Effects of Player's Participation Patterns on Economic Performance of Entertainment Shopping Website
Main Article Content
Abstract
Article Details
Authors who publish with IJIIS : International Journal on Informatics and Information Systems agree to the following terms: Authors retain copyright and grant the IJIIS : International Journal on Informatics and Information Systems right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) the work for any purpose, even commercially with an acknowledgement of the work's authorship and initial publication in IJIIS : International Journal on Informatics and Information Systems. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in IJIIS : International Journal on Informatics and Information Systems. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
A. Ghose and S. P. Han, A dynamic structural model of user learning on the mobile Internet. Working Paper. Leonard N. Stern School of Business, New York University. Available at http://people.stern.nyu.edu/aghose/research.html, 2011.
B. C. Platt, J. Price and H. Tappen, The role of risk preferences in pay-to-bid auctions. Management Science, 59 (9), 2117-2134, 2013.
C. I. Teng and W. W. Chen, Team participation and online gamers loyalty. Electronic Commerce Research and Applications, 13 (1), 24-31, 2014.
C. L. Hsu and H. P. Lu, Consumer behavior in online game communities: A motivational factor perspective. Computers in Human Behavior, 23 (3), 1642-1659, 2007.
D. Choi and J. Kim, Why people continue to play online games: in search of critical design factors to increase customer loyalty to online contents. Cyber ​​Psychology & Behavior, 7, 11-24, 2004.
D. Gefen, Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27 (1), 51-90, 2003.
D. L. Davies and D. W. Bouldin, A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1 (2), 224-227, 1979.
E. L. Deci and R. M. Ryan, Accessibility and stability of predictors in the theory of planned behavior. Journal of Personality and Social Psychology, 63 (5), 754-765, 1987.
F. F. Reichheld and P. Schefter, E-loyalty. Harvard Business Review, 78, 105-113, 2000.
F. Zhu and X. Zhang, Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. Journal of Marketing, 74 (2), 133-148, 2010.
G. Ku, Auctions and auction fever: Explanations from competitivearousal and framing. Kellogg Journal of Organization Behavior, 1-32, 2000.
G. Zacharia and P. Maes, Trust management through reputation mechanisms. Applied Artificial Intelligence, 14, 881-907, 2000.
H. Lu and J. C. Lin, Predicting customer behavior in the market-space: a study of Rayport and Sviokla's framework. Information and Management, 40, 1-10, 2003.
J. B. Walther and M. R. Parks, Cues filtered out, cues filtered in: computer-mediated communication and relationships. In Handbook of interpersonal communication (3rd ed., Pp. 529-563), Knapp, M. L., & Daly, J. A. (Eds.), Thousand Oaks, CA, Sage, 2002.
J. K. Murnighan, A very extreme case of the dollar auction. Journal of Management Education, 26, 56-69, 2002.
J. W. Byers, M. Mitzenmacherand G. Zervas, Information asymmetries in pay-per-bidauctions: how Swoopo makes bank. The 11th ACM Conference on Electronic Commerce, Cambridge, 1-12, 2010.L. Escartin-Sorjonen, DealDash - Turning shopping into a game. The Huffington Post, June 14, 2012.
M. T. P. Adam, J. Krämer, C. Jähnig, S. Seifert and C. Weinhardt, Understanding auction fever: a framework for emotional bidding. Electron Markets, 21 (3), 197-207, 2011.
N. Augenblick, Consumer and producer behavior in the market for penny auctions: a theoretical and empirical analysis. Working Paper. Haas School of Business, University of California, Berkeley. Available at http://faculty.haas.berkeley.edu/ned/, 2011.
P. A. Pavlou and D. Gefen, Building effective online marketplaces with institution-based trust. Information Systems Research, 15 (1), 37-59, 2002.
P. J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics, 20, 53-65, 1987.
R. B. Calinski and J. Harabasz, J, A dendrite method for cluster analysis. Communication in Statistics, 3, 1-27, 1974.
R. E. Anderson and S. S. Srinivasan, E-satisfaction and e-loyalty: a contingency framework. Psychology & Marketing, 20, 123-138, 2003.
R. Iyengar, Al. Ansari and S. Gupta, A model of consumer learning for service quality and usage. Journal of marketing research, XLIV, 529-544, 2007.
R. J. Vellerand, Toward a hierarchical model of intrinsic and extrinsic motivation. Advance Experiment Social Psychology, 29, 271-360, 1997.
R. Kieschnick and B. D. McCullough, Regression analysis of variates observed on (0,1): percentages, proportions and fractions. Statistical Modeling, 3, 193-213, 2003.S. Li, K. Srinivasan and B. Sun, Internet auction features as quality signals. Journal of Marketing, 73, 75-92, 2009.
S. Narayanan and P. Manchanda, Heterogeneous learning and the targeting of marketing communication for new products Marketing Science, 28 (3), 424-441, 2009.
S. S. Al-Gahtani and M. King, Attitudes, satisfaction and usage: factors contributing to each in the acceptance of information technology. Behavior and Information Technology, 18 (4), 277-297, 1999.
T. Erdem and M. P. Keane, Decision-making under uncertainty: Capturing dynamic brand choice processes in turbulent consumer goods market. Marketing Science, 15 (1), 1-20, 1996.
T. Erdem, M. P. Keane and B. Sun, B, A dynamic model of brand choice when price and advertising signal product quality. Marketing Science, 27 (6), 1111-1125, 2008.
T. Hinnosaar, Penny auctions are unpredictable. Working Paper. Department of Economics, Northwestern University. Available at http://toomas.hinnosaar.net/, 2010.
Y. Chen and J. Xie, Third-party product review and firm marketing strategy. Marketing Science, 24 (2), 218-240, 2005.
Y. Huang, P. V. Singh and K. Srinivasan, Crowdsourcing new product ideas under consumer learning. Management Science, 60 (9), 2138-2159, 2014.
Y. Zhao, S. Yang, V. Narayan and Y. Zhao, Modeling consumer learning from online product reviews. Marketing Science, 32 (1), 153-169, 2013.