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ISSN : 1738-3110
The Influence of a New Product's Innovative Attributes and Planned Obsolescence on Consumer Purchase Intention
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Abstract
Purpose - To vitalize a market or develop a new one, companies frequently release new products into the market, often by shortening the time to market, called the release period. This research aims to investigate the purchase intention behavior of consumers in terms of buying new products at the time of product release based on the release speed. Research Design, Data, and Methodology - The research reviews the influence of relative advantage, complexity, and compatibility among innovative attributes of new products, as proposed by Rogers. Moreover, it examines the moderating effect of the innovative new product attributes in terms of speed of obsolescence of old products and how that influences consumer purchase behavior. Additionally, this study tests the research hypotheses using empirical analysis. Results - The analysis demonstrated that the relative predominance (H1) and suitability (H3) of new products had a statistically significant positive influence on new product purchase intention. However, the complexity (H2) of new products had a statistically significant positive influence on new product purchase intention in contrast to its predicted sign (-). The results of the moderating effect of the old product use period were as follows. H4-1 was not supported since the difference between the path coefficients of the group with the low level old product use period and the group with the high level, represented by the relationship of relative predominance and new product purchase intention, was not statistically significant. H5-1 was also not supported since the difference between the path coefficients of the group with the low level of old product use period and the group with the high level, represented by the relationship of complexity and new product purchase intention, was not statistically significant. However, H4-2 was supported since the difference between the path coefficients of the group with the low level of old product use frequency and the group with the high level, represented by the relationship of relative advantage and new product purchase intention was statistically significant. H5-2 was not supported since the difference between the path coefficients of the group with the low level of old product use frequency and the group with the high level, represented by the relationship of complexity and new product purchase intention, was not statistically significant. H6-2 was also not supported since the difference between the path coefficients of the group with the low level of old product use frequency and the group with the high level, represented by the relationship of compatibility and new product purchase intention, was not statistically significant. Conclusion - According to the results, only H4-2 among the hypotheses on the moderating effect of the old product use period and use frequency was statistically significant. Future research should focus on carrying out a detailed review of the hypothesis on the moderating effect of the old product usage period and frequency, find the cause, and connect this to potential new research.
- keywords
- Relative Advantage, Complexity, Compatibility, Planned Obsolescence
Reference
Al-Gahtani, S. S., & King, M. (1999). Attitudes, Satisfaction and Usage: Factors Contributing to Each in the Acceptance of Information Technology. Behavior and Information Technology, 4, 277-297.
Bayus, B. L., & Gupta, S. (1992). An Empirical Analysis of Consumer Durable Replacement Intention. International Journal of Research in Marketing, 9(3), 257-267.
Chau, P. Y. (1996). An Empirical Assessment of a Modified Technology Acceptance Model. Journal of Management Information Systems, 13(2) Fall, 185-204.
Davis, F. D. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems:Theory and Result. Doctoral Dissertation, Sloan School of Management, Massachusetts Institute of Technology.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology : A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003.
Fishbein, M. (1967). Readings in Attitude Theory Measurement. New York, NY : Wiley.
Fishbein, M., & Azjen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.
Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the Determinants Microcomputer Usage via a Structural Equation Model. Journal of Management Information Systems, 11(4), Spring, 87-114.
Jong, G. D. (1996). A Disaggregate Model System of Vehicle Holding Duration, Type of Choice and Use. Transportation Research, 30(4), 263-276.
Kono, Tomoaki (2009). The Effects of Using period and Using Frequency on Consumer Acceptance of Innovative New Product. Journal of Keio Marketing Ronkyu, 5(Spring), 295-319.
Kotler, P., & Armstrong, G. (2012). Principles of Marketing (14th Edition). NY: Prentice Hall.
Lancaster, K. (1971). Consumer Demand: A New Approach. New York, NY : Columbia University Press.
Lee, Hwak-Sik, & Lim Ji-Hoon (2011). Structural Equation Modeling with AMOS 16.0. Seoul, Korea: Bobmunsa.
Lee, Yang-Kyu, Park, Sang-Youn, & Hwang, Il-Young (2014), A Comparative Analysis of Customer Choice and Satisfaction Factors among Three Types of Coffee Shops. Journal of Distribution Science, 12(2), 49-57.
Lee, Young-Chul, & Yang, Hoe-Chang (2012). The Effects of College Students’ Perceived Attribute of Traditional Market and Relationship Quality : Moderating Effects of Consumption Emotion and Mediating Effects of Consumer’s Value. Journal of Distribution Science, 10(1), 33-42.
Malhotra, Y., & Galletta, D. F. (1999). Extending the Technology Acceptance Model to Account for Social Influence:Theoretical Bases and Empirical Validation. Proceedings of the 32nd Annual Hawaii International Conference on System Sciences, IEEE Computer Society, January.
Ono, Akinori (2008). Consumer Acceptance theory of New Product. Mita Shougaku Kenkyu, 51(1), 1-18.
Pickering, F. J. (1981). A Behavioral Model of the Demand for Consumer Durables. Journal of Economic Psychology, 1(1), 59-77.
Rogers, E. M. (1962). Diffusion of Innovation. New York, NY:Free Press.
Rogers, E. M. (1995). Diffusion of Innovation (Fourth Edition). New York, NY : Free Press.
Rogers, E. M. (2003). Diffusion of Innovations. New York, NY:Free Press.
Rosen, S. (1974). Hedonic Prices and Implicit Markets : Product Differentiation in Pure Competition. Journal of Political Economy, 82(1), 34-55.
Serge, L. (2014). Bon Pour La Casse : Les Deraisons de l’obsolescence Programmee. Seoul, Korea: Minumsa.
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 45(2), 186-204.
Venkatesh, M., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology:Toward a Unified View. MIS Quarterly, 27(3), 425-478.
Yi, Weon-Yi, Kim, Su-Ok, Lee, Sang-Youn, & Youn, Myong-Kil (2012). Study on the Effects of Shop Choice Properties on Brand Attitudes : Focus on Six Major Coffee Shop Brands. Journal of Distribution Science, 10(3), 51-61.
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