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2020 | 4(38) | 1-20

Article title

PRELIMINARY RESEARCH OF INFORMATION OVERLOAD FROM INFORMATION SEARCH AND INFORMATION FOLLOW

Content

Title variants

PL
BADANIE WSTĘPNE NAD PRZECIĄŻENIEM INFORMACYJNYM W WYNIKU WYSZUKIWANIA INFORMACJI ORAZ ŚLEDZENIA INFORMACJI

Languages of publication

EN PL

Abstracts

EN
The major objective of this research is to test if two types of information overload are different: Information overload from searching for the information someone needs to search, and information overload from following all the information someone needs to follow. These two types of information overload may be labelled information search overload and information follow overload, corresponding to the concepts of information search and information follow. Using the data of a survey from a sample of about 1600 respondents across 50 states in the United States, the research identified 2 items corresponding to information search overload and information follow overload, and ran analyses including correlation and logistic regression with the 2 items separately as the dependent variables, and with some other items about consumers' activities involving information as independent variables. Results of the various analyses suggest that information search overload and information follow overload are different, especially in terms of how they associate with different variables of consumer activities involving information, therefore indicate as a preliminary research that we may separate the two types of information overload in our future research.
PL
Głównym celem tego badania jest sprawdzenie, czy dwa rodzaje przeciążenia informacyjnego są różne: przeciążenie informacjami w wyniku wyszukiwania potrzebnych informacji i przeciążenie informacyjne wynikające ze śledzenia wszystkich informacji, które ktoś musi śledzić. Te dwa typy przeciążenia informacyjnego są nazywane „przeciążenie informacją wyszukiwaną” i „przeciążenie informacją śledzoną”, co odpowiada pojęciom wyszukiwania informacji i śledzenia informacji. Posługując się danymi z ankiety przeprowadzonej na próbie 1600 respondentów z 50 stanów w Stanach Zjednoczonych, w badaniu zidentyfikowano dwie pozycje odnoszące się do przeciążenia informacją wyszukiwaną oraz przeciążenia informacją śledzoną i przeprowadzono analizę uwzględniającą korelację i regresję logistyczną z obydwiema pozycjami oddzielnie jako zmiennymi zależnymi, a także innymi pozycjami dotyczącymi działań konsumentów, uwzględniając informacje jako niezależne zmienne. Wyniki różnych analiz sugerują, że przeciążenie informacją wyszukiwaną i przeciążenie informacją śledzoną są odmienne, szczególnie pod względem sposobu, w jaki wiążą się z różnymi zmiennymi działań konsumenckich dotyczących informacji, dlatego też jako badanie wstępne wskazują, że możemy odseparować dwa rodzaje przeciążenia informacyjnego w naszych przyszłych badaniach.

Year

Issue

Pages

1-20

Physical description

Dates

online
2020-12

Contributors

  • Governors State University, College of Business, Division of Management, Marketing and Entrepreneurship 1 University Pkwy, University Park, IL 60484, USA

