Investigating Factors Affecting Elderly's Intention to

Original Research

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Investigating Factors Affecting Elderly’s Intention to Use m-Health Services: An Empirical Study

G.M. Azmal Ali Quaosar, MSC,1 Md. Rakibul Hoque, PhD1,2 and Yukun Bao, PhD1 1

Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan, People’s Republic of China. 2 Department of Management Information Systems, University of Dhaka, Dhaka, Bangladesh.

Abstract

Background: m-Health as an important part of e-health has recently become one of the most influential initiative in healthcare sector all over the world. In developing countries healthcare service providers started to provide m-health services from the last few years. Despite the widespread acceptance of mobile phones, the adoption of m-health among elderly is significantly low in developing countries. However, little research has been conducted to explore factors influencing elderly’s intention to use m-health services particularly in developing countries. The objective of this study is to identify the factors that influence the elderly’s intention to use m-health services. Materials and Methods: To assess elderly’s intention to use m-health services, this study applied the Unified Theory of Acceptance and Use of Technology (UTAUT). Data were collected from participants of age 60 years and above. The partial least square method based on structural equation modeling was used to analyze data. Results: The study found that performance expectancy, effort expectancy, social influence, and perceived credibility (p < 0.05) had significant influence on elderly’s intention to use m-health services. However, facilitating condition (p > 0.05) had no significant influence on elderly’s intention to use m-health services. Conclusions: The findings of this study may become beneficial for the governments, policy makers, and healthcare service providers in developing countries. Keywords: m-health, developing country, elderly, UTAUT

DOI: 10.1089/tmj.2017.0111

Introduction

T

he rapid spread of mobile and wireless technologies and their application to healthcare sectors have developed a new field of e-health, known as m-health. m-Health as an important part of e-health has recently become one of the most influential initiative in healthcare sector all over the world. m-Health is defined as ‘‘the application of wireless technologies to transmit different data contents and services which are accessible by health workers through mobile devices such as mobile phones, personal digital assistants, smart phones, and tablet computers.’’1 m-Health can reduce cost, be reached easily, and increase interaction between patients and health professionals concerning the diseases and health. It has been successfully used in developed countries to access healthcare, emergency medical services, health education, real-time treatment, and in-home health monitoring.2 Globally, the number of the elderly people is increasing faster than any other age group. In 2015, about 900 million people were aged 60 years or more, comprising 12.3% of the world’s population. By 2050, this figure is expected to reach to about 2.0 billion, representing 16% of the world’s population.3 In developing countries, where 80% of the elderly people live, elderly people will account for 20% of the population by 2050. When people become older, they suffer different diseases particularly chronic diseases. A study showed that 80% of elderly people have at least one chronic disease and 60% of elderly suffered from several chronic diseases.4 The readmission rates of elderly people with chronic diseases have recently gained attention from healthcare providers and policy makers in developing countries. The ageing population and the prevalence of their chronic conditions demand for an increase in both the quality and variety of healthcare services being offered. m-Health services can be used to manage diseases effectively and expand the quality of life for the elder people. Elderly people with chronic disease have to meet frequently with the health professionals to manage their care appropriately.5 It is quite difficult for the elderly people to meet with doctors regularly because of their physical disabilities. Both health

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professionals and users can benefit by using m-health services. Health professionals communicate with other health professionals to get support in their work and elderly people may take a number of services, including medical advice, appointment with doctors, seeking medical test result, and seeking to personal health information through m-health.6 However, the adoption of m-health services among elderly is significantly low, particularly in developing countries. Limited research has been conducted to explore factors influencing the adoption m-health services in this population. The objective of this study was to identify the factors that influence the elderly’s intention to use m-health services. The following specific research question was formulated in this study. ‘‘What are the factors affecting the elderly’s intention to use m-health services in developing countries?’’

