Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Research Article: 2021 Vol: 25 Issue: 1

The Impact of Social Media Sharing on Brand Association of Startups: A Study on It Startups in Hyderabad, India

D. Prasanna, Associate Professor, KL Business School, KL University, Guntur, AP, India

Dillip Kumar Parida, Research Scholar, KL Business School, KL University,Guntur, AP, India


Sharing of information and content on social media makes brand more interactive and makes easy to spread the word about product offerings. The most ideal approach to compose and advantage from this social media marketing factor is as yet developing and we are learning as we go along. From one viewpoint, there are considerable success stories for new start-ups and then again, there are numerous failed cases of utilizing social media strategy.There are numerous factors of Social media marketing and focus on most influenced factor can help to optimise the marketing budget. Social media is an ideal domain for start-ups to build and develop the brand association. The purpose of this paper is to understand the relationship between sharing and brand association along with how a startup company could improve brand association for consumers by sharing information on social media. This article investigates this area by studying and exploring with the help of the Honey Comb of social media framework and brand association from Aaker’s Brand equity Model highlighting how social media sharing can influence the brand association. The findings identify the relationship between sharing elements of social media that affects the brand association towards brand equity.


Social Media Marketing, Brand Association, Social Media Sharing and Startups.


The internet started as a tool for users to share information with each other(Kaplan & Haenlein 2010). With the change in the use of the internet, people started sharing their interests and information on the internet. It gradually moved toward companies’ product information and corporate pages where companies could present their solution offering through the internet. The two-way communication through the internet in the large network becomes social media activity. Nowadays companies use social media to market themselves through communication with consumers (Kaplan & Haenlein 2010). Social media can be a tempting tool for start-up companies to use since it is cheap (Ramp Up Marketing in a Downturn, 2009), relatively simple to use and the company can reach a big audience (Weinberg, 2011).

The major problem is that small start-ups face many challenges to survive. The biggest problem is to finance the business and creating brand value in the market. As a consequence of the frequently changing environment, the shutdown of the start-up rate is expected to rise. Even though companies have a hard time acquiring new customers, especially in times of competitive environment. Small start-ups cut their budget in marketing activities and maximize the effort to reach the customer with a minimum resource.

In many start-up companies, brand management receives little or no attention in the daily run of affairs.

Although the owners or directors are the ones to take the lead in this area, they either seldom have the time for it or are not even aware of “brand management” as a concept. And because this concept is not ingrained within, there are no other employees available to give it sufficient attention (Krake, 2005). This paper first discusses Aaker’s Brand equity model (Aaker, 1991) and Smith’s Honeycomb of social media framework (Kietzmann et al., 2011) through a review of the literature. After that, the paper discusses the necessity of brand building and development in start-ups; and try to find out the relationship between social media sharing and brand association. The result of the research is to understand and explain the important elements in using the sharing of data on the social media platform for brand management for start-ups or small companies. More precisely how social media can engage with their customer and inform more about their services and product to understand the strategic importance behind social media marketing for a company.

Literature Review

Start-up in India

There is no common definition for a start-up that is widely accepted globally. A start-up could be defined as a new business or company that drives towards the innovation or improvement of technology with the help of investors or self-financing. According to the Grant Thornton report, currently, a clear definition of a ‘Start-up’ does not exist in the Indian context due to the subjectivity and complexity involved (HV et al., 2016). Considering various parameters about any business such as the stage of their lifecycle, the amount and level of funding achieved, the amount of revenue generated, the area of operations, etc, some conceptual definitions are available in the public domain. According to the startupindia, an entity shall be considered as a Start-up if it is incorporated as a private limited company or registered as a partnership firm or a limited liability partnership in India. The firm can be considered as a start-up for up to ten years from the date of its incorporation/registration. The nature of business should be working towards innovation, development, or improvement of products or processes or services, or if it is a scalable business model with a high potential of employment generation or wealth creation (Startup Recognition & Tax Exemption 2020).

