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Digital sustainability involves the ability of industries and professionals to adapt to rapidly changing technological landscapes. Digitalisation and artificial intelligence (AI) are expected to radically change the maritime industry’s job landscape, especially with autonomous ships. International organisations currently do not formalise the education of maritime professionals and deck officers and need new formal modules. This study aims to contribute to this aspect by investigating learners’ experiences and knowledge gaps in the fundamentals of, as supported by the andragogy theory, topics such as computer programming, cybersecurity and statistics.

The research was carried out at Southampton Solent University, with samples of 105 students attending various MSc courses in maritime operations and deck cadet courses. The data was collected through an online survey. The two groups were compared and analysed using a chi-square test.

The results show that the percentage of MSc students with previous training in statistics, computer programming and cybersecurity courses was 37%, 13% and 16%, respectively. The deck officers’ training in the same areas was 06%, 09% and 09%. The results of this study were used to develop a new maritime digital module to focus on these topics.

The paper highlights digital sustainability’s significance in adapting education and training courses. Ship management companies and higher education institutions must urgently meet the demands of digitalisation and AI in the maritime industry. It highlights the necessity of addressing current knowledge gaps and implementing new educational modules to ensure the sustainable development of digital skills among maritime professionals and cadets.

This chapter focuses on unearthing the metaverse technology impacts on banking services, both the underlying opportunities it brings and the challenges it imposes, along with its long-term effects on the industry. The tremendous strides made in the field of technology have impacted every industry, and banking is no different. Metaverse technology further catapults banking services into an unknown realm of possibilities and challenges since the advent of modern technology. Metaverse technology is an upcoming virtual interconnected universe where users can connect with each other and digital entities at the same time in real time. Banking will open up a lot of avenues to redefine customer experience, build new financial products and make the customers sticky. Deploying metaverse technology in a banking system comes with a whole host of challenges, including technical complexity, security, privacy and compliance. More insights on the dos and don'ts of this as well as what to measure in terms of success would be found by looking at cases from those banks that have successfully rolled out metaverse technology. So, for banks to come out of their shells and sustain themselves in a competitive world of financial services, understanding metaverse technology in banking is essential. This chapter attempts to uncover the subtlety of metaverse technology on banking services, their promise and perils and the consequences that may stick with this industry forever.

In this chapter, we emphasise how Creative Artificial Intelligence (AI) can and will transform the practice of financial operations (FinOps). To do this, we first place AI in the context of FinOps and how operations need to change, explicitly using Creative AI to be faster, more accurate and more creative when assessing client needs. This is achieved by explaining how traditional approaches fall well short of the mark by highlighting their fundamental limitations and showcasing how AI helps to address those shortcomings. We also provide a detailed discussion of how AI is transforming finance operations when we focus on four discursive areas: (1) risk, (2) fraud detection, (3) predictive analytics and (4) trading algorithms. In all four areas, Creative AI supports many decisions that benefit the clients, improves customer service and guides financial institutions to allocate their resources more effectively. We elaborate throughout this text how AI, in particular by using methods such as natural language processing, generative adversarial networks (GANs) and other related techniques, can be understood as what we have termed ‘Explainable AI’ to address operational issues in the modern financial world creatively. As AI offers great disarming power, we also discuss the threats, limitations and specific pitfalls of AI adoption and use in financial contexts. This includes addressing clearly ethical and regulatory concerns, in addition to the technical ones.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

The rise of the metaverse and technology's disruptive visualisation spell a changing landscape for digital banking. As consumers increasingly conduct financial transactions in virtual worlds and immersive digital environments, it is imperative that advanced analysis tools are developed to measure and improve the virtual banking experience with this emerging metaverse. This chapter enriches the new notion of Metalytics and how it can be successfully employed as an approach to quantify and measure customer satisfaction and quality in digital banking services with today's changing scenarios. Now, it is a complex digital world of social engagement, entertainment and commerce. Traditional banking services, in that sense, have even started seeping into the metaverse and offering users a taste of virtual financial institutions (the meta bank), digital currencies as well as other financial products. The use of advanced analytics, tools and metrics designed for the unique features of a multiverse. This chapter goes on to discuss, through some of its key components and methodologies, how this could usher an epochal change in interstellar nodes for evaluating digital banking services. Financial institutions that embrace the peculiarities of the metaverse and leverage data-driven insights can offer comprehensive digital banking solutions. This is necessary for the digital frontier, and Metalytics propulsion fits this part perfectly, managing to keep their services up to date, safe, secure and, most importantly, customer-centred while navigating through the metaverse landscape.

Building on the relationship marketing and stimulus-organism-response (SOR) theory, the purpose of this paper is to study the impact of the integrated store service quality (ISSQ) on the omnichannel customer lifetime value (CLV). The mediating role of customer commitment (affective, normative and continuance) and relationship program receptiveness with the moderating role of customer relationship proneness were relied upon to better understand the omnichannel customer profitability metric (CLV).

The study is descriptive and relies upon the cross-sectional data collected using the self-administered structured questionnaires from 785 omnichannel shoppers. A purposive sampling technique was performed in the study. Structural equation modeling was performed using the SMART-PLS 4.0 software to analyze the data.

The results indicate that omnichannel customer commitment (affective, normative and continuance) differentially mediates the relationship between ISSQ and relationship program receptiveness, subsequently impacting the omnichannel CLV. The customer relationship proneness significantly and positively moderated the relationships between different dimensions of customer commitment and relationship program receptiveness.

