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Based on the sociotechnical systems theory, we examined the human–technology interactions in the context of future works conditioned by machine learning (ML) and artificial intelligence (AI). Skills needed to support career sustainability and the future of the workforce, particularly for the middle-skilled workforce in the contemporary United States America (USA) context, were also studied.

We conducted a scenario analysis to demonstrate the potential roles that human resource professionals may perform to fill the skill gaps given their expertise in the shaping and skilling processes.

Assessing the success of the integration of AI and ML into the middle-skilled workforce requires a multi-faceted approach that considers performance metrics, cost-effectiveness, job satisfaction, environmental impact and innovation. Employees with AI skills can be more competitive in the workforce and forward to high-skilled positions.

Empirical research and related studies focusing on evaluations of reskilling and upskilling processes and outcomes would support career sustainability and the future development of middle-skilled workers.

Through a proactive strategic career development plan with AI integration, middle-skilled workers may enhance their career sustainability and be prepared for future higher-skilled work.

The economic downturn caused by technology-induced unemployment may be addressed by unleashing middle-skilled workforce potentials for future work created by AI and robotics and sustaining economic competitiveness.

This article offers important implications for human resource development theory-minded researchers and scholarly practitioners.

Notwithstanding their potential benefits especially for individuals with low health literacy, users are still somewhat skeptical about the reliability of healthcare chatbots. The present study aims to address this challenge by investigating strategies to enhance users’ cognitive and emotional trust in healthcare chatbots. Particularly, this study aims to understand the effects of chatbot design cues in increasing trust and future chatbot use intention for low health literacy users.

We conducted two experimental studies with a final sample of 327 (Study 1) and 241 (Study 2). Three different chatbots were developed (Chatbot design: Bot vs Male-doctor vs Female-doctor). Participants were asked to have a medical consultation with the chatbot. Participants self-reported their health literacy scores. The PROCESS model 7 was used to analyze the hypotheses.

The results showed that the female-doctor cues elicited greater cognitive and emotional trust, whereas the male-doctor cues only led to greater cognitive trust (vs bot-like cues). Importantly, this study found that users’ health literacy is a significant moderating factor in shaping cognitive and emotional trust. The results indicated that both the female and male-doctor cues’ positive effects on cognitive trust were significant for those with lower levels of health literacy. Furthermore, the positive effect of the female-doctor cues on emotional trust was also significant only for those whose health literacy level was low. The increased cognitive and emotional trust led to greater future intention to use the chatbot, confirming significant moderated mediation effects.

Despite the strong economic and educational benefits of healthcare chatbots for low health literacy users, studies examining how healthcare chatbot design cues affect low health literate users surprisingly remained scarce. The results of this study suggest that healthcare chatbots can be a promising technological intervention to narrow the health literacy gap when aligned with appropriate design cues.

The Fourth Industrial Revolution is a prominent area of interest in the field of manufacturing that is bringing about revolutionary changes. In this study, the authors sought to determine whether professionals in academia or industry could have predicted the composition of advanced technologies associated with Industry 4.0 before Germany's Industrie 4.0 policy announcement. The purpose of this paper is to use the process for identifying technologies that can be included in industrial policy to improve national competitiveness in manufacturing.

Relevant documented research from 2000 to 2012 was identified and captured using a systematic literature review. The significant technological advancements during this period were analyzed to determine how technological innovations may have affected predictions about the future of manufacturing. The identified predictions were analyzed using an open-source natural language processing code that clustered relevant topics in the predictions that indicated common themes. The results were then compared to the ideas within “Industry 5.0”.

The results of this study showed that an aggregate analysis of manufacturing predictions would have preemptively revealed the Fourth Industrial Revolution and could have been used to inform industrial policy that could accelerate technology adoption. Also, contrary to popular belief, the popular Industry 5.0 is a sematic exemplification of a concept already embedded within the origins of the Fourth Industrial Revolution.

By examining the provenance of the Fourth Industrial Revolution, lessons are learned that bring light to Industry 4.0 and the measures that can be taken to enable the advancements that it brings. The results of this study show that is would be wise for government policymakers to enact programs that monitor the manufacturing predictions coming out of academia and to analyze them aggregately using natural language processing as a means to identify the next evolutions and revolutions and to mobilize policymakers to enhance outcomes of enacting policy.

Despite high hopes for the realization of Industry 4.0, there has been little discussion about the technological innovations and events that occurred to enable it. To the best of the authors’ knowledge, this is the first study that has determined that an aggregate analysis of manufacturing predictions would have preemptively revealed the Fourth Industrial Revolution. The success of the methodology used in this study has theoretical implications in support of natural language processing (NLP) being used to inform national policy.

This chapter examines three common fintech use cases transforming the financial industry. First, the chapter introduces fintech's role in enhancing financial services and promoting financial inclusion, especially through digital platforms. Second, it investigates various fintech applications that support financial institution management by harnessing the power of artificial intelligence (AI) and machine learning (ML). Finally, the chapter explores fintech use cases related to the regulatory environment, including regulatory technology (regtech), blockchain technology, and cryptocurrencies. The insights presented in this chapter cater to researchers and practitioners keen on better understanding fintech's diverse applications in the ever-evolving financial industry landscape.

