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

Research Article: 2018 Vol: 22 Issue: 2

Factors Affecting Female Consumer's Online Buying Behavior

Madasu Bhaskara Rao, ICFAI Foundation for Higher Education

Ch Lakshmi Hymavathi, ICFAI Foundation for Higher Education

M Mallika Rao, Koneru Lakshmiah Education Foundation

Abstract

Substantial rise in internet penetration is revolutionising online retailing in emerging markets in general and India in particular. Realizing this, retailers of traditional store formats are venturing into e-retailing. The Indian online retail industry is estimated at USD 38.5 billion in 2017 and is growing at a rate of 45 to 48 per cent CAGR. This is a trickle compared to the Indian retail market of USD 672 billion in 2016 with a CAGR of over 15 per cent. Online retailing in India is poised to grow by leaps and bounds. The challenge before the companies and marketing professionals is to understand what drives online-shopping? What factors influence online-shopping buying behaviour? Considering that the growth rate for onlineshopping is high for food (80%), ticketing (26%), jewellery (25%) and perfumes (18%) categories, women consumers play a significant role and their buying behaviour is important. This study focuses on factors that influence female online-shopping behaviour. The factors that motivate the female buyers to shop-online and their attitudes towards online-shopping are studied. Insights are provided into female shoppers’ expectations with respect to onlineshopping. The findings of this paper benefit online retailers, marketing managers, policymakers and academicians.

Keywords

Consumer Behaviour, E-Commerce, Female Buying Behaviour, Online-Shopping, Online Retailing, Retail Sector

JEL classification

L81, M31, N30

Introduction

Factors Affecting Female Consumers’ Online Buying Behaviour

Internet is redefining the shopping behaviour of people across the globe. It has become a hotbed of advertising, shopping and commercial activity (Rowley, 1998). People’s daily life is influenced by internet more so as compared to past (Hsieh et al., 2013). Consumers are getting used to virtual experience from physical experience, adapting to online purchases. According to Lee and Zhang (2002) after e-mail usage, instant messaging and web browsing, online shopping is the third most popular internet activity. The process a customer takes to purchase a service or product over the internet is referred as online shopping (Jusoh & Ling, 2012) where a consumer buys from an online store from home at his/her convenience. Online shopping is growing in India (Suresh & Shashikala, 2011). According to the reports of IAMAI-KPMG1, the total number of Internet users in India (out of a total population of 1.25 billion) would reach 600 million by 2020. According to PricewaterhouseCoopers2 report, e-commerce sector in India has grown by 34% (CAGR) since 2009 and was expected to be in the range of USD 60-70 billion by 2019.

Extensive research on attracting and retaining consumers from a consumer-oriented or a technology-oriented view was triggered by the rapid growth of online shopping (Jarvenpaa & Todd, 1996). Examining consumers’ prominent beliefs about online shopping, that may influence purchase channel selection is the focus of consumer-oriented view. Predicting consumer acceptance of online shopping by analysing technical specifications of an online store is the scope of technology-oriented view. Internet is being used as a channel of information and commerce in the rapidly growing online businesses.

Literature Review

Researchers have attempted to identify factors affecting Indian consumers’ online purchase behaviour from various perspectives (Bhatnagar & Ghose 2004; Jarvenpaa & Todd, 1996; Vijaysarathy & Jones, 2000); purchase behaviour from consumer demographics (O'Keefe et al., 2000; Chau et al., 2002; Brown, Pope & Voges, 2003; Li, Kuo & Russell, 1999; Korgaonkar, Silverblatt & Becerra, 2004; Park, Lee & Ahn, 2004); cognitive and psychological characteristics (Huang, Schrank & Dubinsky, 2004; Novak, Hoffman & Yung, 2000; Wolfinbarger & Gilly, 2001; Xia, 2002); risk perceptions and benefits of online shopping (Jarvenpaa & Todd, 1996; Liang & Jin-Shiang, 1998; Jarvenpaa, Tractinsky & Vitale, 1999; Jarvenpaa & Tractinsky, 1999; Solomon, 1999; Bhatnagar & Ghose 2004a; Liao & Cheung, 2001; Featherman & Pavlou, 2003; Joines, Scherer & Scheufele, 2003; Featherman & Pavlou, 2003; Bhatnagar & Ghose 2004a; Bhatnagar & Ghose, 2004b; Garbarino & Strabilevitz, 2004; Huang, Schrank & Dubinsky, 2004; Kolsaker, Lee-Kelley & Choy, 2004; Park, Lee & Ahn, 2004; Pires, Stanton & Eckford, 2004); shopping motivation (Novak, Hoffman & Yung, 2000; Wolfinbarger & Gilly, 2001; Johnson et al., 2004) and orientation for shopping (Korgaonkar & Wolin, 1999; Donthu & Garcia, 1999; Li, Kuo & Russell, 1999; Swaminathan, Lepkowska-White & Rao, 1999).