Materials and Methods THEORETICAL FRAMEWORK AND HYPOTHESIS This study applied the Unified Theory of Acceptance and Use of Technology (UTAUT), widely accepted behaviors and acceptance analysis model, to identify the factors influencing elderly’s intention to use m-health. The UTAUT model is developed by extensive assessment of different models, such as the Theory of Planned Behavior (TPB), the Innovation Diffusion Theory, theory of Reasoned Action, the Technology Acceptance Model (TAM), the combined TAM-TPB, the Motivational Model, the Model of perceived credibility (PC) Utilization, and the Social Cognitive Theory, can show 70% variance in intention. It is an effective and mostly used model in technology adoption, including e-health and m-health. The research model used in this study is shown in Figure 1. According to UTAUT, the four constructs, such as (1) perfor-

mance expectancy (PE), (2) social influence (SI), (3) effort expectancy (EE), and (4) facilitating condition (FC), act as determinants of users’ behavioral intention. In this study, the UTAUT model has been extended with additional variable-perceived credibility. The perceived credibility was rated the most significant variable in the context of health and elderly people.7,8 Performance expectancy. PE is defined as ‘‘the degree to which an individual believes that using the system will help him or her to attain gains in job performance.’’9 It shows the measurements of user of a system whether the system is advantageous, performance enhancer, user friendly, or not. Carlsson et al.10 revealed that PE has direct effect on intention to use mobile phones. Hoque and Sorwar11 have found that PE is one of the significant determinants of users behavior intention to use m-health services. From this discussion, we propose the following hypothesis:

H1: Performance Expectancy is positively associated with elderly’s intention to use m-Health services. Effort expectancy. EE is defined as ‘‘the degree of ease asso-

ciated with the use of the system.’’9 It is the determinant that shows how much a technology or a system is easy to use. Studies show that EE influences the intention of a user to accept and adopt a health technology.12,13 Venkatesh et al.9 have identified EE as the most effective determinant of behavioral intention of any user to use technology. Hence, it was hypothized that: H2: Effort Expectancy is positively associated with elderly’s intention to use m-Health services. Social influence. SI is defined as ‘‘the degree to which an

individual perceives that important others believe he or she should use the new system.’’9 Sun et al.14 empirically indicated the positive relationship between SI and usage of mobile health services. Pan and Jordan-Marsh.15 revealed that SI was a significant predictor of technology use intention among older adults. Hence, it was hypothized that: H3: Social Influence is positively associated with elderly’s intention to use m-Health services.

FIG. 1. Research model. BI, behavioral intention; EE, effort expectancy; FC, facilitating conditions; PC, perceived credibility; PE, performance expectancy; SI, social influence; UB, use behavior.

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Facilitating conditions. FC is defined as ‘‘the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system.’’9 Aggelidis and Chatzoglou.16 indicated that FCs significantly affect behavioral intention to use health information systems. Mun et al.17 reported that FC has direct impact on behavioral intention and use of technology. From this discussion, we propose the following hypothesis:

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H4: Facilitating Condition is positively associated with elderly’s intention to use m-Health services. H5: Facilitating Condition is positively associated with actual use of m-Health services.


Perceived credibility. PC is defined as ‘‘the protection of users’ personal information and transaction details from unauthorized entrance.’’ It is one of the key dimension of behavioral intention to use an information system.7 It is proved that perceived credibility has important influence on intention to use technology.18,19 Oni and Ayo.20 reported that perceived credibility affects positively on user’s intention to use any systems such as m-health. Hence, it was hypothized that:

H6: Perceived credibility is positively associated with elderly’s intention to use m-Health services. Behavioral intention. Behavioral intention (BI) and actual use

behavior are strongly associated and behavioral intention is a predictor of actual use behavior.21 Venkatesh and Davis22 tested that behavioral intention assesses actual use behavior of users. Hence, it was hypothized that: H7: Behavioral intention is positively associated with actual use of m-Health services.

