Keywords

Advertising awareness, Brand Awareness, Price awareness, E-commerce, Social Media Marketing, MSME.

Introduction

E-commerce and social media marketing has revolutionized the way businesses market their products and services (Abed et al, 2015). With the snowballing popularity of online shopping & social media usage, businesses are leveraging these platforms to reach out to their target audience. This research paper focuses on the impact of advertising, brand, and price on brand image of MSME (Micro, Small and Medium Enterprises) in the context of e-commerce and social media marketing. The study examines how these factors influence customers' perception of a brand and how businesses can use them to build a strong brand image. Through this study, an insight into effective marketing strategies that businesses can adopt to enhance their brand image in the competitive world of e-commerce & social media marketing is given.

E-commerce has brought a paradigm shift in the retail industry (Kumar and O.G., 2021), enabling businesses to reach a wider customer base beyond geographical boundaries. Social Media has occurred as an influential tool for businesses to unite with the customers for products and services promotion. However, in cluttered digital space, it becomes essential for business houses to create a sturdy brand image which can reverberate with their customers. Advertising, brand and price are critical factors that can impact a customer's perception of a brand.

E-commerce and social media marketing have become essential components of business strategies in the digital age. With the widespread use of online platforms, businesses have the opportunity to reach out to a global customer base. However, to establish a strong brand image, businesses need to understand how advertising, brand, and price impact customer perception. This study examines the impact of these factors on brand image in the context of e-commerce and social media marketing.

For instance, Jaiman and Sharma (2019) establish that advertising & brand image had a substantial positive impact on loyalty of shoppers towards e-commerce platforms in Rajasthan. Similarly, another study by Singh and Jindal (2020) revealed that price was a critical factor in customer decision-making in the e-commerce industry in Rajasthan. The study by Gupta et al. (2021) found that social media marketing ominously impacted brand image & customer engagement in tourism industry in Rajasthan. The authors suggest that businesses can leverage social media platforms to create engaging content and build a strong brand image that resonates with customers.

The results of these studies high spot the position of advertising, brand & price in shaping customer perception in the e-commerce and social media marketing landscape in Rajasthan. Businesses need to carefully consider these factors when developing marketing strategies to establish a strong brand image and increase customer loyalty.

Literature Review

E-commerce and social media marketing have significantly altered the habits in which businesses operate and commute with customers. In the digital age, businesses need to establish a strong brand image that resonates with customers to foster the competition. The impact of advertising, brand & price on brand image in the framework of e-commerce & social media marketing has been a widely researched area in recent years.

Advertising and brand image are critical factors in shaping customer perception in the ecommerce and social media marketing landscape. Boshoff and Greyling (2018) found that advertising significantly impacted brand image and customer loyalty in the e-commerce industry. The authors suggest that businesses should focus on creating engaging and informative advertisements that highlight the brand's unique selling points to enhance customer perception.

Similarly, De Vries et al., (2019) examined the outcome of brand image on customer behavior in referring to social media marketing context. The study found that a strong brand image occasioned in amplified customer engagement and positive WOM (word-of-mouth) references, indicating the prominence of construction a strong brand image in social media marketing.

Price is another critical factor that influences customer perception in e-commerce & social media marketing. Zhuang et al., (2018) instituted that price perception had significant impact on customer fulfillment and constancy in e-commerce industry. The study recommends the businesses to focus on providing competitive pricing for attracting & retaining customers.

However, Anselmsson et al., (2019) establish that consumers' price sensitivity varied depending on the product category and the level of involvement. The authors suggest that businesses should carefully consider the product category and target audience to determine the optimal pricing strategy to enhance brand image.

In conclusion, the impact of advertising, brand and price on brand image is a critical area of research in the e-commerce and social media marketing landscape. The literature suggests that businesses need to carefully consider these factors when developing marketing strategies to establish a strong brand image and increase customer loyalty. A combination of effective advertising, a strong brand image, and competitive pricing can help businesses build a sustainable competitive advantage in the digital age.