Several factors affect online buying decisions of consumers. Consumers’ attitude and shopping intentions on the Internet are guided by a wide variety of situational factors, like geographical distance, lack of mobility, time pressure, attractiveness of alternatives and need for special items. Important attributes of online shopping are convenience and accessibility to most consumers (Wolfinbarger & Gilly, 2001). Online shopping decisions are also influenced by the type of product or service. Absence of aid and the lack of physical contact in shopping on the Internet is one factor that influences this suitability. The need to feel, touch, smell, or try the product, which is not possible when shopping online is another factor. An individual’s response to a task involving judgment is based on three aspects viz., individual’s past experiences, the context or background and the stimulus (Helson, 1964).

Internet shopping history of the consumer influences intention to shop online. Trust and risk are the major factors that influence customer participation in web-based commerce, which have the potential to increase the frequency of online shopping activity (Al-Mowalad, 2013). In an Internet environment, business is being transformed to a social relationship from a transactional relationship. The advent of social media has opened a new avenue of marketing for corporations. As consumers are increasingly referring to social media sites before making a purchase, the word-on-web replaced the word-of-mouth publicity, greatly influencing buying behaviour. In online shopping, trust is interwoven with risk (McAllister, 1995). Trust reduces the consumer’s perception of risk associated with opportunistic behaviour by the seller (Ganesan, 1994). The reason for consumers not purchasing from Internet shops is reported to be lack of trust.

Under conditions of uncertainty and risk, trust is an important factor in traditional theories. Mayer, Davis & Schoorman, (1995) developed a model combining traditional marketing philosophy on consumer motivation to buy and the trust model, where propensity for trust, a personality trait possessed by buyers is an important antecedent. Buyer with a high propensity for trust is likely to be a potential customer than a buyer with a lower propensity. Ability, benevolence and integrity are the main elements of trustworthiness. According to Gefen & Straub (2002) trust is very important in e-commerce and the lack of it is the major factor for consumers to avoid online purchases (Gefen & Straub, 2002; Emurian & Wang, 2005). Humans must decrease their social uncertainty, that is, to try to control their environment and behaviour of other people. This is usually done by rules and customs. As Internet is a new sales channel, there are few established customs and rules, which is why trust is so important in e-commerce (Gefen & Straub, 2002).

Varma and Aggarwal (2014) in a study on Mumbai’s western suburbs homemakers found that online shopping for them is both utilitarian and hedonic experience and is a leisure activity directed to reduce their boredom. According to Stone (1954), shoppers are classified as the economic, personalizing, ethical and apathetic shoppers. Consumers are motivated by purchase needs or experiential needs or a combination of both when they shop (Westbrook & Black, 1985).

