Perception towards the Acceptance of Digital Health Services among the

People of Bangladesh

K. M. SALAH UDDIN1, MOHAMMAD RAKIBUL ISLAM BHUIYAN2,*, MARUFA HAMID3

1Department of Management Information Systems, Faculty of Business Studies,

University of Dhaka, Dhaka-1000, Dhaka,

BANGLADESH

2Department of Management Information Systems, Faculty of Business Studies,

Begum Rokeya University,

Rangpur, Rangpur-5404,

BANGLADESH

3Department of Management Information Systems, Faculty of Business Studies,

University of Dhaka,

Dhaka-1000,

BANGLADESH

*Corresponding Author

Abstract: - The research intends to determine the influential factors of individual willingness to use digital health

services in Bangladesh. The quantitative research method was conducted to obtain the purposes of this study. To

collect primary data, a questionnaire link and direct interaction with a purposive sample of 300 people were used.

The sample for this study was made up of people who use digital health services. The study discovered that six

factors, totaling 24 items, influence Bangladeshis' acceptance of digital health services. The reliability test for 24

variables and 6 determinants is reliable because Cronbach's alpha is 0.569, which is greater than the standard 0.5.

This study discovered a positive correlation between social and cultural, technological, economic, convenience,

security, and perceived utility using a two-tailed test with a significance level of 0.01 or less. The study found that

economic advantages and technology literacy understanding greatly influence digital health care acceptability, with

greater statistically significant outcomes than other determinant factors. Policymakers, healthcare practitioners, and

technology developers can use the data to customize their plans and solutions to Bangladeshi requirements.

Promoting positive perceptions and removing barriers will increase digital health service use in Bangladesh,

increasing healthcare outcomes and accessibility.

Key-Words: - Digital Health (DH), Digital Health Services (DHS), Perception, Economic Benefits, Technological

Literacy, Social and Cultural, Bangladesh.

Received: October 21, 2023. Revised: May 13, 2024. Accepted: June 14, 2024. Published: July 12, 2024.

1 Introduction

The field of digital health services (DHS) focuses on

the provision of healthcare in the era of digital

technology, encompassing the integration of

informatics and technological advancements in

medicine and healthcare, [1]. This integration is

applied to various aspects of clinical practice, patient

experiences, and the broader political, social, and

economic implications of healthcare, [2]. DHS

encompasses a range of technological advancements

and applications in the healthcare sector, [3].

The rapid development and advancement of

mobile and wireless technologies over the last few

decades have paved the way for global health service

delivery to be transformed, [4]. Almost everyone has

used a cell phone to access some type of electronic

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information at some point, generally through voice

calls to an office (such as a local agriculture office or

bank) to request information. Nonetheless, despite

extensive promotion and advertising, many people

were unfamiliar with the provision of electronic

health information or services, as well as the term

digital health, [5].

The utilization of DH technologies holds promise

in contributing to the attainment of the Sustainable

Development Goals (SDGs) in India conducted, [6].

By bolstering health systems and augmenting health

promotion and disease prevention efforts, digital

health has the potential to make significant strides

toward achieving these goals.

Despite their unfamiliarity with formal terms

such as DH, some of them utilize mobile phones to

access health-related information. Some of those with

relevant knowledge were hesitant to use

technological devices to acquire health information,

such as the internet and call centers. As a result,

many use their phones to seek friends, relatives, or

social acquaintances for guidance but are unaware of

or do not use established DHS, [7].

Because of the unprecedented spread of mobile

phone technologies and other digital devices, as well

as their innovative applications for addressing health

priorities, a new field of E-health known as digital

health services has emerged. Digital health is an

exciting field with the potential to significantly

improve healthcare delivery. However, the

Bangladeshi people's perception of the acceptance of

DHS is behind the world's ongoing development.

Bangladesh faces numerous challenges, including

inadequate ICT infrastructure, financial issues,

resistance to change, usability, a lack of policy, and

interoperability, all of which contribute to a negative

perception of DHS acceptance, [8].

