Determining Trustworthiness of Health Information Shared in Social

Networking Sites (SNS)

Nurul Ulfa Abdul Aziz, Siti Nurul Hanifah Che Mohammad, Zainuddin Zakaria, Nik Noor Afizah

Azlan

and Zalinawati Abdullah

Faculty of Bisiness Management, University Teknologi MARA Cawangan Terengganu, Terengganu, Malaysia partment of

Keywords: Trustworthiness, Health Information, Social Networking Sites

Abstract: This study investigates the level of trustworthiness of health information shared in Social Networking Sites

(SNS) by examining three main factors namely benevolence, integrity and ability. The research adopted

survey approach where a questionnaire has been tailored to ascertain the level of trustworthiness of health

information shared via SNS. The questionnaire was distributed among health administration staff in IIUM

Medical Center and the respondents were randomly selected. The collected questionnaire was than analysed

using SPSS version 26. Descriptive statistics was used to obtain the frequency and percentage of the

respondent profile. Finding of the research was obtained by using reliability analysis, frequency analysis,

correlation analysis and multiple regression analysis. The result proved that all three (3) variables can

influence trustworthiness of health information. Analysis outcome indicate the hypothesis testing of the

relationship between three (3) variables with trustworthiness of health information is significant. The variables

benevolence and integrity have a positive relationship with trustworthiness while variable ability have a

negative relationship with trustworthiness. Between all three factors, result show that the most influential

factor towards trustworthiness is benevolence.

1 INTRODUCTION

The use of Internet has created a culture of depending

and trusting information shared on the Internet sites.

According to Malaysian Communications and

Multimedia Commission (MCMC) (2019), the

percentage of Internet Users at national level

increases from 76.9% (2016) to 87.4% (2018) and

majority of the users relied on Internet for

information seeking (85.5%). Evidence shows that

among Internet users one of the most common

activities is social networking (85.6%). Currently

SNS has become an important platform for

communications and socializing. SNS is the tool to

connect people, building communities, interest group,

expressing one’s opinion, educating, creating

awareness and business purposes. Findings estimated

in 2018 there are 24.6 million SNS users, the most

preferred SNS platform in Malaysia is Facebook;

97.3% owned a Facebook account, followed by

Instagram 57.0% have, YouTube 48.3% and Twitter

23.8%. MCMC also reported that the majority of

Internet user do shared content online via social

media (73.8%) and educational content are among the

most frequently shared content. The importance and

impacts of SNS towards users is very significant and

undeniable (Zhou, Zhang, Yang & Wang, 2018)

Thus, the situation has created windows of

opportunities for many parties to share and spread

information with the intention to help creating health

awareness (Lapointe, Ramaprasad, & Vedel, 2014).

The most common problem faced by Internet

users is that there is various and overloaded of

information shared in the Internet specifically SNS.

This have led to caused confusion whether the

Information is genuinely true with the right facts or

the information is just a myth created by parties that

uses misleading facts for personal satisfaction or self-

interest. Li, Wang, Lin, & Hajli (2018) stated that

having untrue and unreliable information causes

health problems among individual that tend to believe

all sort of information and may also causes effect to

the society and also the government. Similarly

Waszak, & Kubanek, (2018) also addressed the

alarming harm of false and unauthorised health

information as the information influences the

mentality and action of the people that read and

Abdul Aziz, N., Che Mohammad, S., Zakaria, Z., Aﬁzah Azlan, N. and Abdullah, Z.

Determining Trustworthiness of Health Information Shared in Social Networking Sites (SNS).

DOI: 10.5220/0009201602090216

In Proceedings of the 2nd Economics and Business International Conference (EBIC 2019) - Economics and Business in Industrial Revolution 4.0, pages 209-216

ISBN: 978-989-758-498-5

209

accept such information. Therefore, this study seeks

to determine the trustworthiness of health information

as it able to help in creating solution to control the

damage and harm caused by unauthorised and

irresponsible personal or organization. Health

practitioner can do an intervention through SNS as an

effort to form a better understanding of health issues

and uses SNS as a tool to educate the people to choose

the right and true information regarding health. SNS

can overcome the constraint of physical location and

differences of time.

