Purpose This paper aims to extend Kruger’s (2008) strategy-oriented knowledge management (KM) maturity model by integrating organizational learning as an explicit mechanism to explain KM maturity progression. It addresses the limitation of existing models that describe maturity levels but provide limited explanation of why organizations progress or stagnate. Design/methodology/approach This paper adopted a sequential explanatory mixed-methods design at Isfahan Regional Electric Company. Quantitative data were collected from 100 managers and specialists using Kruger’s Knowledge Management Maturity Questionnaire and Watkins and Marsick’s DLOQ, followed by 11 semi-structured interviews analyzed through thematic network analysis. Field observations and documentary analysis complemented the findings. Findings Four of the six KM maturity stages were below their desired benchmark, and individual and team learning levels were below their respective benchmarks. Individual and team learning emerged as foundational capabilities, while analytical thinking, systems thinking and strategic leadership functioned as stage-specific enabling mechanisms. An exploratory non-linear U-shaped maturity pattern was identified, with Stages 1 and 6 outperforming intermediate stages. Research limitations/implications The single-site design may limit generalizability. Future research can validate the framework via longitudinal studies, structural equation modeling and diverse contexts. Practical implications This paper highlights developing organizational learning capabilities alongside analytical thinking, systems thinking and strategic leadership. Social implications Improving KM maturity can support knowledge retention, organizational resilience and network sustainability in infrastructure-intensive utilities. Originality/value This paper develops an organizational learning-based explanatory framework and identifies mechanisms shaping non-linear KM maturity progression, contributing to deeper understanding of how maturity advances.
Purpose This study aims to examine how narcissistic leadership motivates employees to engage in knowledge sabotage through perceived competitiveness and knowledge-based psychological ownership (KBPO). Design/methodology/approach A three-wave, time-lagged survey collected data from 282 full-time researchers in Indonesia. Partial least squares structural equation modeling (PLS-SEM) tested the proposed mediation model. Findings Results show that narcissistic leaders catalyze subordinates’ knowledge sabotage directly and indirectly through perceived competitiveness and KBPO. By fostering competitive work climates and possessive orientations toward knowledge, it encourages knowledge sabotage. Originality/value This study extends destructive leadership literature by identifying narcissistic leadership as a covert yet consequential predictor of subordinates’ knowledge sabotage. It advances the application of social learning theory and cooperation–competition theory to explain how narcissistic leadership shapes counterproductive knowledge dynamics. The findings offer organizations guidance for mitigating sabotage in competitive, knowledge-driven workplace.
Purpose The application of artificial intelligence (AI) is increasingly being used in organizational decision-making. Concerns about its “dark side” are mounting and warrant attention. This paper aims to examine the impact of these aspects on ethical vigilance and on the emergence of dysfunctional results in AI-enabled environments. Design/methodology/approach The study uses a mixed-methods approach based on the socio-technical systems theory and the technology acceptance model by incorporating theme analysis of interviews with experts to determine gaps in the research and using them to structure equation modeling to analyze survey data consisting of 369 professionals working with Indian tech-enabled companies. Findings Algorithmic opacity was found to be positively associated with organizational overreliance and negatively associated with trust in AI systems. Organizational overreliance was found to statistically mediate the association between algorithmic opacity and ethical vigilance. Practical implications The paper calls on policymakers to leave behind the discourse of efficiency at the cost of AI and take a holistic, governance-focused approach to harmonize the use of AI with transparency, confidence and moral responsibility. This alignment should be achieved not only by law but also by a strategy for long-term organizational integrity and stakeholder trust. Originality/value The study provides preliminary empirical support for ethical vigilance as a relevant construct in AI governance and organizational decision-making. It provides useful insights into how AI systems can be designed to be explainable, accountable and ethically sound to counter rising organizational risks.
Purpose This study aims to conduct a bibliometric analysis of research data management (RDM) in library science to illuminate the evolving landscape of the field. In addition, this study aims to unveil key trends, contributors and thematic patterns of the underlying research field. Design/methodology/approach This research adopted systematic research planning, collected data from the Scopus database and analyzed that data using bibliometric techniques. The methodology involved performance analysis to assess research constituents such as authors, sources and countries and science mapping to visualize collaborative networks and thematic evolution. Findings Results revealed a surge in publications over time, with a notable emphasis on topics such as open data-sharing norms, institutional patronage, infrastructure augmentation and the need for prudent investments in RDM infrastructure. This study highlighted the integration of emerging technologies in enhancing data management practices. The overall analysis revealed the key trends and knowledge structure of RDM scholarship, as well as its pivotal role in fostering scientific progress and innovation within library science. Originality/value This study provided a focused overview of the RDM research landscape. In particular, the key findings on the knowledge structure of RDM within library science, its thematic evolution and projections of future research areas offer valuable implications for scholars and practitioners seeking to navigate the dynamic landscape of RDM research.
