Factors influencing actual usage of fitness tracking devices: Empirical evidence from the UTAUT model

Anubha Mishra · Lori Jo Baker-Eveleth · Prachi Gala · Julia Stachofsky

29 citations0/0 files storedHealth Marketing Quarterly

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Technology Adoption and User Behaviour Mobile Health and mHealth Applications Innovative Human-Technology Interaction

Abstract

performance and effort expectancy. Based on 124 responses using the structural equation approach, most hypotheses were supported. The social influence had the strongest indirect effect through performance expectancy, while user efficacy had the strongest indirect effect through effort expectancy. Data accuracy and device engagement had a positive influence on actual usage and privacy concerns negatively affected the device's use.

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References

1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error 2003User Acceptance of Information Technology: Toward A Unified View1 1977Self-efficacy: Toward a unifying theory of behavioral change.1977Self-efficacy: Toward a unifying theory of behavioral change.1989User Acceptance of Computer Technology: A Comparison of Two Theoretical Models 1998The partial least squares approach for structural equation modeling.1991Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation 2001Understanding Information Systems Continuance: An Expectation-Confirmation Model1

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2022Investigating Residents’ Acceptance of Mobile Apps for Household Recycling: A Case Study of New Jersey 2022The Moderating Role of Privacy Concerns on Intention to Use Smart Wearable Technologies: An Integrated Model Combining UTAUT2 Theoretical Framework and Privacy Dimensions 2022Electronic Wearable Device Use for Physical Activity in Older Adults: A Qualitative Study 2024Determinants of internet of things (IoT) smart home technology (SHT) adoption behavior of consumers: Empirical evidence from a least developed country (LDC)

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