Search results

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.

This study examines the temporal dynamics shaping our understanding of search in education and the role language plays in legitimising these dynamics. It critiques the way online search is discursively constructed using home-education as a case study, and problematises how particular discourses are privileged, whom this privileging serves, as well as the likely consequences.

The study employs Faircloughian Critical Discourse Analysis (CDA) as its methodological framework. Search and discursive practices were recorded during observations, search-tasks and interviews with five Australian home-educating families. Discursive features from the Google interface were also analysed.

A discursive privileging of hasty search practices was identified. This was found alongside largely ineffectual search, but participants continued to discursively represent search as fast and easy. The study highlights the complex co-option of discourses surrounding online search that privilege particular temporal and commercial landscapes.

This study contributes new knowledge regarding time as a context for understanding search behaviours, locating the perception of temporal scarcity in education within broader discursive and social structures. To date, no studies are found which investigate the temporal factors surrounding search in home-education. Increasing global reliance upon online search means the findings have broad significance, as does the proliferation of home-education induced by COVID-19. Additionally, while much work problematises the power search engines wield to privilege certain discourses, few investigate the day-to-day discursive practices of searchers affording Google and others this power.

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.

The substantial expansion of technology and the efficacy of digital platforms in reaching young audiences have led to enhanced targeting and customization of promotional communications. Notwithstanding the expansion and efficacy of contemporary advertising platforms, scholarly attention has not kept pace with this domain of inquiry. This study aims to assess the antecedents of Google Shopping Ads (GSA) on intention to purchase behavior among the Generation Y and Z cohorts.

The current study used a quantitative approach and snowball sampling technique to gather primary data via a questionnaire and Google Forms, which resulted in the collection of 5,808 questionnaires among the cohort members. A principal component analysis and multigroup confirmatory multigroup structural equation modeling (between Generation Y and Z) were used to assess the research data and model.

The results show positive trust and perceived value associations with intention to purchase, particularly among Generation Y and Z consumers. The findings also show negative irritation, product risk and time risk associations with intention to purchase, especially among the Generation Y cohort, which indicates that young consumers generally do not observe perceived risk due to the usage of GSA.

GSA will continue to grow and become an increasingly important integrated marketing communications tool as the digital landscape develops. It can be concluded that young consumers show a high degree of perceived value and low levels of perceived risk due to the use of GSA. This study, therefore, promotes improved understanding among academics, marketers and businesses of search engine advertising among young cohorts of consumers (Generation Y and Z) in a developing country context.

Informational conflict and uncertainty are common features across a range of sources, topics and tasks. Search engines and their presentation of results via search engine results pages (SERPs) often underpinned by knowledge graphs (KGs) are commonly used across tasks. Yet, it is not clear how search does, or could, represent the informational conflict that exists across and within returned results. The purpose of this paper is to review KG and SERP designs for representation of uncertainty or disagreement.

The authors address the aim through a systematic analysis of material regarding uncertainty and disagreement in KG and SERP contexts. Specifically, the authors focus on the material representation – user interface design features – that have been developed in the context of uncertainty and disagreement representation for KGs and SERPs.

Searches identified n = 136 items as relevant, with n = 4 sets of visual materials identified from these for analysis of their design features. Design elements were extracted against sets of design principles, highlighting tensions in the design of such features.

The authors conclude by highlighting two key challenges for interface design and recommending six design principles in representing uncertainty and conflict in SERPs. Given the important role technologies play in mediating information access and learning, addressing the representation of uncertainty and disagreement in the representation of information is crucial.

The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs.

The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The URLs were then executed directly in the internet browser (Chrome), search engine (Google) and Internet Archive (WayBack Machine). The collected data were recorded in an excel file and presented in tables/diagrams for further analysis.

From the total of 1,083 web references, a maximum number was retrieved by the WayBack Machine (786; 72.6 per cent) followed by Google (501; 46.3 per cent) and the lowest by Chrome (402; 37.1 per cent). The study concludes that the WayBack Machine is more efficient, retrieves a maximum number of missing web citations and fulfills the mission of preservation of web sources to a larger extent.

A good number of studies have been conducted to analyze the persistence and decay of web-references; however, the present study is unique as it compared the dead URL retrieval effectiveness of internet explorer (Chrome), search engine giant (Google) and WayBack Machine of the Internet Archive.

The web references of a single journal, namely, Library Hi Tech, were analyzed for 5 years only. A major study across disciplines and sources may yield better results.

URL decay is becoming a major problem in the preservation and citation of web resources. The study has some healthy recommendations for authors, editors, publishers, librarians and web designers to improve the persistence of web references.

This paper investigated whether a bank’s popularity and depositors' fear of Google search volume could affect bank deposits and credit.

