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Tourism is one of the key economic activities in the world. Sri Lanka is a country that deeply relies on income from tourism. Hence, identifying new horizons to develop the tourism sector is vital. Since wellness tourism is a trending concept in world tourism, this chapter aimed at Ayurveda-based wellness tourism and ways to uplift the existing outfit through lessons from other Asian counterparts. The researcher conducted 19 in-depth interviews with industry professionals to collect qualitative data inputs to understand the real scenario in Ayurveda wellness service providers. Results showed that Sri Lankan Ayurveda wellness tourism (AWT) is impacted by the actions of three main stakeholders, tourism-related authorities, Ayurveda-related authorities, and service providers. Tourism and Ayurveda-related authorities identified the importance of AWT to some extent, but actions are insufficient or rather bottleneck the sustainable development in the industry. Further, the bureaucracy of authorities prevents the expansion of AWT through medium- or small-scale service providers. The results suggest that Sri Lankan AWT requires long-, mid-, and short-term-based corrective actions.

Science mapping is an essential application of visualization technology widely used in safety, construction management and environmental science. The purpose of this study was to explore thermal comfort in residential buildings (TCinRB) research in India, identify research trends using a science mapping approach and provide a perspective for recommending future research in TCinRB.

This study used the VOSviewer tool to conduct a systematic analysis of the development trend in TCinRB studies in India based on Scopus Index articles published between 2001 and 2020. The annual numbers of articles, geographical locations of studies, major research organizations and authors, and the sources of journals on TCinRB were presented based on the analysis. Then, using co-authorship analysis, the collaborations among the major research groups were reported. Furthermore, research trends on TCinRB studies were visually explored using keyword co-occurrence analysis. The emerging research topics in the TCinRB research community were discovered by analyzing the authors’ keywords.

The findings revealed that studies had been discovered to pay more attention to north-east India, vernacular architecture, Hyderabad apartments and temperature performance in the past two decades. Thermal adaptation, composite climate, evaporative cooling and clothing insulation are emerging research areas in the TCinRB domain. The findings summarized mainstream research areas based on Indian climatic zones, addressed current TCinRB research gaps and suggested future research directions.

This review is particularly significant because it could help researchers understand the body of knowledge in TCinRB and opens the way for future research to fill an important research gap.

Those who believe they excel at architecture or engineering education are more likely to succeed based on self-efficacy principles. To investigate educator self-efficacy and success in the Online Learning Environment (OLE), a set of relationships are observed which describe correlations between experience and potential.

An online survey instrument was distributed the fall after COVID-19 university closures. Respondents were asked to reflect on their level of experience teaching and their ability to teach online. All analyzed data were subjected to descriptive and inferential statistics using the SPSS 22.0 statistical software package. The compatibility of the variables with normal distribution was tested using the Kolmogorov–Smirnov and Shapiro–Wilk methods. Variables comparisons were performed using non-parametric tests (Kruskal Wallis test, Mann Whitney U test). The relationships between quantitative variables were examined using the Spearman Rank Correlation and comparisons formed from the qualitative variables were tested using the Pearson Chi-Square and Fisher Exact methods.

Educator self-efficacy was determined throughout the COVID-19 transition. Possessing online teaching experience is related to the perceptions that architectural education can be delivered entirely online. A relationship was found for educators who previously taught using OLE and who had experience with delivering and developing OLE.

It is incumbent on educators and administrators to continue to learn how to best accommodate student learning. The strong relationship for R1: Total teaching experience (IV) and perceptions of whether AEC education can be delivered completely online, points to having educators with a depth of experience and being open to change. The strong relationship shown for R2: Have you ever taught using an online method before January 2020 and Experience in developing online materials demonstrates that a variety of experience will also support educators in a time of change. These relationships illustrate how educator efficacy can provide support for educators during times of crisis.

