RISK BEHAVIOR, RISK PERCEPTION AND ONLINE SHOPPING: AN EXPERIMENTAL APPROACH

Transcription

1 IPEK UNIVERSITY DEPARTMENT OF ECONOMICS WORKING PAPER SERIES RISK BEHAVIOR, RISK PERCEPTION AND ONLINE SHOPPING: AN EXPERIMENTAL APPROACH Zafer Akın & İ. Erdem Seçilmiş No: September 2015

2 Risk Behavior, Risk Perception and Online Shopping: An Experimental Approach Zafer Akın a and İ. Erdem Seçilmiş b1 a Department of Economics, İpek University, Ankara, Turkey b Department of Public Finance, Hacettepe University, Ankara, Turkey June 2015 Abstract In this paper, we analyze and link self-reported risk perception data obtained from surveys and experimentally elicited risk parameters and use them to explain online shopping behavior. We find that self-reported risk data turn out to be not correlated with the actual risk parameters. Although risk perceptions do not play a significant role in explaining binary online shopping behavior, risk parameters of the subjects elicited via a new hybrid experimental methodology play a very important role. The data also reveals that actually risk-averse subjects tend to report themselves to be just as risk-loving as the ones who exhibit risk-loving behavior. Keywords: perceived risk, risk elicitation, experiment, survey, online purchasing 1 İ. Erdem Seçilmiş is the corresponding author, ies@hacettepe.edu.tr, phone: +90 (532) , fax: + 90 (312) Zafer Akın: zakin@ipek.edu.tr, phone: +90 (312) , fax: + 90 (312)

3 1. Introduction The volume of online shopping has been rapidly growing all over the world. The global e- commerce 2 sales, which were around $763 billion in 2011, are expected to be over $1 trillion by the year In addition, the percent of online users who have made an Internet purchase was 85% in 2010 in the US 4, and the number of US digital shoppers is expected to grow from 137 million in 2010 to 175 million in Clearly, the statistics imply the crucial role of e-commerce in world trade and there have been significant benefits both for sellers and buyers. Online shopping improves market efficiency and enhances welfare because of the ability of shoppers to make better quality decisions while buying online (Punj, 2012). This is because there appears to be a strong link between product varieties of online shopping and social welfare (Lee et al., 2014). Brynjolfsson et al. (2003) demonstrate how increased product variety through electronic markets enhances consumer surplus. In addition, Kulviwat et al. (2004) show that increased consumer intelligence, associated with the product variety expansion in electronic market, improves consumer welfare as well. As a result, most firms have changed or modified firm strategy in response to the rapid rise of electronic commerce (Daniel & Klimis, 1999). However, in spite of the increase of the overall volume of e-commerce, there is still a need to enhance the total value created by online shopping in order to provide further benefits in terms of raising welfare. Apparently this is a challenging process because of the dynamic nature of e-commerce and multi-actor decision making. One of the biggest and probably the most significant obstacle to reaching this goal is the high level of consumers perception of the risk associated with the shopping on the Internet (Bhatnagar et al., 2000). Consumers have realized the benefits of online buying, but at the same time they feel uncomfortable because of various concerns (Chen & Tan, 2004). 2 The definition used here is the same as suggested by Grandon and Pearson (2004, p. 197): The process of buying and selling products or services using electronic data transmission via the Internet and the www. 3 Available at Last accessed on April 11, Available at Last accessed on May 15, See The Global Trends in Online Shopping Consumer Report of Nielsen Company in Available at Sales/ Last accessed on May 15, 2014.

4 Perceived risk toward online shopping affects purchasing intention through cognition and affect based attitudes. Lower perceived risk can be anticipated as a way of increasing both attitudes and enhancing purchasing intention (Chang & Wu, 2012). Perceived risk is an important barrier for online consumers because it threatens the operation of the e-commerce, making security issues a fundamental concern. Although a vast body of the literature focuses on ways to increase the volume of electronic commerce and to overcome barriers to enhance social welfare created by electronic economic activities (Wigand, 1997); risk issues, especially financial security, are still cited as the main deterrents for the rapid growth of electronic commerce (Farhoomand et al., 2000; Szymanski & Hise, 2000; Van Slyke et al., 2004). Risk is a much more salient concern in e-commerce than in many other social and economic interactions involving uncertainty due to the nature of the financial transactions and multiple levels of uncertainties. Hence, both scholars and online retailers are increasingly concerned about reducing risk perceptions. However, this is not an easy task as it might appear at first, because of the several types of perceived risks of buying online (Garbarino & Strahilevitz, 2004). Numerous empirical studies examined this problem by using different methodological approaches. There are two main methodologies to elicit and assess individual risk attitudes. First, most of the studies in the literature tested their hypotheses using survey data. Second, although the literature is not extensive, some experimental research regarding risk elicitation exists. However, there is still an ongoing dispute about which method would be a better choice in terms of explaining actual risk taking behavior (Harrison & Rutström, 2008; Lönnqvist et al., 2011). Although it is a very challenging task to evaluate the influence of the risk factors (financial risk in particular) on online shopping behavior in a consistent and objective way, with a proper methodology, this is possible. In this paper, we use both surveys that provide a sense of risk preferences when financial issues related to online shopping are of concern and a hybrid experimental methodology that enables us to elicit general financial risk preferences in an incentive compatible way. Using surveys is a conventional way of assessing risk in the context of online purchasing. On the other hand, experiments are widely used to elicit risk preferences in various applications but not especially in the current context.

