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This chapter delves into the evolving identity of professionals within the field of research management and administration (RMA), examining the shifts in their roles and expectations in the changing landscape in higher education. After the introductory section, Section 2 offers a conceptual framework that emphasises identity as a dynamic process rather than a static concept. This framework sheds light on the changing roles and expectations that define the RMA profession. In Section 3, we explore the contextual backdrop of shifting expectations surrounding RMA roles while stressing the importance of recognizing the multiplicity of identities to comprehend the nuances of the RMA profession. Section 4 analyzes empirical data and explore the diverse pathways that lead individuals into the RMA profession. We uncover that a notable proportion of RMAs possess scientific training and research experience and highlight the complexities surrounding the identity of RMAs with doctoral training (DRMAs). Lastly, Section 5 discusses key observations that yield valuable insights for future research on the evolving professional identity of RMAs. We emphasise that, through self-exploration and introspection, practitioners in the field can contribute to a deeper understanding of their roles and actively shape their professional identity.

Purpose: This chapter examines the effect of green banking practices (GBPs) on environmental performance (EP), specifically focussing on the Sri Lankan banking industry. Additionally, the study explores the mediating impact of green finance in the association between GBPs and the EP of banks listed in the Colombo Stock Exchange in Sri Lanka.

Methodology: The survey included 233 banking employees from Sri Lanka, and data for this study were collected via questionnaires. The formulated hypotheses were tested employing a regression analysis.

Findings: GBPs such as employee, customer, operation, and policy-related practices significantly predicted the banks’ EP. Furthermore, the study highlights that green finance partially mediates the relationship between GBPs and banks’ EP in Sri Lanka.

Implications of the study: The study’s results indicate that banks should prioritise integrating GBPs in their organisations to enhance environmental and overall performance. Moreover, strategically utilising green financing techniques might be a substantial channel for banks to further strengthen their ecological dedication and influence.

Originality: This is the first study to investigate the impact of GBPs on banks’ EP with the mediating effect of green finance in the Sri Lankan context.

The rise of artificial intelligence (AI) and machine learning has largely promoted the emergence of “autonomous decision-making” (ADM). This paper aims to establish a personalized artificial intelligent housekeeper (AIH) that knows more about our hobbies, habits, personality traits, and shopping needs than ourselves and can replace us to do some habitual purchasing behavior.

We propose an AI decision-making method based on machine learning algorithm, a novel framework for personalized customer preference and purchase. First, the method uses interactive big data to predict a potential consumer’s decision possibility. Then, the method mines the correlation between consumer decision possibility and various factors affecting consumer behavior. Finally, the machine learning algorithm is used to estimate the consumer’s purchase decision according to the comprehensive influencing factors data of the target consumer.

The experimental results show that the method can predict the regular consumption behavior of consumers in advance and make accurate decision-making behavior. It can find correlations from a large amount of data to help predict many simple purchase decisions in our life, and become our AIH.

This study introduces a new approach that not only has the auxiliary decision-making function but also has the decision-making function. These findings contribute to the research on automated decision-making process of AI and on human–technology interaction by investigating how data attributes consumer purchase decision to AI.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

The high turnover of new graduate employees has become a concern for many organizations in Korea. This study explores when new graduate employees leave first jobs and what makes these employees decide to leave employees' organizations.

Using national panel data from South Korea, the authors employed a survival analysis and examined the factors that explain the turnover of new graduate employees.

The findings of this study reveal that many new graduate employees leave the employees' organizations within two years. Moreover, work conditions, work satisfaction and job-skill match were associated with new graduate employee turnover.

Based on the results of survival analysis derived from actual turnover data, not turnover intentions, the authors emphasize appropriate human resources (HR) intervention, a working environment and organizational culture, and employee development opportunities.

In a world with over 8 billion people, ensuring sustainable food sources is paramount. This chapter explores the pivotal role of aquaculture in addressing the challenges of marine conservation and sustainable resource use. Aligned with the United Nations’ Sustainable Development Goal 14, aquaculture emerges as a solution to relieve pressure on wild fish stocks and enhance food security. The chapter emphasises the rapid growth of this sector and underscores the importance of international cooperation and policies like the Global Ocean Treaty in ensuring marine biodiversity. While acknowledging the potential of aquaculture, the chapter delves into environmental concerns surrounding fishmeal and fish oil in feed. It advocates for innovative technologies and ingredients to establish a circular bioeconomy. The significance of higher education in advancing sustainable aquafeed technology, breeding, and genetics is highlighted, with a discussion on milestones achieved by experts like Dr John E. Halver and Professor Simon J. Davies. Examining technological advances, the chapter explores molecular genetics, transgenics, and gene editing, particularly CRISPR biosciences, as transformative tools for enhancing aquaculture productivity and sustainability. Environmental impacts are addressed, proposing solutions such as Recirculating Aquaculture Systems (RAS) and Multitrophic Aquaculture Systems (MTA) to minimise ecological footprints. Throughout, there is a strong emphasis on the integral role of research and education in fostering sustainable aquaculture practices. The chapter advocates for specialised courses and programs in higher education to prepare the next generation for the challenges and opportunities in aquaculture, ensuring its contribution to global food security and environmental stewardship.

Growing research attention has been given to both the circular economy and digitalisation in accounting research in recent years, but there are few studies exploring how digital tools are used to develop, analyse and respond to information for circular decision-making in industrial organisations. Therefore, this paper addresses how the data from digital technologies are leveraged in the aftermarket of an industrial firm for circular control.

The paper develops an analytical framework that is then used to frame the findings through a single case study of an international heavy equipment manufacturer for circular control.

The case provides examples of how digital technologies are used for circular control, framed within the analytical model as the key contribution. The study illustrates the different ways through which the accounting information from such technologies supports the service marketing function through circular control and the types of controls needed for this.

Managers in large industrial organisations should ensure customer-facing staff have adequate digital competences and knowledge of circular products and services for marketing, product design improvements and material recovery that can help decrease costs and improve customer satisfaction. The digital systems need to be integrated with upstream and downstream partners.

Understanding the transition towards increasingly circular product-service systems in industrial firms is important for current and future generations.

The originality lies in providing an empirical example of how digital technologies can be used to facilitate circular control and support the service marketing function in the aftermarket of an industrial firm.