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Upon graduating from university, many engineers will work in new product development and/or technology adoption for continuous improvement and production optimization. These jobs require employees to be cognizant of ethical practices and implications for design. However, little engineering coursework, outside the traditional ABET (Accreditation Board for Engineering and Technology) required Engineering Ethics course, accounts for the role of ethics within this process. Because of this, engineering students have few learning opportunities to practice and reflect on ethical decision-making.

This paper highlights one approach to integrating ethics into an engineering course (outside of engineering ethics). Specifically, the study is implemented within a five-week module with a focus on big data ethics, as part of a Supply Chain Management Technology course (required for Industrial Engineering Technology majors), using metacognition as the core assessment.

Four main themes were identified through the qualitative data analysis of the metacognitive reflections: (1) overreliance on content knowledge, (2) time management skills, (3) career connections and (4) knowledge extensions.

Three notable points emerged which contribute to the literature. First, this study showcased one example of how an ethics module can be integrated into an engineering course (other than Engineering Ethics). Second, this study demonstrated how metacognitive reflections can be used to reinforce student self-awareness of the learning process and connections to big data ethics in the workplace. Finally, this study exhibited how metacognitive reflection assignments can be deployed as a teaching and learning assessment tool, providing an opportunity for the instructor to make immediate changes as needed.

Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA.

This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL.

Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA.

This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.

This paper argues for the need to use multiple sources and methods that respond to research challenges presented by new forms of war. There are methodological constraints and contention on the superiority given to positivist and interpretivist research designs when doing fieldwork in war situations, hence there is a need to use integrated data generation techniques. The combined effect of severe limitations of movement for both the researcher and researched fragmented data because of polarized views about the causes of the war and unpredictable events that make information hard to come by militate against systematic, organised and robust data generation. The purpose of this paper, therefore, is to make fieldwork researchers understand significant research problems unique to war zones.

This research was guided by the postmodernist mode of thought which challenges standardised research traditions. Fieldwork experiences in Cabo suggest the need to use the composite strategies that rely on the theoretical foundation of integrative and creative collection of data when doing research in violent settings.

The fieldwork experiences showed that the standardised, conventional and valorised positivist and ethnographic research strategies may not sufficiently facilitate understanding of the dynamics of war. There should not be firm rules, guidelines or regulations governing the actions of the researcher in conflict. As such, doing research in violent settings require reflexivity, flexibility and creativity in research strategies that respond to rapid changes. Research experiences in Mozambique show the need to use blended methods that include even less structured methodologies.

Fieldwork experiences in Cabo challenges researchers who cling to standardised research traditions which often hamper awareness of new postmodernist mode of thought applicable to war settings. It is essential to study the nature of African armed conflicts by combining creativity and flexibility in the selection of research strategies.

This study aims to investigate how living lab (LL) activities align with responsible research and innovation (RRI) principles, particularly in artificial intelligence (AI)-driven digital transformation (DT) processes. The study seeks to define a framework termed “responsible living lab” (RLL), emphasizing transparency, stakeholder engagement, ethics and sustainability. This emerging issue paper also proposes several directions for future researchers in the field.

The research methodology involved a literature review complemented by insights from a workshop on defining RLLs. The literature review followed a concept-centric approach, searching key journals and conferences, yielding 32 relevant articles. Backward and forward citation analysis added 19 more articles. The workshop, conducted in the context of UrbanTestbeds.JR and SynAir-G projects, used a reverse brainstorming approach to explore potential ethical and responsible issues in LL activities. In total, 13 experts engaged in collaborative discussions, highlighting insights into AI’s role in promoting RRI within LL activities. The workshop facilitated knowledge sharing and a deeper understanding of RLL, particularly in the context of DT and AI.

This emerging issue paper highlights ethical considerations in LL activities, emphasizing user voluntariness, user interests and unintended participation. AI in DT introduces challenges like bias, transparency and digital divide, necessitating responsible practices. Workshop insights underscore challenges: AI bias, data privacy and transparency; opportunities: inclusive decision-making and efficient innovation. The synthesis defines RLLs as frameworks ensuring transparency, stakeholder engagement, ethical considerations and sustainability in AI-driven DT within LLs. RLLs aim to align DT with ethical values, fostering inclusivity, responsible resource use and human rights protection.

The proposed definition of RLL introduces a framework prioritizing transparency, stakeholder engagement, ethics and sustainability in LL activities, particularly those involving AI for DT. This definition aligns LL practices with RRI, addressing ethical implications of AI. The value of RLL lies in promoting inclusive and sustainable innovation, prioritizing stakeholder needs, fostering collaboration and ensuring environmental and social responsibility throughout LL activities. This concept serves as a foundational step toward a more responsible and sustainable LL approach in the era of AI-driven technologies.

This study aims to explore the experiences of living through the COVID-19 pandemic for people who faced homelessness and dealt with mental health and/or substance use challenges.

This qualitative study was comprised of 26 1:1 interviews (16 men and 10 women), conducted between February and May 2021 with people who experienced homelessness in North East England during the COVID-19 pandemic. An inductive reflexive thematic analysis was undertaken, with input from individuals with lived experience who were involved throughout the study.

