Future of B2C E-commerce (Buyers Perspective) in India: An Empirical Analysis

Abstract

India is one of the hottest destinations for online retailers. It provides the emerging prospect of business because of young generation. The present study is an evaluation of the future of business-to-consumer (B2C) commerce in India with major focus on identifying the satisfactory and dissatisfactory factors for online buyers. The study collects 206 respondents, out of which 161 respondents were used for further analysis. A total of 20 factors were considered, which were thought to make an impact on e-commerce consumer buying behaviour. After applying Kaiser–Meyer–Olkin (KMO) test for sampling adequacy and Bartlett’s test for sphericity, the study adopts principal component analysis for extracting relevant factors. The study identifies four core factors, namely, service quality, security and privacy, content and design, and customer delight. The finding of the study depicts that most of the consumers purchasing online are youngsters, educated and financially well off. The study shows that three-fourth buyers visit online shopping once or twice in six months. The top four products purchased online are books (40.99 per cent), electronics (39.75 per cent), apparel and accessories (36.02 per cent) and computer and peripherals (32.92 per cent). The top three attractor for buyers to buy from e-commerce portals are home delivery (70.19 per cent), time saving (61.49 per cent) and 24 × 7 availability (60.25 per cent). Security issues (65.84 per cent) and unclear and complex policies (60.87 per cent) are the major detractors for buyers in B2C e-commerce. The results of our analytical study indicate the relationship between the consumers’ perceptions about the factors that influence their intention while buying online, more specifically, consumers’ perceptions of the service quality, security and privacy, content and design on website, and customer delight factors.

Keywords

B2C e-commerce KMO test principal component

Introduction

The role of online retailers is to know that which factors affect the Indian consumers’ buying behaviour and also try to establish the relationship between the types of online buyers and these factors, which help the online retailers to formulate the marketing strategies in line with potential customers. If we look into data size of e-commerce market, then e-travel share is the maximum with a share of more than 70 per cent. The size of e-tailing market, which is a mixture of the online retail and online marketplaces, has now become the fastest growing segment in the larger market with Compound Annual Growth Rate (CAGR) growth rate of around 56 per cent over the period of 2009–2014. Around US$6 billion is the size of e-tail market. Apparel, accessories, electronics and books are nowadays the largest selling products with the help of e-tailing, having product distribution of more than 80 per cent. The increasing use of the tablets and smartphones, Internet broadband and, nowadays more popular, 3G has led to help the retailers to develop the stronger consumer base which is likely to increase further (Figure 1).

Figure 1.

Source: Retrieved April 16, 2016, from www.deloitte.com/in

Market Size

The Indian digital commerce market has seen a steady growth from 2010 to 2014 having US$4.4 billion to US$13.6 billion value, respectively. According to the industry estimates, by the end of 2015 there was an expectation that Indian digital commerce market will cross the US$16 billion mark due to the rapid growth of online shoppers and increase in the Internet population. Online travel is having a major share of business of e-commerce with 61 per cent, while the rest of the 29 per cent is contributed by e-tailing. According to the data of Visa India spend, there has been around 53 per cent growth in the total number of e-commerce transactions in 2014. At the same pace of growth, the size of Indian e-retail industry will, by 2017–2020, touch the mark of 10–20 billion US$. The said growth rate is expected to be influenced further by increased consumer-leading purchases of the durables, apparel, accessories and electronics, besides the traditional products like audio–visuals and books.

Similarly, the same growth trend is seen in the global market of e-commerce. The reports of eMarketer had already estimated that the e-commerce business-to-consumer (B2C) model sales revenue will reach around US$1.5 trillion in 2014 worldwide, with the increment of 20 per cent over the year 2013. As the e-commerce players, like Europe, Japan and the USA, are seeing slower growth in their home markets, therefore, they are increasingly looking forward to enter in developing economies, like Brazil, China and India, which have the expected growth rate of even higher than 20 per cent in the upcoming years.

Business Models

Indian e-commerce has now started to become more complex and crowded with many players battling together for a piece of fair share of customers’ wallet and mind. With an increase in the competition in the e-commerce, companies are now exploring multiple business models, in the hope to get the customers’ attention including:

Inventory based model (e.g., Croma, Shoppers Stop)

The social networks (e.g., TripAdvisor)

The aggregator model (e.g., Uber, Ola Cabs)

The e-marketplace (e.g., Snapdeal, Flipkart)

The transaction broker (e.g., IRCTC)

The Click and Collect service (e.g., Flipkart)

Since there has been more frequent changes in the business model and the delivery system has become more complex, increasing the innovation in sale, pricing, promotion and even the distribution channels will soon be seen going forward in future. The ecosystem of entrepreneurship is now rising with the innovative start-ups attracting more the mass market of India and further taking the opportunity in solving some of the more big challenges that are being faced by the country; for example, MyGrahak and BigBasket in delivering the groceries at a customer’s home, Portea with the medical care at home and Housing.com for the realty based challenges.

