Search results

The landscape of search engine usage has evolved since the last known data were used to calculate click-through rate (CTR) values. The objective was to provide a replicable method for accessing data from the Google search engine using programmatic access and calculating CTR values from the retrieved data to show how the CTRs have changed since the last studies were published.

In this study, the authors present the estimated CTR values in organic search results based on actual clicks and impressions data, and establish a protocol for collecting this data using Google programmatic access. For this study, the authors collected data on 416,386 clicks, 31,648,226 impressions and 8,861,416 daily queries.

The results show that CTRs have decreased from previously reported values in both academic research and industry benchmarks. The estimates indicate that the top-ranked result in Google's organic search results features a CTR of 9.28%, followed by 5.82 and 3.11% for positions two and three, respectively. The authors also demonstrate that CTRs vary across various types of devices. On desktop devices, the CTR decreases steadily with each lower ranking position. On smartphones, the CTR starts high but decreases rapidly, with an unprecedented increase from position 13 onwards. Tablets have the lowest and most variable CTR values.

The theoretical implications include the generation of a current dataset on search engine results and user behavior, made available to the research community, creation of a unique methodology for generating new datasets and presenting the updated information on CTR trends. The managerial implications include the establishment of the need for businesses to focus on optimizing other forms of Google search results in addition to organic text results, and the possibility of application of this study's methodology to determine CTRs for their own websites.

This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.

In this study, STEM resources reviewed in Goodreads were investigated to determine their authorship, linguistic characteristics and impact. The analysis reveals gender disparity favoring titles with male authors.

This paper applies theoretical concepts of knowledge commons to understand how individuals leverage the affordances of the Goodreads platform to share their perceptions of STEM-related books.

The analysis reveals gender disparity favoring titles with male authors. Female-authored STEM publications represent popular science nonfiction and juvenile genres. Analysis of the scholarly impact of the reviewed titles revealed that Google Scholar provides broader and more diverse coverage than Web of Science. Linguistic analysis of the reviews revealed the relatively low aesthetic disposition of reviewers with an emphasis on embodied experiences that emerged from the reading.

This study contributes to the understanding of the impact of popular STEM resources as well as the influence of the language of user-generated reviews on production, consumption and discoverability of STEM titles.

This study aims to examine Big Data and the management of libraries in the era of the Fourth Industrial Revolution and its implications for policymakers in Nigeria.

A qualitative methodology was used, involving the administration of open-ended questionnaires to librarians from six selected federal universities located in Southwest Nigeria.

The findings of this research highlight that a significant proportion of librarians are well-acquainted with the relevance of big data and its potential to positively revolutionize library services. Librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

This study exclusively focuses on the Nigerian context, overlooking insights from other African countries. As a result, it may not be possible to generalize the study’s findings to the broader African library community.

To the best of the authors’ knowledge, this study is unique because the paper reported that librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

This study aims to present a framework for automatically collecting, cleaning and analyzing text (news articles, in this case) to provide valuable decision-making information to destination management organizations. Keeping a record of certain aspects of the projected destination image of an attraction (Cancun in this study) will grant the design of better strategies for the promotion and administration of destinations without the time-consuming effort of manually evaluating high quantities of textual information.

Using Web scraping, news articles were collected from the USA, Mexico and Canada over an interval of one year. The documents were analyzed using an automatic topic modeling method known as Latent Dirichlet Allocation and a coherence analysis to determine the number of themes present in each collection. With the data provided, the authors were able to extract valuable information to understand how Cancun is presented to the countries.

It was found that in all countries, Cancun is an important destination to travel and vacation; however, given the period defined for this study (from July 2021 to July 2022), an important part of the articles analyzed was concerned with the sanitary measures derived from the COVID-19 pandemic. Besides, given the rise of violence and the threat of organized crime, many articles from the three countries are focused on warning potential tourists about the risks of traveling to Cancun.

The examination of the relevant literature revealed that similar analyses are manually performed by the experts on a set of predefined categories. Although those approaches are methodologically sound, the logistic effort and the time used could become prohibitively expensive, precluding carrying out this analysis frequently. Additionally, the preestablished categories to be studied in press articles may distort the results. For these reasons, the proposed framework automatically allows for gathering valuable information for decision-making in an unbiased manner.

The current predominant delivery format resulting from digitization is PDF, which is not appropriate for the blind, partially sighted and people who read on mobile devices. To meet the needs of both communities, as well as broader ones, alternative file formats are required. With the findings of the eBooks-On-Demand-Network Opening Publications for European Netizens project research, this study aims to improve access to digitized content for these communities.

In 2022, the authors conducted research on the digitization experiences of 13 EODOPEN partners at their organizations. The authors distributed the same sample of scans in English with different characteristics, and in accordance with Web content accessibility guidelines, the authors created 24 criteria to analyze their digitization workflows, output formats and optical character recognition (OCR) quality.

In this contribution, the authors present the results of a trial implementation among EODOPEN partners regarding their digitization workflows, used delivery file formats and the resulting quality of OCR results, depending on the type of digitization output file format. It was shown that partners using the OCR tool ABBYY FineReader Professional and producing scanning outputs in tagged PDF and PDF/UA formats achieved better results according to set criteria.

The trial implementations were limited to 13 project partners’ organizations only.

This research paper can be a valuable contribution to the field of massive digitization practices, particularly in terms of improving the accessibility of the output delivery file formats.

In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.

Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.

A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.

This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.

It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.

Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.

Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

This study aims to identify the updated areas in family business innovation (FBI) and propose a future research agenda for scholars in the domain.

Using VOSviewer and Bibliometrix-R, this study conducts a bibliometric analysis on 699 Scopus-indexed journal articles/reviews to analyse FBI’s performance and intellectual structure.