The objectives of this research are to identify the

relationship between the independent variables

(benevolence, integrity, and ability) and the

dependent variable (trustworthiness of health

information) and to determine the most influential

factor towards trustworthiness of health information.

The research hypotheses are shown in Table 1:

Table 1: Research Hypothesis

H1:

Benevolence

There is a significant relationship

between benevolence and

trustworthiness.

H2: Integrity

There is a significant relationship

between integrity and

trustworthiness.

H3: Ability

There is a significant relationship

between ability and

trustworthiness.

2 LITERATURE REVIEW

2.1 Social Network Sites

Social network sites have become the current trend of

communicating and engaging with people that we

know and also to strangers. People tend to use SNS

for various reason such as a platform to communicate

and socializing with family, friends and people with

the same interest and sharing information in different

form like video, text, images and infographic.

According to Boyd & Ellison (2008) Social network

sites is a web-based services that allow individuals to

(1) construct a public or semi-public profile within a

bounded system, (2) articulate a list of other users

with whom they share a connection, and (3) view and

traverse their list of connections and those made by

others within the system. The way SNS function will

may vary from each other although all SNS share the

same purposes. The are many SNS in the Internet

among the major SNS are Facebook, Instagram,

Twitter, YouTube and LinkedIn.

2.2 Uses of SNS for Health Information

Communication

Health information in this research referred as any

information related to health wellness, health concern,

facts regarding diseases and illness, clinical

information and clinical notes. This study focuses

about health information in a digital format. SNS have

become one of the platforms used to seek and shared

information. The main objective of SNS to increase

interaction with others, facilitating, sharing and

obtaining health messages have been acknowledged

and welcome by the Ministry of Health in Malaysia

(Balchin, 2017). According to Denecke & Nejdl

(2009); Hwang et. Al (2010); Hu & sundar (2010);

Sanford (2010) the general public mainly use SNS to

obtain and share information on a wide range of health

issues. The advance of technology will provide more

opportunities and challenge to use social media for

health care purposes. The challenge will vary from the

communication between health professional and

patient and also between patient and patient. Thus, a

study to know and understand the behaviour and

trustworthiness of information should be done and

used to ensure that this trend brings more benefit than

harm.

2.3 Trustworthiness

Colquitt, Scott, and LePine (2007) identify the

trustworthiness for consumer is when they make

decision based on their trust towards a certain belief

which influence them to have trusting attitude that

lead towards a certain behaviour. Previous literature

on trustworthiness has consider a variety of beliefs

while the present research focused on four specific

factors, which are, ability, benevolence, integrity and

predictability, which capture the concept of

trustworthiness (Colquitt et al., 2007; Mayer, Davis,

& Schoorman, 1995; McKnight, Cummings, &

Chervany, 1998). Akter, D'Ambra, and Ray (2011)

defined trustworthiness as set of beliefs on the other

party that enable willingness while Yahia, Al-Neama,

& Kerbache, (2018) explain trustworthiness as a

perceptions of competence, benevolence, and

integrity.

Majority of Internet users relied on Internet for

information seeking (85.5%). Moreover, various

forms of media presentation – such as video, audio

and infographic – for information dissemination has

made Internet an interactive and commonly preferred

source of knowledge. Recently people choose SNS as

a platform to share and read information regarding all

EBIC 2019 - Economics and Business International Conference 2019

210

kind of issues as it provides access to all type of result

in an instant. Besides that, different forms of media

presentation as such as video, audio and infographic

have made the usage of SNS as an interactive and

preferred source of information and knowledge.

However, since SNS have different views on a variety

of topics, it is difficult to distinguish which

information is considered to be the most accurate and

trustworthy and which information is not (Warner-

Søderholm et al., 2018).

For this research, trust research constructs will be

used as the most valid and parsimonious resource in

this study to determine the trustworthiness of health

information in SNS. Lin, Zhang, Song, and Omori

(2016) have indicated that people trust of online

health information is a major factor that influence

their follow- up actions after information search, for

example, to further discuss health topics, or to be

willing to share health information and this statement

also been supported by Metzger and Flanagin (2011).