Review of Literature

According to Ha and Perks (2005), the relevant exogenous factors have been brought by this article in the context of ‘the consumer traits’, ‘the situational factors’, ‘the product characteristics’, ‘the trust in online shopping’ and ‘the previous online shopping experiences’. Collectively, these influences and effects on the consumers’ perception towards the online shopping that provide us a framework in order to understand consumers’ intentions, while shopping via the Internet. Apart from these said four latent dimensions, ‘the site characteristics’ like the download speed, navigation and the search functions also play an important role in the process of shaping the ease of use. As the above-mentioned site characteristics merely influence ‘the ease of use’ of a particular online store or the website and not the Internet as a shopping medium in general, we decided not to give much attention on these sites.

According to Mathwick (2002), the online shopping features, in general, can be either perception of the consumers for the functional and the utilitarian dimensions like ‘the usefulness’ and ‘the ease of use’ or their perception, in particular, for emotional and the hedonic dimensions like the enjoyment by including both the dimensions, the utilitarian and the hedonic. Aspects from the technology or the information systems literature, along with consumer behaviour literature, are basically integrated in the framework of our study. Apart from these online shopping features the exogenous factors are also taken care of, so that they could moderate the relationships between the core constructs of its framework.

It further stressed that if the consumers are enjoying their e-shopping experiences, they will tend to have more positive attitude towards the online e-shopping and they are most likely to adopt the Internet as their major shopping medium. In our present study, we have identified the three basic latent dimensions: the ‘enjoyment’ constructs, which includes the ‘pleasure’, the ‘arousal’ and the ‘escapism’.

Rationale of Study

To evaluate the future of B2C e-commerce in India, the first part considers the major challenges and recommends possible solutions. The second part focuses on B2C e-commerce sector in India. The major focus was to identify the major satisfactory and dissatisfactory factors for buyers, understand their buying patterns and evaluate the important influencing factors that mould their perception.

Data and Research Methodology

Primary Data

In this study, quantitative primary data was obtained through survey from buyers who had bought some items from any of the e-commerce portal present in the county in last one year. We have gathered data through online survey and personal interviews with consumers of any of the e-commerce portals. The survey was shared with Facebook friends, LinkedIn friends, batchmates, seniors in college, juniors in college, family, associated businessmen and employees working in a division of two leading organisations.

Questionnaire Design

The information required was:

Demographic, background or respondent characteristics;

Internet usage, pattern and purchase pattern;

Major influencing factors for online purchase; and

Major attractors and detractors.

Question Formulation

The questions included were:

The open-ended questions;

The close-ended questions; and

Rank order scale.

Sampling Technique Used

In the survey, random sampling was used; this implies that selecting any respondent had equal chance of selection.

Sampling Design

For sampling, respondents were identified through social websites, social groups and previous organisations I worked with. Bulks of respondents were from one state of the nation.

Sample Size

Out of 206 respondents, 25 respondents gave self-contradictory answers and were dropped from the data analysis; 20 respondents never did online purchase, hence those were also dropped from the analysis. For all analysis of B2C e-commerce, 161 respondents were considered.

Secondary Data

Secondary sources were used to collect qualitative data collection; it is important to have an instrument that guides data collection. Sources used are:

Website of e-commerce portals;

Some published journals and articles in the field of B2C e-commerce;

Industry reports;

Public forum for e-commerce customers; and

B2C e-commerce.

Buyers Perspective

In this part, researchers have studied the sector evolution with the recent trends and different business models. A primary data collection has been done through online questionnaires. For this 206 responses were received. After critical examination, 161 responses were considered for final evaluation of the individuals who have done online shopping atleast once in last one year. The study analysed the demographic distribution of the buyers, understood their current perceptions and future expectations from the industry. Through the study, it has been evaluated that the perception of online shopping is influenced by gender, age, educational level and income. The research also empirically investigated the factors that are being considered as the thought which will build the Indian consumers’ perception. The present study has tried to achieve the objective through the application of the statistical technique of factor analysis and the extracted basic four most important factors out of the initial twenty factors. The result showed the most influencing factors were service quality, security and privacy, content and design on website, and customer delight factors. The research was concluded by providing recommendations to further increase the trust and improve the perceptions of both buyers and sellers towards e-commerce sector.

