IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v10y2011i02ns0219622011004336.html
   My bibliography  Save this article

An Optimal Trust Management Method To Protect Privacy And Strengthen Objectivity In Utility Computing Services

Author

Listed:
  • JUNSEOK HWANG

    (Technology Management, Economics and Policy Program, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea)

  • SO YOUNG KIM

    (Technology Management, Economics and Policy Program, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea)

  • HAK-JIN KIM

    (School of Business, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, Republic of Korea)

  • JIHYOUN PARK

    (Korea Insititue of Science and Technology, Europe Forschungsgesellschaft mbH, Universität des Saarlandes, Campus E71, 66123 Saarbrücken, Germany)

Abstract

Utility computing services constitute business opportunities in the customer-to-customer (C2C) marketplace. Providers deal with the risks and uncertainties faced by users while transacting with unfamiliar counterparts. Ensuring trustworthy transactions among unfamiliar users is an important condition of utility-computing service. Many peer-to-peer (P2P) sites utilize reputation systems; however, such systems are subject to lack of reviewer objectivity and robustness against attacks as well as unfair ratings. In this study, we propose the manual adjustment of reputation ratings by means of transaction monitoring and establish that the resulting enhanced objectivity and robustness in utility-computing service transactions will strengthen the trust management system. We also propose the optimal level of monitoring and penalizing activities based on the level of privacy concerns among users and the appropriate complementary service.

Suggested Citation

  • Junseok Hwang & So Young Kim & Hak-Jin Kim & Jihyoun Park, 2011. "An Optimal Trust Management Method To Protect Privacy And Strengthen Objectivity In Utility Computing Services," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 287-308.
  • Handle: RePEc:wsi:ijitdm:v:10:y:2011:i:02:n:s0219622011004336
    DOI: 10.1142/S0219622011004336
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622011004336
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0219622011004336?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Janice Y. Tsai & Serge Egelman & Lorrie Cranor & Alessandro Acquisti, 2011. "The Effect of Online Privacy Information on Purchasing Behavior: An Experimental Study," Information Systems Research, INFORMS, vol. 22(2), pages 254-268, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Miaomiao Yin & Asghar Afshar Jahanshahi, 2018. "Developing Knowledge-Based Resources: The Role of Entrepreneurs’ Social Network Size and Trust," Sustainability, MDPI, vol. 10(10), pages 1-15, September.
    2. Soyoung Kim & Junseok Hwang & Jorn Altmann, 2012. "Dynamic Scenarios of Trust Establishment in the Public Cloud Service Market," TEMEP Discussion Papers 201298, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jun 2012.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheng, Junjun & Chen, Bo & Huang, Zihang, 2023. "Collective-based ad transparency in targeted hotel advertising: Consumers’ regulatory focus underlying the crowd safety effect," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    2. Jacopo Arpetti & Antonio Iovanella, 2019. "Towards more effective consumer steering via network analysis," Papers 1903.11469, arXiv.org, revised Nov 2019.
    3. Esther Gal-Or & Ronen Gal-Or & Nabita Penmetsa, 2018. "The Role of User Privacy Concerns in Shaping Competition Among Platforms," Information Systems Research, INFORMS, vol. 29(3), pages 698-722, September.
    4. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    5. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    6. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(2), pages 115-129, March.
    7. Erjonilda Hasrama & Ervin Myftaraj & Brunela Trebicka, 2024. "Exploring User Attitudes Toward Online Behavioral Advertising: Insights into Trust, Transparency and Privacy," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 13, March.
    8. Beresford, Alastair R. & Kübler, Dorothea & Preibusch, Sören, 2012. "Unwillingness to pay for privacy: A field experiment," Economics Letters, Elsevier, vol. 117(1), pages 25-27.
    9. Pallant, Jason I. & Pallant, Jessica L. & Sands, Sean J. & Ferraro, Carla R. & Afifi, Eslam, 2022. "When and how consumers are willing to exchange data with retailers: An exploratory segmentation," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    10. David A. Schweidel & Yakov Bart & J. Jeffrey Inman & Andrew T. Stephen & Barak Libai & Michelle Andrews & Ana Babić Rosario & Inyoung Chae & Zoey Chen & Daniella Kupor & Chiara Longoni & Felipe Thomaz, 2022. "How consumer digital signals are reshaping the customer journey," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1257-1276, November.
    11. Regner, Tobias & Riener, Gerhard, 2012. "Voluntary payments, privacy and social pressure on the internet: A natural field experiment," DICE Discussion Papers 82, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    12. Schudy, Simeon & Utikal, Verena, 2017. "‘You must not know about me’—On the willingness to share personal data," Journal of Economic Behavior & Organization, Elsevier, vol. 141(C), pages 1-13.
    13. Idris Adjerid & Sonam Samat & Alessandro Acquisti, 2016. "A Query-Theory Perspective of Privacy Decision Making," The Journal of Legal Studies, University of Chicago Press, vol. 45(S2), pages 97-121.
    14. Helia Marreiros & Mirco Tonin & Michael Vlassopoulos & M.C. Schraefel, 2016. "“Now that you mention it”: A Survey Experiment on Information, Salience and Online Privacy," BEMPS - Bozen Economics & Management Paper Series BEMPS34, Faculty of Economics and Management at the Free University of Bozen.
    15. Simeon Schudy & Verena Utikal, 2012. "The Influence of (Im)perfect Data Privacy on the Acquisition of Personal Health Data," Working Paper Series of the Department of Economics, University of Konstanz 2012-12, Department of Economics, University of Konstanz.
    16. Anjuli Franz & Alexander Benlian, 2022. "Exploring interdependent privacy – Empirical insights into users’ protection of others’ privacy on online platforms," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2293-2309, December.
    17. Xu, Xun & Jackson, Jonathan E., 2019. "Examining customer channel selection intention in the omni-channel retail environment," International Journal of Production Economics, Elsevier, vol. 208(C), pages 434-445.
    18. Morando, Federico & Iemma, Raimondo & Raiteri, Emilio, 2014. "Privacy evaluation: what empirical research on users' valuation of personal data tells us," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 3(2), pages 1-12.
    19. Qing Zhu & Renxian Zuo & Shan Liu & Fan Zhang, 2020. "Online dynamic group-buying community analysis based on high frequency time series simulation," Electronic Commerce Research, Springer, vol. 20(1), pages 81-118, March.
    20. Maciej Sobolewski & Michał Paliński, 2017. "How much consumers value on-line privacy? Welfare assessment of new data protection regulation (GDPR)," Working Papers 2017-17, Faculty of Economic Sciences, University of Warsaw.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijitdm:v:10:y:2011:i:02:n:s0219622011004336. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.