# Understanding the Determinants of Adoption of Healthcare Information Technology

Abir Alsideiri, Zaihisma Che Cob, Sulfeeza Drus

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Abir Alsideiri, Zaihisma Che Cob, Sulfeeza Drus. Understanding the Determinants of Adoption of Healthcare Information Technology. 6th International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2023, Chennai, India. pp.178-189, 10.1007/978-3-031-38296-3_14. hal-05466052

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# Understanding the Determinants of Adoption of Healthcare Information Technology

Abir AlSideiri¹,²✉, Zaihisma Che Cob¹, and Sulfeeza Bte Mohd Drus¹

¹ College of Computing and Informatics, Universiti Tenaga Nasional (UNITEN), Kajang, Selangor, Malaysia

abir@buc.edu.om, {zaihisma,sulfeeza}@uniten.edu.my

² Department of Information Technology, Buraimi University College, Buraimi, Oman

**Abstract.** Online pharmacies are often cited as an exemplar of evolving personal innovation in the physical health field, particularly in the areas of value-added information, consumer convenience and pharmacist outreach. Although online pharmacies have been widely adopted in developed countries, this trend has yet to catch on in developing nations like Oman due to the presence of numerous barriers. Unfortunately, there is a dearth of data regarding the factors that influence the acceptance and actual use of online pharmacies in Oman. By fusing the UTUAT-2 Theory with the TAM theory, this study proposes a conceptual model for understanding the factors that contribute to the growth of the online pharmacy industry in Oman. Three hundred and seventy-eight smartphone owners were asked to fill out a questionnaire. SPSS and the SEM method were used for data analysis. There was a significant correlation between the respondents' Perceived Expectation (PE), Effort Expectation (EE), Technology Trust (TR), and Technology Awareness (TA) and the rate at which Online Pharmacy was adopted in Oman. We also discovered a moderating effect of BI on the decision to use an online pharmacy (OPA). Health literacy, consumer awareness, and the understanding needed to make informed health decisions are all areas that need to be strengthened for the successful adoption of Online Pharmacy at the highest levels of management and policymaking.

**Keywords:** Healthcare IT · Adoption · TAM · UTAUT2 · CFA Model · SPSS

## 1 Introduction

Oman went from a remote wilderness 50 years ago to a place of cutting-edge technology and staunch social conservatism today. Such a combination is impossible to attain unless there is a conflict of interest. An obvious case in point is the utilization of the Internet (Al Sideiri et al. 2021). A recent survey found that 44% of the population uses the Internet as of 2011 (Alalwan et al. 2016), whereas a study using Internet World Stats estimated a growth in Internet use of 75.8% between 2007 and 2008. One possible explanation for the low rate of adoption is the constant process of upgrading the information and communication technology (ICT) infrastructure (Yin et al. 2016). It is not the focus of this
research, however, because there are likely other sociocultural factors at play in people's willingness to use and embrace e-government (Al Sideiri et al. 2021). The core of the problem, as stated by (El-Said & Tall 2020), is that new technologies are founded on the convictions and values of their creators, which may be at odds with those of the host environment. According to the findings of several scholars, creatives frequently fail to consider the context of their work (Fittler et al. 2018). Despite claims that Arabs' luke-warm adoption of the Internet is attributable to cultural differences, research (AlSideiri et al. 2022) found that Arabs have a strong propensity for their cultural beliefs and values to accept technologies that are introduced into the Arab world. The problem isn't with technology per se, but rather with the society that embraces it, which stands in stark contrast to Arab values. Actually, Arab governments and institutions are slow to adopt new technologies; despite sharing a common Arab heritage with other Arab countries, Oman has many distinctive features that set it apart, including a distinct language, culture, and religion (AlSideiri et al. 2022). Arab nations and organizations spend millions of dollars annually on technology transfer, according to (Chen et al. 2021). According to the research (AlSideiri et al. 2022), the level of Internet use among Omani citizens is influenced by their cultural backgrounds and social norms. Such elements can play a role in determining whether or not people will adopt and make use of digital services like online shopping and government. There have traditionally been many obstacles to the widespread adoption of e-commerce and e-banking in Oman (El-Said & Tall 2020). E-commerce acceptance in Oman was studied, and factors like skepticism toward online security and privacy, low levels of computer and internet literacy, and ignorance about available Internet services were discussed (Punakivi 2019). These elements influence the extent to which e-commerce and pharmaceuticals are accepted. It is clear from the aforementioned studies that research demonstrating Internet and Web infrastructure adoption in Oman is extremely rare. Recent years, however, have seen increased adoption of e-banking technologies as more Omanis have adopted the new technology and younger generations have helped older generations learn and use it (Sales et al. 2019).