Data Interpretation and the Outcomes

Evaluation of the Demographic Data of Samples (Table 1-24)

Table 1.

B2C Consumer Demographic Analysis: Gender

Gender	Responses	Percentage
Male	91	56.52
Female	70	43.48
Other	0	0.00

Source: Authors’ findings.

Key Finding: Over 56 per cent of the buyers are males and the rest are females. No third gender is part of the sample.

Table 2.

Sample Age

Age	Responses	Percentage
18–20	11	6.83
21–30	63	39.13
31–40	41	25.47
41–50	22	13.66
51–60	18	11.18
Above 60	6	3.73

Source: Authors’ findings.

Key Findings: Over 78 per cent of the buyers are in the age bracket of 21–50 years.

Table 3.

Sample Marital Status

Marital Status	Responses	Percentage
Married	95	59.01
Unmarried	61	37.89
Other	5	3.11

Source: Authors’ findings.

Key Findings: 59.01 per cent of the online buyers are married and 37.89 per cent of the online buyers are unmarried.

Table 4.

Sample Highest Level of Education

Highest Level of Education	Responses	Percentage
Never attended	0	0.00
Secondary (10th) and below	6	3.73
Higher secondary (12th)	27	16.77
Graduation	69	42.86
Postgraduation	54	33.54
Doctorate (PhD)	5	3.11

Source: Authors’ findings.

Key Findings: 79.50 per cent of the buyers are at least graduates; all buyers in the sample had some level of education.

Table 5.

Sample Occupation

Occupation	Responses	Percentage
Student	58	36.02
Job professional/service	61	37.89
Businessman/self-employed	19	11.80
Home maker	15	9.32
Retired	8	4.97

Source: Authors’ findings.

Key Findings: Close to 50 per cent of the online buyers are bread earners and while 36.02 per cent of the online buyers are students.

Table 6.

The Monthly Family Income of the Buyers (in Indian Rupees)

Family Income (per months)	Responses	Percentage
Less than Rs.10,000	0	0.00
Rs.10,001–Rs.20,000	2	1.24
Rs.20,001–Rs.30,000	3	1.86
Rs.30,001–Rs.40,000	62	38.51
Rs.40,001–Rs.50,000	71	44.10
Greater than Rs.50,000	23	14.29

Source: Authors’ findings.

Key Finding: Around 96.89 per cent of the buyers’ monthly income is more than Rs. 30,001.

Table 7.

How Frequently Does Sample Use Internet

How Frequently Do You use Internet?	Responses	Percentage
Never	0	0.00
Once in a week	7	4.35
Once in 2–3 days	18	11.18
Once in a day	81	50.31
More than once in a day	55	34.16

Source: Authors’ findings.

Key Findings: Over 84 per cent of the buyers use the Internet at least once in a day. The respondents with ‘Never’ option were self-contradicting as this survey was floated online. Hence, such participants were dropped from the list.

Table 8.

Daily Average Time Spent on the Internet by Buyers

Daily average Time Spent on the Internet	Responses	Percentage
Less than 1hr	16	9.94
1hr–2 hr	114	70.81
More than 2 hr	31	19.25

Source: Authors’ findings.

Key Finding: Over 90 per cent of the buyers use the Internet daily for at least 1 hour on an average.

Table 9.

How Buyers Access the Internet

How You Access the Internet	Responses	Percentage
Mobile/I-pad	78	48.45
Computer/laptop	83	51.55
Cybercafé	0	0.00

Source: Authors’ findings.

Key Findings: No buyer was identified in the sample that uses cybercafés. While almost equal amount of buyers use mobile and computer.

Table 10.

Main Purpose of Using the Internet

Main Purpose of Using the Internet	Responses	Percentage
For entertainment	11	6.83
For shopping	18	11.18
For collecting information	59	36.65
For communication	73	45.34
Never Uses	0	0.00

Source: Authors’ findings.

Key Findings: Shopping as the prime purpose for using the Internet is third on the list. The respondents with ‘Never Uses’ option was self-contradicting, as this survey was floated online. Hence, such participants were dropped from the list.

Table 11.

How Frequently Does a Buyer Purchase Online?