E-commerce offers a number of benefits for businesses which includes amplified reach, concentrated costs, and better-quality customer experience. Chaffey et al. (2019) quoted that e-commerce enables businesses to reach customers in different geographic locations and time zones, providing a global reach. Additionally, e-commerce shrinks the costs that is linked with the traditional brick and mortar stores in terms of rent and services. Finally, e-commerce provides a more convenient and personalized customer experience through features such as personalized recommendations and easy checkout processes.

Social Media Marketing (SMM)

Social media marketing has turned out to be a quintessential component of modern marketing strategies. An assortment of applications that uses the technologies of Web 2.0 for communication & sharing of data is often referred to as social media (Kaplan & Haenlein (2010);Tritama and Tarigan, 2016). According to a study by Statista (2021), social media platforms such as Instagram, Facebook, and Twitter have over 3 billion users, providing businesses with a vast audience to connect with. SMM empowers businesses to engross with customers through interactive content, such as videos, images, and polls, to build brand awareness and loyalty. Social media marketing helps in relationship building, promotion, publicity, brand development and market study (Priansa & Suryawardani, 2020).

E-commerce and Social Media Marketing

The integration of e-commerce and has provided SMM with new opportunities to connect with customers and improve the customer experience. According to a study by Wang et al. (2020), e-commerce businesses that integrated social media marketing into their strategy experienced increased customer engagement, improved customer experience, and increased sales. The study proposes that businesses should focus on mounting an integrated ecommerce & social media marketing strategy to stay ahead of the competition.

Challenges of E-commerce and SMM (Social Media Marketing)

Despite aids of e-commerce and social media marketing, businesses face several challenges when implementing these strategies. One of the main challenges is the rapidly evolving technology and platforms. According to a study by Alalwan et al. (2018), businesses struggle to keep up with the ever-changing technology and platforms, which can lead to a lack of consistency in marketing efforts. Additionally, businesses face challenges with data privacy and security, as the collection and storage of customer data can be a target for cyber-attacks.

Research Methodology

For the purpose of the study quantitative method of research is adopted. A cross sectional survey was run in Rajasthan with 85 participants to gather data. The participants were chosen using a non-probability sampling approach, namely convenience sampling. A standardized questionnaire with 5 point Likert-scale was used to gather data. On a scale of 1 to 5, the participants will be asked to score their perceptions of advertising, brand, pricing, and brand image. Smart PLS and SPSS programmers were used for data analysis. Descriptive stats like mean-median, and standard deviation were computed. The hypotheses were tested, and the connection between the variables was ascertained, using inferential statistics like correlation and regression analysis.

Throughout the study, ethical issues were taken into justification wherein participants were made aware of the study's objectives and their consent was taken. Information gathered was kept private and used solely for research. The small sample size and convenience sampling methodology were undertaken. Data analysis tools like SPSS and Smart PLS were used to achieve the overall objectives Figure 1.

Research Objectives

1. To know the impact of advertising, price and brand awareness on Brand image.
2. To know the impact of advertising and price awareness on brand awareness.

Research Framework and Hypothesis

H₁. There is no positive relationship in between advertising awareness and brand awareness

H₂. There is no positive relationship between price awareness and brand awareness

H₃. There is no positive relationship between advertising awareness and brand image

H₄. There is no positive relationship between price awareness and brand image

H₅. There is no positive relationship between brand awareness and brand image

Analysis

Table 1 presents the demographic profile of MSMEs owners/managers in the study. The table consists of three demographic variables: age, gender, and occupation, along with income levels. The frequency column represents the number of MSMEs falling under each category, and the percentages column represents the percentage of MSMEs falling under each category.

