Next Article in Journal
Towards a Better Integration of Fuzzy Matches in Neural Machine Translation through Data Augmentation
Previous Article in Journal
The Influence of Sociological Variables on Users’ Feelings about Programmatic Advertising and the Use of Ad-Blockers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Informatics
Volume 8
Issue 1
10.3390/informatics8010006
informatics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Social Media Adoption by Health Professionals: A TAM-Based Study

by
Md Irfanuzzaman Khan
*,
M Abu Saleh
and
Ali Quazi
Canberra Business School, University of Canberra, Canberra 2617, Australia
*
Author to whom correspondence should be addressed.
Informatics 2021, 8(1), 6; https://doi.org/10.3390/informatics8010006
Submission received: 23 November 2020 / Revised: 6 January 2021 / Accepted: 6 January 2021 / Published: 29 January 2021
(This article belongs to the Section Health Informatics)
Browse Figure
Versions Notes

Abstract

:
This research identifies the underlying drivers impacting on healthcare professionals’ social media usage behaviours using the technology acceptance model (TAM) as the theoretical lens. A self-administered survey questionnaire was developed and administered to 219 healthcare professionals. Data are analysed applying the structural equation modelling (SEM) technique. The SEM model demonstrated an acceptable model fit (χ2 = 534.241; df, 239, χ2/df = 2.235, RMSEA = 0.06, IFI = 0.92, TLI = 0.93, and CFI = 0.92) and indicates content quality, perceived risk, perceived credibility, peer influence, confirmation of expectations, supporting conditions, and perceived cost significantly influence the notion of perceived social media usefulness. Furthermore, perceived social media usefulness positively affects social media usage behaviour of healthcare professionals. This research generates important insights into what drives the adoption of social media by healthcare professionals. These insights could help develop social media guidelines and strategies to improve the state of professional interactions between health professionals and their clients.

1. Introduction

In the healthcare domain, social media has transformed our interactions with one another through community building, message amplification, rapid dissemination, and active engagement [1]. Social media offers healthcare organisations an abundance of potential benefits [2,3,4]. These benefits include patient engagement, social support and enhanced community outreach, health message implications, and improved quality of patient care [5,6]. In a practical sense, social media is leading a true revolution in the healthcare industry [7]. Extant literature suggests that social media can help healthcare professionals promote and advertise their products and services locally and globally [8,9,10,11,12]. Various healthcare social media sites make it easier for consumers to locate the products and services they want to find. For example, PatientsLikeMe is a web-based application, built on the philosophy of value-based care, and it allows the user to share in-depth quantifiable data about health conditions, symptoms, and possible treatments. Users can learn from the experience of other patients and improve their health outcomes. These data are provided to members through graphical health profiles and combined into reports accessible on-line. Members can discuss the datasets either within a group forum or individually through private messages [13].
However, social media also poses significant challenges to the healthcare domain, particularly with reference to privacy and safety considerations for both patients and healthcare professionals [14]. Existing studies also highlight misinformation and disinformation, organisational resistance, privacy threats, inefficiency, adverse health consequences as significant challenges emerging when applying social media in the healthcare sector [15,16,17,18,19,20,21,22]. For example, in a survey of US medical school administrators, it was discovered that 60% reported incidents in which medical students exhibited unprofessional conduct, and 13% reported violations of patient confidentiality [15]. However, it is still unknown how the above challenges influence health professionals’ perceptions regarding the use of social media in the healthcare setting.
Research on the usage patterns revealed mixed results. While the use of social media by both health professionals and public is on the increase, there are considerable concerns about its usefulness in a healthcare setting [23]. Recent findings suggest that despite the advances made by social media in the healthcare domain, many medical professionals are reluctant to use it for health communication [24,25,26]. The question is: What are the factors influencing health professionals’ perceived usefulness social media?
The primary aim of this research is to understand the factors affecting the perceived usefulness of social media usage from the perspective of health professionals. The following section introduces the theoretical background and focuses on the dearth of theory-based research on social media in the healthcare context.

1.1. Theoretical Background and Research Model

Extant social media-related research in healthcare is built on either a standard survey of medical professionals or exploratory studies without a strong theoretical underpinning [10,27]. This study proposes an updated theoretical framework for investigating the antecedents of perceived usefulness to healthcare professionals’ social media usage. The proposed framework derives from the Technology Acceptance Model (TAM) [28]. TAM has been a dominant model in the domain of technology acceptance. The TAM has gained considerable reputation, particularly due to its transferability to various contexts and samples, its potential to explain variance in the intention to use or the use of technology, and its simplicity of specification within structural equation modelling frameworks [29,30]. Besides, TAM is a powerful vehicle to understand the acceptance of healthcare technologies [31] and other generic studies concerning technology adoption [32].
The theoretical basis of the TAM is the theory of reasoned action [33] and it denotes that the antecedents of technology usage occur because of two fundamental factors: the perceived usefulness (PU) and perceived ease of use (PEOU) of a technology [34]. Perceived usefulness refers to the extent a user believes that using a particular system will improve how they do their job. Perceived ease of use (PEOU) refers to the extent a potential user expects that the target system to be free of unproductive effort [34]. The model explains that perceived usefulness and perceived ease of use influence the user’s attitudes toward using a certain technology (intention). In turn, intentions to use will determine whether a person will use the application or not. However, several TAM-based studies recommended the exclusion of attitude from the model. For instance, the revised TAM model (i.e., TAM without the attitude construct) has been validated in several user technology acceptance/adoption investigations because attitude did not appear to mediate fully the effect of perceived usefulness and perceived ease of use on behavioural intention as originally anticipated [35,36,37]. Thus, in this study, the construct “attitude” was excluded from the examination of healthcare professionals’ social media usage behaviour.
Previous studies concerning information system (IS) and information technology (IT) adoption has consistently accepted perceived usefulness (PU) as the most influential determinant of an individual’s decision to adopt a new technology [38,39]. Extant literature indicates that the influence of PU is greater than that of PEOU concerning the adoption of new technology [40]. In the health technology domain, physicians adopt a more pragmatic attitude to acceptance. They focus on the usefulness of health technology adoption rather than on the ease of using it [41]. Consequently, health professionals concentrate more on system utility and functionality than users in other industries. Therefore, their motivation for a new technology acceptance is directly related to the fact that the new technology acts as an enabler of their daily tasks. Furthermore, PU has a greater impact on behavioural intention to use than PEOU [42]. Prior studies that employed TAM have demonstrated that PU the most important predictor of behavioural intention to use new technology, while perceived ease of use becomes less important over periods of sustained usage [43]. Therefore, the construct PEOU is not considered in the proposed theoretical framework, leading to the inclusion of PU as the fundamental predictor of health professionals’ social media usage behaviour.
Although PU is well accepted as the major antecedent affecting technology usage behaviour, it is necessary to examine the determinants of PU in the context of social media usage in the healthcare domain. In our quest for identifying and validating the antecedents of health professionals’ perceived usefulness of the social media within the healthcare context, the proposed framework contextualises the TAM by adding variables to study the impact of content quality, perceived credibility, perceived risk, peer influence, supporting conditions, confirmation of expectations and perceived cost on health professionals’ social media usage behaviour. It is evident that the integration of external variables is a common trend in this research field [44,45]. An added variable approach enables researchers and practitioners to understand the drivers of intention better and assist in developing new theories [46]. The conceptual framework depicts the possible effects of the factors influencing health professionals’ perceived usefulness and social media usage behaviours. These factors are yet to be contextualised and tested within the healthcare social media context. To sum up, the proposed theoretical framework is an important addition to the mainstream social media adoption literature. Figure 1 illustrates the conceptual research model.

