




































AMERICAN INTERNATIONAL JOURNAL OF BUSINESS AND MANAGEMENT STUDIES 4(1) (2022), 30-40 

 
30 

 

        Business and Management Studies 

                                                              AIJEFR VOL 4 NO 1 (2022) P-ISSN 2641-4937  E-ISSN 2641-4953 
                                                  

                                                                                                                        Available online at www.acseusa.org      

                                                                                                                                      Journal homepage: https://www.acseusa.org/journal/index.php/aijbms 
                                                                                                                                             Published by American Center of Science and Education, USA 

INVESTIGATING THE DETERMINANTS OF MOBILE HEALTH APPS 

ADOPTION AMONG ELDERLY CITIZENS IN BANGLADESH   
                     

  Hamida Akhter (a)1   Md. Arif Hossain (b)     
  

 (a)  Assistant Professor, Department of Management Information Systems, Faculty of Business Studies, University of Dhaka, Dhaka, Bangladesh; E-mail: 

hamida.mis@du.ac.bd 
(b) Assistant Manager, Sylhet Gas Fields Limited, Sylhet, Bangladesh; E-mail:arifhossain.sgfl@gmail.com 

 

 
A R T I C L E I N F O 
 

 

Article History: 

 

Received: 20th October 2022 

Revised: 30th November 2022 
Accepted: 20th December 2022 

Published: 30th December 2022 

 

Keywords: 

M-Health Apps, UTAUT Model, Perceived Risk, 

Social Influence, Healthcare Profession. 

 

JEL Classification Codes:  

 

G91, F14, L11 

 

 

 

 

 

 
 

 
A B S T R A C T 
 
In this modern era, healthcare services are provided through technology, one of which is m-health apps. 

As a developing country, Bangladesh pursues to offer healthcare facilities to its citizen by using modern 

technology. However, IT adoption is different among younger and older generations, and several factors 

impact the adoption intention. This research aims to investigate determinants influencing elderly citizens 

of Bangladesh to adopt m-health apps. This study applies PLS (Partial Least Squares) statistical 

technique based on Structural Equation Modeling (SEM) to achieve research objectives. A quantitative 

research methodology approach was adopted, and a structured questionnaire was disseminated to the 

112 target respondents. Purposive random sampling technique was used in this study. The underpinning 

theory used in this research endeavor is the UTAUT model (Unified Theory on Acceptance and Use of 

Technology), incorporating several variables such as the quality of m-health apps, perceived risk, and 

cost. The findings demonstrate that social influence and app quality have a significant positive impact 

on older people's willingness to adopt m-health apps. In addition, the behavioral intention of users and 

actual usage behavior have a significant positive association. By extending the UTAUT model with some 

rationally related variables, this research has contributed to the ICT of the healthcare profession. M-

health app providers need to consider improving the features of apps as the quality of apps is regarded 

as a critical criterion for users. 

 
 

© 2022 by the authors. Licensee ACSE, USA. This article is an open-access article distributed under 

the terms and conditions of the Creative Commons Attribution (CC BY) license 

(http://creativecommons.org/licenses/by/4.0/).                           

 

INTRODUCTION 

Health information systems are becoming prominent in our daily life, and in the last few decades, it has grabbed the attention 

of scholars (Sam, 2017). Developed countries provide better healthcare services to their citizens. Educated citizens are 

needed, and healthy citizens are also necessary for economic development. The number of mobile phone users is increasing 

day by day in the world. The Internet population is becoming familiar with different app services. M-health services enable 

people to quickly get healthcare services at a low cost (Kallander et al., 2013). 

Bangladesh is a developing country, and most people live in rural areas. Urban people get better health services 

instead of rural areas citizens. Moreover, older citizens are lagging in society. The digital divide still exists in developing 

countries. The government of Bangladesh develops IT infrastructure to provide modern facilities to all its citizens. During 

COVID-19, Bangladeshi citizens used m-health services and mobile health apps for registration to get the vaccine. 

Therefore, patients and healthcare service providers are now realizing the benefit of mobile health app services. In 

Bangladesh, numerous studies have assessed e-heath service adoption, m-health service adoption, and continuation 

intention. However, m-health app adoption among elderly citizens has not yet been addressed previously. As elderly citizens 

are more prone to illness, they frequently need healthcare services. Consequently, this study examines factors influencing 

older people to adopt m-health apps. 

Usually, when new information technology is introduced, users are reluctant to accept it as they are familiar with 

legacy systems. Therefore, investigating crucial factors influencing adoption behavior is essential to establish an information 

                                                      
1Corresponding author: ORCID ID: 0000-0003-2377-1505 -1505  

© 2022 by the authors. Hosting by ACSE. Peer review under responsibility of ACSE, USA.  

https://doi.org/10.46545/aijbms.v4i1.282   
 

To cite this article: Akhter, H., & Hossain, M. A. (2022). INVESTIGATING THE DETERMINANTS OF MOBILE HEALTH APPS ADOPTION 

AMONG ELDERLY CITIZENS IN BANGLADESH. American International Journal of Business and Management Studies, 4(1), 30–40. 
https://doi.org/10.46545/aijbms.v4i1.282  

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://doi.org/10.46545/aijbms.v4i1.282
https://orcid.org/0000-0003-2377-1505
https://orcid.org/0000-0002-4183-0030


   Akhter & Hossain, American International Journal of Business and Management Studies 4(1) (2022), 30-40

  

31 
 

system. 

LITERATURE REVIEW 

Mobile phone usage has increased in the past few decades; wireless technology to get health services has also enlarged. As 

a result, researchers have focused on these issues (Cameron et al., 2017). In addition, assessing the drivers influencing m-

health app adoption with changing technology features has made this issue more attractive to researchers (Baabdullah et al., 

2018). 