U.S. Architectural and Architectural Engineering educator pandemic OLE self-efficacy has not been previously been a focus of research efforts. This research adds to the body of knowledge by demonstrating how relationships between teaching experience and OLE can encourage educator self-efficacy during a crisis. Statistical analyses found a strong relationship between total teaching experience and perceptions that AEC education can be delivered completely online. A strong relationship was found between online teaching experience and positive experiences in developing online materials.

Industries throughout the world have gone through four phases of the industrial revolution. Today, the authors are encountering the Fourth Industrial Revolution (Industry 4.0 or 4IR), which is characterized by the application of information and communication technologies in the industry. This study aims to evaluate the growth of literature in the context of libraries and the Fourth Industrial Revolution and then identify the active research actors, countries, affiliations and sources and the status of collaboration across various geographical locations.

A systematic literature review was conducted. The data were extracted from the Scopus database incorporating scientometric and altmetric indicators. The search terms were TITLE-ABS-KEY “Industry 4.0” OR “4IR” OR “Fourth Industrial Revolution” AND “Library 4.0” OR “librarian 4.0” OR “libraries” OR “library” OR “library service” without any limitation. The results identified 218 documents out of which 67 core documents are related to libraries. Analysis was performed using MS Excel and VOSviewer.

South Africa, Nigeria and South Korea were the three most productive countries in the field. Library Hi Tech News is the most productive journal. Collaboration among African countries like South Africa, Nigeria and Zimbabwe seems to be good and they constituted the largest share of documents. The research on the Fourth Industrial Revolution in the context of libraries still lacks social media attention.

This study is the first systematic review in this area using a combination of scientometrics and altmetrics. The results will serve as a valuable reference for future researchers and policymakers.

The paper aims to analyze the potential of artificial intelligence (AI) in enhancing marketing capabilities for Indian fast-moving consumer goods (FMCG) firms, with appropriate emphasis on dynamic capabilities (DC). By integrating AI into their operations, companies can enhance diverse marketing practices, leading to improved efficiency and effectiveness. In addition, the study offers valuable insights and guidance for effectively implementing AI in marketing strategies.

In-depth interviews of 26 seasoned professionals from various positions in the Indian FMCG sector were conducted for the study. A systematic approach of coding that includes open, axial and selective coding was made to recognize the themes that represent AI-enabled marketing practices. This approach ensured that the themes were precisely identified and comprehensively analyzed. Interviews followed by a rigorous coding process provide valuable insights into the practical implementation of AI-enabled marketing practices for the Indian FMCG industry.

The study’s results underscore the importance of AI in enhancing marketing practices for FMCG firms, particularly in four critical areas: personalization, consumer engagement, marketing automation and strategic goals. By adopting AI in these areas, Indian FMCG firms can significantly enhance their marketing capabilities, increasing efficiency, effectiveness and gaining a competitive advantage.

The study uses qualitative data analysis to explain how DC through AI technologies can enhance digital marketing practices for Indian FMCG organizations. Furthermore, the study provides valuable insights into how AI technologies can improve marketing capabilities and emphasizes the importance of DC. The study also proposes a framework for AI-enabled Marketing 4.0 that can guide FMCG firms seeking to leverage AI technologies to enhance their marketing practices.

This study outlines the potential for enhancing marketing practices by adapting AI in the Marketing 4.0 environment for Indian FMCG companies, highlighting the importance of DC in achieving this goal.

This paper examines the early pandemic experience in a large metropolitan area to differentiate the roles of the lifestyle and built environment factors associated with differing case rates across neighborhoods.

This paper develops a simple empirical methodology for sorting out the separate effects of lifestyle and the built environment factors along with their interactive effects when individuals’ behaviors not only reflect their observable characteristics but also are influenced by the physical environments in which they live and work, indirect connections implied by the early insights of Jacobs (1961) and more recently Hawley and Turnbull (2019).