1.2. Hypothesis Development

1.2.1. A. Content Quality

Content quality refers to social media content that is appropriately customised, relevant, easy to understand, complete, and secure during an online transaction [47]. The theoretical underpinning of this research is based on the information system success model which describes information quality as an antecedent of system use [48]. Scholars have previously found that barriers to social media included poor quality and the lack of reliability of health information [6,49]. Medically incorrect information has resulted in “wrong” instructions and contributed to serious negative outcomes. Thus, this creates a cascading effect which leads to the distrust of online social applications and services in medical healthcare [50,51]. Prior studies have consistently confirmed that information quality is a fundamental construct in evaluating the usefulness of a new system [52,53]. However, the effect of social media content quality on health professionals’ social media usage decision is not very clear and warrants further investigation. Thus, we propose the following hypothesis:
Hypothesis 1 (H1). 
Content quality significantly influences perceived usefulness of social media.

1.2.2. B. Perceived Credibility

In the online context, perceived credibility denotes feelings of confidence and security while using digital technology [40,54,55]. Prior studies have shown that credibility affects people’s voluntary acceptance of digital transactions [40,49]. Thus, perceived credibility can be a significant factor for engaging in health information searching and related activities via social media [56,57]. If online participants are unsure about the credibility of the health information provided through social media channels, they may stop seeking online health information [10,19,56,57,58,59]. Based on extant literature we propose the following hypothesis:
Hypothesis 2 (H2). 
Perceived credibility is associated with the perceived usefulness of health professionals’ social media usage.

1.2.3. C. Perceived Risk

Perceived risk is a frequently used variable in consumer behaviour studies and known as a multi-dimensional construct. There are six components of perceived risk and they are as follows: (i) financial, (ii) performance, (iii) social, (iv) physical, (v) privacy, and (vi) time loss [60]. Prior studies have consistently shown that perceived risk negatively influences users’ intention to use IT/IS systems [61,62,63].
In the social media context, previous studies reported privacy and confidentiality concerns as a major deterrent of social media usage in the healthcare industry [58,64,65,66]. This is because online patient privacy breaches can result in greater harm compared to privacy breaches that occur face-to-face, especially given that online privacy breaches reach a wider audience and remain permanently online. Consequently, health professionals’ concerns about social media use predominantly revolve around the potential risks arising from breaching patient confidentiality [67,68] or publicising unprofessional content [69]. For example, an assessment of 271 medical blogs revealed that around 17% included ample information for patients to identify their doctors or themselves [70]. Furthermore, a content analysis of physicians’ Twitter posts (5156 tweets) revealed that 4% were potentially unprofessional, including 38 potential patient privacy violations [71]. This outcome explains why many health professionals consider the integration of medicine and social media as a daunting challenge often posing severe threats to people’s privacy [72,73]. From the review, the privacy-related risks arising from social media technologies can be significant and can influence medical personnel’s capacity to use social media. Therefore, the following hypothesis is proposed:
Hypothesis 3 (H3). 
Perceived risk negatively influences health professionals’ perceived usefulness of social media.

1.2.4. D. Peer Influence

Peer influence refers to the extent to which an individual perceive that the important members of his/her social circle believe that he/she should use the new system [45]. Prior research also measured this variable in the healthcare context and reported a positive relationship with behavioural intention to use technology [74]. Close peers’ personal evaluations of technological interventions tend to be more credible and help to minimise uncertainty in adoption of a new technology. Thus, in a healthcare context the subjective influence of close peers or colleagues is important [18]. The benefits of social media are primarily based on social aspects such as social support, patient engagement, greater interactivity and social listening [54,56,59]. However, empirical studies concerning the effect of peer influence on health professionals’ social media adoption behaviours are relatively scarce and warrants further investigation. Thus, the following hypothesis is developed:
Hypothesis 4 (H4). 
Peer influence positively influences the notion of perceived social media usefulness.

1.2.5. E. Supporting Conditions

Supporting condition refers to the extent of broader social, organisational and technical infrastructure existing to support the use of a new system or technology [45]. Recent literature raised various concern related to supporting conditions for adopting social media in a professional setting. For example, internal and external support in terms of resources, staffing, and technical expertise is necessary for social media to be used effectively [75,76,77,78]. A survey of 366 healthcare workers in the USA revealed that healthcare workers are not familiar with their workplace policies concerning social media use, which may cause a privacy breach of confidential medical information [79]. Recent studies also highlighted the importance of supporting conditions on social media usage behaviour [80,81,82]. Thus, it will be interesting to observe the relationship between supporting conditions and perceived usefulness in healthcare context. Drawing on the extant research the following hypothesis has been posited:
Hypothesis 5 (H5). 
Supporting conditions positively influence the perceived social media usefulness.

1.2.6. F. Confirmation of Expectations

Confirmation is the outcome of a considerable “over-fulfilment” of initial expectations [83,84]. The confirmation of expectations suggests that when experiences with specific system exceed expectations, expectations exert a positive influence on satisfaction [85]. Consequently, lower expectation and higher perceived performance may lead to a greater sense of confirmation [86]. Prior studies in different contexts show that when users’ actual experience exceeds their expectations, confirmation positively influences perceived usefulness [87,88,89,90,91,92]. It will be interesting to examine the relationship between confirmation of expectations and perceived social media usefulness in healthcare context. Indeed, further empirical investigation of the relationship between confirmation of expectations and perceived social media usefulness is warranted. Hence, the following hypothesis is put forward:
Hypothesis 6 (H6). 
Confirmation of expectations is significantly associated with perceived social media usefulness.

1.2.7. G. Perceived Cost

The costs associated with a new information system or technology have always been a major driver of usage behaviour. In this research, perceived cost entails health professionals’ perceptions concerning the initial subscription, transaction and communication costs involved in using social media [93]. Extant research also represented perceived cost as an important variable [93,94,95] and supports the hypothesis that perceived cost negatively affects users’ behavioural intention to use technology [96,97]. This research argues that social media reduces the economic, time and human costs associated with clinical practice. Thus, perceived cost is expected to be an important factor for social media adoption in a professional setting. Based on the extant research, the following hypothesis is proposed:
Hypothesis 7 (H7). 
Perceived cost negatively affects health professionals’ perceived social media usefulness.

1.2.8. H. Perceived Usefulness and Usage Behaviour

The theoretical underpinning of the usage behaviour construct is grounded on the ‘actual system usage’ construct which refers to the degree a user applies one or more system features to accomplish a specific task [98]. Based on TAM, perceived usefulness is expected to positively influence social media usage behaviour of healthcare professionals [28]. Prior technology acceptance studies have shown that perceived usefulness is a significant predictor of intention to use information technology [46,97]. In accordance with the TAM framework, the following hypothesis is proposed:
Hypothesis 8 (H8). 
Perceived social media usefulness positively influences health professionals’ social media usage behaviour.
Table 1 below provides definitions and sources of root constructs.

2. Methods

This study used quantitative technique for data collection and analysis. The major attribute of quantitative research is the use of a formal and methodical statistical technique [106]. The population for this study comprised Australian health professionals who used social media, and worked in hospitals, clinics and other clinical environments.

2.1. Data Collection

A quantitative survey of 219 healthcare professionals was conducted. The sampling frame was derived from publicly available online databases. In an attempt to eliminate any inherent bias, a number of measures were taken. For example, steps recommended by Ryan and Bernard [107] followed, such as ensuring that the clarity and overt judgments during questionnaire instrument preparation, data processing, entry, cleaning and different phases of analyses are monitored and undertaken. A simple random sampling method was used to select the respondents. A personalised email invitation containing the link to the survey was sent to 824 health professionals. A total of 219 complete responses were received with a 27% response rate.