This article aims to investigate the current state

of digitalization in healthcare services, with a

particular focus on the findings derived from

previous studies conducted in diverse contexts within

Morocco, [9]. By examining the existing research,

this study seeks to shed light on the extent to which

digital technologies have been adopted and integrated

into healthcare practices in Morocco. The digitization

phenomenon has also made its way into the

healthcare sector, as evidenced by the integration of

digital processes and tools. The emergence of digital

formulas in Morocco can be attributed to the

extensive implementation of telemedicine, e-learning

platforms, and the exchange of information among

key stakeholders such as the pharmaceutical industry,

healthcare professionals, and patients, [10].

One of the most significant barriers to the

development of digital health services was the

connectivity and compatibility of electrical devices,

[11]. Users are more comfortable using digital health

services applications because of the connectivity and

compatibility of electric devices.

1.1 Research Problem

Although digital health services are becoming

increasingly popular in both developed and

developing countries, they are likely to be novel and

limited in Bangladesh because most people are

unfamiliar with them, [12]. Nobody has ever heard of

digital health services, regardless of age or gender.

Several companies in Bangladesh have already begun

to offer digital health services, but many people in

this country are still unaware of the benefits of digital

health services, [13]. The goal of this study is to learn

more about people who use digital technology or a

mobile phone and their attitudes towards digital

health acceptability in Bangladesh. As a result, the

study's purpose was to examine how factors such as

perceived social and cultural, technological,

economic, convenience, security, and perceived

usefulness influence the perception of those who use

DHS in Bangladesh.

1.2 Research Objective

The research intends to determine the influential

factors of individual willingness to use DHS in

Bangladesh.

2 Materials and Methods

A vast number of individuals suffer from various

health conditions all around the world, and many

people are denied health care due to poverty, a lack

of medical professionals, and a variety of other

challenges. The United Nations Development

Program (SDGs) are a collection of 17 global goals

aimed at altering our world: the 2030 Agenda for

Sustainable Development. SDG 3 stresses health and

well-being for all ages, particularly in specific areas

such as mental health, maternal mortality, infectious

illnesses, and the healthcare workforce, [14].

Significant progress towards SDG 3 targets is

possible if effective education, outrage support, and

digital health in the form of telemedicine and DH are

implemented on a low-resource platform, [15].

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Numerous factors contribute to the complex

interplay that shapes the well-being of individuals

and civilizations. The health of individuals is

influenced by their circumstances and the

surrounding environment, [16]. The determinants of

health encompass various factors that influence an

individual's well-being. These factors include the

geographical location of one's residence, the

condition of the surrounding environment, genetic

predispositions, socioeconomic status, educational

attainment, and the quality of interpersonal

relationships with friends and family. While the

availability and utilization of healthcare services are

commonly acknowledged as influential factors, their

impact on health outcomes is often less pronounced

compared to the aforementioned determinants, [17].

Research on resistance to change has primarily

examined how individuals perceive the influence of

organizational and environmental factors on their

decision to either comply with or resist change. This

decision ultimately leads to an intention to either

accept or resist the implementation of a particular

technology. The individual's perception of the value

and potential threats associated with the technology

plays a significant role in shaping their intention,

[18].

Many people were unaware of e-health services

due to a misunderstanding of the benefits of eHealth

services. They have a number of issues with DHS,

including a lack of expertise to handle the device and

platform, a lack of awareness about the associated

costs of device integrations, resistance to change, a

lack of trust, and a reluctance to use DHS, [19].

E-health is not just a technological advancement

but also a mental attitude. Health informatics is a

cognitive approach characterized by a certain attitude

and a commitment to interconnected, worldwide

thinking. It leverages information and

communication technologies to enhance healthcare

on local, regional, and global scales, [20]. By

lowering economic costs, eHealth provides more

potential for transformation at every stage of the

patient's medication management journey, [21].

