Tülay Yeniçeri, Eyup Akin

Aksaray University, Turkey

Determining Risk Perception Differences between

Online Shoppers and Non-Shoppers in Turkey

Abstract:

In last decade internet has become a popular media for most of the societies including the shopping opportunity. By a reason of having place in human daily life a lot, it is a potential for management to reach different type of consumer at the same time without much effort than in real market. But using internet is sometimes problematic and/or risky for all of the users, particularly online shoppers and risk perception of users is one of the -may be the most important- factors which affects shopping via internet, limits its potential. Aiming to identify the major discriminative factors between online shoppers and non-shoppers concerning their risk aversion attitude and risk perceptions about online-shopping, this study was conducted on internet users to analyze quantitative data which was gathered by a questionnaire. In the questionnaire development stage, a pilot test was done on 30 respondents in order to test the clarity of the questions and to identify the average completion time. The questionnaire was applied after the necessary improvements and simplifications via internet. 237 questionnaires were carried out for analyses and discriminant analysis was used in order to test the research hypotheses. As a result of the analysis, it was found that there exist statistically significant differences between online shopper and non-shoppers in terms of risk perception about online shopping. In our study, risk perception was measured by six dimensions namely; financial, social, psychological, performance, privacy, and time. In addition, it was also found that online shoppers are different from non-shoppers concerning their risk aversion attitude.

Keywords: Online Shopping, Risk Aversion, Perceived Risk.

JEL Classification: M31 - Marketing

1. Introduction

In last decade internet has become a popular media for most of the societies and business since it includes a lot of benefits and usage areas involving the shopping opportunity for people and selling for the firms and brands. From the consumers’ side convenience is the major reason for internet

shopping, but time savings, greater variety of products and services, and absence of sales pressure are also key rationales for using the internet (McQuitty and Peterson, 2000; Szymanski and Hise, 2000). From the business side, by a reason of having place in human daily life a lot, independent from the location it is a potential for management to reach different type of consumer at the same time without much effort than in real market. Owing to its characteristics, the evolution of internet directly interacts with the development of globalization.

According to Tan, the increased globalization of the world economies has created many opportunities for marketers. At the same time, this has also intensified competition among

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businesses so that many companies are looking towards unconventional forms of marketing. Marketing through the Internet is one such unconventional form of marketing that many companies have turned to (Tan, 1999). This transformation in business is directly related with internets potential for the business and marketing. But the potential for Internet shopping is underexploited, however, because as recent market surveys have confirmed, many users are reluctant to make purchases on the Internet. Lack of trust is one of the most frequently cited reasons for consumers not purchasing from Internet shops (Lee and Turban, 2001). Lack of trust sometimes makes usage of internet and shopping via internet problematic and risky by consumers. And this trust problem has place in marketing and consumer behavior literature with the concept of perceived risk by the consumers. Particularly this risk perception of users is one of the -may be the most important-factors which affects shopping via internet.

The main objective of this empirical quantitative study is to specify the difference the online shoppers and non-shoppers in risk perception levels.

2. Perceived Risk and Online Shopping

Perceived risk is a situational and personal construct that has been defined in several ways, with considerable debate occurring on the merit of each (Pires et. al. 2004). It is a function of two components: uncertainty and the seriousness of the consequences of the purchase (Cases, 2002). So, it can be considered a function of the uncertainty about the potential outcomes of a behavior and the possible unpleasantness of these outcomes. It represents consumer uncertainty about loss or gain in a particular transaction conceptualized perceived risk as ‘‘the nature and amount of risk perceived

by a consumer in contemplating a particular purchase decision.’’ (Forsythe and Shi, 2003) When

perceived risk falls below an individual’s acceptance value, it has little effect on intended behavior

and is essentially ignored (Cunningham et.al, 2005)

Since a person’s risk-taking behavior is determined by her perception of risk as well as her acceptance of risk, it follows that a consumer’s risk profile (risk-neutral or risk-averse) would affect her shopping behavior online: Risk neutral consumers are more likely than risk-averse consumers to consummate a purchase transaction when faced with buying a product (or service) with uncertain outcomes or possible loss. (Gupta et. al. 2004) So risk aversion is another factor may be a personal attitude or characteristic that possibly has an effect on the purchase decisions and behaviors. According to Tan’s (1999) findings risk aversion is one of the main factors that affect online

shopping.