 

Mobile Health Apps Services 

The term m-health app services are provided through a mobile platform, encompassing health advice from professional 

physicians, medical registration, and geographical-based services (Zhang et al., 2017). In addition, m-health services are 

cost-effective and help assess health risks and positively modify patients' health habits (Brown-Connolly et al., 2014). 

Mobile health technologies have become popular because of the features of wireless technology, such as portability and 

ubiquitous (Akter et al., 2010). The benefits of the m-health app service were accessibility to qualified physicians at 

affordable cost at any time (Khatun et al., 2016). 

Numerous research has been experimented to identify factors that impact m-health adoption. For example, Quaosar 

et al. (2018) found the significant drivers affecting the willingness of patients to accept m-health services: improved 

performance, social influence, anxiety about new technology, convenience, and resistance to change. Zhao et al. (2018) 

showed a meta-analysis in which they demonstrated that user intention was significantly influenced by observed usefulness, 

assumed convenience, perceived vulnerability, and perceived severity. Besides, Cajita et al. (2018) researched the USA 

using the TAM model to examine important facilitators and obstacles to adopting mobile health services. Their findings 

showed that user-friendly, adequate experience, training programs, and equipment support influenced m-health adoption 

behavior. 

Moreover, Lee and Han (2015) demonstrated that users’ age, gender, and earnings did not influence m-health 

adoption, while effectiveness, ease of accessibility, and economic values positively impact to use of mobile health services. 

Furthermore, Kaium et al. (2019) revealed that social influence, privacy issues (Hoque, 2016), monetary value, etc., 

impacted users' desire to use mobile health. Finally, Phichitchaisopa & Naenna (2013) asserted that the factors that 

significantly affect m-Health usage are performance improvement expectation, user-friendly expectation, facilitating 

settings, and behavioral willingness on technology adoption. 

There are several established models for examining technology adoption, such as Theory of Reasoned Action 

(TRA) (Fishbein & Ajzen, 1977); Technology Acceptance Model (TAM) (Davis,1989) ; Technology Acceptance Model-2 

(Venkatesh & Davis, 2000); Technology Acceptance Model-3 (Venkatesh & Bala, 2008); Theory of Planned Behavior 

(TPB) (Ajzen, 1991); Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). Some 

similar constructs are identified among UTAUT, TRA, and TPB models (Kapoor et al., 2014). 

Socio culture contexts are different in emerging countries compared to advanced countries; therefore, theories do 

not apply to all contexts. Identifying which factors are more influential in adopting technology in developing countries is 

challenging (Dwivedi et al., 2016). However, the user’s adoption intention and external variables that impact the user’s 

adoption willingness are being predicted more accurately using the TAM model compared to the TRA and TPB models 

(Zhang et al., 2017). Many scholars use the TAM model to identify the newest m-health technologies acceptance (Kang, 

2014; Hoque, 2016; Sezgin et al., 2018; Cho et al., 2014; Byomire & Maiga, 2015; Chang et al., 2016). 

Davis, Bagozzi, and Warshaw (1989) demonstrated that TAM explains why users accept or reject information 

systems based on the concept developed by Davis (1985). In the original TAM model, assumed effectiveness and perceived 

user-friendliness instigate an individual’s intention to use technology. In 2008, Venkatesh and Bala presented TAM3 by 

incorporating different factors in perceived ease of use in the previous TAM2 model (Venkatesh & Davis 2000). The 

UTAUT model is a widely popular theory to examine IT adoption, and this study applied the UTAUT model (Hoque & 

Sorwar, 2017; Nunes et al., 2019). In addition, some new variables, such as app quality, perceived risk, and cost, are 

incorporated due to contextual demand. The following figure 1 represents the research model of this study. 

 

 

 

 

  

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1. Research Model 

Apps Quality 

Cost 

Perceived Risk 

Performance 

Expectancy 

Actual Usage of M-

Health Apps 

M- Health Apps 

Adoption Intention 

Social Influence 

Effort Expectancy 



   Akhter & Hossain, American International Journal of Business and Management Studies 4(1) (2022), 30-40

  

32 
 

MATERIALS AND METHODS 

After investigating the previous study, several research constructs are developed to attain the purpose of this study. The 

hypotheses of this study are derived from prior studies. The operational definition of the research constructs of this study 

are discussed in the following section. 

Quality of apps refers to the apps' features, display, and content. Users want updated content, attractive 

presentation, and informative features (Calisir et al., 2014). Therefore, when m-health apps do not meet users' expectations, 

users are not willing to accept m-health apps. Consequently, we propose the following hypothesis:   

 

H1: App quality positively impacts the patients’ intention to adopt m-health apps. 

 

Another vital element is cost issues while adopting m-health apps. Users compare the cost and benefits when using 

any new IT (Lin et al., 2011). They prefer the lower cost and higher benefits of using IT. If the usage cost of m-health apps 

is within the benefits, they are willing to accept m-health apps. Thus, the following hypothesis is developed: 

 

H2: Cost negatively impacts the patients’ intention to adopt m-health apps. 

 

Effort expectancy denotes the extent of ease of use of a system (Venkatesh et al., 2003). Users search for a system 

that is more convenient, easy to use, comfortable, and fulfills requirements. Therefore, end users' adoption of new 

technology is strongly influenced by effort expectancy. As a result, the following hypothesis is proposed: 

 

H3: Effort expectancy positively impacts the patients’ intention to adopt m-health apps.  

