From Old to New Technologies: Exploring the Role and Impact of Value Resonance

Abstract

In the research of technology switch, theories such as Push-Pull-Mooring (PPM), Behavioral Reasoning (BRT), Innovation Resistance (IRT), and Status Quo Bias (SQB) have been employed to extend the Technology Acceptance Model (TAM), focusing on how old technology factors such as inertia as barriers to embracing new technological innovations. However, our research contends that the impact of these old technology factors on the technology switch is not unequivocal, as they may either hinder or facilitate the transition, depending on whether the new technology retains valued features of the old technology. To delve into this complexity, we introduce the concept of value resonance between old and new technologies.

Utilizing Nintendo Switch for empirical analysis, two quantitative studies were conducted. Out of 630 distributed questionnaires, 553 valid responses were collected, yielding an 87.7% response rate. The first study used multi-groups analysis within TAM to differentiate between the technology acceptance group (TAG) and the technology switch group (TSG), contributing to the development of the Technology Switch Model (TSM). The second study applied Covariance-Based Structural Equation Modeling (CB-SEM) to examine value resonance’s mediating role in the TSM.

The empirical results revealed discernible differences between TAG and TSG within the TAM framework, providing a robust theoretical basis for the TSM. Furthermore, the result indicated that factors associated with old technology could enhance users’ switching intentions through the mediating mechanism of value resonance. These findings aim to provide a more nuanced understanding of technology switch, highlighting that the dynamics between old and new technologies are more complex and variable than previously assumed.

Keywords:

• Technology switch
• value resonance
• switching cost

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by The National Science and Technology Council, Republic of China. [NSTC 111-2410-H-025-005-].

Notes on contributors

Kuo-Wei Lee

Kuo-Wei Lee, a professor at National Taichung University of Science and Technology, specializes in E-commerce and knowledge management. His work is published in journals such as Electronic Commerce Research and Applications, Academy of Management Learning & Education, Journal of Knowledge Management, and International Journal of Project Management.