Online apparel shopping hesitation in India

Abstract

Though online shopping is increasingly receiving the attention and appreciation there still are various factors which adversely affect the phenomenon of online purchases by making consumers being hesitant towards online purchase making. Shopping via internet is tremendously being practices by citizens of India, this increase in online shopping practice has been witnesses and researched and there are various reasons proved for it including the cheap internet availability across the nation to the increase in consumers’ trust and acceptance towards online purchase and payments. However, on other hand online shopping is not preferred by many individuals across the nation for various reasons. This study focuses on this gap to know the basic factors which adversely affect consumer buying intention via online in apparel segment. A convenient sample of 319 students was collected and analysed employing SPSS for factor analysis and two major factors were found which adversely affect consumers’ buying intention via online in apparel segment. Poor packing and fear of fake brands were found to be major variables which are the reason for the consumers’ hesitation towards online shopping of apparels.

Keywords: Online consumer hesitation, Perceived fear, Perceived website incompetence

Introduction

While a large number of consumers across India use internet to fulfil their shopping needs and wants, there is undebatable need to research on why still there are majority of consumers who are hesitant towards using online shopping methods, what factors are adversely affecting them from choosing online shopping to fulfil their needs. According to Swinyard & Smith (2011), the behaviour of consumers who do not shop online is under-researched field. There is a lot of research already done on why consumers shop online but a very few researchers really focused on why consumers do not use internet to shop and on this note this piece of work focuses on the gaps which the earlier researches have left to understand the major factors because of which consumers do not opt internet shopping. In this research an attempt has been made to increase researchers understanding of consumers’ buying intentions by providing factors which influence their online buying.

According to many researchers (Grewal et al., 2002; Chen and Leteney, 2000; Ha¨ubl and Trifts, 2000; Alba et al., 1997) online shopping or otherwise known as internet shopping or electronic shopping fulfils several consumer needs more efficiently and effectively than that of traditional in store shopping. According to Kim & Park, 2005, e-retailing could be used to target a much larger consumer group because as compared to traditional way of markets, the online markets provide a much flexible way to sell products and services (Doherty & Ellis-Chadwick, 2006). Given these facts, figures and research finding by many researchers why there are still a lot of people who do not choose online shopping over traditional shopping methods is yet to be deeply understood.

In online shopping the role of consumers or in this perspective e-consumers is major. According to Brynjolfsson, Hu and Rahman (2013), as the diminution of the boundaries between offline and online channels, this world is turning into one big showroom (or a virtual showroom). It is mentioned in their work that the consumers are becoming more technology oriented and the deep penetration of technology to an extent that a lot of target consumers own some kind of device (majorly mobile phone/smart phone) on which they can access the information is encouraging them to be much more technology dependent. According to Chaffey et al., 2009, online shopping is preferred because online purchases are much convenient and less time consuming and the availability of information in online sites is abundant and also discounts and incentives are comparatively very high in online stores than that of traditional stores. On the contrast many researchers suggest that the aspects of security, tangibility, after sales services etc. are the important factors which affect online shoping intentions in consumers. (M. Kumar, 2016; Ling & Yazdanifard, 2014; Al-Debei et al., 2015; Bilgihan (2016); Faqih (2016)).

Though internet shopping has a beneficial edge over traditional instore shopping in more than one way there are still a large chunk of population how retain reasons to not shop online. Out of many reasons the ability of handling internet is one (Lee and Turban, 2001), however the inability to handle internet or browse through websites has definitely decreased since 2001 and perhaps this reason could be very minimal in urban young people (the research sample is fron urban young people) the inability to use internet as a medium to shop online is almost vanished and this is the reason why in this paper inability to use internet has not been considered the reason why consumers hesitate to shop online. On the other hands, in India nearly 70% of the population live in rural areas and there is a high possibility that many of the consumers can possibly possess an inability to handle internet to shop but this situational factor is not a point of research in this paper. The other factors could be trust, delivery, intangibility, etc. In this paper all such factors are discussed which are the reasons why online apparel shopping is neglected by young consumers in India. On this line the researcher has come up with a research question to know what are the factors affecting consumers’ online buying intention. And the objective of the study is to point out the factors which are giving reasons for not opt online shopping for the purchase of apparel.

Literature review:

According to IBEF reports, India is the fastest growing market for e-commerce sector. The report highlighted that the revenue of the e-commerce sector in India is growing at a never before rate of 51% annually which is highest in the world. To put it into perspective in 2017 the revenue of e-commerce in India was USD 39 billion which is anticipated to grow to USD 120 billion by the year 2020. As of December 2018, the internet subscribers in India pegged at 604.21 million people according to IBEF reports. With growing internet usage the online shopping in India is also growing at a faster rate. With the growth of online consumers, e-retailers needs to have a deeper understanding about the factors associated with the online shopping orientation so as to further develop their strategies accordingly to target the consumer base. This present study focuses on this very issue of which factors influence consumers to be hesitant to online shopping.