One of the pivotal questions facing all firms is “Who owns the customer?” Despite the longstanding acknowledgment that customer ownership is critical to a firm’s success, to date, little research attention has been afforded to conceptualizing and measuring customer ownership. This study aims to address this research gap by exploring, measuring and validating a customer ownership scale through the lens of the business-to-business salesperson.

The classical multi-item scale development involving a multistep process was used in developing and validating this scale measuring customer ownership. Using a grounded theory approach, the customer ownership scale is developed and justified as distinctive from customer loyalty.

The two-factor customer ownership scale reflects the underlying factors of the salesperson–customer bond and provides a pathway to empirically assess mechanisms for addressing customer migration. The findings suggest an opportunity for greater precision in both meaning and measurement for both academics and practitioners.

The question “Who owns the customer?” has been a venerable enigma in sales organizations, and it remains an underdeveloped construct in sales and marketing research. This research empirically explores the construct of customer ownership in a systematic manner that is conspicuously absent from extant studies.

This chapter explores the role of artificial intelligence (AI), particularly its subfield of machine learning (ML) methods, as a core technology of the fintech revolution in the financial services industry. It simplifies some of the complex concepts related to AI by introducing the main ML paradigms and related techno-methodic aspects. This chapter uses real-world examples to illustrate how next-generation AI powered by ML is transforming the financial services industry. Next, in illustrating the risks associated with AI adoption, this chapter discusses the need for regulation to address the essential facets of AI governance, including transparency, accountability, ethics, and responsible use. Lastly, it looks at emerging regulatory approaches across leading global jurisdictions. The primary goal is to give readers an initial understanding of AI's profound impact on the financial sector.

In recent years, new and technologically innovative financial products and services, generally subsumed under the fintech umbrella, have permeated all areas of capital markets at an exponential rate. Primarily driven by developments in Web3 and advancements in artificial intelligence (AI), fintech solutions offer valuable benefits to all existing markets and participants and are the basis for introducing wholly new segments to classic capital market ecosystems. However, this increasing fintech adaptation does not come without challenges. Due to the technologies' nascent nature and often unregulated status, many products are susceptible to manipulation and fraud. The result can be sizable investor losses and excessive regulatory and public scrutiny. This chapter highlights the most essential and prominent fintech solutions used in capital markets today, along with their features, value additiveness, and degree of adaptation.

This study, a conceptual paper, analyses the growth of curation in tourism and hospitality and the curator role in selecting and framing products and experiences. It considers the growth of expert, algorithmic, social and co-creative curation modes and their effects.

Narrative and integrative reviews of literature on curation and tourism and hospitality are used to develop a typology of curation and identify different curation modes.

Curational techniques are increasingly used to organise experience supply and distribution in mainstream fields, including media, retailing and fashion. In tourism and hospitality, curated tourism, curated hospitality brands and food offerings and place curation by destination marketing organisations are growing. Curation is undertaken by experts, algorithms and social groups and involves many of destination-related actors, producing a trend towards “hybrid curation” of places.

Research is needed on different forms of curation, their differential effects and the power roles of different curational modes.

Curation is a widespread intermediary function in tourism and hospitality, supporting better consumer choice. New curators influence experience supply and the distribution of consumer attention, shaping markets and co-creative activities. Increased curatorial activity should stimulate aesthetic and stylistic innovation and provide the basis for storytelling and narrative in tourism and hospitality.

This is the first study of curational strategies in tourism and hospitality, providing a definition and typology of curation, and linking micro and macro levels of analysis. It suggests the growth of choice-based logic alongside service-dominant logic in tourism and hospitality.

This chapter focuses on how alternative data can change the nature of financial forecasting through improved short-term forecasting techniques and decreased informativeness from longer term sources. Increased use of social media data leads the charge in transforming this transition. Alternative data are data not from standard financial statements or formal reports. This chapter looks at alternative data from new sources (e.g., social media, Internet of Things [IoT], and digital footprints) and alternative data from new collection methods like web scraping for textual analysis, image analysis, and vocal analysis). It first discusses standard data in financial forecasting. Next, this chapter examines alternative data in financial forecasting. Finally, it discusses alternative data used in studying finance more broadly.

As the hospitality business adapts to the digital age, the importance of using Virtual Design (VD) to create memorable visitor experiences has grown. This study aims to investigate the potential of VD in the hospitality sector, particularly regarding the improvement of guests' overall experiences.

The research study examines how Virtual Reality (VR), Augmented Reality (AR) and other digital technologies are currently used in VD firms. It explores how these simulated architectural features are implemented in other facets of the hospitality experience, like the decor of guest rooms and restaurants and staff responsiveness to guests' needs.

The study also examines VD's potential outcomes and advantages for the hotel industry and its clients. It investigates the potential of VD to help hospitality businesses offer more customised services, boost customer loyalty and gain an edge in the market. VD implementation in the hospitality business may face several obstacles, some of which are discussed in this study.

Methods include both qualitative and quantitative techniques, such as interviews with experts, guest surveys and an examination of the use of VD in specific hotels. This study intends to help the hotel industry benefit from a VD by analysing real-world case studies and gathering empirical data that can be used to draw conclusions and formulate recommendations.