Technology is widely used worldwide to provide

and distribute healthcare services. E-health, which

refers to the application of information, computer,

and communication technology in the field of health

or healthcare, is widely acknowledged as an essential

remedy for addressing the difficulties faced by

healthcare systems. These issues encompass the

increasing need caused by a growing elderly

population and breakthroughs in medical therapies,

along with the limitations imposed by scarce

resources. However, despite the universal

acknowledgment of the importance and potential

benefits of e-health, its implementation has often

faced difficulties in meeting early expectations,

primarily due to challenges connected to its

execution. The importance of personnel involved in

e-health implementation having a thorough grasp of

the elements that impact implementation. In addition,

they should possess advanced expertise in

formulating strategies and interventions that promote

the extensive and effective adoption of e-health while

also tackling any barriers to implementation.

Incorporating end users in the design and

development of e-health technologies is a beneficial

strategy for overcoming obstacles related to

adaptability, meeting performance standards,

increasing preference for flexibility, and promoting a

positive reception of new health service methods,

[22].

The benefits and drawbacks of utilizing online

health consultations might be perceived as favorable

or unfavorable. Security is recognized as one of the

determinants influencing the development and

growth of DHS, and it has been stated that, while

security and privacy are closely related, both

constructs are distinct in nature, [23]. The advantages

and disadvantages might be viewed as favorable and

unfavorable aspects of utilizing online health

consultations, [24].

Various social and cultural factors influence

people's acceptance of DHS in Bangladesh. A culture

is a set of shared beliefs and behaviors among a

group of people, [25]. A society can have multiple

cultures, and inequalities in social status exist across

cultures. Social and cultural factors will always

interact with biology to influence health, [26]. This

confluence of factors influences a person's perception

and definition of health and illness, access to

healthcare, response to treatment, and treatment

expectations and options. Disease, pain, disability,

pain experience, and healing are all health outcomes,

[27].

Resistance to technological change has always

had an impact on our decision to accept new

technology. People who are digitally literate and

have internet access can easily understand the

benefits of DHS. As a result, there is a positive

relationship between some of these technological

determinants and DHS acceptance, [28].

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Transportation cost savings, reduction in doctor's

fees, unavailability of medical resources, and

inadequate health center infrastructure are some

economic determinants associated with the

acceptance of digital health among the people of

Bangladesh. As a result, it is proposed that there is a

positive relationship between some of these

economic determinants related to the cost and

acceptance of DHS, [29].

Convenience is a major motivator for people to

use DHS. In other words, the more useful health

applications are perceived to be, the more convenient

they are. It was discovered that convenience has a

positive relationship with perceived usefulness, [30].

Security is defined as the protection of systems

or data from unauthorized outflows or intrusions.

One of the major barriers to DH adoption has been

identified as perceived security, [31].

Perceived utility refers to the user's expectation

that using a specific application system will improve

the efficiency of their activity, [32]. Perceived

usefulness pertains to the user's expectation of

improving their service performance by using DHS

applications. Suggest a connection between the

characteristics that impact how useful DHS are

judged to be and how acceptable they are, [33].

There are interconnected relationships between

security, perceived utility, convenience, and cultural

and social behavioral intentions in the context of

online health consultations, [34]. Since online health

consultation is considered an e-commerce application

in the field of eHealth, it is crucial to incorporate

economic benefits into the proposed study design.

Previous research has shown that service quality has

a crucial role in influencing behavioral intentions,

[35]. When assessing the performance of online

physicians, patients typically evaluate aspects such as

doctors' fees, transportation costs, and doctor-patient

contacts, [36].

2.1 Conceptual Framework

The conceptual framework of this study was

informed by the literature survey, as depicted in

Figure 1: The present study has successfully

identified six key determinants through an extensive

review of the existing literature. These determinants

encompass various aspects, including social and

cultural factors, economic considerations,

technological proficiency, convenience, perceived

usefulness, and security. By thoroughly examining

the available scholarly works, this research has

synthesized and presented these determinants as

crucial factors influencing the subject under

investigation. According to the comprehensive

analysis of existing literature and considering all

relevant factors, Figure 1 presents a conceptual

research model that aims to tackle the research

questions at hand. Despite the growing interest in

DHS, there is a lack of research examining the

determinants of acceptance behavior in this field.

