance (TPM) practices in healthcare, aligned with Industry 5.0 (I5.0) principles. Design/methodology/approach: This study adopts a multi-phase methodology, starting with a literature review on technology integration in TPM within healthcare settings. It presents the chatbot development pipeline and conducts a large-scale validation study across 250 queries covering five medical devices (MDs) to demonstrate the chatbot's real-time, context-aware guidance capabilities. Performance analysis further evaluates MedMaintBot's potential to optimize TPM practices and support sustainability goals in healthcare maintenance. Findings: The study reveals that MedMaintBot enhances TPM within healthcare by delivering accurate, context-aware guidance (Accuracy = 0.713, Relevance = 0.810), supporting nurse autonomy in routine maintenance and reducing technician dependency. While clarity and completeness were slightly below ideal for complex tasks, over 80% of autonomy-related queries were validated, showing strong support for first-level interventions. Combined with dynamic Large Language Model (LLM) switching between GPT-4 and MedLLaMA2, MedMaintBot strikes a balance between performance, cost and privacy, positioning it as a scalable and sustainable tool for healthcare maintenance. Research limitations/implications: This research provides valuable insights for practitioners and researchers on enhancing autonomous maintenance (AM) through AI–chatbot integration, offering a scalable framework for integrating AI into TPM practices. It also encourages further studies to address gaps in procedural completeness and contextual continuity and assess scalability across diverse maintenance environments. Practical implications: By providing real-time, context-aware guidance, the chatbot helps reduce user-induced errors, allowing non-expert users, such as nurses, to perform maintenance tasks. This not only reduces the burden on specialized technicians but also ensures better equipment availability, contributing to more streamlined healthcare operations and improved patient care. Social implications: MedMaintBot promotes a more inclusive and resilient healthcare environment by empowering non-expert users with AI-driven support. Its adaptability aligns with the human-centric principles of Industry 5.0, fostering collaboration between technology and healthcare personnel. Originality/value: This research is among the first to examine the integration of innovative AI chatbot with TPM practices within the healthcare sector, particularly in the context of I5.0. It demonstrates how such a system can significantly enhance operational efficiency, empower non-expert users and support sustainability in healthcare, offering a roadmap extending AI-assisted maintenance to broader industrial and resource-constrained environments.

Adapting to the business environment and surviving in the digital economy requires businesses to innovate and rethink their strategy more often than before. This is complex for all organizations, but even more so for SMEs. Collaborative Innovation (CI) offers ways to overcome the scarcity of resources, typical in the context of SMEs. However, most of time CI requires strategic changes to adopt and integrate innovation. Literature suggests that Dynamic Capabilities (DC), which include sensing, seizing and resources reconfiguration capabilities, may help businesses in strategic changing. More, DC seem to support CI since the same capabilities are needed. Although both CI and DC have been well articulated in the literature, empirical evidence supporting their relationship is scarce. Our study contributes to reducing this gap by conducting an exploratory study based on a Systematic Literature Review. The objective of this paper is to explore the relationship between CI and DC.

While the issue of business intelligence is rapidly gaining popularity across a wide range of domains, the majority of research treats it as a single capability or technique, such as big data analytics capability. However, as a tool for Big Data Decision-making or technique for enhancing operational research technique, there is still a low amount of work that examines business intelligence as a tool to develop dynamic capabilities of the organization and to contribute to sustainable innovation, in particular in the digital age. Therefore, to address this gap, this chapter aims to discuss how organizations can use technologies, including business intelligence as a tool for creating new knowledge, which in turn helps organizations to improve their dynamic capabilities and achieve sustainable innovation. Recognizing how these firms’ dynamic capabilities are started building, achieved sustained, enlarged, utilized, evolved, and phased out in phrases of their constituent micro-foundations. So, this study suggests business intelligence as a process that helps organizations collect and transform data into information and knowledge, which contributes to building dynamic capabilities. It is important for managers to understand how these firms’ dynamic capabilities are started building, achieved sustained, enlarged, utilized, evolved, and phased out in phrases of their constituent micro-foundations.

In a global context marked by the transition to a more sustainable economy, this study investigates how green technologies, supported by digital leadership, contribute to strengthening organizational sustainability, particularly within the agricultural sector. Adopting a qualitative approach, the study relies on empirical data collected through an interview with the CEO of an innovative Tunisian company specializing in smart irrigation solutions. Thematic analysis reveals that the integration of green technologies not only enhances the productivity and profitability of farms, but also preserves natural resources and promotes environmentally friendly practices. The role of the manager, characterized by a proactive vision, adaptability, and a commitment to sustainability, proves to be decisive in guiding the company towards a successful ecological transition. Furthermore, the study identifies several obstacles, such as the initial cost of technologies, technical complexity, and resistance to change, while emphasizing the importance of strategic and institutional support to facilitate adoption. Ultimately, this research highlights the crucial managerial role of digital leadership in linking technological innovation and sustainability, while acknowledging the exploratory nature and limited generalizability inherent in a single-case study.

Purpose: This study aims to investigate how entrepreneurial firms transform turbulence within their ecosystems, originating from clients, competitors and suppliers, into innovation success. Drawing on contingency theory, the study emphasizes analytical capacity as a key mediating mechanism. Design/methodology/approach: A cross-sectional survey was conducted among 140 entrepreneurial manufacturing firms in Québec, drawn from the Centre de Recherche Industrielle du Québec database. Using the key informant approach, data were collected from chief executive officers, directors and senior managers. The research model was tested with partial least squares structural equation modeling and evaluated for predictive relevance and effect size to ensure the robustness of results. Findings: The results reveal that entrepreneurial ecosystem turbulence (EET) positively influences innovation performance but only indirectly through analytical capacity. Entrepreneurial firms with stronger analytical capacity are more effective in capturing signals from clients, competitors and suppliers, which enables them to adapt their strategies and turn environmental disruptions into opportunities. The findings confirm that analytical capacity fully mediates the relationship between ecosystem turbulence and innovation outcomes, highlighting its central role as a dynamic capability in turbulent environments. Originality/value: The study introduces EET as a second-order construct comprising client, competitor and supplier turbulence and demonstrates that analytical capacity functions as a contingent resource that aligns internal capabilities with external conditions. By adopting an informational perspective, the research advances entrepreneurship studies and shows how data analytics can transform turbulence into sustained innovation success.