## 2 Literature Review

While there is strong evidence that e-commerce has altered the healthcare industry as a whole, particularities such as the online pharmacy industry have presented obstacles to this transformation. Some research, for instance, has suggested that, despite outward appearances, the online pharmacy market in some developing nations has run into a number of obstacles (Xu et al. 2021). In addition, Yin et al. (Chen et al. 2021) point out that these difficulties are twofold. The first factor is the government's stringent oversight of pharmacies operating online. Because of these strict policies, the proliferation of online pharmacies in the country has been slowed. In some ways, the government's strict policies can be seen as enabling conditions. Second, they said the low uptake was due to the fact that most people are still wary of purchasing drugs from an online pharmacy. People's ingrained prejudices against online pharmacies are regarded as a habit.

It was reported by Mohamed (Enaizan et al. 2020) that the online pharmacy easily be subsectors. First, there is the tried-and-true online pharmacy, which requires a doctor's
prescription before dispensing any medicine. This online pharmacy model is widely regarded as the gold standard in the industry (Hussain et al. 2018). Second, there are the online pharmacies that offer consultation services. Here, the user will detail his or her medical condition and past experiences online, and a doctor will examine the data before prescribing a course of treatment. This is popular among its users because it offers access to both medical professionals (doctors and pharmacists) from the comfort of their own homes (Chu & Chen 2016). The third category of internet drugstore is the “rogue” pharmacy, also known as a “online drug shop,” where customers can buy drugs without first consulting with a doctor or providing a valid prescription (Khan et al. 2018). Authorities crack down on pharmacies that sell prescription drugs online despite the law. Online pharmacies like these are popular among drug abusers.

Because of the availability of online pharmacies, people can now quickly and easily order the medications they require, based on the available instructions.

The low level of patronage or lack of awareness that has makes online drug purchasing a challenge in modern-day Oman. Many people still resist the online pharmacy paradigm, according to the findings of some studies, so there are still some problems to be solved despite the widespread adoption of online pharmacies in both developed and developing countries (Tarhini et al. 2019). It has been determined through further research that there are significant problems associated with the legislation and executive authorities that govern the laws and ethical standards that govern the online pharmacy industry and commerce. This set of measures is an intentional attempt to rein in the rapidly expanding online pharmacy industry around the world. Most people aren't on board with or convinced by the procedure of buying medications online, according to some studies. A procedure that culminates in the lowest possible rating (Singh et al. 2020).

Despite the proliferation of high-speed internet that has made it easier to conduct business online, communicate with colleagues and clients, shape consumer behavior when shopping online, and increase loyalty among existing clients, online marketing for the pharmaceutical field (online pharmacies) and healthcare management is still in its infancy in developing countries like Oman (AlSideiri et al. 2022; English et al. 2017; Yin et al. 2016). Lack of knowledge and awareness has been identified as the root cause of the problems plaguing the online pharmaceutical e-business.

Consumers and residents of Oman are not showing much hope in the areas of power generation and online drug ordering (e-ordering) (Al Sideiri et al. 2021; AlSideiri et al. 2022; Tiwari 2020). There are those who believe that the current climate is ideal for e-business, and that as a result, consumer readiness to adopt internet technology will increase (Albertsen et al. 2020; Punakivi 2019; Yamin & Zhe 2020). This shift in attitude toward online drugstores has been largely attributable to the development of other medical facilities in Oman in recent years.

# 3 Methodology

Significant time and effort was put into locating articles that presented various acceptance and technology adoption models. It should now be able to identify the most common technology acceptance models and theories applied to the spread of online pharmacies. This section provides an overview of the two most widely-used models and describes how they work.
**3.1 Technology Acceptance Model (TAM)**

A variant of the TAM theory is shown in Fig. 1. This shows how trust, perception, and risk all play a role in uncovering the TAM model. To avoid excessive efficiencies during analysis and justify the new proposed model, redesigning the framework to remove any two variables with high similarity is shown in Fig. 2 (V Venkatesh et al. 2012a, b). We will use the TAM model shown in Fig. 1.

