{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T16:05:24Z","timestamp":1768320324785,"version":"3.49.0"},"reference-count":42,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AJIM"],"published-print":{"date-parts":[[2025,4,17]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The landscape of search engine usage has evolved since the last known data were used to calculate click-through rate (CTR) values. The objective was to provide a replicable method for accessing data from the Google search engine using programmatic access and calculating CTR values from the retrieved data to show how the CTRs have changed since the last studies were published.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In this study, the authors present the estimated CTR values in organic search results based on actual clicks and impressions data, and establish a protocol for collecting this data using Google programmatic access. For this study, the authors collected data on 416,386 clicks, 31,648,226 impressions and 8,861,416 daily queries.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The results show that CTRs have decreased from previously reported values in both academic research and industry benchmarks. The estimates indicate that the top-ranked result in Google's organic search results features a CTR of 9.28%, followed by 5.82 and 3.11% for positions two and three, respectively. The authors also demonstrate that CTRs vary across various types of devices. On desktop devices, the CTR decreases steadily with each lower ranking position. On smartphones, the CTR starts high but decreases rapidly, with an unprecedented increase from position 13 onwards. Tablets have the lowest and most variable CTR values.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>The theoretical implications include the generation of a current dataset on search engine results and user behavior, made available to the research community, creation of a unique methodology for generating new datasets and presenting the updated information on CTR trends. The managerial implications include the establishment of the need for businesses to focus on optimizing other forms of Google search results in addition to organic text results, and the possibility of application of this study's methodology to determine CTRs for their own websites.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ajim-04-2023-0107","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T22:39:02Z","timestamp":1704839942000},"page":"513-529","source":"Crossref","is-referenced-by-count":6,"title":["Device-dependent click-through rate estimation in Google organic search results based on clicks and\u00a0impressions data"],"prefix":"10.1108","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3487-0971","authenticated-orcid":false,"given":"Artur","family":"Strzelecki","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3318-534X","authenticated-orcid":false,"given":"Andrej","family":"Miklosik","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"issue":"4","key":"key2025041704031162300_ref001","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1287\/isre.2015.0593","article-title":"Do organic results help or hurt sponsored search performance?","volume":"26","year":"2015","journal-title":"Information Systems Research"},{"key":"key2025041704031162300_ref002","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/1148170.1148175","article-title":"Learning user interaction models for predicting web search result preferences","year":"2006"},{"issue":"1","key":"key2025041704031162300_ref003","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1108\/AJIM-07-2015-0106","article-title":"Improving retrieval relevance using users' explicit feedback","volume":"68","year":"2016","journal-title":"Aslib Journal of Information Management"},{"issue":"1","key":"key2025041704031162300_ref004","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1111\/jems.12141","article-title":"Search engine optimization: what drives organic traffic to retail sites?","volume":"25","year":"2016","journal-title":"Journal of Economics and Management Strategy"},{"key":"key2025041704031162300_ref005","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1145\/1507509.1507511","article-title":"Analysis of long queries in a large scale search log","year":"2009"},{"issue":"18","key":"key2025041704031162300_ref006","doi-asserted-by":"publisher","first-page":"3825","DOI":"10.1016\/j.comnet.2012.10.007","article-title":"Reprint of: the anatomy of a large-scale hypertextual web search engine","volume":"56","year":"2012","journal-title":"Computer Networks"},{"key":"key2025041704031162300_ref007","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1109\/ICDM.2008.78","article-title":"DECK: detecting events from web click-through data","year":"2008"},{"issue":"2","key":"key2025041704031162300_ref009","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1002\/hbe2.142","article-title":"Growing up with Google: how children's understanding and use of internet\u2010based devices relates to cognitive development","volume":"1","year":"2019","journal-title":"Human Behavior and Emerging Technologies"},{"key":"key2025041704031162300_ref010","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116352","article-title":"An information retrieval benchmarking model of satisficing and impatient users' behavior in online search environments","volume":"191","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"key2025041704031162300_ref011","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1145\/1718487.1718510","article-title":"A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine","year":"2010"},{"key":"key2025041704031162300_ref012","doi-asserted-by":"crossref","unstructured":"Erola, A. and Castell\u00e0-Roca, J. 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