Abstract
A micro-blog is a social media tool that allows users to write short text messages for public and private networks. This research focuses specifically on the micro-blog on Facebook. The main purposes of this study are to explore and compare what recommendation sources influence the intention to use micro-blogs and to combine the personal characteristics/attributes of gender, daily internet hour usage and past use experience to infer the usage of micro-blogs decision rules using a dominance-based rough-set approach (DRSA) with flow network graph. Data for this study were collected from 382 users and potential users. The analysis is grounded in the taxonomy of induction-related activities using a DRSA with flow network graph to infer the usage of micro-blogs decision rules. Finally, the study of the nature of micro-blog reflects essential practical and academic value in real world.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ajzen, I.: Attitudes, Personality, and Behavior. Dorsey Press, Chicago (1988)
Ajzen, I., Fishbein, M.: Understanding Attitudes and Predicting Social Behavior. Prentice Hall, Englewood Cliffs (1980)
Akhter, S.H.: Digital divide and purchase intention: Why demographic psychology matters. Journal of Economic Psychology 24(3), 321–327 (2003)
Andreasen, A.R.: Attitudes and customer behavior: A decision model. In: Kassarjian, H.H., Robertson, T.S. (eds.) Perspectives in Consumer Behavior, pp. 498–510. Scott, Foresman and Company, Glenview (1968)
Awareness: Enterprise Social Media: Trends and Best Practices in Adopting Web 2.0 (2008), http://www.awarenessnetworks.com/resources/resources-whitepapers.asp
Bagozzi, R.P., Baumgartner, H., Yi, Y.: State versus action orientation and the theory of reasoned action: An application to coupon usage. Journal of Consumer Research 18(4), 505–518 (1992)
Błaszczyński, J., Greco, S., Słowiński, R.: Multi-criteria classification - A new scheme for application of dominance-based decision rules. European Journal of Operational Research 181(3), 1030–1044 (2007)
Carchiolo, V., Malgeri, M., Mangioni, G., Nicosia, V.: Emerging structures of P2P networks induced by social relationships. Computer Communications 31(3), 620–628 (2008)
Chan, C.-C., Tzeng, G.-H.: Dominance-Based Rough Sets Using Indexed Blocks as Granules. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 244–251. Springer, Heidelberg (2008)
Chau, P.Y.K., Hu, P.J.H.: Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management 39(4), 297–311 (2002)
Cheong, M., Lee, V.: Integrating Web-based intelligence retrieval and decision-making from the Twitter trends knowledge base. In: Proceeding of the 2nd ACM Workshop on Social Web Search and Mining, pp. 1–8 (2009)
Cox, D.F.: Risk taking and information handling in consumer behavior. In: Cox, D.F. (ed.) Risk Taking and Information Handling in Consumer Behavior, pp. 604–639. Boston University Pres, Boston (1967)
Crisci, R., Kassinove, H.: Effects of perceived expertise, strength of advice, and environmental setting on parental compliance. The Journal of Social Psychology 89(2), 245–250 (1973)
Crow, S.M., Fok, L.Y., Hartman, S.J., Payne, D.M.: Gender and values: What is the impact on decision making? Sex Roles 25(3-4), 255–268 (1991)
Davis, F.D.: Perceived usefulness, Perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3), 319–340 (1989)
Fang, S.K., Shyng, J.Y., Lee, W.S., Tzeng, G.H.: Combined data mining techniques for exploring the preference of customers between financial companies and agents based on TCA. Knowledge-Based Systems 27(1), 137–151 (2012)
Ford, L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton (1962)
Gefen, D., Straub, D.W.: Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly 21(4), 389–400 (1997)
Gilly, C.M., Graham, J.L., Wolfinbarger, M.R., Yale, L.J.: A dyadic study of interpersonal information search. Journal of the Academy of Marketing Science 2(2), 83–100 (1998)
Greco, S., Matarazzo, B., Słowiński, R.: A new rough set approach to evaluation of bankruptcy risk. In: Zopounidis, C. (ed.) Operational Tools in the Management of Financial Risk, pp. 121–136. Kluwer Academic Publishers, Boston (1998)
Greco, S., Matarazzo, B., Słowiński, R.: Rough set theory for multicriteria decision analysis. European Journal of Operation Research 129(1), 1–47 (2001)
Greco, S., Matarazzo, B., Słowiński, R.: Dominance-Based Rough Set Approach as a Proper Way of Handling Graduality in Rough Set Theory. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W.P. (eds.) Transactions on Rough Sets VII. LNCS, vol. 4400, pp. 36–52. Springer, Heidelberg (2007)
Günther, O., Krasnova, H., Riehle, D., Schöndienst, V.: Modeling microblogging adoption in the enterprise. In: Proceedings of the 15th Americas Conference on Information Systems, San Francisco, CA, USA (2009)
Häubl, G., Trifts, V.: Consumer decision making in online shopping environments: the effects of interactive decision aids. Marketing Science 19(1), 4–21 (2000)
Hoadley, C.M., Xu, H., Lee, J.J., Rosson, M.B.: Privacy as information access and illusory control: The case of the Facebook news feed privacy outcry. Electronic Commerce Research and Applications 9(1), 50–60 (2010)