Purpose This study aims to empirically examine the influence of environmentally specific servant leadership (ESSL) on pro-environmental behavior (PEB), with a specific focus on the mediating role of green knowledge sharing (GKS). Design/methodology/approach Data were collected from 487 supervisor-subordinate dyads across seven textile companies in Egypt. A time-lagged, multisource data collection strategy was employed, utilizing separate questionnaires completed by employees and their immediate supervisors. Partial least squares structural equation modeling (PLS-SEM) was used to test the proposed relationships and to evaluate the reliability and validity of the measurement instruments. Findings The findings confirm that ESSL positively and significantly influences employees’ PEB. Furthermore, GKS was found to serve as a significant mediator in this relationship, thereby offering a deeper understanding of the underlying psychological mechanism that facilitate PEB in organizational settings. Originality/value This study represents the first empirical attempt to investigate the mediating role of GKS in the relationship between ESSL and PEB. By doing so, it contributes novel insights to the growing literature on sustainable leadership and employee PEB.
Purpose This study aims to examine how nonprofit and civil society initiatives create measurable public value when responding to unmet social needs in the context of institutional ambiguity. Design/methodology/approach Focusing on “Spin Time,” a civic welfare hub in Rome developed within an abandoned public building, the authors integrate Faulkner and Kaufman’s public value framework with social return on investment methodology. Drawing on knowledge translation theory, the authors analyze how measurement practices function as translation mechanisms that render community-generated value legible to institutional actors, while examining the tensions inherent in this translation process. Findings Spin Time delivers multidimensional outcomes in housing, education, health and inclusion, despite operating outside formal governance arrangements. The case illustrates hybrid forms of collaboration and adaptive governance, showing how measurement tools can mediate between community legitimacy and institutional recognition. Originality/value Theoretically, this study contributes to nonprofit management scholarship by conceptualizing public value measurement as a knowledge translation process operating within contested governance spaces, demonstrating how such translation both empowers grassroots organizations and creates risks of “measurement capture.” The authors further show how strategic resource dependency may serve a value preservation function in civil society initiatives, contrary to conventional assumptions regarding organizational autonomy. This study’s central theoretical contribution is the reconceptualization of public value measurement as a knowledge translation process. Two interrelated arguments support this core claim: first, that such translation simultaneously empowers grassroots organizations and creates risks of measurement capture; second, that strategic resource dependency may serve a value preservation function, contrary to conventional assumptions of organizational autonomy
Purpose Artificial intelligence (AI) is fundamentally reconfiguring knowledge practices, yet its effective integration into existing knowledge management (KM) infrastructures remains complex. This paper aims to address: How do organisations meaningfully integrate AI into operational practices in light of existing KM? This study moves beyond descriptions of individual AI tools to develop a holistic, empirically grounded theory of AI-driven knowledge transformation.Design/methodology/approach Using a rigorous, qualitative, interpretive grounded theory approach, this study analysed 100 publicly available AI implementation case studies. Initially, open codes were created through line-by-line coding and then systematically abstracted into coherent axial codes, which in turn informed the final selective codes.Findings Our analysis reveals a novel theoretical framework that posits useful AI integration in four distinct archetypes, shaped by three primary organisational decisions: externalise selective expert knowledge (vertical vs horizontal), consolidate knowledge into stable artefacts (verified vs non-verified) and workflow configuration (automation vs augmentation).Research limitations/implications This study is limited by its reliance on publicly available success stories, which may bias findings towards positive outcomes. Future research should examine instances of AI implementation failure to provide a more balanced perspective. Furthermore, the constructs developed from this secondary data require empirical validation through primary data collection.Originality/value This paper's primary contribution is its novel, empirically grounded typology of AI integration strategies. These frameworks offer insight into how KM-AI integration is implemented in practice. It moves beyond abstract principles to offer a structured, actionable model for meaningful organisational transformation in the age of AI.
