Purpose Large firms invest heavily in digital platforms but struggle to govern information flows, integrate external knowledge, and convert infrastructure into enterprise innovation capability. This study aims to examine how UK technology firms build digital platform capability (DPC) to support knowledge integration and innovation management. Design/methodology/approach A qualitative multiple-case study of five large UK technology firms, drawing on 46 elite interviews (June 2023–April 2024), 20 annual reports (2020–2024), and public digital traces. Abductive analysis using NVivo 12 developed a technology–organization–environment–dynamic capabilities view–institutional pressures (TOE-DCV-IP) framework integrating technological compatibility, organisational orchestration, and institutional pressures. Findings DPC emerges when technological compatibility (application programming interface (API)/legacy alignment) combines with organisational mechanisms (leadership orchestration, absorptive capacity, boundary-spanning roles) to convert institutional pressures into platform governance routines. Firms A and B showed smoother rollout progression and fewer reported integration difficulties under stronger cross-functional governance, whereas Firms C and D experienced greater implementation friction under weaker integration conditions. Orchestration resolves TOE framework's process limitations by linking contexts to dynamic capability processes. Research limitations/implications The TOE-DCV-IP framework extends enterprise information management literature by explaining DPC as a governance capability rather than an adoption outcome. It clarifies how orchestration integrates external knowledge and legitimises platform-based innovation under regulatory and competitive pressures. Practical implications The managerial implications follow directly from the TOE-DCV-IP framework. First, managers should conduct compatibility audits and sequence interface integration because technological alignment determines whether external knowledge can move effectively across legacy and platform systems. Second, firms should formalise cross-functional decision rights, leadership prioritisation, and boundary-spanning roles because orchestration is the mechanism through which platform investments are converted into repeatable governance routines. Third, managers should translate regulatory and competitive pressures into API rules, participation protocols, and absorptive knowledge-integration routines rather than treating those pressures as stand-alone compliance demands. Originality/value Grounded in multi-case evidence, this study advances a process-based explanation of how large firms convert digital platform investments into governance capability for enterprise knowledge integration.
Purpose This study investigates how big data analytics capability appears to support competitive strategy development in the Palestinian dairy industry, focusing on the role of data utilization in developing organizational capabilities, creating value and positioning the firm strategically. Design/methodology/approach This study adopts an exploratory multiple-case study approach supported by descriptive survey evidence and Visualization-Based Pattern Analysis. Data were collected through unstructured interviews with managers and employees from leading Palestinian dairy companies, alongside descriptive questionnaire-based evidence. The qualitative findings were analyzed using thematic and VRIO-based analysis to identify patterns related to big data practices, capability development, organizational maturity and competitive strategy dimensions. Findings The findings indicate that participating firms recognize the value of data for customer understanding, product development and operational decision-making. However, analytics practices remain fragmented and rely largely on conventional or manual processes. Limited system integration, analytical expertise, data governance and real-time processing restrict the incorporation of analytics into organizational routines and strategic decision-making. These conditions reflect a gap between awareness of data's potential value and the development of integrated analytics capabilities. Practical implications Managers should prioritize the gradual integration of customer, operational and market data, supported by appropriate infrastructure, analytical skills and governance procedures. These measures may improve the consistency of data-informed decision-making, operational coordination, customer responsiveness and market positioning. Originality/value This study provides context-specific empirical evidence on data utilization and analytics capability development within the Palestinian dairy industry, an underexplored resource-constrained setting. Its primary contribution is contextual rather than the development of a new theoretical construct. The study uses the term data maturity gap as an interpretive lens to organize evidence on the disconnect between firms' recognition of data value and their ability to integrate analytical, technological and organizational capabilities. The findings illustrate how mechanisms already identified by the Resource-Based View, Dynamic Capabilities Theory, absorptive capacity and analytics maturity research operate within this particular industrial context.