<figure><img src="image_4.png" /><figcaption>Fig. 1. TAM model version (V. Venkatesh et al. 2003)</figcaption></figure>

**3.2 Unified Theory of Acceptance and Use of Technology (UTAUT-2)**

Figure 2 shows the architecture of.UTAUT-2. People’s acceptance and use of IT tools is empirically verified (AlSideiri et al. 2022; Ammenwerth 2019; Onwezen et al. 2021), but so is the rate at which new technologies are adopted and is newly evaluated. Agencies that advocate the use of technological solutions without first determining whether they actually improve outcomes such as worker productivity or task/work performance. Key factors (achievement expectations, effort expectations, social impacts, and supportive conditions) and moderators (age, gender, experience, and willingness) associated with predicting behavioral intentions to use technology and existing technology nature) is identified in a unified theory of acceptance, Use Technology (UTAUT) by Erjavec and Manfreda (2022). According to UTAUT theory, behavioral intentions and supportive context determine technology use, whereas performance expectations, average effort, and social impact determine technology use.

**4 Research Model**

The purpose of this investigation is to examine hypothetical relationships in the context of technology adoption using well-defined conceptual paradigms developed and validated in theories and models of technology adoption and acceptance. As shown in Fig. 3, the proposed model of online pharmacy conditioning model is based on the integration of UTUAT-2 theory and TAM theory. This model was created by justifying the importance of each independent variable in determining whether a customer would benefit from an online pharmacy so that companies can better meet their customers’ needs. Did.
<figure><img src="image_5.png" /><figcaption>Fig. 2. UTAUT-2 Model (V Venkatesh et al. 2012a, b)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Theoretical Framework</figcaption></figure>

For this proposed study, the data collection would be conducted by sending a web link; the web link would use the customers' database at Pharmaceutical Company Chain to start with and send the link to their phones or emails. 384 samples were being determined based on (Morosan & DeFranco 2016) table to generalize the study findings for more than 100,000 populations. The questionnaire proceeds with a few questions that context can exclude unmatched respondents with the inclusion/exclusion criteria. The final collected data will be exported to a.csv file for further data sanitization and analysis. Each respondent will take approximately 10 min to answer the questionnaire, and every
questionnaire will be immediately collected after it is answered. The duration of the survey will take around 2 months to complete.

# 5 Result and Discussion

Using SPSS version 23, the descriptive analysis was carried out to describe the respondents' profiles. Table 1 shows the main results for this purpose.

Over 378 collected questionnaires, 233 useful responses were received from the male 61.6%) and 15 from the female (38.4%). The responders were asked to specify their age. As the result, 0.3% of the respondents stated that they are below age 20, 15.6% are 21 to 30 years old, 36.8% are age of 31 to 40, 38.9% are age of 41 to 50 and 8.5% are above 51. In specifying the experience of the respondents, 13% stated they have less than 5 years of experience, 16.9% have 6-10 years, 28.8% have 11-15 years, 15.6% have 16-20 years, 19.3% have 21-25, 6.1% have 26-30 and 0.3% have more than 31 of experience. In specifying the educational qualification of the respondents, 6.1% of them had high school degree, 15.1% had 2 Years Diploma, 60.8% had bachelor degree, 15.9% had master degree and 2.1% had PhD.

## 5.1 Reliability and Convergent Validity

Table 1 represents the result of major conditions and factors of the whole model

<table><caption>Table 1. Results of Cronbach Alpha, CR and AVE for the Model</caption><thead><tr><th>Factor</th><th>Item</th><th>FL</th><th>(AVE)<sup>a</sup></th><th>(CR)<sup>b</sup></th><th>Cronbach Alpha</th></tr></thead><tbody><tr><td rowspan="5">Perceived Expectancy (PE)</td><td>PE1</td><td>.869</td><td rowspan="5">.743</td><td rowspan="5">.935</td><td rowspan="5">.914</td></tr><tr><td>PE2</td><td>.853</td></tr><tr><td>PE3</td><td>.875</td></tr><tr><td>PE4</td><td>.847</td></tr><tr><td>PE5</td><td>.866</td></tr><tr><td rowspan="8">Effort Expectancy (EE)</td><td>EE1</td><td>.847</td><td rowspan="8">.693</td><td rowspan="8">.919</td><td rowspan="8">.890</td></tr><tr><td>EE2</td><td>.812</td></tr><tr><td>EE3</td><td>.817</td></tr><tr><td>EE4</td><td>.844</td></tr><tr><td>EE5</td><td>.843</td></tr><tr><td>HB2</td><td>.790</td></tr><tr><td>HB3</td><td>.793</td></tr><tr><td>HB4</td><td>.824</td></tr></tbody></table>

