Factors influencing search engine usage behavior

2018 ◽  
Vol 46 (1) ◽  
pp. 1-10
Author(s):  
Yi Li ◽  
Zhihui Yuan ◽  
Yujie Li ◽  
Jing Liu

We analyzed the effect of individual factors, contextual factors, and perception of search engine advertising on users' search engine usage behavior. The sample comprised 404 Chinese who used search engines in the context of their paid employment. Results showed that (a) perceived search skills and perceived search engine reliance significantly and positively impacted users' general search engine usage, (b) perceived advertising clutter reduced the beneficial effects of perceived search skills on users' general search engine usage, (c) users with higher perceived search engine reliance preferred search engines to other online search methods, and (d) prior negative experience reduced the positive link between perceived search engine reliance and users' specific search engine usage. Our findings suggest that search engine designers and operators should focus on individual and contextual factors influencing search engine usage behavior, and should consider users' perception of advertising on search engine programs.

2019 ◽  
Vol 47 (4) ◽  
pp. 1-12 ◽  
Author(s):  
Yujie Li

Researchers have examined avoidance of traditional media advertising (e.g., television advertising) and general Internet advertising (e.g., banner advertising), but less attention has been paid to search engine advertising (SEA) avoidance, particularly in the Chinese context. Therefore, I analyzed the effects of 3 components of user perception of SEA (perceived goal impediment, perceived advertising clutter, and prior negative experience) and 2 components of user characteristics (monthly income and advertising location awareness) on SEA avoidance in a sample of 348 working professionals who use Chinese search engines. Results showed that user perception had a significantly positive impact on SEA avoidance, monthly income attenuated the positive impact of perceived advertising clutter but intensified the positive impact of prior negative experience on SEA avoidance, and advertising location awareness enhanced the positive impact of perceived advertising clutter on SEA avoidance. Implications of the findings for effective advertising on search engines are discussed.


2011 ◽  
Vol 467-469 ◽  
pp. 129-133 ◽  
Author(s):  
Qing Wei Zeng ◽  
Jie Jiang

With the rapid development of Internet, how to find useful information rapidly is becoming more and more important. General search engines somewhat satisfy users’ search needs. However, they do not consider users’ interests or background. Search engine will be more personal, intelligent and professional. It is necessary that personalized search engines come to reality. This paper designed and realized personalized search engines system by learning user feedback information. System can be able to optimize searching results and return the results that user is most interested in, also can tell users about other users’ interested modes, in order to make users share searching results with each other and improve the efficiency of searching.


Author(s):  
S. Naseehath

Webometric research has fallen into two main categories, namely link analysis and search engine evaluation. Search engines are also used to collect data for link analysis. A set of measurements is proposed for evaluating web search engine performance. Some measurements are adapted from the concepts of recall and precision, which are commonly used in evaluating traditional information retrieval systems. Others are newly developed to evaluate search engine stability, which is unique to web information retrieval systems. Overlapping of search results, annual growth of search results on each search engines, variation of results on search using synonyms are also used to evaluate the relative efficiency of search engines. In this study, the investigator attempts to conduct a webometric study on the topic medical tourism in Kerala using six search engines; these include three general search engines, namely Bing, Google, and Lycos, and three metasearch engines, namely Dogpile, ixquick, and WebCrawler.


Author(s):  
Carla Ruiz Mafé ◽  
Silvia Sanz Blas

The aim of this chapter is to analyse antecedents of search engines use as prepurchase information tools. Firstly, there is a literature review of the factors influencing search engines use in online purchases. Then, there is an empirical analysis of a sample of 650 Spanish E-shoppers. Logistical regression is used to analyse the influence of demographics, surfing behaviour and purchase motivations on willingness to use search engines for E-shopping. Data analysis shows that experience as Internet user and as Internet shopper are negative key drivers of search engine use. Most of the utilitarian shopping motivations analyzed predict comparison shopping behaviour. Demographics are not determinant variables in the use of search engines in online purchases. This research enables companies to know the factors that potentially affect search engine use in E-shopping decisions and the importance of using search engines in their communication campaigns.


