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Construction of information search behavior based on data mining

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Abstract

With the increasing maturity of Web 2.0–related technologies and the expansion of applications, a large number of social network services have emerged at home and abroad. These network platforms have greatly enriched the lives of netizens and become an important platform for studying user information behavior. At the same time, the development of technologies such as global positioning system technology, search engine, and data mining has made users’ data in the mobile social network platform receive extensive attention. This paper constructs a model of information search behavior based on data mining technology in mobile socialized networks and tests it with empirical methods. The results show that the usefulness of information plays a mediating role in the path of information usability impact on information search behavior. Product interaction and human-computer interaction can significantly affect the information search behavior. Trust plays a mediating role in the interaction of virtual community interaction (product interaction, interpersonal interaction, and human-computer interaction) on information search behavior. Using data mining technology to mine user needs, mining relevant data in search and mining information utilization, can improve user information search efficiency and efficiency. What’s more, it can provide basis and support for users and website decision-making.

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References

  1. CNNIC The 42nd “Statistical Report on the Development of China’s Internet”. https://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201808/t20180820_70488.htm 2018. Accessed 11 Feb 2019

  2. Whiting A, Williams DL (2013) Why people use social media: a uses and gratifications approach. Qual Mark Res 16(4):362–369(8)

    Google Scholar 

  3. Ye-Bing C (2013) Research on individual tacit knowledge sharing in SNS virtual community. Inf Sci

  4. Laumann E, Galaskiewicz J, Marsden PV (1978) Community structure as inter-organizational linkages. Annu Rev Sociol 4(3):455–484

    Google Scholar 

  5. Kilduff M, Tsai W (2003) Social networks and organizations. Sage, London

    Google Scholar 

  6. Lai LSL, Turban E (2008) Groups formation and operations in the Web 2.0 environment and social networks. Group Decis Negot 17(5):387–402

    Google Scholar 

  7. Ellison N, Steinfield C, Lampe C (2009) The benefits of Facebook ‘friends’: social capital and college students use of online social network sites. J Comput-Mediat Commun 12(4):1143–1168

    Google Scholar 

  8. Fishbein M, Ajzen I (1975) Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading, MA

    Google Scholar 

  9. Davis F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarte 13(3):319–341

    Google Scholar 

  10. Xun L (2006) Data mining: modeling, algorithms, applications and systems. Comput Technol Dev 16(1)

  11. Fayyad UM (2001) Knowledge discovery in databases: an overview. Elational data mining. 28–47

  12. Berlin (2008) Analysis of the relationship between data mining and knowledge discovery. Intell Theory Pract 4:507–510

    Google Scholar 

  13. Han J, Kamber M, JianPei YJ, Campbell HJ et al (2012) Data mining: concepts and technology. Mechanical Industry Press

  14. Viet D, Tran L, Adislav Hluchy, Ondrej Habala (2010) Data mining environmental scenarios. Symposium on Information Technology

  15. Xiaoling L, Bangchang X (2009) Data mining: methods and applications. Renmin University of China Press

  16. Wilson TD (2002) Human information behavior, informing. Science 3(2):49–55

    Google Scholar 

  17. Solomon MR (2012) Consumer behavior (10th edition). Prentice Hall, New Jersey

    Google Scholar 

  18. Moorthy S, Ratchford BT, Talukdar D (1997) Consumer information search revisited: theory and empirical analysis. J Consum Res 23(4):263–277

    Google Scholar 

  19. Guo C (2001) A review on consumer external search: amount and determinants. J Bus Psychol 15(3):505–519

    Google Scholar 

  20. Wilson TD (1981) On user studies and information needs. J Doc 37(6):658–670

    Google Scholar 

  21. Kuhlthau CC Accommodating the user’s information search process: challenges for information retrieval system designers[EB/OL]. http://www.asis.org/Bulletin/Feb-99/kuhlthau.html [2005-05-12]. Accessed 9 Feb 2019

  22. Choo CWC Losing the cognitive gaps: how people process information. Financial Times of London, Information Management Series, March 22. 1999