2.2. Data Analysis

This research employed Structural Equation Modelling (SEM) technique to investigate the hypotheses proposed in the research model. The structural equation modelling (SEM) is a technique that embodies the observed data through some structural parameters characterized by a theoretical framework [108,109,110]. SEM helps to conduct a simultaneous analysis, where the relationships among multiple independent and dependent constructs are modelled concurrently. This capability differs significantly from most first-generation statistical tools such as correlation, regression and factor analysis, which can analyse only one layer of linkages between independent and dependent variables at a time [111]. SPSS Amos 25 (IBM, New York, NY, USA) statistical package was used to analyse the data. SPSS Amos software (IBM, New York, NY USA) is a preferred statistical tool to examine causal and effect relationship between a number of independent and dependent variables [112]. The source of the constructs measures is documented in Table 2.

2.3. Construct Measures

The survey questionnaire consisted of certain factors that influence the perceived usefulness and usage behaviour of healthcare professionals. These factors were content quality, perceived credibility, perceived risk, peer influence, supporting conditions, confirmation of expectations and perceived cost. The scales for all the constructs were adapted from prior studies, many of which have already established their reliability and validity. The language of the items was modified to reflect the measurement of these constructs for social media users. The items were measured on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Prior studies indicated that 7-point Likert scale increases scale sensitivity [113] and likely to provide a more accurate measure of a participant’s true evaluation and are more appropriate for electronically distributed or unsupervised usability questionnaires [114]. Details of the construct measures and their operational indicators are provided in Table 2 below.
The above table confirms that all composite reliabilities exceeded the baseline criterion of 0.70 [120]. The average variance extracted (AVE) for each construct is also greater than the baseline limit of 0.50 [121]. The factor loadings, and reliability scores were also found to be above the required threshold. Therefore, all constructs are retained for confirmatory analysis and all results indicate the instrument’s convergent validity. During the assessment of multicollinearity, the correlation matrix (Table 3) confirmed that no correlation between any two items was above 0.8; thus, bivariate high collinearity did not exist. Construct correlations were compared with the square root of the AVE, and it was found that all square roots of the AVEs were consistently higher than the correlations between constructs. In this way, the discriminant validity of the constructs used in this research was confirmed [109].

2.4. The Demographic Profile

The completed survey includes 48% male participants and 52% female participants. Regarding the participants’ age range, 2% were 18–29 years old, 30% were in the 30–39 bracket, 35% were 40–49 years old, 25% were 50 to 59 and 8% were over 60 years of age. Regarding the participants’ experience in healthcare, 48% had 1–15 years, 48% had 16–30 years, and 4% had more than 30 years. Table 4 summerises the patterns of personal and professional use of social media among health professionals.
In terms of personal use, it emerges that Facebook, YouTube, Twitter, and LinkedIn are the most popular social media applications. Facebook is the most frequently used platform as 47% are regular users of it. YouTube is the second most popular platform among health professionals (45%). This is followed by 25% of respondents frequently using Twitter, and 21% of respondents’ access LinkedIn. 15% of respondents employ various other social media platforms for personal use. However, concerning the professional use of social media, only 19% use Facebook, 12% use Twitter, 25% use YouTube, 7% utilise blog sites, 21% use LinkedIn and 11% use other platforms. This finding point towards limited use of social media for professional purposes.

3. Results

Hypothesis Testing

The individual measurement model fit was tested for all dependent and independent constructs included in the theoretical framework. CFA of the initial 36 items measurement model revealed the following model indices: CMIN = 1393, df = 467 and CMIN/df ratio = 2.984; CFI = 0.857; and RMSEA = 0.09, indicating a scope for model refinement. Subsequently, an assessment of the modification indices (MI) was conducted [122]. Accordingly, seven items were excluded from different constructs to achieve a better fit to the data. This reduces the χ2 value to 408 (df, 224, p < 0.001) and significantly improved other fit indices in the final overall measurement (CFA) model. The retained items in the construct measures suggest a satisfactory fit between data and the measurement model. The final overall measurement model was tested with 29 items during the SEM stage. The results of final CFA demonstrate an adequate model fit (χ2 = 438.355; df, 224, χ2/df = 1.957, RMSEA = 0.06, IFI = 0.95, TLI = 0.93, and CFI = 0.95).
The subsequent phase examined the structural model to test the structural relationship between measured variables and latent constructs. [109]. The SEM analysis indicated a reasonable model fit (χ2 = 534.241; df, 239, χ2/df = 2.235, RMSEA = 0.06, IFI = 0.92, TLI = 0.93, and CFI = 0.92). Furthermore, the research model explains (R2 = 47.6%) variance in social media usage behaviour, which indicates a moderate explanatory power as recommended by Hu and Bentler [123].
The R2 value of 47.6% is also consistent with the findings of a recent meta-analysis which showed that most of the TAM based studies explained 40–70% of variance in behavioural intention and actual usage [46]. Table 5 below provides an overview of the results of the hypothesis testing performed in SEM.

4. Discussion

The results demonstrate that all hypothesised relationships are supported. This research confirms that content quality (H1) is a significant predictor of perceived usefulness (p < 0.05). The finding is consistent with previous studies in different contexts which have found that online content quality positively influences the notion of perceived usefulness in digital information exchange settings [124,125]. This study concludes that if the health professionals perceive that social media channels facilitate dissemination of high-quality health-related content, they will use it more extensively.
The insertion of perceived credibility (H2) as an independent variable also provides a new perspective for studying social media acceptance research in healthcare. Although several studies [55,126,127,128] have looked at the role of trust as a direct antecedent of behavioural intentions, this study investigates the effect of credibility on perceived usefulness in the context of social media technologies. The SEM analysis indicates that perceived credibility is significantly associated with the perception of usefulness (p < 0.01) and this finding is consistent with previous studies [59,129]. Therefore, if health professionals perceive social media as a credible platform to professionally interact with their patients, they will perceive it as useful. Therefore, the role of credibility is pivotal in shaping productive relationships between health professionals and patients.
Consistent with prior research [6,63], findings confirm a significant and negative association (p < 0.01) between perceived risks and perceived usefulness (H3). This result reflects the health professionals’ concern about privacy and confidentiality, the main items that represent the construct of perceived risk in the healthcare context. Privacy threats as a universal phenomenon are playing a critical role in the communication mechanisms of healthcare professionals and patients.
The study has also examined the relationship between peer influence and perceived usefulness. It confirms there is a significant association (p < 0.05) between these two constructs. The findings are consistent with prior studies [45,74,102]. As mentioned earlier, past studies predominantly examined the influence of peers in assessing information technology usage behaviour. However, studies in relation to the influence of peers on social media usage decisions in healthcare are relatively scarce. The results confirm that peer influence is a significant predictor of health professionals’ perceived usefulness of social media [130]. The social aspect of social media motivates users to embrace the way information and knowledge is disseminated between health professionals, their patients and members of the public [131]. Consequently, establishing a network with other relevant players is an essential factor in community wellbeing.
The relationship between supporting conditions and perceived usefulness (H5) was also supported (p < 0 0.05). This result is consistent with prior research which exhibited a positive effect of supporting conditions on perceived usefulness [132]. The results indicate that a favourable environment helps to reduce uncertainty in relation to the usage of social media by health professionals. Thus, a favourable social, technical and organisational environment will help healthcare providers to realise the full potential of social media [133,134,135].
The result indicated a significant relationship (H6) between confirmation of expectations and perceived usefulness (p < 0.01). The finding is consistent with the notion that the higher the confirmation, the higher the perceived usefulness [85,86,87,88,89,90]. Moreover, this research further validates that physicians are likely to use social media when its benefits in healthcare are confirmed.
The effect of perceived cost was assumed to be a strong indicator of social media adoption (H7). The results indicate that the perceived cost of social media negatively influences (p < 0.05) the perception of usefulness. Prior research highlighted cost as an important factor affecting users’ behavioural intention [124,125,126,127]. Thus, this research confirmed some of the arguments put forward in the literature [82,128]. Extant literature revealed that social media applications make it possible for healthcare professionals to engage with their patients, enhance brand image and increase patient satisfaction in a very cost-effective way [46,71,74,77,129,130]. Thus, professional use of social media would allow physicians to share and exchange health information on a large scale [60].
Finally, the findings demonstrate a significant association between perceived usefulness (H8) and usage behaviour (p < 0.05), confirming the notion that health professionals’ perception of usefulness is a key predictor of their actual social media usage behaviour.