Previous literature suggests that the process of online shopping includes a lot of aspects from need recognition, information search, product or service search, decision making, order placing, payments, receiving and aftersales support. While an emphasis on each aspect of the procedure of online shopping keep their own prominence, in this paper we will emphasis on those factors which are to be blamed for a negative impact on buying intention of a consumer in online platform. Know and Lee (2003), in their study about consumers, details about payment security and its relationship to online shopping attitude and actual purchases, observed that concerns about online payment and online shopping attitudes are negatively related.

Consumers are concerned about receiving the product they bought online in an efficient manner. According to Vijayasarathy and Jones (2000), the perceptions on delivery of goods and services bought from online stores as promised by the websites are closely associated to risk. This perception might be developed due to the trust issues of consumer over online retailers regarding the inabilities in keeping up their promises when it comes to the quality of delivery services of products. Na Li and Ping Zhang (2002) suggested that the online consumer expect the promised quality of the product to remain the best and to receive the product on time as assured by the retailer. Online service providers who deliver the promised services within promised time frame only will be considered as reliable (Syed et al. 2008). Also extra delivery charges turns down e-consumer. Clark (2000) suggested in his survey that free delivery makes 46% of people to choose online shopping.

The fear factor is another aspect e-consumer are concerned about. Loss of money while making online payments and threat to the privacy are many times observed by many researchers like M. Kumar, 2016; Ling & Yazdanifard, 2014. While the technical face of online shopping is considered a threat the personal and emotional aspects were also highlighted by many authors. Iglesias-Pradas et al.  (2013) suggested that the absence of option like physically see and touch the product or in other words being intangibility before making the purchase is one of the barrier to potential consumer. After sales service is also a factor which affects consumer online buying intention. According to Almousa (2013), weak or no after sales service is the most prominent barrier faced by the consumers which may hinder them to shop online. They also mentioned that the high international shipping cost and fear of product un-arrival are the next most important factors which affect adversely towards consumers online shopping intention. Earlier researches like Ernst and Young (2000), Know and Lee (2003) have expressed their study findings are that the payment security is one of the major factor which hinder consumer online shopping intention. Ernst and Young (2000) suggested the merits and demerits of online shopping as online shopping is practices because of wide selection, ease to use, convenience, and price attractions while on other hand online shopping is not practices for the reasons like high or extra shipping costs, intangibility and security issues.

Methodology

Data Collection

A convenient sample of 319 respondents were taken using snowball sampling technique. The professors and lecturers were approached with the questionnaire and were requested to get them filled by their students, all thanks to the professors and lecturers that they got them filled as requested. It has been suggested by various researchers that college students sample is quite appropriate to be analysed when the analysis is related to young apparel consumers (Eriksson et al. 2017). The aim was to come up with factors or online attributes which influence negatively towards online purchase of apparels.

Demographic variables	Number of respondents
Gender
Male	178
Female	146
Age
Below 20 years	66
20 – 30 years	236
30 – 40 years	25
Education
Bachelors	150
Masters	106
Doctoral/Post-doc	38

According to the demographic data, the number of male respondents were higher than the female respondents. The male respondents were 178 where are female were 146. Out of all the respondents the highest numbers of respondents were in the age group of 20 to 30 years and were all educated with 150 respondents having bachelor’s degree and 106 respondents have master’s degree and only 38 people have a doctoral degree.

The aim of this paper is to present and discuss the factors which are the reason for online apparel purchase hesitation. In the scope of the analysis, the IBM SPSS Statistics software package was applied, just like others (Fenyves et al., 2019a, Fenyves et al., 2019b). Factor analysis has been conducted to generate factors which explain the why consumers hesitate to shop online for apparels. Tables 1 to 5 shows various factor analysis results like KMO and Bartlett’s test to measure an index of sampling adequacy to determine the appropriateness of the factor analysis and to see if factor analysis is an appropriate method; communalities; total variance; rotated component matrix and factor loading.

Results

Factor analysis is carried out to identify the attributes which influence positively towards online apparel purchase performance. The results are as presented,

Table 1: KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.	.642
Bartlett’s Test of Sphericity	Approx. Chi-Square	187.066
	df	21
	Sig.	.000

Kaiser-Meyer-Olkin (KMO) measure is an index of sampling adequacy which determines the appropriateness of the factor analysis. The KMO value in this study is .642 which is apparently an acceptable measure and this indicates that factor analysis could be considered as an appropriate technique to analyse this data.

Bartlett’s Test of Sphericity is a test statistic to examine the hypothesis that variables are uncorrelated in the population. The factors must correlate for approprieate factor analysis. At the level of 0.05 and above the null hypotheis is considered as significant. In this study the significance level is .000.

Table 2: Communalities

Initial Extraction
Fake brand	1.000	.602
Late delivery	1.000	.390
Fear of losing money	1.000	.577
Size issue	1.000	.375
packing	1.000	.679
Intangibility	1.000	.328
Difficult or no after sales service	1.000	.203

Communalities indicate the amount of variance in each subjective parameters that is accounted for. Initial communalities are estimates of the variance in each variable accounted for by all components or factors. For principal components extraction, this is always equal to 1.0 for correlation analyse. Extraction communalities are estimates of the variance in each variable accounted for by the components. The communalities in this table are all at acceptable levels, which indicates that the extracted components represent the variables well.