  23. Dervin B (1983) An overview of sense-making research: concepts, methods, and results to date. Chicago, USA

  24. Awasthy D, Banerjee A, Banerjee B (2012) Understanding the role of prior product knowledge to information search: an application of process theory to the Indian market. Asia Pac J Mark Logist 24(2):257–287

    Google Scholar 

  25. Saracevic T (1997) The stratified model of information retrieval interaction: extension and applications. Proc Am Soc Inf Sci Technol 34(3):313–327

    Google Scholar 

  26. Bock GW, Kankanhalli A, Sharma S (2006) Are norms enough? The role of collaborative norms in promoting organizational knowledge seeking. Eur J Inf Syst 15(4):357–367

    Google Scholar 

  27. Kankanhalli A, Tan BCY, Wei KK (2005) Understanding seeking from electronic knowledge repositories: an empirical study. J Am Soc Inf Sci Technol 56(11):1156–1166

    Google Scholar 

  28. Desouza KC, Awazu Y, Wan Y (2006) Factors governing the consumption of explicit knowledge. J Am Soc Inf Sci Technol 57(1):36–43

    Google Scholar 

  29. Zha X, Zhang J, Yan Y, Xiao Z (2015) Does affinity matter? Slow effects of e-quality on information seeking in virtual communities. Libr Inf Sci Res 37(1):68–76

    Google Scholar 

  30. Evans BM, Chi EH (2010) An elaborated model of social search. Inf Process Manag 46(9):656–678

    Google Scholar 

  31. Libai B, Bolton R, Bugel MS et al (2010) Customer-to-customer interactions: broadening the scope of word of mouth research. J Serv Res 13(3):267–282

    Google Scholar 

  32. Valck KD et al (2007) Satisfaction with virtual communities of interest: effect on members’ visit frequency. Br J Manag 18(3):241–256

    Google Scholar 

  33. Shanji Y, Dewen L (2016) Customer participation, customer interaction and new product value under crowdsourcing mode. Financial Theory 10:85–95

    Google Scholar 

  34. Liu Z, Jansen BJ (2013) Almighty Twitter, what are people asking for? Proc Am Soc Inf Sci Technol 49(1):1–10

    Google Scholar 

  35. Belkin NJ (1978) Information concerts of information science. J Doc (07):34–81

  36. Changping H (2008) Information services and users. Wuhan University Press, Wuhan

    Google Scholar 

  37. Krikelas J (1983) Information-seeking behavior: patterns and concepts. Drexel Library Quarterly 19(2):5–20

    Google Scholar 

  38. Kulkarni R, Haynes KE, Stough P, Jean HP (2009) Forecasting housing prices with Google econometrics. GMU School of Public Policy

  39. Haiyan S, Pan B (2011) Forecasting demand for hotel rooms with search engine query volume data. Working Paper

  40. Lachowska M (2013) Expenditure, confidence, and uncertainty: identifying shocks to consumer confidence using daily data, working paper

    Google Scholar 

  41. Penna ND, Huang H (2009) Constructing a consumer confidence index for the US using web search volume, Working Paper

  42. Bergman O, Yanai N (2018) Personal information retrieval: smartphones vs. computers, emails vs. files. Pers Ubiquit Comput 22(4):621–632

    Google Scholar 

  43. Amela Karahasanovic´, Jan Heim. Understanding the behaviour of online TV users. Pers Ubiquit Comput, 19(5–6), pp.839–852, 2015

  44. Gallaugher J (1999) Challenging the new conventional wisdom of net commerce strategies. Commun ACM 42:27–29

    Google Scholar 

  45. Smith D, Menon S, Sivakumar K. Online peer and editorial recommendations, trust, and choice in virtual markets, J Interact Mark, (19), pp.15–37, 2005

  46. Taylor R, Wolburg J (1997) Mess ages of individualism in French , Spanish and American television advertising. Paper Presented at the 1997 AEJMC Convention, Chicago

  47. Wong YK, Hsu CJ (2008) A confidence-based framework for business to consumer (b2c) mobile commerce adoption. Pers Ubiquit Comput 12(1):77–84

    Google Scholar 

  48. Wilson TD (1999) Models in information behavior research. J Doc 55(3):249–270

    Google Scholar 

  49. Kamvar M, Baluja S (2006) A large scale study of wireless search behavior: Google mobile search. Sigchi Conference on Human Factors in Computing Systems