Fig. 1: Digital Health Adoption Model

The study goal is to determine the efficacy

variables that, either directly or indirectly, influence

people's acceptance of DHS. The factors on which

this study will focus are divided into six categories.

These include social and cultural factors, economic

factors, technological factors, security, convenience,

and perceived usefulness, [37]. While DHSs have

potential benefits, they also face numerous

difficulties and challenges as a new phenomenon.

Problems are most noticeable during the promotion

and implementation stages. Processes that can

encourage user acceptance are required for the

development of DHS. As a result, there is an urgent

need for research on the factors influencing people's

acceptance of digital health services; however,

current academic studies on DHS are insufficient to

meet this need, [38]. This investigation focuses on

Bangladeshi users of DHS. A standardized

questionnaire is used to collect responses from

population groups. A non-probability sampling

method is used in this study. Non-probability

sampling approaches provide the researcher with a

variety of methodologies to use during the data

collection process.

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3 Results

3.1 Profile of Respondents for the Study

The frequency distribution analysis is used in this

section to describe the demographic characteristics of

the study's respondents in relation to their use of

DHS. Table 1 shows that 300 responses were deemed

valid (with no missing data). The respondents'

demographics, such as gender, educational

background, age, and ownership of digital

technology, are summarized. The following are the

demographic characteristics of the respondents.

3.2 Cronbach's Alpha Reliability Analysis

Reliability is defined as a measure's ability to

produce consistent results when the same entities are

evaluated under different conditions. This indicates

how well a test can be evaluated consistently. There

is, however, no clear consensus on the specific

criteria for interpreting Cronbach's alpha. The

coefficient is commonly interpreted as 0.5 for low

reliability, 0.5 0.8 for moderate (acceptable)

reliability, and > 0.8 for high (good) reliability, [39].

In reliability analysis for the impact of 24 variables

on the acceptance of DHS, Cronbach’s alpha is 0.569

in Table 2, which is an acceptable value because it is

greater than the standard 0.5.

3.3 Factor Analysis

The initial result of the study is a tabular

representation of descriptive statistics for all

variables being examined. Typically, the survey

includes the mean, standard deviation, and number of

respondents (N) who have participated. The mean

value represents the central tendency of a given

dataset, indicating the typical or most often occurring

response. Based on the mean values in Table 3, one

can conclude that the easiest variable to install and

save transportation costs is the most important factor

influencing people to accept DHS. When people take

digital technology-based health services on the

perception acceptance role, the lowest value of 3.89

for 'Easy to integrate' indicates that respondents

roughly agree and DH technology integration is

moderately easy. All of the variables' roles in the

perception and acceptance of technology-based

health services can be interpreted similarly.

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Table 2. Reliability of Constructs of This Study

