Purpose This study examines how knowledge identification capability (KIC), knowledge acquisition capability (KAC) and knowledge sharing capability (KSC) are enacted from different functional positions during the fuzzy front end (FFE) of innovation, and how participants describe the interfaces through which these knowledge-processing activities connect. Design/methodology/approach The study adopts an abductive, multiple-case design and a Gioia-inspired analytical procedure. It draws on 48 expert interviews with senior professionals working in R&D, innovation, consumer intelligence and supply chain roles across eight FMCG and technology organisations. Findings The findings indicate that KIC, KAC and KSC are connected through overlap, iteration and reciprocal feedback rather than operating as an exclusively linear sequence. Participants described differentiated functional contributions, with R&D providing technical interpretation, Consumer intelligence framing behavioural relevance, supply chain contributing supplier and feasibility knowledge, and innovation facilitating translation and integration. The sampled FMCG accounts particularly emphasised signal expiry and timely insight translation, whereas technology accounts more frequently highlighted technical complexity and cross-functional interpretation. Research limitations/implications The findings are based on functionally situated managerial accounts and do not establish complete firm-level configurations or objective innovation-performance effects. Future research should use multi-function, longitudinal and project-level designs to assess whether the reported coordination patterns recur across organisations and sectors. Practical implications The study provides a diagnostic framework for identifying knowledge-processing bottlenecks, including identification without translation, acquisition without prioritisation, sharing without interpretive capacity, asynchronous activation and functional gatekeeping. Originality/value The study's originality lies in showing how established knowledge-processing capabilities are described from different functional positions and how participants report reciprocal and temporal interfaces among these activities during the FFE.
Purpose This study examines how knowledge workers make sense of GenAI-enabled co-production, with particular attention to authorship ambiguity and its implications for competence attribution, self-evaluation and meaningful work. Design/methodology/approach The study uses a qualitative approach based on in-depth interviews with 32 knowledge workers who work directly with GenAI tools, combining inductive coding with abductive interpretation. We used the Gioia methodology to analyze participants' reported experiences and interpretations of human–AI collaboration, with sensemaking informing the interpretation of emergent themes and aggregate dimensions. Findings The findings identify authorship ambiguity as a central interpretive challenge in GenAI-mediated knowledge work. When GenAI contributes to ideation, analysis, drafting, coding or other knowledge-intensive outputs, workers may find it difficult to distinguish their own contribution from that of the technology. Participants responded to this ambiguity differently. Some reconstructed their expertise around judgment, validation and contextualization and experienced GenAI as extending their professional capability. Others struggled to internalize successful outputs as evidence of their own competence and reported impostor-like experiences and reduced meaningfulness. The findings suggest that experiences of GenAI-enabled co-production depend on whether workers interpret and reconstruct their contribution. Practical implications Managers in comparable knowledge-intensive settings may consider making human judgment visible, clarifying legitimate uses of GenAI and recognizing contributions beyond output speed and quality. Originality/value This study identifies authorship ambiguity as a central interpretive challenge in GenAI-mediated knowledge work. It shows how uncertainty regarding the respective contributions of the worker and GenAI can complicate competence attribution and initiate sensemaking about professional contribution. By explaining how workers' interpretations of authorship ambiguity are associated with divergent identity and meaning outcomes, including enhanced capability, impostor-like experiences and changing meaningfulness, the study extends research on human–AI collaboration beyond its predominant focus on performance and productivity.
Purpose This paper examines the extent to which information technology projects (ITP) measure and integrate energy and carbon emissions into planning and execution and proposes a structured framework for embedding environmental sustainability into ITP. Design/methodology/approach A systematic literature review was conducted using a three-stage process of planning, conducting and reviewing. Inclusion and exclusion were governed by the PRISMA method across three databases (Scopus, Web of Science and ProQuest), covering 2012 to 2024. Thematic synthesis was applied to 72 academic papers and 16 grey literature sources to identify emergent themes through open, axial and selective coding. Findings Five interconnected themes were identified: Sustainable IT Skills, Sustainable IT Strategy, Implementing Sustainable IT, Sustainable IT Operations and Sustainable IT Regulations. The Integrated Sustainable Software (ISS) Framework is proposed to address these gaps by providing a structured, methodology-agnostic approach to integrating sustainability into ITP governance and execution. Research limitations/implications Future research should examine framework adoption in practice and explore sector-specific sustainability metrics. Practical implications The ISS Framework provides project managers and IT leaders with actionable guidance for embedding carbon and energy metrics into project governance, planning and execution. Social implications Embedding sustainability into ITP supports broader societal progress towards net-zero targets and the UN Sustainable Development Goals. Originality/value This paper presents a systematic literature review focused specifically on energy and carbon emissions within ITP. It introduces the ISS Framework as a methodology-agnostic instrument for sustainable software project management.