The variance explained by the initial solution, extracted components, and rotated components is displayed. This first section of the table shows the Initial Eigenvalues. The Total column gives the eigenvalue, or amount of variance in the original variables accounted for by each component. The % of Variance column gives the ratio, expressed as a percentage, of the variance accounted for by each component to the total variance in all of the variables. The Cumulative % column gives the percentage of variance accounted for by the first n components. For example, the cumulative percentage for the second component is the sum of the percentage of variance for the first and second components. For the initial solution, there 7 components as variables.  The second section of the table shows the extracted components. They explain nearly 45% of the variability in the original 7 variables, so you can considerably reduce the complexity of the data set by using these components.

Table 4: Rotated Component Matrix^a

	Component
	1	2
Packing	.798
Late delivery	.575
Intangibility	.561
Size issue	.477
Difficult or no after sales service	.441
Fake brand		.770
Fear of losing money		.755

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a.) Rotation converged in 3 iterations.

The rotated component matrix helps you to determine what the components represent. The first component is most highly correlated with ‘Packing’, the second component is highly correlated with ‘Fake brand’.

Table 5: Factor loading

	Loading
Factor 1: Website incompetence
Packing	.798
Late delivery	.575
Intangibility	.561
Size issue	.477
Difficult or no after sales service	.441
Factor 2: Fear of consumer
Fake brand	.770
Fear of losing money	.755

Factor 1: Website incompetence – This factor explains the incompetence of the website which may potentially affect consumers’ online apparel purchase intentions. This underlying factors consists of 5 variables which are packing; late delivery; intangibility; size issue; difficult or no after sales service. The highest loading (.798) was for packing which means that the consumers are highly concerned about the packing of the apparels they purchase. The next highest loading (.575) was for late delivery which suggests that the consumers are many times willing not to shop online out of the fear of receiving there ordered apparels late than the promised time and this could be a terrible things as occasions and plans could flop and the garments they intent to show up in can be wasted and this could potentially make consumers think that traditional instore shopping is the best for them as they can pick the apparel at the same time as they pay. Intangibility is the third highest loaded variable (.561). The concern of not being able to touch and see the garment they are about to pay is for obvious reason valuable and this gives rise to the next highest loaded variable which is size issues being forth highest loaded (.477), this variable is somewhat connected to intangibility, as there is no option of touch or view of the apparels the consumer purchase the size and fitting has a uncertainty in it but for people who stick to certain apparel brands usually to an extent get out of the trouble of size and fitting. The final variable (.441) in this factor is difficult or no after sales service. When an apparel is bought online, due to the above mentioned issues there is a high possibility that the apparel could have got dirty due to unwell packing, could have arrived late and there is no more need of the apparel by the time of delivery, due to intangibility the quality could be realised to be low post delivery, the fitting of the garment isn’t well and for one or more of these reasons when a consumer need to return the product a proper after sales service is very important.

Factor 2: Fear of consumer – This factor explains the fear of the consumer which may make a consumer not want to purchase apparels online. The highest loading (.770) was for fake brands, due to intangibility and just based on pictures and information put up by the retailer, there is a high possibility that the apparels bought with an expectation of an original brand turn up to be a fake one. One such experience can potentially make a consumer to stay away from online purchase of apparels. The next highest loading (.755) was for the fear of losing money. The security concerns of consumers while making online payment is apparently one reason why many consumers stay away from online shopping of apparels. This perhaps could be solved to an extent by the option on cash on delivery which allows consumers to pay when they receive the apparel unlike online payment option in which they need to pay while check out.

Conclusion

The aim of this research was to understand the main factors which influence a consumer to not shop online in apparel sector. The research was carried out with a convenient sample of 319 respondents who were asked the main reasons why they preferred instore shopping over online shopping and the results were analysed to form 2 major factors which are indeed responsible for the negative effect on consumers’ online apparel buying intention. The first factor found was the website incompetency, different website offerings and attributes results in consumers to be away from online apparel shopping. In this research the website’s in abilities to maintain a good packing standers was found to be the top most reason why consumers do not shop apparels online followed by websites not keep up their promise of on time delivery, problem of website characteristic of intangibility and size issues and finally difficult or no aftersales service were found to be the reasons why consumers don’t prefer online medium to shop apparels. The second factor was found to be the fear of the consumer. The fear of receiving a fake brand which could be related to the intangibility factor was found to be one reason and the other was the fear of security which means that the consumer is afraid of losing money while online payments as they have to share their card details and mischief can possibly happen.

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Shivam Sakshi
PhD Student,
University of Debrecen,
Faculty of Economics and Business.

Károly Pető
Professor,
University of Debrecen,
Faculty of Economics and Business.