Reliability Value

Cronbach's Alpha

Cronbach's Alpha on Standardized Items

N of Items

.569

.579

24

Table 1. Frequencies and Percentages for Demographics Information

Demographics Statistics

Frequency

Percent

Valid Percent

Cumulative Percent

Gender

Female

57

19.0

Male

243

81.0

100.0

Educational Background

Bachelor

103

34.3

HSC

168

56.0

90.3

Masters

18

6.0

96.3

Others

5

1.7

98.0

SSC or below

6

2.0

100.0

Age

15 or below

2

.7

16-25

129

43.0

43.7

26-35

127

42.3

86.0

36 above

42

14.0

100.0

Adoption of Digital

Health Technology

Yes

300

100.0

Table 3. Descriptive Statistics

Descriptive Statistics

N

Mean

Std. Deviation

DHS uses

300

4.02

.856

Ownership influence on DHS

300

3.95

.835

Resistance to move

300

4.02

.882

Social connection

300

4.08

.828

Cultural belief

300

4.20

.855

Awareness

300

4.05

.825

Internet availability

300

4.06

.896

Digital literacy Knowledge

300

4.00

.869

Access to mobile phone technology

300

4.17

.941

Use of full technology-based systems

300

4.16

.843

Save transportation cost

300

4.23

.843

Reduction of waiting time

300

4.14

.907

Reduction of doctor’s fee

300

4.02

.842

Unavailability of medical resource

300

4.05

.852

Easy to use

300

4.14

.943

Easy to install

300

4.43

1.024

Easy to integrate

300

3.89

1.142

Anonymity of large people

300

3.94

.932

Privacy of data

300

4.00

.922

Privacy of home activities

300

3.93

.962

Performance expectancy

300

3.99

.925

Timely response

300

4.06

.867

Flexibility of preference

300

3.97

.903

Enjoyable to use

300

4.00

.922

Table 4. KMO and Bartlett's Test

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.702

Bartlett's Test of Sphericity

Approx. Chi-Square

2237.414

Df

276

Sig.

.000

The current investigation employed the Kaiser-

Meyer-Olkin (KMO) measure and Bartlett's Test of

Sphericity to evaluate the appropriateness of factor

analysis. Bartlett's test is an additional measure that

may be utilized to assess the robustness of the

association between variables. The Kaiser-Meyer-

Olkin (KMO) measure is utilized to assess the

sufficiency of sampling in factor analysis. A KMO

value of about 0.5 is considered good, indicating that

the responses obtained from the sample are suitable

for further factor analysis. According to Kaiser's

(1974) recommendations, a value of 0.5 is considered

the least acceptable level, while values falling

between 0.7 and 0.8 are deemed good. Additionally,

values beyond 0.9 are regarded as outstanding.

Perform and analyze a factor analysis. Based on the

findings shown in Table 4, the Kaiser-Meyer-Olkin

(KMO) measure yielded a value of 0.702, falling

within the acceptable range of 0.7 to 0.8. This

suggests that the data can be deemed suitable for

factor analysis. Additionally, Bartlett's Test of

Sphericity yielded a statistically significant result

(Chi-Square = 2237.414, p < 0.001), indicating that

the correlation matrix is not an identity matrix and

supports the appropriateness of doing factor analysis.

The result of factor analysis in a rotated

component matrix is shown in Table 4.

The eigenvalues, which represent the fraction of

total variance explained by each component, are

presented in the Table 5, [40]. The eigenvalue of a

factor may be computed by summing the squared

factor loadings over all variables. The calculation of

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the percentage of variance accounted for by a factor

involves dividing its eigenvalue by the number of

variables, which is equivalent to the sum of variances

due to the fact that the variance of a standardized

variable is equal to 1. The eigenvalue ratio quantifies

the extent to which the variables explain the factors'

explanatory power. Factors with low eigenvalues

(e.g., 1.0) have little explanatory power in accounting

for the variances of variables. Consequently, it is

advisable to avoid such factors as they redundantly

overlap with other factors, [41]. The provided source

is an illustration of Exploratory Factor Analysis,

without a specified date. Table 5 displays a total of

24 variables, with each variable representing a

distinct component. In the present scenario, the

cumulative number for 9 components indicates that

the overall percentage of variation explained is

60.10%.

Factor loadings play a fundamental role in the

process of giving labels to different factors. Loadings

that exceed 0.6 are categorized as "high," whilst

loadings that fall below 0.6 are classified as "low."

Four of them are considered to be of modest

magnitude. The above image depicts an illustration

of Exploratory Factor Analysis. Consequently, a

positive loading is indicative of a negative sentiment

towards a certain element, whereas a negative

loading suggests a favorable sentiment, [42]. The

rotated solution is given in Table 6. SPSS was used

to extract principal factors with varimax rotation

from the 24 factors. In Table 6, all variables

exceeding 0.45 loadings and under 0.45 were left

blank for ease of use. The first factor (three

variables) is strongly related to the second factor. As

a result, all other factors can be used as variables in

further analysis.

