A review on Effect of e trust and e risk on Consumers of retail e markets in India: A Comparative Study Based on Sociodemographic Variables

 Anuraag Agarwal¹, Dr.Sanskrity Joseph²

¹Research Scholar, Institute of Management, PRSU Raipur

²Supervisor, Institute of Management, PRSU Raipur

[Received: 17 January 2020; Accepted: 29 September 2020; Published Online: 12 February 2021]

Abstract: Most companies run their online portals to sell their products / services online. The potential growth of online shopping has given rise to the idea of conducting online shopping research in India. Trust is one of the biggest barriers to success in Internet media. Lack of confidence and the risks involved can prevent online customers from participating in e-commerce. This investigationgoals to investigate how electronic consumers develop their initial confidence orobtainingpurposeswith e retailers. The present study attempts to draw value information that impacts the e trust and e risk on shopping behaviour of Indian e shopper and their possible implications one retailer's product offerings. The study intends to identify key variables and construct which has a significant influence one trust and e risk in India. The researcher through literature review has identified few dimensions of trust and risk which will be explored on the basis of sociodemographic variables to get broad picture and to arrive at conclusions. The data was collected through Questionnaires.

Keywords: e risk, e trust, e retail markets, online shopping, e-commerce

1.Introduction

E-commerce refers to "buying information, products and services through computer networks" (Kalakota&Whinston, 1996). Bloch, Pigneur, and Segev (1996) expanded it to include "support for any kind of business transaction through digital infrastructure". Online shopping is a process of e-commerce through which patrons can directly contact electronic suppliers orobtaining goods or facilities from online stores (Chaffey, 2009, p. 88).

The retail ecosystem is made up of consumers, retailers and partners who are rapidly transforming the retail landscape. They deliver very difficult consumer demands by combining ecommerce, chat, streaming, gaming or payment services into a single platform or application adopted by customers worldwide. Since the last five years, the Indian e-commerce market has grown exponentially. With the introduction of better marketing technologies, the market is predictable to grow at great leaps.

Driven by strong investments in this area and the rapid growth in the number of Internet users, India is predictable to develop world's fastest growing e-commerce market. Various institutions have high hopes for the growing of Indian e-commerce market.

Consumers are increasingly buying and buying products online, arranging financing, arranging transportation or collecting various products and getting after-sales services. Business-to-consumer (B2C), often referred to as e-commerce, including retail, often referred to as e-tail, and other online purchases such as airline tickets, entertainment venues, hotel rooms and warehouses (Joseph, 2009). There are two different categories of e-retailers, pure play and physical click. A pure e-retailer uses the Internet to market its goods or services, but also has a traditional physical store that customers can use (Murugavel, 2010).

Online shopping has great potential in India. This is especially true as the cost of real estate in India is rising. Every day, more and more websites offer different products and services. The scope of research in trust in e-commerce is very wide, especially in developing countries like India. Trust has received a lot of attention because belief is ansignificant factor that has a importantimpression on website sales.

2. Literature review

Mainardes et al. (2019)Introduces the antecedents of e-commerce purchase intentions in developing markets (Brazil). To this end, they used new methods in the structure to fully characterize the Brazilian market. They surveyed 345 e-commerce users. The consequences show that purchasing intent will be adversely affected by the lack of customer confidence, while the website logo and site quality will be positively affected. They also suggested that the factors lacking consumer confidence are perceived as risk and consumer resistance to innovation, whereas past experiences have a negative impact.

Malaquias et al. (2019)This article aims to compare the determinants of mobile banking use between respondents in two countries with different levels of development (Brazil and the United States). Our theoretical model includes six variables, which are key factors in mobile banking. To analyze the path coefficients and test these six hypotheses, they adopted a structural equation model.

Christino et al. (2019)A conceptual model is proposed, which is based on the overall theory of technology acceptance and application 2 (UTAUT2). The following constructions were extra to original model: usability, personal capabilities, visible risks and behavioralfeatures. The perfect effectively explained its 67.2% alteration.

Rajavi et al. (2019)Using multi-source datasets (including studies of 15,073 respondents from 589 brands in 46 CPG groups in 13,073 countries / regions and scanner panel data) through use of consumer, category, and country / region characteristics Reduce CTB sensitivity to activities in marketing mix. The 4maindeveloping markets), which together make up half of the world's population. The author found that the confidentpossessions of publicityor new product launch strengths are strong, the positive properties of price orcirculation power are weak, and the adverse effect of CTB price promotion power is small.