 

Data theft, unauthorized access by thirst parties, and privacy violation have become significant issues for users as 

they share their personal information in the m-health app. Sometimes, different app installation on the mobile device requires 

access to personal information; consequently, users are unwilling to install apps. The more users perceive the risk of using 

m-health apps, the more they are reluctant to accept m-health apps (Laxman et al., 2015; Becker, 2016). Hence, we develop 

the following hypothesis: 

 

H4: Perceived risk negatively impact the patients’ intention to adopt m-health apps. 

 

Performance expectancy refers to the individual’s perception of the benefit they gain from using the systems. 

Performance expectancy is a vital determinant to influence adoption behavior among users, which is established by several 

previous studies (Cimperman et al., 2016; Hsu & Wu, 2017; Hoque & Sorwar, 2017). Based on prior studies the following 

hypothesis is developed: 

 

H5: Performance expectancy positively impacts the patients’ intention to adopt m-health apps. 

 

Social influence represents that individuals are influenced by other members of society, especially the person whom 

they consider important (Venkatesh et al., 2003; Venkatesh et al., 2012). Social influence is another significant element that 

strongly effected users’ intentional behavior in accepting digital technology and health information systems (Sun et al., 

2013). This research proposes the following hypothesis: 

 

H6: Social influence positively impacts the patients’ intention to adopt m-health apps. 

 

The association between behavioral intention (BI) and actual usage (AU) has been examined by scholars, and they 

asserted that there is a positive relationship between BI and AU (Sheppard, 1988; Venkatesh & Davis, 2000). In addition, 

in the health information systems discipline, similar findings were found in several studies (Kijsanayotin et al., 2009). 

Hence, the following proposition is made in this study: 

 

H7: M- health apps adoption intention positively impacts the actual usage of m-health apps. 

 

Measurement of the Research Constructs 

The structured questionnaire was adapted after a rigorous studying previous literature. To confirm the questionnaire's clarity, 

accuracy, and readability, a pre-testing was conducted. The structured questionnaire of the study has two sections: 

demographic information of the respondents and user's acceptance of m-health apps services. A Likert five-point scale was 

used to assess the user's acceptance of m-health app services. The scale ranges from 1 to 5, where 1 = Strongly Disagree 

and 5= Strongly Agree. The measurement scale of the major research constructs of this study is given below: 

 

Apps Quality (AQ) 

The elements of mobile health apps quality are adapted from past studies, and these are: (1) M-health apps are very much 

organized; (2) M-health apps are updated regularly and available 24 hours; and (3) Looking m-health apps are attractive 

(Alaiad et al., 2019). 

 

 



   Akhter & Hossain, American International Journal of Business and Management Studies 4(1) (2022), 30-40

  

33 
 

Cost (CT) 

The indicators which measure the construct of cost are: (1) The cost of using m-health apps is expensive to me; (2) The 

benefit getting from using m-health apps is lower than the usage cost; and (3) Overall, I think cost factor is a barrier to me 

for adopting m-health apps (Alaiad et al., 2019). 

 

Effort Expectancy (EE) 

The effort expectancy construct is constituted of three elements such as (1) Using m-health apps is easy for me; (2) Learning 

how to use m-health apps is easy for me; and (3) Interaction with m-health apps is clear and understandable to me (Venkatesh 

et al., 2012; Johnston & Warkentin, 2010; Zhang et al., 2017; Alaiad et al., 2019). 

 

Perceived Risk (PR) 

Perceived risk is measured through three items such as (1) M-health apps would not keep my personal information 

confidential; (2) Information that I share with m-health apps may be attacked by hackers anytime; and (3) M- health apps 

are not trustworthy (Xue et al., 2012; Hoque & Sorwar, 2017; Alaiad et al., 2019). 

 

Performance Expectancy (PE) 

The items of performance expectancy are derived from previous studies, and these are (1) M-health apps help me to get 

better treatment quickly; (2) M-health apps enable me to manage my health problems more effectively; and (3) Overall, M-

health apps are helpful to me (Venkatesh et al., 2003). 

 

Social Influence (SI)  

After studying prior literature, three items are adapted for measuring subjective norms, and these are (1) Individuals who 

are important to me think that I should use mobile health services; (2) Individuals whose opinions are valuable to me think 

that I use m-health services; and (3) People who are using m-health services have more high status in our social system 

(Venkatesh et al., 2012; Alam et al., 2020). 

 

Adoption Intention (AI) 

The construct of adoption intention is comprised of three items and these are (1) I intend to use mobile health service 

regularly in the future; (2) I plan to use mobile health services recurrently; and (3) I will always try to use mobile health 

services in my daily life( Zhang et al., 2017; Alam et al., 2020). 

 

Actual Usage (AU)  

For assessing actual usage construct, three components are adapted from previous literature, and these are (1) I actually use 

mobile health services to keep me healthy; (2) I use mobile health services often; and (3) Mobile health services provide me 

good experience (Moon & Kim, 2001; Alam et al., 2020). 

 

Participant Characteristics 

The following Table 1 demonstrates the demographic information of survey respondents. Among the respondents, male and 

female respondents were approximately 53% and 46%, respectively. As we considered only elderly citizens, the respondents' 

age was above 50 years. The majority of the participant completed their graduation and engaged in working. In terms of m-

health app usage experience, nearly 70 percent of respondents have less than one year of experience. Moreover, thirty-four 

percent of the respondents stated that the frequency of m-health usage is once per month. 