References

  • 1. Agnew, J. R. & Szykman, L. R. (2005). Asset allocation and information overload: The
  • influence of information display, asset choice, and investor experience. Journal of
  • Behavioral Finance, 6(2), 57–70. https://doi.org/10.1207/s15427579jpfm0602_2
  • 2. Allen, D. K. & Shoard, M. (2005). Spreading the load: Mobile information and
  • communications technologies and their effect on information overload. Information
  • Research, 10(2), 1–13.
  • 3. Anderson, M. J. (1988). A comparative analysis of information search and evaluation
  • behavior of professional and non-professional financial analysts. Accounting,
  • Organization and Society, 13(5), 431–446. https://doi.org/10.1016/0361-3682(88)90015-3
  • 4. Anderson, S. P. & de Palma, A. (2012). Competition for attention in the information
  • (overload) age. The RAND Journal of Economics, 43(1), 1–25. https://doi.org/
  • 10.1111/j.1756-2171.2011.00155.x
  • 5. Anderson, S. P. & de Palma, A. (2009). Information Congestion. The RAND Journal of
  • Economics, 40(4), 688–709. https://www.jstor.org/stable/25593734
  • 6. Baranetsky, V. (2017). Information overload is driving us crazy — And the media can
  • help. CNN Opinion. Pozyskano z https://www.cnn.com/2017/12/01/opinions/
  • information-overload-new-media-opinion-baranetsky/index.html
  • 7. Bawden, D. & Robinson, L. (2009). The dark side of information: Overload, anxiety and
  • other paradoxes and pathologies. Journal of Information Science, 35(2), 180–191.
  • https://doi.org/10.1177/0165551508095781
  • 8. Berghel, H. (1997). Cyberspace 2000: Dealing with information overload.
  • Communications of the ACM, 40, 19–24. https://doi.org/10.1145/253671.253680
  • 9. Blair, A. (2003). Reading strategies for coping with information overload ca. 1550–1700.
  • Journal of the History of Ideas, 64(1), 11–28. https://doi.org/10.2307/3654293
  • 10. Blair, A. (2011). Information overload's 2,300-year-old history. Harvard Business
  • Review. Pozyskano z https://hbr.org/2011/03/information-overloads-2300-yea.html
  • 11. Branco, F., Sun, M. & Villas-Boas, J. M. (2016). Too much information? Information
  • provision and search costs. Marketing Science, 35(4), 605–618. https://doi.org/
  • 10.1287/mksc.2015.0959
  • 12. Bray, D. A. (2008). Information pollution, knowledge overload, limited attention spans,
  • and our responsibilities as IS professionals. Emory University Working Paper.
  • https://doi.org/10.2139/ssrn.962732
  • 13. Chen, M. (2018). Improving website structure through reducing information overload.
  • Decision Support Systems, 110, 84–94. https://doi.org/10.1016/j.dss.2018.03.009
  • 14. Chen, W. & Lee, K. H. (2013). Sharing, liking, commenting, and distressed? The
  • pathway between Facebook interaction and psychological distress. Cyberpsychology,
  • Behavior and Social Networking, 16(10), 728–734. https://doi.org/10.1089/
  • cyber.2012.0272
  • 15. Chen, Y., Shang, R. & Kao, C. (2009). The effects of information overload on consumers'
  • subjective state towards buying decision in the internet shopping environment.
  • Electronic Commerce Research and Applications, 8(1), 48–58. https://doi.org/
  • 10.1016/j.elerap.2008.09.001
  • 16. Dean, D. & Webb, C. (2011). Recovering from information overload. McKinsey Quarterly.
  • Pozyskano z https://www.mckinsey.com/business-functions/organization/ourinsights/
  • recovering-from-information-overload
  • 17. Edmunds, A. & Morris, A. (2000). The problem of information overload in business
  • organizations: A review of the literature. International Journal of Information
  • Management, 20(1), 17–29. https://doi.org/10.1016/S0268-4012(99)00051-1
  • 18. Ellison, K. E. (2017). Fatal News: Reading and information overload in early eighteenthcentury
  • literature. Routledge.
  • 19. Eppler, M. J. & Mengis, J. (2004). A framework for information overload research in
  • organizations: Insights from organization science, accounting, marketing, MIS and
  • related disciplines. The Information Society: An International Journal, 20(5), 325–344.
  • 20. Gooding, P, Terras, M., & Warwick, C. (2013). The myth of the new: Mass digitalization,
  • distant reading, and the future of the book. Literary and Linguistic Computing, 28(4),
  • 629–639. https://doi.org/10.1093/llc/fqt051
  • 21. Greenwood, S., Perrin, A. & Duggan, M. (2016). Social media update 2016. Pew
  • Research Center Report. Pozyskano z https://www.pewresearch.org/
  • internet/2016/11/11/social-media-update-2016/
  • 22. Griffiths, M. (2000). Internet addiction-Time to be taken seriously? Addiction Research,
  • 8(5), 413–418. https://doi.org/10.3109/16066350009005587
  • 23. Griffiths, M. D. & Pontes, H. M. (2014). Internet addiction disorder and Internet gaming