Purpose This study examines how human lean practices (HLP), Industry 4.0 (I4.0) technologies, technical lean practices (TLP), operational performance (OP) and sustainable performance (SP) interact within the manufacturing sector of emerging economies under the constraints of a lean environment. Design/methodology/approach We collected multi-stage data from 214 respondents in manufacturing firms using a structured questionnaire. A hybrid approach of structural equation modelling and ordinary least squares hierarchical linear regression was employed to analyse the data and assess the robustness of the proposed hypotheses. Findings The findings confirm that I4.0 technologies have a significantly positive impact on TLP, HLP and OP. Moreover, HLP and TLP have similar effects on TLP and OP, respectively. Additionally, TLP, HLP and OP significantly positively influence SP. All of the proposed moderations (except I4.0 × HLP impact on SP) and mediated paths are significant. Lastly, certain control variables such as firm size, firm type and duration of lean implementation significantly affect OP and SP. Practical implications This research holds significant practical implications by enhancing the comprehension of lean processes, thereby aiding managers in crafting a strategic roadmap for the implementation of HLP to embrace lean methodologies and enhance both OP and SP efficiently. In light of the impending 5th industrial revolution, our findings underscore the indispensability of HLP in harnessing the benefits of forthcoming technological advancements, particularly within the realm of I4.0, and leveraging them to elevate both OP and SP to new heights. Originality/value Departing from conventional research approaches, the study underscores the substantial impact of contingencies on endogenous variables, lending robust support to the contingency theory. Anchored in the principles of the resource orchestration theory, the findings elucidate that the isolated deployment of technology may not yield significant results. Instead, the integration of I4.0 with lean practices is important, serving as an orchestrated mechanism to propel organisational goals to fruition.
Purpose Distributed Denial of Service (DDoS) attacks on critical information infrastructures (CII) cause operational disruptions and result in financial and reputational damage to organisations. Our study provides an integrated framework to assess, quantify and mitigate the cyber-risk of DDoS attacks on CII organisations in the energy and power sectors. Our model adopts a socio-technological perspective and draws on protection motivation theory (PMT) and rational choice theory (RCT). Design/methodology/approach Our study adopts a mixed-method approach. In the quantitative section, we estimate the likelihood of misdetection of different DDoS attacks by using observable attackers’ strategy. These observations influence how CISOs implement the organisation’s cybersecurity posture and IT governance. Next, we compute the expected loss. Lastly, the study recommends CISO for various mitigation strategies based on the NIST Cybersecurity Framework by creating a 2×2 risk-impact heat matrix. Subsequently, Linear Programming is used to determine the priority of optimal allocation of investment across different mitigation strategies. In qualitative section, in-depth interviews with cybersecurity executives corroborate findings. Findings The likelihood of the misdetection of DDoS attacks by the CISO of an organisation is low. Most DDoS attacks result in small financial losses, but rare, severe incidents can cause disproportionately serious damage. The study further finds that organisations must invest in technological interventions, complemented by financial tools, to mitigate DDoS attacks. Originality/value The study uses a mixed-methods approach, combining quantitative analysis with executive interviews to assess the CISO's misdetection rate for DDoS attacks, compute the expected financial loss, and recommend a mitigation and investment strategy based on the NIST framework for CII organisations.
Purpose To investigate when the use of generative AI (GenAI) in knowledge-intensive work remains sustainable vs when users intend to discontinue, this study models how multilevel stressors generate cognitive strains that drive discontinuance intentions. Drawing on the stress–strain–outcome (SSO) framework, supported by cognitive load theory (CLT), the study explains how task, technology and organizational demands translate into cognitive strain and discontinuance. Design/methodology/approach A cross-sectional survey of 460 knowledge workers was analyzed using covariance-based structural equation modeling (CB-SEM). The model incorporates task-level, technology-level and organizational-level stressors; identifies cognitive overload and decision fatigue as parallel strains; and specifies GenAI discontinuance intention as the outcome. Findings All stressors positively relate to cognitive overload and decision fatigue except content governance, which showed no significant effect on decision fatigue. Cognitive overload emerged as a significant factor while mapping decision fatigue, and both strains significantly contributed to GenAI Discontinuance Intention. Practical implications To achieve the sustainable use of GenAI, policymakers and facilitators should minimize task complexity, limit time-related pressures and implement measures that lower the risk of hallucination. They should also provide templates and establish standard operating procedures to minimize perceived overload and fatigue. Originality/value Extends the SSO framework for GenAI by integrating hallucination-related and governance stressors and by distinguishing dual cognitive strain pathways (cognitive overload, decision fatigue) leading to discontinuance.