QUESTIONNAIRE DEVELOPMENT AND DATA COLLECTION The survey method was used to collect data. The survey questionnaire was divided into two sections, Part A and Part B. The first section Part A consists of 10 questions based on the respondents’ demographic profile like gender, educational background, experience of mobile phone usage, etc. Part B included questions on the subject of each variable in the model. The PE was measured with three items adapted from Venkatesh et al.9 EE items were adapted from Boontarig et al.23 and Venkatesh et al.,24 and the items for SI and FCs were adopted from Venkatesh et al.24 Perceived credibility with three items were adapted from Wang et al.25 and Tung et al.26 BI items were adopted from Boontarig et al.23 and use behavior items were adapted from Taylor and Todd,27 and Davis and Venkatesh.28 All items except the demographic profile were measured using a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5). Initially a structured questionnaire was developed in English and translated by a professional translator skilled in Bengali language. The questionnaire, also examined by a pool of experts and a pilot survey, was conducted to refine the questions. The ethical approval was obtained from the Ethics Committee of research of the Center for Modern Information Management, School of Management, Huazhong University

of Science and Technology, People’s Republic of China before commencing the research. All respondents were given consent forms and information sheets, which explained the purpose of the study. In total, 250 questionnaires were collected from respondents in Bangladesh. Five incomplete questionnaires were excluded from the analysis. There were 245 questionnaires for further analysis. To assess the relationship among the hypothesized concepts and validating the conceptual research model, the structural equation modeling (SEM) was used. As per Go¨tz et al.,29 SEM is a widely accepted model to validate theory with empirical data. Initially, Microsoft Excel was used to organize raw data from questionnaire and imported to SmartPLS software, one of the mostly used statistical software for analyzing data.30

Results THE MEASUREMENT MODEL The reliability and validity should be evaluated before testing the hypothesis.31 The reliability was assessed by using Cronbach’s alpha and composite reliability. The composite reliability and Cronbach’s alpha values of 0.70 or more are acceptable. According to Hair and Hult.,30 the composite reliability and Cronbach’s alpha values below 0.60 indicate a lack of internal reliability. Table 1 shows that all the constructs have Cronbach’s alpha and composite reliability values of more than 0.80, which is higher than the recommended value. Thus, the constructs were deemed to have adequate reliability. The validity was measured by considering convergent and discriminant validity. The convergent validity is considered to be satisfactory when measurement constructs have an average variance extracted (AVE) of at least 0.50 and items loading are well above 0.50.32 The measurement model table shows that AVE ranged from 0.715 to 0.916, whereas crossloading matrix shows that item loading ranging from 0.838 to 0.968 are greater than the recommended value. Therefore, conditions for convergent validity were met. The discriminant validity was assessed by the square root of the AVE and crossloading matrix. The square root of the AVE of a construct must be larger than its correlation with other constructs for satisfactory discriminant validity.33 The square roots of AVE, shown in the Table 2, were greater than their corresponding correlation, representing that our data had good discriminant validity. STRUCTURAL MODEL The structural model was constructed to find the path relationships among the constructs in the research model. Bootstrap method was used to test the hypothesis. The study tests the relationship between endogenous and exogenous


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Table 1. The Measurement Model and Crossloading Matrix COMPOSITE CRONBACH’S CONSTRUCTS ITEMS LOADINGS AVE RELIABILITY ALPHA


BI

EE

FC

PE

SI

PC

UB

BI1

0.896

BI2

0.857

BI3

0.863

EE1

0.887

EE2

0.899

EE3

0.865

EE4

0.879

FC1

0.888

FC2

0.885

FC3

0.880

PE1

0.896

PE2

0.901

PE3

0.882

SI1

0.858

SI2

0.838

SI3

0.842

PC1

0.948

PC2

0.955

PC3

0.968

UB1

0.923

UB2

0.921

UB3

0.910

0.761

0.905

0.843

0.779

0.934

0.906

0.782

0.915

0.862

0.797

0.922

0.873

0.715

0.883

0.804

0.916

0.970

0.954

0.843

0.942

0.907

Discussion

AVE, average variance extracted; BI, behavioral intention; EE, effort expectancy; FC, facilitating conditions; PC, perceived credibility; PE, performance expectancy; SI, social influence; UB, use behavior.