Currently people engage in online health information

seeking to verify the prescriptions offered by medical

professionals, especially when they experience

uncertainties about a certain prescription (Lin et al.,

2016). The level of trust in communication channels

and organisations has been found to be important in

determining the respond and action of oneself Lin et

al. (2016) found that the greater level of trust in online

drug information, the more likely it would be to

engage in three types of behaviour, such as

communicating with doctors, talking to others and

attempting more health information. Thus, research

regarding the trustworthiness information in SNS is

crucial as it have potential of influencing people

lifestyle and mindset. It is also operationalized to

consider whether the probability of trust is high

enough for us to consider cooperating with this party

in some way.

2.4 Benevolence

According to Mayer et al. (1995) benevolence stands

for loyalty, tolerance, caring and support.

Benevolence is crucial to trust because it implies to

what extent an individual feels interpersonal care and

concern for others and is prepared to do well for

reasons besides ego and profit (Robert et al., 2009).

Benevolence is described by other scholars as a key

component in close relationships and a history of

trustworthiness (Elangovan & Shapiro, 1998; Koscik

& Tranel, 2011; Mayer et al., 1995). As been

discussed by Urbano, Rocha, and Oliveira (2013)

benevolence is either a willingness to do good and an

act of kindness, in which the person has a sense of

goodwill towards the interacting partner, excluding

any intention to harm him. Benevolence means that

one cares for the benefits of the other person and is

motivated to act in the interest of the other person

without any act of opportunistically. Therefore,

perceived benevolence significantly linked to

positive attitude towards the continued use of health

information. Benevolence can be seen as the

healthcare provider ability to satisfy patients with best

intentions, apart from any reasons for profit (Akter et

al., 2011). .

2.5 Integrity

Akter et al. (2011) defined integrity as the believes to

confirm moral and ethical principles. Integrity is the

assumption that others will act in accordance with a

socially accepted level of ethics or a set of values

accepted by the person. In the interim, Colquitt et al.

(2007) referred integrity as logical reason for trusting

someone based upon the sense of fairness or moral

character offers for long term predictability that help

during uncertainties occurring. While Gefen,

Karahanna, and Straub (2003), believed integrity

could decrease a variety of social unacceptable

behaviours. Perceived integrity shows adherence to

an appropriate set of principles and integrity is

important because it instils trust in person behaviour

and lowers doubt and potential consequences

(Bhattacherjee, 2002). Lee et al. (2008) states it will

influence overall trust because it allows for future

events to be predicted, especially under great

uncertainty.

2.6 Ability

Ability in this research, according to Akter et al.

(2011) is a set of skills, competencies, and

characteristics that enable a party to have an influence

within some specific domains whereas Urbano et al.

(2013) have described ability as a potential

competence to perform a specific task. By having

ability, it allows an individual to have influence in a

certain area. This applies in the context of virtual

communities because Internet users usually focus on

a specific common interest, hobby, life event,

occupation, and concerns. Therefore ability is

believed to have potential to be a factor in

determining trustworthiness as referred to Jarvenpaa,

Knoll, and Leidner (1998) ability is critical to trust,

because the believer must be assured that the trustee

is able to perform the task that he or she trusts.

Determining Trustworthiness of Health Information Shared in Social Networking Sites (SNS)

211

3 METHOD

As regards to the literature review the variables

benevolence, integrity and ability is being used to

determine the trustworthiness. The purpose

theoretical framework is been adapted and adopted

from Mayer et al. (1995),Warner-Søderholm et al.

(2018), and Colquitt et al. (2007)

Figure 1: The Theoretical Framework of the trustworthiness

of health information shared through social media.

3.1 Data Collection Method

Questionnaires have been distributed at Islamic

University Malaysia Medical Centre (IIUMMC)

Kuantan, Pahang. The questionnaire has been divided

into Parts A and B. Part A contains demographic

issues while Part B represents both the dependent

variable and independent variables. The research

sampling technique is non probability sampling

convenience and the administration staff at IIUM

Medical Centre were the target population. The

population is 93 according to Krejcie Morgan’s table

sample size should be 81 which involved employees

as the respondents.

