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The present work aimed to identify the impact of accrual-based earnings management on the cost of equity (KE) through corporate social responsibility (CSR) as a moderating variable on European Environmental, Social, and Governance (ESG) companies.

The authors used data from a sample of 366 European firms over the 2012–2022 period. The data were collected from the Thomson Reuters Asset 4 and I/B/E/S database and analyzed using STATA 17 as a statistical software package.

As expected, the results showed a negative relationship between accruals, CSR and KE. Moreover, they suggest that the moderating variable negatively affects the relationship between accruals and the KE.

The results are pertinent to stakeholders and investors, who would pressure companies to enhance the quality of disclosed information and mitigate risks facing the company.

The main contribution lies in examining the relationship between accruals and KE through CSR in the European ESG context.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

Sherwood Forest is a mosaic of heritage, habitats and stakeholder relations. Scheme Manager, Steve Little, and Senior Lecturer in Museum Studies, Charlie Gregson, share their story of developing a working methodology in this complex landscape. By evaluating their relationship through the lenses of knowledge brokering and collaborative mentoring, they identify six themes relating to how their working environment evolved and functioned. Discussion finds significant overlap between collaborative mentoring, KE and the attainment of the Sustainable Development Goals in their ability to enable more nuanced and holistic changemaking that is contextualized in a deep understanding of need.

Knowledge brokering, a process by which an individual (or an organization) supports the transfer of research evidence into policy and practice, can improve evidence-based decision-making through knowledge exchange (KE) but is, on the whole, poorly defined in academia (Cvitanovic et al., 2017). This chapter seeks to contribute to the ‘necessary and urgent’ need for evaluation of KE in practice (Rycroft-Smith, 2022) by providing edited snippets of dialogue, analysis and key learning points. It is intended as inspiration and encouragement for academics, professionals, students and volunteers developing human-centric projects or design-thinking methodologies between universities and external partners.

This study aims to apply for the first time in literature a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm and test the sensors' configuration found in a delamination identification case study.

This work aims to study the damage identification in an aircraft wing using the Lichtenberg and multi-objective Lichtenberg algorithms. The former is used to identify damages, while the last is associated with feature selection techniques to perform the first sensor placement optimization (SPO) methodology with variable sensor number. It is applied aiming for the largest amount of information about using the most used modal metrics in the literature and the smallest sensor number at the same time.

The proposed method was not only able to find a sensor configuration for each sensor number and modal metric but also found one that had full accuracy in identifying delamination location and severity considering triaxial modal displacements and minimal sensor number for all wing sections.

This study demonstrates for the first time in the literature how the most used modal metrics vary with the sensor number for an aircraft wing using a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm.

Although numerous studies have been conducted to explore the impact of various factors on employees' turnover intention and intention to remain with the organization, the relationship between these two constructs remains largely unexplored. Considering the significance of these constructs, particularly in the context of the COVID-19 pandemic, the authors aimed to investigate their association within an academic environment using a dynamic modeling approach.

This study follows a quantitative approach and utilizes a longitudinal survey design. The authors utilized a cross-lagged panel model (CLPM) and employed the parametric efficient partial least squares (PLSe2) methodology to estimate the dynamic model using data gathered from lecturers associated with both public and private universities in Malaysia. In order to offer methodological insights to applied higher education researchers, the authors also compared the results with maximum likelihood (ML) estimation.

The findings of the authors' study indicate a reciprocal relationship between turnover intention and intention to remain with the organization, with intention to remain with the organization being a stronger predictor. Moreover, situational factors were found to have a greater influence on eliciting turnover intention within academic settings. As anticipated, the use of the PLSe2 methodology resulted in higher R² values compared to ML estimation, thereby reinforcing the effectiveness of PLS-based methods in explanatory-predictive modeling in applied studies.

The authors' findings suggest prioritizing policies that enhance training and consultation sessions to foster positive attitudes among lecturers. Positive attitudes significantly impact judgment-driven behaviors like turnover intention and intention to remain with the organization. Additionally, improving working environments, which indirectly influence judgment-driven behaviors through factors like affective work events, affect and attitudes, should also be considered.

This study pioneers the examination of the causal relationship between turnover intention and intention to remain with the organization, their stability over time and the association of changes in these variables using a dynamic CLPM in higher education. It introduces the novel application of the cutting-edge PLSe2 methodology in estimating a CLPM, providing valuable insights for researchers in explanatory-predictive modeling.