Purpose This study aims to examine brand-level co-occurrence structures in retail transaction data using the FP-Growth algorithm. It explores how validated association-rule outputs may support enterprise decision-making by serving as interpretable decision-support artefacts within enterprise information management environments. Design/methodology/approach The FP-Growth algorithm was applied to approximately 28,000 transactions collected from 2 supermarket branches in distinct retail contexts (student-area and city-centre) in Denizli, Turkiye. Association rules were evaluated using support, confidence and lift metrics and further validated through threshold-sensitivity analysis and cross-store stability testing to assess the consistency of the extracted association rules across parameter settings and retail contexts. Findings The findings reveal context-specific brand co-occurrence patterns, with national-brand clustering dominating in the student-area store and hybrid national–private label configurations emerging in the city-centre store. Several association rules remained stable across parameter settings and retail contexts, indicating their potential use as interpretable decision-support inputs for retail analysis. Originality/value Rather than proposing a new mining algorithm, this study contributes by demonstrating how validated association-rule outputs can be interpreted as decision-support artefacts within enterprise information management. It introduces context-sensitive rule stability as a validation approach and presents a conceptual framework illustrating how FP-Growth outputs may support DSS and business intelligence environments.
Purpose This study conceptualizes the capabilities of Generative AI (GenAI) within Supply Chain Management (SCM) through the Resource-Based View (RBV). It examines how GenAI-enabled capabilities emerge from the combination of GenAI and complementary organizational resources, and, through the VRIO framework, assesses the resource bundles that underpin each capability and the conditions under which they may support sustained competitive advantage in increasingly volatile operational environments. Design/methodology/approach A qualitative multi-method design was adopted. A systematic literature review (SLR) identified 14 candidate GenAI-enabled capabilities, which were refined and validated by a panel of 20 SCM experts, yielding a final taxonomy of 12 capabilities. The RBV guided both the capability categorization and the VRIO-based assessment of the enabling resource bundles at a given point in time. Findings The validated capabilities are organized into three dimensions: Operational Efficiency, Operational Responsiveness, and Strategic. Each dimension is linked to a distinct causal mechanism through which resource bundles translate into performance, progressing from variance reduction (efficiency) through speed of adjustment (responsiveness) to knowledge-based value creation (strategic). Sustained competitive advantage arises primarily from the Strategic dimension, which relies on rare, path-dependent, inimitable, and organized-to-capture-value resource bundles, reinforcing that strategic value stems from resource heterogeneity rather than from the technology itself. Originality/value This study advances SCM theory by shifting attention from technology-centric applications to a resource-level reinterpretation of GenAI-enabled capabilities grounded in RBV. It systematically assesses the VRIO attributes of enabling resource bundles across three capability dimensions, providing managers with a conceptual diagnostic framework for identifying resource gaps and prioritizing investments that may foster defensible differentiation.
Purpose Tools of generative artificial intelligence (Gen AI) have gained immense significance recently, particularly in higher education. While these tools enhance educational efficiency, there are still unanswered questions about the challenges of incorporating this technology into learning processes and its effects on academic outcomes, despite its promise to improve academic work. The purpose of this research is thus to address this gap by developing and validating a comprehensive scale covering perceived risks of Gen AI - such as hallucinations, dysfunctional AI use behavior and biases - and measuring their impact on students' academic performance.Design/methodology/approach The study develops a scale through a three-step process. The study used a mixed-method approach to develop a scale on risks associated with Gen AI. The first step involved qualitative interviews to arrive at themes, followed by thematic analysis. Data were collected from students of diverse backgrounds and were subjected to exploratory factor analysis and confirmatory factor analysis. Lastly, we applied structural equation modeling using AMOS to validate the hypothesized relationships.Findings The results indicated significant impacts of hallucinations, dysfunctional AI use behavior and biases on students' attitudes and perceived productivity, which in turn influence their academic performance.Originality/value To the best of the authors' knowledge, this study is the first to develop and validate a reliable scale to measure the challenges students encounter when using Gen AI tools and the associated risks. Drawing on the theory of epistemic risk, this study conceptualizes hallucinations, dysfunctional AI usage behavior and biases not merely as technical flaws but as epistemic predispositions that affect users' ability to improve their academic performance when interacting with Gen AI. Hence, this research translates and extends the theory of epistemic risk by converting abstract constructs into measurable instruments that can be validated across diverse contexts.
