Research
I do not have a lot of publications yet, but I hope that the ones I have right now are of interest for you. If you have any questions or are interested in commenting my work I will be happy to receive any comments.
1.
Font-Julian, Cristina I.; Orduña-Malea, Enrique; Ontalba-Ruipérez, José-Antonio
Hit count estimate variability for website-specific queries in search engines: The case for rare disease association websites Journal Article
In: Aslib Journal of Information Management, vol. 70, no. 2, pp. 192-213, 2018, ISSN: 2050-3806.
Abstract | Links | BibTeX | Tags: Bing, Google, Hit Count Estimates, Rare Diseases, Search Engines, Website Page Count
@article{nokey,
title = {Hit count estimate variability for website-specific queries in search engines: The case for rare disease association websites},
author = {Cristina I. Font-Julian and Enrique Orduña-Malea and José-Antonio Ontalba-Ruipérez},
doi = {10.1108/AJIM-10-2017-0226},
issn = {2050-3806},
year = {2018},
date = {2018-02-07},
urldate = {2018-02-07},
journal = {Aslib Journal of Information Management},
volume = {70},
number = {2},
pages = {192-213},
abstract = {Purpose
The purpose of this paper is to determine the effect of the chosen search engine results page (SERP) on the website-specific hit count estimation indicator.
Design/methodology/approach
A sample of 100 Spanish rare disease association websites is analysed, obtaining the website-specific hit count estimation for the first and last SERPs in two search engines (Google and Bing) at two different periods in time (2016 and 2017).
Findings
It has been empirically demonstrated that there are differences between the number of hits returned on the first and last SERP in both Google and Bing. These differences are significant when they exceed a threshold value on the first SERP.
Research limitations/implications
Future studies considering other samples, more SERPs and generating different queries other than website page count (<site>) would be desirable to draw more general conclusions on the nature of quantitative data provided by general search engines.
Practical implications
Selecting a wrong SERP to calculate some metrics (in this case, website-specific hit count estimation) might provide misleading results, comparisons and performance rankings. The empirical data suggest that the first SERP captures the differences between websites better because it has a greater discriminating power and is more appropriate for webometric longitudinal studies.
Social implications
The findings allow improving future quantitative webometric analyses based on website-specific hit count estimation metrics in general search engines.
Originality/value
The website-specific hit count estimation variability between SERPs has been empirically analysed, considering two different search engines (Google and Bing), a set of 100 websites focussed on a similar market (Spanish rare diseases associations), and two annual samples, making this study the most exhaustive on this issue to date.
},
keywords = {Bing, Google, Hit Count Estimates, Rare Diseases, Search Engines, Website Page Count},
pubstate = {published},
tppubtype = {article}
}
Purpose
The purpose of this paper is to determine the effect of the chosen search engine results page (SERP) on the website-specific hit count estimation indicator.
Design/methodology/approach
A sample of 100 Spanish rare disease association websites is analysed, obtaining the website-specific hit count estimation for the first and last SERPs in two search engines (Google and Bing) at two different periods in time (2016 and 2017).
Findings
It has been empirically demonstrated that there are differences between the number of hits returned on the first and last SERP in both Google and Bing. These differences are significant when they exceed a threshold value on the first SERP.
Research limitations/implications
Future studies considering other samples, more SERPs and generating different queries other than website page count (<site>) would be desirable to draw more general conclusions on the nature of quantitative data provided by general search engines.
Practical implications
Selecting a wrong SERP to calculate some metrics (in this case, website-specific hit count estimation) might provide misleading results, comparisons and performance rankings. The empirical data suggest that the first SERP captures the differences between websites better because it has a greater discriminating power and is more appropriate for webometric longitudinal studies.
Social implications
The findings allow improving future quantitative webometric analyses based on website-specific hit count estimation metrics in general search engines.
Originality/value
The website-specific hit count estimation variability between SERPs has been empirically analysed, considering two different search engines (Google and Bing), a set of 100 websites focussed on a similar market (Spanish rare diseases associations), and two annual samples, making this study the most exhaustive on this issue to date.
The purpose of this paper is to determine the effect of the chosen search engine results page (SERP) on the website-specific hit count estimation indicator.
Design/methodology/approach
A sample of 100 Spanish rare disease association websites is analysed, obtaining the website-specific hit count estimation for the first and last SERPs in two search engines (Google and Bing) at two different periods in time (2016 and 2017).
Findings
It has been empirically demonstrated that there are differences between the number of hits returned on the first and last SERP in both Google and Bing. These differences are significant when they exceed a threshold value on the first SERP.
Research limitations/implications
Future studies considering other samples, more SERPs and generating different queries other than website page count (<site>) would be desirable to draw more general conclusions on the nature of quantitative data provided by general search engines.
Practical implications
Selecting a wrong SERP to calculate some metrics (in this case, website-specific hit count estimation) might provide misleading results, comparisons and performance rankings. The empirical data suggest that the first SERP captures the differences between websites better because it has a greater discriminating power and is more appropriate for webometric longitudinal studies.
Social implications
The findings allow improving future quantitative webometric analyses based on website-specific hit count estimation metrics in general search engines.
Originality/value
The website-specific hit count estimation variability between SERPs has been empirically analysed, considering two different search engines (Google and Bing), a set of 100 websites focussed on a similar market (Spanish rare diseases associations), and two annual samples, making this study the most exhaustive on this issue to date.