Table 1 Demographic Profile Of Msmes
		Frequency	Percentages
Age Gender Occupation   Income (PA)	20 to 23 Years 23 to 26 Years 26 to 29 Years 29 to 32 Years 32 and above Male Female Services Sector Manufacturing Sector Contact bases Others > Rs. 200,000 Rs. 2 00,000 to Rs. 5 00, 000 Rs. 5 00,000 to Rs. 800,000 Rs. 8 00,000 to Rs. 12, 00,000 Rs. 12,00,  000 and more	09 42 13 6 15 85 49 36 85 13 20 18 34 85 05 43 17 06 14 85	10.6% 49.4% 15.3% 7.1% 17.6% 100% 57.60% 42.40% 100% 15.3% 23.5% 21.2% 40.0% 100% 5.9% 50.6% 20.0% 7.1% 16.5% 100%
SPSS View

In terms of age, it can be seen that the majority of MSMEs (49.4%) belonged to the age group of 23 to 26 years that was 17.6% in the age group of 32 and above. The gender distribution shows that 57.6% of the MSMEs owners/managers in the study were male, while 42.4% were female. Regarding occupation, the majority of the MSMEs were in the services sector (50.6%), followed by the manufacturing sector (20%). It can be seen that 21.2% of MSMEs were contact-based businesses, and the remaining 7.1% were classified as "others."

Lastly, the income level of the MSMEs is presented in the table. It can be observed that the majority of MSMEs (40%) fall under the income range of Rs. 800,000 to Rs. 12, 00, 000, followed by 34% falling under the range of Rs. 500,000 to Rs. 800,000. Only 5.9% of MSMEs have an income of less than Rs. 200,000. Overall, this table 2 provides insights into the demographic profile of MSMEs in the study, which can be useful in understanding the characteristics of these businesses and designing effective policies and strategies to support their growth and development.

The results of the KMO (Kaiser-Meyer-Olkin) test & Bartlett's test of sphericity, are taken to determine whether the data is appropriate for factor analysis, are shown in Table 2. Values closer to 1 show that the data are suitable for factor analysis. The K-M-O measure of sampling adequacy goes from 0 to 1. The K-M-O score of 0.856 in this instance shows that the data is suitable for factor analysis.

Table 2 Kmo & Bartlett's Test
Kaiser- Meyer- Olkin Measure of Sampling Adequacy.		.856
Bartlett's Test of Sphericity	Apx. Chi Square	1391.223
	Df	120
	Sig.	.000

Bartlett's test of sphericity is taken to evaluate if correlation matrix between variables in data set is appropriate for factor analysis. In order to determine if there are any correlations between the variables, the test analyses that the null hypothesis in correlation matrix is an identity matrix. With an estimated chi-square value of 1391.223 and 120 degrees of freedom, the findings in this example demonstrate that this test was statistically significant, showing that the correlation matrix is not an identity matrix and the variables are appropriate for component analysis. The p-value of 0.000, which shows that there are substantial correlations between the variables, further supports the rejection of the null hypothesis.

The reliability data for the survey utilised in the study are displayed in Table 3. The consistency or stability of a measurement tool or instrument is referred to as reliability. Cronbach's alpha, which has a scale from 0 to 1, is the most often used dependability indicator. The survey questions are internally consistent and trustworthy if Cronbach's alpha is high.

Table 3 Reliability Statistics
Cronbach's Alpha	N of Items
.954	16

The Cronbach's alpha in this table is 0.954, which is regarded as extremely high. This shows that the survey questions accurately and consistently measure the research constructs. There were 16 survey questions used to measure the constructs, as shown by the N of items, which is 16. The research's survey was a valid measuring method for evaluating the constructs of interest, according to the data in this table.

Reliability and validity test results for the study's included variables are shown in Table 4. Advertising awareness, brand awareness, brand image, and price awareness are the contributing elements. Cronbach's alpha scores vary from 0.809 to 0.89, indicating a high level of internal consistency and dependability.

