Keywords

Online pharmacy, UTAUT, Purchase, Intention, e-pharmacy.

Introduction

The internet has completely changed how we live, communicate, and even make purchases. The internet is being used more and more in the healthcare industry as access to it grows. Healthcare-related queries and information are thought to account for 4.5% of all global internet searches (Das and Faxvaag, 2014). Furthermore, modern consumers utilize the internet to obtain a variety of other healthcare-related services and goods as well as to self-diagnose and retrieve lost healthcare information. E-pharmacy, also known as an online pharmacy, is the practice of trading pharmaceuticals over the internet and delivering them straight to customers (Karahoca et al., 2018). The rise of e-pharmacies in the last few years has been driven by factors such as simple means of getting online, the consciousness of digital knowledge, the move away from in-person doctor-patient interactions towards self-diagnosis, and an integrated online marketing supply chain. However, research indicates that buying prescription drugs online carries some risks, including the possibility of receiving fake goods.

Frost and Sullivan predicted that by 2022, the Indian e-pharmacy market will reach a valuation of approximately INR 25,000 crores, or US$3,657 million, indicating a significant potential to enhance medication adherence and accessibility for the populace (Kim and Kim, 2017; Hu et al., 2021; Tanchareon, 2023). This study conceptualizes the factors affecting consumers’ purchase intentions towards buying medicines online on different websites and application software.

Literature Review

Various factors impact the purchase intention of consumers as per many of the researchers. A lot of studies are done by taking the theories like TAM Model, the TAM 2 Model, and the UTAUT Model into consideration. This study utilized the Unified Theory of Acceptance and Use of Technology (UTAUT Model) in order to establish the fundamental conceptual framework.

UTAUT Theory (Unified Theory of Acceptance and Use of Technology)

Kandambi & Wijayanayaka (2013) evaluated the relevance of technology acceptance theories in assessing the inclination to make online purchases and verified the discrepancies between online consumers and employees with regard to processes, theoretical frameworks, and circumstances—precursors of consumer intent. The UTAUT (Unified Theory of Acceptance and Use of Technology) is acknowledged as a notable progression from Ajzen and Fishbein's Theory of Reasoned Action (TRA) in academic investigations. By the year 2023, the UTAUT framework had garnered over 9,000 references. It has been cited about a diverse range of technologies, such as Internet platforms, tax payment systems, mobile devices, and hospital information networks (Williams, et al., 2015). Social influence, Effort expectancy, performance expectancy, and various enabling conditions are recognized as crucial elements of the UTAUT Model. Perceived risk, perceived trust, social influence, and performance expectancy exhibit direct and substantial impacts on the intention of consumers to embrace online pharmaceutical transactions (Meng, et al. 2016).

A Structural Equation Model (SEM) technique, and an extended UTAUT model are utilized to study the determinants influencing the adoption of online pharmacies among young consumers in Bangladesh (Sabbir, et al., 2021). Behavioural intentions are known to be significantly positively impacted by performance expectations, effort expectations, social influence, facility conditions, hedonic motivation, and utilitarian motivation (Prasarry et al., 2023). A research investigation was undertaken through a survey to explore a theoretical framework that merges three significant theories: the DeLone and McLean Information Success Model, the Protection Motivation Theory (PMT), and the Unified Theory of Acceptance and Use of Technology (UTAUT). The main objective of this framework is to pinpoint the factors that impact individuals' behaviors about the adoption of telepharmacy services (Indriyati et al., 2023).

Antecedents of Consumers' Intentions towards Online Pharmacy

Social Persuasion (SP)

The term "social influence" describes how much friends and family influence how a certain system is used. The level to which an individual perceives that the system is backed by organizational and technological infrastructure is referred to as "facilitating conditions" (FC) (Venkatesh et al., 2003). Technology can indeed change society, but it's also true that social dynamics have a big impact on how technology develops and moves forward. Social influence includes a person's interpretation of the opinions of important people in their surroundings as well as their specific suggestions and actions (Eckhardt et al., 2009; Vannoy and Palvia, 2010).

There is a strong observation that broadens the comprehension of the role of social factors in consumer behaviour by investigating the influence of young celebrity endorsements on the purchase intentions of young consumers through social media plaforms. This study adds a level of intricacy to our understanding of consumer behaviour by highlighting the unique role that celebrity endorsements play in influencing consumer choices in addition to the significance of social influence in doing so (Ahmad et al., 2019). Social media significantly influences the decision-making process for purchasing complex products, as individuals often use it to research brands, assess potential risks, and evaluate high acquisition costs. Moreover, social media has a substantial impact on customer satisfaction at various stages of the product purchase cycle (Varamontri, et al., 2019).

