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Assistant Professor Senior, VIT University
Vellore, Tamil Nadu, India
Tel: +8870581145
Email: [email protected]

Assistant Professor, Commerce Division, School of Social Sciences and Languages, VIT University, Vellore, Tamil Nadu, India

Professor, Commerce Division, School of Social Sciences and Languages, VIT University, Vellore, Tamil Nadu, India

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Digital universe in India is doubling in size every two years and will multiply nine-fold between 2014 and 2020. As per ASSOCHAM, the value of Indian e-commerce market in 2012 was $8.5 billion and $16 billion in 2013 and it is estimated to be $56 billion in 2023. Sources of e-commerce depend on the effective shopping, prompt delivery and increased use of online payment mechanism. Online shopping has changed the face of marketing globally. It has helped in easier, simpler and faster business transactions. Today each and every household is using online shopping. India being a highly populated country is positively transforming towards online shopping. Therefore there is a huge scope for both business and teenagers in India for online selling and buying household goods. As the Indian population is adding more educated and expert in internet technology, online shopping is moving drastically. In this background, the present descriptive study is an attempt to investigate the important factors influencing teenager’s behaviour, attitude and perception towards online shopping in Vellore district of Tamil Nadu in India.


Online Shopping, Behaviour and E-Commerce


Intensification of Indian E-commerce is the result of mounting customer convenience to online shopping websites due to the rising mobile internet diffusion in India. Mobile phones, laptops and personal computers are the new sources for admission to online shopping sites which are more popular due to varying lifestyle of teenagers and decrease in tariffs 3G or 4G data plans by all popular internet service providers. In a noticeable change from the previous age group of shoppers, consumer behaviour has come out from a choice of touch and feel buying to ease and comfort, E-mode of payment like debit card, credit card or net banking, EMI option, onsite replacement, cash on delivery and free home delivery. These facilities have added the value of E-commerce.

Overview off E-Market

As per E-market survey 2014, India’s E-commerce sales growth will be 30.3% in 2015 which is much higher than UK and France. As per I-cube 2011 estimates, the active internet users in India stands 65 million of which 7.5 million represents small towns which shows that within 5 years, India’s rural market will be twice bigger than the urban market today.

As per Mckinsey’s E-commerce policy index, there is possibility to get better the online payment and internet speediness in India. In India, share of internet is 1.6% of GDP in contributing $30 billion in total. As per Google India, Indian users will be around 500 million online by 2018. As per E-marketer report 2014, the projection of business to business E-commerce sales growth worldwide from 2012 to 2015 with the percentage of change states that China stands first with 43.3% growth in 2015 and India stands second with 30.3% growth in 2015.

The data pertaining to projection of BCB E-commerce sales growth are presented in Table 1.

Country Percentage change
2012 2013 2014 2015
China 93.7 78.5 63.8 43.3
India 35.9 34.9 31.5 30.3
France 32.3 10.3 10 9.8
UK 13.7 13.7 12.2 10.2
Japan 12.3 10.2 7.1 6.7

Table 1: Projection of BCB E-commerce sales growth worldwide.

Triumph of Online Shopping

Technology has transformed the way in which teenagers are buying books, tickets and other things. Now one needs to visit these sites like flipkart, Snapdeal, Amazon, E-bay etc in place of going physically to the market. In India, Indian Railways is the best model where one can book ticket online but then there are number of websites like make my trip, travel guru etc. The other fields where it has changed the way of doing the business are job portals like monster, Naukri. In real estate examples are India property, 99acres, magic bricks. Cinema theatres example book my show, Stock market example is money control. The success tasted by these companies show the potential market and the new way forward for the other companies to motivate [1].

Online Shopping Frenzy

According to Coupon Dunia GOSF, 2014 trends, the increase in E-tailers’ transactions in percentage, given in Table 2, indicates that the Amazon stands first with 820 transactions and Snapdeal stands second with 553 E-tailing transactions.

E-Tailer Percentage of transactions
Amazon 820
Snapdeal 553
Myntra 459
Jabong 275
Shopclues 204

Table 2: E-Tailers transactions.

In addition to above, the E-tailers saw a steep spike in the number of visitors by age logging via smartphones during Google’s Great Online Shopping Festival (GOSF). Table 3 states the GOSF 2014 visitors by age.

Age Percentage of visitors
18-24 36.1
25-34 39.6
35-44 9.7
45-54 7.3
55-64 3.5
65+ 3.6

Table 3: Online shopping visitors by age.