According to Kstart Survey on Digital Marketing, for startups, digital marketing is a more viable option than traditional media because even with a small budget, businesses can test the effectiveness of their marketing strategy, control costs, and reach out to targeted prospects. It’s why every type of business (big and small, old, and new) is recognizing the importance of leveraging digital marketing. Not surprising then, that the digital media industry is growing at 40% y/y growth when other industries are struggling at 5% or 6% (Exploring India’s Digital Marketing Landscape n.d.). According to digital India 2016, 80% of India Marketers believe that integrated campaigns (Email, Social, and Mobile) can result in a moderate to a significant increase in conversion rates. 85% of the marketers are tracking revenues generated through e-Marketing activities for their business. 50% of respondents report that e-Marketing activities are contributing more than 10% of the share of their revenues (Sharma, 2020).

Social Media and Social Media Marketing

The term ‘Social Media’ is a construct from two areas of research, communication science, and sociology. A medium, in the context of communication, is simply a means for storing or delivering information or data. In the realm of sociology, and particular social (network) theory and analysis, social networks are social structures made up of a set of social actors (i.e., individuals, groups, or organizations) with a complex set of dyadic ties among them (Winship et al., 1996) The definition of social media as a “platform to create profiles, make explicit and traverse relationships”.

Social media marketing is defined as the use of social media as a tool of marketing to create brand awareness. According to the American Marketing Association, Marketing is the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large (American Marketing Association 2004) Social media marketing can be defined as the use of social media platforms to communicate with the customer about the product and service offering. Social media marketing is using social media channels to promote your company and products. This type of marketing should be a subset of your online marketing activities, complementing traditional web-based promotional strategies like email newsletters and online advertising campaigns. Social media marketing qualifies as a form of viral or word-of-mouth marketing (Barefoot & Szabo 2010).

Social Media and Start-ups

According to the Social media marketing industry report, the top two benefits of Social media marketing are increasing exposure and increasing traffic. A significant 90% of all marketers indicated that their social media efforts have generated more exposure for their businesses. Increasing traffic was the second major benefit, with 77% reporting positive results.(Stelzner, 1965) Social media enables firms to engage consumers constantly and directly at relatively low cost and a higher level of efficiency than with more traditional communication tools. Businesses want their message to reach as many people as possible. To maximize this reach, a business must have a presence where customers are hanging out. Increasingly, they are hanging out on social networking sites (Halligan & Sha 2009).

Social media marketing allows businesses to get access to customers with a customized and individual message. It can also aid the use of a firm's worthiness and increase customer contacts. There is no limit to social media use and flexibility to increase the network. Social media can be beneficial for product selling and services as well. There is a shift of paradigm towards use the internet as one of the primary channels of marketing for start-up organizations. This provides greater access to target customers within a short period and is proved as the most influential medium. Mangold & Faulds (2009) recognize that social media allows an enterprise to connect with both existing and potential customers, engage with them and reinforce a sense of community around the enterprise’s offering(s).

Benefits of Social Media Marketing

The major benefit of Social media marketing is to leverage the huge social network to increase brand awareness. Social media marketing makes it easy to spread the news about the product and its objective. This directly or indirectly Increases traffic by linking to the website or any product information material. The primary objective of any marketing channel is to advertise products and services and this low-cost channel is the right way to do it.

Social listening is the act of watching social conversations regarding products and services. It helps you to perceive what’s vital to your audience and helps to understand the trends your target market is interested in. Social listening is more about feedback from the customer which you never asked. This helps the start-up to gauge the requirement and taste of consumers. Social listening data can be mined to get an insight into the sentiment of conversation. Negative sentiments can be analyzed to find the real reason for negative sentiments. Positive sentiment helps to find the brand advocates and these advocates can be engaged to spread the goodwill of the product. Billions of conversations happen a day across the numerous social media networks on the web regarding brands, services, and products.

Startup businesses can advertise products and their services; companies can provide instant support to any queries or services, the building of an online community of brand advocates through all forms of social media channels such as social networking sites like Facebook, Blogs, Communities and professional network like LinkedIn. Additionally, social media enables consumers to share information with their friends and this generates support for the brand psychologically. These sharing and peer conversations provide startup another cost-effective way to increase brand awareness, brand recall, and eventually that increases brand loyalty.

