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Data-driven smart product design, smart service design, and smart product-service system design – A comprehensive review
Full text linkThe increasing complexity of smart products in the era of Artificial Intelligence (AI) presents new challenges for designing smart products, services, and product service systems. This paper aims to summarize the latest progress in the design of smart products and services, focusing on the concepts, design methods, and data types used in the design process of smart products and services. It also aims to explore how a data-driven approach can enhance product performance, improve user experience, and drive service innovation. A systematic literature search was conducted for studies published between 2004 and 2024 in the Web of Science (WoS) database. Keywords such as “smart product-service system (SPSS)”, “smart product design (SPD)”, “smart service design (SSD)”, “intelligent product service system design”, “intelligent product design”, and “intelligent service design” are used to retrieve relevant literature. A total of 803 research articles were searched and screened for relevance and eligibility based on predefined inclusion criteria, focusing on journals, papers, and conference proceedings. Ultimately, 694 valid articles were identified. Text analysis includes Term Frequency-Inverse Document Frequency (TF-IDF), Keywords cluster and knowledge graph, combined with research categories, data type, case study, and publication years to find core concepts and trends. The review identifies key applications of data in SPD, SSD, and SPSS, including requirements analysis, product optimization, fault diagnosis, and enhancing user experience. The findings highlight the importance of interdisciplinary integration and continuous innovation for developing SPD, SSD, and SPSS
Recent Methodologies on AI and Labour - a Desk Review
Full text linkThis desk review toolkit brings together recent methodologies used to analyse AI’s effects on employment, wages, and productivity. Shift from Occupation-Level to Task-Level Analysis: Recent methodologies increasingly focus on tasks rather than entire occupations, recognising the heterogeneity within jobs. For example, Felten et al. (2023) and Eloundou et al. (2023) use AI exposure indices to measure task-specific impacts.Integration of Advanced Technologies: Methods now incorporate Natural Language Processing (NLP) (e.g., BERT, LSTM) and LLMs (e.g., GPT-4) to analyse job descriptions and predict automation risks (Xu et al., 2025; Hampole et al., 2025).Scenario Planning and Policy Focus: Think tanks like TBI and IPPR, and organisations like IMF emphasise scenario-based modelling to inform policy, highlighting the need for reskilling and labour market reforms (TBI, 2024; IPPR, 2024, Korinek, 2023). They assume different ‘initial conditions’ in adoption to estimate different conclusions in employment, wages, productivity ect. Studies consistently find that AI’s impact varies by skill level, with low-skilled workers facing higher displacement risks and high-skilled workers benefiting from augmentation (Brynjolfsson et al., 2023; Chen et al., 2024). Many methodologies struggle with static assumptions, lack of causal evidence, and overreliance on theoretical models. For instance, Acemoglu & Restrepo (2022) assume fixed comparative advantage, while Webb (2020) ignores adaptation. As of year 2025, conclusions of some of the pre-2020 research is already redundant in terms of what professions will remain or not. The pre-2020 conclusion that creative and intellectual occupations will remain in high demand, has already been disproven
From patents to predictive analytics: Leveraging R-GCNs for technological opportunity discovery in converging industries
No full textTechnological Opportunity Discovery (TOD) is increasingly critical for innovation strategy in converging industries, where opportunities emerge from interactions among technologies, firms, and inventors rather than from isolated artifacts. However, most existing TOD approaches rely on homogeneous representations and retrospective indicators, limiting their strategic relevance for Technology Management (TM). This study proposes an advanced TOD framework based on Relational Graph Convolutional Networks (R-GCNs) that models innovation ecosystems as heterogeneous, multi-actor networks linking patents, inventors, assignees, and technological domains. Using a longitudinal dataset of over 9 million USPTO patents (1976–2025), the framework integrates relational signals—such as collaboration, citation, and classification co-occurrence—with domain-specific semantic embeddings derived from PatentSBERTa. The framework is illustrated through a focused empirical application in the solar energy domain; tracing how localized opportunity spaces emerge around a focal semiconductor patent. To address the interpretability limitations of graph-based TOD models, the approach incorporates a human-in-the-loop layer using Large Language Models (LLMs), enabling post-hoc explanation of latent clusters, semantic characterization of emerging domains, and strategic querying by decision-makers. By jointly modeling structural, semantic, and temporal dimensions, this study advances TOD as an early sensing and managerial capability embedded within the technology management decision cycle, rather than as a retrospective analytical exercise. From a TM perspective, the proposed framework provides scalable and explainable foresight tools to support R&D portfolio design, alliance strategy, and strategic investment decisions in complex and convergent innovation environments. Beyond firm-level strategy, the framework contributes to technology forecasting practice by providing policy-relevant early warning signals that can inform public R&D prioritization and innovation policy design in converging technological domains
