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

Research Article: 2021 Vol: 25 Issue: 3

Factors Influencing Young Consumers on E-Retailing An Empirical Study To Analyze E-Satisfaction Leading To E-Loyalty In the Indian Context

Sashikala P, ICFAI Business School

Vijayudu G, ICFAI Business School


In 21st century, the evolution of e-retailing in India is growing phenomenally. The role of young consumers’ participation especially the age group of 18 to 25 years is highly appreciable in the e-retailing growth in India. This has triggered the researchers to study and find more relevant factors behind the youth involvement in e-retailing sector especially towards fashionable items. The purpose of this research paper is to provide an empirical framework for determining factors effecting young consumers’ online re-purchasing. This paper is testing whether the young consumer satisfaction is leading to young consumers’ repeat purchase behaviour or not? Hence, we proposed a model ‘young consumers’ e-loyalty over e-satisfaction’. Towards this, twenty variables (items) were taken from previous literature and framed the hypotheses. The primary data was collected from young consumers by following simple random sampling technique. The primary data was analysed by using factor, regression and logistic regression analysis. The results were found that the Product-specific factors, website design, price & payment methods and product delivery are the most determining factors influencing young consumers’ online satisfaction in e-retailing industry. The proposed model was proved through logistic regression that there is high significance between e-satisfaction and e-loyalty.


Young Consumer, E-Satisfaction, E-loyalty, Q-sort, Logistic Regression.


Michael Aldrich (1979) was the man behind the invention of online shopping (e-retailing). Since then, the e-retailing business across the world is in high progress. E-retailing is a web-enabled interface between a retailer and its target consumers for selling products and services on the web (Chen, Ayanso, & Lertwachara, 2018). In recent times, India has seen an amazing growth in online retail shopping. Customers are individual buyers especially from metros, tier one and tier two cities. There was a huge expectation by a joint study of ASSOCHAM and Resurgent that the annual growth rate is all about 115 percent for the year 2018-19 (ASSOCHAM, 2018). The report was also noted that for the year 2017-18 as many as 108 million consumers did online shopping. The most preferred categories by Indian consumers were apparels & accessories which is about 54 percent, beauty and personal care is about 43 percent (ASSOCHAM, 2018; Sagar Malviya, 2020). In all purchases, the quality is the predominant at any price that led to online customers’ satisfaction (Gehrt, 2012). Consumers also feel very convenient to shop online since they find themselves free from personally visiting the stores (Williams, et. al. 2006). It has given a new market space for the retailers (e-retailers) to approach e-retailing for better and direct access to online customers. Almost all big retailers are now electronically present on World Wide Web (www).

E-retailing companies strategized their operations to nurture customer satisfaction which retains the customer (Hansemark, et al. 2004). E-retailing companies were introducing wide verities of products that give the wider scope for the product selection that fulfil the potential needs of the customers (Mary Ellen Holden, 2017). In India, since the beginning of e-retailing companies, they are focusing more on young consumers and techno savvy customers, because they are the most powerful target customers, more adaptable and more convincible in this technological era. E-retailing companies were offering fanciful offers to their customers often. In this process, e-retailing companies are managing the marketing activities that identify, anticipate and supply customer requirements profitably (Gay, et al. 2007).

There are number of studies on customer e-satisfaction based up on developed countries. These studies indicate that the technology acceptance factors influence online customer satisfaction positively that led to customer online loyalty (Lin, & Sun, 2009). Website service quality can positively influence customer e-satisfaction and e-loyalty (Bai, et al. 2008) and specific holdup cost can positively influence customer e-loyalty but cannot positively influence customer e-satisfaction directly (Grace, et al. 2009). There are plenty of studies which tells about consumers’ psychological state, consumers’ attitude and consumer behaviour derived from online shopping experiences (Pennanen, (2009); Anderson and Srinivasan, 2003).

There is a paucity of literature on customer e-satisfaction based on developing and under developed countries. Being India is a developing country, Internet usage is about 45 percent i.e. mostly from metropolitan and tier one cities (Sagar Malviya, 2020)), the participation from all geographical locations is a major constraint. Even though some customers are interested from remote locations, the scope of e-retailers’ reachability is very less due to several reasons. For example: Internet signalling, transportation & infrastructure are very conservative in most of the rural India. Another major reason is lack of customer awareness about e-retailing and its process.