EE

FC

PC

PE

HYPOTHESIS SI

BI

0.872

EE

0.373

0.882

FC

0.288

0.209

0.884

PC

0.500

0.237

0.250

0.957

PE

0.545

0.225

0.380

0.266

0.893

SI

0.415

0.141

0.266

0.268

0.347

0.846

UB

0.458

0.308

0.209

0.341

0.260

0.265


It is the first study that extended UTAUT model with perceived credibility in the context of m-health services in developing countries. The findings of this study indicate that the extended UTAUT is a good predictive model of elderly’s intention to use m-health services. Regarding UTAUT-related variables, the findings show that PE, EE, and SI had significant influence on intention to use m-health services. The findings of this study are consistent with previous studies.11,34–36 Veer et al.37 demonstrated that PE, EE, and SI are highly related to intention to use e-health and m-health. In addition to the UTAUT variables, our findings show the positive relationship between perceived credibility and intention to use m-health services. Hu and Shyam Sundar7 also found significant relationship between perceived credibility and intention to use health technology. However, this study did not find any significant relationship between FC and elderly’s behavioral intention and actual use of m-health services. This finding is apparently surprising, given that many previous studies confirm a direct relationship between FC and technology adoption. It is a reflection of the fact that most of the elderly in developing countries such as Bangladesh are dependent on their adult children for living. Table 3. Structural Model

Table 2. Correlation Matrix and Square Root of the Average Variance Extracted BI

variable by path coefficient (b) and t-statistics. The study found that PE (t = 5.635, b = 0.359), EE (t = 3.484, b = 0.197), SI (t = 3.565, b = 0.185), and perceived credibility (t = 5.365, b = 0.302) had a significant effect on elderly’s intention to use m-health. Thus H1, H2, H3, and H6 were supported. However, FC (t = 0.263, b = -0.016, t = 1.170, b = 0.082) had no significant effect on elderly’s intention and actual use of m-health services. So, H4 and H5 were not supported. Finally, the result confirmed that behavioral intention to use is positively associated with actual use (t = 4.523, b = 0.426), supporting H7 (Table 3).

UB

0.918

PATH

b

t-STATISTICS

COMMENTS

H1

PE/BI

0.359

5.635

Supported

H2

EE/BI

0.197

3.484

Supported

H3

SI/BI

0.185

3.565

Supported

H4

FC/BI

-0.016

0.263

Not supported

H5

FC/UB

0.082

1.170

Not supported

H6

PC/BI

0.302

5.365

Supported

H7

BI/UB

0.426

4.523

Supported

Significant at p < 0.05.


ELDERLY’S INTENTION TO USE M-HEALTH SERVICES


Adult children must provide care for their aged parents in shared cultural values of developing countries. It is also true that FC may be insignificant if both PE and EE are present in a model. The developed model in this study can easily be applied for examining the adoption of m-health services in other developing countries. The findings of the study are important for the development of policies and strategies to enhance m-health services in developing countries because it identified the key factors that influence the elderly’s intention to use m-health services. Healthcare service provider and policy makers should consider the facilitating factors before the implementation of m-health program. Training should be provided to healthcare professionals to improve the m-health services. Healthcare service providers and telecommunication companies can invest money for promotional activities for increasing the willingness of users to use m-health services. Finally, special attention should be given to users who do not have any experience with m-health services.

Disclosure Statement No competing financial interests exist.