4 RESULTS AND DISCUSSION

4.1 Reliability Analysis

81 questionnaires were distributed to four different

departments which is finance department, human

resource department, operation department and

quality management department. The reliability

analysis was tested after all questionnaires were

collected. Table 2 indicates the results of the actual

reliability analysis test. This analysis has shown that

every item in every variable is reliable. Two variables

have excellent internal consistency, one has excellent

internal consistency, the other has good internal

consistency

Table 2: Cronbach’s Alpha of Variables

Variables

Cronbach's Alpha

N of Items

Trustworthiness

.931

Benevolence

.860

Integrity

.915

Ability

.735

4.2 Descriptive Analysis

Analysis of the frequency distribution was carried out

to analyse the respondent demographic profile. The

demographic profile comprises age, gender, race and

level of education. The demographic profile results

are shown as shown below in Table 3.

Table 3: Demographic Profile

No.

Profile Description

Frequency

Percentage

(%)

Gender

Male

38.3

Female

61.7

Total

100.0

Age

20-29

48.1

30-39

38.3

40-49

11.1

50 and

above

2.5

Total

100.0

Race

Malay

100.0

Education

Level

Secondary

40.7

Diploma

and

Degree

44.4

Master and

above

14.8

Total

100.0

The survey found that 63% of the respondent visit

SNS within hour and will browse through the SNS

more than once in a day. While 32.1% of the

respondent visit SNS daily meaning once in a day and

only 4.9% of the respondent use SNS minimally as

they visit SNS once a week. This show how SNS is

one of the main platform that is visited by the Internet

users as all respondent have an SNS account and do

browse through their SNS everyday. Table 4

summarize the frequent usage of SNSs.

Benevolent

Integrity

Ability

Trustworthiness

Independent

Variable

Dependent

Variable

EBIC 2019 - Economics and Business International Conference 2019

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Table 4: Frequency Usage of SNS

Frequent

Hourly

63.0

Daily

32.1

Weekly

4.9

Total

100.0

According to the collected data the most frequent

SNS that is used by the respondent is Facebook

39.5% followed by Instagram that 34.6%. The third

most popular SNS is twitter 17% and the lesser

amount of used SNS by the respondent is YouTube

4.9%.

Table 5: Types of SNS

SNS

Frequency

Facebook

39.5

Instagram

34.6

Twitter

21.0

YouTube

4.9

Total

100.0

The mean value for trustworthiness is 3.58

respectively. The standard deviation 0.944 and the

skewness are -0.297. While for Benevolence the

mean is 4.22 respectively. The standard deviation

0.859 and the skewness coefficient is -0.858. Result

for Integrity shows mean value is 3.36 respectively.

The standard deviation 0.860 and the skewness

coefficient is -0.189. From the variables data mention

above indicates the values the distribution data are

skewed to the left. This means that a large portion of

sample concentrated at the larger values (agree and

strongly agree) with a few extremely small values

(strongly disagree). Differently for ability the mean

value is 2.86 respectively. The standard deviation

0.660 and the skewness coefficient 0.440 that

indicates the value the distribution data are skewed to

the right. This means that a large portion of sample

concentrated at the small value (disagree and strongly

disagree) with a few extremely larger values

(Strongly agree). This index skewness indicates that

using mean is not a good indicator to measure the

central location of average value. Median needed to

be used as the average value for all variables.