2018 ◽  
Vol 1 (1) ◽  
pp. 15-22
Author(s):  
Diki Arisandi ◽  
Sukri Sukri ◽  
Salamun Salamun ◽  
Roni Salambue

Access to information from the internet is become the thing that is needed by almost all society, and also the student of SMK N II Taluk Kuantan. In this community service, the material that has been given is about how the search results obtained to be more effective by using power searching. Power searching delivered to students of SMK N II Taluk Kuantan is by inserting mathematical symbols on the keywords entered into the search engine on the internet. In addition, the material also discussed about more specific search by combining mathematical symbols, host names and file types to search. The method In this community service was giving the material and demo about how the implementation of power searching on search engines. After this activity is implemented, the community service team evaluates the material that has been given before. The result was the students in SMK N II Taluk Kuantan can implement power searching well.\  


2021 ◽  
pp. 026666692110102
Author(s):  
Mehrdad (Mozaffar) CheshmehSohrabi ◽  
Elham Adnani Sadati

This experimental study used a checklist to evaluate the performance of seven search engines consisting of four Image General Search Engines (IGSEs) (namely, Google, Yahoo DuckDuckGo and Bing), and three Image Specialized Search Engines (ISSEs) (namely, Flicker, PicSearch, and GettyImages) in image retrieval. The findings indicated that the recall average of Image General Search Engines and Image Specialized Search Engines was found to be 76.32% and 24/51% with the precision average of 82/08% and 32/21%, respectively. As the results showed, Yahoo, Google and DuckDuckGo ranked at the top in image retrieval with no significant difference. However, a remarkable superiority with almost 50% difference was observed between the general and specialized image search engines. It was also found that an intense competition existed between Google, Yahoo and DuckDuckGo in image retrieval. The overall results can provide valuable insights for new search engine designers and users in choosing the appropriate search engines for image retrieval. Moreover, the results obtained through the applied equations could be used in assessing and evaluating other search tools, including search engines.


2011 ◽  
Vol 6 (1) ◽  
pp. 81
Author(s):  
Laura Newton Miller

A Review of: Jamali, H. R., & Asadi, S. (2010). Google and the scholar: The role of Google in scientists' information seeking behaviour. Online Information Review, 34(2), 282-294. Objective – To determine how Google’s general search engine impacts the information-seeking behaviour of physicists and astronomers. Design – Using purposive stratified non-random sampling, a mixed-methods study was conducted which included one-on-one interviews, information-event cards, and an online questionnaire survey. Setting – Department of Physics and Astronomy at University College London. Subjects – The researchers interviewed 26 PhD students and 30 faculty members (23% of the department’s 242 faculty and students), and 24 of those participants completed information-event cards. A total of 114 respondents (47.1% of the department members) participated in the online survey. Methods – The researchers conducted 56 interviews which lasted an average of 44 minutes each. These were digitally recorded, fully transcribed, and coded. The researchers asked questions related to information-seeking behaviour and scholarly communication. Four information-event cards were given to volunteer interviewees to gather critical incident information on their first four information-seeking actions after the interview. These were to be completed preferably within the first week of receiving the cards, with 82 cards completed by 24 participants. Once initial analysis of the interviews was completed, the researchers sent an online survey to the members of the same department. Main Results – This particular paper examined only the results related to the scholars’ information-seeking behaviour in terms of search engines and web searching. Details of further results are examined in Jamali (2008) and Jamali and Nicholas (2008). The authors reported that 18% of the respondents used Google on a daily basis to identify articles. They also found that 11% searched subject databases, and 9% searched e-journal websites on a daily basis. When responses on daily searching were combined with those from participants who searched two to three times per week, the most popular method for finding research was by tracking references at the end of an article (61%). This was followed by Google (58%) and ToC email alerts (35%). Responses showed that 46% never used Google Scholar to discover research articles. When asked if they intentionally searched Google to find articles, all except two participants answered that they do not, instead using specific databases to find research. The researchers noted that finding articles in Google was not the original intention of participants’ searches, but more of a by-product of Google searching. In the information-event card study, two categories emerged based on the kinds of information required. This included participants looking for general information on a specific topic (64%, with 22 cases finding this information successfully), and participants knowing exactly what piece of information they were seeking (36%, with 28 cases finding information successfully). There was no occurrence of using Google specifically to conduct a literature search or to search for a paper during this information-event card study, although the researchers say that Google is progressively showing more scholarly information within its search results. (This cannot be ascertained from these specific results except for one response from an interviewee.) The researchers found that 29.4% of respondents used Google to find specific pieces of information, although it was not necessarily scholarly. Conclusion – Physics and astronomy researchers do not intentionally use Google’s general search engine to search for articles, but, Google seems to be a good starting point for problem-specific information queries.


2021 ◽  
pp. 089443932110068
Author(s):  
Aleksandra Urman ◽  
Mykola Makhortykh ◽  
Roberto Ulloa

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.


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