Carrying the load: a moderated mediation study exploring the link between perceived organizational support and burnout amongst management consultants
Full text linkPurpose This study investigates the relationship between perceived organizational support (POS), employee resilience and workload on burnout in the consulting sector, which is characterized by long working hours and high pressure.Design/methodology/approach The proposed hypotheses were tested using data collected from a sample of 169 management consultants. The key constructs were examined using the PROCESS statistical package.Findings The findings indicate that POS has a positive effect on exhaustion, cynicism and professional inefficacy. This effect is partially mediated by employee resilience for all three dimensions. A significant moderation between workload and POS has been found for the cynicism dimension of burnout, suggesting that the positive effect of high POS is especially useful for consultants with high workloads (exceeding 60 working hours per week).Practical implications These findings highlight the importance of making employees feel supported in high-pressure work environments, as this has both a direct effect on employees' mental health and an indirect effect by increasing resilience, which in turn reduces the risk of burnout.Originality/value The study addresses the paucity of research on the workloads of management consultants and how they navigate burnout. The findings show that both personal resources (in this case, resilience) and organizational resources (POS) have a favorable impact on preventing burnout
Older people: strategies for maintaining independence
No full textCommentary on: Crocker TF, Ensor J, Lam N, et al. Community based complex interventions to sustain independence in older people: systematic review and network meta-analysis. BMJ 2024 Mar 21;384
Fear of goal failure and unethical behavior – the mediating role of ego depletion and moderating role of moral attentiveness
Full text linkPurposeThis article examines how fear of goal failure leads to unethical behavior at work. The study further explores whether ego depletion mediates the positive link between employees’ fear of failure in meeting their goals and their unethical behavior. In addition, the moderating role of moral attentiveness on the mediated relationship is examined.Design/methodology/approachTime-lagged data were collected from the sales staff working across various industries in the USA and Pakistan. The final samples from the USA and Pakistan were n = 334 and n = 381, respectively.FindingsFear of goal failure was significantly related to employees’ unethical behavior, and ego depletion mediated this positive relationship. In addition, employees’ moral attentiveness attenuated the link between fear of goal failure and unethical behavior.Practical implicationsThis study contributes to the existing literature by testing an unexplored relationship between fear of goal failure and employee unethical behavior at work. It further confirms the role of an individual’s morality in shaping this relationship.Originality/valueThis study contributes to the existing literature by testing an unexplored relationship of fear of goal failure with employee unethical behavior at work. It further confirms the role of individual’s morality in shaping this relationship
Driving out risk: A taxonomy of factors influencing perceived safety in automated vehicles and the role of knowledge-based variation
No full textDespite ongoing technological advancements, public acceptance of automated vehicles (AVs) remains limited, with perceived safety (PSAV) emerging as a pivotal determinant of trust and adoption. While prior research has identified factors such as cybersecurity, legal accountability, and functional performance as influential, these elements are often examined in isolation and without a unifying framework. Furthermore, the role of individuals' Knowledge Levels of AVs (KLAV) in shaping the salience of safety concerns remains underexplored. This study addresses these gaps through a qualitative investigation involving 66 interviews with members of the public and AV experts in the United Kingdom. We develop an empirically grounded taxonomy of PSAV comprising thirteen factors, organized into three overarching categories: Technological Safety, Psychological Safety, and Social Safety. Our findings suggest that perceptions of safety are not uniform but vary with participants’ KLAV, which is associated with differences in how safety concerns are interpreted and prioritized. The study advances theoretical understanding by reconceptualizing PSAV as a multidimensional and knowledge-sensitive construct. Practically, the taxonomy and KLAV-based insights offer actionable guidance for AV research, public engagement, and anticipatory governance, supporting more inclusive and socially responsive pathways for AV deployment
Quantum Deep Reinforcement Learning for URLLC Satellite-Air-Ground Integrated Networks with Digital Twin Applications