Table 5. Total Variance Explained

Total Variance

Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

3.189

13.289

3.189

13.289

3.071

12.797

2

2.162

9.010

22.299

2.162

9.010

22.299

1.754

7.310

20.107

3

1.838

7.657

29.956

1.838

7.657

29.956

1.740

7.252

27.359

4

1.535

6.394

36.350

1.535

6.394

36.350

1.519

6.329

33.688

5

1.266

5.275

41.625

1.266

5.275

41.625

1.417

5.906

39.594

6

1.206

5.025

46.649

1.206

5.025

46.649

1.280

5.333

44.927

7

1.115

4.645

51.294

1.115

4.645

51.294

1.277

5.323

50.250

8

1.064

4.432

55.726

1.064

4.432

55.726

1.243

5.178

55.427

9

1.050

4.376

60.102

1.050

4.376

60.102

1.122

4.674

60.102

10

.990

4.126

64.227

11

.909

3.789

68.016

12

.873

3.636

71.651

13

.838

3.491

75.142

14

.822

3.427

78.569

15

.760

3.168

81.736

16

.737

3.072

84.809

17

.708

2.949

87.758

18

.696

2.899

90.657

19

.623

2.596

93.252

20

.583

2.430

95.682

21

.506

2.109

97.791

22

.464

1.933

99.724

23

.045

.189

99.912

24

.021

.088

100.000

Extraction Method: Principal Component Analysis.

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Table. 6 Rotated Component Matrix

Rotated Component Matrixa

Component

1

2

3

4

5

6

7

8

9

Reduction of doctor’s fee

.979

Digital literacy Knowledge

.977

DHS uses

.973

Flexibility of preference

.653

Privacy of data

.629

Internet availability

.564

Use of full technology-based

systems

.499

Timely response

.745

Reduction of waiting time

.685

Access to mobile phone

technology

.589

Awareness

.707

Social connection

.671

Ownership influence on DHS

.569

Cultural belief

.740

Easy to install

.491

Performance expectancy

.476

Easy to use

Easy to integrate

.756

Resistance to move

.539

Save transportation cost

.742

Unavailability of medical

resource

.572

Anonymity of large people

-.672

Enjoyable to use

.519

Privacy of home activities

-.727

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a

a. Rotation converged in 14 iterations.

3.4 Correlation Analysis

Correlation is employed as a means of examining the

extent of association between two variables. The

correlation coefficient is a statistical metric utilized

to quantitatively assess the extent of association

between variables. The correlation coefficients for

both variables are standardized between -1 and +1. A

value of 0 signifies the absence of a linear or

monotonic relationship, while a value of 1 suggests a

progressively stronger link that finally approaches a

straight line. The observed interpretations of

correlation coefficients are as follows: a correlation

coefficient ranging from 0.00 to 0.10 is considered

inconsequential, a correlation coefficient ranging

from 0.10 to 0.39 is considered weak, a correlation

coefficient ranging from 0.40 to 0.69 is considered

moderate, a correlation coefficient ranging from 0.70

to 0.89 is considered high, and a correlation

coefficient ranging from 0.90 to 1.00 is considered

extremely strong.

According to the data shown in Table 7, there

exists a positive link between social and cultural

factors, technological knowledge, economic factors,

convenience, security, and perceived usefulness, and

the acceptance rate of DHS when using a two-tailed

test with a level of significance of 1%.

4 Discussion

The relationship between health service acceptance

rate and social and cultural factors is statistically

significant and somewhat favorable (r (300) = 0.106,

p = 0.066). Furthermore, the correlation between

technological and DH acceptance was found to be

moderately positive and statistically significant (r

(300) = 0.479, p = 0.000).