Janda, S.: Does gender moderate the effect of online concerns on purchase likelihood? Journal of Internet Commerce 7(3), 339–358 (2008)
Java, A., Song, X., Finin, T., Tseng, B.: Why We Twitter: An Analysis of a Microblogging Community. In: Zhang, H., Spiliopoulou, M., Mobasher, B., Giles, C.L., McCallum, A., Nasraoui, O., Srivastava, J., Yen, J. (eds.) WebKDD 2007. LNCS, vol. 5439, pp. 118–138. Springer, Heidelberg (2009)
Kim, S.S., Malhotra, N.K., Narasimhan, S.: Two competing perspectives on automatics use: A theoretical and empirical comparison. Information Systems Research 16(4), 418–432 (2005)
Lin, C.S., Tzeng, G.H., Yang-Chieh Chin, Y.H.: Combined rough set theory and flow graph to predict customer churn in credit card accounts. Expert Systems with Applications 38(1), 8–15 (2011)
Liou, J.H., Tzeng, G.H.: A dominance-based rough set approach to customer behavior in the airline market. Information Sciences 180(11), 2230–2238 (2010)
Liou, J.H., Yen, L., Tzeng, G.H.: Using decision-rules to achieve mass customization of airline service. European Journal of Operation Research 205(3), 680–686 (2010)
Mark, B., Munakata, T.: Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3. European Journal Operational Research 136(1), 212–229 (2002)
Murray, K.B.: A test of services marketing theory: Consumer information acquisition activities. Journal of Marketing 55(1), 10–25 (1991)
Ou Yang, Y.P., Shieh, H.M., Tzeng, G.H., Yen, L., Chan, C.C.: Combined rough sets with flow graph and formal concept analysis for business aviation decision-making. Journal of Intelligent Information Systems 36(3), 347–366 (2011)
Park, D.H., Lee, J., Han, I.: The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce 11(4), 125–148 (2007)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11(1), 341–356 (1982)
Pawlak, Z.: Rough sets, decision algorithms and Bayes’ theory. European Journal of Operational Research 136(1), 181–189 (2002)
Pawlak, Z.: Decision rules and flow networks. European Journal of Operation research 154(1), 184–190 (2004)
Pawlak, Z.: Flow graphs and intelligent data analysis. Fundamenta Informaticae 64(1), 369–377 (2005)
Richins, M.L., Root-Shaffer, T.: The Role of Involvement and Opinion Leadership in Consumer Word-of-Mouth: An Implicit Model Made Explicit. Advances in Consumer Research 15(1), 32–36 (1988)
Rogers, E.M.: The ’Critical Mass’ in the diffusion of interactive technologies in organizations. In: Kraemer, K.L. (ed.) Information Systems Research Challenge.: Survey Research Methods. Harvard Business School Research Colloquium, vol. 3, pp. 245–264. Harvard Business School, Boston (1991)
Rogers, E.M.: Diffusion of Innovations, 5th edn. Free Press, New York (1995)
Sankey, D.: Networking sites used for background checks, http://www2.canada.com/components/print.aspx?id=ba0dcc0d-f47d-49c9-add4-95d90fd969ab
Shyng, J.Y., Shieh, H.M., Tzeng, G.H.: Compactness rate as a rule selection index based on rough set theory to improve data analysis for personal investment portfolios. Applied Soft Computing 11(4), 3671–3679 (2011)
Sledgianowski, D., Kulviwat, S.: Social network sites: antecedents of user adoption and usage. In: Proceedings of the Fourteenth Americas Conference on Information Systems (AMCIS), Toronto, ON, Canada, pp. 1–10 (2008)
Słowiński, R.: New Applications and Theoretical Foundations of the Dominance-based Rough Set Approach. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS (LNAI), vol. 6086, pp. 2–3. Springer, Heidelberg (2010)
Słowiński, R.: The international summer school om MCDM 2006. Class note. Kainan University, Taiwan (2006), Software available on line, http://idss.cs.put.poznan.pl/site/software.html
Słowiński, R., Greco, S., Matarazzo, B.: Rough sets in decision making. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 7753–7786. Springer, New York (2009)
Smith, D., Menon, S., Sivakumar, K.: Online peer and editorial recommendation, trust, and choice in virtual markets. Journal of Interactive Marketing 19(3), 15–37 (2005)
Sung, S.K., Malhotra, N.K., Narasimhan, S.: Two competing perspectives on automatics use: a theorectical and empirical comparison. Information Systems Research 17(2), 418–432 (2005)
Wang, C.H., Chin, Y.C., Tzeng, G.H.: Mining the R&D innovation performance processes for high-tech firms based on rough set theory. Technovation 30(7-8), 447–458 (2010)
Yi, Y., Wu, Z., Tung, L.L.: How individual differences influence technology usage behavior? Toward an integrated framework. Journal of Computer Information Systems 46(2), 52–63 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Chin, YC., Chang, CC., Lin, CS., Tzeng, GH. (2013). The Impact Rules of Recommendation Sources for Adoption Intention of Micro-blog Based on DRSA with Flow Network Graph. In: Skowron, A., Suraj, Z. (eds) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Intelligent Systems Reference Library, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30341-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-30341-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30340-1
Online ISBN: 978-3-642-30341-8
eBook Packages: EngineeringEngineering (R0)