Table 4 Factors, Cronbach’s Alpha, Cr, And Ave Values
		Factors	Cronbach's alpha	Composite reliability (rho_a)	Composite reliability (rho_c)	Average variance extracted (AVE)
Advertising Awareness	AA1	.818	.792	.794	.878	.706
	AA3	0.878
	AA4	0.824
Brand Awareness	BA1	0.849	0.873	0.876	0.913	0.724
	BA2	0.872
	BA3	0.858
	BA4	0.824
Brand Image	BI1	0.809	0.86	0.862	0.905	0.704
	BI2	0.852
	BI3	0.827
	BI4	0.867
Price Awareness	PA1	0.89	0.878	0.887	0.916	0.733
	PA2	0.842
	PA3	0.808
	PA4	0.882
Note: AA= Advertising Awareness, PA= Price Awareness, BA= Brand Awareness, BI= Brand Image

Measures of construct reliability called composite reliability (rho a) and (rho c) show how well the components reflect the underlying construct. The factors have a high level of construct reliability, as seen by the values of composite reliability, which vary from 0.792 to 0.887. The amount to which the components share common variance is shown by average variance extracted (AVE), a convergent validity indicator. The components have a high level of convergent validity, as seen by the values of AVE, which vary from 0.704 to 0.733.

Overall, the findings support the use of the variables in determining the objectives of the study since they show that the factors employed in the study have high levels of reliability and validity.

The discriminant validity in between the study's variables is assessed using Fornell-Larcker criterion in Table 5. The diagonal numbers are square roots of the ‘average variance extracted (AVE)’ for each component. The off-diagonal numbers reflect the correlations between the variables. Each factor's AVE should be higher than its correlation with other components in accordance with the Fornell-Larcker criterion.

Table 5 Fornell-Larcker Criterion
	Advertising Awareness	Brand Awareness	Brand Image	Price Awareness
Advertising Awareness	0.840*
Brand Awareness	0.852	0.851*
Brand Image	0.905	0.861	0.839*
Price Awareness	0.834	0.816	0.777	0.856*
Note: AA= Advertising Awareness, PA= Price Awareness, BA= Brand Awareness, BI= Brand Image

The table 6 demonstrates that the diagonal values are bigger than the off diagonal values, demonstrating that each component has a higher level of internal variability than external variability. The discriminant validity of the components is therefore validated. In conclusion, the Fornell-Larcker criteria results indicate that the elements utilised in the study have sufficient discriminant validity, which supports their use in analysing the effects of advertising, brandand pricing on brand image in e-commerce and social media marketing.

Table 6 Cross Loading
		Advertising Awareness	Brand Awareness	Brand Image	Price Awareness
Advertising Awareness	AA1	0.818	0.629	0.751	0.596
	AA3	0.878	0.69	0.767	0.745
	AA4	0.824	0.815	0.761	0.751
Brand Awareness	BA1	0.636	0.849	0.632	0.73
	BA2	0.76	0.872	0.768	0.733
	BA3	0.77	0.858	0.812	0.709
	BA4	0.725	0.824	0.702	0.602
Brand Image	BI1	0.731	0.733	0.809	0.521
	BI2	0.671	0.759	0.852	0.616
	BI3	0.83	0.712	0.827	0.773
	BI4	0.793	0.687	0.867	0.677
Price Awareness	PA1	0.845	0.786	0.785	0.89
	PA2	0.747	0.577	0.678	0.842
	PA3	0.607	0.742	0.634	0.808
	PA4	0.632	0.661	0.53	0.882
Note: AA= Advertising Awareness, PA= Price Awareness, BA= Brand Awareness, BI= Brand Image

The findings of hypothesis testing performed using SEM to look at the correlations between the components are shown in Table 7. The p-values reveal the likelihood of getting a result as severe as the one observed, provided that the null hypothesis is correct, while the T statistics reveal the importance of the relationship between the components.