Aaker’s Brand equity Model and Brand Association

Aaker (1992) provided the most comprehensive brand equity model which consists of five different assets that are the source of value creation. These assets include brand loyalty; brand name awareness; perceived brand quality; brand associations in addition to perceived quality; and other proprietary brand assets - e.g., patents, trademarks, and channel relationships (Aaker, 1991) in Figure 1.

Figure 1 Brand Equity Model Van Aaker

In his Brand equity model, David A. Aaker identifies five brand equity components: (1) brand loyalty, (2) brand awareness, (3) perceived quality, (4) brand associations, and (5) other proprietary assets.

Aaker defines brand equity as the set of brand assets and liabilities linked to the brand - its name and symbols - that add value to, or subtract value from, a product or service. These assets include brand loyalty, name awareness, perceived quality, and associations. This definition stresses ‘brand-added value’; however, his model does not make a strict distinction between added value for the customer/ consumer and added value for the brand owner/ company (EURIB, 2009)

Brand Associations

Brand associations or brand image is perhaps the most accepted aspect of brand equity. It is anything linked in customers’ memory to a brand. Brand association includes product attributes, customer benefits, uses, users, lifestyles, product classes, competitors, and countries. Associations can help customers process or retrieve information, be the basis for differentiation and extensions, provide a reason to buy, and create positive feelings. Consumers use brand associations to process, organize, and retrieve information in memory and this helps them to make purchase decisions (EURIB & Aaker, 1991). Brand associations consist of all brand-related thoughts, feelings, perceptions, images, experiences, beliefs, attitudes, and is anything linked in memory to a brand (Kotler & Keller, 2003).

A brand association is anything “linked” in memory of a brand. The association not only exists but has a level of strength. A link to a brand will be stronger when it is based on many experiences or exposures to communications, rather than a few. It will also be stronger when it is supported by a network of other links. (Aaker, 1991) In other words the association is not only a link between customer and brand but has a level of strength. Brand association is stronger when a consumer experiences more of the product and services. The brand association creates a positive feeling and satisfaction with the product or services. The greater the association, the higher is the brand equity.

The Honeycomb of Social Media

Kietzmann et al. (2011) developed the honeycomb framework Figure 2 which represents seven functional building blocks: identity, conversations, sharing, presence, relationships, reputation, and groups. Each block allows us to unpack and examine (1) a specific facet of social media user experience, and (2) its implications for firms. These building blocks are neither mutually exclusive nor do they all have to be present in a social media activity. They are constructs that allow us to make sense of how different levels of social media functionality can be configured (Kietzmann et al., 2011).

Figure 2 The Honeycomb of Social Media

Sharing” refers to the sending and receiving of content between users on the same social media platform, such as photos, comments, and videos (Kietzmann et al., 2011). The “sharing” block of the honeycomb has two implications for companies with the ambition to engage in social media. First, companies need to understand “what objects of sociality their users have in common, or to identify new objects that can mediate their shared interests”. Second, companies need to evaluate “the degree to which the object can or should be shared” (Kietzmann et al., 2011); (Silva et al., 2020).

This building block is associated with the extent to which consumers exchange, distribute, and receive content (Ozanne & Ballantine 2010). There are at least three fundamental managerial implications that the sharing block offers to firms whose ambition is to engage in social media: first, there is a need to identify these linkages or to select new objects that could mediate their shared interests (e.g. Photos for Flickr and videos for youtube consumers); second is related to the degree to which these objects can or should be shared (e.g. Copyright concerns, legality, offensive or improper contents); and third, what motivates consumers to share these objects of sociality (Kietzmann et al., 2012). The sharing block requires a lot of motivation for the consumer to share win their network. This may drive by different kinds of values and objectives. Few consumers are brand loyal and advocates, they love to share the content as they associate themselves with the brand. Few consumers may share because of some benefits like monetary, coupon, or discount. Thus sharing can create a positive impact or negative impact as well.

Theoretical Framework

Sharing block presents the extent to a social media user shares, receives, and distributes content online on a social media platform (Jokela, 2013). The term ‘social’ often implies that exchanges between people are crucial. In many cases, however, sociality is about the objects that mediate these ties between people (Engestro¨m, 2005); the reasons why they meet online and associate with each other. Sharing is interacting with another user through the distribution of content.

