Abstract
Due to the scarcity of medical infrastructure including doctors and hospitals, ICT based healthcare services is getting popular around the world including low facilities rural areas of Bangladesh. Portable Health Clinic (PHC) system is one of the ICT based healthcare systems. Speciality of this system is that the clinic box is carried and operated by a pre-trained healthcare worker. However, longitudinal study in this context wasn’t undertaken before. In order to draw strong inferences about new technology use we need to do longitudinal study. Therefore, the aim is to identify key determinants of actual use of the PHC system and to understand how their influence changes over time with increasing experience to explain detailed action sequences that might unfold over time. Face to face survey will be conducted to collect data. Structural Equation Modeling will be used to analyze data. By analyzing data using AMOS 25.0 this study will identify most important time that are key to increase actual use of the PHC system. The proposed model can make it possible to offer important practical guidelines to service providers in enhancing actual use of the PHC system. The study can suggest way of increasing health awareness to policy makers and way to build awareness to use the system. The study can also contribute to make policy to improve health care situation i.e., reduce morbidity rate in the country.
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1 Introduction
Though dramatic advances in hardware and software capabilities, low usage of already developed information systems continue and low return from organizational investment has been identified. Therefore, understanding and creating the conditions under which the information system will be accepted by human remains a high priority research issue of information systems research and practice. Better understanding of the determinants of use behavior would increase user acceptance and usage of new system [1]. Significant changes have been made in the most of the countries in the world over the last decades in explaining and determining user acceptance of information technology at health care sectors [2]. In addition, due to the scarcity of medical infrastructure including doctors and hospitals, and to expensive access to quality health care services remote healthcare services by using advanced Information and Communication Technology (ICT) is getting popular around the world including Bangladesh.
Currently Grameen, Bangladesh and Kyushu University, Japan have developed one of remote healthcare systems called portable health clinic (PHC) system which is carried and operated by one pre-trained health care lady to deliver health care services to the unreached people in Bangladesh who are deprived of quality health care services where doctors from urban area can consultant with patients.
Due to potential benefits and various eHealth initiatives in place, many recent studies have been done to enhance the acceptance of eHealth services by all citizens. Therefore, through proper study of growing popularity of ICT based health care services, it is possible to enhance the actual use of the PHC system. There are four broad categories of factors that can influence consumers’ usage of any new product or service - (i) demographic factors (ii) socio-economic factors (iii) cultural factors, and (iv) psychological factors [3]. Understanding factors that influence technology acceptance is essential for its successful adoption [4]. However, there are only few studies conducted in regards to consumer acceptance [5]. A study among 600 families in one particular area in Bangladesh to determine the demographical and socio-economic factors found – consumers’ age, occupation and purchasing power have significant influence on their acceptance of eHealth services from PHC system. But this study identified temporary and contemporary solutions or dynamics of determinants of acceptance of the PHC system [6].
Moreover, longitudinal study of a cohort of consumers in the context of Portable Health Clinic (PHC) system wasn’t undertaken before to understand the consistent causal relationship among factors of PHC and how their influence changes over time. In order to draw strong inferences about acceptance of new technology we need to do longitudinal study.
Therefore, the main aim of this study is to identify key determinants of actual use of the PHC system and to understand how their influence changes over time with increasing user experience by using the PHC system to explain detailed action sequences that might unfold over time. More detailed objectives are following:
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To develop a general decision making model across time and geographic area for the actual use of PHC system in Bangladesh.
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To identify consistently cited most significant factors in predicting actual use.
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To identify cause and effect relationships among factors over time.
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To explore changing factors of use the system over a 1 year period.
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Testing significant differences among 3 times survey per year: survey 1, survey 2, and survey 3 data, to examine whether the respondents’ use of PHC service changed over the period.
A sample of 300 people from both areas (urban and rural) of Bangladesh have been collected by face to face questionnaire survey.
2 Theoretical Basis for Building the Proposed Model
There have been many previous studies targeted to explore the factors influencing the use behavior of a new technology. The present study reviewed previous literature and develops the proposed model. Theory of Reasoned Action (TRA) is the major theory to explain people’s behavior [7].
Based on the reasoned action theory, theory of planned behavior was developed which is an extension of TRA [8]. Based on the above models Technology Acceptance Model (TAM) was developed [9]. TAM is the major theory to understand how users come to accept and use a new technology/system. By 10 years, TAM has become a well-established, robust, powerful, and parsimonious model for predicting user acceptance but this model did not include some important external variables. TAM2 model supported well basic TAM relationships and extended TAM model by adding additional determinants of the basic TAM model (TAM1)’s perceived usefulness and usage intention constructs. Later Unified Theory of Acceptance and Use of Technology (UTAUT) model was developed by adding four constructs in the basic technology acceptance model: facilitating conditions, social influence, performance expectancy, and effort expectancy [10].