 

Table 1. Demographic information of respondents 

 
Category Frequency Percentage (%) 

Gender 
  

    Male 60 53.57 

    Female 52 46.43 

Age (Years) 
  

   50 to 55 42 37.50 

   56 to 60 39 34.82 

   61 to 65 19 16.96 

   66 to 70 12 10.71 

Educational Qualification 
  

   Higher Secondary 19 16.96 

   Bachelor's degree 65 58.04 

   Master's degree 28 25.00 

Employment Status 
  

   Yes 58 51.79 

   No 54 48.21 

Employment Nature 
  

   Full Time 36 32.14 

   Self-employed 49 43.75 

   Part time 16 14.29 

   Others 11 9.82 



   Akhter & Hossain, American International Journal of Business and Management Studies 4(1) (2022), 30-40

  

34 
 

M-Health Apps Usages Experiences 
  

  Less than 1 years 79 70.54 

  1-3 years 16 14.29 

  4-6 years 11 9.82 

  More than 6 years 6 5.36 

M-Health Apps Usage Frequency 
  

  Once per week 34 30.36 

  Once per month 39 34.82 

  2-5 times per month 24 21.43 

  More than 5 times per month 15 13.39 

Income Level 
  

  Less than 15000 BDT 38 33.93 

  15001 to 30000 BDT 16 14.29 

  30001 to 45000 BDT 24 21.43 

  45001 to 60000 BDT 25 22.32 

  More than 60000 9 8.04 

 

Sampling Procedures and Sample Size 

The study's target population is users of m-health app services in Dhaka. Because of resource and time limitations, others 

cities in Bangladesh are not included in the study. A structured questionnaire was sent to the target population through 

Google and printed forms. Only completed questionnaires were used to conduct the analysis. Smart PLS (v3) was used to 

analyze the data. In PLS-SEM, the minimum sample size is one hundred to conduct an investigation (Reinartz et al., 2009). 

To attain the research objective purposive random sampling technique was used. The authors of this research paper used 

their judgment to select respondents with attributes to serve the survey purpose. This study used the G*power 3.1 software. 

The suggested sample size was 109, whereas this research has taken 112 respondents. The configuration was as follows: 

effect size f2 = 0.15; α err prob= 0.05; power = 0.85; number of predictors= 6 have been used to identify sample size. 

 

Research Design 

A quantitative research approach is appropriate for this study, and a survey is used as a research technique. Furthermore, a 

structured questionnaire is used for conducting the survey. Participants were asked questions regarding some personal 

attributes and evaluated some statements related to m-health app services. 

 

RESULTS 

Common Method Variance (CMV) 

This research uses SPSS software to conduct Harman’s single-factor test to identify whether any common method bias exists 

(Podsakoff et al., 2003). The result of Harman’s single factor is approximately 41% which is lower than the threshold value 

of 50%. Consequently, this study established there is no common method variance. 

 

Measurement Model Analysis 

The outer model analysis must be performed before evaluating the structural model. Therefore, it is suggested by Hair et al. 

(2019) to examine the loadings of the indicators for measuring measurement model. In addition, composite reliability and 

Average variance extracted also need to be measured to ensure convergent validity (Hair et al., 2010). The following Table 

2 shows the outer model assessment result. All the indicator loadings values are greater than 0.70 and therefore meet the 

threshold value. Moreover, the CR and AVE values of the elements confirm the threshold values. As a result, the convergent 

validity of measurement has been established in this research. 

 

Table 2. Validation of measurement model 

 
Constructs Indicators Loadings Composite Reliability (CR) Average Variance Extracted (AVE) 

AI AI1 0.886 0.885 0.720 

AI2 0.802 

AI3 0.855 

AQ AQ1 0.819 0.840 0.638 

AQ2 0.844 

AQ3 0.728 

AU AU1 0.84 0.866 0.683 

AU2 0.782 

AU3 0.855 

CT CT1 0.549 0.769 0.535 

CT2 0.891 

CT3 0.715 

EE EE1 0.841 0.862 0.675 

EE2 0.86 

EE3 0.761 

PE PE1 0.604 0.789 0.560 

PE2 0.786 

PE3 0.834 

PR PR1 0.825 0.875 0.701 

PR2 0.85 



   Akhter & Hossain, American International Journal of Business and Management Studies 4(1) (2022), 30-40

  

35 
 

PR3 0.835 

SI SI1 0.783 0.875 0.700 

SI2 0.877 

SI3 0.846 

Notes: AQ =Apps Quality; CT =Cost; EE= Effort Expectancy; PR =Perceived Risk; PE =Performance Expectancy; SI =Social Influence; AI =Adoption 

Intention; AU =Actual Usage 

 

To ensure the discriminant validity of the research, the Fornell –Larcker criterion, HTMT.90 criterion, and Cross 

Loadings are used, and these results are shown in Tables 3, 4, and 5. According to Fornell and Larcker (1981), to attain 

discriminant validity, the off-diagonal values should be lower than the diagonal value in the Fornell-Larcker criterion. Table 

3 demonstrates that discriminant validity has been achieved using the Fornell-Larcker criterion. Another test result is 

HTMT.90 criterion which is represented in Table 4, and all the values are below 0.90; thus, sufficient discriminant validity 

has been ensured (Henseler et al., 2015; Kline, 2015). Cross-loading is another criterion to establish discriminant validity. 

Table 5 depicts the cross-loading result, and it is seen that an indicator’s loadings are greater than all of its cross-loadings, 

therefore, achieving discriminant validity (Hair et al., 2011). 