5. Conclusions and Implications

This study contributes to the limited research on social media in the healthcare context by building on previous research [24,26]. Prior technology adoption theories were mostly operationalised without taking into account the characteristics of social media. Although many researchers have tested the existing adoption theories in the healthcare context, the unique characteristics of social media needed a more context-specific approach to model development. This research enriches the traditional technology acceptance model (TAM) by operationalising additional constructs such as content quality, perceived credibility, perceived risk, peer influence, supporting conditions, confirmation of expectations and perceived cost affecting the social media usage decisions of Australian healthcare professionals. These constructs were formulated, operationalised, and tested exclusively, drawing on the social media use behaviours of healthcare professionals.
Prior research explored the potential of social media technologies in healthcare [2,3,4,5,6,7]. However, compared to previous studies, this research backed its arguments with empirical data collected from the healthcare industry but also explored and identified new factors that are relevant to social media adoption behaviours. This research operationalised some relatively unknown factors regarding the usage of social media in healthcare.
Current wisdom suggests that although social media can alleviate many challenges pertaining to the practical, social and emotional aspects of healthcare service delivery, there is a growing concern in the minds of healthcare professionals about some negative aspects of social media usage in their medical practice. The findings point towards the need for more strategic initiatives concerning security related issues associated with health professionals. Healthcare authorities need to undertake practical initiatives to attract health professionals in this interactive sphere by firstly, promoting the benefits, and secondly, ensuring the availability of support structures to improve community health.
The findings also point towards the need to raise more awareness, training, and support for practitioners so that the adoption trends of social media are maintained and further enhanced. The findings also point towards the fear or scepticism in health professionals’ minds concerning professional use or abuse of social media. This scepticism is attributed to the challenges associated with poor content quality, credibility issues, perceived risk and unfavourable supporting conditions.
The study findings may provide an opportunity for healthcare providers and policymakers to understand and take necessary actions with regard to the current status of healthcare practitioners perceptions of the potential factors affecting the usage of social media for health communication. Healthcare providers, administrators and system professionals can consider the relevant factors affecting adoption behaviour and formulate a clear social media usage guideline, offering health professionals advice on how to maximise potential benefits through engagement with social media. These insights could be used in developing social media guidelines effectively and to maximise the benefits for the specific needs of the health community. This is paramount, as there is currently a widespread scepticism about the viability of social media applications in the healthcare domain [6].
To sum up, adequate knowledge of the factors driving adoption behaviour discussed above may provide guidelines for the healthcare sector. Appropriate strategies that address these issues and improve the current status of social media usage amongst Australian healthcare professionals and consumers should be designed.

6. Limitations and Future Research

Like any other research, this study has its limitations. This research has been undertaken in an Australian context which tends to limit the generalisation of the findings internationally. Future research can overcome this limitation by using large samples drawn from other Western countries with similar social and economic characteristics, for example, Canada, New Zealand, the UK and the USA. Furthermore, the cross-sectional design does not consider the responses at different time intervals. Notably, some constructs (like usage behaviour) need a period of time to pass for them to be measured correctly. This is thought to be a limitation of most TAM-based research. Future studies may take the form of comparative studies or judge the pre-adoption and post-adoption behaviour of social media in healthcare. The time factor is worth investigating where some time is needed for the conclusive data to emerge and mean something. The issue of generalisability is a widely known limitation in most of the research on technology acceptance. Finally, the research posed considerable challenges to access health professionals who are active users of social media and could be recruited for the study. Due to the busy nature of the profession, only a small number of participants was enthused to participate in this research since they are usually too busy with their everyday tasks. However, the sample was chosen not only for practical reasons and convenience but also because healthcare professionals are amongst those whose opinions and perceptions will influence the adoption of social media in healthcare organisations.