  50. Park JH et al (2014) An investigation of information sharing and seeking behaviors in online investment communities. Comput Hum Behav 31:1–12

    Google Scholar 

  51. Jin XL et al (2009) How to keep members using the information in a computer-supported social network. Comput Hum Behav 25(5):1172–1181

    Google Scholar 

  52. Nambisan S, Baron RA (2009) Virtual customer environments: testing a model of voluntary participation in value co-creation activities. J Prod Innov Manag 26(4):388–406

    Google Scholar 

  53. Novak HTP (1996) Marketing in hypermedia computer-mediated environments: conceptual foundations. J Mark 60(3):50–68

    Google Scholar 

  54. Vallerand RJ (1997) Toward a hierarchical model of intrinsic and extrinsic motivation. Adv Exp Soc Psychol 29(08):271–360

    Google Scholar 

  55. Yun, Woong G (2007) Interactivity concepts examined: response time, hypertext, role taking, and multimodality. Media Psychol 9(3):527–548

    Google Scholar 

  56. Allam A, Kostova Z, Nakamoto K, Schulz PJ (2015) The effect of social support features and gamification on a web-based intervention for rheumatoid arthritis patients: randomized controlled trial. J Med Internet Res 17(1)

  57. Jeon YJ, Rieh SY (2015) Social search behavior in a social Q&A service: goals, strategies, and outcomes. Proc Assoc Inf Sci Technol 52(1):1–10

    Google Scholar 

  58. Ba S (2001) Establishing online trust through a community responsibility system. Decis Support Syst 31(3):323–336

    Google Scholar 

  59. Ridings C, Gefen D, Arinze B (2006) Psychological barriers: lurker and poster motivation and behavior in online communities. Commun Assoc Inf Syst 18:329–354

    Google Scholar 

  60. Brodie RJ, Hollebeek LD, Jurić B, Ilić A (2011) Customer Engagement. J Serv Res 14(3):252–271

    Google Scholar 

  61. Huvila I (2017) Distrust, mistrust, untrust and information practices[J/OL]. Information Research, http://InformationR.net/ir/22-1/isic/isic1617.html. 22 (1) ,paper isic1617. Accessed 11 Feb 2019

  62. Moon JW, Kim YG (2001) Extending the TAM for a World-Wide-Web context. Inf Manag 38(4):217–230

    MathSciNet  Google Scholar 

  63. Alraimi KM, Zo H, Ciganek AP (2015) Understanding the MOOCs continuance: the role of openness and reputation. Comput Educ 80:28–38

    Google Scholar 

  64. Preece J (2001) Sociability and usability in online communities:determining and measuring success. Behav Inf Technol 20(5):347–356

    Google Scholar 

  65. Fiske AP (2002) Socio-moral emotions motivate action to sustain relationships. Self Identity 1(2):169–175

    Google Scholar 

  66. Venkatesh V, Morris MG, Davis GB et al (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478

    Google Scholar 

Download references

Funding

This research was supported by National Natural Science Foundation of China (No.71562020), Thirteenth Five-Year Planning (2017) research project of Jiangxi Social Science (No.17GL05), and Jiangxi Universities Humanities and Social Sciences Research on Young Fund (GL17115).

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Authors and Affiliations

  1. School of Business, Jiangxi Normal University, No. 99, Ziyang Road, Nanchang, 330022, Jiangxi, China

    Hong Jin & Yunting Miao

  2. Virtual Augmented Reality Center, Namseoul University, Daehak-ro, Seonghwan-eup, Seobuk-gu, Cheonan, Chungnam, 31020, South Korea

    Jae-Rim Jung

  3. Business School, Nankai University, Baidi Road, Nankai District, Tianjin, 300071, China

    Dongjin Li

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  1. Hong Jin
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  2. Yunting Miao
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Correspondence to Dongjin Li.

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Jin, H., Miao, Y., Jung, JR. et al. Construction of information search behavior based on data mining. Pers Ubiquit Comput 26, 233–245 (2022). https://doi.org/10.1007/s00779-019-01239-8

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