 

Table 3. Assessing discriminant validity using Fornell –Larcker criterion 

 
Constructs AU AI AQ CT EE PR PE SI 

AU 0.826 
       

AI 0.745 0.849 
      

AQ 0.683 0.706 0.798 
     

CT 0.405 0.436 0.534 0.731 
    

EE 0.696 0.662 0.773 0.463 0.822 
   

PR -0.621 -0.620 -0.649 -0.454 -0.680 0.837 
  

PE 0.629 0.556 0.637 0.405 0.507 -0.480 0.748 
 

SI 0.773 0.758 0.638 0.459 0.629 -0.602 0.621 0.837 

Notes: AQ =Apps Quality; CT =Cost; EE= Effort Expectancy; PR =Perceived Risk; PE =Performance Expectancy; SI =Social Influence; AI =Adoption 

Intention; AU =Actual Usage 
 

Table 4. Assessing discriminant validity using HTMT .90 criterion 

 
Constructs AU AI AQ CT EE PR PE SI 

AU - 
       

AI 0.891 - 
      

AQ 0.847 0.871 - 
     

CT 0.572 0.614 0.830 - 
    

EE 0.849 0.841 0.849 0.702 - 
   

PR 0.796 0.771 0.851 0.649 0.877 - 
  

PE 0.885 0.783 0.672 0.717 0.655 - 
 

SI 0.837 0.872 0.842 0.646 0.816 0.768 0.877 - 

Notes: AQ =Apps Quality; CT =Cost; EE= Effort Expectancy; PR =Perceived Risk; PE =Performance Expectancy; SI =Social Influence; AI =Adoption 

Intention; AU =Actual Usage 

 

Table 5. Cross Loadings 
 

Constructs AI AQ AU CT EE PE PR SI 

AI1 0.886 0.655 0.689 0.441 0.610 0.543 -0.600 0.690 

AI2 0.802 0.549 0.610 0.263 0.461 0.396 -0.433 0.618 

AI3 0.855 0.586 0.591 0.397 0.610 0.469 -0.536 0.617 

AQ1 0.611 0.819 0.631 0.410 0.673 0.490 -0.616 0.552 

AQ2 0.630 0.844 0.506 0.481 0.612 0.558 -0.485 0.519 

AQ3 0.411 0.728 0.497 0.381 0.566 0.479 -0.442 0.450 

AU1 0.651 0.644 0.840 0.382 0.649 0.547 -0.485 0.660 

AU2 0.548 0.498 0.782 0.311 0.477 0.493 -0.482 0.599 

AU3 0.640 0.543 0.855 0.309 0.586 0.518 -0.572 0.655 

CT1 0.229 0.252 0.196 0.549 0.225 0.208 -0.202 0.247 

CT2 0.430 0.459 0.427 0.891 0.405 0.395 -0.452 0.468 

CT3 0.249 0.446 0.200 0.715 0.369 0.244 -0.284 0.230 

EE1 0.557 0.684 0.634 0.393 0.841 0.439 -0.545 0.572 

EE2 0.591 0.679 0.561 0.389 0.860 0.508 -0.615 0.514 

EE3 0.478 0.532 0.518 0.358 0.761 0.283 -0.510 0.461 

PE1 0.312 0.328 0.228 0.313 0.226 0.604 -0.153 0.291 

PE2 0.429 0.549 0.538 0.213 0.462 0.786 -0.379 0.527 

PE3 0.487 0.525 0.583 0.387 0.419 0.834 -0.486 0.537 

PR1 -0.560 -0.575 -0.552 -0.356 -0.557 -0.456 0.825 -0.480 

PR2 -0.500 -0.554 -0.495 -0.402 -0.571 -0.384 0.850 -0.484 

PR3 -0.490 -0.495 -0.507 -0.383 -0.579 -0.356 0.835 -0.551 

SI1 0.558 0.503 0.586 0.311 0.532 0.454 -0.492 0.783 

SI2 0.680 0.569 0.677 0.436 0.531 0.506 -0.547 0.877 

SI3 0.655 0.528 0.671 0.396 0.519 0.592 -0.473 0.846 

Notes: AQ =Apps Quality; CT =Cost; EE= Effort Expectancy; PR =Perceived Risk; PE =Performance Expectancy; SI =Social Influence; AI =Adoption 
Intention; AU =Actual Usage 



   Akhter & Hossain, American International Journal of Business and Management Studies 4(1) (2022), 30-40

  

36 
 

Structural Model Analysis 

The first step of structural model analysis is confirming non-collinearity issues. Inner VIF (Variance Inflation Factor) should 

be lower than three to ensure non-collinearity matters, which Hair et al. (2019) recommend. All the inner VIF values are 

less than three, shown in Table 6. As a result, this study has no collinearity issues.  

Hair et al. (2017) recommended that to examine hypotheses, some tests such as co-efficient, t-statistics, and p 

values need to be considered. Therefore, for hypotheses testing, bootstrapping with 5000 resamples has been executed, and 

the result is demonstrated in Table 6, and the graphical representation is shown in figure 2. 

 

Table 6. Hypothesis Result 

 
Hypothesis Path Std. Beta Std. Error t statistics p values Supported? VIF R2 Q2 f2 

H1 AQ -> AI 0.280 0.109 2.568 0.005 Yes 2.407 0.668 0.462 0.069 

H2 CT  -> AI -0.016 0.073 0.220 0.413 Yes 1.468 0.001 

H3 EE -> AI 0.089 0.114 0.782 0.217 Yes 2.995 0.008 

H4 PR -> AI -0.103 0.083 1.246 0.106 Yes 2.152 0.015 

H5 PE -> AI -0.001 0.090 0.015 0.494 No 1.950 0.000 

H6 SI -> AI 0.469 0.094 4.968 0.000 Yes 2.248 0.295 

H7 AI -> AU 0.745 0.042 17.643 0.000 Yes 1.000 0.554 0.370 1.244 

Notes: AQ =Apps Quality; CT =Cost; EE= Effort Expectancy; PR =Perceived Risk; PE =Performance Expectancy; SI =Social Influence; AI =Adoption 

Intention; AU =Actual Usage 
 

Participants’ responses support all the hypotheses except H5. Apps quality (β = 0.280, t = 2.568, p < 0.01) and 

social influence (β = 0.469, t = 4.968, p < 0.001) have a significant positive impact on the adoption intention of the 

participants. Similarly, adoption intention has the most significant effect on the usage of the m-health app services (β = 

0.745, t = 17.643, p < 0.001). However, performance expectancy is not considered a major driver for the participants to 

adopt m-health app services (β = -0.001, t = 0.015, p > 0.05). The f2 value of adoption intention is 1.244, which denotes a 

large effect size, whereas app quality (f2= 0.069) has a small effect size. In addition, the f2 value of social influence is 0.295, 

which is greater than 0.15 and thus has a medium effect (Cohen, 1988). 