How Frequently Do You Purchase Online	Responses	Percentage
Never	0	0.00
Once in a year	11	6.83
Once in 6 months	55	34.16
Once in 3 months	61	37.89
Once in a month	21	13.04
More than once in a month	13	8.07

Source: Authors’ findings.

Key Findings: 72 per cent of online buyers do the online shopping once or twice in six months. The respondents with ‘Never’ option were irrelevant for this research. Hence, such participants were dropped from the list.

Table 12.

What Have Buyers Purchased Online?

What Have You Purchased Online?	Responses	Percentage
Nothing	0	0.00
Books	66	40.99
Electronics	64	39.75
Apparel and accessories	58	36.02
Computer and peripherals	53	32.92
Home appliances	26	16.15
Beauty products	19	11.80
Health and fitness products	19	11.80
Other	11	6.83

Source: Authors’ findings.

Key Findings: Top four products purchased online are: books (40.99 per cent), electronics (39.75 per cent), apparel and accessories (36.02 per cent) and computer and peripherals (32.92 per cent). These are followed by home appliances, beauty products, and health and fitness products.

Table 13.

Major Attractor to Shop Online or Would Shop Online

Why You Shop Online or Would Shop Online	Responses	Percentage
Home delivery	113	70.19
Time saving	99	61.49
24 × 7 availability	97	60.25
Return availability	93	57.76
Ease of use	72	44.72
Multi category platform	64	39.75
Product comparison	63	39.13
Wide product variety	49	30.43
Discounts	45	27.95

Source: Authors’ findings.

Table 14.

Major Detractors to Not to Shop Online or Would Not Shop Online

Why You do Not Shop Online or Would Not Shop Online	Responses	Percentage
Security issues	106	65.84
Unclear policies	98	60.87
Inaccurate information listed	85	52.80
Poor grievance mechanism	76	47.20
Feel of product is missing	65	40.37
Technical website	63	39.13
Bargain not available	55	34.16
Identity theft issue	37	22.98

Source: Authors’ findings.

Table 15.

Buyer’s Satisfaction Level from E-commerce Website in Total

How Satisfied Are You from E-commerce Website(s)	Responses	Percentage
Highly dissatisfied	8	4.97
Dissatisfied	13	8.07
Neither satisfied nor dissatisfied	27	16.77
Satisfied	84	52.17
Highly satisfied	29	18.01

Source: Authors’ findings.

Table 16.

B2C Consumer Analysis: Age and Gender

Observed value		18–20	21–30	31–40	41–50	51–60	60+	Total
	Male	8	30	22	14	12	5	91
	Female	3	33	19	8	6	1	70
	Total	11	63	41	22	18	6	161

Source: Authors’ findings.

Key Findings: The top three reasons that attractor buyers buy from e-commerce portals are home delivery (70.19 per cent), time saving (61.49 per cent) and 24 × 7 availability (60.25 per cent). Long-return policy, With no question and hassle, also atcts the buyers by 57.76 per cent. Other motives to buy online include discounts, ease of use, multi category of products available in one portal and product comparison feature.

Key Findings: With 65.84 per cent and 60.87 per cent issues, security issues and unclear and complex policies are the major detractors, respectively, for buyers in B2C e-commerce. While the incorrect information listed in the portal is another major challenge which has been faced by over 50 per cent of buyers, the results of our analytical study indicate the minor challenges faced by less than 35 per cent of buyers are: bargain power is lost for the seller and identity theft issues. The issues of poor grievance mechanisms, technical websites and actual feel of the products missing falls in mid-range of challenges with issues faced by less than 50 per cent of sellers and more than 35 per cent of sample buyers.

Key Findings: 70.19 per cent of the respondents were satisfied from the e-commerce portals and most likely will be the repeat customers. Only 13.04 per cent of the respondents were dissatisfied from the e-commerce portal and are less likely to purchase again online.

Hypothesis Testing for Demographic Data

Following are the hypotheses of the present study:

H1: The perception of the online shoppers is independent from the age and gender.

H2: The perception of the online shoppers is independent from the educational qualifications and the gender.

H3: The perception of the online shoppers is independent from the income and the gender.

The Chi-square test has been conducted in order to test that whether the age and gender have any significant impact on the Internet usage for the online shopping.

Table 17.