4.3 Correlation Analysis

Correlational studies have led to the knowledge of the

relationship between four variables: trustworthiness,

benevolence, integrity and ability. Pearson

Correlation Matrix is referred to Hair, Babin, Money,

Samuel (2006). Result show that there is moderate

and significant relationship between trustworthiness

and benevolence (r=.560, p-value =.000) and

Integrity is moderate and significant relationship

(5=0.443 and p-value=.000). While the relationship

between trustworthiness and ability is a negative

weak significant relationship (r=-.346, .002)

Table 6: Correlation Analysis

Benevolence

Integrity

Abilit

Trustworthiness

Pearson

Correlation

0.560

0.443

-0.346

Sig. (2-tailed)

0.000

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

4.4 Regression Analysis

Trustworthiness and Independent

Variable (Benevolence, Integrity,

and Ability)

R-Square is the proportion of variance in the

dependant variable (Trustworthiness) which can be

predicted from the independent variable. This value

indicates that 31.3% of the variance in

Trustworthiness can be predicted from the variable

benevolence, 19.6% of the variance in

trustworthiness can be predicted by integrity and only

12% of the variance in Trustworthiness can be

predicted by Ability.

Table 7: Summary of Regression Analysis for Each

Variables

Unstandardized

Coefficient

Stand.

Coefficie

nts

Sig.

Std. Err

Beta

Constant

1.105

.421

2.624

.010

Benevolent

.615

.102

.560

6.006

.000

Constant

1.941

.384

5.053

.000

Integrity

.487

.111

.443

4.395

.000

Constant

4.988

.441

11.317

.000

Ability

-.493

.150

-.346

-3.283

.002

The p-value associate with the F value for

Benevolence and integrity is very small (0.000) and

the p-value associate with F value for ability is also

Determining Trustworthiness of Health Information Shared in Social Networking Sites (SNS)

213

small (0.002) the result show statistically

benevolence, integrity and ability can be used to

reliable predict Trustworthiness.

Table 8: The P-value associate with the F value

Model

Sum of

Squares

Mean

Square

Sig.

Benevolent

Regression

22.369

36.070

.000

Residual

48.991

.620

Total

71.360

Integrity

Regression

14.022

19.319

.000

Residual

57.338

.726

Total

71.360

Ability

Regression

8.566

10.777

.002

Residual

62.794

.795

Total

71.360

The coefficient for benevolence is 0.615 hence for

every unit increase in benevolence is expected 0.615

increases the trustworthiness. This is statistically

significant at t= 6.006, p<0.05. For Integrity the

coefficient is 0.487 hence for every unit increase in

integrity is expected 0.487 increases the

trustworthiness. This is statistically significant at t=

4.395, p<0.05. The coefficient value for ability is -

0.493 for every unit increases in ability is expected -

0.493 decreases the trustworthiness. This is

statistically significant at t= -3.283, p<0.05.

Table 9: Coefficient Result of Regression Analysis

Trustworthiness

Benevolent

Integrity

Ability

Mean

3.58

4.02

3.36

2.86

Median

3.74

4.22

3.47

2.74

Mode

Std.Dev.

.944

.859

.860

.664

Skewness

-.297

-.858

-.189

.493

4.5 Multiple Regression Analysis

R-Square is the proportion of variance in the

dependant variable (Trustworthiness) which can be

predicted from the independent variable

(benevolence, integrity and ability. The value

indicates that 32.3% of the variance of

trustworthiness can be predicted from the variables

mention above. The p-value associate with the F

value 12.266 is very small (0.000) this show that the

independent variable consisting benevolence,

integrity and ability can be used reliable predict

trustworthiness of health information in SNS. This

means that the model is valid and the relationship's

outcome is not by chance, the independent variable

which are benevolence, integrity and ability can

Influence trustworthiness. Further analysis by

regression produces standardized measurements (beta

weights) of the strength of the association of each

dimension with trustworthiness. The results of the

three independent variables are: benevolence (β

0.503, p<0.001), integrity (β 0.008, p<0.955), and

ability (β -0.110, p<0.325). This result shows that

benevolence is the most influential factor towards

trustworthiness.

Table 10: Summary of Multiple Regression Analysis

Summary

ANOVA

R Square

Sig.

.569

.323

12.266

.000

Dimensions

Standardized

Coefficients

Sig.