REFERENCES 1. Consulting VW. m-Health for development: The opportunity of mobile technology for healthcare in the developing world. Washington DC and Berkshire, UK: UN Foundation-Vodafone Foundation Partnership, 2009. 2. Khatun F, Heywood AE, Ray PK, Hanifi SM, Bhuiya A, Liaw ST. Determinants of readiness to adopt m-Health in a rural community of Bangladesh. Int J Med Inform 2015;84:847–856. 3. UN. World Population Prospects: The 2015 Revision, Department of Economic, United Nations Publications, New York, NY, 2015. 4. Khanam MA, Streatfield PK, Kabir ZN, Qiu C, Cornelius C, Wahlin A˚. Prevalence and patterns of multimorbidity among elderly people in rural Bangladesh: A cross-sectional study. J Health Popul Nutr 2011;29:406–414. 5. Aldrich N, Benson WF. Disaster preparedness and the chronic disease needs of vulnerable older adults. Prev Chronic Dis 2008;5:A27. 6. Wickramasinghe NS, Fadlalla AM, Geisler E, Schaffer JL. A framework for assessing e-health preparedness. Int J Elect Healthc 2005;1:316–334. 7. Hu Y, Shyam Sundar S. Effects of online health sources on credibility and behavioral intentions. Commun Res 2010;37:105–132. 8. Mueller-Johnson K, Toglia MP, Sweeney CD, Ceci SJ. The perceived credibility of older adults as witnesses and its relation to ageism. Behav Sci Law 2007;25:355–375.

Conclusion

The higher rate of mobile phone adoption among the people in developing countries has opened a prodigious opportunity to use information technology in healthcare. The number of healthcare institutions is not enough in developing countries, therefore elderly people are mostly deprived of proper healthcare services. Elderly people who suffer different diseases, particularly, chronic diseases have to meet frequently with the health professionals to avoid any disaster. It is quite hard for them. Healthcare support through m-health can be the useful tool for elderly people all over the world, especially in developing countries. It reduces complexities, ensures better reach, increases interaction between patients and health professionals, and saves cost and time to get basic healthcare support. To achieve greater adoption and use of m-health services, an effective roadmap needs to be set and followed by both public and private healthcare service providers. This study is the milestone for developing countries to understand factors affecting the adoption of m-health services. The findings of this study can be used by policy makers, government, and mobile phone company to increase the adoption of m-health services in developing countries.

Acknowledgments This research is supported by the Ministry of Education in China Project of Humanities and Social Science (Project No. 13YJA630002), and a grant from the Modern Information Management Research Center at Huazhong University of Science and Technology (Project No. 2015AA030).

9. Venkatesh V, Morris, MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q 2003;27:425–478. 10. Carlsson C, Carlsson J, Hyvonen K, Puhakainen J, Walden P. Adoption of mobile devices/services—searching for answers with the UTAUT. Proceedings of the 39th Annual Hawaii International Conference on System Sciences, 2006. HICSS’06. IEEE, New Jersey, 2006;6:132a–132a. 11. Hoque R, Sorwar G. Understanding factors influencing the adoption of m-Health by the elderly: An extension of the UTAUT model. Int J Med Inform 2017;101:75–84. 12. Hoque MR, Albar A, Alam J. Factors influencing physicians’ acceptance of e-health in developing country: An empirical study. Int J Healthc Inf Syst Inform 2016;11:58–70. 13. Kijsanayotin B, Pannarunothai S, Speedie SM. Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. Int J Med Inform 2009;78:404–416. 14. Sun Y, Wang N, Guo X, Peng Z. Understanding the acceptance of mobile health services: A comparison and integration of alternative models. J Electr Commerce Res 2013;14:183. 15. Pan S, Jordan-Marsh M. Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Comput Hum Behav 2010;26:1111–1119. 16. Aggelidis VP, Chatzoglou PD. Using a modified technology acceptance model in hospitals. Int J Med Inform 2009;78:115–126. 17. Mun YY, Jackson JD, Park JS, Probst JC. Understanding information technology acceptance by individual professionals: Toward an integrative view. Inform Manag 2006;43:350–363. 18. Karjaluoto H. Selection criteria for a mode of bill payment: Empirical investigation among Finnish bank customers. Int J Retail Distrib Manag 2002;30:331–339. 19. Lallmahamood M. Privacy over the internet in Malaysia: A survey of general concerns and preferences among private individuals (Doctoral dissertation, Malaysian Institute of Management), 2008. 20. Oni AA, Ayo CK. An empirical investigation of the level of users’ acceptance of e-banking in Nigeria. J Internet Bank Commerce 2010;15:1–13.