(continued)
<table><caption>**Table 1.** (continued)</caption><thead><tr><th>Factor</th><th>Item</th><th>FL</th><th>(AVE)<sup>a</sup></th><th>(CR)<sup>b</sup></th><th>Cronbach Alpha</th></tr></thead><tbody><tr><td rowspan="5">Technology Trust (TR)</td><td>HB5</td><td>.815</td><td rowspan="5">0.749</td><td rowspan="5">0.937</td><td rowspan="5">.916</td></tr><tr><td>TR1</td><td>.790</td></tr><tr><td>TR2</td><td>.878</td></tr><tr><td>TR3</td><td>.883</td></tr><tr><td>TR4</td><td>.891</td></tr><tr><td>TR5</td><td>.881</td><td rowspan="5">.727</td><td rowspan="5">.930</td><td rowspan="5">.906</td></tr><tr><td rowspan="4">Technology Awareness (TA)</td><td>TA1</td><td>.823</td></tr><tr><td>TA2</td><td>.882</td></tr><tr><td>TA3</td><td>.874</td></tr><tr><td>TA4</td><td>.867</td></tr><tr><td>TA5</td><td>.816</td><td rowspan="8">.728</td><td rowspan="8">.930</td><td rowspan="8">.906</td></tr><tr><td rowspan="7">Behavioural Intention (BI)</td><td>BI1</td><td>.881</td></tr><tr><td>BI2</td><td>.871</td></tr><tr><td>BI3</td><td>.893</td></tr><tr><td>BI4</td><td>.830</td></tr><tr><td>BI5</td><td>.788</td></tr><tr><td>WOM2</td><td>.886</td></tr><tr><td>WOM3</td><td>.876</td></tr><tr><td>WOM4</td><td>.894</td><td rowspan="5">.635</td><td rowspan="5">.912</td><td rowspan="5">.885</td></tr><tr><td>WOM5</td><td>.914</td></tr><tr><td rowspan="6">Online Pharmacy Adoption (OPA)</td><td>OPA1</td><td>.750</td></tr><tr><td>OPA2</td><td>.799</td></tr><tr><td>OPA3</td><td>.774</td></tr><tr><td>OPA4</td><td>.817</td></tr><tr><td>OPA5</td><td>.803</td></tr><tr><td>OPA6</td><td>.834</td></tr><tr><td>OPA7</td><td>.274<sup>c</sup></td><td></td><td></td><td></td><td></td></tr></tbody></table>

According to Table 1, the initial standardised factor loading of OPA7 was.274, which is below the cut-off 0.6, as determined by testing factor items. Following the advice of Hair et al. (2006), these features were cut from the model. All remaining items had standardized factor loadings greater than 0.6, between 0.750 and 0.947. From Table 1, the AVE values, are all greater than the 0.6 threshold proposed by Hair et al. (2006).
For all constructs, the composite reliability (CR) values, which show how well the construct indicators represent the latent construct, were above the Peterson & Kim (2013)-recommended value of 0.6. 904 and 0.974. Cronbach's alpha, a measure of reliability, ranged from 0.868 to 0.966, well above the 0.7 threshold recommended by Nunnally and Bernstein (1994).

## 5.2 Discriminant Validity

This section shows the Table 2 represents the discriminant validity as a whole model.

<table><caption>**Table 2.** Discriminant validity</caption><thead><tr><th></th><th>BI</th><th>EE</th><th>OPA</th><th>PE</th><th>TA</th><th>TR</th></tr></thead><tbody><tr><th>BI</th><td>.939</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>EE</th><td>367</td><td>.920</td><td></td><td></td><td></td><td></td></tr><tr><th>OPA</th><td>.452</td><td>.385</td><td>.866</td><td></td><td></td><td></td></tr><tr><th>PE</th><td>.364</td><td>.206</td><td>.260</td><td>.932</td><td></td><td></td></tr><tr><th>TA</th><td>.530</td><td>.216</td><td>.250</td><td>.184</td><td>.907</td><td></td></tr><tr><th>TR</th><td>.519</td><td>.157</td><td>.222</td><td>.170</td><td>.461</td><td>.889</td></tr></tbody></table>

<table><caption>**Table 3.** Examining Results of Hypothesized Causal & Direct Effects of the Constructs</caption><thead><tr><th>Causal</th><th>P Coefficient</th><th>S. Error</th><th>T-value</th><th>P-value</th><th>H Result</th></tr></thead><tbody><tr><th>PE → BI</th><td>.106</td><td>.032</td><td>3.339</td><td>.001</td><td>H1) Supported</td></tr><tr><th>EE → BI</th><td>.090</td><td>.036</td><td>2.496</td><td>.013</td><td>H2) Supported</td></tr><tr><th>TR → BI</th><td>.179</td><td>.035</td><td>5.077</td><td>.000</td><td>H8) Supported</td></tr><tr><th>TA → BI</th><td>.239</td><td>.044</td><td>5.484</td><td>.000</td><td>H9) Supported</td></tr><tr><th>BI → OPA</th><td>.456</td><td>.041</td><td>11.222</td><td>.000</td><td>H10) Supported</td></tr></tbody></table>