The examination of the influence of psychological reactance (threat to liberation and rage), attitudes towards public service announcements (PSAs), and intentions for viral behaviour on the purchasing intention of medications online via social media recommendations has been undertaken in various studies (Eckhardt et al., 2009; Chan et al., 2010; Jenifer, et al., 2022; Alhabash, et al., 2022). Within the realm of Information Systems research, social influence has been considered a crucial factor that impacts interpersonal facets associated with the adoption and use of technology (Chan et al., 2010; Alhabash, et al., 2022). Investigations into the influence of online influencers on consumer purchase intentions have indicated that these influencers wield significant power over the purchase decisions of consumers or potential consumers; nevertheless, in order to effectively target the right consumer segment, marketers must carefully choose the appropriate platform (Jenifer, et al., 2022). Various key individuals such as members of each family, colleagues, extension representatives, or other prominent figures play crucial roles at different stages of adoption of technology (Eckhardt et al., 2009; Mgendi et al., 2022). Rabia et al., (2023) delved into the correlation between consumers' levels of social media addiction (SMA) and its intentions to make online purchases (OPI), with a specific emphasis on the intervening function of attitudes toward online advertisements (ATOA).

Self-motivation (SM)

The factors influencing buyers' purchase intentions when using TikTok shop are analysed using a modified version of the Unified Theory of Acceptance and Use of Technology (UTAUT2) model and the Electronic Word of Mouth (e-WOM) theory. Additionally, it is ascertained if price values and hedonic motivation significantly influence consumers' purchase intentions (Maulida, et al. 2022).

Purchase intentions and perceived benefits are influenced by hedonic and utilitarian values, but effort expectancy can also operate as a moderator between the them. It was found that while effort expectancy does not significantly moderate these relationships, perceived benefits are positively influenced by hedonic and utilitarian values (Anni et al., 2023). Ducoffe's web advertising model is used as a framework to investigate how YouTube advertising affects Indian consumers' purchase intentions. It puts forth a theoretical framework that combines hedonic motivation with elements of entertainment, informativeness, credibility, and annoyance (Nagarajan and Shanmugavel. 2023).

Ease of Use Expectation (EE)

Ease of use expectations, or effort expectancy, pertains to the perception customers have regarding the simplicity of utilizing these platforms for purchasing prescription medications (Venkatesh et al., 2012). According to earlier research, users who are proficient with technology no longer find the ease of use to be a barrier when utilizing new technologies (Wong et al., 2014). Results from previous studies conducted in the context of online pharmacies indicate that EE positively and statistically significantly influences the utilization intention (Sabbir et al., 2020). It is implied that the likelihood of adoption among young people is increased when the use of e-pharmacy services is viewed as straightforward, easy to use, and reasonable.

There is a study that utilizes a quantitative approach and a descriptive research methodology to analyze the impact of performance expectancy (PE) and effort expectancy (EE) on use behaviour in e-audit adoption by consumers (Muhammad, et al., 2021).

Utility expectation (UE)

Utility expectation or performance expectation is a person's conviction that a system can improve their ability to do their job. Perceived usefulness, motivation from outside sources, job fit, comparative advantage, and result expectations are the five essential components of this construct that are taken from different models (Venkatesh et al., 2003).Utility expectation or performance expectancy clarifies how an individual’s confidence in utilizing a technological system can assist him in reaching the intended goal (Tharob et al., 2017).

Utility expectation is essential to the adoption of technology. It establishes a strong and favorable relationship, intricately connected to elements such as online banking, web design, and service quality (Rahi et al., 2019). Regarding the adoption of e-pharmacy, prior studies have discovered that Utility expectation favorably correlates to recommend as well as the adoption of e-pharmacy (Sabbiret al., 2020; Srivastava and Raina, 2020). A study demonstrates the relationship among customer satisfaction and intention to make purchase in e-commerce. It also looks at how purchase intention is favorably and considerably influenced by effort anticipation and trust, but negatively and barely at all by performance expectancy and self-efficacy (Ni et al., 2022)

Technology (TE)

The intention to make online purchases is significantly influenced by how well technology is perceived. A customer’s online purchase is directly influenced by the perceived quality of technology. Customers are more likely to decide to make purchases online if they find a website to be both helpful and simple to use (Buttner and Goritz, 2008). On the other hand, it is observed that the perceived quality of technology, which is mediated by online trust, influences consumers' intentions to make purchases online.

Consumer intention is significantly impacted by technology. Technological developments have simplified information access, buying processes, and brand interactions for customers. Due to this, businesses now sell their goods and services differently, with many using digital marketing techniques to connect and interact with their target market (Li and Huang, 2009). Customers now have greater influence over their purchases because of the growth of social media and mobile devices, which make it simple to compare costs, read reviews, and make purchases whenever and wherever they want. In general, technology has had a significant impact on consumer intent and behavior, which has shaped how businesses operate and how customers make judgments about what to buy (Ling et al., 2011; Goswami, 2016; Lee et al., 2017).