Based on TAM1, TAM2, UTAUT and other existing user acceptance model we would like to propose a generalized theoretical model that can investigate factors behind actual use of PHC system in Bangladesh. In our proposed model, we incorporated TAM1 [9], TAM2 [1], and UTAUT [10] model moreover; we are arguing that illness, health awareness, and self-efficacy directly affect actual use of Portable Health Clinic (PHC) system (Figs. 1 and 2).
3 Methodology
3.1 Study Place
We have selected two areas of Bangladesh to collect our data. One is the urban area and another one is the rural area. (i) Urban area: Grameen Bank Complex, Mirpur, Dhaka, and (ii) Rural area: Ekhlaspur village, Matlab, Chandpur.
3.2 Study Population
The target population is those who have used the PHC system at least once. The target population size is: 300 from both areas. And participants must be: (1) Adult aged older than or equal to 35 years, (2) Residents who live at the targeted survey areas, (3) Those who provides informed written consent, and (7) Those who are healthy enough to be able to participate in this research.
3.3 Statistical Basis of the Sample Size and Sampling Procedures
There is considerable variation in the opinions observed in the literature in regard to the selection/calculation of optimum sample size in different types of statistical analysis [11]. For example, statistical analysis including structural equation modeling (SEM) recommends sampling of 200 as fair and 300 as good [12]. Hair et al. also recommended a sample size of 200 to test a model using SEM. A ‘critical sample size’ that can be used in any common estimation procedure for valid results [13]. As per previous studies, a sample size of 300 was selected in this study for data analysis using SEM [14].
The sample was drawn by a simple random sampling procedure. In this study, we informed people first about our PHC system. After initial introduction about PHC system, survey1 will be conducted among those who come to the service point and have received the PHC service at least once. The same respondent will be interviewed in the post-survey.
3.4 Data Collection Methods
Longitudinal field survey through structured questionnaire will be conducted to measure factors/variables. Data will be collected in 3 different points in time per year: First survey (survey 1) - after initial introduction of the PHC system (T1), Second survey (survey 2) - 4 months after the first survey (T2), Third survey (survey 3) - 4 months after the second survey (T3). The same respondent will be interviewed in the post-survey.
3.5 Pretesting
Pilot survey was conducted in both areas. 35 respondents were interviewed after receiving the PHC healthcare service. Furthermore, we have analyzed their responses. Based on feedback from the pilot survey, we have finalized our questionnaire.
3.6 Questionnaire Design
The questionnaire contains the following two parts: (1) Part A (Socio-demographic information)-Name, age, education level, gender, having phone, having access to internet, past experience, and, having any kind of illness etc. and (2) Part-B (Cognitive or Perceptional questions)-13 Psychological factors which are used in the initial hypothetical model (Fig. 3). Every factor will be measured by 3 questionnaire items. Respondent will read each statement and rate each statement on 5-point Likert scales by putting √ (tick) in the number that best describes she/he. All statements will be measured on a 5-point Likert scale, where 1 = Strongly disagree, 2 = Disagree, 3 = Neither Disagree nor agree, 4 = Agree, and 5 = Strongly agree.
3.7 Outcome Variables
Our outcome variable is the actual use of the Portable Health Clinic (PHC) system. We have asked three questionnaire items to measure the dependent variable.
3.8 Hypotheses of the Study
Based on previous related researches we propose following hypotheses:
H1. Perceived usefulness will have a positive and direct effect on actual use of the Portable Health Clinic (PHC) system.
Perceived usefulness is defined as the extent to which a person believes that using the PHC system will improve his or her health [1].
H2. Social influence will have a positive and direct effect on actual use of the Portable Health Clinic (PHC) system.
Social influence is defined as the degree to which an individual perceives that important others believe he or she should use the system. Social influence is similar to subjective norm and social norm [10]. Social norm or subjective norm is defined as an individual’s perception most people who are important to him think that he should or should not perform the specific behavior [7]. In other words, social influence means reference from friends or family members or loved one. Social norm consists of two influences: 1. Informational norm to enhance knowledge and 2. normative to conforms expectations of others [15]. Subjective norms concern the perceived social pressures to undertake or not undertake a behavior [8, 16]. And moral norms are personal feelings of moral responsibility or obligation to perform a certain behavior which may have a significant contribution to explain the variance of the behavior [8, 17]. Social reference was a direct determinant of acceptance of eHealth technology [18]. Study analyzed energy saving behavior among University students in Vietnam and identified Social norm was the most important determinants for energy saving behavior such as avoiding AC use [19]. Social norm had also direct positive influence on waste reduction behavior [20].