Purpose Data Science encompasses various tools and techniques from statistics and computer science to solve specific business problems. Organizations are often overburdened with the choices of deciding among the vast number of available tools and techniques for enhancing business value, increasing revenues, reducing costs and improving overall efficiency. Despite the advances in existing body of literature there is a significant gap in providing a holistic multi-perspective framework that can systematically guide tool selection based on the nature of problem in a specific business context with a desired outcome. Therefore, the purpose of this paper is to address this gap and the pressing issues of selecting the right data science tool for solving specific business problems. Design/methodology/approach The paper presents a systematic literature review based on the guidelines provided by Tranfield et al. (2003), focusing on widely used data science tools for solving business problems across different business functions and industry verticals. The study yielded 71 research articles deemed relevant after a thorough search strategy and included for the final analysis. Findings The contribution of this paper is that it presents a multi-perspective synthesis and categorization of a myriad of data science tools: a conceptual framework based on (a) business problems, namely descriptive, predictive, and prescriptive, (b) business outcomes, namely operational, tactical and strategic and (c) business context or industry verticals. The findings will be helpful for academicians and practitioners across various disciplines with a practical guiding framework to solve business problems with the right data science tools from the toolkit effectively, thereby improving decision-making and solution efficacy. Originality/value The novel conceptual framework uniquely integrates multiple perspectives to map data science tools to specific business problems, outcomes and industry contexts. This integration offers a tailored solution with significant theoretical and practical value by enabling a more structured, industry-specific context and outcome-driven approach to tool selection for business value creation.
Purpose This research examinations the dual mediating role of green creativity (GC) in linking environmental innovation practices (EIP) to both sustainable competitive advantages (SCA) and environmental performance (EP) in the context of the renewable energy sector.Design/methodology/approach A quantitative, cross-sectional design was used. Data were collected via a survey from 637 engineers and managers working in renewable energy companies across Turkey. The reliability and validity of the scales were determined using confirmatory factor analysis (CFA), and the hypothesized mediation effects were tested using mediation analysis using the Jamovi program. The risk of common method bias (CMB) was assessed using the Harman Single Factor Test and found to be acceptable.Findings The results strongly support the conceptual model. Green creativity was found to have a significant mediating role between environmental innovation practices and sustainable competitive advantage (H1 supported). Furthermore, there was a significant mediating role in the relationship between green creativity, environmental innovation practices, and environmental performance (H2 supported).Research limitations/implications The primary limitation is the use of cross-sectional survey data, which limits causal inference. Future research should use longitudinal designs and replicate this model across different sectors and emerging economies to increase generalizability.Practical implications For managers in the renewable energy sector, these findings highlight the need to actively cultivate an organizational environment that supports and rewards green creativity. This proactive investment in creative processes is essential for fully leveraging environmental innovations, maintaining a strong competitive stance, and achieving superior environmental outcomes.Originality/value The research presents a novel and empirical examination of a dual mediation model incorporating green creativity and provides baseline data from the rapidly developing renewable energy sector in T & uuml;rkiye - a significant and under-researched market.