Figure 2. Structural model for m-health apps adoption by elderly citizens 

 

Hair et al. (2017) advised that researchers should examine the value of R2 for ensuring model predictive accuracy 

and Q2 for establishing model predictive relevance. The value of R2 in this research is 0.55, which indicates that this model 

explains around 55% variation of the actual usage of the m-health services. Furthermore, model relevance is examined 

through the Q2 value, which should be more than zero. The Q2 value of this study is 0.37, which is higher than zero; hence, 

model predictive relevance is acquired. 



   Akhter & Hossain, American International Journal of Business and Management Studies 4(1) (2022), 30-40

  

37 
 

DISCUSSIONS 

After performing hypotheses testing, social influence and quality of m-health apps are significant drivers that impact the 

users’ intentional behavior. Besides, willingness to accept is also significantly positively associated with the actual course 

of action to use m-health apps.  Social influence was found significant by Holtz and Krein (2011) as well as Hoque and 

Sorwar (2017), and this finding is consistent with our study. 

The cost of using m-health apps influences the adoption intention is supported by collected data but not 

significantly. This may be due to the increased internet package cost because most m-health apps require an internet 

connection while using it. However, as the price of mobile devices is reasonable, the cost issue is treated as an insignificant 

barrier to the users. Alaiad et al. (2019) revealed that participants were not concerned about the cost of using m-health apps 

which is partially similar to the finding of this study. 

Hoque and Sorwar (2017) and Nunes et al. (2019) demonstrated that performance expectancy was a major 

determinant of adopting m-health service. However, surprisingly our study reveals performance expectancy as a non-vital 

element, which is the opposite finding of prior studies (Hoque & Sorwar , 2017; Nunes et al., 2019). Furthermore, this study 

shows a negative association between perceived risk and older people's intention to use m-health apps. This finding is also 

reinforced by previous studies (Guo et al., 2013; Alaiad et al., 2019). In addition, this study established a positive relationship 

between effort expectancy and behavioral intention, which is also similar to prior research findings (Phichitchaisopa & 

Naenna, 2013; Dwivedi et al., 2016; Hoque & Sorwar ,2017; Alaiad et al.,2019). Finally, willingness to adopt technology 

and actual usage behavior has a significant association found in our study. Again, this is supported by prior research 

endeavors in the m-health discipline (Ifinedo, 2012; Hoque & Sorwar, 2017). 

 

CONCLUSIONS  

From the findings of this research, policymakers need to concentrate on the features, content, upgrade, accuracy, and quick 

response of the m-health apps. Developers of m-health apps need to consider several aspects, such as privacy, trust, and 

security risk issues. This study incorporates several variables rationally related to ICT in healthcare and thus contributes to 

the literature. However, some constraints limit the research findings from being generalized. At first, the sample size was 

insufficient, and only Dhaka metropolitan citizens were considered participants. In addition, a longitudinal study was not 

taken due to time and resource constraints. Several variables, such as the health condition of the participants, age, gender, 

social status, and education, can be considered moderating variables for future research in this sector. 

 

 
Author Contributions: Conceptualization, H.A. and M.A.H.; Methodology, M.H.A.; Software, H.A.; Validation, F.T.J.T.; Formal Analysis, H.A.; 

Investigation, H.A. and A.H.; Resources, H.A.; Data Curation, H.A.; Writing – Original Draft Preparation, H.A. and M.A.H.; Writing – Review & Editing, 

H.A. and M.A.H.; Visualization, H.A.; Supervision, H.A.; Project Administration, H.A.; Funding Acquisition, H.A. and M.A.H. Authors have read and 

agreed to the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to the fact that the research does not deal with 

vulnerable groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Acknowledgement: Not applicable.  

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   

 

REFERENCES 

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. 

https://doi.org/10.1016/0749-5978(91)90020-T 

Akter, S., D’Ambra, J., & Ray, P. (2010). Service quality of mHealth platforms: development and validation of a 

hierarchical model using PLS. Electronic Markets, 20, 209-227. https://doi.org/10.1007/s12525-010-0043-x 

Alaiad, A., Alsharo, M., & Alnsour, Y. (2019). The determinants of M-health adoption in developing countries: an 

empirical investigation. Applied clinical informatics, 10(5), 820-840. 10.1055/s-0039-1697906 

Alam, M. Z., Hu, W., Kaium, M. A., Hoque, M. R., & Alam, M. M. D. (2020). Understanding the determinants of mHealth 

apps adoption in Bangladesh: A SEM-Neural network approach. Technology in Society, 61, 101255. 

https://doi.org/10.1016/j.techsoc.2020.101255 

Baabdullah, A. M., Alalwan, A. A., & Al Qadi, N. S. (2018). Evaluating the current situation of Mobile services (M-

services) in the Kingdom of Saudi Arabia. Emerging Markets from a Multidisciplinary Perspective: Challenges, 

Opportunities and Research Agenda, 149-160. https://doi.org/10.1007/978-3-319-75013-2_13 