(Constant)

2.173

.033

Benevolence

.503

3.525

.001

Integrity

.008

.056

.955

Ability

-.110

-.991

.325

5 CONCLUSIONS

The research analysis result represents three main

findings. Firstly, there are three significant factors

that can contribute to trustworthiness which are

benevolence, integrity, and ability. Based on the

result between all variable there is a significant

relationship with benevolence and integrity have a

positive relationship and concurrently there is a weak

negative relationship between ability and

trustworthiness. This indicates that benevolence can

influence positively towards the trustworthiness of

health information shared through SNS. While ability

show tendency to affect trustworthiness in inverse

ways. Finally, the most influential factor towards

trustworthiness due to the respond of the users is

benevolence. This means that users believed

information shared by those who have good intention

without any profitable intuition and mindset do share

EBIC 2019 - Economics and Business International Conference 2019

214

genuinely true information for the beneficial proposes

gain by others. From the foregoing therefore, the

following of result hypotheses testing are displayed in

Table 11.

Table 11: Summary of Hypotheses Result

Hypotheses

Result

There is a significant

relationship between

benevolence and the

trustworthiness.

Supported

P = 0.00

(p<0.01)

There is a significant

relationship between integrity

and the trustworthiness.

Supported

P = 0.00

(p<0.01)

There is a significant

relationship between the ability

and the trustworthiness.

Supported

P = 0.002

(p<0.01)

REFERENCES

Akter, S., D'Ambra, J., & Ray, P. (2011). Trustworthiness

in mHealth information services: an assessment of a

hierarchical model with mediating and moderating

effects using partial least squares (PLS). Journal of the

American Society for Information Science and

Technology, 62(1), 100-116.

Bhattacherjee, A. (2002). Individual trust in online firms:

Scale development and initial test. Journal of

Management Information Systems, 19(1), 211-241.

Balchin, Imelda [Imelda Blachin] (2017, 18 November)

Terimakasih rakyat Malaysia kerana menghargai ilmu

pengetahuan kesihatan [Facebook status update].

Retrieved from

https://www.facebook.com/story.php?story_fbid=1015

5092066651659&id=679746658

Boyd, D. M. & Ellison, N. (2007). Social Network Sites:

Definition, History, and Scholarship. Journal of

Computer-Mediated Communication. Doi:

https://doi.org/10.1111/j.1083-6101.2007.00393.x

Colquitt, J. A., Scott, B. A., & LePine, J. A. (2007). Trust,

trustworthiness, and trust propensity: a meta-analytic

test of their unique relationships with risk taking and

job performance. Journal of applied psychology, 92(4),

909.

Denecke K, Nejdl W. How valuable is medical social media

data? Content analysis of the medical web. Inform

Sciences 2009;179:1870-1880

Elangovan, A., & Shapiro, D. L. (1998). Betrayal of trust

in organizations. Academy of Management review,

23(3), 547-566.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust

and TAM in online shopping: An integrated model. MIS

quarterly, 27(1), 51-90.

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham,

R. (2006). Multivariate data analysis (6th ed.). Uppersaddle

River, N.J.: Pearson Prentice Hall.

Hagg, E., Dahinten, V. S., & Currie, L. M. (2018). The

emerging use of social media for health-related

purposes in low and middle-income countries: A

scoping review. International Journal of Medical

Informatics, 115, 92-105.

doi:https://doi.org/10.1016/j.ijmedinf.2018.04.010

Hu Y, Sundar S. (2010) Effects of online health sources on

credibility and behavioral Intentions. Commun Res

2010;37:105-132.

Hwang KO, Ottenbacher AJ, Green AP, Cannon-Diehl MR,

Richardson O, Bernstam EV, et al. (2010). Social

support in an Internet weight loss community. Int J Med

Inform 2010 Jan;79(1):5-13

Hwang, Y., & Lee, K. C. (2012). Investigating the

moderating role of uncertainty avoidance cultural

values on multidimensional online trust. Information &

Management, 49(3), 171-176.

doi:https://doi.org/10.1016/j.im.2012.02.003.

Koscik, T. R., & Tranel, D. (2011). The human

amygdala is necessary for developing and expressing

normal interpersonal trust. Neuropsychologia, 49(4),

602-611.

Krejcie, R. V., & Morgan, D. W. (1970). Determining

sample size for research activities. Educational and

psychological measurement, 30(3), 607-610.