Author Contributions

Conceptualization, M.I.K., M.A.S. and A.Q.; methodology, M.I.K. and A.Q.; Data analysis, M.A.S. and M.I.K.; writing—original draft preparation, M.I.K., M.A.S. and A.Q.; writing—review and editing, M.I.K.; supervision, A.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the University of Canberra, Human Research Ethics Committee (Approve project 15-234, 3/11/2015).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Fernández-Luque, L.; Bau, T. Health and social media: Perfect storm of information. Healthc. Inform. Res. 2015, 21, 67–73. [Google Scholar] [CrossRef] [PubMed]
  2. Leary, V.G.; Grant, G. Social media in healthcare. In Proceedings of the 50th Hawaii International Conference on System Sciences, Puako, HI, USA, 4–7 January 2017. [Google Scholar]
  3. Park, A.; Bowling, J.; Shaw, G., Jr.; Li, C.; Chen, S. Adopting social media for improving health opportunities and challenges. North Carol. Med. J. 2019, 80, 240–243. [Google Scholar] [CrossRef] [PubMed]
  4. Gabarron, E.; Bradway, M.; Fernandez-Luque, L.; Chomutare, T.; Hansen, A.H.; Wynn, R.; Årsand, E. Social media for health promotion in diabetes: Study protocol for a participatory public health intervention design. BMC Health Serv. Res. 2018, 18, 1–5. [Google Scholar] [CrossRef] [Green Version]
  5. Barnes, S.S.; Kaul, V.; Kudchadkar, S.R. Social media engagement and the critical care medicine community. J. Intensive Care Med. 2019, 34, 175–182. [Google Scholar] [CrossRef] [PubMed]
  6. Moorhead, S.A.; Hazlett, D.E.; Harrison, L.; Carroll, J.K.; Irwin, A.; Hoving, C. 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. [Google Scholar] [CrossRef] [Green Version]
  7. Korda, H.; Itani, Z. Harnessing social media for health promotion and behavior change. Health Promot. Pract. 2013, 14, 15–23. [Google Scholar] [CrossRef]
  8. Sarasohn-Kahn, J. The Wisdom of Patients: Health Care Meets Online Social Media; California HealthCare Foundation: Oakland, CA, USA, 2008. [Google Scholar]
  9. Sarringhaus, M.M. The great divide: Social media’s role in bridging healthcare’s generational shift. J. Healthc. Manag. 2011, 56, 235–244. [Google Scholar]
  10. Fisher, J.; Clayton, M. Who gives a tweet: Assessing patients’ interest in the use of social media for health care. Worldviews Evid. Based Nurs. 2012, 9, 100–108. [Google Scholar] [CrossRef]
  11. Lambert, K.M.; Barry, P.; Stokes, G. Risk management and legal issues with the use of social media in the healthcare setting. J. Healthc. Risk Manag. 2012, 31, 41–47. [Google Scholar] [CrossRef]
  12. Andersen, K.N.; Medaglia, R.; Henriksen, H.Z. Social media in public health care: Impact domain propositions. Gov. Inf. Q. 2012, 29, 462–469. [Google Scholar] [CrossRef]
  13. Wicks, P.; Massagli, M.; Frost, J.; Brownstein, C.; Okun, S.; Vaughan, T.; Bradley, R.; Heywood, J. Sharing health data for better outcomes on PatientsLikeMe. J. Med. Internet Res. 2010, 12, e19. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Miller, A.R.; Tucker, C. Active social media management: The case of health care. Inf. Syst. Res. 2013, 24, 52–70. [Google Scholar] [CrossRef] [Green Version]
  15. Chretien, K.C.; Greysen, S.R.; Chretien, J.P.; Kind, T. Online posting of unprofessional content by medical students. JAMA 2009, 302, 1309–1315. [Google Scholar] [CrossRef] [Green Version]
  16. McKee, R. Ethical issues in using social media for health and health care research. Health Policy 2013, 110, 298–301. [Google Scholar] [CrossRef] [PubMed]
  17. Grobler, C.; Dhai, A. Social media in the healthcare context: Ethical challenges and recommendations. S. Afr. J. Bioeth. Law 2016, 9, 22–25. [Google Scholar] [CrossRef]
  18. Sahin, I. Detailed review of Rogers’ diffusion of innovations theory and educational technology-related studies based on Rogers’ theory. Turk. Online J. Educ. Technol.-TOJET 2006, 5, 14–23. [Google Scholar]
  19. Antheunis, M.L.; Tates, K.; Nieboer, T.E. Patients’ and health professionals’ use of social media in health care: Motives, barriers and expectations. Patient Educ. Couns. 2013, 92, 426–431. [Google Scholar] [CrossRef] [PubMed]
  20. Cuan-Baltazar, J.Y.; Muñoz-Perez, M.J.; Robledo-Vega, C.; Pérez-Zepeda, M.F.; Soto-Vega, E. Misinformation of COVID-19 on the internet: Infodemiology study. JMIR Public Health Surveill. 2020, 6, e18444. [Google Scholar] [CrossRef] [Green Version]
  21. Kasemsap, K. Professional and business applications of social media platforms. In Social Entrepreneurship: Concepts, Methodologies, Tools, and Applications; IGI Global: Hershey, PA, USA, 2019; pp. 824–847. [Google Scholar]
  22. Kouzy, R.; Abi Jaoude, J.; Kraitem, A.; El Alam, M.B.; Karam, B.; Adib, E.; Zarka, J.; Traboulsi, C.; Akl, E.W.; Baddour, K. Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic on Twitter. Cureus 2020, 12, e7255. [Google Scholar] [CrossRef] [Green Version]
  23. Smailhodzic, E.; Hooijsma, W.; Boonstra, A.; Langley, D.J. Social media use in healthcare: A systematic review of effects on patients and on their relationship with healthcare professionals. BMC Health Serv. Res. 2016, 16, 442. [Google Scholar] [CrossRef] [Green Version]
  24. Kind, T.; Greysen, S.R.; Chretien, K.C. Pediatric clerkship directors’ social networking use and perceptions of online professionalism. Acad. Pediatrics 2012, 12, 142–148. [Google Scholar] [CrossRef] [PubMed]
  25. Brown, J.; Ryan, C.; Harris, A. How doctors view and use social media: A national survey. J. Med. Internet Res. 2014, 16, 12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Usher, W. Types of social media (Web 2.0) used by Australian allied health professionals to deliver early twenty-first-century practice promotion and health care. Social Work Health Care 2011, 50, 305–329. [Google Scholar] [CrossRef]
  27. McGowan, B.S.; Wasko, M.; Vartabedian, B.S.; Miller, R.S.; Freiherr, D.D.; Abdolrasulnia, M. Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information. J. Med. Internet Res. 2012, 14, 5. [Google Scholar] [CrossRef] [PubMed]
  28. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
  29. Šumak, B.; Heričko, V.; PušNik, M. A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Comput. Hum. Behav. 2011, 27, 2067–2077. [Google Scholar] [CrossRef]
  30. Scherer, R.; Siddiq, F.; Tondeur, J. The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Comput. Educ. 2019, 128, 13–35. [Google Scholar] [CrossRef]
  31. Nadri, H.; Rahimi, B.; Lotfnezhad, A.H.; Samadbeik, M.; Garavand, A. Factors affecting acceptance of hospital information systems based on extended technology acceptance model: A case study in three paraclinical departments. Appl. Clin. Inform. 2018, 9, 238. [Google Scholar] [CrossRef] [Green Version]
  32. Palos-Sanchez, P.R.; Hernandez-Mogollon, J.M.; Campon-Cerro, A.M. The behavioral response to location-based services: An examination of the influence of social and environmental benefits, and privacy. Sustainability 2017, 9, 1988. [Google Scholar] [CrossRef] [Green Version]
  33. Ajzen, I.; Fishbein, M. Understanding Attitudes and Predicting Social Behavior; Pearson: London, UK, 1980. [Google Scholar]
  34. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User acceptance of computer technology: A comparison of two theoretical models. Manag. Sci. 1989, 35, 982–1003. [Google Scholar] [CrossRef] [Green Version]
  35. Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef] [Green Version]
  36. Koh, C.E.; Prybutok, V.R.; Ryan, S.D.; Wu, Y. A model for mandatory use of software technologies: An integrative approach by applying multiple levels of abstraction of informing science. Inf. Sci. 2010, 13, 177–203. [Google Scholar]
  37. Yang, H.-D.; Yoo, Y. It’s all about attitude: Revisiting the technology acceptance model. Decis. Support Syst. 2004, 38, 19–31. [Google Scholar] [CrossRef]
  38. Abdullah, F.; Ward, R.; Ahmed, E. Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Comput. Hum. Behav. 2016, 63, 75–90. [Google Scholar] [CrossRef]
  39. Kucukusta, D.; Law, R.; Besbes, A.; Legohérel, P. Re-examining perceived usefulness and ease of use in online booking. Int. J. Contemp. Hosp. Manag. 2015, 27, 185–198. [Google Scholar] [CrossRef]
