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Search engines, the most popular online services, are associated with several concerns. Users are concerned about the unauthorized processing of their personal data, as well as about search engines keeping track of their search preferences. Various search engines have been introduced to address these concerns, claiming that they protect users’ privacy. The authors call these search engines privacy-preserving search engines (PPSEs). This paper aims to investigate the factors that motivate search engine users to use PPSEs.

This study adopted protection motivation theory (PMT) and associated its constructs with subjective norms to build a comprehensive research model. The authors tested the research model using survey data from 830 search engine users worldwide.

The results confirm the interpretive power of PMT in privacy-related decision-making and show that users are more inclined to take protective measures when they consider that data abuse is a more severe risk and that they are more vulnerable to data abuse. Furthermore, the results highlight the importance of subjective norms in predicting and determining PPSE use. Because subjective norms refer to perceived social influences from important others to engage or refrain from protective behavior, the authors reveal that the recommendation from people that users consider important motivates them to take protective measures and use PPSE.

Despite its interesting results, this research also has some limitations. First, because the survey was conducted online, the study environment was less controlled. Participants may have been disrupted or affected, for example, by the presence of others or background noise during the session. Second, some of the survey items could possibly be misinterpreted by the respondents in the study questionnaire, as they did not have access to clarifications that a researcher could possibly provide. Third, another limitation refers to the use of the Amazon Turk tool. According Paolacci and Chandler (2014) in comparison to the US population, the MTurk workers are more educated, younger and less religiously and politically diverse. Fourth, another limitation of this study could be that Actual Use of PPSE is self-reported by the participants. This could cause bias because it is argued that internet users’ statements may be in contrast with their actions in real life or in an experimental scenario (Berendt et al., 2005, Jensen et al., 2005); Moreover, some limitations of this study emerge from the use of PMT as the background theory of the study. PMT identifies the main factors that affect protection motivation, but other environmental and cognitive factors can also have a significant role in determining the way an individual’s attitude is formed. As Rogers (1975) argued, PMT as proposed does not attempt to specify all of the possible factors in a fear appeal that may affect persuasion, but rather a systematic exposition of a limited set of components and cognitive mediational processes that may account for a significant portion of the variance in acceptance by users. In addition, as Tanner et al. (1991) argue, the ‘PMT’s assumption that the subjects have not already developed a coping mechanism is one of its limitations. Finally, another limitation is that the sample does not include users from China, which is the second most populated country. Unfortunately, DuckDuckGo has been blocked in China, so it has not been feasible to include users from China in this study.

The proposed model and, specifically, the subjective norms construct proved to be successful in predicting PPSE use. This study demonstrates the need for PPSE to exhibit and advertise the technology and measures they use to protect users’ privacy. This will contribute to the effort to persuade internet users to use these tools.

This study sought to explore the privacy attitudes of search engine users using PMT and its constructs’ association with subjective norms. It used the PMT to elucidate users’ perceptions that motivate them to privacy adoption behavior, as well as how these perceptions influence the type of search engine they use. This research is a first step toward gaining a better understanding of the processes that drive people’s motivation to, or not to, protect their privacy online by means of using PPSE. At the same time, this study contributes to search engine vendors by revealing that users’ need to be persuaded not only about their policy toward privacy but also by considering and implementing new strategies of diffusion that could enhance the use of the PPSE.

This research is a first step toward gaining a better understanding of the processes that drive people’s motivation to, or not to, protect their privacy online by means of using PPSEs.

This paper aims to give an overview of the history and evolution of commercial search engines. It traces the development of search engines from their early days to their current form as complex technology-powered systems that offer a wide range of features and services.

In recent years, advancements in artificial intelligence (AI) technology have led to the development of AI-powered chat services. This study explores official announcements and releases of three major search engines, Google, Bing and Baidu, of AI-powered chat services.

Three major players in the search engine market, Google, Microsoft and Baidu started to integrate AI chat into their search results. Google has released Bard, later upgraded to Gemini, a LaMDA-powered conversational AI service. Microsoft has launched Bing Chat, renamed later to Copilot, a GPT-powered by OpenAI search engine. The largest search engine in China, Baidu, released a similar service called Ernie. There are also new AI-based search engines, which are briefly described.

This paper discusses the strengths and weaknesses of the traditional – algorithmic powered search engines and modern search with generative AI support, and the possibilities of merging them into one service. This study stresses the types of inquiries provided to search engines, users’ habits of using search engines and the technological advantage of search engine infrastructure.

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.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

The internet has evolved into an indispensable platform for seeking health information, particularly among transgender individuals. With an abundance of online resources available, extensive research into the credibility and reliability of this information is essential, as concerns about the quality of online resources persist. Transgender individuals are drawn to online health information due to the anonymity it offers, providing them with a sense of freedom from social isolation and the discomfort of experimenting with their transgender identity. However, it is crucial to assess the accuracy and reliability of the transgender health information available on the internet. This article aims to evaluate the quality of online transgender health resources by utilizing ten credibility indicators, along with six indicators to assess the veracity of the content.

A total of 179 online resources were meticulously reviewed after excluding any unnecessary and irrelevant ones, to ensure a comprehensive assessment.