This study provides up-to-date assessment through performance analysis. Through the co-citation, co-word and thematic evolution analysis, this study unpacks FBI themes/topics to propose possible future avenues.

The findings provide insights into resilient innovation-driven family businesses to enlighten the next generation of family business leaders with essential innovation knowledge for sustainable growth.

This study complements past FBI reviews by offering renewed perspectives that future research can focus on, in turn, enhancing literature on contemporary, relevant topical issues in the FBI post-COVID-19 pandemic.

The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers at a job site are paramount as they face both immediate and long-term risks such as falls and musculoskeletal disorders. To mitigate these dangers, sensor-based technologies have emerged as a crucial tool to promote the safety and well-being of workers on site. The implementation of real-time sensor data-driven monitoring tools can greatly benefit the construction industry by enabling the early identification and prevention of potential construction accidents. This study aims to explore the innovative method of prototype development regarding a safety monitoring system in the form of smart personal protective equipment (PPE) by taking advantage of the recent advances in wearable technology and cloud computing.

The proposed smart construction safety system has been meticulously crafted to seamlessly integrate with conventional safety gear, such as gloves and vests, to continuously monitor construction sites for potential hazards. This state-of-the-art system is primarily geared towards mitigating musculoskeletal disorders and preventing workers from inadvertently entering high-risk zones where falls or exposure to extreme temperatures could occur. The wearables were introduced through the proposed system in a non-intrusive manner where the safety vest and gloves were chosen as the base for the PPE as almost every construction worker would be required to wear them on site. Sensors were integrated into the PPE, and a smartphone application which is called SOTER was developed to view and interact with collected data. This study discusses the method and process of smart PPE system design and development process in software and hardware aspects.

This research study posits a smart system for PPE that utilises real-time sensor data collection to improve worksite safety and promote worker well-being. The study outlines the development process of a prototype that records crucial real-time data such as worker location, altitude, temperature and hand pressure while handling various construction objects. The collected data are automatically uploaded to a cloud service, allowing supervisors to monitor it through a user-friendly smartphone application. The worker tracking ability with the smart PPE can help to alleviate the identified issues by functioning as an active warning system to the construction safety management team. It is steadily evident that the proposed smart PPE system can be utilised by the respective industry practitioners to ensure the workers' safety and well-being at construction sites through monitoring of the workers with real-time sensor data.

The proposed smart PPE system assists in reducing the safety risks posed by hazardous environments as well as preventing a certain degree of musculoskeletal problems for workers. Ultimately, the current study unveils that the construction industry can utilise cloud computing services in conjunction with smart PPE to take advantage of the recent advances in novel technological avenues and bring construction safety management to a new level. The study significantly contributes to the prevailing knowledge of construction safety management in terms of applying sensor-based technologies in upskilling construction workers' safety in terms of real-time safety monitoring and safety knowledge sharing.

Our purpose is to reconnect memetics to information, a persistent and unclear association. Information can contribute across a span of memetic research. Its obscurity restricts conversations about “information flow,” the connections between “form” and “content,” as well as many other topics. As information is involved in cultural activity, its clarification could focus memetic theories and applications.

Our design captures theoretical nuance in memetics by considering a long standing conceptual issue in memetics: information. A systematic review of memetics is provided by making use of the term information across literature. We additionally provide a citation analysis and close readings of what “information” means within the corpus.

Our initial corpus is narrowed to 128 pivotal memetic publications. From these publications, we provide a citation analysis of memetic studies. Theoretical directions of memetics in the informational context are outlined and developed. We outline two main discussion spaces, survey theoretical interests and describe where and when information is important to memetic discussion. We also find that there are continuities in goals which connect Dawkins’s meme with internet meme studies.

To our knowledge, this is the broadest, most inclusive review of memetics conducted, making use of a unique approach to studying information-oriented discourse across a corpus. In doing so, we provide information researchers areas in which they might contribute theoretical clarity in diverse memetic approaches. Additionally, we borrow the notion of “conceptual troublemakers” to contribute a corpus collection strategy which might be valuable for future literature reviews with conceptual difficulties arising from interdisciplinary study.

Social mediametrics is a subfield of measurement in which the emphasis is placed on social media data. This paper analyzes the trends and patterns of paper comprehensively mentions on Twitter, with a particular focus on Twitter's mention behaviors. It uncovers the dissemination patterns and impact of academic literature on social media. The research has significant theoretical and practical implications.

This paper explores the fundamental attributes of Twitter mentions by means of analyzing 9,476 pieces of scholarly literature (5,097 from Nature and 4,379 from Science), 1,474,898 tweets and 451,567 user information collected from Altmetric.com database and Twitter API. The study uncovers assorted Twitter mention characteristics, mention behavior patterns and data accumulation patterns.

The findings illustrate that the top academic journals on Twitter have a wider range of coverage and display similar distribution patterns to other academic communication platforms. A large number of mentioners remain unidentified, and the distribution of follower counts among the mention users exhibits a significant Pareto effect, indicating a small group of highly influential users who generate numerous mentions. Furthermore, the proportion of sharing and exchange mentions positively correlates with the number of user followers, while the incidence of supportive mentions has a negative correlation. In terms of country-specific mention behavior, Thai scholars tend to utilize supportive mentions more frequently, whereas Korean scholars prefer sharing mentions over communicating mentions. The cumulative pattern of Twitter mentions suggests that these occur before official publication, with a half-life of 6.02 days and a considerable reduction in the number of mentions is observed on the seventh day after publication.

Conducting a multi-dimensional and systematic analysis of Twitter mentions of scholarly articles can aid in comprehending and utilizing social media communication patterns. This analysis can uncover literature's distribution patterns, dissemination effects and social significance in social media.