  40. Ong, C.S.; Lai, J.Y.; Wang, Y.S. Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Inform. Manag. 2004, 41, 795–804. [Google Scholar] [CrossRef]
  41. Chismar, W.G.; Wiley-Patton, S. Does the extended technology acceptance model apply to physicians. In Proceedings of the 36th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA, 6–9 January 2003. [Google Scholar]
  42. Lin, C.; Lin, I.-C.; Roan, J. Barriers to physicians’ adoption of healthcare information technology: An empirical study on multiple hospitals. J. Med. Syst. 2012, 36, 1965–1977. [Google Scholar] [CrossRef]
  43. Phang, C.W.; Li, Y.; Sutanto, J.; Kankanhalli, A. Senior citizens’ adoption of e-government: In quest of the antecedents of perceived usefulness. In Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA, 6 January 2005; p. 130a. [Google Scholar]
  44. Davis, F.D. User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. Int. J. Man Mach. Stud. 1993, 38, 475–487. [Google Scholar] [CrossRef] [Green Version]
  45. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef] [Green Version]
  46. Holden, R.J.; Karsh, B.-T. The technology acceptance model: Its past and its future in health care. J. Biomed. Inform. 2010, 43, 159–172. [Google Scholar] [CrossRef] [Green Version]
  47. Delone, W.H.; McLean, E.R. The DeLone and McLean model of information systems success: A ten-year update. J. Manag. Inf. Syst. 2003, 19, 9–30. [Google Scholar]
  48. DeLone, W.H.; McLean, E.R. Information systems success: The quest for the dependent variable. Inf. Syst. Res. 1992, 3, 60–95. [Google Scholar] [CrossRef]
  49. Al-Gahtani, S.S. Modeling the electronic transactions acceptance using an extended technology acceptance model. Appl. Comput. Inform. 2011, 9, 47–77. [Google Scholar] [CrossRef] [Green Version]
  50. Lopez, D.; Blobel, B.; Gonzalez, C. Information quality in healthcare social media—An architectural approach. Health Technol. 2016, 6, 17–25. [Google Scholar] [CrossRef]
  51. Gutierrez, N.; Kindratt, T.B.; Pagels, P.; Foster, B.; Gimpel, N.E. Health literacy, health information seeking behaviors and internet use among patients attending a private and public clinic in the same geographic area. J. Community Health 2014, 39, 83–89. [Google Scholar] [CrossRef]
  52. Mohammadi, F.; Abrizah, A.; Nazari, M. Is the information fit for use? Exploring teachers perceived information quality indicators for Farsi web-based learning resources. Malays. J. Libr. Inf. Sci. 2017, 20, 1. [Google Scholar]
  53. Alsabawy, A.Y.; Cater-Steel, A.; Soar, J. Soar. Determinants of perceived usefulness of e-learning systems. Comput. Hum. Behav. 2016, 64, 843–858. [Google Scholar] [CrossRef]
  54. Considine, D.M. Social Media for Health Professionals—Benefits and Pitfalls. Available online: https://www.doctorportal.com.au/social-media-for-health-professionals-benefits-and-pitfalls/ (accessed on 17 March 2018).
  55. Gefen, D.; Karahanna, E.; Straub, D.W. Trust and TAM in online shopping: An integrated model. MIS Q. 2003, 27, 51–90. [Google Scholar] [CrossRef]
  56. Hou, J.; Shim, M. The role of provider–patient communication and trust in online sources in Internet use for health-related activities. J. Health Commun. 2010, 15, 186–199. [Google Scholar] [CrossRef]
  57. Bansal, G.; Gefen, D. The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decis. Support Syst. 2010, 49, 138–150. [Google Scholar] [CrossRef]
  58. Van Der Velden, M.; El Emam, K. Not all my friends need to know: A qualitative study of teenage patients, privacy, and social media. J. Am. Med. Inform. Assoc. 2013, 20, 16–24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Lin, W.-Y.; Zhang, X.; Song, H.; Omori, K. Health information seeking in the Web 2.0 age: Trust in social media, uncertainty reduction, and self-disclosure. Comput. Hum. Behav. 2016, 56, 289–294. [Google Scholar] [CrossRef]
  60. Jacoby, J.; Kaplan, L.B. The components of perceived risk. In Proceedings of the Third Annual Conference of the Association for Consumer Research, Chicago, IL, USA, 3–5 November 1972. [Google Scholar]
  61. Stone, R.N.; Grønhaug, K. Perceived risk: Further considerations for the marketing discipline. Eur. J. Mark. 1993, 27, 39–50. [Google Scholar] [CrossRef]
  62. Mitra, K.; Reiss, M.C.; Capella, L.M. An examination of perceived risk, information search and behavioral intentions in search, experience and credence services. J. Serv. Mark. 1999, 13, 208–228. [Google Scholar] [CrossRef]
  63. Rahman, M.M.; Sloan, T. User adoption of mobile commerce in Bangladesh: Integrating perceived risk, perceived cost and personal awareness with TAM. Int. Technol. Manag. Rev. 2017, 6, 103–124. [Google Scholar] [CrossRef] [Green Version]
  64. Crane, G.M.; Gardner, J.M. Pathology image-sharing on social media: Recommendations for protecting privacy while motivating education. AMA J. Ethics 2016, 18, 817. [Google Scholar]
  65. Melnik, T. Avoiding violations of patient privacy with social media. J. Nurs. Regul. 2013, 3, 39–46. [Google Scholar] [CrossRef]
  66. Al-Muhtadi, J.; Shahzad, B.; Saleem, K.; Jameel, W.; Orgun, M.A. Cybersecurity and privacy issues for socially integrated mobile healthcare applications operating in a multi-cloud environment. Health Inform. J. 2019, 25, 315–329. [Google Scholar] [CrossRef]
  67. Hader, A.L.; Brown, E.D. LEGAL BRIEFS. Patient Privacy and Social Media. AANA J. 2010, 78, 270–274. [Google Scholar]
  68. Hawn, C. Take two aspirin and tweet me in the morning: How Twitter, Facebook, and other social media are reshaping health care. Health Aff. 2009, 28, 361–368. [Google Scholar] [CrossRef]
  69. Cain, J. Social media in health care: The case for organizational policy and employee education. Am. J. Health Syst. Pharm. 2011, 68, 1036–1040. [Google Scholar] [CrossRef] [PubMed]
  70. Lagu, T.; Kaufman, E.J.; Asch, D.A.; Armstrong, K. Content of weblogs written by health professionals. J. Gen. Intern. Med. 2008, 23, 1642. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Chretien, K.C.; Azar, J.; Kind, T. Physicians on twitter. JAMA 2011, 305, 566–568. [Google Scholar] [PubMed]
  72. Thompson, L.A.; Black, E.; Duff, W.P.; Black, N.P.; Saliba, H.; Dawson, K. Protected health information on social networking sites: Ethical and legal considerations. J. Med. Internet Res. 2011, 13, 1. [Google Scholar] [CrossRef] [PubMed]
  73. Farnan, J.M.; Snyder Sulmasy, L.; Worster, B.K.; Chaudhry, H.J.; Rhyne, J.A.; Arora, V.M.; American College of Physicians Ethics; Professionalism and Human Rights Committee, American College of Physicians Council of Associates; Federation of State Medical Boards Special Committee on Ethics and Professionalism. Online medical professionalism: Patient and public relationships: Policy statement from the American College of Physicians and the Federation of State Medical Boards. Ann. Intern. Med. 2013, 158, 620–627. [Google Scholar] [CrossRef]
  74. Vanneste, D.; Vermeulen, B.; Declercq, A. Healthcare professionals’ acceptance of BelRAI, a web-based system enabling person-centred recording and data sharing across care settings with interRAI instruments: A UTAUT analysis. BMC Med. Inform. Decis. Mak. 2013, 13, 1–4. [Google Scholar] [CrossRef] [Green Version]
  75. Panahi, S.; Watson, J.; Partridge, H. Social media and physicians: Exploring the benefits and challenges. Health Inform. J. 2016, 22, 99–112. [Google Scholar] [CrossRef] [Green Version]
  76. Martinasek, M.P.; Panzera, A.D.; Schneider, T.; Lindenberger, J.H.; Bryant, C.A.; McDermott, R.J.; Couluris, M. Benefits and barriers of pediatric healthcare providers toward using social media in asthma care. Am. J. Health Educ. 2011, 42, 213–221. [Google Scholar] [CrossRef]
  77. Vitak, J.; Ellison, N.B. There’sa network out there you might as well tap’: Exploring the benefits of and barriers to exchanging informational and support-based resources on Facebook. New Media Soc. 2013, 15, 243–259. [Google Scholar] [CrossRef]
  78. Kamal, S.; Ayesha, M.S.; Priyanka, K. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technol. Soc. 2020, 60, 101212. [Google Scholar] [CrossRef]