Users share the content with their network if it sounds interesting or the network is benefited from that. Sometimes user shares to distribute about their opinion in the social network. Organizations can leverage the sharing by providing interesting facts about the industry or line of business. There are organizations they share the social message so that user can share the content with their network. When the user shares the news or distributes the message, they associate themselves with the brand. Social media site that enables its users to share pictures and videos where other users are also allowed to comment on what other users have shared in it.

Brand association is anything linked in memory to a brand (Aaker, 1991). This link becomes stronger when it is based on a consumer’s frequent experiences with a specific brand. Brand associations help a start-up to differentiate their brands in the market and can gain a competitive advantage (Aaker, 1991). When consumers share anything about a particular brand, it is attached to a reason which affects their feelings and attitudes. Some associations influence consumers’ perceptions of the brand and create a positive view of the brand. “Brand image is defined as perceptions about a brand as reflected by the brand associations held in consumer memory”. Keller classifies brand associations into three major categories: attributes, benefits, and attitudes. Attributes are those descriptive features that characterize a brand, such as what a consumer thinks the brand is or has. Benefits are the personal value consumers attach to the brand attributes, that is what consumers think the brand can do for them. Brand attitudes are consumers’ overall evaluations of a brand (Keller, 2008) Figure 3.

Figure 3 Theoretical Framework


This study purposed to examine the relationship between the social media sharing factor from social media of honeycomb model and its influence on Brand Association. After conducting a literature review, we proposed a hypothesis based on existing constructs. The proposed hypothesis in this study describes the relationship between sharing and brand association. This study also helps to understand the predictive relevance of exogenous factor on endogenous factor.

H1: Sharing on social media platform has a positive impact on brand Association.


In this research paper, the methodology used is the collection of data for analysis through Smart PLS. This study uses quantitative methods that emphasize testing the theory by measuring the research variables with numbers and perform data analysis with statistical procedures.

The Information about start-up companies as well as data about their nature of the business was collected from the Ministry of Corporate Affairs, Govt. Of India website. The State-wise master details of companies registered with Registrar of Companies after Apr 2015 are considered. The rationale behind consideration of start-up with the age of more than 5 years so that the active and stable start-ups can be considered for research studies.

According to the survey by the Institute for Business Value and Oxford Economics, 90% of India’s startups fail within the first five years. It added that the lack of pioneering innovation is the major reason for the failure of Indian start-ups — in essence, they are copycats of start-up ideas of the west, the study said (2018 In Review: 10 Of The Biggest Startup Failures In India). Here the focus is on established start-up companies of 5 years old, and seek to understand how this company has established its brand identity and how it has been perceived by external stakeholders.

There were 2313 companies registered in Hyderabad, Telangana region for the year 2015. Out of which only 2250 companies are active and in progress. There are 1291 technology and IT start-ups out of 2250 companies registered in 2015 and are active in Hyderabad, India. Our population size is 1291 number of Start-Ups for this study. These companies’ product offerings and services are common in nature and have the same characteristics. These companies are into Information Technology services and deal with software or hardware engineering. We focused on one study of start-ups in one location so that the companies can be visited for interview and better understanding.. We have used simple random sampling to select the sample from the population. Thus, our population size is 1291 number of Start-Ups for this study. Thus, 296 number of startups are the minimum required sample size for this study. The structured questionnaire is designed and created by the Google Forms online platform (Google, nod). Google Forms is an internet-mediated or known as web-based questionnaires that are widely accessible through internet connectivity on various devices such as on computers or tablets/smartphone. The technique chosen to measure the users’ attitudes in the survey was a 5 step Likert scale ranging from strongly disagree to strongly agree.

Construct Measurement

The questionnaire items were adapted based on numerous sources through literature reviews and analysis of various articles. The following Table 1 comprised the questions where it was measured on a 5-point Likert scale ranging from strongly disagree to strongly agree.

Table 1 Construct Measurement
  Category Questions
BA1 Brand awareness Does the social media marketing campaign help the consumer to know your organization?
BA2 Do you agree that social media marketing helps your customer to recognize your product?
BA3 Does social media help your organization to become familiar with your product or services?
SH1 Sharing Social media is the best way to share your view about a product or service.
SH2 News or announcements about the products on SM helps in sharing between like-minded people.
SH3 Social Media content motivates the consumer to share in their network.