Table 7 Mean, Stdev, T Values, P Values
	(O)	(M)	(STDEV)	(|O/STDEV|)	P
AA -> BA	.568	.564	.135	4.192	.000*	Supported
AA -> BA	.651	.646	.112	5.824	.000*	Supported
BA -> BI	.353	.343	.105	3.363	.001*	Supported
PA -> BA	.342	.337	0.13	2.638	.008*	Supported
PA -> BI	-.056	-.047	0.085	0.661	.509	Not Supported
Note: AA= Advertising Awareness, PA= Price Awareness, BA= Brand Awareness, BI= Brand Image

The original sample (O), sample mean (M), standard deviation (STDEV), T statistics (|O/STDEV|), and p-values for each correlation between the components are displayed in the table.

The findings point to a substantial positive link between advertising awareness (AA) and brand awareness (BA) (T=4.192, p0.001), demonstrating that advertising has a big influence on brand awareness in SMM and e-commerce. The statistics lend credence to this association. Similar to the previous finding, there stands a strong and positive correlation in between advertising awareness (AA) and brand image (BI) (T=5.824, p0.001), indicating that advertising has a big influence on brand image. The facts also corroborate this association.

Also, brand awareness (BA) and brand image (BI) have a substantial positive association according to the results (T=3.363, p0.001), demonstrating that brand awareness significantly affects brand image in social media and e-commerce marketing. Another evidence that pricing awareness has a major influence on brand awareness comes from the significant positive connection between price awareness (PA) and brand awareness (BA) (T=2.638, p0.01). The facts also corroborate this association.

However, there turns out to be no significant relationship in between Price Awareness (PA) and Brand Image (BI) (T=0.661, p > 0.05), indicating that price awareness does not have a significant impact on brand image. Overall results of Table 7 provide support for the hypotheses.

Discussion

The study examined how advertising, brand, and pricing affected brand perception in social media marketing and e-commerce. In order to assess the data gathered from a sample of clients in the setting of an online shopping platform, the study used structural equation modelling (SEM). The findings showed that brand awareness and advertising had a substantial influence on brand image, highlighting the value of making advertising investments and increasing brand recognition in social media and e-commerce marketing. The study also discovered that brand-awareness has a substantial impact on brand image, underscoring the need of creating and upholding a strong brand awareness to improve brand image in the online market.

It's interesting that the study indicated that brand image was not much impacted by pricing knowledge. The conventional wisdom that pricing strategy plays a key role in creating a positive brand image is called into question by this research. The findings imply that brand image in e-commerce and social media marketing may be more significantly prejudiced by other factors for example advertising and brand awareness.

Overall, the research's conclusions have significant ramifications for businesses engaged in social media marketing and e-commerce. The study offers proof that businesses may improve their brand image in the internet market by spending in advertising and raising brand awareness. The findings also imply that corporations may need to concentrate on other elements in order to develop a strong brand image and that pricing strategy may not be as crucial a role as previously believed.

Conclusion

The research's findings imply that advertising and brand recognition are essential elements in creating a strong brand-image. The findings emphasise the value of spending money on advertising and raising brand recognition in order to improve brand perception and client perceptions. The study also casts doubt on the conventional wisdom that a strong pricing strategy is essential for developing a strong brand image. While this study indicated that pricing awareness was unimportant, other factors like advertising and brand awareness had a bigger influence on brand image.

To strengthen their brand image and increase client views, businesses need to concentrate on creating a strong brand awareness and spend in advertising efforts. Therefore, organisations might not need to put as much emphasis on price strategy as previously believed in order to develop a strong brand image.

For businesses involved in social media marketing and e-commerce, the study has significant practical consequences. According to the findings, businesses should spend money on advertising efforts to raise brand recognition and concentrate on creating a strong brand image through different marketing techniques. In order to strengthen their brand reputation in the internet market, businesses may also need to reassess their pricing approach.

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Received: 02-Aug-2023, Manuscript No. AMSJ-23-13849; Editor assigned: 03-Aug-2023, PreQC No. AMSJ-23-13849(PQ); Reviewed: 29-Sep-2023, QC No. AMSJ-23-13849; Revised: 19-Oct-2023, Manuscript No. AMSJ-23-13849(R); Published: 01-Nov-2023