A mixed-method approach to assess users' intention to use mobile health (mHealth) using PLS-SEM and fsQCA
| DOI | https://doi.org/10.1108/AJIM-07-2021-0211 |
| Published date | 11 January 2022 |
| Date | 11 January 2022 |
| Pages | 589-630 |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
| Author | Najmul Hasan,Yukun Bao |
A mixed-method approach to
assess users’intention to use
mobile health (mHealth) using
PLS-SEM and fsQCA
Najmul Hasan and Yukun Bao
Center for Modern Information Management, School of Management,
Huazhong University of Science and Technology, Wuhan, PR China
Abstract
Purpose –Despite the enormous potential of mobile health (mHealth), identifying the asymmetric relationship
among the predictors towards intentionto use (ITU) of mHealth tends to remain unresolved. This study aims to
investigate the predictors and their asymmetric effects on ITU of mHealth through patients and healthcare
professionals.
Design/methodology/approach –An integrated information systems (IS) model with four additional
constructs has been developed to analyze symmetric and asymmetric effects on ITU of mHealth. An
exploratory survey on 452 mHealth users with prior experience was conducted to evaluate the model using a
mixed-method approach including partial least squares-based structural equation modeling (PLS-SEM) and
fuzzy-set qualitative comparative analysis (fsQCA) technique.
Findings –The findings show that facilitating conditions, personal awareness building, perceived enjoyment,
effort expectancy and perceived usefulness have predictive power for ITU of mHealth. In contrast, fsQCA
reveals four more alternative solutions, including the main drivers explored by PLS-SEM. The results indicate
that various conditions that were not crucial in PLS-SEM analysis are shown to be sufficient conditions
in fsQCA.
Research limitations/implications –This study contributes to theory by integrating self-actualization
factors (i.e. personal awareness building, patients as decision support unit) into the IS model. And practically,
this study makes an essential contribution to users’ITU of mHealth, enabling relevant stakeholders to build
strategies to implement mHealth successfully.
Originality/value –While mHealth has revolutionized healthcare and the prior literature only showed linear
relationships, this empirical study revealed asymmetrical relationships among the determinants of ITU of
mHealth. Thus, this study extends to the growing body of literature on the use of mHealth technology in the
least developing nation.
Keywords mHealth, Mixed-method, Integrated IS model, ITU, SEM-fsQCA
Paper type Research paper
1. Introduction
Healthcare consumers prefer to use digital technologies like mobile health (mHealth) apps for
self-care management to enhance quality healthcare (Gamble, 2020). In response, academic
and market researchers have begun investigating how healthcare consumers’intention to use
(ITU) of mHealth is shifting due to these evolving smart technologies (Yan et al., 2021). Recent
studies (e.g. Balapour et al., 2019) have investigated the factors that influence individuals’
intention to adopt mHealth apps, which would potentially enhance healthcare delivery
systems and patient care. Literature reveals that determining the most critical factors that
influence consumers’ITU of this technology remains contradictory and unresolved. As the
healthcare industry has seen a growing trend, researchers argue for further study to uncover
Users’
intention to use
mHealth
589
The authors are grateful to the editor in chief, associate editor and anonymous reviewers for their
valuable comments to enhance the quality of their manuscript.
Funding: This study was conducted under and supported by the National Natural Science
Foundation of China (Project No. 71810107003).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2050-3806.htm
Received 4 August 2021
Revised 3 November 2021
21 December 2021
Accepted 25 December 2021
Aslib Journal of Information
Management
Vol. 74 No. 4, 2022
pp. 589-630
© Emerald Publishing Limited
2050-3806
DOI 10.1108/AJIM-07-2021-0211
how effectively health technologies are being used. Similarly, Veeramootoo et al. (2018) and
Zha et al. (2020) suggested that there has been a call from academics to extend IS theories
relevant to particular settings, and Adnan et al. (2019) emphasized the need for more formal
integration of innovative constructs to keep up with the continuous usage model.
Traditional health-seeking behaviors are evolving rapidly due to the growing demand for
healthier lifestyles. Compared to developed nations, developing and least developing
countries suffer from a scarcity of medical facilities, and providing appropriate health care is
challenging due to the high cost of clinical care (Kim et al., 2016). Besides, the World Health
Organization’s (WHO) recommended doctor-patient ratio adversely affects numerous
developing and least developing countries. According to the WHO, Bangladesh has the
worst doctor-patient ratio, with an average of 5.2 doctors treated per 10,000 population,
placing the country in second-to-last place in South Asia (Mohiuddin, 2020). Similarly, in
other South Asian countries, the average number of physicians is as follows: 7.77 in India,
9.75 in Pakistan, 9.5 in Sri Lanka, 6.5 in Nepal, 8.6 in Myanmar and 22.3 in the Maldives for
every 10,000 people. In an extensive global survey conducted by Irving et al. (2017),
Bangladeshi physicians treat their patients for a median time of just 48 s. Statistics show that
not only is there a dearth of healthcare professionals, but the shortage raises substantial
concerns about ethics. However, the healthcare system will undergo a dramatic
transformation if mHealth delivers on its promises.