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Table 7. Correlation between Acceptance of DH and Six Determinants

Correlations

Acceptance

of DHS

Social &

cultural

Technological

skill

Economical

Convenience

Security

Perceived

usefulness

Acceptance of

DHS

Pearson

Correlation

1

.106

.479**

.423**

.045

.072

.035

Sig. (2-tailed)

.066

.000

.442

.216

.545

Social &cultural

Pearson

Correlation

.106

1

.172**

.178**

.165**

-.033

.073

Sig. (2-tailed)

.066

.003

.002

.004

.566

.208

Technological

skill

Pearson

Correlation

.479**

.172**

1

.376**

.074

.088

.305**

Sig. (2-tailed)

.000

.003

.000

.202

.127

.000

Economical

Pearson

Correlation

.423**

.178**

.376**

1

.211**

.104

.223**

Sig. (2-tailed)

.000

.002

.000

.073

.000

Convenience

Pearson

Correlation

.045

.165**

.074

.211**

1

.058

.034

Sig. (2-tailed)

.442

.004

.202

.000

.317

.557

Security

Pearson

Correlation

.072

-.033

.088

.104

.058

1

.073

Sig. (2-tailed)

.216

.566

.127

.073

.317

.208

Perceived

usefulness

Pearson

Correlation

.035

.073

.305**

.223**

.034

.073

1

Sig. (2-tailed)

.545

.208

.000

.557

.208

**. Correlation is significant at the 0.01 level (2-tailed).

The level of significance for the correlation (a

level of.05 or lower is considered "statistically

significant"), so a significance level of.000 does not

imply that the level of significance is completely

zero.

It simply means that the number cannot be

greater than 0.0004. ("Interpreting Correlation

Tables"). This suggests that as technological

knowledge grows, so will the acceptance of DHS.

The study revealed a statistically significant and

moderately favorable correlation (r (300) = 0.423, p

= 0.000) between the acceptability of DH and the

associated economic advantages. This observation

suggests a positive correlation between the growth of

economic advantages and the level of acceptability

towards DHS, [43]. The study revealed a statistically

significant and moderately favorable association (r

(300) = 0.045, p =.442) between convenience and the

acceptability of DH. The study revealed a statistically

significant and somewhat favorable association (r

(300) = 0.072, p = 0.216) between the adoption of

DH and security. The study revealed a statistically

significant and moderately favorable association (r

(300) = 0.035, p = 0.545) between the perceived

utility of DH and its acceptability. Furthermore, there

is a negative relationship between security and

culture and beliefs; this means that if security

increases, culture and beliefs decrease, [44].

The observed negative correlation suggests that

people who are more sure that their personal health

information is safe and secure in DH systems are less

likely to be affected by cultural or traditional beliefs

that might make them less likely to adopt and use

these services. There is a considerable effect on

individuals' behaviors and attitudes toward the latest

technologies, [45]. It is important to recognize that

cultural norms, religious beliefs, and societal

perceptions can initially hinder the widespread

acceptance of DHS within its domain, [46].

However, it is important to acknowledge that the

perceived importance of cultural and ideological

barriers may diminish when individuals have a high

level of confidence in the security procedures

implemented to safeguard their data. Gaining an

understanding of this negative relationship can offer

policymakers and healthcare professionals significant

knowledge, illuminating the significance of

addressing security concerns to reduce the impact of

cultural and belief barriers on the acceptance and use

of DHS. Highlighting the need to implement robust

security protocols and promoting transparent and

effective communication channels can alleviate

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DOI: 10.37394/23207.2024.21.127

K. M. Salah Uddin,

Mohammad Rakibul Islam Bhuiyan,

Marufa Hamid

E-ISSN: 2224-2899

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Volume 21, 2024

concerns and foster greater receptiveness across

diverse cultural groups in protection data. Future

research might be conducted based the perception of

individual belief, perception and perspective, [47].

The fundamental objective of this research work

was to determine the factors which influences on

individual perceptions regarding digital health

systems in Bangladesh. Different approaches were

used to collect 300 intended respondents. The

application of this approach facilitated the collection

of a diverse array of responses in a comparatively

expedient fashion. Furthermore, a direct interaction

with the participants was also carried out. The

application of this particular approach afforded a

valuable avenue for the acquisition of more

comprehensive and detailed understandings, as well

as the clarification of specific of the research, [48].