NO. 4 APRIL 2018


QUAOSAR ET AL.

21. Bhattacherjee A, Hikmet N. Reconceptualizing organizational support and its effect on information technology usage: Evidence from the health care sector. J Comp Inform Syst 2008;48:69–76.


22. Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag Sci 2000;46:186–204. 23. Boontarig W, Chutimaskul W, Chongsuphajaisiddhi V, Papasratorn B. Factors influencing the Thai elderly intention to use smartphone for e-Health services. IEEE Symposium on Humanities, Science and Engineering Research (SHUSER). IEEE, New Jersey, 2012:479–483. 24. Venkatesh V, Thong JY, Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q 2012;36:157–178. 25. Wang YS, Wang YM, Lin HH, Tang TI. Determinants of user acceptance of internet banking: An empirical study. Int J Serv Ind Manag 2003; 14:501–519. 26. Tung FC, Chang SC, Chou CM. An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. Int J Med Inform 2008;77:324–335. 27. Taylor S, Todd P. Assessing IT usage: The role of prior experience. MIS Q 1995;19:561–570. 28. Davis FD, Venkatesh V. Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Trans Eng Manage 2004;51:31–46. 29. Go¨tz O, Liehr-Gobbers K, Krafft M. Evaluation of structural equation models using the partial least squares (PLS) approach. In: Vinzi V, Chin WW, Henseler J, Wang H, eds. Handbook of partial least squares. Berlin Heidelberg: Springer, 2010:691–711. 30. Hair Jr. JF, Hult GTM. A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage Publications, 2016. 31. Bagozzi RP, Yi Y, Phillips LW. Assessing construct validity in organizational research. Admin Sci Q 1991;421–458. 32. Hair Jr. JF, Anderson RE, Tatham RL, William C, Black. Multivariate data analysis with readings. Upper Saddle River, NJ: Prentice Hall: 1995.


33. Henseler J, Ringle CM, Sinkovics RR, The use of partial least squares path modeling in international marketing. In: Sinkovics RR, Ghauri PN, eds. New challenges to international marketing. Bingley, UK: Emerald Group Publishing Limited, 2009:277–319. 34. Hoque MR, Bao Y, Sorwar G. Investigating factors influencing the adoption of e-Health in developing countries: A patient’s perspective. Inform Health Soc Care 2017;42:1–17. 35. Hoque MR, Bao Y. Cultural influence on adoption and use of e-Health: Evidence in Bangladesh. Telemed J E Health 2015;21:845–851. 36. Bao Y, Hoque R, Wang S. Investigating the determinants of Chinese adult children’s intention to use online health information for their aged parents. Int J Med Inform 2017;102:12–20. 37. Veer AJ, Peeters JM, Brabers AE, Schellevis FG, Rademakers JJJ, Francke AL. (2015). Determinants of the intention to use e-Health by community dwelling older people. BMC Health Serv Res 2015;15:103.

Address correspondence to: Yukun Bao, PhD Center for Modern Information Management School of Management Huazhong University of Science and Technology Room No. 551 Wuhan 430074 People’s Republic of China E-mail: [email protected]; [email protected] Received: April 30, Revised: June 26, Accepted: June 29, Online Publication Date: October 4,

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