Purpose - This study investigates how micro-level GenAI infrastructure optimisation - specifically CPU thread tuning on NPU-accelerated inference - affects enterprise knowledge management, organisational resilience, and digital transformation outcomes. Design/methodology/approach - We propose the Infrastructure-to-Knowledge Outcomes (I2KO) pathway as an infrastructure-level operationalisation linking service performance distributions to SECI knowledge flows, Kolb's learning cycle, and dynamic capabilities. Using Qwen2.5-3B on KT ATOM + NPUs, we benchmarked 70 workloads across eight categories, including n = 15 multiturn scenarios for socialisation-phase analysis. We introduce the Resilience Degradation Index (RDI = P95/P50) to capture tail-risk exposure invisible to average-centric metrics. Findings - Optimal thread configurations improved average throughput (+8.8%) and mean latency (-1.6%) but increased P95 latency (+12.1%) and context scaling sensitivity (+30.4%). The multiturn analysis suggests that tail-latency degradation increases with conversational turn depth across the n = 15 workload set, with optimisation benefits concentrating in single-turn tasks while tail-risk accumulates in conversational and large-context workloads; This directional pattern (anchored by n = 13, five-turn) requires replication. Organisational implications are theoretically inferred and await field validation. Practical implications - We propose a four-layer governance stack (Policy, Control, Monitoring, Review) and deployable design patterns - Lite Tier, Analytical Tier, Memory Broker, Context Pipeline - with illustrative SLO thresholds (e.g. P95 <30s for Socialisation; scaling factor <9.0 for Externalisation) derived from HCI response-time research and enterprise SLA precedents. Originality/value - This study provides an initial empirical operationalisation of performance-distribution effects on enterprise knowledge capabilities, extending IT business value and dynamic capabilities theory by disaggregating infrastructure performance into efficiency-oriented (P50) and predictability-oriented (P95) dimensions.
Purpose This study assesses the moderating impact of technology in the relationship between cyberfraud perpetration and organisation cybersecurity outcomes such as blocked attacks, response to cyber threats, increase in uptime, reduction in financial loss due to cyberfraud, data breaches and operational disruption. Design/methodology/approach The study uses a quantitative survey using a questionnaire as the survey instrument to obtain a primary dataset from the 17 licensed banks in South Africa. It draws insights from the criminological and information systems theories to determine the moderating role of technology in the relationship between cyberfraud occurrence and cybersecurity outcomes. Primary data were collected and moderation analysis was carried out in the Statistical Package for Social Sciences (SPSS, version 29 environment). Findings The outcome of the interaction term between cyberfraud perpetration and organisation's technological ability is statistically significant and negative (β = −1.462, p < 0.05). This shows that technology moderates the relationship between cyberfraud perpetration and organisation's security outcomes. The results show that banks experiencing a high rate of cyberfraud perpetration are also the ones investing more in cybersecurity measures and reporting more controls and vice versa. While the preventive and especially the detective technologies significantly moderate and mitigate cyberfraud perpetration effect, the response technologies exhibit no significant buffering role, thus indicating limited effectiveness. Research limitations/implications The article contributes to knowledge by establishing the dual role of technology as a cyberfraud enabler and mitigator, thus advocating for its moderating role. The outcome of this study as well as the policy recommendations can assist policymakers and financial institutions in moderating technology to mitigate cyberfraud perpetration. Practical implications The findings and policy recommendations can assist policymakers and the banking institutions in moderating technology to mitigate cyberfraud perpetration. Originality/value The study is novel in that it contributes conceptually, methodologically and empirically to technology as a moderating variable between cyberfraud perpetration and organisation's security outcomes.
Purpose As the use of ChatGPT continues to expand, understanding its impact on users remains an emerging area of study. This research investigates how individuals form a sense of identity around ChatGPT, applying the information technology (IT) identity framework, with an added outcomes component, to assess the psychological relationship users develop with the tool. Design/methodology/approach A survey of 312 active ChatGPT users was conducted to evaluate the framework’s applicability in this context and to explore the resulting behavioral and experiential outcomes. The model was tested using partial least squares structural equation modeling. Findings The results provide partial support for the IT identity framework in the ChatGPT context. The analysis indicates that regular interaction with ChatGPT is associated with the development of a distinct IT identity. Moreover, the presence of sufficient resources and organizational or contextual support significantly moderates this relationship, shaping user behaviors in meaningful ways. These behaviors, in turn, were linked to improvements in both individual performance and perceived social well-being. The findings also suggest that some relationships established in traditional IT contexts may operate differently in generative AI environments. Originality/value By extending the IT identity framework to include specific outcome measures, this study offers a nuanced understanding of how AI-based tools like ChatGPT influence user identity and behavior based on the IT identity framework. The findings further contribute to understanding how AI-related identities form and highlight opportunities for refining IT identity theory in the context of generative AI assistants.