Studies on Indian consumer-buying behaviour are scarce, and more so in case of the role of gender. Shopping dynamics in India are different from that of the west, where shopping is a family activity with nearly 70% of shoppers going to stores always with the family, with 74% of them seeing shopping as the best way to spend time with the family. The family-oriented shopping as a preference was found to be consistent across regions and cities, income segments and age groups in India (Sheth &Vittal, 2007). Women do most of the shopping in the traditional world, but it is the reverse in online shopping (Dennis et al., 2002b; Ballard & Mander, 2006; OFT, 2007). Why is this phenomenon? Is this because online shopping lacks the social experience of brick and mortar shopping? Research-based evidence suggests that females and in particular, young adults, regard the social aspect of shopping as an important element (Dholakia, 1999). Internet retailers (e-retailers) face difficulty in satisfying customers’ higher-level needs for personal interaction. Yet for young females, the Internet is the new social space (Social Networks, 2007). The researchers suggest that there is a major opportunity waiting for e-retailers to combine e-shopping with social networking.

The focus of openness to change is on diversity and thrill. Interestingly, internet shoppers are found to be more convenience seeking, impulsive, innovative and less risk averse than non-Internet shoppers (Donthu & Garcia, 1999). Online grocery shopping also has similar pattern. Hansen (2005) showed that non-online grocery shoppers regard online grocery shopping as less compatible with their daily lives as compared with adopters of online grocery shopping.

The primary reason for online grocery shopping is convenience and the time saved (Morganosky & Cude, 2000). Ramus and Nielsen (2005) found that consumers prefer online shopping as it allows them to shop without leaving home and in a less stressful way than going to the grocery store during rush hours. Hansen (2005) detected that consumers are concerned with the missing social interaction when shopping online. Self-transcendence emphasizes equality and maintaining good social relations, whereas self-enhancement focuses on wealth and power and getting things done effectively. Shang suggest that online shopping is not much of a goal-oriented activity rather it is a result from cognitive absorption experiences from the Internet, a personality characteristic that influences perception and especially perceptual differentiation (Antonides & Van-Raaij, 1998).

While online shopping has several advantages over the traditional method, few pitfalls are there pertaining to Internet buying. In virtual shops, the consumer is not able to see and check the product quality, as would be the case in physical store. In fact, for the consumer, buying products on internet appears to be more complex decision as it is more difficult to form an impression as to whether the products on offer are appropriate (Raijas, 2002). The second area of complexity concerns the mode of payment for the ordered products. Most of the consumers who are used to pay by cash at a checkout may find the electronic transfer and security checks unfamiliar and more complex.

Mishra (2007) examined the demographic characteristics of online consumers and their attitude towards online shopping behaviour for clothing. Perceived usefulness reinforces an online shopper’s intention to continue using a website, such that when a person accepts a new information system, one may be more than willing to alter practices and expand time and effort to use it (Gefen & & Straub, 2002).

The changing attitudes can be difficult as they fit into a pattern. To change one’s attitude requires radical adjustments to be made to others (Grant & Graeme, 2005). Forsythe & Shi (2003) mentioned Internet users can be categorized in to two viz., Internet shoppers and Internet browsers. Internet shoppers are the people who shop online, whereas Internet browsers are the people who just browse the Internet for other than shopping purposes.

According to Grant & Graeme (2005), purchasing behaviour is influenced by many factors, including social (culture, sub-culture, reference groups, social class and family), political, technological, economic and personal factors (self-image, motivation, personality, learning, perception, beliefs and attitudes). Improvements in technological infrastructure will make consumers more comfortable shopping on-line, and vendors will exploit advantage of the internet’s strengths more effectively to market and sell goods and services (Brown, Durrett & Wetherbe, 2004). For instance, British consumers shopping has drastically changed due to the rapid expansion of the Internet since the 1990s (Hengst, 2001), where the business sector accounts for most of the value of Internet-related business (Monsuwe, Dellart & De-Ruyter, 2004). Rapid growth in the retail sector is pushing retailers to tap into the virtual business environment. As Schlosser, Shavitt & Kanfer (1999) observed, a common strategy used by marketers and fashion retailers is adding Internet advertising to the promotional mix. Businesses are conducted both through traditional means as well as online. Technological advancements have led to the growth in technology-based self-service and thereby impacted the way business is transacted (Dabholkar, 1994; Moncrief & Cravens, 1999). This has provided opportunities to fulfil several consumer needs such as detailed product information of different brands, good bargains, convenience of shopping at home and saving of time and effort-all that leading to shopping more effectively and efficiently than conventional shopping in the highly competitive environment (Chen & Leteney, 2000).