The authors used two different quarterly data from Google Trends and banking data from 2012 Q1 to 2020 Q1. Based on available data, Google Trends data start from 2012. The authors exclude data after 2020 Q1 because the Covid-19 pandemic arguably increased the volume of Internet users due to shifting behavior to online activities. They merged and cleaned the data by winsorizing at 5 and 95 percentiles to avoid any outlier problems, reaching 74 banks in the sample. They used panel data estimation of quarterly data following Levy-Yeyati et al. (2010) and Trinugroho et al. (2020).

The results show that a higher search volume of a bank’s name leads to higher deposits. A higher search volume of depositor fear reduces deposits and credit. The authors also found that banks with high risk and a high search volume of their name have a significantly lower volume of deposits.

To the best of the authors’ knowledge, not many papers in banking and finance have used Google Trends data to gauge related issues regarding depositors' behavior. The authors have filled a gap in the literature by investigating whether the popularity of Google search and depositors' fear could impact deposits and credit. This study also attempted to establish whether Google Trends data could be a reliable source of information to predict depositors' behavior by using a Zscore to measure bank risk.

The purpose of this research paper is to explore and offer insightful information on the useful use of Google Tag Manager (GTM) in the context of library websites and to bridge the gap between GTM’s technical features and the practical requirements of libraries. It gives libraries the ability to use GTM’s capabilities to increase user engagement, data-driven decision-making and improve online services.

This study reviews existing literature on GTM in the context of websites and libraries. The methodology involves identifying keywords and searching terms related to GTM, digital marketing, user engagement, Web analytics and library websites. Sources and databases were consulted, including library science journals, marketing journals, academic databases, publications on digital marketing and search platforms such as Google Books, Google Scholar, Google Search Engine, JSTOR and library associations like the American Library Association. Initial screening was done based on titles and abstracts, followed by a thorough-text review, categorization and synthesizing of the findings.

GTM provides libraries with a potent tool to improve their online presence, customize user experiences and collect insightful real-time data. Libraries may harness GTM’s potential to better engage people and provide services by properly implementing it and maintaining it over time. It can be a flexible instrument that supports contemporary library services in the digital era. The findings of this study indicate that GTM technology may be used in library services; nevertheless, there are several barriers, such as librarians’ attitudes and technical abilities, that prevent GTM acceptance in library services.

This study covers the implementation of a free GTM tool in library websites that will help the library and information professionals to leverage the GTM in the library’s online presence. Furthermore, this study recommends that libraries and librarians should develop guidelines and policies for the critical adoption of a free GTM tool in the library environment, which will support improving the library’s user engagement and tracking of library website traffic.

The potential growth in cryptocurrencies has raised serious ethical and religious issues leading to a new investment rethinking. This paper aims to identify the influence of religiosity on cryptocurrency acceptance through an extended technology acceptance model (TAM) model.

In the first phase, this research develops a conceptual model that extends the theory of the TAM by integrating the religiosity component. In the second phase, the proposed model is tested using search volume queries in daily frequencies from 01/01/2018 to 31/12/2022 and structural equation modeling (SEM).

The empirical results demonstrate a significant positive effect of religiosity on the intention to use cryptocurrency, the users' perceived usefulness (PU) and ease of use (PEOU). Besides, the authors note that PEOU positively influences the intention. Furthermore, religiosity indirectly affects the intention through the PEOU and positively impacts the intention through the PU. In the same way, PEOU has a considerable indirect effect on the intention through PU.

This study has practical and theoretical contributions by providing insights into the cryptocurrency acceptance factors. In other words, it contributes to the literature by extending TAM models. Practically, it helps managers determine factors affecting the intention to use cryptocurrencies. Therefore, they can adjust their industry according to the suitable characteristics for creating successful projects.

Identifying the effect of religiosity on cryptocurrency users' choices and decisions has a social added value as it provides an understanding of the evolution of psychological variants.

The findings emphasize the importance of integrating big data to analyze users' attitudes. Besides, most studies on cryptocurrency acceptance are investigated based on one kind of religion, such as Christianity or Islam. Nevertheless, this paper integrates the effect of five types of faith on the users' intentions.

This study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and analyze how public sentiment, measured through Google Trends, can predict stock market fluctuations. We propose a novel framework using Google Trends for financial sentiment analysis, aiming to improve understanding and preparedness for future crises.

Hybrid approach leverages Google Trends as sentiment tool, market data, and momentum indicators like Rate of Change, Average Directional Index and Stochastic Oscillator, to deliver accurate, market insights for informed investment decisions during pandemic.

Our study reveals that the pandemic significantly impacted the Indian financial sector, highlighting its vulnerabilities. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.95% maximum accuracy in forecasting stock market values during such events.

To the best of authors knowledge this model's originality lies in its focus on short-term impact, novel data fusion and methodology, and high accuracy.• Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of COVID-19 on market behavior.• Novel data fusion and framework: A novel framework of sentiment analysis was introduced in the form of Trend Popularity Index. Combining trend popularity index with momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods.• High predictive accuracy: Achieving the prediction accuracy (98.93%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.