Strategic signaling and new technologically superior product introduction: a game-theoretic model with simulation
Abstract
User acceptance of technology is essential to determine its success. The current paper incorporates the main properties of the technology acceptance models (TAMs) developed by management scholars into a pre-commitment signaling duopolistic framework, where two competing firms must decide the level of technological improvement of the products being introduced. As a result, the corresponding equilibria of the duopolistic technological games will be determined by demand-based factors, providing a novel approach and complementing the current supply-based economic and operational research models developed in the literature. The proposed model will be simulated numerically to illustrate the strategic optimality of the update process of smartphone and tablet characteristics defined by Apple and Samsung as the market developed.
Keyword : game theory, simulations, product planning, information sharing, strategic signaling, technological improvement
How to Cite
Tavana, M., Di Caprio, D., & Santos-Arteaga, F. J. (2018). Strategic signaling and new technologically superior product introduction: a game-theoretic model with simulation. Technological and Economic Development of Economy, 24(4), 1466-1498. https://doi.org/10.3846/tede.2018.4531
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Di Caprio, D., & Santos Arteaga, F. J. (2011). Strategic diffusion of information and preference manipulation. International Journal of Strategic Decision Sciences, 2(2), 1-19. https://doi.org/10.4018/ijsds.2011040101
Di Caprio, D., & Santos Arteaga, F. J. (2014). Climbing quality ladders and the evolution of technology dynamics: rethinking the role of demand in technological change. International Journal of Operational Research, 20(2), 121-155. https://doi.org/10.1504/IJOR.2014.061770
Di Caprio, D., Santos Arteaga, F. J., & Tavana, M. (2014). The optimal sequential information acquisition structure: a rational utility-maximizing perspective. Applied Mathematical Modelling, 38(14), 3419-3435. https://doi.org/10.1016/j.apm.2013.12.003
Di Caprio, D., Santos-Arteaga, F. J., & Tavana, M. (2016). An optimal sequential information acquisition model subject to a heuristic assimilation constraint. Benchmarking: An International Journal, 23(4), 937-982. https://doi.org/10.1108/BIJ-01-2014-0008
Dillon, A., & Morris, M. (1996). User acceptance of new information technology: theories and models. In M. Williams (Ed.). Annual review of information science and technology (vol. 31, pp. 3-32). Medford NJ: Information Today.
Gaines, B. R. (2003). Organizational knowledge acquisition. In C. W. Holsapple (Ed.). Handbook of knowledge management 1. Knowledge matters (pp. 317-348). Berlin: Springer-Verlag.
Gao, S. Y., Lim, W. S., & Tang, C. (2016). The impact of the potential entry of copycats: entry conditions, consumer welfare, and social welfare. Decision Sciences, pp. 1-31.
Geroski, P. A. (2000). Models of technology diffusion. Research Policy, 29(4-5), 603-625. https://doi.org/10.1016/S0048-7333(99)00092-X
Gerpott, T. J., Thomas, S., & Weicher, M. (2013). Characteristics and mobile internet use intensity of consumers with different types of advanced handsets: an exploratory empirical study of iPhone, Android and other web-enabled mobile users in Germany. Telecommunications Policy, 37(4-5), 357-371. https://doi.org/10.1016/j.telpol.2012.04.009
Gobble, M. M. (2012). Apple scores a win in the smartphone wars. Research-Technology Management, 55(6), 4-5.
Greenleaf, E. A., & Lehmann, D. R. (1995). Reasons for substantial delay in consumer decision making. The Journal of Consumer Research, 22(2), 186-199. https://doi.org/10.1086/209444
Hameed, M. A., Counsell, S., & Swift, S. (2012). A conceptual model for the process of IT innovation adoption in organizations. Journal of Engineering and Technology Management, 29(3), 358-390. https://doi.org/10.1016/j.jengtecman.2012.03.007
Holsapple, C. W. (Ed.). (2003). Handbook of knowledge management 2. Knowledge directions. Berlin: Springer-Verlag. https://doi.org/10.1007/978-3-540-24748-7
Hsu, W.-C. J., Tsai, M.-H., & Tzeng, G.-H. (2017). Exploring the best strategy plan for improving the digital convergence by using a hybrid MADM model. Technological and Economic Development of Economy, 24(1), 164-198. https://doi.org/10.3846/20294913.2016.1205531
Ji, G., & Gunasekaran, A. (2014). Evolution of innovation and its strategies: from ecological niche models of supply chain clusters. Journal of the Operational Research Society, 65(6), 888-903. https://doi.org/10.1057/jors.2013.136
Kenney, M., & Pon, B. (2011). Structuring the smartphone industry: is the mobile internet OS platform the key?. Journal of Industry, Competition and Trade, 11(3), 239-261. https://doi.org/10.1007/s10842-011-0105-6