A purchase decision involves risk when the consequences connected with the decision are uncertain and some results are more desirable than others (Cunningham et.al 2005), all the shopping including via internet. Even though consumers perceive the Internet as offering a number of benefits, the Internet tends to magnify some of the uncertainties involved with any purchase process, consumers perceive a higher level of risk when purchasing on the Internet compared with traditional retail formats (Forsythe et.al., 2006). Because in a traditional shopping context, consumers essentially are passive recipients of information. With the advent of internet shopping, however, a new paradigm of consumer behavior has emerged. Rather than being merely passive recipients of marketing information, consumers are afforded the opportunity to be active users and co-producers of information through utilization of computer technology. The end result is intended to be a facilitated, and even enhanced, shopping experience. (Haung et. al., 2004) This evolved format of shopping revived another risk concept as “perceived risk in internet shopping” and defined as the

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subjectively determined expectation of loss by an Internet shopper in contemplating a particular online purchase (Forsythe and Shi, 2003).

2.1 Types of Perceived Risk

The behavior of consumers in the face of risk has been the subject of numerous studies over the past 40 years and the studies have focused primarily on analyzing consumers’ perceived risk and on

identifying the different dimensions of risk (Cases, 2002).

All of these risk types were grouped and determined for the online shopping by Cases (2002) as given in Table-1

Table 1. Risk Sources and Risk dimensions in an Electronic Shopping Context

Risk source Risk

dimension

Risk description

Product Performance risk

Disappointment of the buyer in relation to expectations concerning product performance

Remote transaction

Time risk Time spent purchasing the product and time wasted in the case of a bad purchase

Financial risk

Money lost in the case of a bad purchase, additional charges engendered by the shipping of the product or its exchange

Delivery risk

Fear of not receiving the product on time, long delivery time

Internet Social risk Fear of the reaction of friends and family concerning the use of the Internet as a mode of purchase

Privacy risk Invasion of the consumer’s private life, loss of anonymity on the Internet

Payment risk

Financial consequences engendered by giving one’s credit

card number on the Internet

Website Source risk Fear of the level of credibility and reliability of the website

In most of the studies six components or types of perceived risk have been identified and used: financial, product performance, social, psychological, physical, and time/convenience loss (Forsythe and Shi, 2003)

Financial risk is viewed as the potential monetary loss consumers may feel after choosing a particular product or brand (Derbaix, 1983; Haung et. al., 2004). It includes the possibility that one’s credit card information may be misused. Thus, consumers’ apparent sense of insecurity

regarding online credit card usage stems primarily from a concern about financial risk ( Forsythe and Shi, 2003).

Product performance risk, the loss incurred when a brand or product does not perform as expected, is largely due to the shoppers’ inability to accurately evaluate the quality of the product online

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(Forsythe et.al., 2006). Performance risk is related to the likelihood that a product will actually function as expected (Haung et. al., 2004) and is defined as the loss incurred when a brand or product does not perform as expected. Product performance risk may result from a poor product choice due to the shoppers’ inability to accurately judge the quality of the product online. The

ability to judge product/service quality online may be limited by barriers to touching, feeling, and trying the product or service, inaccurate product colors and insufficient information on quality attributes relevant to the consumer resulting in increased product performance risk. (Forsythe et.al., 2006).

Physical risk is considered to be possible safety problems arising from using the product, especially those directly related to health and safety (Haung et. al., 2004). It is about with the probable physical damage to consumer.

Psychological risk is the probability that the selected product will be consistent with the consumer’s self-image (Haung et. al., 2004) and may refer to disappointment, frustration, and shame experienced if one’s personal information is disclosed ( Forsythe and Shi, 2003). Misuse of

consumers’ personal information is the main fear for online shoppers related to psychological risk.

Time/convenience risk includes the inconvenience incurred during online transactions, often resulting from difficulty of navigation and/or submitting orders, or delays receiving products (Forsythe et. al., 2006). This kind of risk includes time loss while online shopping related to shopping malls or web sites system problems and waiting long for receiving the purchased product than it is expected.

Social risk is related to the perceptions that significant others are likely to have towards the purchased item (Haung et. al., 2004). Social risk is refers the possible negative interaction with the people about buying via the internet like being criticized, condemned or sidelined.