Purpose Generative Artificial Intelligence is reshaping higher education by enabling new forms of collaboration between learners and intelligent systems. Drawing on Hybrid Intelligence and Self-Regulated Learning theories, this study proposes and empirically tests a Hybrid–Self-Regulation Model to explain how human–artificial intelligence collaboration influences engagement, self-regulation and learning outcomes. Specifically, the study examines how perceived collaborative artificial intelligence support, trust in artificial intelligence collaboration and artificial intelligence-enabled adaptive personalization drive collaborative engagement (CE), which in turn enhances self-monitoring (SM) and metacognitive reflection (MR). Design/methodology/approach Using data from 307 students across six countries, the study examines how Perceived Collaborative AI Support (PCAS), Trust in AI Collaboration (TAIC) and AI-Enabled Adaptive Personalization (AIAP) drive CE, which in turn enhances SM and MR. Findings The findings reveal that perceived collaborative artificial intelligence support, trust in artificial intelligence collaboration and artificial intelligence-enabled adaptive personalization significantly increase CE and CE strongly predicts SM and MR. MR further positively influences academic achievement, creativity and innovation and responsible and ethical problem-solving. Serial mediation analyses confirm that the impact of perceived collaborative artificial intelligence support and artificial intelligence-enabled adaptive personalization on learning outcomes is transmitted through CE, SM and MR. Originality/value This study contributes theoretical clarity on human–artificial intelligence co-learning mechanisms and offers practical guidance for designing artificial intelligence-enhanced, ethically grounded learning ecosystems in higher education.
Purpose This study examines how digitally embedded transaction taxes within enterprise payment systems influence consumer purchasing behaviour, using Ghana's Electronic Transaction Levy (e-levy) as an empirical context. Rather than treating transaction taxes as external fiscal instruments, the study conceptualises them as system-embedded rules integrated into digital payment infrastructures encountered at the point of transaction. Drawing on the Theory of Planned Behaviour, the research investigates how attitudes toward transaction-embedded charges, subjective norms, and perceived behavioural control shape purchasing behaviour, while assessing whether institutional trust conditions these relationships. The study addresses a critical gap at the intersection of digital taxation, consumer behaviour, and enterprise information management in emerging economies. Design/methodology/approach The study adopts a quantitative, cross-sectional research design. Survey data were collected from 360 customers of a large digitally enabled retail enterprise in Ghana that routinely processes electronic payments subject to the e-levy. Established Theory of Planned Behaviour constructs were operationalised using multi-item Likert scales. Data were analysed using multiple regression analysis to test direct behavioural effects and Hayes' PROCESS macro to examine the moderating role of institutional trust. Reliability and validity were confirmed using Cronbach's alpha and correlation diagnostics. The methodological approach enables theory testing within an enterprise-mediated digital transaction environment affected by system-embedded fiscal rules. Findings The findings show that attitudes toward the electronic transaction levy and perceived behavioural control have statistically significant positive effects on consumer purchasing behaviour within digitally mediated payment systems. Together, the Theory of Planned Behaviour constructs explain approximately 36.7% of the variance in purchasing behaviour. Subjective norms do not exert a significant influence once individual cognitive evaluations and perceived control are accounted for. Institutional trust does not moderate the relationship between transaction-embedded charges and purchasing behaviour, despite low overall trust levels. The results indicate that behavioural responses to digitally embedded transaction taxes are driven primarily by individual evaluations and perceived system manageability rather than social pressure or institutional confidence. Research limitations/implications The study is limited by its focus on a single retail enterprise, which may constrain the generalisability of findings to informal markets or alternative transaction environments. The cross-sectional design captures behavioural responses at one point in time and does not account for dynamic adaptation as users adjust to digitally embedded fiscal interventions. Reliance on self-reported purchasing behaviour may also introduce response bias. Despite these limitations, the study offers strong theoretical implications by extending the Theory of Planned Behaviour to enterprise-mediated digital taxation contexts and by empirically testing, rather than assuming, the behavioural relevance of institutional trust in low-trust environments. Practical implications For policymakers, the findings highlight that the effectiveness of electronic transaction levies depends not only on statutory authority but on how charges are embedded, communicated, and experienced within digital payment systems. Enterprises and payment platform providers should prioritise system transparency, clear cost disclosure, and user-friendly interfaces that enhance perceived behavioural control. Strengthening