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.

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.

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.

The past two decades have witnessed exponential growth in the use of mindfulness-based programmes within professional workplace cultures. From digital media giants such as Google, Apple and Facebook, to Fortune 500 companies, hospitals, universities, and government departments, many contemporary workplaces now offer mindfulness-based programmes as a remedy for workplace challenges such as low productivity, employee stress and staff attrition. Using Google’s Search Inside Yourself Leadership Institute as a case study, this chapter adopts Lauren Berlant’s concept of ‘cruel optimism’ as a critical framework for re-evaluating the affective and relational experiences of mindfulness within the contemporary neoliberal workplace. Specifically, it considers the ways in which corporate mindfulness initiatives commonly use the rubric of ‘employee wellbeing’ and ‘self-care’ to pathologise employee experiences of boredom, dissatisfaction and stress while downplaying the social, political and economic factors that contribute to workplace dissatisfaction and employee burnout.

The present study aims to systematically synthesize the academic and industrial literature on multi-central bank digital currencies (m-CBDCs) arrangements.

The study adopted a unique multivocal literature review methodology that considers both white and grey literature. For white literature searches, the study relied on Scopus, Web of Science (WOS), and Google Scholar bibliometric databases; for grey literature searches, the study used the Google search engine.

The findings of the study illustrated that M-CBDC arrangements, through various design options, have the potential to revolutionize the contemporary international payment system. M-CBDC arrangements will lead to more integrated financial systems and promote economic growth. However, m-CBDC arrangements will also have serious macroeconomic implications, such as contagion and currency substitution risks.

The present review is one of the earliest reviews of m-CBDC arrangements. In addition, the findings of the study offer valuable insights for both academicians and policymakers.

The study is also one of the pioneer studies in management studies that apply a multivocal literature review methodology.

In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.

The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.

The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.

In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.

The COVID-19 pandemic disrupted healthcare systems globally, including antenatal care coverage. Pregnant women, who were considered “at risk” during the pandemic, replaced in-person antenatal visits with telemedicine and accessed health information online. However, little is known about pregnant women’s online information-seeking behaviour during the COVID-19 pandemic in Nigeria. Hence, the purpose of this paper is to investigate the information seeking behaviour of pregnant women online during the COVID-19 pandemic.

This research uses qualitative study and in-depth interviews to obtain data from eight pregnant women during the pandemic. Data were thematically analysed, with responses presented verbatim to illustrate themes.

Findings show that during the COVID-19 pandemic, the unavailability of health professionals and the fear of contracting the COVID-19 virus influenced pregnant women’s information-seeking behaviour online. Pregnant women accessed online sources as alternatives to consultations with health professionals, searched for drug prescriptions and asked pregnancy-related questions online. Findings also revealed that pregnant women conceptualised these online sources and platforms as safe spaces for sharing and dealing with pregnancy-related anxieties and difficulties during the pandemic.

The number of participants sampled in the study is considered satisfactory since data saturation was achieved. However, considering the generalisation and transferability of the research findings, note that the study focused on a limited number of pregnant women in one state in Nigeria (Lagos State). Hence, the design and sample do not provide adequate generalisation to a larger population of pregnant women in Nigeria. Future research may generalise more broadly to other states in Nigeria. Another limitation of the study was using telephone interviews to collect data. Therefore, this paper could not analyse body language and facial expressions, which prevented us from gaining insights into participants’ descriptions of health information-seeking behaviour online. Therefore, further studies should use alternative data collection methods, such as face-to-face or online video interviews, instead of telephone interviews.

This study has implications for health policy interventions. The study’s findings can guide policies on designing digital health systems for pregnant women during health crises.

This study contributes to existing literature on health information-seeking behaviour online among a vulnerable population – pregnant women in a developing country. Specifically, the study contributes to knowledge on how pregnant women’s health information-seeking behaviour can change online within a health-crisis context like the COVID-19 pandemic and its implications for their overall well-being.

The purpose of this study is to conduct a comprehensive systematic literature review using bibliometric approach to investigate the academic structure of World Trade Organization Dispute Settlement research.

The study examines the bibliographic information for 1,858 articles from Scopus and the Australian Business Deans Council-indexed journals published between 1995 and 2024 using Dimensions.ai and Google Scholar search engines. Exploratory-cum-descriptive research design with bibliometric approach is used to answer the stated literature review research questions.

The data shows a gradual decline in WTO-Dispute Settlement System (WTO-DSS) research relative to the total international business area in the three decades. Developed countries appear as key contributors to the research, with the USA and the UK standing out as the most productive and influential research countries. The study shows a significant change in the focus of this research corpus from legalized to non-legalized approaches, with a greater emphasis on transparency and environmental sustainability. The research identifies global politics and international trade law as influential subjects in the discipline.

To the best of the authors’ knowledge, the study is a first of its kind where bibliometric approach has been used to study the evolution of WTO-DSS literature. The study adds to the understanding of WTO Dispute Settlement research patterns and recommends future research options.