Rsha Alghafes
58311215500
Publications - 5
Psychological foundations of ambiguity in the hybrid workplace: The role of managerial risk-taking and AI-induced job insecurity
Publication Name: Acta Psychologica
Publication Date: 2026-02-01
Volume: 262
Issue: Unknown
Page Range: Unknown
Description:
In the modern ever-changing organizational environment, where hybrid workplace arrangement is becoming increasingly common, and artificial intelligence (AI) technology has been used widely, employees tend to face a situation characterized by ambiguity of work and it is difficult to perceive an understanding of role, expectations, and employment. The paper explores the interrelationship between task ambiguity, risk-taking by managers, AI-induced job insecurity, and employee outcomes in a hybrid working environment. This is based on Social Information Processing Theory where we advance a theoretical model that explores how workforce members actively learn and process information in their social context to get through ambiguity and foster resilience. The evidence of the proposed relationships is substantiated by three studies. Study 1 focuses on the way task ambiguity influences active lurking and also job engagement. Study 2 explores with the moderating factor on the relationship among the variables of task ambiguity, active lurking, and job engagement on managerial risk-taking. Study 3 examines how AI-induced job insecurity can moderate the link between task ambiguity and active lurking and job engagement. The results emphasize the need to ensure clear task specification, active lurking, management risk-taking and proactive efforts to reduce the issue of AI-induced job insecurity as factors that enhance employee engagement. The implications of the study are given and recommendations to conduct further research are outlined.
Open Access: Yes
Harnessing Generative AI for Sustainable Supply Chains: Lean, Circular and Green Perspectives
Publication Name: Business Strategy and the Environment
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
Generative artificial intelligence is playing a significant role in the transformation of digital ecosystems by reinventing the processes of content generation, process automation, product innovation and customer experience. At the same time that these technologies are becoming more integrated into routine operations, the focus has shifted to the ethical and environmental consequences associated with their widespread application. An investigation of the operational sustainability associated with the generative artificial intelligence systems would be crucial, as it would provide information about how these systems match ideals such as efficiency, circularity and environmental responsibility. We explore how users understand and engage with sustainability principles, specifically lean, circular and green operational frameworks within generative artificial intelligence environments. We collect user reviews of 72 recently launched generative AI platforms from 2022 to 2024 and utilise advanced machine learning methods, including Word2Vec modelling, sentiment and regression analysis, to reveal how text datasets reflect customer perceptions. We find that the lean theme is the most prominent feature of operational sustainability, with the highest sentiment score, followed by the green and circular themes. Our findings show that there is a growing respect among the general public for artificial intelligence systems that exhibit responsible and efficient design.
Open Access: Yes
DOI: 10.1002/bse.70515
Human–GenAI-based agent collaboration: How employee perceptions shape knowledge sharing, thriving, and well-being
Publication Name: Acta Psychologica
Publication Date: 2026-03-01
Volume: 263
Issue: Unknown
Page Range: Unknown
Description:
The growing pace of the introduction of generative artificial intelligence (GenAI) into organizational processes is changing the way workers cooperate with technology. Based on Social Exchange Theory, we propose that the perception of employees regarding the value of GenAI-based agents, their vulnerability and privacy, and their self-concern would determine the collaboration with GenAI agents, which subsequently would predict the knowledge sharing, job thriving, and well-being. The findings show that perceived GenAI-based value has a strong positive impact on human-GenAI-based agent collaboration, but data vulnerability and privacy concerns have no significance. Interestingly, self-concern has a positive effect, which implies that identity-based fears can be used to drive active use of GenAI-based agents. Knowledge sharing, job thriving, and well-being are highly predicted by human-GenAI-based agent collaboration, and organizational exploratory innovation moderates these correlations. These results extrapolate the Social Exchange Theory to human-AI situations, dispel the assumptions of the consistently negative impact of risk perception, and emphasize the relevance of organizational climate to the implementation of the advantages of AI cooperation. The paper provides both theoretical and practical understanding of the way in which employees interact with GenAI-based agents to ensure that organizational learning and psychological outcomes of employees are achieved.
Open Access: Yes
Linking digital platform design to circular supply chains: Evidence from knowledge sharing and collaboration mechanisms
Publication Name: Technology in Society
Publication Date: 2026-06-01
Volume: 86
Issue: Unknown
Page Range: Unknown
Description:
Digital platforms are increasingly central to circular economy transitions, yet it remains unclear how platform design translates into measurable circular outcomes. This study addresses this gap by developing a socio-technical framework that explains how digital platform information transparency and interoperability enable circular value creation. Drawing on socio-technical systems theory, platform governance, and the dynamic capabilities perspective, the study conceptualizes platform design features as layered affordances that operate through governance and knowledge-based mechanisms. Using survey data from platform-enabled supply chain actors and analysing the model via PLS-SEM, the findings reveal that transparency and interoperability significantly enhance sustainability governance mechanisms, while transparency strengthens knowledge sharing intensity and interoperability supports collaborative decision-making. Sustainability governance mechanisms, in turn, drive both circular business model implementation and circular supply chain performance, whereas knowledge sharing intensity primarily supports business model transformation. Notably, collaborative decision-making does not directly improve circular supply chain performance, suggesting that collaboration without governance alignment remains insufficient. By unpacking the indirect pathways through which platform design shapes circular outcomes, the study advances platform governance and circular supply chain literature. It offers actionable insights for managers and policymakers seeking to design digital ecosystems that move beyond symbolic digitalization toward measurable circular performance and systemic value creation.
Open Access: Yes
Understanding the psychology of knowledge sharing and experience in digital service ecosystems
Publication Name: Acta Psychologica
Publication Date: 2026-07-01
Volume: 267
Issue: Unknown
Page Range: Unknown
Description:
Drawing on service-dominant (SD) logic, which conceptualizes value as emerging through resource integration and use rather than direct technological outputs, the study examines how technology-mediated knowledge-sharing platforms (TMKSP) influence employee and employee-perceived customer experience using the DART (dialogue, access, risk assessment, transparency) framework of value co-creation. Employing a mixed-method approach, a pre-hoc qualitative study (Study A) identified key TMKSP features relevant to value co-creation, which informed the development of a DART-based survey for the quantitative phase (Study B). Data from retail employees were analyzed using PLS-SEM with two-tailed bias-correct bootstrapping. The findings show that TMKSP significantly improves employee experience via platform access and reduced perceived risk, while enhancing employee-perceived customer experience through employee-customer dialogue and platform transparency. Mediation analysis confirms the explanatory role of DART-based constructs in linking TMKSP with experience outcomes, although the mediating role of perceived platform risk was not supported. The study contributes theoretically by operationalizing SD logic within an internal service ecosystem and demonstrating how value-in-use is shared through employee-perceived co-creation conditions rather than through direct technological effects. It offers practical guidance for managers aiming to design employee and customer-centric knowledge-sharing ecosystems.
Open Access: Yes