Many of the customers do not know who is selling the products, who are responsible for the product damages, failures, or any misleads in product delivery. Several studies proved the awareness level of e-retailing in India is very less (Harish Pal Kumar, 2017). ‘After sale service’ is almost zero in many of the e-retailing cases. Customers are sending several complaints but the response rate is minimal or sometimes zero from e-retailers (Gong, 2013). Most of the consumers are stopping their on-line purchases after first or second purchase due to several issues mentioned just before these lines. Hence, there is a need to understand the customers’ e-satisfaction especially the young and enthusiastic customers interested in purchases from e-retailing that is specific to India. It also needs to study about their regularity and frequency of purchases through e-retailing.

Hence, this research has taken up to study online customer satisfaction among young consumers in India. The main objective of the study is to study the online customer satisfaction and his repeat purchase behaviour with the online retailers. The second objective is to find the influencing factors towards online customer satisfaction. And finally, this study has looked into whether the satisfaction is leading to repeat purchase behaviour of a customer or not. This paper enlightens various issues in e-retailing and gives the conceptual framework to measure the e-satisfaction of young consumers who are in between eighteen and twenty five years of age. It also articulates the young consumers’ philosophy towards e-retailing. This will help e-retailers to develop new strategies in concern of major online purchase chunk i.e. young consumers.

Conceptual Framework and Study Measures

Customer Satisfaction is defined as an outcome of purchase and use resulting from the buyer’s comparison of the rewards and costs of a purchase in relation is similar to attitude in that it can be assessed as the sum of the satisfaction with the various attributes of the product or service (Churchill Jr, et al., 1982). Online customer satisfaction (e-satisfaction) is defined as the contentment of the customer with respect to his or her prior purchasing experience with a given electronic commerce firm (Oliver, 1997). Several researchers recognised several determinants behind the e-satisfaction. They are as customers’ convenience, product offerings, product information, web site design, and operational security. The convenience is the time and effort consumed by the online shopper (Farquhar and Rowley, 2009). The merchandise is measured through wide range of product offerings or product assortment and rich product information (Rigby, 2011). The web site performance should be very fast, no clutters in product or information display and ease in site navigation (Geissler, 2001). Finally, the operational security must be very high being the website is using for cashless transactions (Evanschitzky, et al. 2004).

Customer satisfaction is a strong determinant of the continuity of a customer’s relationship or association with a brand (Roy, et al., 2017). It measures that how products and services supplied by a company to meet or surpass customer expectation (Bell, et al., 2011). It is determined based on perceived service quality, customer mood, emotions, social interactions, and experiences (Pitchayadejanant and Nakpathom, 2016). The perceived service quality is defined by Parasuraman (1985) as ‘a global judgment, or attitude, relating to the superiority of the service’. This has been agreed upon the above statement by several researchers (Spreng & Mackoy, 1996). He and Liu, (2008) stated that the level of e-satisfaction has determined by the quality of e-services. E-S-Qual (e-service quality) proposed by Parasuraman (2005) which is the one among those prominent studies to measure service quality of an e-retailor. The central purpose of E-S-Qual is to measure efficiency of the website, fulfilment of the customer needs, system availability and privacy & security for the entire transactions on e-commerce website. Liu et al, (2008) is also agreed partially upon the Parasuraman’s E-S-Qual and expressed his ideas as website design (Cyr, et al. 2008), information quality, customer services, payment options and delivery are the strong indicators of customer e-satisfaction. User interface and overall navigation of particular websites make the customer experience very convenient thus increase customer e-satisfaction level (Lee and Lin, 2005). Jiradilok, et al, (2014) opined that the product delivery has the strongest influence on customers' satisfaction which leads to future purchase intentions.

Purchase convenience, availability of product varieties (Felipe, 2010; Da Silveira, 1998), offering various kinds of services and the social interaction with the customers will help e-retailers to predict online customer satisfaction (Christodoulides and Michaelidou, 2010). From the consumer's perspective, obtaining value is a substantial consumption goal in the successful shopping experience (Davis and Hodges, 2012) that leads to satisfaction.