H3. Illness will have a positive and direct effect on actual use of the PHC system.
H4. Health awareness will have a positive and direct effect on actual use of the PHC system.
Health awareness is measured to assess the degree of readiness to undertake health actions [21]. This construct reflects a persons’ readiness to do something for his or her own health [22].
H5. Privacy will have a positive and direct effect on actual use of the PHC system.
Privacy is defined as the extent to which a respondent believes that PHC system will not compromise his or her privacy [23].
H6. User self-efficacy will have a positive and direct effect on actual use of the PHC system.
Perceived self-efficacy is defined as the judgement of one’s ability to use a technology [10, 24]. That is belief that using the PHC system can improve or benefit his or her health condition.
H7. Past experience of using any eHealth system other than PHC system will have a positive and direct effect on actual use of the PHC system.
H8. Internet access will have a positive and direct effect on actual use of the PHC system.
H9. Having mobile will have a positive and direct effect on actual use of the PHC system.
H10. Intention to actual use will have a positive and direct effect on actual use of the PHC system.
The theory of reasoned action (TRA) by Ajzen et al. [25] has identified that intention to perform behavior as the immediate determinants of behavior.
3.9 Statistical Methods of Data Analysis
We will apply two well established statistical methods to analyze our data, which are: Exploratory Factor Analysis (by using Statistical Package for Social Sciences (SPSS) 25.0): We will use factor analysis to measure factors that were used in our proposed research model. Structural Equation Modeling (by using Analysis of a Moment Structures (AMOS) 25.0 software tool): We will use Structural Equation Modeling statistical technique to identify cause and effect relationship among factors.
4 Expected Results
The result can identify the degree (value) of relationship between statistically significant variables. We will examine the direct and indirect roles of social norm, perceived usefulness, user self-efficacy, health awareness, privacy, illness, past experience of using eHealth, and access to internet connection on actual use of the eHealth system in Bangladesh. The study can also offer important practical guidelines to the PHC service providers in enhancing actual use of the PHC system. Results can suggest way of increasing health awareness and to build awareness to use PHC system to policy makers. The results of this study can also contribute to make policy to reduce morbidity rate in the country.
5 Limitations and Future Directions
The study will be conducted to only two specific geographic areas due to time and budget constrain. Thus the results may raise concern about the generalizability of the findings. Due to the small sample size our model could able to explain less % of the variance. In future, we could include more geographic areas. Future study could also increase sample size.
6 Conclusion
This study will theoretically develop a modified and improved eHealth acceptance model. The study will be the first longitudinal study to identify the key psychological factors and socio-demographic characteristics behind actual use of the PHC system by potential users. It can offer important practical guidelines to service providers and policy makers in promoting actual use of the PHC system.
We will examine the direct and indirect roles of social norm, perceived usefulness, user self- efficacy, health awareness, privacy, illness, past experience of using eHealth, and access to internet connection on actual use of the eHealth system in Bangladesh. Structural equation modeling statistical technique will be used to test the hypotheses of the study. Some factors those who have direct and positive effects on actual use of the PHC system will be also identified.
The proposed model through this study will make it possible to offer important practical guidelines to service providers and policymakers in promoting actual use of the eHealth system in Bangladesh.
For policy recommendation, we should take into account more the most important predictor of actual use of the PHC system. And for this identified predictor we can inform policy maker the way of increasing this predictor. We should also focus on followed by predictors of the actual use of the PHC system. In addition, our result can also suggest the way of increasing significant predictors based on indirect effects.
Therefore, the result of this study can offer important practical guidelines to the PHC service providers in enhancing actual use of the PHC system. Results can also suggest way of increasing health awareness and to build awareness to use PHC system to policy makers. The results of this study can also contribute to make policy to improve the healthcare system in the country i.e., to reduce morbidity rate in the country.
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Acknowledgement
This research work has been supported by multiple organizations. JSPS KAKENHI, Grant Number 18K11529 and the Future Earth Research Fund, Grant Number 18-161009264 jointly financed the core research. Institute of Decision Science for a Sustainable Society (IDS3), Kyushu University, Japan provided travel expenses for data collection, and Grameen Communications, Bangladesh provided technical assistance.
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Sampa, M.B. et al. (2019). A Framework of Longitudinal Study to Understand Determinants of Actual Use of the Portable Health Clinic System. In: Streitz, N., Konomi, S. (eds) Distributed, Ambient and Pervasive Interactions. HCII 2019. Lecture Notes in Computer Science(), vol 11587. Springer, Cham. https://doi.org/10.1007/978-3-030-21935-2_24
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