Becker, D. (2016). Acceptance of mobile mental health treatment applications. Procedia Computer Science, 98, 220-227. 

https://doi.org/10.1016/j.procs.2016.09.036 

Brown-Connolly, N. E., Concha, J. B., & English, J. (2014). Mobile health is worth it! Economic benefit and impact on 

health of a population-based mobile screening program in new Mexico. Telemedicine and e-Health, 20(1), 18-23. 

https://doi.org/10.1089/tmj.2013.0080 

Byomire, G., & Maiga, G. (2015, May). A model for mobile phone adoption in maternal healthcare. In 2015 IST-Africa 

Conference (pp. 1-8). IEEE. https://doi.org/10.1109/ISTAFRICA.2015.7190562 

https://doi.org/10.1089/tmj.2013.0080


   Akhter & Hossain, American International Journal of Business and Management Studies 4(1) (2022), 30-40

  

38 
 

Cajita, M. I., Hodgson, N. A., Lam, K. W., Yoo, S., & Han, H. R. (2018). Facilitators of and barriers to mHealth adoption 

in older adults with heart failure. Computers, informatics, nursing: CIN, 36(8), 376. 

https://doi.org/10.1097%2FCIN.0000000000000442 

Calisir, F., Altin Gumussoy, C., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web‐based 

learning system: Perceived content quality, anxiety, perceived system quality, image, and the technology 

acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515-531. 

https://doi.org/10.1002/hfm.20548 

Cameron, J. D., Ramaprasad, A., & Syn, T. (2017). An ontology of and roadmap for mHealth research. International 

journal of medical informatics, 100, 16-25. https://doi.org/10.1016/j.ijmedinf.2017.01.007 

Chang, I. C., Chou, P. C., Yeh, R. K. J., & Tseng, H. T. (2016). Factors influencing Chinese tourists’ intentions to use the 

Taiwan Medical Travel App. Telematics and Informatics, 33(2), 401-409. 

https://doi.org/10.1016/j.tele.2015.09.007 

Cho, J., Quinlan, M. M., Park, D., & Noh, G. Y. (2014). Determinants of adoption of smartphone health apps among college 

students. American journal of health behavior, 38(6), 860-870. https://doi.org/10.5993/AJHB.38.6.8 

Cimperman, M., Brencic, M. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance 

behavior—applying an Extended UTAUT model. International journal of medical informatics, 90, 22-31. 

https://doi.org/10.1016/j.ijmedinf.2016.03.002 

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (pp. 20–26). Erlbaum. 

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and 

results (Doctoral dissertation, Massachusetts Institute of Technology). 

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS 

quarterly, 319-340. 

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two 

theoretical models. Management science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982 

Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., & Weerakkody, V. (2016). A generalised adoption model for 

services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, 33(1), 

174-187. https://doi.org/10.1016/j.giq.2015.06.003 

Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. 

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement 

error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/ 002224378101800104 

Guo, X., Sun, Y., Wang, N., Peng, Z., & Yan, Z. (2013). The dark side of elderly acceptance of preventive mobile health 

services in China. Electronic Markets, 23, 49-61. https://doi.org/10.1007/s12525-012-0112-4 

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation 

Modeling (2nd Edition). Sage. 

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS- SEM: indeed a silver bullet. Journal of Marketing Theory and 

Practice, 19(2), 139–152. https://doi.org/ 10.2753/MTP1069-6679190202   

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. 

European business review. https://doi.org/10.1108/EBR-11-2018-0203 

Hair, J., Black, W., Babin, B. Y. A., Anderson, R., & Tatham, R. (2010). Multivariate Data Analysis (7th ed). Pearson 

Prentice Hall. 

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based 

structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. 

https://doi.org/10.1007/s11747-014-0403-8 

Holtz, B., & Krein, S. (2011). Understanding nurse perceptions of a newly implemented electronic medical record 

system. Journal of Technology in Human Services, 29(4), 247-262. 

https://doi.org/10.1080/15228835.2011.639931 

Hoque, M. R. (2016). An empirical study of mHealth adoption in a developing country: the moderating effect of gender 

concern. BMC medical informatics and decision making, 16(1), 1-10. https://doi.org/10.1186/s12911-016-0289-0 

Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension 

of the UTAUT model. International journal of medical informatics, 101, 75-84. 

https://doi.org/10.1016/j.ijmedinf.2017.02.002 

Hsu, H. H., & Wu, Y. H. (2017). Investigation of the effects of a nursing information system by using the technology 

acceptance model. CIN: Computers, Informatics, Nursing, 35(6), 315-322. 10.1097/CIN.0000000000000313 

Ifinedo, P. (2012, January). Technology acceptance by health professionals in Canada: An analysis with a modified 

UTAUT model. In 2012 45th Hawaii international conference on system sciences (pp. 2937-2946). IEEE. 

https://doi.org/10.1109/HICSS.2012.556 

Johnston, A. C., & Warkentin, M. (2010). Fear appeals and information security behaviors: An empirical study. MIS 

quarterly, 549-566. https://doi.org/10.2307/25750691 

Kaium, M. A., Bao, Y., Alam, M. Z., Hasan, N., & Hoque, M. R. (2019). Understanding the insight of factors affecting 

mHealth adoption: A systematic review. International Journal of Research in Business and Social Science (2147-

4478), 8(6), 181-200. https://doi.org/10.20525/ijrbs.v8i6.522 

Kallander, K., Tibenderana, J. K., Akpogheneta, O. J., Strachan, D. L., Hill, Z., ten Asbroek, A. H., ... & Meek, S. R. 