Price of a product or service is another major influencing factor that impacts the purchase decision of a consumer. Even for the marketers ‘pricing’ is a major challenge even to sell an item at a fixed price especially in a running trend of online sales (Guo et al., 2018) because the bundling or coupon discount pricing strategies are more popular on online purchase (Jiang et al., 2018). Coupons are used as a promotional and marketing campaign with a key objective of boosting the sale through new customers or retaining the existing customers (Poisson, 2018). However, ‘price’ will be as one of the elements either as a major or minor but it depends on the consumer. It is not the only factor that affects the purchase decision process (Faith & Agwu, 2018). However, the value based price is always preferred by the consumers (Koschate-Fischer, 2018).

Oliver (1999) defined customer loyalty as ‘a deeply held commitment to rebuild and re-patronize a preferred product or service in the future despite situational influences and marketing efforts having the potential to cause switching behaviours’. Customer loyalty is viewed as the strength of the relationship between an individual’s relative attitude and re-patronage. Thomas and Tobe (2012) emphasize that ‘loyalty is more profitable’. The expenses to gain a new customer is much more than retaining existing customer. Loyal customers will encourage other fellow customers to buy the same. Loyalty is a deeply held commitment to re-buy a preferred product or service consistently in the future (Wong, et al. 2019).

Thus, after reviewing various literatures, it can be said that the customer satisfaction is depended on various items. It is important to study each one of these items carefully so that one can understand the importance of each item and its level of contribution to the e-satisfaction. Once similar kinds of items formed under one factor, one can understand the importance of that factor. The e-satisfaction level can be improved upon the improvement of most influential factor (Eid, 2011). Hence, this study is framed based on so many individual factors from review of literature, and followed some critical issues in Indian scenario and tested them in a pilot study and validated them. Finally the factors are framed and tested through primary data.


Youth buying pattern is more dynamic in nature. Brand belief is a major concern in youth segment (De Mooji, 2019). They are prejudiced to watch online retail fashion often (Christel and Dunn, 2018). The referrals or attitude towards intended to buy things, or the impulse buying behaviour of the young consumers, collectively or independently is tempting the young shoppers to buy things online on different payment modes (Yang, et al., 2007). Most often, youth are using mobile apps, websites for buying things online. In this study, customer satisfaction is as a dependent variable. A number of independent items that influence the customers are product information, product variety, payment and delivery methods, customer convenience, last purchase experience which he or she has done in last six months (Juaneda-Ayensa, et al. 2016), the website design (Szymanski and Hise, 2000), and the online operational security. We understand the young consumers who are more flexible/ brand switchers. For example; customer A is buying things from an online store when the offers are in a row, because the customer is finding more or less the same brands/ products in all online stores except exclusive store brands or some specific brands that are available in that specific online store. Hence, the online retailer cannot estimate the future demand based on the present sales (Guo, et al. 2018).

Objectives of the Study

The primary objective of the study is to analyse the factors which are giving satisfaction for his or her online purchase followed by the secondary objective which to analyse the role of customer satisfaction in repeat purchase behaviour of the customer from the same website.

Sampling Procedure

For this study, we have taken simple random technique in which each sample of the population will get the same chance being selected as a subject (Al Ghayab, et al. 2016). The research respondents are young, pursuing Management / Engineering / Law / Science graduates from various B-schools / Degree colleges. The young generation between the age 18 and 25 years who are the most frequent game changers / mobile application changers and also most adoptive customers (Knežević, B., & Delić, M. 2017).We have kept the data set in excel and then followed ‘RANDBETWEEN’ function to select fifteen hundred random numbers including mail ids and demographic data like present age, education, occupation, income (family income), residential place, the recent purchase date (approx.), frequency of visits online stores, frequency of buying things online. Responses have come during Jul 2019 to Nov 2019. Fourteen hundred and twenty four structured questionnaires were valid out of fifteen hundred. The purpose of this study is to measure the customer satisfaction towards online purchase which has the role in building customer loyalty towards online platform.


