Estimating Google’s search engine ranking function from a search engine optimization perspective

Luh, Cheng-Jye; Yang, Sheng-An; Huang, Ting-Li Dean

doi:10.1108/oir-04-2015-0112

Cited by 64 publications

(40 citation statements)

References 28 publications

(29 reference statements)

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“…However, although prior studies have shown that the implementation of SEO strategies returns higher rankings and possibly better organic traffic [16,18,19], it remains unclear whether the size of the website affects the organic search traffic percentage, that is, the bigger the content of the website in terms of webpages, images, internal, and external links, the greater the percentage of organic search engine traffic. This implication led us to develop the first hypothesis:…”

Section: The Size Of Cultural Heritage Websitesmentioning

confidence: 99%

Big Data Analytics for Search Engine Optimization

Drivas

Σακάς

Γιαννακόπουλος

et al. 2020

BDCC

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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.

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Section: The Size Of Cultural Heritage Websitesmentioning

confidence: 99%

Big Data Analytics for Search Engine Optimization

Drivas

Σακάς

Γιαννακόπουλος

et al. 2020

BDCC

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“…The use of target keywords related to the business or themes of the site in the HMTL code of webpages (title tag, description tag, headings, alt-tags and URL) affects the website relevance on search engines (Luh et al, 2016; Pérez-Montoro and Codina, 2017). Search engines use the metadata supplied in the webpages to extract keywords and other related information, and use them as index terms in their databases (Zhang and Dimitroff, 2005: 693).…”

Section: Relevance Of the Banking Sector Websitesmentioning

confidence: 99%

“…Search engines and their algorithms are neither neutral nor independent tools (Haider, 2016), ranking webpages according to a broad range of factors. Google is said to employ more than 250 factors in its ranking algorithm, most of which held as closely guarded secrets (Luh et al, 2016). Although the exact algorithms used differ across search engines, the major players in the field (e.g., Google, Bing) rank and display search results by taking into account the similarity of a website's content to the users' query, as well as kojih se većina drži kao strogo čuvana tajna (Luh et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Google is said to employ more than 250 factors in its ranking algorithm, most of which held as closely guarded secrets (Luh et al, 2016). Although the exact algorithms used differ across search engines, the major players in the field (e.g., Google, Bing) rank and display search results by taking into account the similarity of a website's content to the users' query, as well as kojih se većina drži kao strogo čuvana tajna (Luh et al, 2016). Iako se korišćeni algoritmi razlikuju među pretraživačima, glavni igrači (npr.…”

Section: Introductionmentioning

confidence: 99%

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Organic search visibility of Serbian banks: Common ailments and lost opportunities

Krstić¹,

Gajić²

2019

Bankarstvo

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This paper aims to analyze the organic search engine visibility applied on the case of Serbian banks, employing an exploratory approach to the use of search engines as one of the major information, sales and interaction channels in the banking sector. In the methodology, web crawling and SEO software tools have been used as a source for collecting the website performance data. The generated data was intersected with some of the finance industry ranking factors, aiming to determine how the Serbian banks are performing on Google and what are their common ailments and lost opportunities in organic search visibility. The research findings showed that the country's largest banks are using search engine optimization as an online marketing strategy, but it still lacks strategic orientation in the areas of content relevance, mobile site speed, web traffic from referrals, social and email marketing, backlinking quality and social signals.

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“…Norris [12] proves in her study that Google has an influence on libraries, while a librarian is the secondary player when the user has access to Google and other search engines. A variety of studies from 2001 onwards such as [12][13][14][15][16] have been conducted which investigate and validate the use of Google as one of the favorite search engines among academia in general and among library professionals in particular. One aspect of Googlization is related to the declined usage of physical library material, which has also influenced the library's reference desk and caused swift modification of reference services from human to digital and from physical to virtual.…”

Section: Related Literaturementioning

confidence: 99%

Virtual Reference Services through Web Search Engines: Study of Academic Libraries in Pakistan

Khan

Malik

et al. 2017

Publications

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Web search engines (WSE) are powerful and popular tools in the field of information service management. This study is an attempt to examine the impact and usefulness of web search engines in providing virtual reference services (VRS) within academic libraries in Pakistan. The study also attempts to investigate the relevant expertise and skills of library professionals in providing digital reference services (DRS) efficiently using web search engines. Methodology used in this study is quantitative in nature. The data was collected from fifty public and private sector universities in Pakistan using a structured questionnaire. Microsoft Excel and SPSS were used for data analysis. The study concludes that web search engines are commonly used by librarians to help users (especially research scholars) by providing digital reference services. The study also finds a positive correlation between use of web search engines and quality of digital reference services provided to library users. It is concluded that although search engines have increased the expectations of users and are really big competitors to a library's reference desk, they are however not an alternative to reference service. Findings reveal that search engines pose numerous challenges for librarians and the study also attempts to bring together possible remedial measures. This study is useful for library professionals to understand the importance of search engines in providing VRS. The study also provides an intellectual comparison among different search engines, their capabilities, limitations, challenges and opportunities to provide VRS effectively in libraries.

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Estimating Google’s search engine ranking function from a search engine optimization perspective

Cited by 64 publications

References 28 publications

Big Data Analytics for Search Engine Optimization

Big Data Analytics for Search Engine Optimization

Organic search visibility of Serbian banks: Common ailments and lost opportunities

Virtual Reference Services through Web Search Engines: Study of Academic Libraries in Pakistan

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