Adoption of fitness wearables. Insights from partial least squares and qualitative comparative analysis

Date12 March 2018
Published date12 March 2018
Pages103-127
DOIhttps://doi.org/10.1108/JSIT-04-2017-0025
AuthorPável Reyes-Mercado
Subject MatterInformation & knowledge management,Information systems,Information & communications technology
Adoption of tness wearables
Insights from partial least squares and
qualitative comparative analysis
Pável Reyes-Mercado
School of Business and Economics, Universidad Anahuac Mexico Norte,
Huixquilucan, Mexico
Abstract
Purpose This paper aims to analyse the adoption of tness wearables by using the unied theory of
acceptance and use of technology (UTAUT). The study analyses the relative weights and causal
combinationsof antecedent variables on use and intentionto use tness wearables.
Design/methodology/approach The study design involves two stages: rst,from the perspective of
variable-orientedanalysis, a structural equation modelis tested using partial least squares (PLS) techniqueon
a sample of 176 adopters and a second sample of 187 non-adopters. Second, from the perspective of case-
oriented analysis, a fuzzy set qualitative comparative analysis (fsQCA) identies causal combinations of
variablesthat lead to use of wearables by adopters and intention to useby non-adopters.
Findings PLS results show that performanceexpectancy and effort expectancy have high net effectson
use and intention to use for adopters.FsQCA analysis shows that current users follow a streamlined path to
adoption. High beliefson performance expectancy and effort expectancy arethe main inuences of intention
to use a tness wearablefor non-adopters. In contrast to adopters, non-adoptersmay follow a number of paths
to intention to use through performanceexpectancy, effort expectancy or facilitating conditions.This insight
was apparentonly after analysing the data sets by using fsQCA.
Research limitations/implications For sake of parsimony, this papertested UTAUT model instead
of the more complex uniedtheory of acceptance and use of technology 2.
Practical implications Marketers in the tness category can enhance use and intention to use by
utilising not one but a combinationof causal factors such as performance expectancy, effort expectancyand
facilitatingconditions. Wide societal deployment of wearables depends on performanceand expectations.
Social implications The widespread use of mobile devices depends on performance expectancy and
effort expectancy.To transit to a real knowledge economy,co-creation should occur at early stages of product
developmentso that these expectations are shared and better productsbe developed.
Originality/value This paper offersa nuanced understandingof tness wearable adoption by analysing
adopters and non-adopters through variable- and case-oriented techniques. It complements the one-linear-
path perspective witha number of alternative causalcombinations of variables that lead to use and intention
to use tness wearables. While the causal path for adopters is unique, there are a number of causal
combinationsof antecedents that lead to high intention to usein potential adopters.
Keywords UTAUT, Partial least squares, Technology adoption, Technology acceptance,
Qualitative comparative analysis, Fitness wearables
Paper type Research paper
1. Introduction
Health is at the centre of many lifestyles today. The wide penetration of smartphones and
wearable devices has enabled consumersto track, store and transmit information about the
parameters related to their physical activities (heart rate, temperature, burned calories and
elapsed time since the last physical activity). Names of tness devices range from tness
trackers, electronic activity monitors, wearables, smart wristbands, and smart watches etc.
Fitness
wearables
103
Received3 April 2017
Revised20 December 2017
Accepted17 January 2018
Journalof Systems and
InformationTechnology
Vol.20 No. 1, 2018
pp. 103-127
© Emerald Publishing Limited
1328-7265
DOI 10.1108/JSIT-04-2017-0025
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1328-7265.htm
For sake of clarity, this paper uses the term tnesswearables. These devices supply visual
dashboards of performance and can be complemented with other healthy lifestyle
applications such as dietarytracking and medical follow-up.
The availability of tness trackers has increased in recentyears. One exemplary case is
the start-up Jawbone, which is currently valuated at $US$3.3bn (Fortune, 2016). Nike and
Fitbit, along with Jawbone, capture more than90 per cent of the market (NPD Group, 2014).
The global wearable market is expected to grow from US$750m in 2012 to $US$5.8bn in
2018 (Transparency Market Research, 2014). Despite this forecasted growth, other studies
show that after an initial 100 per cent volume in purchases, continuous use of tness
wearables drops to 70 per cent after six months and to about 55 per cent after one year
(Endeavour Partners, 2014). Hence, it becomes critical to assess the antecedents of use and
intention to use tnesswearables.
This paper focuses on tness wearablesin relation to cognitive approaches to technology
adoption and use. The research on this approach dates back several decades with the
seminal models of theory of reasoned action (Fishbein and Ajzen, 1975) and the theory of
planned behaviour (Ajzen, 1991), from which the technology acceptance model (TAM,
Davis, 1989) was rst formulated. Academics and practitioners need to understand the
drivers of technology adoption, which include perceived usefulness and perceived ease of
use. For this reason, this paper opted to use the unied theory of acceptance and use of
technology (UTAUT, Venkatesh et al.,2003). This model involves performance-expectancy
theory, facilitating conditions and socialinuence to explain use and behavioural intention.
Another motivation to use the UTAUT model is that the growing and increasingly larger
market for these devices, along with the proliferationof brands, makes an interesting case to
study technology adoptionin the enabling technologies category.
Criteria to choose tness wearables as a subject of study include the following: rst,
tness wearablesare situated in the growthstage of a products life cycle. The rapid adoption
of smartphoneswith applications enables thesedevices to perform new tasks, i.e. monitoring
physical activities. Smart phones have become a ubiquitous platform and have moved
beyond the traditional devices that typicallytrack health parameters. Second, as technology
evolves and newwearable devices, applications and informationservices are being launched
across markets, there is a need to assess the magnitude, direction and signicance of the
relationships between variables. Specically, for wearables, relationships among variables
and signicance in previous empirical studies may differ in reference to other technology
contexts(Venkatesh et al.,2012).
The existing literature traditionally addresses technology research under the paradigm
of variable signicant net effect on another dependent variable. However, a nuanced
understanding of adoption needscomplementary perspectives to enquire into the necessary
and the sufcient conditions of UTAUTvariables that lead to high use and intention to use.
This paper aims to be one of the studies to combine a qualitative method along with the
traditional mainstreamresearch approaches in technology research.
In summary, this paper has two researchobjectives, i.e. to analyse the adoption of tness
technology by using the UTAUTmodel to understand the effects of antecedent variables on
the use and intention to use tness wearables and to analyse the causal combinations of
antecedent variablesthat lead to use and intention to use tness wearables.
The contribution of this paper to literatureis twofold. First, to the best of our knowledge,
this is one of the rst empirical studies that test the UTAUT model to examine tness
wearables. By doing so, this paper addresses the need to understand the motivational
variables associated with the use of newer tness wearables and the intention to use them.
Structural equation modellinghas been largely utilised in business (Richter et al.,2015) and
JSIT
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