A set of questions were developed with each question corresponding to an item which measures a specific characteristic of the variable understudy. The primary stage is to develop a binding measure that majorly depends on strong requirement of the domain of the construct which is developed with a proper literature review (Churchill, 1979). A proper literature review along with the interviews of academicians and practitioners helped in generating effective and consistent dimension. The literature review provides the basis for the development of new constructs and corresponding construct definitions. These definitions are to be clearly stated for understanding the domain of each construct developed based on the previous research. The constructs are like this (1) Product-specific factors: product quality, product variety, product availability, product customization and product comparability. (2) Website Design: ease of finding, site design, product information, product selection process and perceived usefulness. (3) Price: price of the product, price comparison, special offers, lowest price and shipping charges. (4) Payment and Delivery: payment options, delivery location options, delivery options (speed, medium, and free), responsiveness and special charges.

Since the constructs were taken from both the literature and practitioner’s articles, after the successful generation of the initial stage, a basic validity test was carried out followed by scale purification (Anderson, Gerbing 1991). In the stage of pilot testing, the irrelevant items were removed based on the agreement in a discussion with two academicians. In consultation with two academicians the unrelated items were removed or altered and pilot testing was done. In this study, Q-sort technique (Moore and Benbasat, 1991) was implemented till the main Q sort indicators of raw-agreement, item placement ratio and Cohen’s Kappa were all above 0.9. In the first round the values did not reach the requisite values and hence after incorporating the necessary changes the items entered second round of Q-sorting which yielded all the three values above 0.9 with a different set of judges In this process of item generation, some items are adapted from the context of existing scales and some others were generated for the new scales. The items were exposed to pretesting in order to adapt them so that there would be strength and improved understanding and validity. In the later stages after pretesting some items were edited, modified and removed if required under the supervision of experts based on logical descriptions and opinions in Table 1.

Table 1 Q-Sorting
Indices Round 1 Round 2
Raw agreement Score 0.79  0.93
Placement (hit) ratio 0.81 0.95
Cohen‘s Kappa 0.80 0.96

After using Q-sort, the thirteen subjective items were used for the final large-scale study representing four constructs (1) Product specific factors with five items, (2) Website Design with three items, (3) Price with three items, and (4) Payment and delivery with two items with a total of thirteen items across four constructs. Though based on literature and Q-sort exercise the four constructs stood out, whether after the Q-sorting still the factor structure stood out or not was investigated using exploratory factor analysis (EFA) using Varimax rotation.

The structured questionnaire contains multiple choice questions, and then followed the Demographics (Gender, Age, Education, occupation, Family Income, Residential Location) of the respondents. The analysis has taken place using SPSS and SAS software for multiple analysis and accuracy. The proposed model is mentioned below in Figure 1

Figure 1 The Proposed Model for Customer E-Loyalty Through E-Satisfaction

Formulating Hypotheses

Product has several factors to fulfil the needs of heterogeneous customers.

H1: Product-specific factors: Product quality, product variety, product availability, product customization and product comparability influence positively the Customer Satisfaction.

H2: Website Design: Website design, clarity of product information, and product selection process influence positively the Customer Satisfaction.

H3: Price: Price comparison, special offers, and shipping charges influence positively the Customer Satisfaction.

H4: Payment and Delivery: Payment options and delivery location options influence positively the Customer Satisfaction.

H5: E-loyalty: Customer online-satisfaction positively influences the customer online-loyalty.

The above proposed model is to explain that the customer e-satisfaction rely on the product specific factors, payment & delivery factors, website design and price. These factors are influencing the satisfaction level of online customers. The present study has explored the satisfaction score of each factor and explained the relationship between the customer online satisfaction and the customer online repurchase behaviour.


This study also has some limitations. They are: (1) the respondents are in the age group of eighteen to twenty five which the age group is emotionally more involved rather than logic (Pascual, et al. 2020). (2) The data has come from internet through a structured questionnaire, so, there are no personal observations or personal interviews to understand the personal hesitations of the customers. (3) The data may be biased for some percentage. (4) This study is not considering store brand loyalty. Customers would have bought things from anywhere but it was online.