Wang et al. (2019)Through four ecommerce models: consumer-to-consumer (B2C), online-to-offline distribution (O2O delivery), online-to-online delivery, consumer food shopping motivation and community are demonstrated with the consumer's attitude towards food shopping demographic characteristics. Offline physical stores (O2O physical stores) and new retail. It also examines customerfavourites for precisenutritiongroups under the four e-commerce models.

Bhatet al. (2019) It introduces impression of belief, invention, utility, key product categories orpurchaser support services on commitments, which in turn affect eWOMor sustainable consumption. Supportableconsumption will ultimately affect a maintainable competitive benefit. The study used the SEM method in which a tool for the above variables was developed in the form of aorganisedsurvey (using both EFA and CFA). In Jam and Kashmir, India, a survey was conducted through online and offline models and 589 respondents were randomly selected from the e-shopper group as an example.

Higueras et al. (2019)Proposals to assess consumer attitude towards electric vehicles. Similarly, it is recommended to use consumer perceived efficiency (PCE) as a moderator. The examination was performed using aincomplete least quadranglesmethod in a structural equation model with a sample of 404 users.

Jibrilet al. (2019)Proposed a framework for stimulus-biological reply (SOR) and focused on five variables of attention, specificallyprofessed ease of use, state-sponsored substructureor financial considerations regarding online store valuing, apparentsuitability, and intention to use. Evidence composed from 294 research contributors supports our investigation claims

Rosillo et al. (2019) suggested important influence of discretenationalextents on perceived producesuperiority, professed risk, or purchase purpose in e-commerce stages.

Tedja et al. (2019) presented the buyingpurpose of communityspending as the dependent variable, based on the professedmaterialsuperiority, professedschemesuperiorityor perceived provisionsuperiority as liberated variables, with customer gratification, commitment and trust as the mediating variables. The target population for this research were active university students in Tangerang who are taking a bachelor degree. The technique used for sample collection is Snowball Sampling and the amount of sample used for this research is 270 students. 

Trabelsi et al. (2018)Describes how patrons can appraisemovable service superiority (MSQ) in mobile banking applications Examines significances such as electronic trust, electronic satisfaction, electronic devotionor word of mouth (WOM). The physicalcalculation model was recycled to examine the responses of 337 users from the Tunisia MB application. Research shows that customer age and gender play a curbing role in relationship among MSQ or certain dimensions of electronic trust.

Amirtha et al. (2018)A method of using consumer segmentation according to the Family Life Cycle (FLC) phase has been proposed and it is found that these factors have a greater impact on personal life changes than age. By covering women of all ages, it solves prejudice against young people in most studies on the use of e-shopping orappearance of adult e-shoppers as anbeautiful market area. It uses technological acceptance models to assess the impact of discernmentsorarrogances on e-shopping acceptance and their changes throughout the FLC phase.

Meesala et al. (2018)Presenting greatest critical featuresconnected to hospital quality of service, these factors will ensure future enduranceorvictory. The study was conducted using data from consumers in 40 different secluded hospitals in Hyderabad, India. Feasibility, reliability, responsiveness, security orsympathy (service quality dimension), persistentgratificationordevotion to the hospital are the variables considered in this education.

Khan et al. (2018)An adventure plan was proposed to understand the impact of the e-commerce strategy applied in Nigeria to endorse use of e-commerce. Unlike preceding studies, this education focuses on the opinions of e-commerce operatives and regulars. This is a larger data-based survey that collects opinions from customers and operators about the impact of e-commerce strategies on e-commerce exploitation levels. A research tool survey was designed and sent to 225 customers via Google links, of which 200 questionnaires were completely completed and returned.

Yu et al. (2018)It is optional that stores can reduce professed risk by adding quality brands to luxury goods. However, efficiency of this excellence brand be contingent on cultural orientation of the consumer. Therefore, they hope that the quality label is effective only to avoid consumers with great uncertainty. Kearney from AT reveals that there is a negative correlation between the degree of uncertainty avoidance (UA) in a country and the degree to which its consumer base is in favor of online transactions, which may indicate that even general online consumer behavior (the is not a luxury product per se) is also considered a high risk. Culture with high uncertainty avoidance.