  • disorder are not the same. Journal of Addiction Research and Therapy, 5(4), 1–3.
  • https://doi.org/10.4172/2155-6105.1000e124
  • 24. Harper, R. H. R. (2010). Texture: Human Expression in the Age of Communications
  • Overload. The MIT Press.
  • 25. Hemp, P. (2009). Death by information overload. Harvard Business Review, 87(9),
  • 82–89.
  • 26. Heylighen, F. (2002). Complexity and information overload in society: Why increasing
  • efficiency leads to decreasing control. Projekt dla Information Society. Pozyskano z
  • http://pcp.vub.ac.be/Papers/Info-overload.pdf.
  • 27. Holton, A. E. & Chyi, H. I. (2012). News and the overloaded consumer: Factors
  • influencing information overload among news consumers. Cyberpsychology, Behavior
  • and Social Networking, 15(11), 619–624. https://doi.org/10.1089/cyber.2011.0610
  • 28. Horrigan, J. B. (2016a). Information overload. Pew Research Center Report. Pozyskano
  • z http://www.pewinternet.org/2016/12/07/information-overload
  • 29. Horrigan, J. B. (2016b). Libraries 2016. Pew Research Center Report. Pozyskano z
  • https://www.pewresearch.org/internet/2016/09/09/libraries-2016
  • 30. Hunt, R. E. & Newman, R. G. (1997). Medical knowledge overload: A disturbing trend
  • for physicians. Health Care Management Review, 22(1), 70–75.
  • 31. Jacoby, J. (1984). Perspectives on information overload. Journal of Consumer Research,
  • 10(4), 432–435. https://doi.org/10.1086/208981
  • 32. Jacoby, J., Speller, D. E. & Berning, C. K. (1974). Brand choice behavior as a function of
  • information load: Replication and extension. Journal of Consumer Research, 1(1), 33–42.
  • https://www.jstor.org/stable/2488952
  • 33. Jones, Q., Ravid, G. & Rafaeli, S. (2004). Information overload and the messagedynamics of online interaction spaces: A theoretical model and empirical
  • exploration. Information Systems Research, 15(2), 194–210.
  • https://doi.org/10.1287/isre.1040.0023
  • 34. Koroleva, K. & Bolufe-Rohler, A. J. (2012). Reducing information overload: Design and
  • evaluation of filtering and ranking algorithms for social networking sites. ECIS 2012
  • Proceedings.
  • 35. Koroleva, K. & Kane, G. C. (2016). Relational affordances of information processing on
  • Facebook. Information and Management, 54(5), 560–572. https://doi.org/10.1016/
  • j.im.2016.11.007
  • 36. Koulayev, S. (2014). Search for differentiated products: Identification and estimation.
  • The RAND Journal of Economics, 45(3), 553–575. https://doi.org/10.1111/1756-
  • 2171.12062
  • 37. Lee, A. R., Son, S., & Kim, K. K. (2016). Information and communication technology
  • overload and social networking service fatigue: A stress perspective. Computers in
  • Human Behavior, 55(A), 51–61. https://doi.org/10.1016/j.chb.2015.08.011
  • 38. Levitin, D. J. (2014). The Organized Mind: Thinking Straight in the Age of Information
  • Overload. Penguin.
  • 39. Lewis, D. (1996). Dying for information? An investigation into information overload in
  • the UK and worldwide — A Reuters report. UK: Reuters Business Information.
  • 40. Li, C. Y. (2016). Why do online consumers experience information overload? An
  • extension of communication theory. Journal of Information Science, 43(6), 835–851.
  • https://doi.org/10.1177/0165551516670096
  • 41. Li, P. & Sun, Y. (2014). Modeling and performance analysis of information diffusion
  • under information overload in Facebook-like social networks. International Journal of
  • Communication Systems, 27(9), 1268–1288.
  • 42. Lin, C. (2006). Optimal Web site reorganization considering information overload and
  • search depth. European Journal of Operations Research, 173(3), 839–848.
  • https://doi.org/10.1016/j.ejor.2005.05.029
  • 43. Ljungberg, F. & Sorensen, C. (1998). Interaction overload: Managing context and
  • modality. Proceedings of the HICSS-31: Collaboration Technology — Theory and
  • Methodology Minitrack. Big Island, Hawaii.
  • 44. Malhotra, N. K. (1982). Information load and consumer decision making. Journal of
  • Consumer Research, 8(4), 419–430. https://doi.org/10.1086/208882
  • 45. Melinat, P., Kreuzkam, T., & Stamer, D. (2014). Information overload: A systematic
  • literature review. Referat wygłoszony na: Perspectives in Business Informatics Research.
  • Lund, Schweden. https://doi.org/10.13140/2.1.4293.7606
  • 46. Misuraca, R. & Teuscher, U. (2013). Time flies when you maximize-maximizers and
  • satisficers perceive time differently when making decisions. Acta Psychologica, 143(2),
  • 176–180. https://doi.org/10.1016/j.actpsy.2013.03.004
  • 47. Moorthy, S., Ratchford, B. T. & Talukdar, D. (1997). Consumer information search