Review of Literature

The review of literature collected for this study on factors influencing teenagers behaviour towards online shopping reflects the opinion of various experts based on their experience. In order to validate the importance of this study, the following literature has been reviewed to get the statement of the problem.

Peter et al. [2] advocated consumers’ reference groups and their influence on consumers’ thoughts, feelings, behaviour and purchase decisions.

Crawford in his paper found that traditional consumer behavior shopping has its own model, which the buying process starts from the problem recognition, information search, evaluation of alternatives, then purchase, and at last post-purchase behaviour. Menon et al.; Childers et al.; Mathwick et al. found that online shopping actions depends either on consumer awareness like user-friendliness, convenience or it can be arousing like enjoyment by including both utilitarian and hedonic dimensions.

Pealtie et al. [3] have indicated that environmentally responsive consumerism is addressing the implication of buying behaviour.

Zhu et al. [4] found that users’ personal characteristics such as technological readings, motivation, ability, role clarity, inherent novelty seeking, need for interaction, trust in technology and self-consciousness and so forth influence adoption behaviour.

Mauer et al. state that decrease in operational cost is possible as the whole business can be moved online. The want for the physical stores has become outdated. It is more simple and faster to judge the prices of goods online, equipping the client with the information to make a decision about the right price or terms for themselves.

Ahn et al., Lee and Joshi found that delivery performance has significant influence on customer satisfaction.

Alam et al. [5] concluded that factors which influence a consumer to go for online shopping include product variety, design, reliability and performance.

Gomathi [6] expressed that computers have become indispensable to the human life. The world has been bubbling with a new technology about which newspapers have been writing and people are admiring about E-commerce.

Crisil Research [7] pointed out that the online retailing scenes in India has witnessed strong growth in recent years and is poised for explosive growth. Although in a recent stage now, the business is expected to grow up 55% annually to become a Rs. 50,000 crore business in the next three years.

Selvam [8] found that the key factors that drive growth in retail industry are young demographic profile, increasing consumer aspiration, growing middle class incomes, improving demand, rising incomes and improvement in infrastructure, enlarging consumer markets and accelerating the convergence of consumer tastes. The study found that FDI in retail is not only necessary for economic growth but also it provides better value and more opportunities to end consumers in India.

Sridevi et al. [1] analysed that the consumer exposure varies to a great extent nothing like traditional media like billboards, newspapers and television. Visibility is the prime precondition for online marketing. People must know about the company, its product and services Traffic cannot increase if people do not know about the company, its product and its services. Website marketing services increase website visibility by optimising and promoting the website.

Based on the above various literatures, the study makes further attempt to identify the problem of the study on the above topic in Vellore district of Tamil Nadu, India.

Statement of Problem

The researcher identified the statement of problem through various literature cited above is that there have been very significant factors that change the behavior of teenagers in the recent past. They are willing to experience and try new technology. Therefore the educated teenagers are showing more interest in online shopping. But in real life situations teenagers’ behavior towards online shopping is not up to their satisfaction [9]. They face more problem like they cannot touch and feel products, order takes several days to deliver, shipping costs are often excessive, poor after sale service, return of product is difficult, lack of access of require technology, perceived risk of electronic shopping, security of online transactions, computer literacy and use of credit card etc.

Based on the above identified few problems, there are some other studies stressed on other areas like customer satisfaction, challenges, opportunities, growth and online fraud on E-commerce in India but no study found so far in Vellore district on shopping behavior of teenagers and researcher want to fill this research gap.

Objectives of Study

1. To find out the factors influencing youngsters to go for online shopping.

2. To study the behaviour of youngsters towards online shopping.

Research Methodology

On the basis of the objectives, descriptive research design is used. Primary and secondary data is taken for the analysis. To be more interactive, personal interview is also conducted. Well framed questionnaire was used to collect data from respondents with 16 questions having close end and likert’s scale. The population of the study was internet users in Vellore district of Tamil Nadu India [10]. The sample size of the study was 48 consisting of both male and female students from university situated in Vellore district aged between 18-24 years.

To fulfill the objectives of the study the factor analysis was applied to identify dominant influencing factors by applying SPSS software.

