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This paper aims to make sense of the social enterprise in a frame of social procurement and conceptualise it as a provider of public welfare based on bibliometric material. Comprehensively, it contributes to developments in social procurement, which has received limited attention.

Scoping literature from Web of Science and using bibliometric methods, the paper identifies and qualitatively explores the literary intersections between social enterprise and social procurement.

Of the 183 articles, four literary clusters are revealed illustrating scholarly intersections and a detailed exploration of social enterprise as a public provider. The alignment and themes of the clusters further indicate the application of, and role played by, social enterprise in social procurement. Collectively, they reveal the dominance of social enterprise in this dyadic relationship and a minor undertaking of research in social procurement.

This “sense-making” groundwork forms a foundational step in developing our understanding of procurements through social enterprises. Furthermore, a positioning and conceptualisation of social enterprise accredits their utility and applicability in delivering public benefits. In this way, the paper informs and supports scholarly and practice-based interest into social enterprises for the delivery of public services.

The paper presents the first bibliometric conceptualisation of social enterprise in relation to social procurement and offers detailed insights through the bibliometric clusters. Furthermore, the paper contributes to the underdeveloped social dimension of procurement and bridges the gap between two distinct fields of scholarship: public management and administration and social entrepreneurship.

The purpose of this study was to investigate trust within human-AI teams. Trust is an essential mechanism for team success and effective human-AI collaboration.

In an online experiment, the authors investigated whether trust perceptions and behaviours are different when introducing a new AI teammate than when introducing a new human teammate. A between-subjects design was used. A total of 127 subjects were presented with a hypothetical team scenario and randomly assigned to one of two conditions: new AI or new human teammate.

As expected, perceived trustworthiness of the new team member and affective interpersonal trust were lower for an AI teammate than for a human teammate. No differences were found in cognitive interpersonal trust and trust behaviours. The findings suggest that humans can rationally trust an AI teammate when its competence and reliability are presumed, but the emotional aspect seems to be more difficult to develop.

This study contributes to human–AI teamwork research by connecting trust research in human-only teams with trust insights in human–AI collaborations through an integration of the existing literature on teamwork and on trust in intelligent technologies with the first empirical findings on trust towards AI teammates.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

This study aims to examine the relationship between high commitment human resource management (HCHRM) practices and employee well-being in the food service industry, with a focus on the mediating roles of job demands and psychological conditions.

A survey was conducted among 296 frontline employees in the food service industry, and the data were analyzed using structural equation modeling (SEM) in AMOS ver26.

The results showed that HCHRM practices do not directly affect employee well-being. However, psychological conditions play a crucial role in mediating the relationship between HCHRM practices and employee well-being. Specifically, the psychological conditions of meaningfulness and availability significantly predicted work engagement and mediated the relationship between HCHRM practices and employee well-being. On the other hand, job demands did not mediate the relationship between HCHRM practices and employee well-being.

Although the study addressed common method variance, the cross-sectional nature of the data limits the ability to infer causal relationships among variables. Future studies could adopt a longitudinal research design to investigate the causal relationships among variables. In addition, the study recommends that managers in the food service industry adopt HCHRM practices and provide necessary psychological conditions to promote employee well-being.

This study extends the current literature on HCHRM and employee well-being in the food service industry by providing new insights into the mediating role of psychological conditions. The findings suggest that HCHRM practices can indirectly promote employee well-being through the enhancement of psychological conditions. These insights could help managers in the food service industry to design effective HRM strategies that foster employee well-being and reduce turnover.

The purpose of this study is to describe the design and validation of a three-dimensional (3D)-printed phantom of a uterus to support the development of uterine balloon tamponade devices conceived to stop post-partum haemorrhages (PPHs).

The phantom 3D model is generated by analysing the main requirements for validating uterine balloon tamponade devices. A modular approach is implemented to guarantee that the phantom allows testing these devices under multiple working conditions. Once finalised the design, the phantom effectiveness is validated experimentally.

The modular phantom allows performing the required measurements for testing the performance of devices designed to stop PPH.

PPH is the leading obstetric cause of maternal death worldwide, mainly in low- and middle-income countries. The proposed phantom could speed up and optimise the design and validation of devices for PPH treatment, reducing the maternal mortality ratio.

To the best of the authors’ knowledge, the 3D-printed phantom represents the first example of a modular, flexible and transparent uterus model. It can be used to validate and perform usability tests of medical devices.