Liébana et al. (2018)Show user acceptance of the mobile imbursement system on social networks. To elucidate acceptance, they combined trust orapparent risk into old-style TAM model. To complete this education, they recognised deciding factor for the payment system by analyzing the user's masculinity, age orknowledge level. The research was conducted through an online review in a state group of 2,012 social system users

Ageeva et al. (2018)The views of attribution and signal theory are proposed to examine the key influences of the determinants that determine the firm's site preferences. In addition, this article examines main effects of gratificationor attractiveness on the pictureorstanding of the company, observes the demographic role of consumers (gender and age) in this relationship, orsuggests research models and investigation principles.

Hubert et al. (2017)By studying two previously unexplored aspects, the existing methods from the technical acceptance literature are introduced. First, the study examines the impact of various mobile and personal benefits (instant connection, contextual value and hedonic motivation), customer characteristics (habits), and risk aspects (financial, performance and security risks) which are prerequisites for mobile shopping acceptance. Second, you assume that several acceptance drivers are different in relevance, which depends on the perception of the three mobile shopping properties (location sensitivity, time pressure and degree of control), while other drivers are considered irrelevant to the context.

Fortes et al. (2017)The issues raised will affect consumers' willingness to buy online. Established a research model and established the mediating role of trust and perceived risk. The results of the empirical study (n = 900) indicate that the relationship between privacy policy issues and purchase intentions is partly mediated by trust, perceived risk, and attitude.Patel et al. (2017)Through a sample survey from India, the role of sociodemographic factors in consumer environmental protection behavior (PEB) (part of ethical behavior) is introduced and its significance in emerging markets is analyzed. As a research method, multivariate analysis of variance (MANOVA) was performed.

Nandi et al. (2017) It introduces consumers ’willingness to buy organic fruits and vegetables (WTP) and related factors that influence consumer desires. A conditional valuation method was chosen to estimate WTP. The empirical data comes from 250 consumer surveys conducted in Bangalore in February 2013. A binomial logistic regression model is used to obtain the value of WTP and determine the factors affecting it.

3. Research Methodology

3.1 Purpose of the Study

e trust is one of the key hindrances in succeeding on the Internet medium; a lack of trust and  perceived risk involved  is likely to discourage online consumers from participating in e-commerce. This research aims to investigate how e consumers develop their initial trust and purchase intentions with e retailers. The study intends to identify key variables and construct which has a significant influence on e trust and e risk in India. The researcher through literature review has identified few dimensions of trust and risk which will be explored on the basis of sociodemographic variables to get broad picture and to arrive at conclusions.

3.2 Objectives of the Study

1. To find out the attitude of consumers regarding e retailers in India.

2. To find out the factors affecting choice of e commerce website amongst consumers across product categories in India. 

3. To analyse the determinants of  e trust amongst consumers of selected e retailers

4.To assess the perception of consumers towards e risk amongst consumers of selected e retailers.

5.To analyse the effect of e trust on purchase intentions of consumers of selected e retailers

6. To assess the effect e risk on purchase intentions of consumers of selected e retailers

7. To find out the e engagement and e satisfaction of consumers towards the services provided by selected e retailers

3.3 Hypothesis Formulated for the study

H_01a = Socio demographic factors affects e trust and e risk of consumers purchasing from e retailers

H_01b =  E trust and E risk has no effect on gender

H_{01c =}  The age factor affects the e trust and e risk of consumers with e retailers

H_01d = There is a significant difference between income of e consumers on e trust and e risk

H_01e = There is no significant difference between occupation of consumers on e trust and e risk

H_01f = Marital status of consumers has a significant effect on e trust and e risk

H_01g = There is a significant difference of education on e trust and e risk

H_01h=There is no significant difference between socio-demographic variables and attitude of consumers regarding e-retailers in India

H_01i =There is no significant difference between socio-demographic variables and factors affecting choice of e-commerce website amongst consumers across product categories in India

H_01j =There is no significant difference between socio-demographic variables and determinants of e-trust amongst consumers of selected e-retailers

H_01k = There is no significant difference between socio-demographic variables and perception of consumers towards e-risk amongst consumers of selected e- retailers

H_01l =There is a significant impact of e-trust on purchase intentions of consumers of selected e-retailers

H_01m = There is a significant impact of e-risk on purchase intentions of consumers of selected e-retailers

H_01hMale and Female does not show any significant difference on e engagement and e satisfaction on services provided by  selected e retailers. 