Limitations of the Study

The present study is restricted to Vellore district of Tamil Nadu India only. The sample size was 48 only [11]. The period of study was six month from August 2015 to January 2016. The result was not similar if it is conducted in other district

Data Analysis and Interpretation

For the purpose of analysis and interpretation, factor analysis has been used. The purpose of this study is to find out minimum number of factors that have maximum variance in the data collected.

16 variables have been used for factor analysis which are Browsing, Desired products, Quality, Price, Speed, Range of products, Delivery, Packing, Time, Cost of Delivery, Savings, Offers, Benefits, Payment, Security and After sales.

For the verification of the data to be appropriate for the purpose of factor analysis, KMO and the Bartlett’s test has been conducted which is shown in Table 4 as follows

KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .775
Bartlett's Test of Sphericity Approx. Chi-Square 447.370
Df 120
Sig. .000

Table 4: KMO and the Bartlett’s Test.

The higher value of KMO and Bartlett’s test favour that the data are appropriate for carrying out factor analysis.

Principal Component Analysis

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components [12]. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components [13]. The resulting vectors are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables (Table 5).

Statistics Variables
Browsing Desired product Quality Price Speed Delivery Packing Time Delivery Cost Saving Offers Benefits After Sales Payment Security Range of Product
Correlation Browsing 1 0.466 0.481 0.521 0.039 0.277 0.33 0.417 0.315 0.494 0.324 0.42 0.608 0.548 0.509 0.338
Desired product 0.466 1 0.714 0.698 0.091 0.309 0.154 0.473 0.317 0.542 0.506 0.523 0.434 0.416 0.514 0.331
Quality 0.481 0.714 1 0.661 -0.024 0.373 0.236 0.397 0.225 0.417 0.598 0.554 0.518 0.447 0.603 0.273
Price 0.521 0.698 0.661 1 -0.011 0.191 0.123 0.447 0.4 0.475 0.363 0.513 0.497 0.506 0.561 0.525
Speed 0.039 0.091 -0.024 -0.011 1 -0.006 -0.081 0.251 0.085 0.085 0.122 0.143 0.033 0.036 0.162 0.321
Delivery 0.277 0.309 0.373 0.191 -0.006 1 0.696 0.559 0.18 0.398 0.473 0.258 0.449 0.291 0.419 0.073
Packing 0.33 0.154 0.236 0.123 -0.081 0.696 1 0.443 0.476 0.372 0.393 0.239 0.475 0.478 0.377 0.113
Time 0.417 0.473 0.397 0.447 0.251 0.559 0.443 1 0.483 0.413 0.401 0.43 0.444 0.453 0.557 0.314
Delivery Cost 0.315 0.317 0.225 0.4 0.085 0.18 0.476 0.483 1 0.502 0.286 0.484 0.368 0.478 0.231 0.238
Saving 0.494 0.542 0.417 0.475 0.085 0.398 0.372 0.413 0.502 1 0.569 0.443 0.517 0.538 0.55 0.141
Offers 0.324 0.506 0.598 0.363 0.122 0.473 0.393 0.401 0.286 0.569 1 0.587 0.423 0.402 0.536 -0.061
Benefits 0.42 0.523 0.554 0.513 0.143 0.258 0.239 0.43 0.484 0.443 0.587 1 0.429 0.36 0.434 0.185
After Sales 0.608 0.434 0.518 0.497 0.033 0.449 0.475 0.444 0.368 0.517 0.423 0.429 1 0.757 0.761 0.346
Payment 0.548 0.416 0.447 0.506 0.036 0.291 0.478 0.453 0.478 0.538 0.402 0.36 0.757 1 0.706 0.311
Security 0.509 0.514 0.603 0.561 0.162 0.419 0.377 0.557 0.231 0.55 0.536 0.434 0.761 0.706 1 0.289
Range of Product 0.338 0.331 0.273 0.525 0.321 0.073 0.113 0.314 0.238 0.141 -0.061 0.185 0.346 0.311 0.289 1

Table 5: Correlation Matrix.

In the analysis under the coefficient display format, the suppress absolute values is taken less than 0.30. The above Table 5 also contains maximum values which are more than 0.30. Therefore data are correlated.

Table 6 shows the total variance attributed to the components. In the study only 4 variables have eigenvalue more than 1 and these 4 components explain the variation of 69.860%. So it can be concluded that we have only 4 components [14]. These factors are shown in the ‘Extracted sums of Squared Loadings.’

Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative % Total
Security 7.074 44.215 44.215 7.074 44.215 44.215 5.819
After Sales 1.641 10.253 54.468 1.641 10.253 54.468 4.694
Payment 1.309 8.180 62.648 1.309 8.180 62.648 3.014
Quality 1.154 7.212 69.860 1.154 7.212 69.860 1.336
Price 0.993 6.206 76.067        
Desired product 0.841 5.257 81.323        
Saving 0.556 3.472 84.795        
Time 0.528 3.299 88.094        
Browsing 0.472 2.952 91.046        
Offers 0.334 2.086 93.132        
Benefits 0.270 1.690 94.822        
Delivery Cost 0.214 1.336 96.158        
Packing 0.187 1.171 97.329        
Delivery 0.181 1.130 98.459        
Range Of Products 0.168 1.052 99.511        
Speed 0.078 0.489 100.000        

Table 6: Total variance explained.

The screen plot clearly shows that component 1 is the most significant component and components 2, 3 and 4 has little less significance as the screen plot shows an elbow at component 2 and 3 (Figure 1).


Figure 1: Screen plot.

Component Matrix has been prepared in Table 7 to shows clear relationship between 4 components and other variables.

  1 2 3 4
Security 0.808      
After Sales 0.795     -0.324
Payment 0.762     -0.332
Quality 0.746   -0.417  
Price 0.737 0.456    
Desired product 0.735 0.316 -0.302  
Saving 0.730      
Time 0.704      
Browsing 0.694      
Offers 0.678   -0.402 0.364
Benefits 0.676     0.320
Delivery Cost 0.570      
Packing 0.555 -0.661    
Delivery 0.468 0.579    
Range Of Products 0.412 0.549 0.582  
Speed   0.313 0.480 0.674

Table 7: Component Matrix.

The above component matrix shows that variables Security, After sales, Payment, Quality, Price, Desired Products, Savings, Time, Browsing, Offers, Benefits, Delivery Cost and Packing are closely associated with Component 1. Variable Delivery is associated with component 2; variable Range of Products is associated with Component 3 and variable Speed is associated with Component 4.

After analyzing these variables, we found that the variables Security, After sales, Payment, Quality, Price, Desired Products, Savings, Time, Browsing, Offers, Benefits, Delivery Cost and Packing which is grouped under component 1 can be named as “Marketing Strategies of the company.” Similarly variable Delivery which falls under component 2 can be named as Delivery system” [15]. Variable Range of Products falls under component 3 and can be named as “Products Diversity” and variable Speed is the only variable having high value and can be grouped under factor 4 and can be named as “Browsing Speed.”

Therefore we can say that Marketing Strategies of the company, Delivery system, Products Diversity and Browsing speed are the major factors identified by the factor analysis. But Marketing Strategies of the company is the most influencing component as it covers most of the variables. At the same time Delivery system, products diversity and browsing speed also influence teenagers but not as much as marketing strategies.

Whatever marketing strategies companies are using to sell its products online are attracting youth towards its website. They offer different offers, price range, variety and so on. Teenagers want a safe and secure system, easy browsing, and wide range of products to compare and after sale services [16]. If all these are facilitated by the online supplier, more teenagers go for online shopping. Another factor which is delivery of the products, plays an important role in influencing people because after all factors taken into consideration, if the products are not delivered in good condition with no proper packing or on time, its affect customers’ expectations and they lose interest in buying for online shopping because they think that if they could have bought the product off line, they could have taken the delivery safe and on time.


The teenagers are very much concerned about the way the online websites sellers are meeting their expectations. The study found that four components are most important in influencing teenagers’ behavior while going for online shopping. These factors are Marketing Strategies of the company, Delivery System, Product diversity and Browsing Speed. These factors if considered in analyzing factors influencing teenagers behaviour, will explain 69.860% of variance in the data collected. It means these four components satisfy 69.860% expectations of the teenagers.


The companies engaged in online shopping should incorporate these four factors while designing or marketing their products online. As this market is entirely different from the traditional market, a customer believe only on the information given or the promises made on the websites, it’s the responsibility of the company to fulfill their expectations by following these factors.


The study reveals the way the teenagers think and behave while going for shopping online. In online shopping platform, a digital market is presented before them in place of real market. They do not make impulsive purchase rather their decisions are affected by a number of reasons discussed above. The study has proved that if these factors are not considered seriously by the company, their decision may be changed. Therefore a company should follow and incorporate these factors.