Are Sponsored Links Effective? Investigating the Impact of Trust in Search Engine Advertising

2017 ◽  
Vol 7 (4) ◽  
pp. 1-33 ◽  
Author(s):  
Yan Lu ◽  
Michael Chau ◽  
Patrick Y. K. Chau
Keyword(s):  
Search Engine ◽  
Search Engine Advertising ◽  
Sponsored Links ◽  
The Impact

scholarly journals Search Engine Advertising (SEA) Or Organic Links: Do Customers See The Difference?

2012 ◽  
Vol 10 (3) ◽  
pp. 179 ◽  
Author(s):  
Andrzej Kobylanski
Keyword(s):  
Search Engine ◽  
Business Students ◽  
Critical Factor ◽  
Internet Users ◽  
Perceptions And Attitudes ◽  
Specific Category ◽  
Search Engine Advertising ◽  
Sponsored Links ◽  
The Difference ◽  
Marketing Tool

This study seeks to investigate the perceptions and attitudes of internet users toward the sponsored link. In light of this study, respondents do not express greater bias with sponsored links in comparison with traditional advertising media, as well as in comparison with organic links. Among factors that mostly affect searchers attitudes, the informativeness is the critical factor; however, no specific category of information will significantly increase the respondents likelihood to explore a sponsored link. It is rather text relevance to the keyword typed by the searcher that makes a link attractive. Results also indicate that business students recognize a great value and potential benefits of using search engine advertising as a marketing tool.

Aggregate effects of advertising decisions

2018 ◽  
Vol 28 (4) ◽  
pp. 1079-1102 ◽  
Author(s):  
Yanwu Yang ◽  
Xin Li ◽  
Daniel Zeng ◽  
Bernard J. Jansen
Keyword(s):  
Search Engine ◽  
Experimental Studies ◽  
Simulation Framework ◽  
Content Type ◽  
Collective Behaviors ◽  
Strategic Bidding ◽  
Competition Level ◽  
Search Engine Advertising ◽  
Search Market ◽  
The Impact

Purpose The purpose of this paper is to model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or vertical industry, and examine resulting market outcomes, via a proposed simulation framework named Experimental Platform for Search Engine Advertising (EXP-SEA) supporting experimental studies of collective behaviors in the context of search engine advertising. Design/methodology/approach The authors implement the EXP-SEA to validate the proposed simulation framework, also conduct three experimental studies on the aggregate impact of electronic word-of-mouth (eWOM), the competition level and strategic bidding behaviors. EXP-SEA supports heterogeneous participants, various auction mechanisms and also ranking and pricing algorithms. Findings Findings from the three experiments show that both the market profit and advertising indexes such as number of impressions and number of clicks are larger when the eWOM effect is present, meaning social media certainly has some effect on search engine advertising outcomes, the competition level has a monotonic increasing effect on the market performance, thus search engines have an incentive to encourage both the eWOM among search users and competition among advertisers, and given the market-level effect of the percentage of advertisers employing a dynamic greedy bidding strategy, there is a cut-off point for strategic bidding behaviors. Originality/value This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

The Impact of Search Engines on Virus Propagation

2021 ◽  
pp. 2150230
Author(s):  
Cai Fu ◽  
Zhaokang Ke ◽  
Yunhe Zhang ◽  
Xiwu Chen ◽  
Liqing Cao ◽  
...  
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Data Sets ◽  
Immune Mechanism ◽  
Virus Propagation ◽  
Immunization Strategy ◽  
Immune Mechanisms ◽  
Social Network Data ◽  
Information Engineering ◽  
The Impact

With the popularization of computers and the development of information engineering, the emergence of search engines makes it possible to get the information needed from big data quickly and efficiently. However, in recent years, a multiplicity of new viruses have been propagated by search engines. Many researchers choose to cut off the source of virus propagation, ignoring the virus immunization strategy based on the search engine. In this paper, we analyze the impact of search engines on virus propagation. First, considering the immune effect and cost, two kinds of immune mechanisms based on the search engine that have greater practicability are defined. Second, immune mechanisms based on the search engine are theoretically analyzed by the iteration method and the dynamic method. The results show that this immunization strategy can slow down or eliminate the propagation of a virus to a certain extent. Third, three real social network data sets are used to simulate and analyze the immune mechanism. We find that when the proportion of nodes being infected and the proportion of infected nodes being identified by the search engine satisfy a certain relationship, our immune mechanism can inhibit the spread of viruses, which confirms our theoretical analysis results.

Differences Across Device Usage in Search Engine Advertising

2018 ◽  
pp. 250-279
Author(s):  
Carsten D. Schultz ◽  
Christian Holsing
Keyword(s):  
Mobile Devices ◽  
Search Engine ◽  
Service Provider ◽  
Performance Indicators ◽  
Target Group ◽  
Search Process ◽  
Advertising Campaign ◽  
Search Engine Advertising ◽  
Multiple Devices

For advertisers, search engine advertising represents an attractive opportunity to selectively reach the target group at a point in time when the prospects are already thematically involved and activated. One question that subsequently arises is if users use various devices during different phases of the search process and if this behavior affects the search engine advertising outcome measured by corresponding performance indicators. The present chapter addresses this question. Based on a search engine advertising campaign of a German service provider, the authors examine the development of performance indicators across multiple devices. Specifically, we retrace the development across desktops, tablets, and mobile devices. Thus, the chapter provides insights into device usage in search engine advertising. The chapter concludes with overall trends in search engine advertising.

scholarly journals Ranking by Relevance and Citation Counts, a Comparative Study: Google Scholar, Microsoft Academic, WoS and Scopus

2019 ◽  
Vol 11 (9) ◽  
pp. 202 ◽  
Author(s):  
Rovira ◽  
Codina ◽  
Guerrero-Solé ◽  
Lopezosa
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Of Science ◽  
Growth Potential ◽  
Google Scholar ◽  
Unexpected Finding ◽  
Ranking Algorithms ◽  
Microsoft Academic ◽  
Citation Counts ◽  
The Impact

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyze and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyze two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, Web of Science (WoS) used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

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