B2C Consumer Analysis: Age and Gender Expected Value

		18–20	21–30	31–40	41–50	51–60	60+
Expected value	Male	6.2174	35.6087	23.1739	12.4348	10.1739	3.3913
	Female	4.7826	27.3913	17.8261	9.5652	7.8261	2.6087
Observed–expected	Male	1.7826	–5.6087	–1.1739	1.5652	1.8261	1.6087
	Female	–1.7826	5.6087	1.1739	–1.5652	–1.8261	–1.6087
Squared (observed–expected)	Male	3.1777	31.4575	1.3781	2.4499	3.3346	2.5879
	Female	3.1777	31.4575	1.3781	2.4499	3.3346	2.5879
Squared (observed–expected)/expected	Male	0.5111	0.8834	0.0595	0.1970	0.3278	0.7631
	Female	0.6644	1.1484	0.0773	0.2561	0.4261	0.9920

Source: Authors’ findings.

Table 18.

B2C Consumer Analysis: Age and Gender Chi-square

Pearson Chi-square	Critical Value	DF	Significance P-value
	6.3063	5	0.00029

Source: Authors’ findings.

We found out that the critical value is 6.3063, since the P-value was found to be around 0.0003. This is less than 0.05. Therefore, the hypothesis is rejected at the 5 per cent level of significance. So, we can say that the perception of online shopping is not independent from age and gender.

Table 19.

B2C Consumer Analysis: Gender and Educational Qualification

Observed value		< = 10th	12th	Graduation	Masters	Doctorate	Total
	Male	4	18	38	29	2	91
	Female	2	9	31	25	3	70
	Total	6	27	69	54	5	161

Source: Authors’ findings.

The Chi-square test has been conducted in order to test that whether the educational qualification and the gender have any significant impact on the Internet usage for the online shopping.

Table 20.

B2C Consumer Analysis: Gender and Educational Qualification Expected Value

		< = 10th	12th	Graduation	Masters	Doctorate
Expected value	Male	3.3913	15.2609	39.0000	30.5217	2.8261
	Female	2.6087	11.7391	30.0000	23.4783	2.1739
Observed–expected	Male	0.6087	2.7391	–1.0000	–1.5217	–0.8261
	Female	–0.6087	–2.7391	1.0000	1.5217	0.8261
Squared (observed–expected)	Male	0.3705	7.5028	1.0000	2.3157	0.6824
	Female	0.3705	7.5028	1.0000	2.3157	0.6824
Squared (observed–expected)/expected	Male	0.1093	0.4916	0.0256	0.0759	0.2415
	Female	0.1420	0.6391	0.0333	0.0986	0.3139

Source: Authors’ findings.

To test, chi-square test is conducted.

Table 21.

B2C Consumer Analysis: Gender and Educational Qualification Chi Square

Pearson Chi-square	Critical Value	DF	Significance P-value
	2.1709	4	0.0045

Source: Authors’ findings.

It was found out through the test results that the critical value is 2.1709, since the P-value was found to be 0.0045. This is less than 0.05. Therefore, this is rejected at the 5 per cent level of significance. So, the perception of the online shopping is not independent from the educational level and the gender.

Table 22.

B2C Consumer Analysis: Gender and Income

		10k–20k	20k–30k	30k–40k	40k–50k	50k <	Total
Observed value	Male	2	2	33	39	15	91
	Female	0	1	29	32	8	70
	Total	2	3	62	71	23	161

Source: Authors’ findings.

The Chi-square test has been conducted in order to test whether the income and the gender have any significant impact on the Internet usage for the online shopping.

It was found out from the test results that the critical value is 2.7191, since the P-value was found to be 0.0009. This is less than 0.05. Therefore, this hypothesis is rejected at the 5 per cent level of significance. So, the perception of the online shopping is not independent from the income and the gender.

Table 23.

B2C Consumer Analysis: Gender and Income Expected Value

		10k–20k	20k–30k	30k–40k	40k–50k	50k <
Expected value	Male	1.1304	1.6957	35.0435	40.1304	13.0000
	Female	0.8696	1.3043	26.9565	30.8696	10.0000
Observed–expected	Male	0.8696	0.3043	–2.0435	–1.1304	2.0000
	Female	–0.8696	–0.3043	2.0435	1.1304	–2.0000
Squared (observed–expected)	Male	0.7561	0.0926	4.1758	1.2779	4.0000
	Female	0.7561	0.0926	4.1758	1.2779	4.0000
Squared (observed–expected)/expected	Male	0.6689	0.0546	0.1192	0.0318	0.3077
	Female	0.8696	0.0710	0.1549	0.0414	0.4000

Source: Authors’ findings.

Table 24.

B2C Consumer Analysis: Gender and Income Chi-square Pearson Chi-square

Critical Value	DF	Significance P-value
2.7191	4	0.0009

Source: Authors’ findings.