Technology has a big influence on what customers want to buy from online pharmacies. Prescription drug purchases are now more convenient and accessible for customers because of the growth of e-commerce platforms and telemedicine developments (Chen, 2013; Kim and Kim, 2017). Additionally, a greater variety of goods and services are provided by online pharmacies, such as virtual consultations with certified pharmacists and medicine refills and prescription renewals (Haba et al., 2017; Moslehpour et al., 2017). Technology has also made it possible for internet pharmacies to provide affordable prices and customized suggestions based on a client's preferences and medical background. In general, technology has improved the convenience and attraction of online pharmacies for customers looking to fill prescription drugs (Athapaththu and Kulathunga, 2018; Purwanto et al., 2021). Online pharmacies have been a rising supplier of medications since the year 2000 (Bootsumran et al., 2021; Sivakumar et al., 2022). Due to a lack of national regulations and systems for verification, poor customer service, and low levels of trust, and relatively few trustworthy websites, the online pharmacy industry was initially perceived as a dubious conduit of dishonest sellers and a potential source of counterfeit medicines (Nguyen et al., 2021; Ang et al., 2023). In developed countries, buying prescription drugs online from reliable and trustworthy online pharmacies is becoming the norm (Maharani and Gorda, 2021; Hu et al., 2021; Tanchareon, 2023; Han and Han, 2023).

Research Gap

Numerous researchers have investigated the purchasing habits of customers in online pharmacies (Moslehpour et al., 2017; Purwanto et al., 2021). Research, however, is lacking in areas such as the influence of technology (Athapaththu and Kulathunga, 2018; Hu et al., 2021), motivation, and performance expectations on the intention of consumers to make purchases (Chen, 2013; Ling et al., 2011; Kim and Kim, 2017). Advancements in technology such as telemedicine, artificial intelligence, and smartphone apps are driving a fast evolution in the online pharmacy sector (Maharani and Gorda, 2021; Shivakumar et al., 2022; Ang et al., 2023). Scholars and business professionals may find valuable insights by examining how technological improvements impact consumers' intent to purchase as well as their overall experience with online pharmacies (Tanchareon, 2023).

Objective of the Study

1. To identify the factors influencing shopper’s purchase intention towards buying medicines on various online platforms.

2. To study the relationship between the factors and shoppers’ consumer intention towards buying medicines on various online platforms.

Research Methodology

This research design is quantitative in nature. This study includes a sample size of 300 participants. The questionnaire was collected as per the given mentioned variables taking into consideration (1) Self Persuasion (ii)Utility Expectation (iii) Ease of Use Expectation(iv) Social Motivation (v) Technology, and (vi) Consumer purchase intention. The current study uses scales of multiple items that have been tested in previous studies to measure the constructs related to factors influencing the intention to buy medicines on online platforms. Furthermore, each item on all the scales uses a five-point Likert scale. Adopted are scales to assess subjective norms, perceived security, perceived utility, perceived ease of use, and intention to use (Venkatesh et al., 2012; Gao et al., 2015; Srivastava and Raina, 2020). The questionnaire has two parts: part A includes the demographic characteristics and Part B includes the twenty-five questionnaires related to the constructs mentioned Table 1.

Table 1 Reliability Statistics Test
Cronbach's Alpha	No. of Items
0.866	25

Reliability and Validity Test

Initially, the data has undergone reliability and validity tests. As per the above the Cronbach’s Alpha value is 0.866 so the data is reliable. From Table 2, it is cleared that the data is valid and sufficient for analysis as the KMO value is 0.746. Since the P value is less than 0.05 the data is ready for further analysis. First the exploratory factor analysis will be used to identify factors; subsequently, the correlation and regression analysis will be conducted to test the hypotheses.

Table 2 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		0.746
Bartlett's Test of Sphericity	Approx. Chi-Square	11613.4
	df	300
	Sig.	0

Analysis of Data

Random purposive sampling technique is used to collect data for this study. Here, the researcher wants to choose responders who meet a specific qualifying requirement. Anyone who had done business with an online pharmacy during the last sixty to ninety days was eligible to take part in this research study. Respondents between the ages of 20 and 70 who satisfied the qualifying requirements were requested to take an online survey. Using an online survey has several advantages, the most important of which are increased public outreach and real-time survey link accessibility. 300 of the 330 responses that were received at the end of the survey were deemed valid for the study.

This study indicates that Table 3 shows a gender distribution of 70% male respondents and 30% female respondents, reflecting a balanced representation of genders. The sample included 14.7% of respondents aged 20 to 30 years, 33.7% aged 31 to 40 years, 41.3% aged 41 to 50 years, and 10.3% aged over 50 years. A significant portion of participants, specifically 35.3%, hailed from urban regions, while 26% originated from municipal areas. Additionally, 23% were from semi-urban locales, and a mere 15.7% came from rural settings. Regarding educational attainment, 3.3% held solely a higher secondary school certificate, 20.7% obtained a bachelor's degree, 32.3% achieved a master's degree, and 43.7% possessed other qualifications such as a doctorate.