The findings of the investigation show that there

exist six distinct factors, comprising a total of 24

individual items, which exert an influence on the

degree of acceptance demonstrated by individuals in

Bangladesh. The conducted reliability test involved

the examination of 24 variables and 6 determinants.

The consequences of the test show that the reliability

is satisfactory, as evidenced by Cronbach's alpha

coefficient of 0.569 where the value exceeds the

commonly accepted threshold of 0.5, additionally

supporting the reliability test of the analysis.

4.1 Key Findings

The crucial findings of the study are given below:

 The coefficient exceeds the commonly accepted

threshold of 0.5, indicating that the reliability of

the test can be considered acceptable in this

study.

 The current study has presented a strong

relationship with cultural, social, technological,

economic, security, and other issues where the

analysis of the correlation significance value is

0.01 or lower result.

 According to this study, social and cultural,

technological, economic, convenience, security,

and perceived usefulness determinants influence

the perception of acceptance of DHS.

 By means of technology adaptability advances,

individuals' attitudes towards the acceptance of

the DHS will shift.

 There is an increasing trend in this study about

the acceptance and perception of DHS.

5 Conclusion

The research intends to determine the influential

factors of individual willingness to use digital health

services in Bangladesh. The quantitative research

method was conducted to obtain the purposes of this

study by using six variables for the acceptance of the

DHS in Bangladesh, [49]. The present study has

revealed a significant positive association between

various factors, including social and cultural aspects,

technological advancements, economic factors,

convenience, security, and perceived utility. This

association was examined using a two-tailed

statistical test with a predetermined significance level

of 0.01 or lower. Our research has revealed that there

is a strong correlation between economic advantages,

technology literacy understanding, and the level of

acceptability of DHS. In fact, these two factors have

been shown to have a much more significant impact

on acceptability compared to other determinant

factors, [50]. Thus, it is hoped that this study will

serve as a catalyst for additional research in this area

and that the recommendations made will be

implemented by the government and DHS providers

to increase the current number of DHS users in

Bangladesh. If the people of Bangladesh gain more

technological knowledge and realize the economic

benefits, their perception of DHS will improve.

Future Directions of the Study

The study laid the groundwork for several

potential future directions that could further enrich

our understanding of the factors influencing

Bangladesh's willingness to use DHS. Conduct

longitudinal studies to track changes in DHS

acceptance and utilization over time, [50]. This

approach can help identify trends, evolving factors,

and the impact of interventions or policy changes.

Supplement quantitative findings with qualitative

research methods such as interviews or focus groups.

Qualitative insights can provide a deeper

understanding of individuals' attitudes, perceptions,

and experiences related to DHS, offering nuanced

perspectives to complement quantitative data.

Investigate contextual factors that may influence the

acceptance of DHS, such as urban-rural disparities,

access to internet connectivity, healthcare

infrastructure, and regulatory frameworks.

Understanding these contextual nuances can inform

targeted interventions and policies. Compare findings

from Bangladesh with those from other countries or

regions to identify similarities, differences, and

cross-cultural factors influencing the acceptance of

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2024.21.127

K. M. Salah Uddin,

Mohammad Rakibul Islam Bhuiyan,

Marufa Hamid

E-ISSN: 2224-2899

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DHS. Insights from cross-cultural comparisons can

inform global strategies and best practices.

Acknowledgement:

I gratitude to Md. Rakibul Hoque, Professor,

Department of Management Information Systems,

University of Dhaka, for his guidance and

cooperation.

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed in the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

The authors acknowledged with gratitude that this

research has received funding for covering the article

processing charge of WSEAS Transactions on

Business and Economics from the University

Research and Funding office at the University of

Dhaka.

Data Availability Statement

Data will be shared upon the researcher request.

Conflicts of Interest

The authors declare no conflict of interest.

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