Generally positive attitude towards shopping online for apparels was shown by women (Hirst & Omar, 2007). Although women are aware of some of the discouraging features of online shopping for apparels, these features do not deter them from buying online. The Internet user base is growing rapidly in India and it is inspiring to see that women user base is also increasing rapidly (Jain, 2014). It is clear that Internet is empowering Indian women with easy access to information and helping them to make more informed decisions in their day-to-day life. Singh (2016) observed that mostly youngsters and youth generation (18-25 age group) are very much interested in online shopping because they know usage of technology.

Attracting, retaining and satisfying female customers’ remains limited, despite the growth in application of technology-based online retail services. A marketer often encounters the difficulty of understanding and managing the dynamics of female consumer behaviour. This necessitates a study of behavioural issues in online retail shopping in establishing an online presence. Although the number of female consumers buying online products and services continue to increase in India, the success of some e-retailers and the failure of some, emphasizes the need for analysis in terms of behavioural issues. Further, what leads a female buyer to shop online also evoked a lot of interest from both the researchers and marketers. An understanding of the effects of the demographic, psychographic and situational factors on female consumer’s decision to shop online needs further exploration. Research pertaining to behavioural issues is fragmented and lacking in online marketing. This leads us to the theoretical framework for the current study.

Theoretical Framework

This study defines online apparel shopping as ‘the use of online stores by women up until the transactional stages of purchasing and logistics.’ A framework is developed adapting the previous research studies in order to develop an in-depth understanding of women’s attitude towards online apparel shopping and their intention to shop on the Internet (Dabholkar & Bagozzi, 2002; Monsuwe, Dellart & De-Ruyter, 2004). The Technology Acceptance Model (TAM), first developed by Davis, is the basis for the core constructs of this framework. This model is helpful to understand the adoption of computer-based technologies on the job or in the workplace. It has also proven to be suitable as a theoretical foundation for the adoption of e-commerce as well (Chen, Gillenson & Sherrell, 2002; Monsuwe, Dellart & De-Ruyter, 2004).

According to Monsuwe, Dellart & De-Ruyter, (2004), the two main determinants of a person’s attitude toward using new technology are-firstly, the degree to which a person believes using the new technology will improve his/her performance or productivity i.e. its “usefulness” and secondly, the extent to which a person believes that using the new technology will be free of effort i.e. the “ease of use”. Further, the extent to which the activity of using the new technology is perceived to provide reinforcement in its own right, apart from any performance consequences that may be anticipated is added as “enjoyment” construct by Davis (cited in Monsuwe, Dellart & De-Ruyter, 2004). More factors such as ‘control’, ‘intrinsic motivation’ and ‘emotion’ may be added to the TAM model (Venkatesh, 2000). Dabholkar & Bagozzi (2002) added two other factors, i.e. “consumer traits” and “situational influences” to this framework, resulting in an attitudinal model of technology-based self-service (Figure 1). This framework suits the investigation as the understanding of the determinants of women’s attitude has both a direct and positive effect on women’s intentions to actually use the Internet for shopping apparels. The two main determinants of a person’s attitude toward using a new technology viz., usefulness, the degree to which a person believes using the new technology will improve his/her performance or productivity and ease of use, the extent to which a person believes that using the new technology will be free of effort.

Figure 1:Theoretical Framework.

For understanding women’s intentions to shop online, this study investigated six factors viz., consumer traits, situational factors, product characteristics, and previous online shopping experience, trust in online shopping and product attributes.

Research Questions

The research questions identified are (i) what are the demographic characteristics of female online shoppers? And (ii) what factors influence female consumers to shop online?

Objectives

The objectives of the study are (a) to study the demographic characteristics of female online shoppers and (b) to identify the factors affecting online shopping preferences of female consumers.