Purpose This study aims to investigate how Dark Triad personality traits - narcissism, Machiavellianism and psychopathy - shape work performance through the mediating role of knowledge sabotage. By integrating trait activation and social contagion theories, this study explains how destructive knowledge behaviors emerge and spread inside competitive work environments.Design/methodology/approach Data were collected from 264 Indonesian sales employees in a context characterized by high competition and intensive knowledge exchange. The proposed model was tested using partial least squares structural equation modeling with SmartPLS 3.0. Measurement validity and reliability were established through outer loadings, composite reliability, Cronbach's alpha and average variance extracted. Hypotheses were evaluated using bootstrapping procedures.Findings Individual and coworker Dark Triad traits significantly predicted knowledge sabotage, while coworker sabotage strongly triggered individual sabotage through behavioral contagion. Knowledge sabotage had a small positive effect on work performance and mediated the influence of Machiavellianism and psychopathy but not narcissism on performance. These results identify knowledge sabotage as a behavioral pathway through which Dark Triad traits translate into performance outcomes.Practical implications Organizations should monitor team dynamics and implement ethical training and collaborative norms to mitigate deceptive knowledge behaviors.Originality/value This study advances trait activation and social contagion theories by identifying knowledge sabotage as a behavioral transmission mechanism that links dark personality traits to performance outcomes. It contributes to the counterproductive knowledge behavior literature by shifting the focus from static antecedents to dynamic processes, showing how destructive knowledge behaviors are activated by situational cues and amplified through social interaction within teams.
Purpose This study aims to examine how knowledge management (KM) systems can be designed and aligned to mitigate this vulnerability and support operational readiness. Design/methodology/approach An exploratory framework-development study was conducted, integrating descriptive survey data from 111 military aviation professionals with expert interactions and thematic analysis of operational practices. Findings Findings indicate a structural imbalance: while procedural documentation and technology-enabled systems are relatively mature, the systematic capture and institutionalisation of tacit operational knowledge remains comparatively weak. This misalignment, compounded by variable leadership engagement, creates significant knowledge vulnerability. Originality/value Rather than proposing a new knowledge management theory, this study advances a context-sensitive extension of KM systems research by introducing readiness orientation as a structuring principle and developing a framework supported by empirically derived design propositions for military helicopter operations.
Purpose - To address mounting environmental, social and economic pressures, this study aims to explore how artificial intelligence (AI) enhances knowledge management (KM) to support sustainable business transformation, particularly within the emerging context of Industry 5.0. Design/methodology/approach - Following preferred reporting items for systematic reviews and meta-analyses guidelines, this mixed-methods systematic review analyzes 80 articles (2004-2025) from Scopus and Web of Science. The methodology combines descriptive bibliometrics (e.g. publication trends) and R-based science mapping (e.g. thematic networks) with qualitative content analysis to decode the AI-KM-sustainability nexus. Findings - Bibliometrics reveal exponential post-2019 growth, Asian geographic dominance and reliance on cross-sectional surveys. Thematic mapping identifies KM, AI and sustainability as anchor motor themes, with green innovation and circular economy as a critical emergent frontier. Qualitatively, six core themes form a tripartite framework: technological foundations, organizational strategy and ecological outcomes. It demonstrates how AI-KM integration enhances dynamic capabilities, while highlighting critical sociocultural and ethical governance barriers. Practical implications - This study provides managers with actionable maturity models and decision-support frameworks to operationalize corporate sustainability strategies and navigate complex digital integrations. Social implications - Within the Industry 5.0 context, this research emphasizes the need for human-AI collaborative ecosystems, advocating for technological democratization, ethical governance and inclusive knowledge sharing. Originality/value - By positioning the knowledge-based view as the primary analytical anchor, supplemented by dynamic capabilities and socio-technical systems theory, this study establishes a comprehensive framework linking AI-driven KM with sustainability. It proposes a targeted research agenda prioritizing longitudinal designs, global inclusivity and ethical accountability.
