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The data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature.

The bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus.

This study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future.

Findings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field.

The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies.

This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.

The purpose of this trends paper is to offer insights into the technological changes affecting our cities and urban tourism destinations, and to explore avenues for further research and practice in the context of smart tourism destinations.

The literature on smart cities and smart tourism destinations is analysed in view of delivering a research agenda for a new generation of “post-smart” tourism destinations, beyond existing paradigms in this field.

Smart tourism research to date is found to be lacking in terms of addressing emerging (“post-smart”) social issues increasingly faced by global tourism cities, such as growing inequalities between host communities and visitors, wellness (e.g. slow tourism and slow cities) and resilience and mental health (e.g. digital detox), among others.

A post-smart approach to tourism city management and marketing calls for rethinking of existing tourism and urban policies that address wider sustainability issues exemplified by the urban transitions debate as well as adopting a more holistic networked approach to smartness involving entire regions. This also calls for the development of a new research agenda in urban tourism through a new prism – the post-smart “wise” tourism destination.

A new tourism futures construct – the “wise” tourism destination – is posited. This is done within the context of a new (“post-smart”) generation of tourism cities. It is argued that “wise” tourism cities will require novel attributes and adopt a visionary strategic positioning well beyond today’s smart tourism destination paradigms. Additionally, a tentative research agenda for “wise” tourism cities is discussed.

The purpose of this paper is to look at how employability is currently embedded within courses to prepare students for their transition into the world of work, identifying the teaching and learning strategies employed.

A review of the relevant literature was conducted. The study analysed experiential data and the logged reports of student work placements over a period of five years, to determine the relationship of such placements to both academic results and long term employability. The study considered placements from the viewpoints of the university, students and employers. It proceeded to look at the current embedding of employability within the teaching curriculum, specifically teaching and learning strategies for personal professional development courses and the employability passport. It also looked at the role of the work placement tutor.

The main revelation was that the employability skills sought by employers were mostly “soft” and therefore behaviours, rather than “hard” teachable skills, which has significant implications for the introduction of the teaching excellence framework (TEF) and the provision of suitable metrics.

The study and findings are limited to a single university in the UK.

The results of the study and conclusions drawn from the analysis of findings, led to the identification of the student, institution, tutor, employer) quartet of actors for employability. The paper also postulates the ramifications of the introduction of the TEF on employability.

Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.

To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.

The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.

Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.

This research has not been published anywhere else.

Hyperautomation is a technological concept whose popularity has been growing continuously since the German manufacturing industry “initiated” the Fourth Industrial Revolution (Industry 4.0), whereas, on the basis of theory, hyperautomation is a term still new and little recognized. This applies equally to scientific studies (articles, conference reports) and empirical studies (quantitative, qualitative). Therefore, this article attempts to fill definition gap that exists in the literature on management and quality sciences on the term hyperautomation.

The authors use literature review approach to identify the gaps in the existing literature on hyperautomation. They present a nominal definition of hyperautomation, discuss related issues and provide a comparative perspective between hyperautomation and automation.

The article’s findings include a precise definition of hyperautomation and the problems it raises. The authors point out that the term “hyperautomation” is still relatively new and underutilized in the management and quality sciences literature. It also compares hyperautomation to automation from several angles and emphasizes how it affects businesses, industries and other economic sectors.

Authors emphasize that in order to deploy hyperautomation successfully, enterprises must take a distributed and integrated approach.

This article addresses a gap in the management and quality sciences literature about the definition of hyperautomation. Authors give a thorough explanation of hyperautomation, along with relevant problems, useful implications and a comparison between hyperautomation versus automation.

The purpose of this study is to investigate through an empirical research the factors that would attract visitors to heritage and cultural sites as museums applied on the Grand Egyptian Museum (GEM). The paper aims to study the impact of the proposed attraction features learning/knowledge, museum facilities, fun/entertainment and socializing and accessibility/location on pre-visit destination image.

The author started by a thorough literature review to arrive to the suggested conceptual model, which is tested by adopting a quantitative approach where data were collected using a self-administered questionnaire from a convenient sample of 300 respondents with 90% response rate and used partial least squares – structure equation modelling using Smart PLS v.3.2.8.

The results show that three of the tested factors were accepted and one was rejected.

Management of GEM, which will be one of the biggest museums world-wide, can make use of the empirical results of this research to enhance their understanding of the factors that impact pre-visit destination image, and thus, most attract visitors to justify the budget set in this huge project and achieve highest visitation and revenue

This research deals with a new museum that has not opened its doors yet and will start functioning in late 2020, and thus, the pre-visit image of the museum is not based on previous experience of visitors but rather on secondary sources as messages sent to visitors based on attraction features, while most previous studies dealt with post-visit image of museums

This paper aims to investigate the mechanisms for managing coordinated benchmarking projects and the outcomes achieved from such coordination. While there have been many independent benchmarking studies comparing the practices and performance of public sector organisations, there has been little research on initiatives that involve coordinating multiple benchmarking projects within public sector organisations or report on the practices implemented and results from benchmarking projects. This research will be of interest to centralised authorities wishing to encourage and assist multiple organisations in undertaking benchmarking projects.

The study adopts a case study methodology. Data were collected on the coordinating mechanisms and the experiences of the individual organisations over a one-year period.

The findings show successful results (financial and non-financial) across all 13 benchmarking projects, thus indicating the success of a coordinated approach to managing multiple projects. The study concluded by recommending a six-stage process for coordinating multiple benchmarking projects.

This research gives new insights into the application and benefits from benchmarking because of the open access the research team had to the “Dubai We Learn” initiative. To the authors’ knowledge the research was unique in being able to report accurately on the outcome of 13 benchmarking projects with all projects using the TRADE benchmarking methodology.

The review aims to facilitate a broader understanding of platform opening and cooperation and points out potential research directions for scholars.

This study searches Web of Science (WOS) database for relevant literature published between 2010 and 2021 and selects 86 papers for this review. The selected literature is categorized according to three dimensions: the strategic choice of platform opening and cooperation (before opening), the construction of an open platform (during opening) and the impact of platform opening and cooperation (after opening). Through comparative analysis, the authors identify research gaps and propose four future research agendas.

The study finds that the current studies are fragmented, and a research system with a theoretical foundation has not yet formed. In addition, with the development of platform operations, new topics such as platform ecosystems and open platform governance have emerged. In short, there is an urgent need for scholars to conduct exploratory research. To this end, the study proposes four future research agendas: strengthen basic research on platform opening and cooperation, deeply explore the dynamic evolution and cutting-edge models of platform opening and cooperation, analyze potential crises and impacts of platform openness and strengthen research on open platform governance.

This is the first systematic review on platform opening and cooperation. Through categorizing literature into three dimensions, this article clearly shows the research status and provides future research avenues.