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This chapter starts by reviewing four broad regulatory approaches that exemplified state-of-the-art in major jurisdictions: market-driven approach (the United States), state-driven approach (China), rights-driven approach (the European Union) and innovation-driven approach (the United Kingdom, Singapore, Hong Kong SAR). This chapter then examines possible regulatory updates with regards to walled gardens and shadow banking, the first two of the challenges first identified in Chapter 3. The next two chapters will then examine possible regulatory updates to address the remaining challenges identified.

The purpose of this paper is to investigate the organizational readiness perspective of adopting artificial intelligence and big data analytics in the textile and garment industry in Bangladesh along with identifying the associated factors.

The research uses a qualitative method using semi-structured interviews with representatives of business organizations and stakeholders of Bangladesh’s textile and garment industry.

The research reveals that the textile and garment industry in Bangladesh currently has low organizational readiness to adopt artificial intelligence and big data analytics. This is due to moderate knowledge- and leadership-readiness along with low human-, finance- and engagement-readiness of most of the business organizations. The readiness aspects interplay with each other and need to be improved holistically.

Considering the significant global and national importance of Bangladesh’s textile and garment industry, gaining insights into the industry’s current state of readiness for adopting artificial intelligence and big data analytics would offer valuable assistance to both national and global economies and may enhance economic outcomes.

Since no exploratory study was conducted to understand the organizational readiness aspects of adopting artificial intelligence and big data analytics of the globally significant textile and garment industry in Bangladesh, the paper analyzes five key aspects of such readiness and offers a basis for conducting similar studies in other emerging economies.

Heritage tourism is an increasingly popular form of tourism that allows individuals to connect with the past and immerse themselves in cultural and historical narratives. Hence, the purpose of this study is to explore the intricate relationships among vicarious nostalgia (VNOS), memorable tourism experiences (MTEXs) and their collective influence on tourists’ behavioral intentions (BINTs). Additionally, this study examines the moderating effect of social return (SN) in the context of heritage tourism.

Data were gathered using a self-administered questionnaire from 259 tourists visiting heritage sites in Jaipur. The proposed model was tested using structural equation modeling.

The results confirmed that VNOS had a significant positive impact on BINT in the context of heritage tourism. The causal relationship between VNOS and BINT was fully mediated by MTEX. The results further verified that the presence of SN strengthens the association between MTEXs and BINT.

This research will guide the firms associated with heritage tourism to target specific cohorts interested in heritage tourism. Policymakers may find it easier to create unique offerings and packages that appeal to visitors interested in historical sites and produce memorable travel experiences. One key implication is to create “social media friendly spaces” at different locations of the sites. To increase tourism, managers may use the findings from this research to create plans for the ethical promotion and protection of cultural and natural heritage sites.

Overall, this research advances the understanding of the role of VNOS in heritage tourism by elucidating its cognitive and emotional aspects and their subsequent influence on the memorability of tourist experiences and BINT s. Additionally, by considering the moderating effect of SN, this study provides a comprehensive view of how these factors collectively shape tourists’ decisions and actions in the context of heritage destinations. This research has been conducted in the heritage city of Jaipur (North-Western India), which, surprisingly – despite its popularity as a heritage tourism site – has not been sufficiently explored in the scholarly research.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

The purpose of this paper is to illustrate the new technical demands and reliability challenges to printed circuit board (PCB) designs, materials and processes when the transmission frequency increases from Sub-6 GHz in previous generations to millimeter (mm) wave in fifth-generation (5G) communication technology.

The approach involves theoretical analysis and actual case study by various characterization techniques, such as a stereo microscope, metallographic microscope, scanning electron microscope, energy dispersive spectroscopy, focused ion beam, high-frequency structure simulator, stripline resonator and mechanical test.

To meet PCB signal integrity demands in mm-wave frequency bands, the improving proposals on copper profile, resin system, reinforcement fabric, filler, electromagnetic interference-reducing design, transmission line as well as via layout, surface treatment, drilling, desmear, laminating and electroplating were discussed. And the failure causes and effects of typical reliability issues, including complex permittivity fluctuation at different frequencies or environments, weakening of peel strength, conductive anodic filament, crack on microvias, the effect of solder joint void on signal transmission performance and soldering anomalies at ball grid array location on high-speed PCBs, were demonstrated.

The PCB reliability problem is the leading factor to cause failures of PCB assemblies concluded from statistical results on the failure cases sent to our laboratory. The PCB reliability level is very essential to guarantee the reliability of the entire equipment. In this paper, the summarized technical demands and reliability issues that are rarely reported in existing articles were discussed systematically with new perspectives, which will be very critical to identify potential reliability risks for PCB in 5G mm-wave applications and implement targeted improvements.