Four themes were developed. The first theme, lack of support and exacerbation of mental health and substance use difficulties, highlighted how the lack of in-person support and increased isolation and loneliness led to relapses or new challenges for many people’s mental health and substance use. The second theme, uncertainty and fear during the pandemic, explored how the “surreal” experience of the pandemic led to many people feeling uncertain about the future and when things would return to normal. The third theme, isolation and impacts on social networks, discussed how isolation and changes to relationships also played a role in mental health and substance use. Finally, opportunity for reflection and self-improvement for mental health and substance use, explored how some people used the isolated time to re-evaluate their recovery journey and focus on self-improvement.

The experiences shared within this study have important implications for planning the future delivery and commissioning of health and social care services for people facing homelessness, such as sharing information accessibly through clear, consistent and simple language.

As one of the few papers to involve people with lived experience as part of the research, the findings reflect the unique narratives of this population with a focus on improving services.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

This final chapter of this book highlights and critically discusses some specific issues concerning talent management in the context of higher education raised in the chapters of this book. It recapitulates the transition higher education is going through. This transition started decades ago but was boosted by the movements of Open Science and Recognition and Rewards. It leads to a reorientation on the conceptualization of academic performance and subsequently also on the meaning of talent and talent management in academia. It points to a shift from an exclusive and performance orientation on talent, to an inclusive, developmental approach to talent management or a hybrid form. Yet, Thunnissen and Boselie state that there is a talent crisis in academia, and this crisis urges the need for more innovative ways of developing and implementing talent management practices. This chapter ends with some recommendations for further talent management research and practice.

In spite of the merits of artificial intelligence (AI) in marketing and social media, harm to consumers has prompted calls for AI auditing/certification. Understanding consumers’ approval of AI certification entities is vital for its effectiveness and companies’ choice of certification. This study aims to generate important insights into the consumer perspective of AI certifications and stimulate future research.

A literature and status-quo-driven search of the AI certification landscape identifies entities and related concepts. This study empirically explores consumer approval of the most discussed entities in four AI decision domains using an online experiment and outline a research agenda for AI certification in marketing/social media.

Trust in AI certification is complex. The empirical findings show that consumers seem to approve more of non-profit entities than for-profit entities, with the government approving the most.

The introduction of AI certification to marketing/social media contributes to work on consumer trust and AI acceptance and structures AI certification research from outside marketing to facilitate future research on AI certification for marketing/social media scholars.

For businesses, the authors provide a first insight into consumer preferences for AI-certifying entities, guiding the choice of which entity to use. For policymakers, this work guides their ongoing discussion on “who should certify AI” from a consumer perspective.

To the best of the authors’ knowledge, this work is the first to introduce the topic of AI certification to the marketing/social media literature, provide a novel guideline to scholars and offer the first set of empirical studies examining consumer approval of AI certifications.

COVID-19 has immensely disrupted business dynamism, providing catalyst innovation opportunities and transposing society's perception of disruptive technology (DT). This research increases the understanding of the impact of the pandemic in influencing the way organizations perceive DT and whether any mitigating factors were considered when deciding to adopt new technology during the pandemic.

A qualitative approach was adopted in this research, consisting of 14 semi-structured interviews with eight senior managers and six employees, representing both the private and public sectors in New Zealand. All participants had in-depth knowledge of organizational DT adoption during the pandemic. Two separate sets of semi-structured interviews were used to enable comparison between senior managers' and employees' experiences of organizational adoption of DT post-emergence of COVID-19. Due to the nature of this research being conducted on organizational adoption of DT during the pandemic, time constraints and sample size were two of the key limitations of this research. Specifically, potential participants widely cited unavailability due to additional pressure from COVID-19. Given the limited research in this area, this study is explorative by nature and adds significant insights to the literature.

The findings suggest that COVID-19 has contributed towards an increased acceptance of, reliance on and adoption of DT across both organizational and social landscapes. The authors found that one of the reasons COVID-19 expedites the adoption of DT correlates with the notion of technology dependency, with organizations citing DT as a viable part of a business continuity plan (BCP) to counter the unpredictability of ongoing disruptive events associated with COVID-19 or any similar disruption which may be on the horizon. These findings are highly relevant as they suggest that the labor market in New Zealand is flexible so organizations and employees can adapt to DT and COVID-19.

This research adds much-needed insight into the emerging field of research that examines COVID-19's impact on the adoption of DT from both management and employee perspectives.

This paper aimed to determine the self- and co-regulation influences on the community of inquiry (CoI) for collaborative online learning.

A quantitative survey was used on a sample of (N = 626) enrolled postgraduate students in a South African Open Distance and e-Learning (ODeL) university. The measuring instruments were the CoI and the shared metacognitive surveys. Correlation and multiple regression analyses were used to determine the association and influence of self- and co-regulation on the CoI.

The results indicated that self- and co-regulation related to the CoI (teaching, cognitive and social) presences. In addition, the results revealed that self- and co-regulation influence the CoI presences. Self-regulation had the highest influence on teaching and cognitive presence, while co-regulation influenced social presence.

The study’s convenience sampling method from a single university limited the applicability of the findings to other online learning environments.

Higher educational teachers who encourage student self- and co-regulation may enhance their online teaching, cognitive and social presence when studying online. The research’s findings may be valuable to teachers to enable them to provide a more collaborative and interactive online learning environment and promote productive online communities.

This study contributes to the body of knowledge about the relationship between teaching, social and cognitive presence and self- and co-regulation within the CoI framework. Furthermore, there has also been limited research focussing on the dynamics of shared metacognition within the CoI framework in an ODeL context.