Analysis on the dynamic relationship among product attributes: VAR model approach |
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| Institution: | 1. Sichuan Normal University, Business School, Chengdu 610101, China;2. College of Management Science, Chengdu University of Technology, Chengdu 610059, China;3. Post-doctorate R & D Base of Management Science and Engineering, Chengdu University of Technology, Chengdu 610059, China;4. Sichuan Normal University, Mathematic School, Chengdu 610101, China;5. Sichuan University, Business School, Chengdu 610065, China;1. East China University of Science and Technology, School of Business, 130 Meilong Road, Xuhui District, Shanghai 200237, PR China;2. Shanghai University, School of Management, No. 99 Shangda Road, Shanghai 200444, PR China;3. Middlesex University, The Business School, London NW4 4BT, United Kingdom |
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| Abstract: | The relationship among various technologies has been a major research theme in high-tech management and thus investigated from several approaches. However, the relationship among product attributes has been ignored chiefly due to product complexities. The dynamic relationship among product attributes is important in terms of both finding product drivers and generating new ideas. Nevertheless, previous approaches have either remained purely conceptual or been conducted case-by-case. In practice, although they have contributed considerably to the product concept generation, uncertainty and risk still remain to a great extent. In this research, we will suggest an exploratory method to bridge the gap between these two approaches. To begin with, focusing on the mobile phone product range by Nokia, user guides and manuals are collected. Then, we build four sets of time-series data using text mining under the guidance of the hierarchy of product attributes composed of three layers. A vector autoregression (VAR) model, usually applied to examine causal relationship among economic time-series variables, is employed to analyze the data and investigate the dynamic causal relationship among product attributes. Applying variance decomposition and impulse–response function, the main product drivers, emerging features and actively interacting sets of attributes are identified, along with the three layers of the hierarchy. Four hypotheses on dynamic relationship among product attributes are also developed and tested to clarify fundamental characteristics. |
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