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The versatility of customer relationship management (CRM) systems has kept these technologies popular over the years. These solutions have been integrated into organizations of all sizes, from large corporations to small- and medium-sized enterprises. Similarly, CRM systems have also found applications in all types of industries and business sectors. All this has been the driving force behind the proliferation of CRM solutions around the world. In this chapter, the author not only reflects on the impact and democratization of CRM systems on business management and marketing strategies but also explores how these technologies can determine the company's income. In particular, the author presents an experiment that analyzes the extent to which the volume of annual investment in CRM solutions can be used to predict annual net income in a sample of companies. Using time series analysis and applying the autoregressive integrated moving average modeling technique, the researcher examines a sample of 10 companies from different industries, and countries, over a 20-year period. The results show the efficiency of the predictive models developed in nine of the 10 companies analyzed. The findings of this study allow us to conclude that there seems to be an association between the investments made in CRM solutions and the income of the companies that invest in these technologies.

The interaction of systems through a designated control channel has improved communication, efficiency, management, storage, processing, etc. across several industries. The construction industry is an industry that thrives on a well-planned workflow rhythm; a change in the environmental dynamism will either have a positive or negative impact on the output of the project planned for execution. More so, raising the need for effective collaboration through workflow and project planning, grid application in construction facilitates the relationship between the project reality and the end users, all with the aim of improving resources and value management. However, decentralisation of close-domain control can cause uncertainty and incompleteness of data. And this can be a big factor, especially when a complex project is being executed.

The purpose of this study is to offer a checklist that can be used for both creating and evaluating digital collections, which are also sometimes referred to as data sets as part of the collections as data movement, suitable for computational use.

The checklist was built by synthesising and analysing the results of relevant research literature, articles and studies and the issues and needs obtained in an observational study. The checklist was tested and applied both as a tool for assessing a selection of digital collections made available by galleries, libraries, archives and museums (GLAM) institutions as proof of concept and as a supporting tool for creating collections as data.

Over the past few years, there has been a growing interest in making available digital collections published by GLAM organisations for computational use. Based on previous work, the authors defined a methodology to build a checklist for the publication of Collections as data. The authors’ evaluation showed several examples of applications that can be useful to encourage other institutions to publish their digital collections for computational use.

While some work on making available digital collections suitable for computational use exists, giving particular attention to data quality, planning and experimentation, to the best of the authors’ knowledge, none of the work to date provides an easy-to-follow and robust checklist to publish collection data sets in GLAM institutions. This checklist intends to encourage small- and medium-sized institutions to adopt the collection as data principles in daily workflows following best practices and guidelines.

This study aims to investigate the extent and pattern of the influence of one of the most important decision-making tools in the context of social commerce. This study demonstrates how much customer testimonials (including verified purchases and ordinary users) can influence the sales rank of experience and search goods.

The data were collected by text mining and performing a content analysis on the XML documents of Web pages and processing them. For search goods, 22,311 opinions were recorded regarding 95 mobile phones. Additionally, for experience goods, 67,817 opinions were recorded regarding 162 books in the Amazon online store. The data were analyzed by functional regression method in longitudinal data analysis.

In terms of importance, the opinions and recommendations of verified purchases had a 60% greater impact on the sales rank of experience goods than the opinions and recommendations of ordinary users. In search goods, the opinions of ordinary users had a greater impact than the opinions of verified purchases. The historical effect of the opinions of ordinary users at the end of the review period on sales rank was evident, while the historical effect of the verified purchase viewpoints during the review period had a nonlinear curve. The results showed that it was necessary to increase the volume of comments to increase their reliability in experience goods.

Measuring the effect of customer testimonials helps the managers of retail websites design algorithms and online suggestion systems, thereby improving the sales of their products by providing information desired by customers.

Individuals can be a source of information and influence the buying decision process of others by sharing their experiences. This issue helps reduce the purchase risk and explains the importance of interaction and sharing the customer’s experience.

Analyzing the impact of customer testimonials by separating verified purchases and ordinary users is one of the advantages of this study. The quantitative estimation of the impact of recommendations and the provision of a model of their historical effect is one of the approaches not addressed in similar studies.

This paper aims to examine the role of Open Artificial Intelligence application (ChatGPT) to identify challenges faced by developers working on React.js libraries.

Scopus and Google Scholar databases were searched for the literature. In addition, ChatGPT application was accessed to extract contents regarding its potential role in identifying challenges and their solutions for developers.

This paper found that ChatGPT has potential in identifying challenges, faced by learners and developers. ChatGPT enables developers to navigate the projects’ libraries and overcome steep learning curve issue. ChatGPT excels in helping the developers by presenting a range of valuable strengths. These include offering tutorials and document support, providing comprehensive programming challenges solutions, assisting with the configuration and adding in debugging process of React.js application.

To the best of the authors’ knowledge, this is one of the first articles presenting the potential role of ChatGPT in identifying and offering solutions to the challenges of learners and programmers about React.js.

This study aimed to examine the mental health information entities and associations between the biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and scientific literature.

Reddit posts and full-text papers from PubMed Central (PMC) were collected. The text analysis was used to create a psychological dictionary. The text mining tools were applied to extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly, social network analysis and visualization were employed to view the associations.

Mental health information on the drug side effects entity was detected frequently in both datasets. In the affective category, the most frequent entities were “depressed” and “severe” in the social media and PMC data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and economy were found repeatedly in the Reddit data. The relationships between the biomedical and psychological processes, “afraid” and “Lithium” and “schizophrenia” and “suicidal,” were identified often in the social media and PMC data, respectively.

Mental health information has been increasingly sought-after, and BD is a mental illness with complicated factors in the clinical picture. This paper has made an original contribution to comprehending the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of mental health informatics that can be analyzed in the laboratory and social media domains.

Innovative modern technologies are utilised in the hospitality and tourist industry to attract prospective consumers. Travel vlogs have emerged as a type of visual travel blog which includes information regarding tourist destinations, accommodation, cuisine, touristic activities, mode of accessibility, etc. The current study aims to investigate the influence of vlogger credibility on triggering the tourist intention to visit an eco-tourism destination.

The present study is a quantitative study, and it was conducted by surveying 218 respondents. A structured, close-ended and self-administrated questionnaire was used to collect data from the respondents.

The findings indicated that vlogger trustworthiness, expertise and attractiveness significantly impact Bangladeshi tourists' intention towards visiting an eco-tourist destination.

The present study has several theoretical and practical contributions. Theoretically, the research framework illustrated how the vloggers' trustworthiness, expertise and attractiveness on Bangladeshi tourists' visit intention to eco-tourist destinations. Practically, tourism industry practitioners, particularly destination marketers, will get benefit from the present study.

Despite the technological paradigm shift presented to the architecture, engineering, construction and operations sector (AECO), the full-fledged acceptance of the building information modelling (BIM) methodology has been slower than initially anticipated. Indeed, this study aims to acknowledge the need for increasing supportive technologies enabling the use of BIM, attending to available human resources, their requirements and their tasks.

A complete case study is described, including the development process centred on design science research methodology followed by the usability assessment procedure validated by construction projects facility management operational staff.

Results show that participants could interact with BIM using openBIM processes and file formats naturally, as most participants reached an efficiency level close to that expected for users already familiar with the interface (i.e. high-efficiency values). These results are consistent with the reported perceived satisfaction and analysis of participants’ discourses through 62 semi-structured interviews.

The contributions of the present study are twofold: a proposal for a virtual reality openBIM framework is presented, particularly for the semantic enrichment of BIM models, and a methodology for evaluating the usability of this type of system in the AECO sector.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.