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Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

This paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.

We followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.

Our results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.

The study’s sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.

ATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.

The originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.

This paper delves into cross-border E-business, unraveling its intricate dynamics and forecasting its future trajectory.

This paper projects the prospective market size of cross-border E-business in China for the year 2023 using the GM (1,1) gray forecasting model. Furthermore, to enhance the analysis, the paper attempts to simulate and forecast the size of China’s cross-border E-business sector using the GM (1,3) gray model. This extended model considers not only the historical trends of cross-border E-business but also the growth patterns of GDP and the digital economy.

The forecast indicates a market size of 18,760 to 18,934 billion RMB in 2023, aligning with the consistent growth observed in previous years. This suggests a sustained positive trajectory for cross-border E-business.

Cross-border e-commerce critically shapes China’s global integration and traditional industry development. The research in this paper provides insights beyond statistical trends, contributing to a nuanced understanding of the pivotal role played by cross-border e-commerce in shaping China’s economic future.

Railways are a well-known example of complex critical infrastructure, incorporating socio-technical systems with humans such as drivers, signallers, maintainers and passengers at the core. The technological evolution including interconnectedness and new ways of interaction lead to new security and safety risks that can be realised, both in terms of human error, and malicious and non-malicious behaviour. This study aims to identify the human factors (HF) and cyber-security risks relating to the role of signallers on the railways and explores strategies for the improvement of “Digital Resilience” – for the concept of a resilient railway.

Overall, 26 interviews were conducted with 21 participants from industry and academia.

The results showed that due to increased automation, both cyber-related threats and human error can impact signallers’ day-to-day operations – directly or indirectly (e.g. workload and safety-critical communications) – which could disrupt the railway services and potentially lead to safety-related catastrophic consequences. This study identifies cyber-related problems, including external threats; engineers not considering the human element in designs when specifying security controls; lack of security awareness among the rail industry; training gaps; organisational issues; and many unknown “unknowns”.

The authors discuss socio-technical principles through a hexagonal socio-technical framework and training needs analysis to mitigate against cyber-security issues and identify the predictive training needs of the signallers. This is supported by a systematic approach which considers both, safety and security factors, rather than waiting to learn from a cyber-attack retrospectively.

This study aims to identify the interfaces between Industry 4.0 (I4.0) technologies and circular supply chains (CSC) in Brazilian foodtechs, focusing on key stakeholders’ perspectives to understand the efficiency and sustainability impacts of these integrations.

Using a qualitative exploratory research design, the study analyzes eight Brazilian foodtechs through interviews and content analysis. It identifies CSC practices and examines the adherence of I4.0 technologies within these enterprises, assessing stakeholder engagement and the implications for CSC optimization.

Fifteen CSC practices were identified across the foodtechs, with notable integration of three distinct I4.0 technologies. The findings suggest that while I4.0 technologies enhance efficiency in CSC, their adoption is in early stages. Stakeholder engagement emerges as a crucial element for optimizing CSC in the context of Brazilian foodtechs.

This study contributes to the academic discussion on the synergy between I4.0 and circular economy (CE) models, providing empirical evidence of their application in the foodtech sector and highlighting the role of stakeholders in facilitating these integrations.

The findings suggest that stakeholder engagement in circular practices is vital for both supply chain and organizational levels, with potential benefits including improved efficiency and sustainability outcomes. The research also underscores the need for public sector support, including regulatory frameworks and incentives for adopting I4.0 technologies.

By demonstrating how I4.0 technologies can support CE practices in foodtechs, the study highlights the potential for these integrations to contribute to more sustainable and efficient food systems, addressing environmental concerns and promoting social well-being.

This study addresses a gap in the literature by exploring the interface between I4.0 technologies and CSC in the emerging context of Brazilian foodtechs, offering insights into the practical and societal benefits of these integrations.

As research emerged in terms of how narcissism, a negative or dark trait, has been found to be constructive in enhancing entrepreneurial behaviour, there are mixed results regarding the significance of narcissism in the field of entrepreneurship. Additionally, this previous research has mostly been conducted on student or nascent entrepreneur samples within developed economies. Therefore, the purpose of this paper is to explore how narcissistic traits of established entrepreneurs in an emerging economy context infuence their entrepreneurial behaviour both positively and negatively.

Gioia methodology was applied in the qualitative study by means of in-depth interviews, which allowed for the unpacking of narcissistic traits among established entrepreneurs in South Africa. Four themes emerged from the data, and included insights related to entrepreneurial experience influencing behaviour; business growth linked to personal development; opportunity identification versus loss; and identity separation in relation to authentic identity versus an entrepreneurial identity.

The findings of the paper contribute to creating an understanding of how to hone individual narcissistic traits for positive influences that develop entrepreneurs while also contributing to their business development, opportunity realization and identity. In addition, the findings highlighted a separation between established entrepreneurs’ authentic personality and the inputs that end up resulting in the entrepreneurial personality.

This paper highlights the possibility of narcissism functioning as a business process involved in entrepreneurship rather than a necessary personality trait. An interesting dynamic contributed to what seems to be a constant battle between the authentic identity and the entrepreneur identity, gaining deeper insight surrounding established entrepreneurs’ experiences to survive and, more importantly, thrive as entrepreneurs.

This study aims to investigate the synergies between the economic environment and the smart living dimension embedded in the current smart city initiatives, focusing on the localization of female entrepreneurship in contemporary cities. This interaction is under-investigated and controversial as it includes cities' practices enabling users and citizens to develop their potential and build their own lives, affecting entrepreneurial and economic outcomes. Building upon the perspective of the innovation ecosystems, this study focuses on the impact of smart living dimensions and R&D investments on the localization of female entrepreneurial activities.

The study uses a Generalized Method of Moments (GMM) and a panel dataset that considers 30 Italian smart city projects for 12 years to demonstrate the relationship between smart living practices in cities and the localization of female entrepreneurship. The complementary effect of public R&D investment is also included as a driver in the “smart” city transition.

The study found that the advancement of smart living practices in cities drives the localization of female entrepreneurship. The study highlights the empirical results, the interaction over the years and a current overview through choropleth maps. The public R&D investment also affects this relationship.

This study advances the theoretical discussion on (1) female entrepreneurial intentions, (2) smart city advancement (as a context) and (3) smart living dimension (as a driver) and offers valuable insight for governance and policymakers.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship. The findings provide valuable insights into the localization of female entrepreneurship in the context of smart cities.

This paper reviews the literature on disability inclusion (DI) in supply and demand chains of hospitality and tourism (H&T) organisations. The purpose of this study is to assess disability support and interventions within H&T organisations. Through the assessment, we identified gaps to recommend H&T scholars’ and practitioners’ knowledge of DI from new perspectives.

An integrative review was conducted to examine the published evidence on DI in H&T organisations. This study used high-ranking H&T journals from the Scopus and Web of Science databases between 2001 and 2023. In total, 101 empirical papers met the criteria for the review analysis.

DI focuses heavily on customer disabilities, with scant research on DI in H&T employment. The review emphasises the critical need for empirical research into the varied disability employment ecosystem within H&T organisations, focusing on social integration for inclusive workplaces.

This study contributes to the H&T literature, which previously overlooked the disability context in diversity. The research offers strategies for creating inclusive environments in the H&T industry for disabled consumers and producers.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.