Skip to main content
Log in

The Search Studies Group at Hamburg University of Applied Sciences

  • Datenbankgruppen vorgestellt
  • Published:
Datenbank-Spektrum Aims and scope Submit manuscript

Abstract

We present an overview of the work of the Search Studies research group, focusing on commercial search engines from a user perspective. This encompasses studying what users of these search engines get to see on the result pages, how users interact with search engines, and the effect both have on knowledge acquisition in society. Our research combines search engine data analysis, by collecting and analysing data from commercial search engines (data science), with understanding information-seeking behaviour through conducting user studies in different settings (information science), ranging from large, representative online surveys to behavioural studies in the lab employing, amongst others, eye-tracking.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

References

  1. Buckland M (2017) Information and society. MIT Press, Cambridge

    Book  Google Scholar 

  2. Haider J, Sundin O (2019) Invisible search and Online search engines. Routledge, Oxford, New York

    Book  Google Scholar 

  3. White RW (2016) Interactions with search systems. Cambridge University Press, New York

    Book  Google Scholar 

  4. Järvelin K (2019) Salton award keynote. ACM SIGIR Forum 52:52–63. https://doi.org/10.1145/3308774.3308782

    Article  Google Scholar 

  5. Belkin NJ (2015) Salton award lecture people, interacting with information. In: SIGIR 2015—Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, New York, pp 1–2

    Google Scholar 

  6. Lewandowski D (2014) Die Macht der Suchmaschinen und ihr Einfluss auf unsere Entscheidungen. Inf Wiss Prax 65:231–238. https://doi.org/10.1515/iwp-2014-0050

    Article  Google Scholar 

  7. Lewandowski D, Kerkmann F, Sünkler S (2014) Wie Nutzer im Suchprozess gelenkt werden: Zwischen technischer Unterstützung und interessengeleiteter Darstellung. In: Stark B, Dörr D, Aufenanger S (eds) Die Googleisierung der Informationssuche – Suchmaschinen im Spannungsfeld zwischen Nutzung und Regulierung. De Gruyter, Berlin

    Google Scholar 

  8. Lewandowski D, Kammerer Y (2020) Factors influencing viewing behaviour on search engine results pages: a review of eye-tracking research. Behav Inf Technol. https://doi.org/10.1080/0144929X.2020.1761450

    Article  Google Scholar 

  9. Lewandowski D, Kerkmann F, Rümmele S, Sünkler S (2018) An empirical investigation on search engine ad disclosure. J Assoc Inf Sci Technol 69:420–437. https://doi.org/10.1002/asi.23963

    Article  Google Scholar 

  10. Schultheiß S, Lewandowski D (2021) Misplaced trust? The relationship between trust, ability to identify commercially influenced results, and search engine preference. J Inf Sci. https://doi.org/10.1177/01655515211014157

    Article  Google Scholar 

  11. European Commission (2016) Special eurobarometer 447—Online platforms. European Commission, Brussels https://doi.org/10.2759/937517

    Book  Google Scholar 

  12. Lewandowski D (2018) Suchmaschinen verstehen, 2nd edn. Springer, Berlin, Heidelberg, New York

    Book  Google Scholar 

  13. Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge

    Book  Google Scholar 

  14. Croft WB, Metzler D, Strohman T (2009) Search engines: information retrieval in practice. Pearson, Boston

    Google Scholar 

  15. Büttcher S, Clarke CLA (2010) Information retrieval: implementing and evaluating search engines. MIT Press, Cambridge

    MATH  Google Scholar 

  16. Lewandowski D (2017) Users’ understanding of search engine advertisements. J Inf Sci Theory Pract 5:6–25. https://doi.org/10.1633/JISTaP.2017.5.4.1

    Article  Google Scholar 

  17. Schultheiß S, Lewandowski D (2021) How users’ knowledge of advertisements influences their viewing and selection behavior in search engines. J Assoc Inf Sci Technol. https://doi.org/10.1002/asi.24410

    Article  Google Scholar 

  18. Li K, Lin M, Lin Z, Xing B (2014) Running and chasing—The competition between paid search marketing and search engine optimization. In: Proc Annu Hawaii Int Conf Syst Sci, pp 3110–3119 https://doi.org/10.1109/HICSS.2014.640

    Chapter  Google Scholar 

  19. Schultheiß S, Lewandowski D (2021) Expert interviews with stakeholder groups in the context of commercial search engines within the SEO effect project. https://osf.io/5aufr/. Accessed: 9 June 2021

  20. Schultheiß S, Lewandowski D (2020) “Outside the industry, nobody knows what we do” SEO as seen by search engine optimizers and content providers. J Doc 77:542–557. https://doi.org/10.1108/JD-07-2020-0127

    Article  Google Scholar 

  21. McCue T (2018) SEO industry approaching $80 billion but all you want is more web traffic. In: forbes.com. https://www.forbes.com/sites/tjmccue/2018/07/30/seo-industry-approaching-80-billion-but-all-you-want-is-more-web-traffic/. Accessed: 9 June 2021

  22. Erlhofer S (2020) Suchmaschinen-Optimierung: das umfassende Handbuch, 10th edn. Rheinwerk Computing, Bonn

    Google Scholar 

  23. Enge E, Spencer S, Stricchiola J (2015) The Art of SEO: mastering search engine optimization, 3rd edn. O’Reilly, Sebastopol