Purpose The purpose of this study is to explore how millennials attain satisfaction through knowledge sharing (KS) in knowledge-intensive environments. Considering growing attention to mental well-being at work, this research investigates how knowledge characteristics - specifically complexity and complementarity - and KS behaviors contribute to satisfaction outcomes.Design/methodology/approach In this study, the authors follow a mixed-methods research design. Drawing on the social cognitive theory, the authors address the antecedents of satisfaction with KS for 213 millennial employees working together in a specific knowledge-intensive context: master's programs in management at Polish and Portuguese business schools.Findings This study shows that knowledge complementarity significantly promotes KS and reduces fear of losing power, while complex knowledge - contrary to Social Cognitive Theory expectations - encourages sharing among millennials seeking recognition. Satisfaction arises when high sharing is paired with low fear. These findings challenge traditional Social Cognitive Theory assumptions by revealing that millennials view complex knowledge as a strategic asset rather than a barrier, guided by self-efficacy, outcome expectations and self-regulation. Configurational analysis confirms that the absence of complementarity and presence of complexity trigger fear and withholding. No cultural differences were found, suggesting generational traits outweigh national influences.Research limitations/implications The authors acknowledge the limitations of the results because of the small sample involved and the cross-sectional approach applied to the data.Originality/value By combining structural equation modeling and fuzzy-set qualitative comparative analysis, this research offers a multidimensional understanding of how millennials navigate emotional and cognitive factors in KS. This study highlights the generational nuances in sharing behavior and challenges traditional assumptions about knowledge complexity.
Purpose - This research aims to investigate the relationship between servant leadership and employee's knowledge hiding behaviors, as mediated by meaningful work. Based on the social learning theory (SLT), the research suggests that servant leaders are exemplary role models, which enhance the employee perception of meaning of work, subsequently counteracting disengaging behaviors like knowledge hiding. Design/methodology/approach - Data were collected from 609 employees in T & uuml;rkiye via a student-recruited sampling. The study used structural equation modelling to test direct and indirect relationships among servant leadership, meaningful work and knowledge hiding. Findings - Servant leadership was found to directly decrease playing dumb, and to not significantly affect evasive or rationalized knowledge hiding. Nonetheless, meaningful work provides insight into how servant leadership serves to decrease knowledge hiding through reduced playing dumb and evasive hiding. This shows that servant leadership reduces knowledge hiding primarily through fostering employees' perceptions of meaningful work. Practical implications - The findings underscore the importance of leadership development and job design that helps employees feel their work is meaningful. Incorporating servant leadership into these practices, organizations can design specific interventions to curb knowledge hiding encourage teamwork or cultivate a trust climate at workplace. Originality/value - This study advances the literature by identifying meaningful work as a key mechanism through which servant leadership affects knowledge hiding. Based on SLT, it describes how servant leaders influence employees' knowledge hiding behaviors through the modelling processes. Expanding beyond the one-dimensional representation of knowledge hiding and focusing on its different behavioral dimensions, the study provides a nuanced account of how servant leadership can deter knowledge hiding behaviors.
Purpose This study aims to analyse how the explainability and perceived autonomy (PA) of agentic artificial intelligence (AAI) influence employees' intentions to use such systems. Recognising that AAI systems differ from previous technologies, this study explored the mediating and moderating roles of knowledge-sharing culture (KSC) and technical self-efficacy (TSE), respectively. The objective was to understand the cognitive, cultural and individual contexts in which employees would like to use AAI. Design/methodology/approach We used structural equation modelling to analyse data collected from 719 employees in different British companies.Findings Results reveal that explainability is a robust predictor of intention to use AAI, exceeding the impact of PA. PA was found to significantly influence KSC, a social and cognitive factor that shapes AI traits and drives adoption intentions. Findings also showed that TSE enhanced the mediating effect of explainability while not affecting autonomy, underscoring the unique psychological mechanisms governing human-AI interactions. Practical implications Organisations should prioritise explainability and invest in systems that encourage employee knowledge-sharing and sense-making, including AI discussion platforms and tools that promote transparency. Adjusting AAI autonomy and boosting employees' TSE can encourage adoption, particularly when AI reasoning must be explained. Originality/value This study extends existing AI adoption and knowledge management perspectives by examining how KSC helps translate explainability and PA into employee adoption intentions. It emphasises explainability as a predictor and identifies KSC as the crucial link connecting AI attributes to employee intentions.
Purpose The purpose of this study is to examine whether benevolent leadership is associated with teacher knowledge sharing and to investigate the mediating roles of relational energy and work engagement in this relationship. Teacher knowledge sharing is a fundamental component of school knowledge management. While prior research has established leadership as a crucial determinant of knowledge-sharing behavior, most studies have predominantly examined leadership styles rooted in Western cultural contexts.Design/methodology/approach The authors conducted a survey in Taiwan (n = 383), a society shaped by Confucian values and embedded within the wider Chinese cultural context.Findings The results indicate that benevolent leadership is positively associated with teacher knowledge sharing. Furthermore, relational energy and work engagement mediate the association between benevolent leadership and teacher knowledge sharing in a serial manner.Research limitations/implications By introducing benevolent leadership into the teacher knowledge-sharing literature and integrating conservation of resources (COR) theory, this study extends leadership research beyond Western paradigms, and offers novel theoretical and practical contributions.Practical implications When leaders show genuine care and support for teachers, they are associated with higher levels of relational energy and work engagement, which are in turn linked to greater knowledge sharing. Leadership training should therefore highlight benevolence to strengthen professional learning communities.Originality/value Grounded in the COR theory, this study investigates the association of benevolent leadership - grounded in a non-Western cultural context - on teacher knowledge sharing, with particular attention to the mediating roles of relational energy and work engagement.