The Analysis of Non-demographic Influencing Factors for Online Purchase

This study empirically analyses the non-demographic factors that are considered as the factors which will build the Indian consumers’ online experience and their perception. The technique of data/variable reduction used is factor analysis. The three reasons behind why this multivariate statistical technique has been used in the present study are as follows:

In order to reduce the number of variables, that is, from large to small;

In order to explain the pattern of the correlations within a set of the observed variables; and

Provide construct validity evidence.

The primary data collection consists of 161 questionnaire responses which were then loaded to the software ‘Statistical Package for the Social Sciences (SPSS)’ for initial analysis. A total of 20 factors were considered that were thought to make an impact on e-commerce’s consumer buying behaviour. For the purpose of identification of the basic/core factors which are affecting the e-commerce consumers’ buying pattern/behaviour, the factor analysis statistical tool was applied.

Test Adequacy of Sample

In order to measure the sampling adequacy, we applied the KMO. The KMO varies between 0 and 1. If KMO value is closer to 1, it is said to be better and the value of 0.6 is the suggested minimum (Table 25 and 26).

Table 25.

KMO Value Evaluation

KMO	Degree of Common Variance
0.90–1.00	Marvellous
0.80–0.89	Meritorious
0.70–0.79	Middling
0.60–0.69	Mediocre
0.50–0.59	Miserable
0.00–0.49	Do not Factor

Source: Standard values as per KMO test.

Table 26.

Test Adequacy of Sample Result

Kaiser–Meyer–Olkin measure of sampling adequacy		0.801
Bartlett’s test of sphericity	Approx. Chi-square	968.536
	Df	190
	Sig.	0.000

Source: Authors’ findings.

In order to test the null hypotheses to find out that the correlation matrix has an identity matrix, the Bartlett’s test of sphericity was used. Considering this, these tests generally provide minimum standard to proceed for the factor analysis.

H4: There is no significant interrelationship statistically between the variables affecting the e-commerce consumer buying behaviour.

Key Findings: In KMO, degree of common variance among 20 variables came out to be 0.801, which is ‘meritorious’ just above ‘middling’. The factors extracted will account for good amount of variance, if the factor analysis is used.

In Bartlett’s test, the Chi-square approximate is 968.536 having 190 degrees of freedom, with significance p-value of 0.000, which is less than 0.05; we, therefore, do not accept the null hypothesis (H4), that is, it is rejected and accept the alternate hypothesis. There is no significant interrelationship statistically between variables affecting the e-commerce consumer buying behaviour. Both the tests also indicate that for the purpose of further analysing the data, the factor analysis is considered as the most appropriate technique.

Principal Component Analysis

The communalities are designed in a way so that it could show the proportion of the variance that the factors are contributing to explain a particular variable. Since, the principal component analysis works only on the assumption that all variances are common, so communalities are all 1 before extraction. Here, the communalities are representing the amount of the variance in each of the variable that can be explained by retained factors.

In initial solution, each variable has to have standardised mean of 0.0 and the standard deviation of ±1.0. Therefore, the variance of each variable = 1.0 (Table 27).

Table 27.

Communalities

S.No	Communalities	Initial	Extraction
1	The online secured payment process	1.000	0.965
2	Clearly stated privacy policy	1.000	0.869
3	Privacy policy can be found easily in the website	1.000	0.584
4	Word of mouth	1.000	0.805
5	Facility of giving feedback online	1.000	0.531
6	Delivered product undamaged	1.000	0.915
7	Testimonials of the users posted on the website	1.000	0.664
8	Product is delivered as per the order specifications	1.000	0.845
9	The clearly stated return policy	1.000	0.883
10	Delivery of the product on time	1.000	0.914
11	Multiple payment option	1.000	0.962
12	Tracking of shipment online	1.000	0.907
13	Lodging of grievance online	1.000	0.705
14	Few and easy steps involved in placing orders	1.000	0.916
15	Website has a search engine	1.000	0.339
16	Detail information about the product features, quality and price	1.000	0.954
17	Easy to read and understand web content	1.000	0.930
18	Website can provide information tailored to customers’ needs	1.000	0.956
19	Visually appealing web content	1.000	0.742
20	Availability of site map	1.000	0.818

Source: Authors’ findings.

Key Findings: In principal component analysis, any factor with variance less than 0.5 after extraction is considered as a factor that does not load onto any other factor; hence, it was dropped out. The variable website has a search engine (S.No 15) extraction value of 0.339, which is less than 0.50; hence, this variable was dropped out.