Research Design

The research is descriptive and exploratory in nature. It is aimed at understanding the factors affecting female online buying behaviour. Vijayawada, an economically, socially and technologically prosperous town in the newly formed State of Andhra Pradesh in India, provided an appropriate setting for making this study.

The dependent variable is female consumers’ attitude towards online shopping. Demographic factors, convenience, time effectiveness, website design/features, security and social media influence are the independent variables. The research model for this study is presented in Figure 2. The constructs in this model are explained below:

Figure 2:Research Framework.

Technological Factors

For online shopping, website design is an important influencing factor. Website design, reliability and fulfilment, customer service, and security and privacy are the most important influencing features on the perception of the consumers of online buying (Shergill & Chen, 2005).

Risk Factors

A dominant factor that affects consumers to shop online is security of the transaction. Many Internet users avoid online shopping due to credit card fraud, privacy, non-delivery risk, post-purchase service, etc.

Demographic Factors

Demographics factors such as age, family status, women’s occupation, individual and family income, etc. Affect pricing, packaging, promotion and place decisions of online consumers.

Social Media

Consumers are increasingly resorting to reviews and feedback on social media sites before buying a product to make sure that the product is the best in its class, and offers good value for money.

Convenience

Browsing or searching product catalogue online is easier than shopping in traditional retail format. Thus convenience is a prime motivation for consumers to shop online.

The sampling design is given below:

Population

Online female consumers of different age groups in Vijayawada town.

Sampling Frame

The female consumers buying online. This includes trial, occasional, frequent and regular consumers.

Sampling Unit

Individual.

Sample Size

For a study of this nature the exact population is not known. Hence, as an estimated population of 4,000 and above is considered in determining the sample size of 316 respondents.

Sampling Method

Simple random sampling method.

Research Instrument

A questionnaire, consisting of two parts viz., demographic factors such as age, gender, education, income (Part A) and items that influence consumers to shop online (Part B) was designed. Questions were in the form of scaled-response as “scaling permits measurement of the intensity of respondents’ answers.” A five-point Likert scale was used.

Data Collection

Primary data from respondents with online shopping experience was collected during 2015 by distributing copies of self-administered questionnaires. Secondary data was collected from published sources such as national and international journals and reports.

Pilot Study

A pilot study with thirty respondents was conducted. The alpha coefficient of 0.789 for this study indicated good internal consistency of the questionnaire as the value is above 0.7.

Analysis

Data was analysed using IBM SPSS 20. The demographic data such as age, gender, designation, educational qualifications, and work experience were analysed for understanding respondent’s profile. Factor analysis was used for analysing data on items that influence consumers to shop online.

Demographic Profile of Respondents

Age

Of the 316 respondents, 157 were in the age group of 21-25 years (49.7%), 63 in 15-20 years (19.9%) and 50 in 26-30 years (15.8%). Female consumers in the age group of 15-30 years is the single largest group (85.4%) shopping online (Table 1). According to census 2011, the percentage population in the age group of 15 to 30 years is around 31% (nearly 40 crore). This age group constitutes a large market for online businesses. With female population constituting around 48.5% of total population, this is a fairly large segment of online consumers.

Table 1:Age
Age Frequency
15-20 63
21-25 157
26-30 50
31-35 9
36-40 7
41-45 14
46-50 10
>50 6
Total 316

Education

Graduates accounted for 168 (53%) of the 316 respondents, followed by post-graduates 80 (25%), class XII 25 (8%) and others 35 (11%). Young graduates constituted the potential market for the online sale of apparels (Table 2).

Table 2: Education
Education Frequency
Class X 16
Class XII 25
Graduate 168
Post-graduate 80
Ph.D 8
Professional course 16
Others 3
Total 316

Marital Status

Singles are more among female online buyers with 219 respondents (69%), followed by married people 82 (26%).

Occupation

Students at the graduation level constitute the modal group. Occupationally, students constitute 118 (37.3%) of the 316 respondents, followed by employees 87 (27.5%) and self-employed 57 (18%). Housewives (12%) and unemployed (5%) make up the remaining respondents (Table 3).