The study relied upon the cross-sectional data from the Indian population aged above 18 years for testing the proposed model. Further studies could test the model across different populations to generalize the study results.

This study addresses the need to investigate the omnichannel retail store customer profitability and their relationship performance with the store. By testing the customer relationship management model in the omnichannel retail store context, this study is the first to show that ISSQ will impact the customer profitability and relationship performance metric (CLV) through omnichannel customer commitment and relationship program receptiveness. The moderating effect of customer relationship proneness on a few proposed hypotheses was also tested to give managerial recommendations.

Increased urbanization has resulted in physical environments, including servicescapes, dominated by functional designs, with nature’s presence becoming scarcer. While “biophilia” designs have received attention in fields like environmental psychology and architecture, studies on biophilia in servicescapes remain scant, fragmented and often contextual. The purpose of this study is to do a semi-systematic review of studies on biophilia in physical servicescape designs (interior and exterior), identify prevailing critical gaps and develop a comprehensive framework for theory advancements in biophilic servicescapes.

Drawing from a critical review of 56 servicescape studies over 33 years and incorporating theoretical frameworks from environmental psychology, this paper introduces a typology of biophilia in physical servicescapes that includes direct, indirect and human–nature relationships. Furthermore, this study develops a conceptual framework using the Stimulus-Organism-Response model to systematically synthesize biophilia’s overall applicability in servicescapes for consumers and service employees, incorporating moderating factors related to service, servicescape and user types.

This review investigates the emergence and definition of biophilia in servicescapes, examines the benefits of biophilic design for consumers and service employees and highlights key design strategies. In the absence of robust frameworks to assess biophilia’s impact on consumer and employee responses, this paper presents a comprehensive framework and offers guidelines for future research in retail environments and servicescapes.

Drawing from the synthesis of research on biophilia in servicescapes, this study introduces a framework that demonstrates how antecedent variables, including both direct and indirect biophilic elements, foster human–nature relationships that lead to affective, cognitive and behavioral responses. These effects are moderated by situational factors (e.g. service and servicescape types) and individual differences (e.g. personality, values and nature-relatedness). Ultimately, these responses influence approach or avoidance behaviors in consumers and employees, with a taxonomy detailing responses aligned with biophilia.

The metaverse marks the beginning in a new era of digital interaction and innovation, having a significant impact on a variety of established sectors, including banking. This chapter exposes readers to the principles of data wrangling, laying the groundwork for comprehending its significance. It also looks at the specific issues presented by Metaverse Banking data, which includes a wide range of data kinds. To ensure consistency and practical relevance, these data must be processed in real time, whether for individuals or organizations. This chapter then transitions to unleash the power of data (which forms the lifeblood of Metaverse Banking), followed by a detailed explanation of advanced data-wrangling techniques and integration with Artificial Intelligence and machine learning. It showcases case studies illustrating how effective data wrangling has helped drive Metaverse Banking platforms by utilising real-world use cases that show the best practices of metaverse entities for a more customer-centric experience. This chapter also explores future trends, expecting the evolution of technologies for data wrangling and their possible repercussions. It further delves into regulatory considerations the nascent industry faces. This chapter underscores the need for a planned strategy in data management and provides suggestions along with best practices to guide stakeholders towards placing metaverse banks based on data. As the metaverse continues to expand and change, wrangling data will remain how banks can win on this digital frontier by keeping themselves nimble, safe and consumer-friendly in an ever more virtual world.

Drawing from stakeholder (ST) and social exchange theory (SET), the purpose of this study is to examine the relationship between customer perceived ethicality (CPE), electronic word of mouth (eWOM), customer trust (CT) and customer loyalty (CL). Furthermore, this study aimed to understand the dual role of CPE and eWOM in obtaining CT and achieving CL.

Using a quantitative, cross-sectional research design, data were collected from face-to-face surveys, yielding 358 responses. The partial least square algorithm was used to test the proposed hypothesis.

The analysis revealed that CPE and eWOM positively affect CT and CL, and CT has a mediating effect on the association between CPE–CL and eWOM–CL. CT was also found to positively affect CL.

Hotel managers can prioritize ethical practices and leverage the power of eWOM to build trust and achieve loyalty. This integrated approach not only enhances customer satisfaction and retention but also creates a competitive advantage.

The novelty of this study lies in the investigation of the dual role played by CPE and eWOM as antecedents of CT and CL within the hotel industry. Finally, this study explains the drivers of CT and CL, thereby making a novel contribution to the literature.

This chapter looks at a number of diverse elements that led to the rise of the digital banking industry. In this age of rapid digitisation, today's bank transactions and activities are mostly done on mobile phones or other smart devices instead of going back and forth between a traditional branch lobby. With more and more customers seeking banking services accessible around the clock from the palm of their hand, based on the numerous experiential data digital platforms have accumulated for many years in the field and other places, traditional financial institutions will have no way but to break open their thinking about how to deliver those services to think. Governments and their regulators are beginning to see the potential risks posed by the new digital banking technology and want consumer protection, competition put under even stricter conditions for players as well and system stability all guaranteed. Above all, it is evidence of something people cannot ignore: the sharp upward trend of cybersecurity risk in recent years. The advent of digitalisation eliminated any excuse for storing sensitive financial data without the most modern cyber defences. Then, at a further level, financial tech start-ups come up like crocuses in spring while Bigtech companies all over the globe are jumping into banking. Both cooperation opportunities and competitive challenges await traditional banks. However, bank customers' changing demographics (millennials and GenZ) are the most vivid examples as they age, together with global universal financial inclusion trends, bringing about social and economic challenges.