3.4 Identification of variables

“Variable” is a term frequently used in research projects. It is pertinent to define and identify the variables while designing quantitative research projects. 

a.       Dependent Variable

The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

In this research work it can be from  e trust and e risk of the e retail market products:

1.      Price

2.      Trust

3.      Quality

4.      Product

5.      Schemes

6.      Services

7.      Level of satisfaction

8.      Reputation

b.      Independent Variable

The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

In our research work these can be from Factors/Parameters of Socio Demographic:

1.      Consumer Age

2.      Consumer Income

3.      Consumer Skills

4.      Marital status

5.      Consumer Education

6.      Recommendation

7.      Consumer Economic status  

3.5 Frame work Model on

3.6 Scope of the Study

The possibility of education is confined to India where the significant and contributory constructs will be identified in growth of belieforprofesseddanger for online shopping with e stores. The present study is being done not considering industrial buyer which forms one of the avenue for study and investigation.

3.7 Population

All the consumers who purchase from e retailers in India will constitute the population for the study.

3.8 Sampling Techniques

A. Determination of Sampling Unit: The sampling unit is actual sample from population for coming to conclusion.

B. Sample Size Determination: Considering a population of 12 million the Cochran’s formula is used to determine the sample size. Determination of sample size for the purpose of research or any empirical study is defined in many books e.g. Cochran (1977), Mark (2005) and Singh and Chaudhury (1985). The aim of the calculation is to determine an adequate sample size which can estimate results for the whole population with a good precision. In other words, one has to draw inference or to generalize about the population from the sample data. The sample size is determined by defining margin of error which is called as confidence interval and confidence level. The present study defines –

Confidence Level to be 95% and confidence interval to be 5%

C. Degree of Variability: Assuming maximum variability of 50% the sample size is determined using Cochran formula, Cochran (1977) developed a formula to calculate a representative sample for proportions as

n_o= Z² pq/e²

The sample size is 2.58²*.5*.5/.05²

=665.65~666

Cochran pointed out that if the population is finite, then the sample size can be reduced slightly. This is due to the fact that a very large population provides proportionally more information than that of a smaller population. He proposed a correction formula to calculate the final sample size in this case which is given below

n₌n_o

      _---------

    ₁₊N_o-1/N

Hence, Sample Size equals

666

-----                                          =665.96~666

1+666-1/120000000

Sample: Hence the researcher will take 1000 for the purpose of study

Sampling Technique: Convenient Sampling will be used during the study

3.9 Sources of Data

The data will be composed from either main orminorbases. For primary data selected respondents will be approached . For secondary data various published articles and informations will used in the study.

3.10. Data Collection:

Data will collected through questionnaires / schedule/interview which will  beadministereed by researcher . Researcher will also attempt to collect data from identified respondents individually and tnperson.The mail questionnaire will also be used for collecting primary data.

4.1  Questionnaire

Taking into account various constructs impacting e trust and risk for online shopping a well structuredquestionnaire  will be developed. The questionnaire will be in two parts taking into consideration both e trust and e risk.

4.2 Conclusions

1.      From the analysis, it is found that risk of performance, finance, time and privacy was perceived more by e-users in India. There psychological risk is also there for some categories of customers. E risk dimention plays important role in the growth of e-commerce industry and e-commerce businesses

2.      It is found that primarily due to risk concerns the Consumers were showing unwillingness to purchase from e-commerce sites.

3.      The Indian consumers are more worried about performance and financial risks of the harvests or facilities while buying operational. Schemeconnectedprofessed risks were found insignificant.

4.      As compared to male e-shoppers, the female e-shoppers are more risk-averse.

5.      As low-income collectionsections are normally price mindful, they are ready to take risks when any product is lauched with a good deal. So as compared to low-income group, the high revenueorintermediatepay groups are supplementary risk-averse.

6.      The Indian customers shop on a particular website, only if they have heard the website’s name from someone or somehow like either from advertisements or newspapers, and the product is able to create positive effect on consumer.

7.      It is also found that if the product or services are not relatable to the customer, the customers’ trust in the e-business decreases. The image of the product also plays a great role in seeking the attention of consumer, provided images are of the actual product and are not mismatched.

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