users' ability to anticipate and manage transaction-embedded charges can mitigate behavioural resistance and sustain purchasing activity. Digital financial literacy initiatives and interface design improvements are therefore critical for balancing revenue mobilisation objectives with enterprise transaction continuity and customer engagement. Social implications Digitally embedded transaction taxes can generate unintended behavioural effects that disproportionately affect digitally dependent and financially vulnerable consumers. The findings suggest that when users feel capable of managing system-embedded charges, purchasing behaviour can be sustained even under additional cost burdens. However, low institutional trust combined with limited perceived control may exacerbate exclusion from digital payment ecosystems. From a social perspective, the study underscores the importance of designing digital taxation systems that preserve financial inclusion, minimise behavioural disruption, and promote equitable participation in digitally mediated markets, particularly in emerging economies with high reliance on mobile and electronic payments. Originality/value This study is among the first to conceptualise electronic transaction levies as digitally embedded rules within enterprise payment systems rather than as external fiscal instruments. It extends the Theory of Planned Behaviour to a novel context of enterprise-mediated digital taxation and provides empirical evidence on how behavioural responses unfold within information-rich transaction infrastructures. By integrating consumer behaviour theory with enterprise information management and digital taxation literature, the study offers a distinctive behavioural systems perspective. The findings generate original insights into why institutional trust may not moderate behaviour in low-trust contexts, advancing theory and informing more behaviourally informed digital tax design.
PurposeThis study investigates the relationships among five key aspects of customer-centric banking services: customer satisfaction, service quality, mobile banking, insurance, and digital channels. The aim is to understand how these interactions impact the customer's digital experience and to construct an empirical network to depict these connections. Design/methodology/approachThe research was conducted in four stages: (1) performing a Systematic Literature Review (SLR) to collect articles; (2) selecting and analysing data; (3) identifying gaps and elements within the research axes; and (4) compiling and synthesising the results. The methodological approach combines the Simple Additive Weighting (SAW) method to assess the relevance of the articles and the Choquet Fuzzy Integral to examine the interactions between the axes. Building the empirical network enabled the visualisation of existing relationships and synergies. FindingsThe analysis indicates that the literature mainly emphasises service quality and customer satisfaction, while topics such as mobile banking remain less studied. The study offers insights into the challenges and opportunities of developing digital strategies that aim to enhance the customer experience. Originality/valueThis research emphasises that the strategic integration of digital technologies and traditional channels can optimise customer satisfaction and improve service quality in the insurance and mobile banking sectors. The originality stems from the systemic analysis of the relationships between satisfaction, quality, and technology, providing valuable insights for managerial strategies and academic research in digital services.
PurposeAlthough mobile payment (m-payment) adoption has grown rapidly worldwide, existing research remains fragmented in explaining the factors that drive users' continued use beyond initial adoption. Prior reviews have predominantly concentrated on adoption-related factors and acceptance models, with limited attention to post-adoption behaviours such as continuance intention (CI). This study aims to address this gap by conducting a systematic review and proposing a conceptual framework grounded in Shannon and Weaver’s communication model. Design/methodology/approachA systematic review of 99 peer-reviewed journal articles from 2014 to 2024 was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles were retrieved from five academic sources and screened through a four-stage process. Influencing factors were categorized using the adapted communication model into three dimensions: source, channel, and recipient, while also considering mediating, moderating and external factors. FindingsThe review identifies three core categories of factors influencing m-payment CI: (1) source-related (e.g. satisfaction, trust and confirmation); (2) channel-related (e.g. usefulness, ease of use and security); and (3) recipient-related (e.g. merchant pro-activeness). Mediating variables (e.g. satisfaction and trust), moderating variables (e.g. gender, age and experience) and external influences (e.g. social influence and subjective norms) also play significant roles. Originality/valueThis study extends communication theory into the digital finance domain by developing a multidimensional source–channel–recipient framework. It applies a classical communication model to synthesize and interpret dispersed continuance research, highlighting underexplored areas such as recipient responsiveness and environmental influences. The framework provides structured implications for researchers, service providers and policymakers seeking to foster sustained m-payment use.