Organizational research on diversity, equity, and inclusion (DEI) is at times siloed; the experience of one minoritized or underrepresented group is treated as completely separate and different from the experience of another group and thus research separately. For example, there are terms that are studied only in the context of one group, and a different term is used to study a very similar (or identical) concept among a different group. Indeed, there are many unique experiences that specific minority groups encounter at work. Because of this end, minority groups should not be fully categorized together, and their individual should not be erased. However, there are shared experiences that many or all minorities experience at work, whether they are a gender minority, racial minority, or a member of any other minoritized group. Recognizing these shared experiences can help scholars develop a deeper understanding of what it's like to be minoritized or underrepresented at work, and therefore help to better serve these communities. To this end, our chapter highlights three such shared but unique minority experiences: three experiences that are common across all minority groups but operationalize slightly differently in different populations. The first experience we discuss is discrimination, as all minorities typically experience some form of negative differential treatment at work. The second experience we discuss is identity management, as many minorities need to actively think about how they present their minority identity to others (regardless of if their identity is “concealable” or not). Finally, we discuss strength through adversity, as many minorities argue that their minority identity is a source of strength and an area that benefits them at work. We conclude the chapter with a call toward intraminority solidarity, suggesting that recognizing shared experiences and working together can help build better workplaces for all minority employees.

Body weight has a long history of functioning as a symbol of one’s beauty, social status, morality, discipline, and health. It has also been a standard inflicted much more intensely on women than men. While US culture has long idealized thinness for women, even at risky extremes, there is growing evidence that weight standards are broadening. Larger bodies are becoming more visible and accepted, while desire for and approval of a thin ideal has diminished. However, the continued widespread prevalence of anti-fat attitudes and stigma leaves uncertainty about just how much weight standards are changing. This study used an online survey (n = 320) to directly compare evaluations of thin, fat, and average size women through measures of negative stereotypes, prejudicial attitudes, and perceptions about quality of life. Results indicated that, as hypothesized, thin women were perceived less favorably than average weight women. However, fat women were perceived less favorably than both average and thin women. Men were harsher than women in their evaluations of only fat women. Additionally, participants being underweight or overweight did not produce an ingroup bias in their evaluations of underweight and overweight targets, respectively. That is, participants did not rate their own group more favorably, with the exception of overweight participants having lower prejudice toward overweight targets. These findings add to the emerging evidence that women’s weight standards are in transition, marked by an increasingly negative perception of thin women, though not necessarily growing positivity toward fat women. This evidence further points toward the need for more extensive research on attitudes of people across the entire weight spectrum.

This study aims to examine the factors influencing trust toward mobile health applications (mhealth apps) among young consumers. By building on the socio-technical systems framework and self-congruence theory, the authors develop and examine a conceptual model of the trusting intentions of young consumers toward mhealth apps.

The data for the study was collected from a sample of 285 young consumers via an online survey by using a purposive sampling method. All the respondents were existing users of health apps and resided in the urban areas of the country. The sample comprised 53% male respondents and 47% female respondents. Subsequently, the data was analyzed using the structural equation modeling technique.

The findings revealed that psychological factors like self-congruence and technical factors such as flow experience and perceived playfulness aid in building trust among young mhealth app users, which in turn influences the purchase intention and user loyalty toward the application. Social factors like perceived critical mass and social influence did not significantly impact trust.

The data was collected from Generation Z, recognized as the most influential consumer demographic worldwide, primarily involved in mhealth app usage. Nonetheless, it is essential to understand that these findings might only partially apply to a specific user group. In addition, the study did not focus on any specific mhealth applications. Hence, it is prudent to exercise caution when generalizing these results to the broader population.

This study advances the comprehension of socio-technical elements in establishing trust, subsequently impacting purchase intentions and loyalty among young users in the context of mhealth apps. In addition, it expands the socio-technical framework by incorporating a psychological factor that could influence trust and purchase intentions toward mhealth apps. Furthermore, the post hoc findings reinforce this comprehension by identifying the moderating effects of operating system type and gender. These insights could provide valuable guidance for developing tailored marketing strategies targeting the young consumer base.

This paper examines how firms respond to local government’s environment initiatives through textual analysis of government work reports (GWRs). This study aims to provide insights into how firms strategically respond to government’s environmental initiatives through their disclosure and investment practices.

This study uses a textual analysis of GWRs from China’s provinces. The frequency and change rate of environmental keywords in these reports are used as a measure of the government’s environmental initiatives.

This study finds that environmental disclosure scores in environmental, social and governance (ESG) reports increase with the frequency or change rate of environmental keywords in provincial GWRs. This effect is more pronounced for non-state-owned enterprises, firms in highly marketized provinces or those listed in a single capital market. However, there is no significant relationship between firms’ environmental investments and government initiatives, except for cross-listed firms in provinces with consistently high frequency of environmental keywords in their GWRs.

The findings indicate that government environmental initiatives can shape firms’ disclosure behaviors, yet have limited influence on investment decisions, suggesting that environmental disclosure could potentially be opportunistic. This underscores the need for more effective strategies to stimulate firms’ environmental investments.

This study provides valuable insights into the differential impacts of government environmental initiatives on firms’ disclosure and investment behaviors, contributing to the understanding of corporate environmental responsibility in the context of government initiatives.