Data Analysis

Exploratory factor analysis is used in the present study to draw the factors by using SPSS 21. Logistic regression analysis is performed to find out the relationship between the e-satisfaction (as an independent variable) and e-loyalty (as a dependent variable). For the data analysis the researchers adopted SPSS 21 and SAS software for the data calculations and data accuracy in Table 2.

Table 2 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.804
Bartlett's Test of Sphericity Approx. Chi-Square 5883.58
df 78
Sig. .000

The KMO value 0.804 indicates that the data is adequate for factor analysis on the overall set of variables. Bartlett’s test of Sphericity finds the correlations within the set of variables and can assess the overall significance of the correlation matrix. The significant value is .000 which indicates that the correlations are significant in Table 3.

Table 3 Total Variance Explained
Rotated Component Matrix
1 2 3 4
Delivery locations 0.37 0.012 -0.012 0.699
Payment Options 0.024 0.085 0.045 0.841
Shipping Charges -0.209 -0.025 0.541 0.424
Special Offers 0.415 0.028 0.716 -0.03
Price Comparison 0.302 0.053 0.781 -0.051
Product Selection -0.023 0.933 0.027 0.046
Product Availability 0.581 0 0.398 0.109
Product Quality 0.617 -0.05 0.346 0.162
Web Site Design -0.114 0.915 0.044 0.031
Product Variety 0.587 -0.028 0.189 0.145
Product Information 0.068 0.659 -0.017 0.017
Product Customization 0.784 0.038 -0.098 -0.14
Product Comparability 0.697 -0.023 0.131 0.12
Extraction Method: Principal Component Analysis.
 Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.

Total Variance explained was: 61.76%, the total variance which satisfies the criteria of 60%. KMO value =.804, Bartlett’s test of sphericity result significant at 0.000. Further the convergent and discriminant validity have also been checked and all the Cronbach Alpha values were above 0.8 which showed very high reliability of the instrument. The validity and reliability of the scale was established. The AVE (average variance extracted) values for each construct were all high which represented squared inter-construct correlation and this showed good convergent and discriminant validity.

Table 3 EFA analysis has given four constructs, Product quality, product variety, product availability; product customization and product comparability are under one construct named Product-specific factors. Product selection Website design and product information are under one construct as Website Design. Shipping charges, special offers, and price comparison are under Price construct. Delivery location options and Payment options are under Payment and Delivery construct.

Considering these four factors as independent variables and Customer Online Satisfaction as dependent variable the regression analysis was carried out in Table 4.

Table 4 Regression Analysis
Model Summary b
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .917a 0.841 0.84 0.523
a. Predictors: (Constant), REGR factor score   4 for analysis 1, REGR factor score   3 for analysis 1, REGR factor score   2 for analysis 1, REGR factor score   1 for analysis 1
b. Dependent Variable: Customer Online Satisfaction (e-satisfaction)

The R2 was found to be 0.841 which indicates that 84.1% of the variation is explained by the independent variables. It also indicates the 84.1% of variation in customer e-satisfaction is explained by these four independent variables. The overall significance of the model is 0.000 which is lesser the level of significance (α = 0.05) which indicates the model is a significant model and the all the independent variables are contributing significantly to the dependent variable i.e. customer e-satisfaction. The model is a significant model. The website design has more contribution towards customer e-satisfaction which indicates that the consumers are more satisfied with the Website Design which includes that there is good clarity of product information, and product selection process which is positively influencing the Customer Satisfaction in Table 4 & Table 5.

Table 5 Anova Analysis
Model Sum of Squares df Mean Square F Sig.
1 Regression 2051.34 4 512.835 1873.5 .000b
Residual 388.424 1419 0.274    
Total 2439.764 1423      
a. Dependent Variable: Customer Online Satisfaction
b. Predictors: (Constant), REGR factor score   4 for analysis 1, REGR factor score   3 for analysis 1, REGR factor score   2 for analysis 1, REGR factor score   1 for analysis 1

The four independent variables Product Specific factors, Website design, Price, and Payment & delivery are having a significant values as 0.000 < the level of significance (α = 0.05) which indicates that all these independent variables are significantly contributing to the dependent variable individually in Table 6-Table 10.