The findings suggest that among the chosen resources, none of them meet all the criteria for maintaining high standards of accuracy and reliability in health information. In other words, none of these sources completely adhere to the established measures for ensuring that the information they provide is trustworthy and of high quality in the context of health.

The study provides valuable insights into the online realm of transgender health information, revealing both the strengths and weaknesses of the existing resources. By pinpointing areas that need enhancement and showcasing commendable practices, this research strives to promote a more knowledgeable and supportive online environment for individuals in search of transgender health information.

This article aims to reveal the factors influencing the sustainable development of mobile e-commerce from both user and operational perspectives. It fills the gap in qualitative research on the sustainable development of artificial intelligence (AI) technology in mobile e-commerce based on the grounded theory. This study provides valuable insights and inspiration for sustainable development in this field and lays the theoretical foundation and research reference for future studies.

Based on the grounded theory (GT), interview method was used to conduct the study.

The impact of AI applications on mobile e-commerce is mainly reflected in three stages of the customer shopping process. They are pre-shopping, mid-shopping and after-shopping AI services and each of the three stages has its own separate dimensions that need attention. The study and its persistence aspects are discussed.

The results of this study can provide forward-looking suggestions and paths for the construction and optimization of future e-commerce platforms, contribute to the sustainable development of e-commerce and contribute to the sustainable and healthy growth of the social economy.

This study proposes sustainable development measures for the application of AI in mobile e-commerce, from operation to supervision, which is an important reference for promoting coordinated and rapid socio-economic development.

The study aims to shed light on the current state of “Dataset repositories” indexed in Directory of Open Access Repositories (OpenDOAR).

From each repository/record information, the Open-Access Policies, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), year of creation and the number of data sets archived in the repositories were manually searched, documented and analyzed.

Developed countries like the United Kingdom and the USA are primarily involved in the development of institutional open-access repositories comprising significant components of OpenDOAR. The most extensively used software is DSpace. Most data set archives are OAI-PMH compliant but do not follow open-access rules. The study also highlights the sites’ embrace of Web 2.0 capabilities and discovers really simple syndication feeds and Atom integration. The use of social media has made its presence known. Furthermore, the study concludes that the number of data sets kept in repositories is insufficient, although the expansion of such repositories has been consistent over the years.

The work has the potential to benefit both researchers in general and policymakers in particular. Scholars interested in research data, data sharing and data reuse can learn about the present state of repositories that preserve data sets in OpenDOAR. At the same time, policymakers can develop recommendations and policies to assist in the construction and maintenance of repositories for data sets.

According to the literature, there have been numerous studies on open-access repositories and OpenDOAR internationally, but no research has focused on repositories preserving content-type data sets. As a result, the study attempts to uncover various characteristics of OpenDOAR Data set repositories.

This paper aims to identify and understand changing research themes within this field and apply a novel technique for text mining.

Statistical text mining methods are applied as an approach to bibliographic analysis to nearly 30 years of papers published in Library Review and Global Knowledge, Memory and Communication to identify key research themes and analyse how they have evolved over this period.

Key stable research themes include students, literacy, learning, research, while emerging research themes include social media, networking and knowledge sharing through information and communication technology.

A novel approach to bibliometric analysis is applied to a large collection of texts published in the library field.

Personal data is a powerful tool. The more someone know about us, the more power they got over us. But who will control the most of our personal data? Does the government and the big tech really care about our personal data? This paper aims to look at data practices, data-related policy making as well as its economic consequences in the context of emerging economies.

Using qualitative methods such as literature review and analysis of numerous government documents, this paper inquires into the dynamics in the use of data by the business sectors, explains how data governance can add value to the business sectors while ensuring customers’ data privacy protection based on the data governance mechanism framework and details what it takes.

Using the case of Indonesian recent development on data privacy regulation, this paper describes the problems and threats to personal data protection. The advent of latest computing and mobile technology is shifting power relations between the governments, the big tech, as well as the end users. To conclude, the strategy and policy recommendations for implementing data privacy protection are also presented.

This paper provides a timely synthesis of data practices in the context of developing countries, particularly in relation to policy making and economic consequences. This paper also identifies and shares several promising future research ideas.

Good abandonment behavior refers to users obtaining direct answers via search engine results pages (SERPs) without clicking any search result, which occurs commonly in mobile search. This study aims to better understand users' good abandonment behavior and perception, and then construct a good abandonment prediction model for mobile search with improved performance.

In this study, an in situ user mobile search experiment (N = 43) and a crowdsourcing survey (N = 1,379) were conducted. Good abandonment behavior was analyzed from a quantitative perspective, exploring users' search behavior characteristics from four aspects: session and query, SERPs, gestures and eye-tracking data.

Users show less engagement with SERPs in good abandonment, spending less time and using fewer gestures, and they pay more visual attention to answer-like results. It was also found that good abandonment behavior is often related to users' perceived difficulty of the searching tasks and trustworthiness in the search engine. A good abandonment prediction model in mobile search was constructed with a high accuracy (97.14%).

This study is the first to explore eye-tracking characteristics of users' good abandonment behavior in mobile search, and to explore users' perception of their good abandonment behavior. Visual attention features are introduced into good abandonment prediction in mobile search for the first time and proved to be important predictors in the proposed model.