Table 3 Descriptive Statistics
Variables	Subcategories	Frequency	Percentage
Gender	Male	210	70
	Female	90	30
Age	20-30 yrs	44	14.7
	31-40 yrs	101	33.7
	41-50 yrs	124	41.3
	More than 50 yrs	31	10.3
Place	Urban	106	35.3
	Municipal	78	26
	Rural	47	15.7
	Semi urban	69	23
Educational Qualification	12th	10	3.3
	Graduation	62	20.7
	Post Graduation	131	32.3
	Others	97	43.7
Apps/ Website	Apps	184	61.3
	Web site	116	38.7
Income Level	10,000-20,000	47	15.7
	21,000-50,000	219	73
	Above 50,000	34	11.3
Total Spend	Less than Rs. 500	52	17.3
	Rs. 500 to Rs. 1000	72	24.0
	More than Rs. 1000	176	58.7
Discount (Like or Dislike)	Like	266	88.7
	Dislike	34	11.3
Satisfaction Level	1	5	1.7
	2	40	13.3
	3	70	23.3
	4	156	52
	5	29	9.7

Out of 300 respondents, it is observed that (Table 3) 184 prefer apps and 116 prefer websites for purchasing medicines. A significant portion of the sample respondents, specifically 73%, fell within the income range of 21,000-50,000 INR. Meanwhile, 15.7% were categorized in the 10,000-20,000 INR per month income group, and 11.3% reported incomes exceeding 50,000 INR per month. The majority of the respondents 88.7% like the discounts and 11.3% dislike the discounts while purchasing the medicines online. Out of the sample respondents, 1.7% are not satisfied, 9.7 % are neutral, 13.3% are somehow satisfied, 23.3% are highly satisfied, and 52% are satisfied.

Exploratory Factor Analysis (EFA)

From Tables 4-10, it is observed that six constructs are extracted from the exploratory factor analysis having Cronbach’s alpha 0.790, 0.712, 0.711, 0.753, 0.715, and 0.797 respectively. This shows the structural validity of the constructs as all are greater than 0.7 Figures 1-6.

Table 4 Rotated Component Matrixa
	Component
	1	2	3	4	5	6
X3	0.895
X22	0.879
X25	0.868
X15	0.821
X12	0.795
X14	0.776
X4	0.73
X21	0.689	0.547
X11	0.665	0.534
X2		0.871
X1		0.859
X9		0.828
X7		0.719
X13		0.631
X20			0.853
X5			0.752
X6			0.751
X19			0.609
X8				0.864
X10				0.707
X17				0.689
X24					0.733
X18					0.697
X23						0.86
X16						0.534

Table 5 Identified Variable and Validity Test
Sl. No	Variables	Eigen Value	Cumulative Variance (%)	Cronbach’s Alpha (>0.7)
1	Self-Persuasion (SP)	8.612	28.8	0.790
2	Utility Expectation (UE)	4.5	48.2	0.712
3	Ease of Use Expectation (EE)	2.742	59.8	0.711
4	Self-Motivation (SM)	2.27	69.4	0.753
5	Technology (TE)	1.5	76.6	0.715
6	Consumer Purchase Intention (PI)	1.01	79.1	0.797

Table 6 Anovaa
Model		Sum of Squares	df	Mean Square	F	Sig.
	Regression	.002	1	.002	.014	.907b
	Residual	46.879	293	.160
	Total	46.881	294

Table 7 Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B
		B	Std. Error	Beta			Lower Bound	Upper Bound
	(Constant)	4.923	.071		69.187	.000	4.783	5.063
	SP	-.002	.020	-.007	-.116	.907	-.042	.037

Table  8 Anovaa
Model		Sum of Squares	df	Mean Square	F	Sig.
	Regression	1.850	1	1.850	12.037	.001b
	Residual	45.031	293	.154
	Total	46.881	294

Table 9 Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B
		B	Std. Error	Beta			Lower Bound	Upper Bound
	(Constant)	4.504	.121		37.357	.000	4.267	4.742
	UE	.100	.029	.199	3.470	.001	.043	.157

Table 10 Anovaa
Model		Sum of Squares	df	Mean Square	F	Sig.
	Regression	.435	1	.435	2.257	.134b
	Residual	56.509	293	.193
	Total	56.944	294

Hypothesis Development

H_01: The Social persuasion (SP) has no impact on the consumers purchase intention (PI). H_1a: The Social persuasion (SP) has impact on the consumers purchase intention (PI).

H_02: There is no relationship between utility expectation (UE) on consumer purchase intention (PI).

H_2a: There is a relationship between utility expectation (UE) on consumer purchase intention (PI).

H_03: There is no impact of utility expectation (UE) on consumer purchase intention (PI).

H_3a: There is an impact of utility expectation (UE) on consumer purchase intention (PI).

H_04: There is no impact of between self-motivation (SM) on consumer purchase intention (PI).

H_4a: There is an impact of between self-motivation (SM) on consumer purchase intention (PI).

H_05: There is no role of technology (TE) on consumer purchase intention (PI).

H_5a: There is a role of technology (TE) in consumer purchase intention (PI).

Hypothesis Testing

H_01: The Social persuasion (SP) has no impact on the consumers purchase intention (PI).

H_1a: The Social persuasion (SP) has impact on the consumers purchase intention (PI).

PI= 4.923 + 0.071 (SP)

The P value is 0.907 and the relationship is insignificant as the value of F is 0.014. The above regression equation shows that consumer purchase intention (PI) is not influenced by social persuasion (SP). This indicates the rejection of alternative hypothesis (H_1a) and the acceptance of null hypothesis (H_01). This indicates that social persuasion does not influence the purchase intention towards medicines on various online platforms.

H_02: There is no relationship of utility expectation (UE) on consumer purchase intention (PI).

H_2a: There is a relationship of utility expectation (UE) on consumer purchase intention (PI).

PI= 4.504 + 0.121 (UE)

The P value is 0.001 and the relationship is significant as the value of F is 12.037. The above regression equation shows that consumer purchase intention (PI) is influenced by utility expectations or the performance of different online platforms. This suggests that the null hypothesis (H_02) has been rejected, while the alternative hypothesis (H_2a) has been accepted. It means there is a significant impact of utility expectation on the intention to buy medicines from various online platforms.

H_03: There is an impact of utility expectation (UE) on consumer purchase intention (PI).

H_3a: There is an impact of utility expectation (UE) on consumer purchase intention (PI).

PI= 4.096+0.292 (EE)

The P value is 0.134 and the relationship is insignificant as the value of F is 2.257. The above regression equation shows that consumer purchase intention (PI) is not influenced by ease-of-use expectation (EE). This demonstrates that the null hypothesis (H_03) is accepted and the alternative hypothesis (H_3a) is dismissed. It means there is no impact of ease-of-use expectation on the intention to purchase medicines from various online platforms.

PI= 4.120+0.234 (SM)

The P value is 0.077 and the relationship is insignificant as the value of F is 3.151. The above regression equation shows that consumer purchase intention (PI) is not influenced by the ease of self-motivation (SM). This demonstrates that the alternative hypothesis (H_4a) is rejected and the null hypothesis (H_04) is accepted. It means there is no impact of self-motivation on the intention to purchase medicines from various online platforms.

H_05: There is no role of technology (TE) on consumer purchase intention (PI).

H_5a: There is a role of technology (TE) on consumer purchase intention (PI).

PI= 2.926 + 0.372 (TE)

The above regression equation shows that consumer purchase intention is positively influenced by technology expectations. This means an increase in the value of technology expectation results in purchase intention. The P value is 0.000 (p< 0.05) and the relationship is significant as the value of F is 28.741. The positive relationship between utility expectation and Consumer purchase intention shows the validity of the hypothesis (H_5a).

Discussion

The study analyzes that technology has a great impact on the consumer’s purchase intention towards online buying of medicines. So technological awareness is much more necessary to adapt the process by the customers (Tables 11-13). According to the research, when it comes to the online drug buying procedure, buyers believe that the e-vendor should assist them every step of the way to make the process simple and hassle-free. Additionally crucial are clear instructions and quick error correction. It should also demonstrate how crucial it is to provide up-to-date details regarding the drugs that are offered together with any exclusive deals. A few participants conveyed their appreciation for the flexibility and impact these virtual portals enable them to demonstrate. When customers can choose the brand of medication they desire and put it in their shopping basket, they feel more in control than when they purchase in a physical store where the pharmacist impacts the decision process.

Table 11 Coefficients
Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.	95.0% Confidence Interval for B
		B	Std. Error	Beta			Lower Bound	Upper Bound
	(Constant)	4.12	0.234		17.619	0	3.659	4.58
	SM	0.086	0.049	0.103	1.775	0.077	-0.009	0.182

Table 12 Anovaa
Model		Sum of Squares	df	Mean Square	F	Sig.
	Regression	4.188	1	4.188	28.741	.000b
	Residual	42.693	293	.146
	Total	46.881	294

Table 13 Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.	95.0% Confidence Interval for B
		B	Std. Error	Beta			Lower Bound	Upper Bound
	(Constant)	2.926	.372		7.870	.000	2.194	3.657
	TE	.403	.075	.299	5.361	.000	.255	.551

Among the responders, few expressed concerns about using the e-pharmacy because they thought this would take too much time. If at all possible, the online marketers should make sure that customers can easily reach a chemist, doctor, or customer support desk if they become stuck during the purchasing process. If not, there is a significant possibility that the transaction will be canceled. As a result, online vendors and marketers must position e-pharmacies as a "positive" and time-saving way for people to buy medications.

According to the study's findings, some respondents had low e-health literacy skills because they were unable to understand medical information even though they could access the online pharmacy. Additionally, some respondents' low literacy levels prevented them from even being able to locate an online pharmacy. Under such circumstances, online vendors or marketers can enhance their website's visibility by implementing better keyword and search engine optimization strategies.

With more and more people turning to the internet as a substitute means of obtaining medical information and making pharmaceutical purchases, the idea of online pharmacies is growing. A two-level strategy can be used to boost e-pharmacies' advantages and lower their risks. The first stage should concentrate on tightening laws governing the distribution and sale of pharmaceuticals online. Legislators ought to assess all potential legal and regulatory ramifications of using e-pharmacies and recommend practical steps that manufacturers and customers can take to reduce such risks. From the standpoint of the user, the second strategy should concentrate on raising the user base's health literacy rate and educating them about the issues surrounding information privacy when using online pharmacies. Throughout the study, we found that the primary driver of pharmaceutical medicine purchases by consumers is the low prices offered by online pharmacies. Consumers may order and buy from online pharmacies while relaxing in their own homes. These online pharmacies entice clients with their goods in addition to presenting engaging healthcare information on their websites. Online pharmacy consumers prefer to utilize trustworthy online pharmacies for recurring purchases, but before choosing one, they compare costs and other special offers from different e-pharmacies. All things considered, a customer's decision to utilize an e-pharmacy depends on how they feel about the service overall and how convenient and valuable they find it.

Implications

This study, which has empirical support, will offer valuable insights for managers regarding the interplay between different constructs and consumers' intentions regarding the acceptance and usage of e-pharmacy. Healthcare marketers will significantly benefit from these aspects of consumer motivation since they will help them craft marketing and advertising campaigns that appeal to consumers' preferences. The sorts of customers who may visit service providers' websites and the demands of individuals who use online pharmacies and look for healthcare information may both be better understood with the use of these insights. The service providers may then gradually increase their market share in their respective industries by providing these prospective online pharmacy customers with beneficial health services. Service providers need to give more gamified tools, mobile applications, and interactive websites to entice users to utilize e-pharmacy portals more constructively.

Customers benefit from the efficiency and convenience offered by online pharmacies. They are very helpful to customers who are confined to their homes or in places where traditional pharmacies are not easily accessible. But as of right now, these internet pharmacies don't offer their customers any assistance with online medication adherence. It appears that online pharmacies will need to create patient adherence online programmes shortly due to the increasing significance information technology in of healthcare industry and consumer preference purchase of medications through online. These programmes might include real-time virtual chats, reminders, and group support.

Limitation/ Future Scope of the Study

This study, like other studies, has a great deal of scope for additional research. The impact of various mediating and moderating variables on online platforms for medicine purchases should be included in research as the use of e-pharmacy grows in popularity. Moreover, the geographical constraint of the online survey conducted in Odisha restricts the applicability of the study's findings to a wider population. The study suggests combining qualitative and quantitative methods to examine additional factors that may be influencing it.

Conclusion

This research shows that the younger populations are increasingly turning to internet pharmacies to buy their medications. It shows a large group of people's ignorance of the advantages of using an e-pharmacy. Therefore, this is imperative to showcase the benefits and applications of e-pharmacies to clients in addition to providing them with improved guidance. Customers would rather buy online in the event of an acute sickness and greatly valued the "home delivery" of the medications they bought online. The outcomes imply that the e-pharmacy application is user-friendly. The rise of e-pharmacies has been fueled by technological advancements, widespread internet accessibility, and shifting consumer behaviors toward digital healthcare solutions. The transition from traditional face-to-face consultations to self-diagnosis, coupled with the convenience of online prescription fulfillment and home delivery, has significantly influenced consumer adoption of these platforms. The COVID-19 pandemic further expedited this transition, reinforcing the demand for contactless healthcare services. By leveraging the broadened Unified Theory of Acceptance and Use of Technology (UTAUT), this study identified major determinants influencing consumers e-pharmacy purchase intention. The five crucial factors—social persuasion, utility expectation, eases of use expectation, self-motivation, and technology—underscore the multidimensional nature of consumer behavior in the e-pharmacy sector. Social persuasion highlights the impact of peer influence and societal norms, while utility expectation and ease of use expectation emphasize the perceived benefits and user-friendliness of online pharmaceutical services. Self-motivation reflects consumers' intrinsic drive to adopt digital healthcare solutions, and technological advancements facilitate seamless interactions, reinforcing consumer confidence in e-pharmacies.

References

Ahmad, A.H., Idris, I., Mason, C. and Chow, S.K. (2019), “The impact of young celebrity endorsements in social media advertisements and brand image towards the purchase intention of young consumers”, International Journal of Financial Research, Vol. 10 No. 5, pp. 54-65.

Indexed at, Google Scholar, Cross Ref

Ang, J. Y., Supramaniam, P., Chiew, S. C., Linus-Lojikip, S., Ibrahim, H. A., & Low, L. L. (2023). Online Purchase of Health Supplements and Traditional and Complementary Medicine (T&CM) Products: A Qualitative Study. Malaysian Journal of Medicine & Health Sciences, 19.

Anni, Rahimah., Zainul, Arifin. (2023). Exploring the Contingent Role of Effort Expectancy on Online Purchase Intention in E-commerce Application. Jurnal Manajemen Teori dan Terapan.

Athapaththu, J. C., & Kulathunga, K. M. S. D. (2018). Factors affecting online purchase intention: Effects of technology and social commerce.

Bootsumran, L., Siripipatthanakul, S., & Phayaphrom, B. (2021). Factors influencing consumers’ purchase intention at pharmacies in Thailand. Journal of Management in Business, Healthcare and Education, 1(1).

Buttner, O. B., & Göritz, A. S. (2008). Perceived trustworthiness of online shops. Journal of Consumer Behaviour: An International Research Review, 7(1), 35-50.

Indexed at, Google Scholar, Cross Ref

Chan, F.K.Y., Thong, J.Y.L., Venkatesh, V., Brown, S.A., Hu, P.J.H. and Tam, K.Y. (2010), “Modeling citizen satisfaction with mandatory adoption of an E-Government technology”, Journal of the Association for Information Systems, Vol. 11 No. 10, pp. 519-549.

Chen, L. Y. (2013). The quality of mobile shopping system and its impact on purchase intention and performance. International Journal of Managing Information Technology (IJMIT) Vol, 5.

Das, A., & Faxvaag, A. (2014). What influences patient participation in an online forum for weight loss surgery? A qualitative case study. Interactive journal of medical research, 3(1), e2847.

Eckhardt, A., Laumer, S. and Weitzel, T. (2009), “Who influences whom Analyzing workplace referents’ social influence on IT adoption and non-adoption”, Journal of Information Technology, Vol. 24 No. 1, pp. 11-24.

Goswami, S. (2016). Investigating impact of electronic word of mouth on consumer purchase intention. In Capturing, analyzing, and managing word-of-mouth in the digital marketplace (pp. 213-229). IGI Global.

Indexed at, Google Scholar, Cross Ref

Haba, H., Hassan, Z., & Dastane, D. O. (2017). Factors leading to consumer perceived value of smartphones and its impact on purchase intention. Global Business and Management Research: An International Journal, 9(1).

Han, L., & Han, X. (2023). The influence of price value on purchase intention among patients with chronic diseases in medical e-commerce during the COVID-19 pandemic in China. Frontiers in Public Health, 11, 1081196.

Hu, J., Zhang, C., & Yang, H. (2021). Analysing consumers'online purchase intention of over-the-counter drugs and its influencing factors: evidence from china. Transformations in Business & Economics, 20(3).

Indriyati, Hadi, Sulistyaningrum., Prasojo, Pribadi., Seftika, Sari. (2023). Adaptation and validation of the telepharmacy service adoption behavior questionnaire during the COVID-19 pandemic crisis. International Journal of Public Health Science.

Jenifer, J. B., Rozek, C. S., Levine, S. C., & Beilock, S. L. (2022). Effort (less) exam preparation: Math anxiety predicts the avoidance of effortful study strategies. Journal of Experimental Psychology: General, 151(10), 2534.

Indexed at, Google Scholar, Cross Ref

Kandambi, G.P.H., & Wijayanayaka, W.M.J.I. (2023). A Review of the Capability to Use Technology Acceptance Theories in the Context of Ecommerce. Current Aspects in Business, Economics and Finance Vol. 10, 67–89.

Cross Ref

Karahoca, A., Karahoca, D., & Aksöz, M. (2018). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47(4), 742-770.

Kim, Y. H., & Kim, Y. K. (2017). A technology-fashion collaborative product: Its impact on consumer attitudes and purchase intention. Journal of Global Fashion Marketing, 8(4), 283-297.

Lee, W. I., Cheng, S. Y., & Shih, Y. T. (2017). Effects among product attributes, involvement, word-of-mouth, and purchase intention in online shopping. Asia Pacific Management Review, 22(4), 223-229.

Li, Y. H., & Huang, J. W. (2009). Applying theory of perceived risk and technology acceptance model in the online shopping channel. World Academy of Science, Engineering and Technology, 53(1), 919-925.

Ling, K. C., Daud, D. B., Piew, T. H., Keoy, K. H., & Hassan, P. (2011). Perceived risk, perceived technology, online trust for the online purchase intention in Malaysia. International journal of Business and Management, 6(6), 167.

Indexed at, Google Scholar, Cross Ref

Maulida, Y. Sari and S. Rohmah, "Influence of Electronic Word Of Mouth (e-WOM), Hedonic Motivation, and Price Value On Consumer's Purchase Intention Using Social Commerce “TikTok Shop”," 2022 Seventh International Conference on Informatics and Computing (ICIC), Denpasar, Bali, Indonesia, 2022, pp. 1-7,

Maharani, A. A. S. N., & GORDA, A. A. N. E. S. (2021). Investigating Community Decisions in Online Medicines Purchasing. International Journal of Pharmaceutical Research (09752366), 13(1).

Meng, Yin., Qi, Li., Zhilin, Qiao. (2016). A study on consumer acceptance of online pharmacies in China.  

Mgendi, B.G., Mao, S. and Qiao, F. (2022), “Does agricultural training and demonstration matter in technology adoption? The empirical evidence from small rice farmers in Tanzania”, Technology in Society, Vol. 70 June, 102024.

Indexed at, Google Scholar, Cross Ref

Moslehpour, M., Thanh, H. L. T., & Van Kien, P. (2017, December). Technology perception, personality traits and online purchase intention of Taiwanese consumers. In International Conference of the Thailand Econometrics Society (pp. 392-407). Cham: Springer International Publishing.

Muhammad, Jefriendy, Ilhamsyah., Pupung, Purnamasari. (2021). Pengaruh Performance Expectamcy dan Effort Expectancy terhadap Use Behaviour Pada Adopsi E-Audit.  

Nagarajan, Shanmugavel. (2023). Hedonic Motivation and Ducoffe’s Web Advertising Model in Estimating Consumer Purchase Intention. Global Business Review,

Nguyen, L. C., Dinh, Q. B., & Hoang, T. H. M. (2022). Factor Affecting Consumer's Willingness of Purchasing Medication Online. In ICRMAT (pp. 181-187).

Indexed at, Google Scholar, Cross Ref

Prasarry, Y. V., Sayoga, R. Y., Marsintauli, F., & Handayani, D. (2023). Investasi Digital Reksa Dana dengan Model Unified Theory Acceptance and Use of Technology (UTAUT) Pada Generasi Y dan Z. Jurnal Ilmiah Manajemen dan Bisnis, 24(1), 42-63.

Purwanto, A., Haque, M. G., Sunarsi, D., & Asbari, M. (2021). The role of brand image, food safety, awareness, certification on halal food purchase intention: An empirical study on Indonesian consumers. Journal of Industrial Engineering & Management Research, 2(3), 42-52.

Rabia, Ekti, Genç., Gülçin, Bilgin, Turna. (2023). The mediating effect of attitude towards online advertising in the influence of social media addiction on online purchase intention. Business And Management Studies: An International Journal, 11(2):511-531.

Indexed at, Google Scholar, Cross Ref

Rahi, S., Othman Mansour, M.M., Alghizzawi, M. and Alnaser, F.M. (2019), “Integration of UTAUT model in internet banking adoption context: the mediating role of performance expectancy and effort expectancy”, Journal of Research in Interactive Marketing, Vol. 13 No. 3, pp. 411-435.

Sabbir, M.M., Islam, M. and Das, S. (2020), “Understanding the determinants of online pharmacy adoption: a two-staged SEM-neural network analysis approach”, Journal of Science and Technology Policy Management, Vol. 12 No. 4, pp. 666-687.

Tanchareon, N. (2023). Factors influencing Thai consumer's purchase intention towards online drug purchase (Doctoral dissertation).

Tharob, G. F., Mingkid, E., & Paputungan, R. (2017). Analisis Hambatan Bisnis Online Bagi Mahasiswa Unsrat. Acta Diurna, 6(2), 1-19.

Vannoy, S.A. and Palvia, P. (2010), “The Social influence model of technology adoption”, Communications of the ACM, Vol. 53 No. 6, pp. 149-153.

Venkatesh, V., Morris, M., Davis, G. and Davis, F.D. (2003), “User acceptance of information technology: toward a unified view”, MIS Quarterly, Vol. 27 No. 3, pp. 425-478.

Indexed at, Google Scholar, Cross Ref

Venkatesh, V., Thong, J.Y. and Xu, X. (2012), “Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology”, MIS Quarterly, Vol. 36 No. 1, pp. 157-178

Williams, M.D., Rana, N.P. and Dwivedi, Y.K. (2015), “The unified theory of acceptance and use of technology (UTAUT): a literature review”, Journal of Enterprise Information Management, Vol. 28 No. 3, pp. 443-448,

Wong, C.H., Wei-Han Tan, G., Loke, S.P. and Ooi, K.B. (2014), “Mobile TV: a new form of entertainment? Industrial Management & Data Systems, Vol. 114 No. 7, pp. 1050-1067

Indexed at, Google Scholar, Cross Ref

Received: 25-Feb-2025, Manuscript No. AMSJ-25-15716; Editor assigned: 26-Feb-2025, PreQC No. AMSJ-25-15716(PQ); Reviewed: 15-Mar-2025, QC No. AMSJ-25-15716; Revised: 20-Mar-2025, Manuscript No. AMSJ-25-15716(R); Published: 23-Mar-2025