Despite the widespread study and application of public-private partnerships (PPPs) since the 1980s, the field lacks a universally accepted definition that captures the concept's complexity. This study aims to offer a definition and foster a more substantive and comprehensive discourse on PPPs to improve communication and understanding between academics and practitioners from diverse disciplines and legislative backgrounds.

Grounded in the family-resemblance concept proposed by German philosopher Ludwig Wittgenstein, this study conducts a comprehensive literature review to identify core and non-core elements frequently cited in PPP descriptions. The authors used these findings to develop the PPP sunflower model as a structured framework for defining PPPs.

The analysis elucidates six core elements consistently present in PPP descriptions: clarity of roles and responsibilities, appropriate risk allocation and sharing, injection of expertise and resources, cooperation and teamwork, a bundle of services, and long-term contracts. Coupled with identified non-core elements, these core components comprise the PPP sunflower model, a structured framework for defining PPPs that accommodates their multi-faceted nature.

The PPP sunflower model distinguishes itself as a unique contribution to the PPP literature. It offers a rigorous theoretical framework that can elucidate the complexity of PPPs for various stakeholders. The model serves as a practical tool for evaluating the authenticity and viability of PPP projects. The study's novelty lies in its adoption of the family-resemblance concept, thereby providing a comprehensive, multi-dimensional framework that enhances the understanding of PPPs across different disciplines and legislative contexts.

The purpose of this study is to examine whether the institutional environment influences auditor reporting.

This study employs China's anti-corruption campaign as an exogenous shock to its institutional environment and compares auditors' issuance of modified audit opinions (MAOs) to small-profit clients before and during the campaign.

This study documents that small-profit clients were more likely to receive MAOs during the anti-corruption campaign period than before, indicating that auditors issued more conservative audit opinions to small-profit clients because of the anti-corruption campaign. Additionally, this study finds that increased auditor conservatism was more pronounced for auditors of large clients.

This study suggests that a weak institutional environment adversely affects auditor conservatism. This offers valuable insights for governments and regulators to improve the audit environment and for audit firms to enhance auditors' integrity and independence.

This study contributes to the research on institutional environments and auditing by observing a unique exogenous event.

External institutional policy and its impact on corporate social responsibility (CSR) have been widely discussed by researchers, but its effect still remains controversial. This study aims to use the minimum wage policy as an illustrative example to analyze its impact on the corporate social responsibility (CSR) of tourist enterprises. Furthermore, the research seeks to examine the boundary conditions that influence the minimum wage’s effect on CSR.

This paper takes the data of 42 listed tourism companies from 2010 to 2020 in China as samples and uses the mixed OLS regression method and the fixed effects panel model to examine the effect of the minimum wage on CSR.

Findings show that increasing wages has a significantly negative impact on their total CSR investment. Also, low-operating-capacity enterprises and private enterprises will react more adversely when faced with increasing minimum wages. And found that the increase of minimum wage has no significant negative impact on the strategic social responsibility of tourism enterprises; however, it has a significantly negative impact on their tactical social responsibility. In addition, as far as employees’ rights and interests are concerned, the minimum wage increase has effectively increased employee salaries, but the nonsalary benefits of the employees have significantly decreased.

The contribution of this paper not only expands the research on the antecedents and boundary mechanisms of CSR but also clarifies the specific effect of the rise of the minimum wage on corporate social responsibility; it further deepens the impact of institutional policy factors on CSR, which also opens new perspectives for policy evaluation and provides a theoretical basis for government policymakers.

This chapter provides an overview of the current state of research policy and research management and administration (RMA) in Kenya. Although RMA is an emerging field globally, it is not yet fully recognised in Kenya. The main objective of this chapter is to provide an overview of the vibrant research environment in Kenya, its most important challenges in the field of management and administration of research, and how some Kenyan Universities are dealing with them.

The findings in this chapter are based first on a research policy documents analysis and on literature review. In a second phase, qualitative data were collected through desk-based research and informant questionnaires and interviews.

In the conclusions, concrete suggestions are formulated that could support the enrichment of the research environment, find solutions for RMA-related challenges, but also lead to the development and establishment of RMA as a profession in the country.