Eigenvalues

Eigenvalues are associated with each factor before the extraction and after the extraction. Basically, Eigenvalues are designed in a way to show how the proportion of the variance is accounted for by each of the factor. In this, total column contains Eigenvalues. In this case, the first factor will always be considered as the most variance and, therefore, have the highest Eigenvalues. The next factor will be considered for the left over variance and same will be continued till the last factor. Here, the percentage of total variance accounted by each factor is represented by the percentage of variance, and the cumulative percentage of variance accounted by the present and preceding factors is given by the cumulative percentage (Table 28).

Table 28.

Total Variance Explained

	Eigenvalues (Initial)			Extraction Sums of Squared Loadings
	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %
Component 1	9.113	45.565	45.565	9.113	45.565	45.565
Component 2	2.224	11.120	56.685	2.224	11.120	56.685
Component 3	1.765	8.825	65.510	1.765	8.825	65.510
Component 4	1.125	5.625	71.135	1.125	5.625	71.135
Component 5	0.802	4.010	75.145
Component 6	0.763	3.816	78.960
Component 7	0.672	3.359	82.320
Component 8	0.595	2.976	85.296
Component 9	0.468	2.340	87.636
Component 10	0.431	2.155	89.791
Component 11	0.396	1.981	91.772
Component 12	0.382	1.908	93.679
Component 13	0.273	1.364	95.044
Component 14	0.253	1.267	96.311
Component 15	0.203	1.015	97.325
Component 16	0.146	0.728	98.053
Component 17	0.124	0.621	98.675
Component 18	0.112	0.558	99.233
Component 19	0.099	0.495	99.728
Component 20	0.054	0.272	100.000

Source: Authors’ findings.

Key Findings: Here, we have extracted all the Eigenvalues greater than 1, where we ultimately resort to only four factors. These four selected factors account for 71.135 per cent of the total variance for the purpose of building the online trust for consumers. Now this is a very good extraction because it can economise on the number of factors (from 20, it has reduced to only 4 factors), so we only lost the 28.865 per cent information content.

Identification of the Core Factors

Rotated component matrix is a matrix of factors loading for the purpose of considering each variable onto each factor. It can be either negative or positive. The factor column is basically representing the rotated factors which have been extracted out of the total factor.

Key Findings: The above-mentioned matrix gives us the correlation of variables with each of the extracted factors. Usually each of the variables is less loaded with one factor and highly loaded towards the others. In order to identify the included variables in each factor, the variable having the maximum value in each row is selected, so that it could be a part of the respective factor. The values have been highlighted in such a manner so that it could make out in each of the rows to group the 20 variables into only 4 core factors. Variable ‘website has a search engine’ (S.No 15) does not load onto any of the factor as the entire factor loading values is less than 0.5 for this variable.

The Four Core Factors’ Names

The four core variables that have been included into each core factor have been named and given as under:

Factor 1: Service Quality

This is one of the most important factors which influence the online trust of consumers. Service quality is represented by values, assurance of quality, consistency of actions, matching customers’ expectations and outcomes. Service quality is generally regarded as the integrity and honesty of e-commerce portal actions. This factor also includes the ease provided by the e-commerce portals to its customers. There are 11 loads to this factor. It is to be noted that there is a need for the online companies to improve their process of online customer services, if they are interested to have more consumers in order to involve them in the online purchasing.

Factor 2: Security and Privacy

Here the security means the protection against loss, crime and danger. It is the state of being free from the intrusion which is unsanctioned. This factor considers three loads to this: namely, clearly stated privacy policy, the online secured payment process and privacy policy can be found easily on the website. If the consumers feel that the online merchant does not compromise with the security and the privacy concerns, it tends to reduce the risk perception of the consumers (Table 29).

Table 29.