Table 3: Occupation
Occupation Frequency
Self Employed 57
Service 87
House Wife 38
Unemployed 16
Student 118
Total 316

Monthly Household Income

Those with lower income levels are frequent online consumers. Families with monthly income of Rs.30,000/- or lower are spending more on online shopping. They accounted for 113 respondents (36%). This is followed by monthly income of Rs.30,001/- to Rs.60,000/- with a frequency of 94 (30%). Those with monthly income levels in excess of Rs.60,001/- account for 109 (35%).

Monthly Individual Income

Ninety three (29%) online women shoppers are dependents, 78 (25%) have income less than Rs.10,000/-, 58 (18%) between Rs.10,001/- to Rs.20,000/-, 49 (15.5%) between Rs.20,001 to Rs.30,000/- and the remaining (12.5%) above Rs.30,001/- (Table 4).

Table 4: Monthly Individual Income In Rs.
Monthly Individual income Frequency
<10,000 78
10,000-20,000 68
20,001-30,000 49
30,000-40,000 16
>40,001 12
Dependent 93
Total 316

Nature of Employment

Dependent female consumers accounting for 134 (42%) are more frequent online buyers, followed by 119 (38%) working females.

Frequency of Online Purchase

The modal value of frequency of purchase is once in a month. Eighty nine (28%) respondents purchased once in a month, followed by 51 (15%) once in three months, 49 (15%) once in a year and 44 (14%) once in three months (Table 5).

Table 5: Frequency Of Online Purchase
Frequency of purchase Frequency
more than twice in a week 25
once a week 31
once in a fortnight 44
once in a month 89
once in 3 months 51
once in 6 months 27
once in a year 49
Total 316

Monthly Average Spending

The modal monthly average spending for online purchases is Rs.1,000/-. Of the total respondents 112 (35%) spent below Rs.1000/- per month, followed by 77 (24%) spent Rs.1,001/- to Rs.2,000/-, 69 (22%) spent Rs.2,001/- to Rs.3,001 and 38 (12%) spent Rs.3,001/- to Rs.4,000. Those who spent more than Rs.4,000/- per month are 58 (18%).

Factor Analysis

The Part B of questionnaire included 24 items relating to female consumer’s online buying behaviour. The alpha coefficient for the data was 0.848 indicating good internal reliability. Further, the factorability of the data was examined. Exploratory Factor Analysis (EFA) detects the constructs i.e. factors that underlie a dataset based on the correlations between variables i.e. questionnaire items. The variables that share the highest proportion of variance are expected to represent the underlying constructs. Factor analysis does not have the presumption that all variance within a dataset is shared, in contrast to the commonly used principal component analysis. Several well-recognised criteria for the factorability of a correlation were used. Firstly, it was observed that all the 24 items correlated at least 0.5 with at least one other item, suggesting reasonable factorability. Secondly, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.801. For the KMO statistic Kaiser (1974) recommends a bare minimum of 0.5 and that values between 0.5 and 0.7 are mediocre, values between 0.7 and 0.8 are good, values between 0.8 and 0.9 are great and values above 0.9 are superb (Hutcheson & Sofroniou, 1999). For these data the value is 0.801 which falls into the range of being great. Bartlett’s test of sphericity was significant (χ2 (276)=4371.979, p<0.05). The communalities were all above 0.5 confirming that each item shared some common variance with other items. Factor analysis was deemed to be suitable given these overall indicators.

Principal components analysis was used as the primary purpose was to identify and compute composite scores for the factors underlying the short version of the items influencing the online purchase behaviour of female consumers. Initial Eigen values indicated that the first three factors explained 27.535%, 19.987% and 6.594% of the variance respectively. The fourth, fifth and sixth factors explained 5.972%, 4.807% and 4.500% of the variance respectively. Solutions for factors were examined using varimax rotations of the factor loading matrix. The six factor solution, which explained 69% of the variance, was preferred because of (a) theoretical support; (b) the ‘levelling off’ of Eigen values on the screen plot after six factors (Figure 3); and (c) the insufficient number of primary loadings and difficulty of interpreting the seventh factor and subsequent factors.