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Lee, J., & Le, J. N. (2009). Understanding the product information inference process in electronic word-of-mouth: an objectivity-subjectivity dichotomy perspective. Information & Management, 46(5), 302-311. https://doi.org/10.1016/j.im.2009.05.004
Li, Y., & Jin, Y. H. (2009). Racing to market leadership: product launch and upgrade decisions. International Journal of Production Economics, 119(2), 284-297. https://doi.org/10.1016/j.ijpe.2009.03.004
Li, Y. L., Tang, J. F., Chin, K. S., Luo, X. G., Pu, Y., & Jiang, Y. S. (2012). On integrating multiple type preferences into competitive analyses of customer requirements in product planning. International Journal of Production Economics, 139(1), 168-179. https://doi.org/10.1016/j.ijpe.2012.03.031
Liao, C.-N., Lin, C.-H., & Fu, Y.-K. (2016). Integrative model for the selection of a new product launch strategy, based on ANP, TOPSIS and MCGP: a case study. Technological and Economic Development of Economy, 22(5), 715-737. https://doi.org/10.3846/20294913.2015.1074951
Lin, C.-Y., Chao, Y.-C., & Tang, T.-W. (2017). Why not be “smarter”? Examining the factors that influence the behavioral intentions of non-smartphone users. Industrial Management & Data Systems, 117(1), 32-49. https://doi.org/10.1108/IMDS-07-2015-0319
Malerba, F., Nelson, R., Orsenigo, L., & Winter, S. (2007). Demand, innovation, and the dynamics of market structure: the role of experimental users and diverse preferences. Journal of Evolutionary Economics, 17(4), 371-399. https://doi.org/10.1007/s00191-007-0060-x
Marcondes Moraes, G. H. S. (2016). Herd behavior in the use of information technology. IEEE Latin America Transactions, 14(8), 3868-3874. https://doi.org/10.1109/TLA.2016.7786374
Maruping, L. M., Bala, H., Venkatesh, V., & Brown, S. A. (2017). Going beyond intention: integrating behavioral expectation into the unified theory of acceptance and use of technology. Journal of the Association for Information Science and Technology, 68(3), 623-637. https://doi.org/10.1002/asi.23699
Mas-Colell, A., Whinston, M. D., & Green, J. R. (1995). Microeconomic theory. New York: Oxford University Press.
Nam, S., Nam, C., & Kim, S. (2015). The impact of patent litigation on shareholder value in the smart-phone industry. Technological Forecasting and Social Change, 95, 182-190. https://doi.org/10.1016/j.techfore.2015.01.015
Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311-329. https://doi.org/10.1086/259630
Phan, K., & Daim, T. (2011). Exploring technology acceptance for mobile services. Journal of Industrial Engineering and Management, 4(2), 339-360. https://doi.org/10.3926/jiem.2011.v4n2.p339-360
Prabhakar Murthy, D. N., Rausand, M., & Østerås, T. (2008). Product reliability. Specification and performance. Springer-Verlag London.
Pun, H., & DeYong, G. D. (2017). Competing with copycats when customers are strategic. Manufacturing & Service Operations Management, 19(3), 403-418. https://doi.org/10.1287/msom.2016.0613
Rogers, E. (2003). Diffusion of innovation (5th ed.). Free Press.
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. The Journal of Political Economy, 82(1), 34-55. https://doi.org/10.1086/260169
Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
Samiee, S., Shimp, T. A., & Sharma, S. (2005). Brand origin recognition accuracy: its antecedents and consumers’ cognitive limitations. Journal of International Business Studies, 36(4), 379-397. https://doi.org/10.1057/palgrave.jibs.8400145
Shaw, K. L. (1996). An empirical analysis of risk aversion and income growth. Journal of Labor Economics, 14(4), 626-653. https://doi.org/10.1086/209825
Shin, D.-H. (2017). Conceptualizing and measuring quality of experience of the internet of things: exploring how quality is perceived by users. Information & Management, 54(8), 998-1011. https://doi.org/10.1016/j.im.2017.02.006
Simon, H. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99-118. https://doi.org/10.2307/1884852
Sutton, J. (1998). Technology and market structure: theory and history. MIT Press.
Swapana, M., & Padmavathy, C. (2017). Factors influencing dependency on smartphone and the impact on purchase behaviour: an empirical research. International Journal of E-Business Research, 13(2), 79-92. https://doi.org/10.4018/IJEBR.2017040105
Tavana, M. (2004). A subjective assessment of alternative mission architectures for the human exploration of Mars at NASA using multicriteria decision making. Computers & Operations Research, 31(7), 1147-1164. https://doi.org/10.1016/S0305-0548(03)00074-1
Tavana, M., Di Caprio, D., & Santos Arteaga, F. J. (2014). An optimal information acquisition model for competitive advantage in complex multiperspective environments. Applied Mathematics and Computation, 240, 175-199. https://doi.org/10.1016/j.amc.2014.04.074
Tavana, M., Di Caprio, D., & Santos Arteaga, F. J. (2016a). Modeling sequential information acquisition behavior in rational decision making. Decision Science, 47(4), 720-761. https://doi.org/10.1111/deci.12193
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