Purpose This study examines how employees' perceptions of STARA (smart technologies, artificial intelligence, robotics, and algorithms) influence their intrapreneurial behaviour (IB) and digital competence (DC), and how digital dynamism (DD) conditions this relationship. Design/methodology/approach Drawing on Dynamic Capabilities Theory, Sociotechnical Systems Theory, and the Antecedents–Decisions–Outcome framework, the study develops an integrated model explaining how employees' perceptions of STARA shape behavioural responses and digital capability development. Data were collected from full-time employees in China's electric vehicle (EV) manufacturing sector, a context characterized by extensive use of STARA technologies, using an online survey and stratified purposive sampling, resulting in 193 valid responses. Partial least squares structural equation modeling was applied to test the proposed model. Findings The results show that positive perceptions of STARA are associated with higher levels of IB and DC. IB serves as a key mechanism through which perceived STARA relates to DC. This highlights the role of employee behaviour in capability development. DD weakens the positive relationship between perceived STARA and DC, suggesting that high environmental turbulence may constrain employees' ability to translate perceived technological opportunities into DC. Research limitations/implications The study offers a socio-technical explanation of digital capability development by showing how employees' interpretations of technological change and their behavioural responses jointly shape capability outcomes. It highlights the importance of aligning technological change with employee-driven adaptation under conditions of environmental uncertainty. Practical implications The findings provide insights for managers in technology-intensive manufacturing firms. They show how employees' perceptions of STARA and intrapreneurial behaviour can contribute to DC. Social implications The findings suggest that fostering IB and DC can enhance employee adaptability, employability, and career resilience, contributing to a more digitally prepared workforce amid ongoing technological transformation. Originality/value This study contributes to the literature on digital transformation and capability development. It integrates employees' perceptions of STARA with employee behaviour within a sociotechnical framework in the EV manufacturing context.
Purpose Privacy-preserving artificial intelligence (AI) technologies increasingly support data-driven decision-making while safeguarding sensitive information. However, peer-reviewed evidence documenting governance integration and operational deployment remains limited. This study examines how the peer-reviewed academic literature documents the technical maturity, governance integration, and operational implementation of privacy-preserving AI systems relevant to enterprise information systems. Design/methodology/approach A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-compliant systematic literature review examined 22 peer-reviewed studies published from 2018 to May 2026. The review synthesized evidence on privacy–utility trade-offs, governance integration, fairness, operational readiness, and implementation maturity. We used Technology Readiness Levels (TRLs) and a proposed Translational Readiness Index (TRI) to classify readiness evidence reported in the included studies. Findings Privacy-preserving AI systems demonstrated favorable privacy–utility trade-offs in the reviewed academic literature, with privacy budgets of e = 1–3 preserving approximately 85–96% of baseline predictive performance. Federated learning combined with differential privacy was the dominant implementation architecture. Most included studies demonstrated intermediate technical maturity (TRL 4–6), primarily through controlled evaluations and prototype development while none documented deployment within operational payer workflows. Studies also inconsistently documented governance integration, privacy accounting, interoperability, and fairness assessment. We characterize this imbalance in the published academic evidence as the proposed Enterprise AI Documentation Gap Hypothesis. The hypothesis concerns the distribution of evidence in peer-reviewed literature and should not be interpreted as evidence that healthcare organizations or insurers are unprepared for deployment. Research limitations/implications This study is limited to reviewing scholarly papers published in the academic literature and did not systematically examine industry literature, company records, patent information, or practitioner views. Therefore, the results reflect the scholarly evidence base and do not reflect health organizations' or insurers' readiness for implementation. The absence of evidence in the literature on deployment does not imply that implementation does not occur outside scholarly journals. Future studies should cross-check scholarly evidence with industry and practitioner evidence on the same topic. Originality/value The TRI offers an exploratory, multidimensional framework for organizing documented evidence across technology, privacy, governance, fairness, and operations. This study proposes the Enterprise AI Documentation Gap Hypothesis to describe the gap between the relative maturity of technical evidence and the lack of published evidence on governance and operations. These are conceptual contributions that require further empirical testing.