Figure 3:Scree Plot.

Alpha coefficient internal consistency for each of the scales was examined. The alpha coefficients were good: 0.894 for factor 1 (10 items), 0.855 for factor 2 (5 items), 0.770 for factor 3 (4 items) and 0.838 for factor 4 (3 items). The six factors were appropriately labelled as ease of use and convenience (factor 1), security (factor 2), utility (factor 3), time management (factor 4), outbound logistics (factor 5) and feedback (factor 6). Factor loadings and communalities based on a principal components analysis is presented in Table 6. The factors are discussed below:

Table 6: Factor Loadings And Communalities Based On A Principal Components Analysis With Varimax Rotation For 24 Items From The Short Version Of The Female Consumers Online Buying Behaviour Scale (N=316)
  Component Commu-nalities
1 2 3 4 5 6
I shop online as I can save myself from market crowd. 0.760           0.710
I shop online as there is no embarrassment if I do not buy. 0.753           0.687
I shop online as I can take as much time as I can to decide. 0.743           0.675
I shop online as I can get detailed product information online 0.707           0.638
I shop online as I can then save myself from chaos of traffic. 0.694           0.732
I use online shopping for buying products which are otherwise not easily available in the nearby market or are unique (new). 0.687           0.666
Online purchasing makes my shopping easy. 0.681           0.661
It is easy to choose and make comparisons with other products while shopping online. 0.605           0.695
I shop online as I can shop from home. 0.594           0.623
Availability of internet makes online shopping easier. 0.567           0.788
I feel that my credit-card details may be compromised and misused if I shop online.   0.830         0.555
I fear overcharging if I shop online as the retailer has my credit-card information.   0.827         0.637
I fear misuse of credit card data in online shopping.   0.780         0.724
Social media triggers me to purchase a product/service online.   0.684         0.697
I cannot get to examine the product when I shop online, hence I don’t prefer.   0.655         0.833
Social media helps me getting customer reviews before buying online.     0.756       0.684
Social media helps me to collect information about the products to buy online.     0.753       0.759
I get on time delivery by shopping online.     0.665       0.723
I do not like being charged for shipping when I shop online.     0.637       0.691
Online shopping doesn’t waste time.       0.869     0.741
Online shopping takes less time to purchase.       0.765     0.636
I feel that it takes less time in evaluating and selecting a product while shopping online.       0.762     0.702
I will have problem in returning product bought online.         0.748   0.644
When I make online purchase my friend's opinion is important to me.           0.803 0.753
Eigen Value 6.608 4.797 1.583 1.433 1.154 1.080  
Explained variance per factor (%) 27.535 19.987 6.594 5.972 4.807 4.500  
Cumulative (%) 27.535 47.522 54.115 60.008 64.985 69.394  

Ease of Use and Convenience

The first factor is labelled as ease of use and convenience. It explained 27.535 per cent variance. It has ten items viz., I shop online as I can save myself from market crowd, I shop online as there is no embarrassment if I do not buy, I shop online as I can take as much time as I can to decide, I shop online as I can get detailed product information online, I shop online as I can then save myself from chaos of traffic, I use online shopping for buying products which are otherwise not easily available in the nearby market or are unique (new), Online purchasing makes my shopping easy, It is easy to choose and make comparisons with other products while shopping online, I shop online as I can shop from home, and Availability of internet makes online shopping easier.

Security

The second factor is labelled as security. It explained 19.987 per cent variance. It has five items viz., I feel that my credit-card details may be compromised and misused if I shop online. I fear overcharging if I shop online as the retailer has my credit-card information. I fear misuse of credit card data in online shopping. Social media triggers me to purchase a product/service online. I cannot get to examine the product when I shop online, hence I don’t prefer.

Utility

The third factor is labelled as utility. It explained 6.594 per cent variance. It has five items viz., Social media helps me getting customer reviews before buying online, Social media helps me to collect information about the products to buy online, I get on time delivery by shopping online and I do not like being charged for shipping when I shop online.