Rotated Component Matrix

Communalities	Component 1	Component 2	Component 3	Component 4
Secured online payment process	0.363	0.838	0.097	0.348
Clearly stated privacy policy	0.128	0.876	0.116	0.267
Privacy policy can be found easily in the website	0.113	0.606	0.396	0.217
Word of mouth	0.712	0.286	0.256	0.388
Facility of giving feedback online	0.606	0.098	0.156	0.361
Delivered product undamaged	0.863	0.121	0.068	0.389
Testimonials of users posted on the website	0.674	0.047	0.356	0.284
Product is delivered as per the order specifications	0.838	0.036	0.012	0.376
The clearly stated return policy	0.794	0.075	0.232	0.439
Delivery of the product on time	0.859	0.128	0.136	0.376
Multiple payment option	0.765	0.385	0.264	0.399
Tracking of shipment online	0.808	0.193	0.389	0.256
Grievance can be lodged online	0.653	0.111	0.376	0.354
Few and easy steps involved in placing orders	0.735	0.287	0.413	0.351
Website has a search engine	0.289	0.043	0.344	0.368
Detail information about the product features, quality and price	0.321	0.099	0.825	0.401
Easy to read and understand web content	0.189	0.086	0.379	0.862
Website can provide information tailored to customers’ needs	0.368	0.041	0.823	0.376
Visually appealing web content	0.173	0.186	0.781	0.259
Availability of site map	0.197	0.159	0.412	0.764

Source: Authors’ findings.

Factor 3: Content and Design

It was also found out that the consumers considered the good quality website of the merchant as a factor which influences the consumers’ decision of buying. The variables which load the said factor are ability of the website that can provide information tailored to customers’ needs and visually appealing web content. It focuses more on the feel and look of the website.

Factor 4: Customer Delight

Customer delights anything that exceeds the expectation of the customer and adds to their comfort or ease. This factor considers the variables easy to read and understand web content and the availability of site map. The consumers will tend to feel more uncomfortable if the e-commerce website is not convenient to use, and moreover, they may also perceive the website difficult to use and it will result in dissatisfaction.

Conclusion

It is revealed from the present study that in India, the consumers who are purchasing online are mostly the young people comprising of the students and the bread earners within the age group of 21–50 years. The study also indicated that most of the consumers who are shopping online are graduates or postgraduates and they are also well off financially. It was also found out from the study that the majority of the consumers are using the Internet every day and they are spending at least 1–2 hours per session of the Internet. The basic objective of using the Internet is to communicate followed by retrieving information, while online shopping is the third priority. Study showed that three-fourth buyers tend to online shopping once or twice in six months. The top four products purchased online are: books (40.99 per cent), electronics (39.75 per cent), apparel and accessories (36.02 per cent) and computers and peripherals (32.92 per cent) (Table 30).

Table 30.

Core Factor Naming

Component	Variable Included	Factor Name
1	• Product delivered undamaged• Delivery of the product on time• Product is delivered as per the order specifications• Shipment can be tracked online• Return policy clearly stated• Multiple payment option• Few and easy steps involved in placing orders• Word of mouth• Testimonials of the users posted on the website• Lodging of grievance online• Feedback can be given online	Service quality
2	• Clearly stated privacy policy• The online secured payment process• Privacy policy can be found easily in the website	Security and privacy
3	• Website can provide information tailored to customers’ needs• Visually appealing web content	Content and design
4	• Easy to read and understand web content• Availability of site map	Customer delight

Source: Authors’ findings.

The top three attractor for buyers to buy from e- commerce portals are home delivery (70.19 per cent), time saving (61.49 per cent) and 24 × 7 availability (60.25 per cent). The long-return policy with no question and hassle also attracts the buyers by 57.76 per cent. Discount and wide product varieties available in one category two factors that differentiate between the two portals. Other motives to buy online include ease of use, multi category of products available in one portal and product comparison features.

With 65.84 per cent and 60.87 per cent issues, security issues and unclear and complex policies are the major detractors, respectively, for buyers in B2C e-commerce. While the incorrect information listed in the portal is another major challenge which has been faced by over 50 per cent of buyers; the minor challenges faced by less than 35 per cent of buyers are: loss of bargain power and identity theft issues. The issues of poor grievance mechanisms, technical websites and actual feel of the products missing falls in mid-range of the challenges with issues faced by less than 50 per cent of sellers and more than 35 per cent of sample buyers.

Through the study, it has been evaluated that the perception of online shopping influenced by educational level and gender, age and gender, and income and gender.

The initially taken 20 variables are reduced to only 4 factors, while we evaluate the influencing factors for the online purchase. Out of these factors the marketer’s integrity towards the service quality is basically considered as the most important factor for influencing online trust of consumers. Our study’s analytical results further indicate the relationships between the consumers’ perceptions of the factors which are influencing their intention to buy via online; more precisely, the consumers’ perceptions of security and privacy, content and design on website, service quality and the factors of customer delight. These analytical results gathered are generally consistent with the findings of the previous researchers. The factors which have received the most consistent support are service quality and web security based on which the consumers’ trust for online shopping is formed and influenced.

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