Lapointe, L., Ramaprasad, J., & Vedel, I. (2014). Creating

health awareness: a social media enabled collaboration.

Health and Technology, 4(1), 43-57.

Lee, D.-J., Park, J. Y., Lee, H. T., & Yu, B. H. (2008). The

effects of trust dimensions on relationship commitment:

the relative efficacy and effect symmetry. ACR North

American Advances.

Li, Y., Wang, X., Lin, X., & Hajli, M. (2018). Seeking and

sharing health information on social media: A net

valence model and cross-cultural comparison.

Technological Forecasting and Social Change, 126,

28-40.

doi:https://doi.org/10.1016/j.techfore.2016.07.021

Lin, W.-Y., Zhang, X., Song, H., & Omori, K. (2016).

Health information seeking in the Web 2.0 age: Trust in

social media, uncertainty reduction, and self-disclosure.

Computers in Human Behavior, 56, 289-294.

doi:https://doi.org/10.1016/j.chb.2015.11.055

Malaysian Communications and Multimedia Commission.

(2019). Internet Users Survey 2018 Statistical Brief

Number Twenty Three. Retrieved from:

https://www.mcmc.gov.my/skmmgovmy/media/Gener

al/pdf/Internet-Users-Survey-2018.pdf

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An

Integrative Model of Organizational Trust. The Academy of

Management Review, 20(3), 709-734.

doi:10.2307/258792

McKnight, D. H., Cummings, L. L., & Chervany, N. L.

(1998). Initial trust formation in new organizational

relationships. Academy of Management review, 23(3),

473-490.

Determining Trustworthiness of Health Information Shared in Social Networking Sites (SNS)

215

Metzger, M. J., & Flanagin, A. J. (2011). Using Web 2.0

technologies to enhance evidence-based medical

information. Journal of health communication,

16(sup1), 45-58.

Moorhead Sa, Hazlett De, Harrison L, Carroll Jk, Irwin A,

Hoving C. (2013). A New Dimension Of Health Care:

Systematic Review Of The Uses, Benefits, And

Limitations Of Social Media For Health

Communication. J Med Internet Res

2013;15(4):E85Url: Https://Www.Jmir.Org/2013/4/E.

Doi: 10.2196/Jmir.1933

Robert, L. P., Denis, A. R., & Hung, Y.-T. C. (2009).

Individual swift trust and knowledge-based trust in

face-to-face and virtual team members. Journal of

Management Information Systems, 26(2), 241-279.

Sanford AA. "I Can Air My Feelings Instead of Eating

Them": Blogging as Social Support for the Morbidly

Obese. Communication Studies 2010 Nov

2010;61(5):567-584.

Urbano, J., Rocha, A. P., & Oliveira, E. (2013). The impact

of benevolence in computational trust Agreement

Technologies (pp. 210-224): Springer.

Warner-Søderholm, G., Bertsch, A., Sawe, E., Lee, D.,

Wolfe, T., Meyer, J., . . . Fatilua, U. N. (2018). Who

trusts social media? Computers in Human Behavior, 81,

303-315. doi:https://doi.org/10.1016/j.chb.2017.12.026

Waszak, P. M., Kasprzycka-Waszak, W., & Kubanek, A.

(2018). The spread of medical fake news in social

media – The pilot quantitative study. Health Policy and

Technology, 7(2), 115-118.

doi:https://doi.org/10.1016/j.hlpt.2018.03.002

Yahia, I. B., Al-Neama, N., & Kerbache, L. (2018).

Investigating the drivers for social commerce in social

media platforms: Importance of trust, social support

and the platform perceived usage. Journal of Retailing

and Consumer Services, 41, 11-19.

doi:https://doi.org/10.1016/j.jretconser.2017.10.021

Zhou, L., Zhang, D., Yang, C. C., & Wang, Y. (2018).

Harnessing social media for health information

management. Electronic Commerce Research and

Applications, 27, 139-151.

doi:https://doi.org/10.1016/j.elerap.2017.12.003

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