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. 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