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This study aims to examine the effects of payment methods used in mergers and acquisitions (M&A) conducted by UK companies spanning the period from 2007 to 2019.

The study used the estimated expected returns method to identify abnormal returns during the deal announcement period, applying event study analysis with both univariate and multivariate regression models to detect cumulative abnormal returns around the announcement timeframe.

The results show a short-term positive return increase for acquiring firms, controlling for deal-specific characteristics like target firm location and payment methods. The authors observed a preference for cash financing across domestic and cross-border transactions. Multivariate analysis revealed insignificance between payment methods and deal characteristics like cross-border acquisitions and diversification.

The study’s focus on publicly traded firms in the UK and the absence of a comparative analysis across different regions and markets limits the sample size and may impact the generalizability of findings.

The study proposes three practical implications. Firstly, firms should tailor payment methods to each transaction, aligning with strategic goals to optimize value and mitigate risks. Secondly, decision-makers must prioritize comprehensive due diligence and strategic alignment throughout M&A processes to enhance success and maximize synergies. Finally, analysing broader strategic contexts and regulatory landscapes when structuring transactions enables goal attainment, such as market expansion or value creation.

The study’s findings can promote transparency and accountability among corporate decision-makers in M&A transactions. Stakeholders can advocate for transparent decision-making processes, enhancing trust in corporate governance.

This study provides valuable insights into the impact of payment methods on shareholder value in M&A transactions involving UK companies, informing strategic decision-making and contributing to the understanding of corporate finance dynamics.

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.

In this case study, we examine how a citrus peel valorising company based in the Netherlands was able to adopt a circular business model while navigating regulatory, managerial, and supply chain-related barriers.

In-depth, semi-structured interviews with key personnel in the company, notes from field observations, photographs of the production process, and documents from a legal judgement served as data for this single, qualitative case study. Data were coded inductively using the in vivo technique and were further developed into four themes and a case description.

Results from our study indicate that the regulatory and political contexts in the Netherlands were critical to the company’s success. Like in the case of most fruitful industrial symbioses, partnerships founded on mutual trust and economically appealing value propositions played a crucial role in ensuring commercial viability. Collaborating with larger corporations and maintaining transparent communication with stakeholders were also significant contributing factors. Lastly, employees’ outlook towards circularity combined with their willingness to learn new skills were important driving factors as well.

In addition to expanding the scholarship on the adoption of circular business models, this research offers novel insights to policymakers and practitioners. It provides empirical evidence regarding the importance of public awareness, adaptable legislation, and harmonised policy goals for supporting sustainable entrepreneurship in the circular economy.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

The purpose of this policy piece is to present headline evidence from the equivalent of a decade of research across three projects focused on improving the educational experiences of incarcerated or justice-involved children and young people across 14 countries. The projects demonstrated a positive impact when using the specially designed innovative methods even with disengaged justice-involved children and young people. Each project had a different focus based on the needs of the children and young people, including (re)engaging them with education and learning opportunities re-engaging young offenders with education and learning (RENYO), developing identified social and emotional competencies through sport active games 4 change (AG4C) and developing skills for when transitioning into communities (S4L).

Each of the three projects designed bespoke interventions and materials based on the focus of the project. Combined, these were piloted across a total of 14 countries with 548 children and young people, 225 educators and 21 youth justice settings (custodial and non-custodial). Qualitative and quantitative data were gathered from educators, children and young people to better understand the effectiveness and limitations of the pedagogical approaches, the materials and experiences of the users.

The findings from across the three projects demonstrated that when custodial settings or youth justice settings have greater access to specifically designed tools and resources, with appropriate training to use them, it is possible to re-engage even the most disengaged children and young people with education and learning (RENYO). It was also possible to show a statistically significant increase in social and emotional competencies (social awareness, responsible decision-making, self-awareness, self-management, relationship skills) in participating country settings (AG4C) and to engage children and young people with a Skills4Life Curriculum in preparation for moving back into the community.

Custodial and other youth justice settings vary widely in different jurisdictions with varying levels of resource. Whilst all programmes had success in implementation, this was to different degrees and extents in each of the country contexts. Much of this success will depend on the skills of educators and the willingness of the leadership.

The programmes and interventions in this policy piece can be used in a range of contexts and settings as shown by the wide range of sites they were piloted in. From a practical perspective, these programmes and interventions have been designed to fit into existing structures and timetables with custodial and youth justice settings, making them easily integrated into business as usual.

Planned and strategically delivered interventions by expert educators in a holistic way means there is real opportunity to better support justice involved children and young people to successful transitioning back into communities, thereby potentially reducing reoffending. This is done by (re)engaging them with education and learning on entry, using the authentic inquiry methodology, developing social and emotional competencies through the active games and sport programme, followed by a tailored Skils4Life Curriculum preparing them for transition back into communities.