Full text linkIn this paper, we explore a maritime 6G-enhanced satellite-air-ground integrated network (SAGIN) that incorporates a UAV-carried reconfigurable intelligent surface (UCR) relay, and low Earth orbit (LEO) satellites equipped with mobile edge computing (MEC) facilities. The system captures dynamic maritime conditions, including ultra-reliable low-latency communication (URLLC) user mobility and UCR movements across harbor environments. The primary objective is to minimize the total system cost by jointly optimizing task offloading decisions, bandwidth allocation, local computational resource distribution, transmission power control, and caching management, while satisfying strict latency and resource constraints. To address this, we formulate a mixed-integer nonlinear programming (MINLP) problem that captures the complexity of resource optimization in the maritime 6G-enhanced SAGIN. Two quantum-enhanced deep reinforcement learning algorithms, namely quantum-enhanced deep deterministic policy gradient (QEDDPG) and quantum-enhanced proximal policy optimization (QEPPO), are proposed to solve the formulated MINLP problem. Moreover, higher-order quantum feature encoding and quantum neural networks are utilized to accelerate learning and enhance decision-making. Simulation results demonstrate that QEDDPG and QEPPO significantly outperform conventional deep reinforcement learning methods by achieving lower system costs and more efficient resource allocation. These findings shows that the potential of quantum-driven reinforcement learning for enabling scalable, efficient, and intelligent resource management in future 6G-enhanced SAGINs. Index Terms-6G networks, quantum deep reinforcement learning, ultra-reliable low-latency communications, space-air-ground integrated networks, maritime communications, digital twins, satellite communications, mobile edge computing
Quantum Deep Reinforcement Learning for Digital Twin-Enabled 6G Networks and Semantic Communications: Considerations for Adoption and Security
No full textRecently, quantum deep reinforcement learning (Q-DRL) has started to gain attention as a potential approach for tackling complex challenges in wireless communication systems. In particular, Q-DRL, integrating quantum operations into deep learning models, can effectively handle dynamic environments and process large-scale optimizations. As future wireless networks continue to evolve, greater emphasis is being placed on context and meaning rather than raw data. New paradigms, such as semantic communications (SemComs) are essential to effectively convey meaning between transmitters and receivers. By linking SemComs with Q-DRL, future wireless networks will be capable of large-scale extractions and decoding of meaning, thereby minimizing reliance on complete context sharing between communicating parties. Together with SemComs, digital twins (DTs) have been considered as key enablers for future wireless networks. As virtual replicas of physical networks, they serve an important role in network operation, optimization, and control. In this regard, Q-DRL will also be highly beneficial for DTs in enhancing critical functions such as data management and security. This study offers fresh outlooks on how to leverage Q-DRL for SemComs in future wireless networks, augmented by the use of DTs
Digital transformation of the harm reduction sector – “Here4UScotland” a case study of a virtual supervised consumption
No full textObjectiveThis study explores the potential of digital technologies in reducing drug-related deaths through virtual supervised drug consumption. It assesses barriers, enablers, and strategies for adopting a remote supervision service app, Here4UScotland, fostering user engagement and ownership.MethodsA mixed-methods evaluation was undertaken, using semi-structured interviews, focus groups, and quantitative data. Interviews and focus groups were undertaken with 26 participants. The Technology, People, Organizations, and Macro-environmental framework guided data collection and analysis, while the Transformative Technology Integration in Health conceptual model enabled analysis of the relationship between digital technology and service delivery. Qualitative data were thematically analyzed.ResultsThe app was piloted in Aberdeen (Scotland) from January to December 2023. Twenty-five users received smartphones and logged 74 calls. Qualitative findings identified user concerns about privacy versus the need for real-time support, challenges in integrating new features, and the impact of police involvement on trust in digital services. The app's functionality and user engagement highlighted the need for ongoing support and improved system integration. Interviews highlighted the importance of relationships, training, and strategic outreach in successfully delivering digital harm reduction services. Technological features, such as location tracking, offer real-time support but raise privacy concerns.ConclusionOrganizational and macroeconomic factors, including marketing, outreach, and law enforcement involvement, may impact service effectiveness and should be considered in future app implementations. Despite challenges, digital tools have enhanced accessibility and support in overdose prevention. Future research should explore cultural differences in digital adoption and improve communication strategies to maximize user engagement
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