Table 3 displays the t-value (3.339) and p-value (0.001) of Perceived Expectancy (PE) as a predictor of Behavioral Intention (BI). What this means is that the odds of obtaining a t-value greater than 3.339 in absolute value are 0.0001. That is to say, at the .01 level of significance, the Perceived Expectancy (PE) regression weight in predicting Behavioral Intention (BI) is not equal to zero. The results backed up H1. There was a positive correlation, as measured by the path coefficient of 0.106. This means that for every one standard deviation increase in perceived expectation 0.106 STD increase in behavioural intention.

Table 3 demonstrates that at the 0.001 level, there is a statistically significant difference between TR and 0 in the prediction of BI. That's why we can accept H8. A positive path coefficient of 0.179 indicates a correlation between two variables. That is, for every
one standard deviation increase in Technology Trust (TR), there is a corresponding increase of 0.179 in Behavioral Intention (BI).

Table 3 displays that at the 0.001 significance level, Awareness (TA) of Behavioral Intention (BI) is significantly not zero. This lends credence to H9. The correlation was positive, with a path coefficient of 0.239. It indicates that for every one-standard-deviation increase in Technology Awareness (TA), 0.239-STD increase in Behavioral Intention (BI).

Table 3 demonstrates that at the 0.001 level, loading of Behavioral Intention (BI) of Online Pharmacy Adoption (OPA) is not equal to zero. Due to this, H10 was confirmed. An optimistic path coefficient of 0.456 was found. This means that for every one standard deviation increase in behavioral intention (BI), 0.456 STD increase in the adoption of online pharmacies.

Table 3 demonstrates that at the 0.05 level of significance, Effort Expectancy (EE) with Behavioral Intention (BI) is not equal to zero. This lends credence to H2. With a value of 0.090 for the path coefficient, a positive correlation can be inferred. This means that for every one standard deviation increase in Effort Expectancy (EE), there is a corresponding 0.9 percentage point increase in Behavioral Intention (BI).

In order to better understand the development, it enhances in social science and information systems that uses the Structural Equation Modelling (SEM) technique to analyze data. An additional implication of this study is that it has raised consciousness of the potential of the online pharmaceutical industry by way of a fully-fledged online pharmacy model. Constructing a conceptual model for the adoption of online pharmacies in Oman allowed for the identification and validation of evidence related to the variables or elements. The results of this study may help pharmaceutical companies better understand initiatives and make informed decisions about their customers' use of online pharmacies by fostering greater technological trust and awareness.

In addition, the practical significance of the research will help in raising awareness which is important when looking at awareness as one of the factors that can influence consumer e-pharmacies and healthcare services in a globalized world perspective. This research has important practical implications because it will help healthcare regulators and professionals (such as those in the pharmaceutical industry) maximize the benefits of consumers' adoption using online pharmacies. By the end, it can raise up consumer awareness, and understanding required to make educated health decisions, as well as to increase familiarity with the dangers of using online pharmacies and the potential benefits they present.

# 6 Conclusion

This study expands our understanding of how people in the Arab world and Oman feel about adopting new technologies. Online pharmacies may offer better prices than Oman due to increased availability, reduced transaction and product fees, greater consumer flexibility, and increased anonymity. There are numerous theoretical and practical ramifications of this study. Customers who are housebound or who live in remote areas may benefit from using an online pharmacy. The main contribution of this study is a conceptual model for the widespread use of online pharmacies in low-income countries;
to increase the final model's predictive power, the authors combined and adapted two frameworks (TAM and UTUAT-2). In the first step, the conceptual model was developed by combining two ideas that are both likely to affect the rate at which people in Oman use electronic pharmacies. To begin, elements of both social and technological aspects were integrated into a single theoretical framework to better predict and understand adoption behavior in Oman. In this model, we examine the relationships between perceived effort (PE), perceived effort (EE), technology trust (TR), and technology awareness (TA) to identify the factors that contribute to the widespread adoption of online pharmacies (TA). To the best of our knowledge, this is also one of the first empirical studies of the prevalence of online pharmacies in Oman.

This research is limited in that it only looks at consumer perceptions of what influences the using of electronic pharmacies in Oman. Youthful smartphone users were the only demographic that participated in the study. In other words, the study is in line with the known facts of the Young Oman case. To gain a more complete picture of online pharmacy adoption, future studies could expand their target population to include people of all ages.

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