(2013). Mobile health (mHealth) approaches and lessons for increased performance and retention of community 



   Akhter & Hossain, American International Journal of Business and Management Studies 4(1) (2022), 30-40

  

39 
 

health workers in low-and middle-income countries: a review. Journal of medical Internet research, 15(1), e17. 

https://doi.org/10.2196/jmir.2130 

Kang, S. (2014). Factors influencing intention of mobile application use. International Journal of Mobile 

Communications, 12(4), 360-379. https://doi.org/10.1504/IJMC.2014.063653 

Kapoor, K., Dwivedi, Y., C. Piercy, N., Lal, B., & Weerakkody, V. (2014). RFID integrated systems in libraries: extending 

TAM model for empirically examining the use. Journal of Enterprise Information Management, 27(6), 731-758. 

https://doi.org/10.1108/JEIM-10-2013-0079 

Khatun, F., Heywood, A. E., Ray, P. K., Bhuiya, A., & Liaw, S. T. (2016). Community readiness for adopting mHealth in 

rural Bangladesh: a qualitative exploration. International journal of medical informatics, 93, 49-56. 

https://doi.org/10.1016/j.ijmedinf.2016.05.010 

Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption 

in Thailand's community health centers: Applying the UTAUT model. International journal of medical 

informatics, 78(6), 404-416. https://doi.org/10.1016/j.ijmedinf.2008.12.005 

Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.    

Laxman, K., Krishnan, S. B., & Dhillon, J. S. (2015). Barriers to adoption of consumer health informatics applications for 

health self management. Health Science Journal, 9(5), 1. Retrieved from 

https://www.proquest.com/openview/3a6a4d71b2ca3950e88b5cbd2db70e4e/1?pq-

origsite=gscholar&cbl=237822 

Lee, E., & Han, S. (2015). Determinants of adoption of mobile health services. Online Information Review, 39(4), 556-

573. https://doi.org/10.1108/OIR-01-2015-0007 

Lin, F. R., Lu, I. Y., & Hsieh, P. S. (2011, May). Understanding the adoption of wireless sensor network service in 

households. In 2011 International Joint Conference on Service Sciences (pp. 218-222). IEEE. 

https://doi.org/10.1109/IJCSS.2011.50 

Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & 

management, 38(4), 217-230. https://doi.org/10.1016/S0378-7206(00)00061-6 

Nunes, A., Limpo, T., & Castro, S. L. (2019). Acceptance of mobile health applications: examining key determinants and 

moderators. Frontiers in psychology, 10, 2791. https://doi.org/10.3389/fpsyg.2019.02791 

Phichitchaisopa, N., & Naenna, T. (2013). Factors affecting the adoption of healthcare information technology. EXCLI 

journal, 12, 413. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4566918/ 

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: 

a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879. 

Retrieved from https://psycnet.apa.org/buy/2003-08045-010 

Quaosar, G. A. A., Hoque, M. R., & Bao, Y. (2018). Investigating factors affecting elderly's intention to use m-health 

services: an empirical study. Telemedicine and e-Health, 24(4), 309-314. https://doi.org/10.1089/tmj.2017.0111 

Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and 

variance-based SEM. International Journal of research in Marketing, 26(4), 332-344. 

https://doi.org/10.1016/j.ijresmar.2009.08.001 

Sam, S. (2017). Towards an empowerment framework for evaluating mobile phone use and impact in developing 

countries. Telematics and Informatics, 34(1), 359-369. https://doi.org/10.1016/j.tele.2016.06.003 

Sezgin, E., Ozkan-Yildirim, S., & Yildirim, S. (2018). Understanding the perception towards using mHealth applications 

in practice: physicians’ perspective. Information Development, 34(2), 182-200. 

https://doi.org/10.1177/0266666916684180 

Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research 

with recommendations for modifications and future research. Journal of consumer research, 15(3), 325-343. 

https://doi.org/10.1086/209170 

Sun, Y., Wang, N., Guo, X., & Peng, Z. (2013). Understanding the acceptance of mobile health services: a comparison and 

integration of alternative models. Journal of electronic commerce research, 14(2), 183. Retrieved from 

https://kd.nsfc.gov.cn/paperDownload/1000006533286.pdf 

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision 

sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x 

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field 

studies. Management science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926 

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a 

unified view. MIS quarterly, 425-478. 

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the 

unified theory of acceptance and use of technology. MIS quarterly, 157-178. https://doi.org/10.2307/41410412 

Xue, L., Yen, C. C., Chang, L., Chan, H. C., Tai, B. C., Tan, S. B., ... & Choolani, M. (2012). An exploratory study of 

ageing women's perception on access to health informatics via a mobile phone-based intervention. International 

journal of medical informatics, 81(9), 637-648. https://doi.org/10.1016/j.ijmedinf.2012.04.008 

Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X., & Lin, J. (2017). User acceptance of mobile health services from users’ 

perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health and 

Social Care, 42(2), 194-206. https://doi.org/10.1080/17538157.2016.1200053 

https://doi.org/10.1016/j.ijresmar.2009.08.001


   Akhter & Hossain, American International Journal of Business and Management Studies 4(1) (2022), 30-40

  

40 
 

Zhao, Y., Ni, Q., & Zhou, R. (2018). What factors influence the mobile health service adoption? A meta-analysis and the 

moderating role of age. International Journal of Information Management, 43, 342-350. 

https://doi.org/10.1016/j.ijinfomgt.2017.08.006 

 
 

Publisher’s Note: ACSE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. 

 

 

© 2022 by the authors. Licensee ACSE, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons 

Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 

American International Journal of Business and Management Studies (P-ISSN 2641-4937 E-ISSN 2641-4953) by ACSE is licensed under a Creative 

Commons Attribution 4.0 International License. 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/