  • revisited: Theory and empirical analysis. Journal of Consumer Research, 23(4), 263–277.
  • https://doi.org/10.1086/209482
  • 48. Nielsen, R. K. (2009). The labors of Internet-assisted activism: Overcommunication,
  • miscommunication, and communicative overload. Journal of Information Technology
  • and Politics, 6(3–4), 267–280. https://doi.org/10.1080/1933168090304884049. Perrin, A. (2016). Book reading 2016. Pew Research Center Report. Pozyskano z
  • https://www.pewresearch.org/internet/2016/09/01/book-reading-2016
  • 50. Peterson, R. A. & Merino, M. C. (2003). Consumer information search behavior on
  • the Internet. Psychology & Marketing, 20(2), 99–121. https://doi.org/10.1002/
  • mar.10062
  • 51. Pew Research Center (2016). March 7–April 4, 2016 — Libraries. Pozyskano z
  • https://www.pewresearch.org/internet/dataset/march-2016-libraries/
  • 52. Ratchford, B. T. & Srinivasan, N. (1993). An empirical investigation of returns to search.
  • Marketing Science, 12(1), 73–87. https://www.jstor.org/stable/183738
  • 53. Roetzel, P. (2019). Information overload in the information age: A review of the
  • literature from business administration, business psychology, and related disciplines
  • with a bibliometric approach and framework development. Business Research, 12(2),
  • 479–522. https://doi.org/10.1007/s40685-018-0069-z
  • 54. Sasaki, Y., Kawai, D. & Kitamura, S. (2015). The anatomy of tweet overload: How
  • number of tweets received, number of friends, and egocentric network density affect
  • perceived information overload. Telematics and Informatics, 32(4), 853–861.
  • https://doi.org/10.1016/j.tele.2015.04.008
  • 55. Savolainen, R. (2007). Filtering and withdrawing: Strategies for coping with information
  • overload in everyday contexts. Journal of Information Science, 20(10), 1–11.
  • https://doi.org/10.1177%2F0165551506077418
  • 56. Scheibehenne, B., Greifeneder, R. & Todd, P. M. (2010). Can there ever be too many
  • options? A meta-analytic review of choice overload. Journal of Consumer Research,
  • 37(3), 409–425. https://doi.org/10.1086/651235
  • 57. Sevinc, G. & D'Abra, J. (2010). The influence of self-esteem and locus control on perceived
  • e-mail overload. ECIS 2000 Proceedings.
  • 58. Shaver, D. (2007). Impact of the Internet on consumer information search behavior in
  • the United States. Journal of Media Business Studies, 4(2), 27–39.
  • https://doi.org/10.1080/16522354.2007.11073450
  • 59. Shields, M. D. (1980). Some effects of information load on search patterns used to
  • analyze performance reports. Accounting, Organizations and Society, 5(4), 429–442.
  • https://doi.org/10.1016/0361-3682(80)90041-0
  • 60. Sicilia, M. & Ruiz, S. (2010). The effects of the amount of information on cognitive
  • responses in online purchasing tasks. Electronic Commerce Research and applications,
  • 9(2), 183–191. https://doi.org/10.1016/j.elerap.2009.03.004
  • 61. Soule, L. C., Shell, L. W. & Kleen, B. A. (2016). Exploring Internet addiction:
  • Demographic characteristics and stereotypes of heavy Internet users. Journal of
  • Computer Information Systems, 44(1), 64–73.
  • 62. Swar, B., Hameed, T. & Reychav, I. (2017). Information overload, psychological illbeing,
  • and behavioral intention to continue online healthcare information search.
  • Computers in Human Behavior, 70, 416–425. https://doi.org/
  • 10.1016/j.chb.2016.12.068
  • 63. Tam, K. Y. & Ho, S. Y. (2006). Understanding the impact of web personalization on user
  • information processing and decision outcomes. MIS Quarterly, 30(4), 865–890.
  • https://doi.org/10.2307/2514875764. Tungare, M. & Perez-Quinones, M. A. (2009). You scratch my back and I'll scratch yours:
  • Combating email overload collaboratively. Referat wygłoszony na: The 27th
  • International Conference on Human Factors in Computing Systems. https://doi.org/
  • 10.1145/1520340.1520725
  • 65. United States Census Bureau (2016). 2016 Population Estimates. Pozyskano z
  • https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk
  • 66. White, J. D. & Carlston, D. E. (1983). Consequences of schemata for attention,
  • impressions, and recall in complex social interactions. Journal of Personality and Social
  • Psychology, 45(3), 538–549. https://doi.org/10.1037//0022-3514.45.3.538
  • 67. Whittaker, S. & Sidner, C. (1996). Email overload: Exploring personal information
  • management of email. Referat wygłoszony na: Conference on Human Factors in
  • Computing Systems. https://doi.org/10.1145/238386.238530

Notes

EN
Available in Open Access (Open Access)
PL
Publikacja w otwartym dostępie (Open Access)

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-0cbbaace-f9c5-4d59-acc1-ecc3b8452572
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