  79. Surani, Z.; Hirani, R.; Elias, A.; Quisenberry, L.; Varon, J.; Surani, S.; Surani, S. Social media usage among health care providers. BMC Res. Notes 2017, 10, 654. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Murire, O.T.; Cilliers, L. Social media adoption among lecturers at a traditional university in Eastern Cape Province of South Africa. SA J. Inform. Manag. 2017, 19, 6. [Google Scholar] [CrossRef] [Green Version]
  81. Puspitasari, I.; Firdauzy, A. Characterizing consumer behavior in leveraging social media for e-patient and health-related activities. Int. J. Environ. Res. Public Health 2019, 16, 3348. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Peñarroja, V.; Sánchez, J.; Gamero, N.; Orengo, V.; Zornoza, A.M. The influence of organizational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice. Behav. Inf. Technol. 2019, 38, 845–857. [Google Scholar] [CrossRef]
  83. Brown, S.A.; Venkatesh, V.; Kuruzovich, J.; Massey, A.P. Expectation confirmation: An examination of three competing models. Organ. Behav. Hum. Decis. Process. 2008, 105, 52–66. [Google Scholar] [CrossRef] [Green Version]
  84. Wijaya, I.; Rai, A.; Hariguna, T. The impact of customer experience on customer behavior intention use in social media commerce, an extended expectation confirmation model: An empirical study. Manag. Sci. Lett. 2019, 12, 2009–2020. [Google Scholar] [CrossRef]
  85. Bhattacherjee, A. Understanding information systems continuance: An expectation-confirmation model. MIS Q. 2001, 351–370. [Google Scholar] [CrossRef]
  86. Hew, J.J.; Lee, V.H.; Ooi, K.B.; Lin, B. Mobile social commerce: The booster for brand loyalty? Comput. Hum. Behav. 2016, 59, 142–154. [Google Scholar] [CrossRef]
  87. Kim, B. An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation–confirmation model. Expert Syst. Appl. 2010, 37, 7033–7039. [Google Scholar] [CrossRef]
  88. Hariguna, T.; Lai, M.T.; Hung, C.W.; Chen, S.C. Understanding information system quality on public e-government service intention: An empirical study. Int. J. Innov. Sustain. Dev. 2017, 11, 271–290. [Google Scholar] [CrossRef]
  89. Park, E. User acceptance of smart wearable devices: An expectation-confirmation model approach. Telemat. Inform. 2020, 47, 101318. [Google Scholar] [CrossRef]
  90. Chow, W.S.; Shi, S. Investigating students’ satisfaction and continuance intention toward e-learning: An Extension of the expectation–confirmation model. Procedia-Soc. Behav. Sci. 2014, 141, 1145–1149. [Google Scholar] [CrossRef] [Green Version]
  91. Wixom, B.H.; Todd, P.A. A theoretical integration of user satisfaction and technology acceptance. Inform. Syst. Res. 2005, 16, 85–102. [Google Scholar] [CrossRef]
  92. Chuttur, M.Y. Overview of the technology acceptance model: Origins, developments and future directions. Work. Pap. Inform. Syst. 2009, 37, 9–37. [Google Scholar]
  93. Kim, S.H. Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Inf. Manag. 2008, 45, 387–393. [Google Scholar] [CrossRef]
  94. Roger, E. Diffusion of Innovations; The Free Press: New York, NY, USA, 1995. [Google Scholar]
  95. Pearson, A.; Suresh, T.; Chris, G. The role of e-service quality and information quality in creating perceived value: Antecedents to web site loyalty. Inf. Syst. Manag. 2012, 29, 201–215. [Google Scholar] [CrossRef]
  96. Sripalawat, J.; Thongmak, M.; Ngramyarn, A. M-banking in metropolitan Bangkok and a comparison with other countries. J. Comput. Inf. Syst. 2011, 51, 67–76. [Google Scholar]
  97. Yarbrough, A.K.; Smith, T.B. Technology acceptance among physicians: A new take on TAM. Med. Care Res. Rev. 2007, 64, 650–672. [Google Scholar] [CrossRef]
  98. Burton-Jones, A.; Straub, D.W., Jr. Reconceptualizing system usage: An approach and empirical test. Inf. Syst. Res. 2006, 17, 228–246. [Google Scholar] [CrossRef]
  99. Legris, P.; Ingham, J.; Collerette, P. Why do people use information technology? A critical review of the technology acceptance model. Inform. Manag. 2003, 40, 191–204. [Google Scholar] [CrossRef]
  100. Aluri, A.; Tucker, E. Social influence and technology acceptance: The use of personal social media as a career enhancement tool among college students. J. Hosp. Tour. Educ. 2015, 27, 48–59. [Google Scholar] [CrossRef]
  101. Westin, A.F. Privacy and freedom. Wash. Lee Law Rev. 1968, 25, 166. [Google Scholar]
  102. Venkatesh, V.; Thong, J.Y.; 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. [Google Scholar] [CrossRef] [Green Version]
  103. Lee, M.C. Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Comput. Educ. 2010, 54, 506–516. [Google Scholar] [CrossRef]
  104. Wei, T.T.; Marthandan, G.; Chong, A.Y.L.; Ooi, K.B.; Arumugam, S. What drives Malaysian m-commerce adoption? An empirical analysis. Ind. Manag. Data Syst. 2009, 109, 370–388. [Google Scholar]
  105. Chang, I.-C.; Hwang, H.-G.; Hung, W.-F.; Li, Y.-C. Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Syst. Appl. 2007, 33, 296–303. [Google Scholar] [CrossRef]
  106. Bryman, A.; Bell, E. Business Research Methods; Oxford University Press: New York, NY, USA, 2015. [Google Scholar]
  107. Ryan, G.W.; Bernard, H.R. Techniques to identify themes. Field Methods 2003, 15, 85–109. [Google Scholar] [CrossRef] [Green Version]
  108. Tabachnick, B.G.; Fidell, L.S. Using Multivariate Statistics; Pearson: London, UK, 2007. [Google Scholar]
  109. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis, 6th ed.; Pearson: London, UK, 2006. [Google Scholar]
  110. Reyes-Menendez, A.; Saura, J.R.; Palos-Sanchez, P.R.; Alvarez-Garcia, J. Understanding user behavioral intention to adopt a search engine that promotes sustainable water management. Symmetry 2018, 10, 584. [Google Scholar] [CrossRef] [Green Version]
  111. Chin, W.W.; Todd, P.A. On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution. MIS Q. 1995, 19, 237–246. [Google Scholar] [CrossRef]
  112. Byrne, B.M. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming (Multivariate Applications Series); Taylor and Francis: Oxfordshire, UK, 2010; Volume 396, p. 7384. [Google Scholar]
  113. Cummins, R.A.; Gullone, E. Why we should not use 5-point Likert scales: The case for subjective quality of life measurement. Proc. Second. Int. Conf. Qual. Life Cities 2000, 74, 93. [Google Scholar]
  114. Finstad, K. Response interpolation and scale sensitivity: Evidence against 5-point scales. J. Usability Stud. 2010, 5, 104–110. [Google Scholar]
  115. Featherman, M.S.; Pavlou, P.A. Predicting e-services adoption: A perceived risk facets perspective. Int. J. Hum.-Comput. Stud. 2003, 59, 451–474. [Google Scholar] [CrossRef] [Green Version]
  116. Duyck, P.; Pynoo, B.; Devolder, P.; Voet, T.; Adang, L.; Vercruysse, J. User acceptance of a picture archiving and communication system. Methods Inf. Med. 2008, 47, 149–156. [Google Scholar] [PubMed]
  117. Bhattacherjee, A.; Hikmet, N. Physicians’ resistance toward healthcare information technology: A theoretical model and empirical test. Eur. J. Inf. Syst. 2007, 16, 725–737. [Google Scholar] [CrossRef]
  118. De Kerviler, G.; Demoulin, N.T.; Zidda, P. Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers? J. Retail. Consum. Serv. 2016, 31, 334–344. [Google Scholar] [CrossRef]
  119. Chau, P.Y.; Hu, P.J.-H. Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories. Inf. Manag. 2002, 39, 297–311. [Google Scholar] [CrossRef]
  120. Hair, J.F. Research Methods for Business; John Wiley & Sons: Hoboken, NJ, USA, 2007. [Google Scholar]
  121. Fornell, C.; Larcker, D.F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. J. Mark. Res. 1981, 18, 382–388. [Google Scholar] [CrossRef]
  122. Byrne, B.M. Structural equation modeling: Perspectives on the present and the future. Int. J. Test. 2001, 1, 327–334. [Google Scholar]
  123. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. J. Econom. Struct. Equ. Modeling 1999, 6, 1–55. [Google Scholar] [CrossRef]
  124. Machdar, N.M. The effect of information quality on Perceived usefulness and ease of ose. Bus. Entrep. Rev. 2019, 15, 131–146. [Google Scholar]
  125. Saeed, K.A.; Abdinnour-Helm, S. Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Inf. Manag. Decis. 2008, 45, 376–386. [Google Scholar] [CrossRef]