Statistical and Data Analysis Approach

Structural Equation Modeling (SEM): Structural Equation Modelling is a part of multivariate statistical techniques employed to examine both direct and indirect relationships between one or more independent latent variables and one or more dependent latent variables (Gefen et al., 2000). With SEM several multivariate statistical analyses may be conducted, including regression analysis, path analysis, factor analysis, canonical correlation analysis, and growth curve modeling (Gefen, et al., 2000); (Urbach & Ahlemann 2010). Structural Equation Modelling allows researchers to assess the overall fit of a model and to test the structural model all together (Gefen et al., 2000; Chin, 1998) Figure 4.

Figure 4 The Structural Model and Measurements

In PLS-path modeling statistical analysis, there are two kinds of models. One is an outer model and the other one is an inner model which is referred to as the measurement model and the structural model, respectively.

An outer or a measurement model reflects the relationship between each ‘unobserved’ construct or latent variable (LV) (blue circles), that needs to be predicted, and the independent ‘predictors’ which are the ‘indicators’ or ‘observed measurement items’ (yellow squares) that are also referred to as ‘manifest variables’ (mvs) (Henseler et al., 2009). The factorial analysis is applied in the analysis of the measurement (outer) model(Mateos-Aparicio 2011).

Convergent Validity and Reliability

Convergent validity of a scale could be achieved if the measured variables of each construct in the scale converge, and convergent validity could be assessed by analyzing factor loadings, variance extracted, and reliability. Factor loadings, which is one indicator of convergent validity, need to be significant, and standardized loadings are required to be 0.50 or higher (Hair et al., 2010).

The construct reliability was assessed with Cronbach's alpha value above 0.60 and composite reliability (CR) values above 0.70 (Hair et al., 2010). As the Cronbach's alpha value of all the latent variables are above 0.60 and all the CR values are above 0.70, the construct reliability was established.

After that, convergent validity is determined by factor loading and average variance extracted (AVE) having a value above 0.50 (Hair et al., 2010). The majority of the factor loading values are above 0.70 and thus acceptable. In addition, an AVE of 0.50 or more means that the latent construct accounts for 50% or more of the variance in the observed variables, on the average.

The Table 2 shows that all AVE values were more than 0.5, so for this research model, convergent validity was confirmed. The findings confirmed that the structure explains at least 50% of the variance of its goods. This shows that more than 50 percent of the variance of the indicator is explained by the structure, thus giving acceptable item reliability. The table demonstrates that for all constructs the composite reliability (CR) is higher than 0.80. The CR showed that the scales were reasonably reliable and indicated that the minimum threshold level of 0.70 is exceeded by all latent construct values.

Table 2 Results of Measurement Model Analysis
Latent variables Items Factor Loading Cronbach's Alpha Composite Reliability Average
Variance Extracted (AVE)
Brand Awareness BA1 0.761 0.635 0.805 0.578
  BA2 0.748      
  BA3 0.773      
Sharing SH1 0.776 0.671 0.820 0.603
  SH2 0.78      
  SH3 0.774      

Structural Model Analysis

After the validity of the full measurement model is confirmed, the structural model is examined (Hair et al., 2010). The structural model analysis is used to test the hypotheses proposed in the theory. Structural model analysis accepts or rejects the stated hypotheses which show the significance of the relationship (Schumacker & Lomax 2004). We confirmed the validity and reliability of the measurement model.

To estimate the structural model, a bootstrapping procedure with a subsample of 1000 had been applied in this study (Sarstedt et al., 2016). In smartpls, the sample size is known as cases within the Bootstrapping context, whereas the number of bootstrap subsamples is known as Samples in Table 3.

Table 3 Results of Structural Model Analysis (Direct Effect)
Direct Paths Path coefficients (b) T Statistics P Values Results
H1: Sharing -> Brand Association 0.596 5.153 0.000* Significant

Path Coefficient Path Coefficient β- Value and T -Statistic Value

The path coefficients in the PLS and the standardized β coefficient in the regression analysis were similar. Through the β value, the significance of the hypothesis was tested.