Purpose This study addresses the research gap in understanding systemic interdependencies among cybersecurity challenges in digital twins (DTs) by proposing a novel framework to model these relationships under uncertainty, supporting anticipatory governance in critical sectors such as healthcare. Design/methodology/approach A mixed methods approach combines a systematic literature review with a Picture Fuzzy Interpretive Structural Modeling and MICMAC (PF-ISM MICMAC) framework. Picture fuzzy sets capture indeterminacy and refusal in expert judgments from a multidisciplinary panel of experts. Robustness is validated through 10,000 Monte Carlo simulations and a leave-one-out sensitivity analysis, complemented by semi-structured interviews. Findings Ten key cybersecurity challenges are identified. Lack of standardization and regulation, infrastructure vulnerabilities and inadequate resilience metrics emerge as foundational drivers. Data poisoning, secure communication and lack of interoperability are linkage factors with high driving and dependence power, forming a dynamic risk core. Insider threats and lack of system resilience are dependent outcomes. Validation confirms high structural stability and practical relevance. Practical implications Policy: Urges global regulatory harmonization and standardized security frameworks for DTs. Managerial: Provides a risk-based prioritization heuristic that invests in high driving factors, integrates responses for linkage factors and monitors dependent outcomes. Originality/value This study does not claim to invent a wholly new methodology. Rather, its originality lies in: (1) the novel application of the PF-ISM MICMAC framework to model systemic interdependencies among DT cybersecurity challenges, a domain where prior research has treated challenges as independent and (2) the empirically grounded and validated six-level hierarchical framework, which enables proactive systemic risk analysis.
Purpose This study examines the organisational and environmental conditions under which Singapore small and medium enterprises (SMEs) realise big data analytics (BDA) business value and how that value translates into firm performance. Drawing on the technology-organisation-environment (TOE) framework, diffusion of innovation (DOI) theory, and the resource-based view (RBV), the study integrates internal organisational factors and external policy instruments into a unified value-creation model.Design/methodology/approach Top management support, organisational readiness, and government advocacy were specified as drivers of a unified BDA business value construct, which in turn predicts a unified firm performance construct. Government grant receipt entered the model as a binary control on both endogenous outcomes. Survey data from 390 Singapore SMEs across six MTI industry clusters were analysed using partial least squares structural equation modelling (PLS-SEM) with 5,000 bootstrap resamples and bias-corrected confidence intervals. An adopter-only robustness check (n = 172) was conducted.Findings BDA value creation in Singapore SMEs is principally an internal organisational achievement: top management support and organisational readiness emerge as the dominant drivers of BDA business value, which substantially predicts firm performance. Government advocacy does not significantly shape BDA business value. Government grant receipt operates as an adoption catalyst rather than a value-creation driver: it exerts a direct effect on firm performance in the full sample but not among confirmed adopters. Once firms adopt BDA, organisational readiness becomes substantially more decisive, marking organisational capability as the binding constraint on post-adoption value realisation.Originality/value The study contributes a refined TOE-DOI-RBV integration that separates the antecedents of BDA adoption from those of post-adoption value creation, novel evidence that government productivity grants function as adoption-stage instruments distinct from value-creation drivers, and an empirical demonstration of the adoption-value gap in a policy-intensive advanced economy. The findings inform both academic theory on SME digital transformation and the design of government support programmes for SME analytics adoption.
Purpose This research aims to delve into the thought-provoking interplay between sustainable technologies, consumer behavior, and the power of social constructs such as sense of belonging and social influence. Design/methodology/approach The authors developed a conceptual research model that integrates key constructs from sense of belonging and social influence. Structural equation modelling (SEM) is used to empirically test the conceptual model using data collected from 132 users in Germany over a 6-month period. Findings The results reveal that socially desirable responses such as praise from others, a sense of prestige, and connectedness with like-minded individuals play a significant role in motivating consumers to engage in the use of sustainable technologies. Originality/value The diffusion of sustainable technologies in the household context is on the rise. For researchers, the study offers insights into the extent to which socially desirable characteristics influence the use of sustainable technologies. Practical implications for product developers, innovators, and marketeers seeking to enhance the acceptability and uptake of sustainable innovations are presented.
