https://bura.brunel.ac.uk/handle/2438/32878 2026-07-17T15:36:18Z https://bura.brunel.ac.uk/handle/2438/33539 Title: Untangling indigenous leadership competences in sustainability challenged firms: A Sustainable Indigenous Network Leadership commitment toward emission mitigation in Bahrain energy industry Authors: AlGhanem, N; Braganza, A; Harrison, C Abstract: This study examines how indigenous leadership competences can be integrated into network leadership frameworks to support emission mitigation in Bahrain's energy sector. Given the lack of culturally aligned leadership models in sustainability-challenged firms, this research addresses a theoretical and practical gap. Drawing on qualitative data from eight firms, the study proposes a Sustainable Indigenous Network Leadership (SINLA) framework comprising four competence dimensions: socio-cultural, socio-political, socio-economic, and socio-knowledge. The findings reveal that embedding indigenous values into network leadership enhances organisational change capacity and supports organisational transformation addressing climate change. This contributes to leadership theory by expanding the applicability of network leadership to non-Western, emission-intensive contexts. Description: Data availability: Data will be made available on request. 2026-06-12T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33535 Title: The Economic Effects of Artificial Intelligence Adoption in Small and Medium-Sized Enterprises Authors: Bolfek, M; Rajko, M; Bolfek, B Abstract: Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution and is increasingly significant for companies’ economic performance. Small and medium-sized enterprises (SMEs), the foundation of economic development in most national economies, face numerous challenges and opportunities in applying artificial intelligence in business. This paper aims to examine the economic effects of applying artificial intelligence in SMEs, with a special emphasis on labor productivity, business process efficiency, and reduced operating costs. Empirical research was conducted on a sample of 228 SMEs using a questionnaire, with the data analyzed using multiple linear regression. The research results show that different applications of artificial intelligence have a statistically significant, positive impact on labor productivity and on reducing operating costs. In contrast, their impact on business process efficiency is moderate and partially limited. The operational application of artificial intelligence, such as automation and data analysis, has proven to be the most important factor in economic effects. At the same time, its application in managerial decision-making also has a significant, but somewhat weaker impact. On the other hand, the mere growth of AI applications over time does not necessarily lead to increased efficiency without targeted and concrete implementation. The paper’s results contribute to understanding the role of AI in transforming SMEs and highlight the importance of targeted investments in operational and management applications of AI. The paper provides practical implications for entrepreneurs and economic policymakers in fostering sustainable, competitive development of SMEs. Description: Data Availability Statement: The data presented in this study are available from the corresponding author upon reasonable request. Data are not publicly available due to confidentiality and anonymity requirements.; JEL Classification: D24; O33 2026-06-18T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33309 Title: Insights and future directions in service design: a global perspective Authors: Marvi, R; Foroudi, P; Mahavarpour, N Abstract: Purpose: This article critically reviews the evolution of the service design literature over the past two decades, with a particular focus on its maturation and application in a global context. The study aims to address gaps and inconsistencies in existing knowledge and to propose a comprehensive research agenda for future studies. Design/methodology/approach: The study applies three co-citation bibliometric methods including multidimensional scaling (MDS), hierarchical clustering (HCA), as well as exploratory factor analysis (EFA) together with text-mining techniques to conduct a systematic review and chart the intellectual and conceptual foundations of the service design field. Findings: The analysis maps the intellectual and conceptual structures of the service design domain. The co-citation analysis produced five clusters/factors from the EFA and HCA, identifying five groups that highlight the theoretical underpinnings of the field. In addition, the text-mining analysis shows that themes such as customers, technology and the international market are among the most dominant in the literature. Taking together, these findings provide a consolidated understanding of the fragmented service design field, which not only highlights existing gaps but also supports researchers in developing and proposing more integrative, globally relevant conceptual frameworks for future studies. Research limitations/implications: While the combination of bibliometric and text mining techniques offers objectivity and breadth, some context-specific nuances may be overlooked. The findings underscore the necessity for future research to develop unified frameworks and region-specific models to address the complexity of global service ecosystems. Practical implications: The proposed integrative framework assists organisations in aligning their service design strategies with emerging global trends and practices, supporting more effective international marketing and service delivery. Originality/value: This study is among the first to provide a holistic, mixed-methods review of service design from a global perspective. It advances the literature by integrating diverse research streams and offering actionable directions for both scholars and practitioners in international marketing. 2026-01-07T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33308 Title: The impact of AI perceived transparency on trust in AI recommendations in healthcare applications Authors: Shabankareh, M; Khamoushi Sahne, SS; Nazarian, A; Foroudi, P Abstract: Purpose: The integration of artificial intelligence (AI) in healthcare has transformed the way users interact with health applications, offering personalized recommendations and decision-making support. However, building trust in AI-driven systems remains a significant challenge, particularly in high stakes environments like healthcare, where user concerns about fairness, control, and privacy are paramount. This study aims to investigate how AI transparency influences trust in healthcare applications, focusing on the mediating roles of perceived fairness and control, and the moderating role of privacy concerns. Design/methodology/approach: A quantitative research design was employed, utilizing survey data collected from healthcare application users. Structural Equation Modeling (SEM) and moderation analysis were used to test the proposed conceptual framework, exploring the interrelationships among the variables. Findings: The results revealed that AI transparency significantly influences trust in healthcare applications indirectly through perceived fairness, while perceived control had a limited mediating effect. Privacy concerns were found to amplify the relationship between fairness and trust but did not significantly moderate the effects of transparency or control on trust. These findings emphasize the central role of fairness and privacy in building trust, highlighting the nuanced interplay between ethical perceptions and user concerns in high-stakes contexts. Originality/value: This study contributes to the literature by integrating fairness, control, and privacy concerns into a unified framework for understanding trust in AI healthcare applications. By demonstrating how transparency operates indirectly and how privacy concerns shape user perceptions, this research provides novel insights for designing ethically robust and user-centric AI systems tailored to sensitive domains like healthcare. 2025-05-20T00:00:00Z