While smartphones are a necessary form of contemporary life and business vitality,
mHealth apps also have the potential for positive economic relevance. In healthcare consumer
apps, the number of downloads jumped from 16% in 2014 to 48% in 2018 (Duarte and Pinho,
2019). There are around 3.5 billion mobile phone users worldwide, and the app store has
nearly 350,000 mHealth apps downloaded. The global mHealth market was valued at
approximately US$45.7 billion in 2020 (Tsakiliotis, 2021). While Zion market research firm is
expecting the mHealth apps market will hit US$111.1 billion by 2025 from US$8.0 billion in
2018 (Peng et al., 2020), Grand View Research has projected to increase the mHealth market at
an annual compound rate of 17.6% from 2021 to 2028 (Tsakiliotis, 2021). Nevertheless, the
Android mobile operating system had a global market share of 74.25% globally by the end of
August 2020, with the highest penetration rates in low and middle-income countries (Moll
et al., 2021).
Along with this, mHealth technology is a valuable self-care decision support tool for
patients in the healthcare industry. According to recent research, mHealth has also become
prominent in the delivery of healthcare. Currently, 83% of physicians employ smart devices
and medical apps to remotely track their patients (Inupakutika et al., 2020). mHealth
technologies can improve patient health, leading to better medical care and the potential to
improve the quality of healthcare systems. Since mHealth apps have increasingly attracted
individuals’attention, a key focus of this study is to investigate howeffectively and efficiently
the ITU of an mHealth strategy may be predicted.
Without a doubt,previous studies have several drawbacksthat must be considered before
implementing mHealth in a new context.First, a great deal of extended empirical studies has
been performedon the ITU of mHealth. Still, manyof them have utilized traditionaltechnology
adoption theories (i.e. the technology acceptance model (TAM), extended unified theory of
acceptance and use of technology (UTAUT), and motivation theory). However, the adoption
and ITU of mHealth apps often reflect health behavior. In this regard, an individual’shealth
habits must be taken into consideration while considering adoption and/or ITU theory, even
thoughthe new body of knowledge alsoincludes conventionaladoption theory aspects(Nabavi
et al.,2016). Second, previous researches have examined essential factors of existing
frameworks suchas TAM and UTAUT. Because of the wide range ofresearch backgrounds,
someresearch findings mightcontradict each other. For example,in terms of mHealth adoption,
the impacts ofeffort expectancy and facilitatingconditions have been examinedand shown to
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be substantial (Hoque and Sorwar, 2017),while the conclusions of some otherstudies suggest
that the effects are insignificant (Duarte and Pinho, 2019). These conflicting findingsnot only
confuse researchers but also result in barriers to improving the mHealth adoption process.
Thereis a need for a consistent and robustframework to better comprehendthe ITU of mHealth
applications (Niknejad et al., 2020) and to bridge the literature gap incurred by contradictory
findings in a specific context (Miles, 2017).
Based on the information provided here, we formulated the primary research questions of
this empirical study as follows:
RQ1. What configurations of the IS model better predict the ITU of mHealth?
RQ2. Which factors are necessarily or sufficiently crucial for the ITU of mHealth?
Unlike theprevious study, this current researchtook a more inclusive approachto the research
design by using an integrated information systems(IS) model. As researchers have called for
the IS paradigm to be extendedby including specific theories or modelsto convey IS settings
(Veeramootooet al., 2018),this study demonstratesthe relationship of the incorporatedIS model
based on our previousqualitative findings (Hasanet al.,2021). Adoption and ITU behavior are
distinct concepts; thus, one may differ tremendously from others associated with certain
factors. ITU will influence users’behaviors after initial implementation and may increase or
decreasefuture usage dependingon the user satisfaction of systems.Researchers concludethat
many of these constructsof IS theories might not be sufficientto predict the continuous use of
technology, although they are valuable to technology adoption (Veeramootoo et al., 2018;
Nabavi et al., 2016). It is expected that incorporating IS theories into concepts would better
expose the extra justification of ITU of mHealth technologies. Extending these concepts to
evaluate the behavioral intentions of technology use, particularly in healthcare, becomes
necessary. This precise concept must be addressed to reveal signs and symptoms of
improvement information from the users’preferences. In addition, Alam et al. (2020a) stated
that incorporating extra contextual elements permits more specific research of users’
acceptance of area-specific technology. The limited functionality of IS thoughts, which are
generally applied in investigating new edge adoptions, is incapable of considering the
complexities, so there is a need to combine a theoretical framework to recognize complex
technology acceptance (Scott and Walczak, 2009). Finally, combining the IS paradigm could
effectively better comprehend healthcare technologies’behavioral intentions.
Conjointly, this ground-breaking research develops a new methodological technique for
examining healthcare consumers’ITU of mHealth using partial least square based structural
equation modelling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA).
Literature reveals that SEM cannot explore all the features of complex variable-forming
configurations if the data is not normally distributed. In linear decision-making analysis,
conventional statistical methods, e.g. multiple linear regression (MLR) and SEM, might also
lead to over-simplification because of the only examination for linear relationships (path
coefficients). To resolve this conflict, the fsQCA technique gives more profound insights into
complex substances when being augmented with SEM (Pappas et al., 2019,Fazal-E-Hasan
et al., 2021). While SEM provides a causal relationship between related constructs, the fsQCA
technique investigates the necessary and sufficient conditions for SEM analysis factors to
lead to an outcome (Fazal-E-Hasan et al., 2021). To draw further conclusions and better clarify
the SEM findings, a combination of the SEM-fsQCA is employed.
To bridge the research gap and contribute to the knowledge in the literature, this study
aims to determine the asymmetric relationship between particular predictors of ITU of
mHealth by developing an integrated IS model using a mixed-method (SEM-fsQCA)
analytical approach. This study also assesses the mediating (patients as decision support unit)
and moderating (age, gender, and education) effects on the ITU of mHealth.
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mHealth
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