  126. Fuller, M.A.; Serva, M.A.; Baroudi, J. Clarifying the integration of trust and TAM in e-commerce environments: Implications for systems design and management. IEEE Trans. Eng. Manag. 2010, 57, 380–393. [Google Scholar]
  127. Chaouali, W.; Yahia, I.B.; Souiden, N. The interplay of counter-conformity motivation, social influence, and trust in customers’ intention to adopt Internet banking services: The case of an emerging country. J. Retail. Consum. Serv. 2016, 28, 209–218. [Google Scholar] [CrossRef]
  128. Ranaweera, H. Perspective of trust towards e-government initiatives in Sri Lanka. SpringerPlus 2016, 5, 22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  129. Mou, J.; Shin, D.-H.; Cohen, J. Understanding trust and perceived usefulness in the consumer acceptance of an e-service: A longitudinal investigation. Behav. Inform. Technol. 2017, 36, 125–139. [Google Scholar] [CrossRef]
  130. Hanson, C.; West, J.; Neiger, B.; Thackeray, R.; Barnes, M.; Mcintyre, E. Use and acceptance of social media among health educators. Am. J. Health Educ. 2011, 42, 197–204. [Google Scholar] [CrossRef]
  131. Williams, M.; Rana, N.; Dwivedi, Y.; Lal, B. Is UTAUT really used or just cited for the sake of it? A systematic review of citations of UTAUT’s originating article. ECIS 2011 Proceedings 231.
  132. Teo, T. Modeling the determinants of pre-service teachers’ perceived usefulness of e-learning. Campus Wide Inf. Syst. 2011, 28, 124–140. [Google Scholar] [CrossRef]
  133. Chretien, K.C.; Kind, T. Social media and clinical care: Ethical, professional, and social implications. Circulation 2013, 127, 1413–1421. [Google Scholar] [CrossRef]
  134. Merolli, M.; Gray, K.; Martin-Sanchez, F. Health outcomes and related effects of using social media in chronic disease management: A literature review and analysis of affordances. J. Biomed. Inform. 2013, 46, 957–969. [Google Scholar] [CrossRef] [Green Version]
  135. Tao, D.; Wang, T.; Wang, T.; Zhang, T.; Zhang, X.; Qu, X. A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies. Comput. Hum. Behav. 2020, 104, 106147. [Google Scholar] [CrossRef]
Informatics 08 00006 g001 550
Figure 1. Social Media Adoption model.
Figure 1. Social Media Adoption model.
Informatics 08 00006 g001
Table
Table 1. Root constructs.
Table 1. Root constructs.
ConstructDefinitionSource
Content quality Inherent value and usefulness of the content provided by social media platforms. [95]
Perceived credibilityRefers to the degree to which a person perceives that using a specific technology and system would be free of privacy and security related threats.[47]
Peer influence Behavioural intention to use a new technology/system is influenced by the perception of important others in their social circle. [45,99,100]
Perceived risk Refers to the degree to which the user perceives that it would be unsafe to utilise social media technologies. [101]
Supporting conditionsAvailability of required conditions to perform a specific task.[102]
Confirmation of expectationsRefers to the degree to which the user perceives that actual performance is consistent with the expected performance of an information system/technology.[103]
Perceived cost The degree to which a user perceived that it is expensive to utilise a specific technology or system to perform a specific task. [104]
Perceived usefulnessThe extent to which a person believes that using the system will enhance his or her job performance.[28]
Usage behaviourRefers to the degree to which a user utilises one or more system features to complete a specific task. [105]
Table
Table 2. Construct measures.
Table 2. Construct measures.
ConstructSourceMeasureStd. Est.
Perceived Risk (α = 0.72, AVE = 0.72)New measures based on [115,116,117,118]Risky to share data0.8
Information can be misused0.68
Might not meet expectation0.66
Concerned about the privacy settings0.74
Content quality (α = 0.85, AVE = 0.83)Adapted measures from [48]Social media provides relevant information0.76
Social media provides reliable information 0.81
Provide accurate content 0.88
Provide timely and complete health information 0.87
Perceived credibility (α = 0.88, AVE = 0.85)Measures based on [55]I am not worried about security issues0.82
Health related social media sites are credible0.81
I am confident in using online health platforms 0.92
I trust online health platforms0.84
Peer Influence (α = 0.91, AVE = 0.88)New measures based on [102] Colleagues influence me to use social media0.87
Patients influence me to use social media 0.88
Friends influence me to use social media0.9
It is expected from healthcare professionals to use social media 0.9
Supporting conditions (α = 0.80, AVE = 0.78)Adapted measure from [119]I am encouraged participate actively in health communities 0.72
Can reach other healthcare professionals0.71
Workplace encourages me to use social media0.81
Industry practices influence me to use social media0.88
Confirmation of expectations (α = 0.89, AVE = 0.89)New measures based on [83,84,85]Social media has improved relationships with patients0.83
Saved time in professional interactions 0.91
Strengthened relationships with colleagues0.86
Ensured better healthcare service0.94
Perceived cost (α = 0.96, AVE = 0.93)New measures based on [93]Social media offers good value0.92
Social media is cost-effective0.92
Attractive in terms of cost0.91
The cost of promotion is reasonable0.97
Perceived usefulness (α = 0.89, AVE = 0.87)New measures based on [28]Social media enables me to communicate better with patients0.8
Improves quality of patient care 0.71
Increases knowledge sharing 0.67
Improves job performance0.68
Usage behaviour (α = 0.92, AVE = 0.87)New measure developed based on [28]I use social media to share medical knowledge with patients 0.88
I follow several health professionals in various social media0.91
I share available medical information using social media platforms0.85
I use social media for self-promotion0.85
Table
Table 3. Bivariate correlations.
Table 3. Bivariate correlations.
123456789
1. Content quality1
2. Perceived risk0.297 **1
3. Perceived credibility0.452 **0.324 **1
4. Peer influence0.496 **0.223 **0.391 **1
5. Supporting conditions0.243 **0.0920.192 *0.0111
6. Confirmation of expectations0.233 **0.306 **0.391 **0.329 **0.241 **1
7. Perceived cost0.408 **0.302 **0.442 **0.525 **0.172 *0.532 ** 1
8. Perceived usefulness0.250 **−0.216 **0.238 **0.325 **0.334 *0.190 **−0.178 *1
9. Usage behaviour0.364 **0.201 **0.323 **0.561 **0.189 *0.311 **0.524 **0.183 *1
** Significant at the 0.001 level; * Significant at the 0.005 level.
Table
Table 4. Health professionals’ social media usage pattern.
Table 4. Health professionals’ social media usage pattern.
TypePersonal UseProfessional Use
Facebook102/47%41/19%
Twitter55/25%27/12%
Instagram9/4%9/4%
YouTube98/45%54/25%
Blogs17/8%15/7%
LinkedIn47/21%47/21%
Others33/15%23/11%
Table
Table 5. SEM Results.
Table 5. SEM Results.
Path Path Coefficient S.E.C.R.P
H1: Content quality→Perceived usefulness0.3600.1602.7520.012
H2: Perceived credibility→Perceived usefulness0.4230.1173.274***
H3: Perceived risks→Perceived usefulness −0.6000.103−5.849***
H4: Peer influence→Perceived usefulness0.2720.1612.816***
H5: Supporting conditions→Perceived usefulness0.4280.1992.0620.019
H6: Confirmation of expectations→Perceived usefulness0.2820.0873.620***
H7: Perceived cost→Perceived usefulness−0.2010.093−2.1640.020
H8: Perceived usefulness →Usage behaviour0.1420.0632.2410.022
R2 = 47.6%, *** Significant at the 0.001 level.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Khan, M.I.; Saleh, M.A.; Quazi, A. Social Media Adoption by Health Professionals: A TAM-Based Study. Informatics 2021, 8, 6. https://doi.org/10.3390/informatics8010006

AMA Style

Khan MI, Saleh MA, Quazi A. Social Media Adoption by Health Professionals: A TAM-Based Study. Informatics. 2021; 8(1):6. https://doi.org/10.3390/informatics8010006

Chicago/Turabian Style

Khan, Md Irfanuzzaman, M Abu Saleh, and Ali Quazi. 2021. "Social Media Adoption by Health Professionals: A TAM-Based Study" Informatics 8, no. 1: 6. https://doi.org/10.3390/informatics8010006

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop