Keywords

Predictive Model, On-Line Shopping, Generation Y, Consumer Behavior, Emerging Countries, Developed Countries, E-Pharmacy.

Introduction

Among the various verticals of e-commerce, one that ruins extensively untouched with excellent likely in the forthcoming online pharmacy market or E-Pharmacy. E-pharmacies are recently competitors in the Global e-commerce sector, with it receiving greater emphasis from government and investors in the last three to five years. India has over 850,000 independent pharmacy retail stores that can meet around 60% of the overall domestic therapeutic demand. (Statista, World Bank, EY, Frost & Sullivian, Deutsche Bank Market Reserch and Televisor’s Research). The old Brick and Mortal Retail pharmacies are responsible for the annual sale of 99% of the pharmaceutical product's sales in which online pharmacy contributes only 1% of the total sales.

The Indian, US, and UK online pharmacy market development have become modest by an increase in the consumers from India, US and UK suffering from long-lasting clinical indications like diabetes, hypertension, Asthma, and Obesity, consequential in an increased demand for medicines. Government of India initiated various step to promote telemedicine, non-specific drugs, and E - healthcare. These steps are likely to provide quality healthcare services in rural India. More than 60% of the Indian population resides in rural areas.

In the USA, the Food and Drug Administration runs BeSafeRx campaign that creates awareness about the risks associated with ordering the drugs online. So, in this context, the National Association of Boards of Pharmacy (NABP) runs the Proved Internet Pharmacy Practice Sites (VIPSS) program which delivers endorsement to pharmacies that sell prescription drug via drug. In fact, “NABP has reviewed over 11,500 online drug outlets [in the US] and found that 96% appear to be operating in conflict with pharmacy laws and practice standards” which increases the consumers buying behavior towards e-pharmacy. Similarly, in the UK, the British National Health Service (NHS) which keep tracking on the e-pharmacy.

Social Media as a Tool for an Advertisement for E-pharmacy

Social media promotion

Internet marketing is Social media marketing (SMM), which employs public networking websites as a tool for marketing. The purpose is to develop content that our target audience will segment with their social network to support. SMM is entertaining in your peer circle and home town. You form a part of assemblies, clubs, and establishments created on your welfares. The number of groups sharing data online is ever increasing. One of the critical anxieties with social media promotion from a professional perception is that it can be highly timewasting. Social media marketing is not a one-shot affair; they need to be encouraged over time.

Social Media Advertising Social media promotion emphases on promoting products or services on social networking sites. With the user's demographic information, the advertisers on social media marketing platforms can focus on their ads. Current targeting group is analyzed and using that analyses it tries to association these selections (like geotargeting, social targeting, socio-psychographic targeting, etc.), to make comprehensive target group empathy possible.

Why social media for E-pharmacy

Generate indications

The objective of all paid promotion must be to get indications. Every social media broadcast shaped ought to be connected to a landing page and therefore, the landing page to embrace a decision to action. The link to the landing page that has a choice to work can maximize the effectiveness of the campaign.

Upsurge Your Perceptibility

Indorsing posts on Facebook may be the right way of skyrocketing the perceptibility of your content. Your job can seem earlier to the peak, and also, it helps people's News Feeds to endorse like never before. Increase consciousness in client Loyalty – after you produce a Facebook page for your commercial then put on the market through Facebook advertising to gather additional "likes" thus serving full awareness.

Influence associate Involved Viewers

Social media users tend to be tremendously involved. As a result of this, persons visit social networking sites many epochs per day and pay an excessive deal of your time on them. Ads on social networks square amount additional opportunity to be seen snapped and communal.

Target Precise Targets

Consumers on social media sites incline to give away an excessive deal of private info in their profiles. Businesses will use that info to focus on user’s interest in specific products or criteria, together with their topographical position, individual benefits, gender, and age. On Facebook, promoters will even have their ads placed on pages that reference explicit keywords.

Growth responsiveness and customers faithfulness

When you generate a Facebook page for your professional and post into the market through Facebook advertising, it accumulates additional “likes” and also increases whole consciousness. Facebook fan pages simplify to outspread client loyalty, and the outcome is that the company can employ it to move directly along with your customers, that retains the business at the forefront of their minds and sources you to appear more approachable.

Test Ambition Elevations

Marketing on social networks is cost-efficient since you target those who have an interest in your merchandise and services. You will solely be charged when somebody clicks on your ad, and you're individually paying for clicks from those who are purchasing the product. It's additionally unbelievably low-cost to run tests and confirm that ads work best and that ads want tweaking; thus, you don't discard your publicity greenbacks on campaigns that aren’t playing awake.

Increase Flexibility

The span of the ad images that Facebook and substitute social networks document square portion stretched than those accessible by Google Ad Words. You've got the choice to use pictures in your ads that helps to attract in additional consideration to them. You’re furthermore able to stop and begin campaigns at your own will. This method is next to not possible to try and do with typical advertising campaigns in Figure 1.

Figure 2, describes the types of social media, definition of social media sites, examples, and some of the available health-related social media sites.

Millennial Consumer Behaviour and Presence in Social media

Indian, the USA, and the UK Millennial-aged 18-35 contributing nearly thirty-four percent, forty-percent and thirty-five percent of the inhabitants have obsessed e-retail industry's development through their growing web usage. Millennia's' increasing usage of the web for searching has driven the growth of online retail. E-retail is anticipated to surge from three percent of total Indian, the US and UK retail market in 2017 to seven percent by 2021," same the report "Trend-setting Millennial: Redefining the buyer story."

Born between the Eighties and 2000, the Indian Millennial, conjointly called 'GenY,' are prime wage earners, accounting for nearly seventy-one percent of the whole home financial gain during a majority of Indian homes, as found by the study conducted in conjunction with the Retailers Association of Asian country (RAI). With a median age of its population as twenty-eight years, the Asian country is one among the youngest significant economies globally. The Millennial is driving the expansion in shopper markets.

Multiple reports highlight India's Gen-Y look employing a mixture of on-line and offline modes, where they compare costs associated with purchasing and see reviews on-line although they look in an offline store to buy the same. "Social media platforms and on-line reviews form a crucial part of the Millennial searching journey because it influences their decision-making journey." Millennial are found to spend almost seventeen hours of per week on online activities like net browsing, film streaming, social media, and online banking.

The convenience of shopping for anyplace and anytime, discounts, and access to product not on the market offline were mentioned as a number of the most important reasons for India's Gen-Y taking to on-line searching.

Understanding the Millennial consumers' perception towards on-line buying behavior of medicine, i.e., e-pharmacy by the inspiration of social media sites. The persistence of this research was to conclude the relationship and impact amongst the variables which includes i.e. Trust, perceived usefulness, Purchase intention, and external factors (independent construct) on online buying behaviour of e-pharmacy through social media influence (dependent variable) across the developed and emerging countries i.e. United States, United Kingdom and India. Further, we also developed the predictive model to forecast whether the consumers shop or do not shop from e-pharmacy in Figure 3.

Social Media Statistics

The data in Figure 3 represents the users checking social media sites in a day to day life.

Literature Review

The emerging use of mobile phones helps in increasing the penetration of healthcare environments via various mobile healthcare information systems. Perceived usefulness has a substantial impact on behavioral intention to use such systems (Mohammed et al. 2013). Health information technology has a vast potential in terms of efficiency, quality, safety, reduced cost, but there are specific barriers in developing countries in terms of awareness, availability, infrastructure, etc. These factors impact the user acceptance of this technology in developing countries (Rahman et al. 2014).

Social media, in no small extent, impacts the decision-making process for the purchase of complex products where individual seek information about the brand, associated risks, and high cost of acquisition. Social media takes a significant part in influencing customer satisfaction at various stages of product purchase cycle (Voramontri et al., 2018). People share their experiences on social media by sharing their experiences, information related purchase, advice, reviews, issues, and problems which helps the other consumers in making purchase decisions. Posting such information has a positive impact on creating awareness among peer groups (Patarawadee, 2013).

Nowadays, many companies widely use social media to increase awareness of the brand by creating pages on social media platforms and engaging with consumers. Such Extensive use of social media has influenced the behavioural changes among consumers to a large extent (Elisabeta et al., 2014). Social media plays a significant role in the lives of Millennial as they spend most of their time on the internet. Hence it is essential to analyze the buying behavior of Millennial before choosing the type of advertising (Sharon et al., 2013).

Millennial is a highly attractive part of the consumer segment, where they are well versed the use of technologies. They tend to spend their income quickly and profoundly dependent on the reviews of peers and their friends. Hence it is essential to segment the market based on the behavior of Millennial (Moreno et al., 2017). Generation of Millennial is widely categorized based on the characteristics 3C such as creativity, confidence, and connected. It was also observed that the unique aspect of one generation in family differs significantly from other age in the same family. This behavior differs in terms of awareness, buying patterns, decision-making process, and post-purchase evaluation. Millennial family is more inclined towards digital platforms, and they participate actively in connecting with open people while making a purchase decision and provide valuable feedback post-purchase (Ayuningtyas et al., 2018).

The explosive development of the internet, along with the emergence of disruptive technologies, has led to the generation of new commercial opportunities. In developing countries, the Internet is mostly being used for the collection of information and purchase of healthcare products. Consumers do give importance to the advantage of doing the online transaction and analyze the risk associated with the acquisition of healthcare products online (Gurau, 2005). With the evolution of E-commerce, there is an increase in the number of transaction and the sharing of information among various individuals and enterprises. E-commerce has provided a more efficient mode of operation to pharmaceutical companies and also helped them to achieve a competitive advantage in the market (Kanungo, 2007).

Advancement in social media has led to an increase in the opportunity to consumers while engaging in social interaction over various platforms. Consumers use social media to share the content with other users, find reviews and ratings of people about their purchase, post their experience related to purchasing. There are various factors in technology acceptance model such as Trust, Purchase intention, Perceived Usefulness, and Social Media activity which impact the buying behavior of consumers. Social media has increased the interaction, which led to an increase in the level of trust and intention to buy (Hajli, 2014).

Various business-to-consumer activities have created the need for active participation of people in E-commerce. Use of technology acceptance model helps in predicting the online shopping activity, which includes consumer's intention to purchase and actual purchases. The task-technology fit model helps in analyzing not only the individual's belief about perceived ease of use and perceived usefulness but also helps in identifying the activities which meet their needs and abilities of the individual (Klopping, 2004).

Some researchers, like Rodgers & Harris (2003), Brown et al. (2003), Van Slyke et al. (2002); Teo (2001), have recognized that there is the noteworthy influence of demographics for making online shopping preferences. Others, however, like Ulbrich et al. (2011), Alreck & Settle (2002), Stafford et al. (2004); Hernandez, et al. (2011) have failed to find a significant gender differential. Others like Lynch & Beck (2001); Dellner (2007) have emphasized that usage of social media sites; glancing and buying designs differ between countries since of differing cultural beliefs, attitudes, and perceptions.

Hypothesis Development and Conceptual Model

The Technology Acceptance Model (Davis, 1989) is the Model used many researchers as per innovative technologies are developing (Horton et al., 2001; Venkatesh et al., 2007). TAM is unique the most operative and broadly adopted information systems theoretical frameworks (Holden & Karsh, 2010; Lee, Kozar, & Larsen, 2003; Li, 2010).

The research mode l is shown in Figure 4.

Methodology

The quantitative data regarding consumer’s perception of e-pharmacy across the country, i.e., US, UK, and India, were used as a methodology. The research questions highlighted in this study is as mentioned in Table 1.

Table 1 Description of the Constructs
Construct	Definition	Reference
Trust	Is a composite public sensation that redirects technological, behavioral, social, psychological, as well as organizational features of interactions among various human and non-human agents?	Lee (1998)
Social Influence	The degree to which an individual considers as inspiration significant the detail those others trusts he or she must use the innovative system.	Venkatesh et al. (2003)
Perceived Usefulness	The grade to which individual trusts that were using IT (i.e., MHCIS) system would recover his/her and job presentation.	Davis et al. (1989) Davis & Cosenza (1993) and Davis (1989)
Purchase intention	The grade to which a healthcare employer’s inspirations aim to use the MHCIS, and this is our goals, aspirations, and predictable reactions to the approach purpose.	Ajzen & Fishbein (1975 and 1979) and Davis & Cosenza (1993)
Consumer’s Demographic	Gender, age, education, and monthly income

Q1: Is there any relationship occur among trust and online purchase behaviour of medicine by using social media influence?

Q2: Is there any amount does an association occur between perceived usefulness and online purchase behaviour of medicine by using social media influence?

Q3: Is there any amount does a relationship occur between purchase intention and online purchase behaviour of medicine by using social media influence?

Q4 : To predict the consumer buying behaviour towards e-pharmacy?

Instrument

The study adjusted procedures of existing instruments and items used for each construct are discussed in Table 2.

Table 2 Items Used in the Study Instrument
Construct	Articles Adapted by Lee (1998); Venkatesh et al. (2003); Davis et al. (1989); Davis & Cosenza (1993); Ajzen & Fishbein (1975 and 1979); Davis & Cosenza (1993)
Trust	1. I am likely to provide information to social media sites to better serve my needs related to purchase of medicines online. 2. I am likely to use Credit/Debit card to purchase medicines online through social media sites 3. On reading positive comments on social media, I am motivated to buy drugs online 4. I changed my purchase preference of online medicine platform based on relevant information obtained from social media sites
Social Influence	1. Information on social media are reliable to purchase medicines online 2. I do not doubt the honesty of my favorite social media site concerning the purchase of drugs online 3. Based on my experience with social media sites, I know they care about buyer's need 4. I believe privacy issues are taken care of by my favorite social media sites 5. I think my favorite social media sites maintain the integrity of user data.
Perceived Usefulness	1. I believe searching and buying medicines online through social media sites are useful. 2. I believe searching and buying online medicines through social media sites makes my life easier 3. My preferred social media site enables me to search and purchase online drugs faster 4. My favorite social media sites keep me updated about online medicine platforms 5. My favorite social media sites address my concerns/doubts and provide quick response to my queries
Purchase intention	1. I use online forums & communities to acquire information about medicines available online for purchase 2. I am receptive to promotional messages available on my favorite social media sites 3. I usually use people's ratings on social media about drugs available online 4. I often use people's reviews & recommendation on social media to purchase medicines online 5. I am proud to be part of the Social Media community 6. Social media sites broaden my understanding of the overall medicine platform

Hypothesis Development

Many empirical research have shown that trust is impacted by social media sites (Pavlou 2003; Wu & Chen 2005) and online purchase behavior (Ha & Stoel, 2009; Pavlou, 2003). Therefore, we propose the following.

H_1a: There is a positive effect of trust on buying behavior to shop online.

H_1b: There is a positive effect of trust on social media influence.

H_1c: Social platform impact mediates the association between trust and online purchase behavior of medicine (e-pharmacy).

Previous studies identified a strong association between Perceived Usefulness with social platform and online purchase behaviour (Davis, 1989). Base on this, we prepare the following:

H_1d: There is a positive effect of Perceived usefulness on buying behavior to shop online.

H_1e: There is a positive effect of Perceived usefulness on social media influence.

H_1f: Social platform impact mediates the association between perceived usefulness and online purchase behavior of e-pharmacy.

Previous studies identified a strong association between purchase behaviour with social media and online buying behaviour (Klopping, 2004):

H_1g: There is a positive effect of Purchase intention on buying behavior to shop online.

H_1h: There is a positive effect of Purchase intention on social media influence.

H_1i: Social platform impact mediates the association between purchase buying behavior and online buying behavior of e-pharmacy.

Social media is a strong platform that helps people to develop a network with others and learn various health related issues to make strong purchase decisions (Toney et al. 2015). Hence, we proposed the following hypothesis:

H_1j: There is a positive effect of social media influence on buying behavior to shop online.

Data Collection

The survey questionnaire was established after reviewing the literature and shared with target population i.e. Millennial. The research reinforced various clarifications developed by researchers and delivered contextual proof into the Millennial generation's behaviour’s.

The review consist of structured questionnaire covered descriptive inquiries, five point Likert scale questions, and qualitative questions. Detailed surveys consist the demographics information about the consumers. Respondents were assured of confidentiality.

The data was collected in two ways ie. an online survey with WhatsApp messenger link and personal communications. It was shared with the respondents that they could be withdraw at any time by ending the browser, and they would not be traced in future. The data was recorded on Google form. To validate the model, a series of questionnaire concerning demographic profile, the amount of time spent on-line, retail sites viewed, product purchased, awareness of e-pharmacy stores, and their experience on it, were discussed.

The previous studies confirms that the youngsters are very much comfortable with the technology and they spend maximum time on social networking sites in day to day life. This study had been carried out on university students (India, UK and USA) because samples drawn from students expedite comparability across cultures (Craig and Douglas 2005). Five-point Likert-type scales (1 = "strongly disagree," and 5 = "strongly agree") were created to record contributors' responses. We used the connivance sampling process for the collection of the data.

For this study, the researchers utilized a measurable analysis (survey). To developing the respondents’ demographic and general characterstics, Statistical descriptive was used by using SPSS 20. Also, the model was tested by using AMOS.

A questionnaire was distributed and sent online to students registered in higher Management education at large universities in India, USA, and the UK. The data collected in between September 2020 to October 2020. We randomly designated four courses in which to manage the survey. The number of students enrolled in each course varied from 60 to 65 in India, in the US it’s between 45 to 55, and in the UK the figure is 35 to 45. We obtained a total of 130 from India, 112 from US and 129 from the UK through the surveys administered in India, USA, and the UK, respectively. Out of these, 15 responses, 12 responses, and 17 responses from India, US and UK were missing on one or two constructs, so removed from our final data. The last data from India, US, and the UK are 115, 100, and 112; Table 3 presents the respondent's profiles on different parameters.

Table 3 Respondent’s Demographics
	India		USA		UK
Variables	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage
Gender
Male	61	53%	51	51%	50	45%
Female	54	47%	49	49%	62	55%
Age
15-19 Years	40	35%	33	33%	37	33%
20-23 Years	38	33%	30	30%	35	31%
23+	37	32%	37	37%	40	36%
Education
SSC/HSC	34	30%	35	35%	39	35%
Undergraduate	36	31%	29	29%	37	33%
Post Graduates	45	39%	36	36%	36	32%

Measurement Model Assessment

To tested the model, we conducted quite a lot of tests of convergent and discriminant validity (Chin, 1998; Fornell & Larcker,1981). The result presented in Table 4 includes Factor loading, Average variance extracted and critical reliability.

Table 4 Items, Constructs, and Measurement Models
Construct	Factor Loading			AVE			CR
Trust	USA	UK	India	USA	UK	India	USA	UK	India
I am likely to provide information to social media sites to better serve my needs related to purchase of medicines online.	0.77	0.60	0.69
I am likely to use Credit/Debit card to purchase medicines online through social media sites.	0.68	0.71	0.67	0.79	0.8	0.8	0.77	0.81	0.80
On reading positive comments on social media, I am motivated to purchase medicines online.	0.76	0.74	0.75
I changed my purchase preference of online medicine platform based on relevant information obtained from social media sites	0.89	0.80	0.75
Perceived Usefulness
I believe searching and buying medicines Online through social media sites are useful.	0.80	0.78	0.77
I believe searching and buying online medicines through social media sites makes my life easier	0.89	0.81	0.80	0.92	0.8	0.7	0.83	0.80	0.89
My preferred social media site enables me to Search and buy online medicines faster	0.87	0.73	0.78
My preferred social media sites keep I updated about online medicine platforms	0.79	0.76	0.81
My preferred social media sites address my concerns/doubts and provide quick response to my queries	0.89	0.85	0.85
Purchase Intention
I use online forums & communities to acquire  information about medicines available online for purchase	0.76	0.72	0.74
I am receptive to promotional messages Available on my favourite social media sites	0.96	0.86	0.73
I usually use people's ratings on social Media about medicines available online	0.97	0.87	0.88	0.83	0.7	0.7	0.85	0.87	0.85
I usually use people's reviews & recommendation on social media to purchase medicines online	0.91	0.79	0.87
I am proud to be the part of Social Media community	0.89	0.84	0.88
Social media sites broaden my Understanding of the overall medicine platform	0.87	0.76	0.86
Social Media Influence
Information on social media are reliable to purchase medicines online	0.79	0.73	0.79
I do not doubt the honesty of my favorite social media site with respect to purchase medicines online	0.69	085	0.67
Based on my experience with social media sites, I know they care about buyer's need	0.81	0.80	0.80	0.72	0.8	0.8	0.71	0.90	0.88
I believe privacy issues are taken care of by my favorite social media sites	0.74	0.76	0.76
I believe my favorite social media sites maintains the integrity of user data.	0.69	0.70	0.71

The above table represented the factor loadings of the entire construct. And, we used average variance extracted (AVE) and composite reliability (CR), to construct reliability. All AVE scores (ranging from 0.72 to .92, 0.76 to 0.82 and 0.75 to 0.87 for the USA, UK, and India, respectively) exceeded the recommended value of .50 (Fornell and Larcker 1981). Similarly, the CR (ranging from 0.71 to 0.85, 0.80 to 0.90 and 0.80 to 0.89 for the USA, UK, and India respectively) exceeded the recommended value used as a cut off of .70 (Straub et al. 2004).

The results of the model fit index presented that the chi-square and other fit indices for the USA sample is 3.41 and comparative fit index [CFI] = 0.89, goodness-of-fit index [GFI] =0.968, root mean square error of approximation [RMSEA] = 0.01, for UK sample is 2.11 and comparative fit index [CFI] = 0.98, goodness-of-fit index [GFI] = 0.900, root mean square error of approximation [RMSEA] = 0.06 and for the India sample is 1.59, CFI = .93, GFI = 0.899 and RMSEA = 0.013 are sufficient according to the guidelines, provided by Hair et. al. (1998). This indicates that the model fit to the data for USA, UK and India samples.

Results and Discussion

Path Analysis and Hypothesis Testing

Direct Effect

Direct Effects of USA sample, the results indicate that social media influence is not related to perceived intention (b = -0.044, p-value =0.742). Thus, H1h is not supported, whereas social media influence has strong direct (positive) impact on trust (b= 0.769, p-value=0.00) , perceived usefulness ( b= 0.329, p-value = 0.00) and demographics (b = 0.011, p-value =0.011) thus H1d, H1l and H1e are supported (Table 5). However, the online buying behaviour has strong direct (positive) impact on trust (b= 0.058, p-value = 0.00), perceived intention (b= 0.067, p-value = 0.00), perceived usefulness (b= 0.070, p-value = 0.00) and demographics (0.129, p-value = 0.00) respectively.

Table 5 Path Analysis Results for a Direct Relationship
Dependent variables	Independent Variables	Hypothesis	USA		UK		India
			Standardized coefficients	p-value	Standardized coefficients	p-value	Standardized coefficients	p-value
Social media Influence R2 (US, UK & India) = 0.63, 0.56 & 0.74	Trust	H1d	0.769	0.00**	0.833	0.00**	0.635	0.00**
	Perceived Usefulness	H1e	0.329	0.00**	0.112	0.00**	0.418	0.00**
	Perceived Intention	H1h	-0.044	0.742	0.999	0.112	-0.015	0.748
	Demographics	H1l	0.011	0.011**	0.061	0.00**	-0.972	0.00**
Online Buying Behaviour R2 (US, UK & India) = 0.73, 0.66 & 0.62	Trust	H1a	0.058	0.00**	0.061	0.00**	0.060	0.00**
	Perceived Usefulness	H1b	0.067	0.00**	0.044	0.00**	0.059	0.00**
	Perceived Intention	H1g	0.070	0.00**	0.055	0.00**	0.073	0.00**
	Demographics	H1g	0.129	0.00**	0.160	0.00**	0.068	0.00**
**statistically significant at 0.05 and 0.01 and 0.10

The study reveals, that direct Effects of UK sample, the results indicate that social media influence is not related to perceived intention (b = 0.999, p-value =0.112). Thus, H1h is not supported.

Whereas, social media influence has strong direct (positive) impact on trust (b= 0.833, p-value = 0.00), perceived usefulness (b=0.112, p-value = 0.00) and demographics (b=0.061, p-value =0.011) thus H1d,H1l and H1e are supported Table 5. It may also be observed that the online buying behaviour has strong direct (positive) impact on trust (b= 0.061, p-value = 0.00), perceived intention (b= 0.055, p-value = 0.00), perceived usefulness (b= 0.044, p-value = 0.00) and demographics (0.160, p-value = 0.00) respectively.

As observed on the table above, the Direct Effects of India sample, the results indicate that social media influence is not related to perceived intention (b = -0.015, p-value = 0.748). Thus, H1h is not supported. It can also be seen that social media influence has strong direct (positive) impact on trust (b=0.635, p-value=0.00).

It can be inferred from the table above, the perceived usefulness (b=0.418, p-value=0.00) and demographics (b = -0.972, p-value=0.011) thus H1d,H1l and H1e are supported.

However, it may be observed that the online buying behaviour has strong direct (positive) impact on trust (b= 0.060, p-value=0.00).

The perceived intention (b= 0.059, p-value = 0.00), perceived usefulness (b= 0.0734, p-value = 0.00) and demographics (0.068, p-value = 0.00) respectively. The most influencing buying behaiviour in all the fields, here in the table we can observe that the trust on social media influencing is significant and positive so we can conclude trust is the most influencing parameters on e-pharmacy. Social media also has a great impact as these e-pharmacy stores promot their companies by either posting their advertisements on social-media platforms like-Facebook,Twitter, Linkedin etc. The hypothesis (H1j) established the positive relationship between purchase intention on social media influence, and, in turn, buying behaviour to online. This reflects that opinions of individual whom buyers contemplate influential or imperative in their peer groups or society does plays a important role of an e-pharmacy services. Past study done by Hajli 2014, also represents that social media influence an users opinion related to adoption of a new technology.

Indirect Effect

The study reveals, that when we test the indirect effects for the USA sample, The R2 of online buying behavior increases from 0.73 to 0.82. This indicates that social media contributes significantly toward online buying behavior of medicine (e-pharmacy).

Now to test H0c, we observed that the trust is having a positive impact on online buying behavior by direct effect, but there is an insignificant relationship between trust and online buying behavior by the mediating result of social media influence.

However, for H1f we observed that perceived usefulness is having the positive impact on online buying behavior from the direct effect; there is also a significant relationship between perceived value and online buying behavior by the mediating result of social media influence.

For H1i, we observed that perceived intention is having the insignificant impact on online buying behavior from the direct effect, but there is a significant relationship between perceived intention and online buying behavior by the mediating result of social media influence. Finally, for H1m, we observed that demographics are having the positive impact on online buying behavior from the direct effect, there is also a significant relationship between demographics and online buying behavior by the mediating effect of social media influence Table 6. Findings indicate that the indirect effects for the UK sample, The R² of online buying behavior increases from 0.66 to 0.76, which indicates that social media contributes significantly toward online buying behavior of medicine (e-pharmacy).

Table 6 Path Analysis Results for in-Direct or Mediating Effect Relationship

Independent Variables →

Trust

Perceived Usefulness

Perceived Intention

Demographics

Mediating Variable ↓

USA

UK

India

USA

UK

India

USA

UK

India

USA

UK

India

Social Media Influence → online Buying Behaviour. R2 (USA, UK & India) = 0.82, 0.76 & 0.71

0.788 (p-value = 0.145)

0.112 (p-value = 0.00**)

0.765 (p-value = 0.344)

0.233 (p-value = 0.00**)

0.112 (p-value = 0.00**)

0.678 (p= 0.000**)

0.034 (p-value = 0.011**)

0.988 (p-value = 0.00**)

0.988 (p = 0.001**)

0.111 (p-value = 0.00**)

0.566 (p-value = 0.00**)

0.767 (p= 0.00**)

** statistically significant at 0.05 and 0.01 and 0.10

Now, to test H1c, we observed that trust is having the positive impact on online buying behavior from the direct effect, but there is also a significant relationship between trust and online buying behavior by the mediating result of social media influence.

However, for H1f we observed that perceived usefulness is having the positive impact on online buying behavior from the direct effect; there is also a significant relationship between perceived value and online buying behavior by the mediating result of social media influence.

For H1i, we observed that perceived intention is having the significant impact on online buying behavior from the direct effect, but there is also a meaningful relationship between perceived intention and online buying behavior by the mediating result of social media influence.

Finally, it may be observed that for H1m, the demographics are having the positive impact on online buying behavior from the direct effect, there is also a significant relationship between demographics and online buying behavior by the mediating result of social media influence (see Table 6).

Finally, when we tested the indirect effect for the India sample, The R² of online buying behavior increase from 0.62 to 0.71, which indicates that social media contributes significantly towards online buying behavior of medicine (e-pharmacy). Now to test H0c, we observed that trust is having a positive impact on online buying behavior from the direct effect, but there is an insignificant relationship between trust and online buying behavior by the mediating result of social media influence. However, for H0f it was observed that perceived usefulness is having the positive impact on online buying behavior from the direct effect, there is also a significant relationship between perceived value and online buying behavior by the mediating result of social media influence.

For H1i, it observed that perceived intention is having a significant impact on online buying behavior from the direct effect, but there is meaningful relationship between perceived intention and online buying behavior by the mediating effect of social media influence. Finally, for H1m, we observed that demographics are having a positive impact on online buying behavior from the direct effect, there is also a significant relationship between demographics and online buying behavior by the mediating effect of social media influence Table 6.

Predictive Modelling

In big data modelling the researcher developed the three predictive model to predict whether the Millennial will get influenced or not influenced with the social media towards e-pharmacy in USA, UK and India. The logistic regression is used to identify the influence of social media on buying behaviour towards e-pharmacy with respect to trust, purchase intention and perceived usefulness. The following model used for the study is used:

Where, Yi defines whether Millennial will get influenced/not influence with the social media towards e-pharmacy, whereas xi’s are all the independent variables i.e. trust, perceived usefulness and purchase intention and εi is the error term. The logistic regression results for the influence/ not influence with the social media towards e-pharmacy are presented in Table 7 below.

Table 7 Variables in the Equation
USA
	B	S.E.	Wald	df	Sig.	Exp(B)
Trust	0.422	0.171	2.055	1	0.000
Perceived Usefulness	-0.156	0.155	2.663	1	0.002
Purchase Intention	0.134	0.154	6.103	1	0.466
UK
	B	S.E.	Wald	df	Sig.	Exp(B)
Trust	0.566	0.112	2.564	1	0.001
Perceived Usefulness	0.145	0.145	2.145	1	0.000
Purchase Intention	0.781	0.113	3.125	1	0.256
India
	B	S.E.	Wald	df	Sig.	Exp(B)
Trust	0.487	0.112	3.254	1	0.001
Perceived Usefulness	-0.598	0.456	2.145	1	0.000
Purchase Intention	0.145	0.789	2.897	1	0.569

From the above Table 7, trust and perceived usefulness are significant (significance of Wald < 0.05) but the purchase intention is not significant. So, there will be no impact of purchase intention consumer buying behaviour towards e- Pharmacy through social media.

The B coefficient for trust is 0.422 > 0, thus consumers have trust so they influence with social media towards e-pharmacy whereas the coefficient for perceived usefulness is -0.156 < 0, so the consumers does not have any inclination on perceived usefulness but still they will get influence with social media towards e-pharmacy for USA sample.

The B coefficients for trust, perceived usefulness and purchase intention are 0.566>0, 0.145>0 and 0.781>0 thus consumers have trust, inclination towards perceived usefulness /purchase intention so they will get influence with social media towards e-pharmacy for UK sample.

The B coefficient for trust is 0.487>0, thus consumers have trust so they influence with social media towards e-pharmacy whereas the coefficient for perceived usefulness is -0.598 < 0, so the consumers does not have any inclination on perceived usefulness but still they will get influence with social media towards e-pharmacy for India. Further, we will represent the predicted classification Table 8.

Table 8 Classification Table
Classification Matrix - USA
Observed ↓	Predicted
	Class 1 = Influence (1)	Class 2 = Not Influence (0)
Class 1 = Influence (1)	65 (True Positive TP)	10 (False negative FN)
Class 2 = Not Influence (0)	13 (False Positive FP)	27 (True Negative TN)
Classification Matrix -UK
Observed ↓	Predicted
	Class 1 = Influence (1)	Class 2 = Not Influence (0)
Class 1 = Influence (1)	63 (True Positive TP)	7 (False negative FN)
Class 2 = Not Influence (0)	15 (False Positive FP)	15 (True Negative TN)
Classification Matrix – India
Observed ↓	Predicted
	Class 1 = Influence (1)	Class 2 = Not Influence (0)
Class 1 = Influence (1)	54 (True Positive TP)	12 (False negative FN)
Class 2 = Not Influence (0)	36 (False Positive FP)	20 (True Negative TN)

From the classification table 8 it can be inferred that out of 75 consumers, 10 consumers are classified as they will not influenced with social media towards e-pharmacy in near future whereas 27 consumers are classified as they will get influenced with social media towards e-pharmacy in near future for USA sample.

For the UK sample, 7 consumers are classified as they will not influenced with social media towards e-pharmacy in near future whereas 15 consumers are classified as they will get influenced with social media towards e-pharmacy in near future, finally from the India sample 12 consumers are classified as they will not influenced with social media towards e-pharmacy in near future whereas 36 consumers are classified as they will get influenced with social media towards e-pharmacy in near future.

Now, we will calculate the sensitivity and specificity percentages for USA sample:

From the sensitivity, we can say that 86% are the chances that consumers are currently influenced with social media towards e-pharmacy will also get influenced with social media towards e-pharmacy.

From the sensitivity, we can say that 53% are the chances that consumers are not currently influenced with social media towards e-pharmacy will also not get influenced with social media towards e-pharmacy.

So, our predictive model accuracy as follows:

So, the model accuracy or predictive accuracy of the model developed by the researcher is 73% which is adequate to adopt the developed model for prediction for USA sample. Now, we will calculate the sensitivity and specificity percentages for UK sample:

From the sensitivity, we can say that 90% are the chances that consumers are currently influenced with social media towards e-pharmacy will also get influenced with social media towards e-pharmacy.

From the sensitivity, we can say that 50% are the chances that consumers are not currently influenced with social media towards e-pharmacy will also not get influenced with social media towards e-pharmacy.

So, our predictive model accuracy as follows:

So, the model accuracy or predictive accuracy of the model developed by the researcher is 75% which is adequate to adopt the developed model for prediction for UK sample.

Now, we will calculate the sensitivity and specificity percentages for India sample:

From the sensitivity, we can say that 81% are the chances that consumers are currently influenced with social media towards e-pharmacy will also get influenced with social media towards e-pharmacy.

From the sensitivity, we can say that 64% are the chances that consumers are not currently influenced with social media towards e-pharmacy will also not get influenced with social media towards e-pharmacy.

So, our predictive model accuracy as follows:

So, the model accuracy or predictive accuracy of the model developed by the researcher is 66.07% which is adequate to adopt the developed model for prediction for India sample.

Conclusion

The objective of this research was to authenticate the classical technology application model (TAM) and test the extended TAM’s suitability for the online buying behavior for medicines (e-pharmacy) through social media influence for the developed and emerging Asian market. Our findings provide several valuable insights for the consumers to accept the e-pharmacy by using social media influence. E-retailers and managers fully understand the importance of social media influence in the process of introducing e-pharmacy; such firms should advertise their medicines and showcase the usage of medicines on different social media which can satisfy the consumers’ needs in different countries. Our study provides that all the three factors trust, perceived usefulness and perceived intention are the important parameters to impact online buying behaviour of medicines i.e. e-pharmacy and which will become more influence consumers through social media (Davis & Venkatesh, 1996; Dillon & Morris, 1996; Drennan et al., 2005; Lee et al., 2003; Park et al., 2013).

As may be expected, we observed differences between USA, UK, and India concerning social media influence towards online buying behaviour of medicines. People with different countries and backgrounds considered information technology is more or less useful (Straub et al., 1997). Trust is the first step towards online buying behavior, so the trust is most impacted parameter towards online buying behavior. The trust is also influenced by the social media comments, blogs and feedback, etc. posted about the company or e-retailers. Irrespective of culture differences building trust is more important parameter and influenced by the social media in the UK context, but it shows that trust is not playing a significant role in USA and India context through social media, which also supports previous research into the importance of trust at different cultures (Pavlou & Chai 2002). Therefore, trust is more critical in an eccentric philosophy because it allows consumers to diminish the essential hazard to a satisfactory level (Lynch & Ariely, 2000).

Perceived usefulness, perceived intention, and demographics are the step forward towards online buying behavior through social media influence, so the perceived usefulness, perceived intention and demographics are influenced by the social media feedback posted across different cultures which impact online buying behavior of medicine (e-pharmacy). The influence of perceived usefulness on intention in previous studies have been varied, either with significant (Chen & Tan, 2004; Pavlou, 2003) or insignificant (Chen et al., 2002; Shan et al., 2005) influences on buying behavior.

Practical Implication for Theory and Practice

Findings of this research shows that Millennials are extremely active on numerous social media platforms and pursue information before making any purchase. It is identified that Millennials depend on facts shared on social media, peer group recommendation, review, and remarks of friends. This study attempts to make available insights on influence on aspects of technology acceptance model such as Trust, Perceived Usefulness, Purchase Intention, and Social media for online medicine purchase transversely the different cultures. From the analysis, we found that Millennials are awake of being drugs sold online but cautious to buy from online pharmacy sites due to lack of trust, unawareness about the benefits of purchasing online across the country.

During the analysis, we found that trust is the most important parameters considered by the consumers from India and the UK, but the consumers from the USA the trust is found to be the insignificant factor. The perceived usefulness and purchase intention are the most impacted parameters for online buying behavior through social media influence across the countries. This research will open a massive opportunity for marketers to change the mind set of consumers regarding online buying behaviour. In the present research, we found the lack of awareness about the benefits of online pharmacy in Millennials which can be used by marketers to design strategies for communication to target Millennials more effectively and design their promotional campaigns which can impact their trust and buying behavior in a positive way. Furthermore, to motivate the customers to buy the medicine online, the practitioners and e-retailers targeting should focus on formulating strategies that could change online customers' perceptions of control. They develop the website which will provide them the more information about the particular drug which can build confidence among the consumers to buy the medicines online.

References

1. Ahmad, B.I.E. (2014). User acceptance of health information technology (HIT) in developing countries: a conceptual model. Procedia Technology, 16, 1287-1296.
2. Alreck, P., & Settle, R.B. (2002). Gender effects on Internet, catalogue and store shopping. Journal of Database Marketing & Customer Strategy Management, 9(2), 150-162.
3. Ayuningtyas, F., Prihatiningsih, W., & Laura, R. (2018). The Pattern of Online Purchase Behavior by Millennial Family. Malaysian Journal of Social Sciences and Humanities (MJSSH), 3(5), 10-16.
4. Brown, M., Pope, N., & Voges, K. (2003). Buying or browsing? An exploration of shopping orientations and online purchase intention. European Journal of Marketing.
5. Chen, L.D., & Tan, J. (2004). Technology adaptation in e-commerce:: key determinants of virtual stores acceptance. European Management Journal, 22(1), 74-86.
6. Gillenson, M.L., & Sherrell, D.L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & Management, 39(8), 705-719.
7. Chin, W.W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
8. Craig, C.S., & Douglas, S.P. (2005). International Marketing Research. Chichester: John Wiley & Sons.
9. Davis, D., & Cosenza, R.M. (1985). Business Research for Decision Making. Kent Publishing Company, Boston, MA.
10. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
11. Davis, F.D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International Journal of Human-Computer Studies, 45(1), 19-45.
12. Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
13. Dellner, T. (2007). European E-commerce, Electronic Retailer Magazine [Online]. Available from: http://www.electronicretailermag.com /info/0607_euro.html [Accessed: 4 August 2011].
14. Dillon, A., & Morris, M.G. (1996). User acceptance of new information technology: Theories and models, Follow Methodologies. Journal of Management Information System, 20(1), 123-151.
15. Drennan, J., Kennedy, J., & Pisarski, A. (2005). Factors affecting student attitudes toward flexible online learning in management education. The Journal of Educational Research, 98(6), 331-338.
16. Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
17. Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
18. Gurau, C. (2005). Pharmaceutical marketing on the Internet: marketing techniques and customer profile. Journal of Consumer Marketing.
19. Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565-571.
20. Hajli, M.N. (2014). The role of social support on relationship quality and social commerce. Technological Forecasting and Social Change, 87, 17-27.
21. Hernández, B., Jiménez, J., & Martín, M.J. (2011). Age, gender and income: do they really moderate online shopping behaviour?. Online Information Review.
22. Holden, R.J., & Karsh, B.T. (2010). The technology acceptance model: its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172.
23. Holden, R.J., & Karsh, B.T. (2010). The technology acceptance model: its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172.
24. Jaradat, M.I.R.M., & Smadi, Z.M.D.A. (2013). Applying the technology acceptance model to the introduction of mobile healthcare information systems. International Journal of Behavioural and Healthcare Research, 4(2), 123-143.
25. Kanungo, R.P. (2004). E‐commerce in the pharmaceutical industry: threshold of innovation. Management Research News.
26. Klopping, I.M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology, Learning & Performance Journal, 22(1).
27. Lee, Y., Kozar, K.A., & Larsen, K.R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(1), 50.
28. Li, L. (2010). A critical review of technology acceptance literature. Referred Research Paper, 4.
29. Ioanăs, E., & Stoica, I. (2014). Social media and its impact on consumers behavior. International Journal of Economic Practices and Theories, 4(2), 295-303.
30. Lynch Jr, J.G., & Ariely, D. (2000). Wine online: Search costs affect competition on price, quality, and distribution. Marketing Science, 19(1), 83-103.
31. Lynch, P.D., & Beck, J.C. (2001). Profiles of internet buyers in 20 countries: Evidence for region-specific strategies. Journal of International Business Studies, 32(4), 725-748.
32. Moreno, F.M., Lafuente, J.G., Carreón, F.Á., & Moreno, S.M. (2017). The characterization of the millennials and their buying behavior. International Journal of Marketing Studies, 9(5), 135-144.
33. Neti, S., (2011). Social Media and its Role in Marketing. International Journal of Enterprise Computing and Business Systems, 1(2), 1-15.
34. Park, N., Kim, Y.C., Shon, H.Y., & Shim, H. (2013). Factors influencing smartphone use and dependency in South Korea. Computers in Human Behavior, 29(4), 1763-1770.
35. Pate, S.S., & Adams, M. (2013). The influence of social networking sites on buying behaviors of millennials. Atlantic Marketing Journal, 2(1), 7.
36. Pavlou, P.A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134.
37. Pavlou, P.A., & Chai, L., (2002). What Drives Electronic Commerce Across Cultures? Across-Cultural Empirical Investigation of the Theory of Planned Behaviour. Journal of Electronic Commerce Research, 3(4), 240-53.
38. Rodgers, S., & Harris, M.A. (2003). Gender and e-commerce: An exploratory study. Journal of Advertising Research, 43(3), 322-329.
39. Van Slyke, C., Comunale, C.L., & Belanger, F. (2002). Gender differences in perceptions of web-based shopping. Communications of the ACM, 45(8), 82-86.
40. Stafford, T.F., Turan, A., & Raisinghani, M.S. (2004). International and cross-cultural influences on online shopping behavior. Journal of Global Information Technology Management, 7(2), 70-87.
41. Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management, 33(1), 1-11.
42. Straub, D., Boudreau, M.C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13(1), 24.
43. Teo, T.S. (2001). Demographic and motivation variables associated with Internet usage activities. Internet Research.
44. Toney, B., Goff, D.A., & Weber, R.J. (2015). Social media as a leadership tool for pharmacists. Hospital Pharmacy, 50(7), 644-648.
45. Ulbrich, F., Christensen, T., & Stankus, L. (2011). Gender-specific on-line shopping preferences. Electronic Commerce Research, 11(2), 181-199.
46. Venkatesh, V., Davis, F., & Morris, M.G. (2007). Dead or alive? The development, trajectory and future of technology adoption research. Journal of the Association for Information Systems, 8(4), 1.
47. Voramontri, D., & Klieb, L. (2019). Impact of social media on consumer behaviour. International Journal of Information and Decision Sciences, 11(3), 209-233.
48. Wu, L., & Chen, J.L. (2005). An extension of trust and TAM model with TPB in the initial adoption of on-line tax: an empirical study. International Journal of Human-Computer Studies, 62(6), 784-808.