Purpose This study aims to examines whether green intellectual capital components, including green human capital (GHC), green structural capital (GSC) and green relational capital (GRC), enhance green process innovation performance in the Vietnamese automotive industry. Design/methodology/approach Drawing on firm-level survey data from the Vietnam’s automotive sector (usable n = 386; response rate = 77.2%), the authors develop and empirically test a process-level framework that links the knowledge-based view with dynamic capabilities. Latent constructs were operationalized using multi-item Likert scales and evaluated for reliability and validity. To evaluate the measurement model, the authors conducted a confirmatory factor analysis (CFA) using IBM SPSS AMOS. Furthermore, partial least squares structural equation model with bootstrapped confidence intervals is also utilized. Findings All three green intellectual capital components exhibit positive and statistically significant associations with green process innovation performance. Practical implications The findings yield actionable implications for managerial policy aimed at strengthening green intellectual capital and, in turn, enhancing sustainable innovation performance. Specifically, our empirical results indicate that targeted green workforce development and collaborative skills programs (GHC), the codification and formalization of environmental routines and metrics (GSC) and the reinforcement of inter-firm ties through supplier development and external partnerships (GRC) are each associated with higher green process innovation performance among Vietnamese automotive firms. Originality/value To the best of our knowledge, this is the first empirical study conducted to examine the role of green intellectual capital on green process innovation performance in automotive firms in emerging markets.
PurposeThis paper aims to examine how knowledge translation (KT) and risk-related practices enable decision-making in early-stage innovation initiatives characterized by high uncertainty and the absence of stabilized governance structures.Design/methodology/approachThe study adopts a qualitative, theory-elaborative single-case design based on an in-depth analysis of DRIVE-F, a research-based spin-off developing a multidimensional decision and impact evaluation framework. Empirical material includes design documents, modelling protocols, pilot application materials and internal reflexive records produced during the development process. Data were analysed abductively through a KT framework focusing on translational artefacts, actors, risk-related practices and early decision outcomes.FindingsThe study shows that KT operates as the process through which a pre-organizational decision infrastructure emerges. Translational artefacts - such as simulations and visual models - function as epistemic scaffolds that render uncertainty discussable. Risk-related practices support alignment and decision readiness by structuring interpretation rather than enforcing control.Originality/valueThe paper reframes KT as a foundational mechanism of organizational emergence and reconceptualizes risk-related practices as translational supports rather than governance instruments. It shifts attention to pre-organizational innovation contexts, contributing to debates on innovation governance under conditions of radical uncertainty.
PurposeInternational Organization for Standardization (ISO) 30401 formalizes knowledge management systems (KMSs), marking a significant milestone in the professionalization of knowledge management (KM) discipline. However, organizational adoption remains limited, ambiguous and surrounded by uncertainty. As a context-specific case study, this research aims to examine how KM professionals perceive the standard's purpose, value, legitimacy and practical relevance.Design/methodology/approachSituated in Israel - a country with a prominent role in initiating and shaping ISO 30401 - the research adopts a qualitative-constructivist paradigm and an inductive interpretive approach. Data were collected through semi-structured interviews with 18 KM professionals and complemented by netnographic observations of two public online KM communities. A grounded-theory thematic analysis was used to identify recurring patterns and systemic interdependencies among adoption challenges.FindingsParticipants acknowledged ISO 30401's potential to provide a shared language and conceptual foundation for KM, but reported low awareness of the standard and minimal organizational motivation to pursue certification. They emphasized the absence of external enforcement or consequences for non-compliance and struggled to identify a clear return on investment. Several practitioners questioned the very feasibility of standardizing a dynamic, context-dependent field. Collectively, these factors formed a mutually reinforcing configuration that delays implementation and positions the standard as symbolically legitimate yet operationally marginal.Originality/valueTo the best of authors' knowledge, this study presents, for the first time, an empirically grounded account of KM practitioners' narratives and critical reflections on ISO 30401 adoption. By uncovering a systemic interplay of perceived barriers - rather than isolated constraints - it advances the limited literature on KM standardization and provides timely, context-sensitive insights to guide ongoing ISO revision efforts.