Time Management

The fourth factor is labelled as time management. It explained 5.972 per cent variance. It has three items viz., Online shopping doesn’t waste time, Online shopping takes less time to purchase and I feel that it takes less time in evaluating and selecting a product while shopping online.

Outbound Logistics

The fifth factor is labelled as outbound logistics. It explained 4.807 per cent variance. It has one item viz., I will have problem in returning product bought online.

Feedback

The sixth factor is labelled as feedback. It explained 4.500 per cent variance. It has one item viz., When I make online purchase my friend’s opinion is important to me.

Discussion

Most of the female online shoppers are-students in the age group of 21-25 years, graduates/students and dependents. Majority of them are not married. Their frequency of purchase is mostly monthly.

The six factors identified through factor analysis viz., ease of use & convenience, security, utility, time effectiveness, outbound logistics and feedback explained more than 69% of variance. Ease of use and convenience play a significant role in attracting online female customers. Website design and features that provide useful information to the customers and convenience of buying from home make them use online shopping. Security of the online transaction and source credibility of the supplier are important too. Female consumers use online reviews on the products that they wish to purchase to assess their utility. For most of the female consumers effective time management is an important consideration for opting to buy online. An important consideration for the female online customers is the process of returning a product when it has to be done for various reasons. These considerations are important for the marketing managers to decide on the strategies for online marketing of their merchandise.

Suggestions

E-Commerce firms should focus on female consumers in the age group of 21 to 25 years, graduates and either during their studentship (i.e. they are dependents) or in the formative years of their career. Female consumers are showing positive attitude towards buying online. Six factors viz., ease of use & convenience, security, utility, time effectiveness, outbound logistics and feedback explain more than 69% variance. E-Commerce firms have to closely monitor these factors and facilitate positive outcomes for the female consumers.

Scope For Further Research

The factors influencing female buying behaviour framework can be useful to both practitioners and academics. It could help online marketers and e-Commerce firms to identify issues requiring special attention to the factors impacting their online business or evaluating their existing online venture. For academic researchers this classification could be a basis for the formulation of new hypotheses and research questions leading to better mapping of the online consumer’s behaviour. This research can be used by online retailers to shape their marketing strategy.

The framework could also be the basis of further research focused on better understanding of the nature and weight of the factors influencing buying behaviour, either in isolation or in interaction with each other and in different e-retailing markets.

The young shoppers are mainly college students who are technologically savvy and don’t mind shopping online. This research doesn’t include people who are aware about online retailing but not participated in online shopping.

Finally, these six factors must be regarded as a dynamic and evolving subject rather than a static one. The developments in the virtual marketplace, changing customer techno-graphics and technological innovation will present e-marketers with new tools and methods for enhancing their customers’ online experience. In that respect this study depicts the current picture of the research done thus far, a good starting point for further research in the direction of developing a comprehensive theory on the online buying behaviour.

The same research can be repeated across different cities in India to understand the differences and similarities in online shopper buying behaviour.

Conclusion

Online shopping is becoming popular by the day. Marketers find understanding customer’s need for online selling a challenge. In particular, understanding attitude of the consumer’s towards online shopping, improvements in the factors that influence consumers to shop online and working on factors that affect consumers to shop online will help marketers to gain the competitive edge over others. Therefore, the focus of this study was mainly on factors that influence female consumers to shop online.

Online retailing is very different from the store formats of retailing. The research made an attempt to find out the triggers that influence shopper buying behaviour in online retail formats. The Exploratory factor analysis concluded factors viz., ease of use & convenience, security, utility, time effectiveness, outbound logistics and feedback as the determinants of female shopper buying behaviour online. Online retailers must take this into consideration while designing their user interface.

Endnote

1. http://www.iamai.in/PRelease_detail.aspx?nid=3604&NMonth=7&NYear=2015

2. http://www.pwc.in/assets/pdfs/publications/2015/ecommerce-in-india-accelerating-growth.pdf

References