3. Research Methodology

3.1 Objectives of Research

The main objective of this research is to determine the differences between online shoppers and non-shoppers in terms of risk perception and risk aversion about online shopping; and based on these differences to estimate their group memberships.

Figure 1: Research Model

Risk Aversion

Perceived Risk

• Financial

• Performance

• Social

• Time

• Psychological

• Privacy

Preferences

Of Consumers for

Online Shopping

Online Shoppers

Non-Shoppers

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A descriptive research model with three main variable groups was used in this research. A descriptive research model was depicted in Figure 1. As can be understood from Figure 1, the research model has the risk aversion of consumers as the first variable group, perceived risk of consumer as the second variable group, and finally preferences of consumers for online shopping as the third variable group.

Two main research hypotheses were formulated by using research model. These hypotheses are as follows:

H1: There exist statistically differences between online shoppers and non-shoppers in terms of risk perception about online shopping and risk aversion.

H2: Meaningful forecasts can be made on group membership estimations by using differential variables of risk perception and risk aversion about online shopping.

3.2 Data Collection

This study was conducted on internet users to analyze quantitative data which was gathered by a questionnaire. In the questionnaire development stage, a pilot test was done on 30 respondents in order to test the clarity of the questions and to identify the average completion time. The questionnaire was applied after the necessary improvements and simplifications via internet. 237 questionnaires were carried out for analyses.

When the distribution of the socio-demographic characteristics of the consumers involved in the study is considered, it was found that 57 % was male and 43% was female, their average age was 31 and their monthly income was 2000-3000 TL(Turkish Lira). In terms of the education level of the respondents, 19.8 % was high school graduates, 42.6 % was faculty graduates and, 22.8% had a master degree, and 14.8 % had a doctorate degree.

3.3 Findings

Discriminant analysis was used in order to test the research hypothesis. The discriminant analysis is a multivariate statistical analysis for distinguishing between two or more groups with respect to several variables simultaneously (Klecka, 1980). In other words, discriminant analysis is a useful technique in the examination of whether significant differences exist among the groups in terms of the predictor variables (Malhotra, 2004).

Once the discriminant function was formed, discriminant analysis was also used to predict the online shopping behavior of internet users. The findings were stated below:

The canonical discriminant function results of the analysis were presented in Table 2 and significance of the function in Table-3.

Table 2. Summary of Canonical Discriminant Functions

Function Eigenvalue % of Variance Cumulative %

Canonical

Correlation

1 .231 100.0 100.0 .433

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Table 3. Wilks' Lambda

Test of Function(s) Wilks' Lambda Chi-square df Sig.

1 .812 48.183 7 .000

As it can be seen from Table 2 and 3, canonical discriminant function accounted for 100 percent of the total variance and chi-square was significant at p= 0.000. Therefore, the first hypothesis (H1) of research was accepted. In other words, there exist statistically differences between online shoppers and non-shoppers in terms of risk perception about online shopping.

Statistics about the variables which was included in discriminant analysis was given in Table 4.

Table 4. Discriminant Analysis : Wilks’Lambda, F Values and Significant Variables

Wilks' Lambda F df1 df2 Sig.

Risk aversion .966 8.269 1 235 .004

Financial risk .876 33.314 1 235 .000

Performance risk .904 24.848 1 235 .000

Social risk .960 9.749 1 235 .002

Time risk .945 13.644 1 235 .000

Psychological risk .836 46.042 1 235 .000

Privacy risk .965 8.527 1 235 .004

The variables that differentiate between online shoppers and non-shoppers can be seen in Table 4. As it can be noticed from the table, there exist seven variables separating online shoppers and non-shoppers, which are “risk aversion”, “financial risk perception”, “performance risk perception”,

“social risk perception”, “time risk perception”, “psychological risk perception”, and “privacy risk

perception”. There exist significant differences between online shoppers and non-shoppers in terms of these seven variables stated herein.

Table 5 provides the structure matrix obtained through discriminant analysis which was implemented for determining the differences of two predefined groups. The structure matrix shows the discriminant loadings that represent the simple correlation between the predictors and the discriminant function. In the structure matrix the discriminant loadings are given in an order from highest to lowest by the size of the loading.