The results demonstrate that lifestyle factors tied to employment show the strongest association with COVID-19 cases. Other lifestyle choices, built environment features, and demographic attributes such as household size, principal cities, highway connectivity, and population density also affect COVID-19 transmission at the onset of the disease outbreak. The analysis reveals a surprising spatial pattern; employment-related lifestyle factors on case rates in outlying neighborhoods are stronger than in neighborhoods within primary cities after accounting for various built environment factors.

This research addresses important questions and the perplexing outcomes related to lifestyle and the built environment’s multi-faceted role in spreading COVID-19. In addition, this study represents a pioneering effort in disentangling the pure lifestyle effect on virus transmission after eliminating potentially confounding impacts of built environment factors on household behavior that in turn influence virus transmission.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

This study aims to identify and analyse critical success factors (CSFs) for the successful implementation of distributed ledger technology (DLT) in the Nigerian construction industry.

This study adopts a quantitative approach that uses snowball sampling techniques to identify professionals participating in the study. A structured questionnaire was used to collect data virtually, using Google Forms, resulting in 217 valid responses. The collected data were subjected to rigorous statistical analysis (descriptive and inferential) to identify and prioritise the CSFs and evaluate the participants’ awareness and knowledge of DLT.

This study revealed 24 key CSFs that are pivotal in ensuring the effective implementation and adoption of DLT in the Nigerian construction industry. Furthermore, the research highlights a moderate level of awareness, but significantly low knowledge of DLT among industry professionals.

The findings of this study will benefit professionals, practitioners and policymakers in the Nigerian construction industry by providing insights into the potential of DLT to improve construction operations.

This study contributes to the literature by identifying the CSFs for implementing DLT in the construction industry and shedding light on the current level of awareness and knowledge within the Nigerian context. The findings offer valuable insights for policymakers, industry practitioners and researchers, providing a solid foundation for informed decision-making and developing effective strategies to enhance DLT adoption in the construction sector.

The purpose is to investigate how to create a new premium new energy vehicle brand.

This study employed a two-stage design. Firstly, a single-case study of NIO was undertaken to execute a thematic analysis, from which propositions were proposed and a theoretical model was constructed. Subsequently, quantitative data were collected through the questionnaire method to empirically test the model developed in the first stage.

NIO creates great user experience through four aspects: product, service, digital touchpoints and lifestyle. Functional experience is shaped by product and digital touchpoints, while emotional experience is affected by service and lifestyle. NIO wins extremely high user satisfaction through great user experience. User satisfaction is affected by both functional and emotional experience. Taking extremely satisfied users as the core, NIO leverages word-of-mouth recommendations to increase brand awareness and build premium brand image, so as to achieve high performance in the long term.

This study contributes to the literature by proposing and testing a theoretical model of creating a new premium new energy vehicle brand. It highlights the significance of emotional factors in the process of creating a new premium brand. It proposes employing the “ripple model” to translate user satisfaction into financial performance. It provides a three-step guide to creating a new premium brand for managers.

This research explores the relationships between financial awareness and investor satisfaction about retirement planning, particularly in the Indian context. We developed a conceptual model involving double moderation effects of post-retirement objectives and external factors influencing investor satisfaction.

A carefully crafted survey instrument was designed to collect data from the 480 employees working in six administrative departments in the northern part of India. After checking the measurement properties of the survey instrument through the Lisrel package of structural equation modeling, the hypotheses were tested using Hayes PROCESS macros.

The results indicate that financial awareness is positively related to (1) investor satisfaction and (2) investment purpose; investment purpose is positively related to investor satisfaction; post-retirement objectives moderate the relationship between financial awareness and investment purpose; external factors moderate the moderated relationship between financial awareness and post-retirement objectives (first moderator) and investor satisfaction mediated through investment purpose.

The outcomes of this study have important implications for the employees embarking on making decisions concerning their retirement. The conceptual model provides a simple framework explaining how financial awareness and investor satisfaction are affected by the post-retirement objectives of individuals. This study highlights the importance of considering the effect of external factors influencing financial decisions.

The three-way interactions in this exploratory research contribute to the growing literature on behavioral finance, particularly concerning retirement planning involving pension.