The β denoted the expected variation in the dependent construct for a unit variation in the independent construct(s).

The β values of every path in the hypothesized model were computed, the greater the β values, the more the substantial effect on the endogenous latent construct. However, the β value had to be verified for its significance level through the T-statistics test.

The bootstrapping procedure was used to evaluate the significance of the hypothesis. To test the significance of the path coefficient and T-statistics values, a bootstrapping procedure using 1000 subsamples with no significant changes was carried out for this study.

In the two-tailed tests, t value is statistically significant when it is out of the range of -1.96 and +1.96, and the p-value is less than 0.05 (Byrne, 2013).

Finding: Therefore, hypotheses number H1 was significant and supported. The variable ‘Sharing’ had the path coefficient (β=0.596) which indicated that when the sharing is increased by 1 standard deviation unit, the brand association will be increased by 0.596 standard deviation unit.

Coefficient of Determination (R2)

The most commonly used measure to evaluate the structural model is the coefficient of determination (R2 value). This coefficient is a measure of the model’s predictive power and is calculated as the squared correlation between a specific endogenous construct’s actual and predicted values. The coefficient represents the exogenous latent variables’ combined effects on the endogenous latent variable. Because the R2 is the squared correlation of actual and predicted values and, as such, includes all the data that have been used for model estimation to judge the model’s predictive power, it represents a measure of in-sample predictive power (Rigdon, 2012; Sarstedt et al., 2014).

The coefficient of determination or R2 is used to assess the explanatory power structural model in PLS. R2 can range from 0 to 1 (Hair et al., 2019). The higher values indicate a better prediction power in Table 4.

Table 4 Coefficient of Determination (R Square)
Latent variables R Square R Square Adjusted Comment
Brand Association 0.381 0.379 Moderate

F Square

Effect size (f2) is used to assess the impact on endogenous or outcome constructs due to removing an exogenous or independent construct (Hair et al., 2019). In other words, the f2 value denotes what changes may happen in the endogenous construct (e.g. Brand association), if one of the predictor variables are omitted (e.g. Sharing). As shown in the following Table 5, removal of sharing will be a large effect on brand awareness.

Table 5 F Square
  Brand association Sharing
Brand association    
Sharing 0.450  


The hypothesized paths of social media sharing for social media marketing were found to be statistically significant. The results revealed that sharing factor potentially impacting brand awareness. Convergent validity and reliability measurement reveals that the observed data is reliable and valid for further study. Hypotheses were significant and supported. The variable ‘Sharing’ had the path coefficient (β=0.596) which indicated that when the sharing is increased by 1 standard deviation unit, the brand association will be increased by 0.596 standard deviation unit. Effect size reveals that the removal of sharing will have impact on brand association.


Several studies on the social media marketing affecting on brand have been published. However, there are few studies conducted on specific social media factor. A specific social media factor may help startup to focus on particular social media channel. On the basis of all acknowledged social media factors, this study evaluates sharing factor by measuring effect on brand association. The principal aim of this paper was to establish and evaluate the impact of brand association within the context of technology startups. The results of this research work confirm the significance of sharing on social media channel to increase brand association.

Theoretical and Practical Implications

This study has given some implications regarding theoretical and practical views. In terms of theoretical part, it has contributed a literature review on the sharing and brand association. Particularly, in the perspective of the startup companies. Hence, findings could be valuable to conduct further research in the area of the social media factors like presence, conversation, and social media groups. From the perspective of practical contributions, it provides direction towards the other brand equity factors for startup companies. Startup can prioritize the social media factors to optimize cost that have more impact on brand equity. Further study can be made to find out the most suitable social media channel for each social media factor. A comparison of impact and cost of each channel may help to focus on particular channel and social media marketing factor based on industry type.

Limitations and Scope for Future Research

This study is limited to one social media factor from honeycomb of social media framework, brand association factor from Aker’s brand equity model and technology startup companies in Hyderabad, India. As current dependent variables focus on social media sharing, within the context of technology startups, other social media marketing variables such as group and conversation can be taken into account for the future research. Moreover, this research focused on technology startups and did not take into account the other industry startups. Thus, further, study could be conducted by taking consideration of other industries.


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