Table 5. Structure Matrix

Function

1

Psychological risk .920

Financial risk .783

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Performance risk .676

Time risk .501

Social risk .423

Privacy risk .396

Risk aversion .390

As it can be understood from Table 5, the variable that makes the greatest contribution to discriminate between the groups was “psychological risk perception”, while the “financial risk

perception” came second, and the “performance risk perception” third. These three were followed

by “time risk perception”, “social risk perception”, “privacy risk perception” and “risk aversion” as

fourth, fifth, sixth, and seventh respectively.

Table 6 displays the arithmetic mean of two groups. The variables given in Table 6 were statistically meaningful and were used for discriminating the predefined two groups.

Table 6. Mean Values of the Groups

Online

Shoppers Non-Shoppers

Psychological risk 2.5474 3.4973

Financial risk 3.0890 3.9016

Performance risk 2.8068 3.5355

Time risk 2.4716 2.9563

Social risk 1.6894 2.0874

Privacy risk 3.3447 3.7049

Risk aversion 3.2857 3.4567

Table 7. Classification Results

Online- Shopping

Predicted Group Membership Total

Online Shoppers Non-Shoppers

Original Count

Online Shoppers

123 53 176

Non-Shoppers 17 44 61

% Online Shoppers

69.9 30.1 100.0

Non-Shoppers 27.9 72.1 100.0

Correct Classification Ratio: 70.5

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It is clear from the mean values in the Table 6 that people who are online shoppers have lower risk perception values than those who are non-shoppers. This means that risk perceptions types and risk aversion are possible factors limiting the online shopping.

After testing the first hypothesis of the research and accepting it, the second hypothesis (H2), which was stated as “Meaningful forecasts can be made on group membership estimations by using differential variables of risk perception and risk aversion about online shopping.” was tested. In

other words, to test the predictive power of the discriminant function, a classification was formed in Table 7.

The findings of the classification analysis that was carried out for finding out the predictability of the group membership of a firm in those two predefined groups (online shoppers and non-shoppers) by means of the functions reached are presented on Table 7. Classification results of discriminant function indicated 70.5% correct classification of membership of two groups. In order to assess the classification accuracy, it should have put forward that the discriminant function classifies better than a random classification. Therefore the second research hypothesis was tested through Morrison’s likelihood analysis. Morrison's likelihood analysis provides a criterion that may be used

to compare the proportion of correctly classified observations with the proportion expected by chance. This proportion was designated the proportional chance criteria and expressed as:

Cpro = p alpha + (1 - p) (1 - alpha) = (176/237) (140/237)+(61/237) (97/237 = 0.53

The classification ratio of discriminant function (0.705) was tested whether it differs significantly from proportional chance criterion (0.53).

H0 : Π0=0.53 H1 : Π0>0.53 p = 0.705 n=237

and 5.401 > 2.33 (Z at α = 0.01) (H2 was accepted)

4. Conclusions and Recommendations

This research indicated that that there exist statistically significant differences between online shopper and non-shoppers in terms of risk perception about online shopping. By using attitude data about risk perception and risk aversion, online shopping behavior of the two groups can be predicted.

Online shoppers were found to have less perceived risk online shopping than non-shoppers, as expected. It was also found that online shoppers are different from non-shoppers concerning their risk aversion attitude. The results show that consumer who don’t shop via internet with a higher

degree of risk aversion than online shoppers tend to perceive internet shopping to be a risky activity.

It is clear from the findings that perceived risk of consumers affect on online shopping behavior. For that reason, the marketing managers of internet stores or online shopping malls should think about how to reduce the risk perception of consumers. Managers should generate some plans to overcome this risk perception of consumers like building more safe systems for users and communicate with the consumers about the safety of systems to build a trust for their processes.

As to the findings psychological risk and financial risk are the main factors describing the difference between the online shoppers and non-shoppers. Result for these risks’ effect was as

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expected and reducing these risk perceptions is totally related with a safe technical system and legal regulations for misuses. Time risk perception problem can also be solved with a functional system and coordinated operation process including an effective distribution channel.

Performance risk perception is an ongoing problem that will be continued in the near future. So the trust to online sellers and brands will be the key for this risk. Guarantees of the brands and online shopping malls for the product will help for building this trust. Social risk and risk aversion has the minimal effects on describing the differences between the groups but since they have affects their social and cultural roots have to be examined in new researches to be overcame.

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