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Exploring factors affecting consumers' adoption of wearable devices to track health data

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Abstract

Mobile health is a rapidly emerging topic due to technological advances, especially in mobile computing and communication technologies. Increased capabilities of mobile devices, including smartphones, smart bands, and other wearables provide vast opportunities to collect health data easily. Health professionals can use this data in order to support medical diagnosis and treatment. In addition to health professionals, consumers can also benefit from the data collected by these devices to assist self-motivation to adopt and track healthier daily life practices. In this research, the factors affecting the adoption of wearable devices to track health information are investigated. We used the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model as a basis for our study as it is focusing on the acceptance of technology from consumers' perspectives. We enhanced the model with the concept of technology use categorization. The original Use construct of the UTAUT2 model addresses technology use only in terms of use frequency. We believe that this is not sufficient to analyze wearable devices that lend themselves to varying degrees of passive and active use. We propose that wearable device usage should be analyzed according to three types of use: Type 1 Use: Users wear the device primarily out of habit with no significant focus on the data; Type 2 Use: Users check the collected data; Type 3 Use: Users take actions based on the collected data. Our quantitative analysis showed that different factors with remarkably different intensities influence these three types of usage. Furthermore, we proposed three new constructs, namely goal clarity, technology stack compatibility, and perceived risk to improve the explanatory power of the UTAUT2 model. A strong relation is found between goal clarity and behavioral intention for type 3 use. Additionally, for all three types of use, it is seen that the Technology Stack Compatibility construct is a strong determinant of behavioral intention to use wearable devices for health tracking purposes.

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Abbreviations

AVE:

Average variance extracted

BI:

Behavioral intention to use

CA:

Cronbach's alpha

DOI:

Diffusion of Innovation Theory

EE:

Effort expectancy

FC:

Facilitating conditions

GC:

Goal Clarity

HM:

Hedonic motivation

MM:

Motivational model

MPCU:

Model of PC Utilization

PDA:

Personal digital assistant

PE:

Performance expectancy

PR:

Perceived risk

SCT:

Social cognitive theory

SI:

Social influence

TAM:

Technology acceptance model

TPB:

Theory of planned behavior

TRA:

Theory of reasoned action

TSC:

Technology stack compatibility

UTAUT:

Unified Theory Of Acceptance And Use Of Technology

UTAUT2:

Extended Unified Theory Of Acceptance And Use Of Technology

VIF:

Variance inflation factor

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  1. Informatics Institute, Middle East Technical University, 06800, Ankara, Turkey

    Tansu Pancar & Sevgi Ozkan Yildirim

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Pancar, T., Ozkan Yildirim, S. Exploring factors affecting consumers' adoption of wearable devices to track health data. Univ Access Inf Soc 22, 331–349 (2023). https://doi.org/10.1007/s10209-021-00848-6

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