PurposeKnowledge sharing promotes organizational learning and innovation. Thus, the aim of this study is to investigate knowledge sharing among university administrators (nonacademic members) from the social identity perspective. This study specifically explores the effect of shared vision, friendship, perceived organizational memberships and supervisors on employees' knowledge sharing behavior.Design/methodology/approachQuestionnaires were used to collect data from 232 administrators at two Ghanaian Technical Universities. Multiple regression was used to analyze the data collected.FindingsThe results of this study showed that shared vision and friendship have a positive significant relationship with knowledge sharing. However, the relationship between perceived organizational memberships and supervisors and knowledge sharing was not supported.Research limitations/implicationsWhile this study may contribute in terms of the unique sampling frame, the responses from administrators in academic communities may limit the generalization of the findings of this study outside of the general business perspective. Future studies should consider collecting data from other nonacademic institutions for comparison.Practical implicationsAcademic managers should provide clear and desirable goals which would motivate employees to share their knowledge. In addition, administrators (nonacademic members) should be involved in committees and other university activities to feel part of the organization.Originality/valueThis study explores how shared vision, friendship, perceived organizational membership and supervision influence organizational knowledge sharing from the social identity perspective. In addition, this study uniquely examines knowledge sharing among administrators (nonacademic members) in the academic community where knowledge sharing behavior may be different from those in the business arena.
PurposeThis study aims to examine the moderating role of religiosity in the relationship between territoriality, personal competitiveness and knowledge-hiding behaviour. Some control variables were also examined, such as gender and generational cohort (i.e. Gen X and Gen Y), to provide a contextual understanding of knowledge-hiding behaviour in Indonesia.Design/methodology/approachThe authors used a quantitative research design by employing a survey to collect the data. A total of 159 academics at various higher education institutions (HEIs) in Indonesia participated in this study. The data were analysed using Warp partial least squares.FindingsThis study provided insights into academics' knowledge-hiding behaviour, confirming symbolic interactionism theory. The authors found that religiosity weakens the relationship between personal competitiveness and knowledge hiding. In addition, the effect of personal competitiveness on knowledge hiding is more substantial among male and Gen Y academics.Practical implicationsHEIs should strengthen the internalisation of religious values, especially teachings that encourage altruism and cooperation among academics to achieve common goals. Such institutions should also develop policies and activities that foster religiosity, reduce knowledge hiding and balance academics' personal goals with collective and institutional goals.Originality/valueWhile knowledge-hiding studies in HEIs are still at an embryonic stage, this study proposes religiosity to mitigate personal competitiveness and knowledge-hiding behaviour. This study also offers contextual insights into knowledge-hiding behaviour among Indonesian academics, examining gender and generational cohorts.
PurposeIn today's dynamic world, innovative technological advancements are reshaping and revolutionizing traditional practices. One such transformation is the advent of artificial intelligence (AI)-driven robo-advisors (RAs). This study aims to examine the effectiveness of AI-powered RAs in decision-making by integrating the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) to elucidate the rationales behind the practical use of RAs.Design/methodology/approachData were collected from 365 respondents using simple random sampling. An integrated approach of structural equation modelling and artificial neural network techniques was used to test the proposed model and examine the robustness of the hypotheses. Emotional and cognitive biases that influence investors' perceptions of RAs were analysed, with age, income and financial literacy used as moderators to measure individuals' behaviours and traits.FindingsThe findings reveal that - perceived usefulness, perceived ease of use and performance expectancy - along with the contextual predictors such as, emotional bias and cognitive bias, act as positive mediators for the adoption of RAs among consumers. The study also finds that age, income and financial literacy significantly moderate the relationships between UTAUT components and behavioural intention to use RAs.Research limitations/implicationsThis study is limited to self-reported data from individual investors, which may introduce common method bias. Future studies could include longitudinal data and cross-cultural samples to enhance generalisability.Practical implicationsThe study provides insights for financial service providers and policymakers to design strategies that promote the adoption of AI-based RAs by addressing emotional and cognitive biases and improving users' perceived usefulness and ease of use of these technologies.Originality/valueTo the best of the authors' knowledge, this study is among the first to explore how behavioural biases can explain the adoption of RAs among investors. It contributes to the existing literature by integrating TAM and UTAUT models with psychological constructs to better understand investors' decision-making in the context of AI-powered financial advisory services.