    Google Scholar 

  24. Petrescu P (2014) Google organic click-through rates in 2014. https://moz.com/blog/google-organic-click-through-rates-in-2014. Accessed: 9 June 2021

  25. Goel S, Broder A, Gabrilovich E, Pang B (2010) Anatomy of the long tail. In: Davison BD, Suel T, Craswell N, Liu B (eds) Proceedings of the third ACM international conference on Web search and data mining—WSDM ’10. ACM Press, New York, p 201

    Chapter  Google Scholar 

  26. Lewandowski D, Sünkler S, Yagci N (2021) The influence of search engine optimization on Google’s results: a multi-dimensional approach for detecting SEO. In: WebSci ’21: Proceedings of the 13th ACM Conference on Web Science. ACM, New York https://doi.org/10.1145/3447535.3462479

    Chapter  Google Scholar 

  27. Beel J, Gipp B, Wilde E (2010) Academic search engine optimization (ASEO/span). J Sch Publ 41:176–190. https://doi.org/10.3138/jsp.41.2.176

    Article  Google Scholar 

  28. Lewandowski D, Sünkler S (2013) Representative online study to evaluate the commitments proposed by Google as part of EU competition investigation AT. 39740-Google: report for Germany. https://searchstudies.org/wp-content/uploads/2021/06/Google_Online_Survey_DE.pdf. Accessed: 9 June 2021

  29. Schultheiß S, Linhart A, Behnert C et al (2020) Known-item searches and search tactics in library search systems: results from four transaction log analysis studies. J Acad Librariansh 46:102202. https://doi.org/10.1016/j.acalib.2020.102202

    Article  Google Scholar 

  30. Behnert C, Lewandowski D (2017) Known-item searches resulting in zero hits: considerations for discovery systems. J Acad Librariansh 43:128–134. https://doi.org/10.1016/j.acalib.2016.12.002

    Article  Google Scholar 

  31. Sanderson M, Croft WB (2012) The history of information retrieval research. Proc IEEE 100:1444–1451. https://doi.org/10.1109/JPROC.2012.2189916

    Article  Google Scholar 

  32. Stock WG, Stock M (2013) Handbook of information science. De Gruyter, Berlin

    Book  Google Scholar 

  33. Lewandowski D (2018) Zugänglichkeit von Information Services und ihren Inhalten über Suchmaschinen. In: Schade F, Georgy U (eds) Praxishandbuch Informationsmarketing. De Gruyter, Berlin, Boston, pp 358–369

    Chapter  Google Scholar 

  34. Behnert C (2019) Investigating the effects of popularity data on predictive relevance judgments in academic search systems. In: Proceedings of the 2019 Conference on Human Information Interaction and Retrieval. ACM, New York, pp 437–440

    Chapter  Google Scholar 

  35. Behnert C (2019) Kriterien und Einflussfaktoren bei der Relevanzbewertung von Surrogaten in akademischen Informationssystemen. Inf Wiss Prax 70:24–32

    Google Scholar 

  36. Singer G, Norbisrath U, Vainikko E et al (2011) Search-logger analyzing exploratory search tasks. In: Proceedings of the 2011 ACM Symposium on Applied Computing—SAC ’11. ACM Press, New York, pp 751–756

    Chapter  Google Scholar 

  37. Sünkler S, Lewandowski D (2017) Does it matter which search engine is used? A user study using post-task relevance judgments. Proc Assoc Inf Sci Technol 54:405–414. https://doi.org/10.1002/pra2.2017.14505401044

    Article  Google Scholar 

  38. Lewandowski D (2019) The web is missing an essential part of infrastructure: an open web index. Commun ACM 62:24–27. https://doi.org/10.1145/3312479

    Article  Google Scholar 

  39. Lewandowski D (2016) Perspektiven eines Open Web Index. Inf Wiss Prax 67:15–21. https://doi.org/10.1515/iwp-2016-0020

    Article  Google Scholar 

  40. Lewandowski D (2014) Why we need an independent index of the web. In: König R, Rasch M (eds) Society of the query reader: reflections on web search. Institute of Network Culture, Amsterdam, pp 49–58

    Google Scholar 

  41. Blank D, Fuhr N, Henrich A, Mandl T, Rölleke T, Schütze H, Stein B (2011) Teaching IR: curricular considerations. In: Efthimiadis E (ed) Teaching and learning in information retrieval. Springer, Berlin, Heidelberg, New York, pp 31–46 https://doi.org/10.1007/978-3-642-22511-6_3

    Chapter  Google Scholar 

  42. Markov I, de Rijke M (2019) What should we teach in information retrieval? ACM SIGIR Forum 52:19–39. https://doi.org/10.1145/3308774.3308780

    Article  Google Scholar 

  43. Lewandowski D, Sünkler S, Schultheiß S (2020) Studies on search: designing meaningful IIR studies on commercial search engines. Datenbank Spektrum 20:5–15. https://doi.org/10.1007/s13222-020-00331-1

    Article  Google Scholar 

Download references

Acknowledgements

We would especially like to thank our student assistants, past and present, whose work often remains unrecognized in research outputs.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dirk Lewandowski.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lewandowski, D., Sünkler, S., Schultheiß, S. et al. The Search Studies Group at Hamburg University of Applied Sciences. Datenbank Spektrum 21, 145–154 (2021). https://doi.org/10.1007/s13222-021-00375-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13222-021-00375-x

Keywords

Navigation