Table 6 Coefficients
Coefficients a
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.907 0.014   281.819 0
Product Specific -0.14 0.014 -0.107 -10.096 0
Website design 1.19 0.014 0.909 85.806 0
Price 0.063 0.014 0.048 4.556 0
Payment and delivery 0.041 0.014 0.031 2.945 0.003
a. Dependent Variable: Online customer satisfaction
Table 7 Logistic Regression Analysis to Find Customer E-Loyal- Response Profile
Response Profile
Ordered Value Online Customer Loyalty Total Frequency
1 0 525
2 1 899
Table 8 Logistic Regression Analysis to Find Customer E-Loyalty
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Table 9 Logistic Regression Analysis – Testing Null Hypothesis
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > Chi Sq
Likelihood Ratio 8.6301 1 0.0033
Score 8.4870 1 0.0036
Wald 8.4334 1 0.0037
Table 10 Analysis of Maximum Likelihood Estimates
Parameter DF Estimate Standard Error Wald
Pr > ChiSq
Intercept 1 0.9597 0.1567 37.5251 <.0001
customer e-satisfaction 1 -0.1101 0.0379 8.4334 0.0037

This lead to the acceptance of the following hypotheses

H1: Product-specific factors: Product quality, product variety, product availability, product customization and product comparability influence positively the Customer Satisfaction.

H2: Website Design: Website design, clarity of product information, and product selection process influence positively the Customer Satisfaction.

H3: Price: Price comparison, special offers, and shipping charges influence positively the Customer Satisfaction.

H4: Payment and Delivery: Payment options and delivery location options influence positively the Customer Satisfaction.

Thence we check whether the customer e-satisfaction leads to customer e-loyalty. For this, the researchers considered customer e-satisfaction as independent variable and customer e-loyalty as dependent variable. Logistic regression was run and the following results were obtained.

Probability modeled is Online Customer Loyalty ='1'

The significant value based on Likelihood ratio, Score and Wald is < 0.05 which indicates that the model is significant with customer e-satisfaction is the only independent variable and it has significant impact on customer e-loyalty which is a dependent variable.

This leads to the acceptance of the Hypothesis:

H5. E-satisfaction: customer e-satisfaction influences the customer e-loyalty.


Customer e-satisfaction influences customer e-loyalty. Customer e-satisfaction is based upon the product specific factors, website design, price and payment & delivery options. At present online retail shopping is providing several user friendly options to attract online customers. Young consumers are more enthusiastic and impulsive customers in online retail shopping. Online retail platforms are providing mobile friendly services to their users. Smart phones are compatible for all kinds of software’s available in the market. E-retailers are providing user friendly mobile apps with less storage capacity and ease of using facilities with a high quality of internet supporting systems. Hence, searching for fashionable items on mobile apps is becoming as an entertainment, but not always with a purchase intention. While searching, consumers might have an impulse purchase, something which they are interested or buying things on referrals which are low price items. In present study, it is not considered the impulse buying or referral buying. There is huge scope to study on referral buying, what kind of goods youth will buy on referrals, impulse buying, time and money they spent on online rather than offline shopping. What is the most trusted thing on e-retailing for a young consumer? Product mix/ assortment are another attractive thing among young consumers. Availability of products, accessible of various payment methods and delivery methods may attract young consumers more.


From the regression analysis we see that the four factors Product, website design, price and payment and delivery influence the e-satisfaction of the young customers towards e-tailing (Kim, et, al. 2009). Out of these factors, the factor Product has a negative influence on e-satisfaction which implies that from the data collected from the young consumers product quality, variety, availability, customization and comparability is not leading to e-satisfaction whereas the other factors such as website design, price and payment and delivery are positively influence the e satisfaction of the young consumers. Since the overall model is significant at 5% level of significance all these factors are significantly influencing the customer e-satisfaction. Further the value of R2 is 0.841 which also indicates that the overall percentage of variation in the model is due to these independent factors.

It is also established that the customer e-satisfaction leads to customer e-loyalty which implies that the young consumers are satisfied with e-retailing and continue to be the loyal customers.


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