5 Our aim in employing these two methods is to see which one relatively explains the underlying behavior more accurately and whether they complement each other. What we argue is that revealed information via surveys about risk behavior -both general and contextual risk perception- may not be the sole risk measure affecting the underlying behavior. Besides, a common risk trait (not necessarily about online purchasing) and its reflections on actual decision making environments that can be measured more accurately via experimental methods may be another relevant component of this interaction. Thus, we aim to disambiguate the base level risk preferences from the contextual ones in e-commerce. Since the aim is to shed light on the relationship between risk measures and online purchasing behavior, we think that our argument is relevant and worth to explore. Moreover, it is suggested in the literature that there is a base risk perception level (e.g., Dohmen et al., 2011) and in different contexts, risk behavior is shaped depending on this base level. Thus, other risk measures trying to capture this base level -not necessarily obtained from a specific context- can have a potential role in explaining behavior in different contexts. This is why we think that methodologically it is not inappropriate to employ a risk elicitation method not directly related to e-commerce to measure risk and use it as an independent variable to explain e-commerce behavior. Even though there is some previous research on how to link different self-reported and experimental measures of propensity of risk taking in various designs, to the best of our knowledge, no attempt has been made to explore this link in online shopping context before. In addition to obtaining self-reported risk parameters, ours is the first paper that also elicits risk parameters by conducting a laboratory experiment and that relates these parameters into this specific context. With this study, we aim to contribute to the methodological debate, which focuses on the question what is the most appropriate way to investigate financial risk perception and risk behavior in electronic commerce research by offering a unique hybrid methodology which analyses individual risk data obtained from both surveys and a controlled experiment.

6 To this aim, we collected all data from a laboratory experiment that involves both an incentivized 6 survey part and a risk elicitation part. Data regarding the propensities of the participants for online purchasing and their own risk perceptions were obtained from the survey part. Financial risk parameters were obtained experimentally in the risk elicitation part by using a hybrid method that combines matching task procedure and multiple price lists -MPL- method and these two techniques were not combined and used together before. All parts were computerized, and payments were done immediately after the experiment was finished. Our analysis yielded interesting results. First of all, self-reported risk measures generally are not correlated with the risk parameters elicited experimentally. This is important because it weakens the reliability of the studies in the literature that heavily rely on survey data. Although this challenge was mentioned in some of the studies before, our results draw attention to these concerns again. Secondly, experimentally elicited risk measures are strong and significant predictors of online shopping decision, whereas risk perceptions obtained from surveys are not significant at all. Thirdly, we find that subjects who exhibits risk aversion tend to report themselves to be as much risk-loving as the ones who exhibit risk-loving behavior. The remainder of the paper is organized as follows. Section 2 presents some basic concepts of risk and summarizes the related literature. Section 3 introduces the experimental design, describes the data and used methodology in detail. Section 4 presents the results and section 5 concludes with a discussion our findings. 2. Risk: Basic Concepts and Background 2.1. Perceived Risk Since the introduction of the concept of perceived risk by Bauer (1967), there have been plenty of studies that seek the role of perception of risk in consumer research (Bettman, 1973; Dowling, 1986). The term perceived risk has been proposed in consumer decision making, defined as the nature and amount of risk perceived by a consumer in contemplating a particular purchase decision (Cox & Rich, 6 Participants are paid a fixed amount for the completion of a set of survey questions. This is obviously not incentive compatible, but we think that it is a better and more effective way to obtain reliable information since participants presumably felt obliged to work on the questions more carefully due to the payment.

7 1964, p. 33). In this regard, the amount of risk depends on both the amount at stake in the purchase decision, and the individual's feeling of subjective certainty (Mitchell, 1999) although these two elements of risk may overlap. The amount at stake is relatively easy to figure out because it depends on what the person hopes to gain as a result of the risky behavior (depending on the nature of the transaction, even this may be a difficult task especially when a non-standard product is being purchased). However, measuring a consumer s feeling of subjective certainty is much more challenging because there are potentially different dimensions of the risk classically listed in the literature under perceived risk: financial risk, performance risk, psychological risk, physical risk, social risk and time risk 7 (Cases, 2002; Stampfl, 1978). Besides, the impossibility of trial and inspection in online shopping environments is a serious disadvantage which rises the perceived risk of consumers (Molina-Castillo et al., 2012). Perceived risk mediates the relationship between mood and purchase intention (Park et al., 2005). Ingman et al. (2015, p. 46) define perceived risk as: a consumer s belief about the potential losses or other negative outcomes from transacting on the Internet. When one focuses on the electronic commerce literature, some additional dimensions of perceived risk exist as well 8. For example; delivery risk, payment risk, personal risk, privacy risk and source risk can be counted in addition to the basic types (Lim, 2003). However, due to the scope of the study, we emphasize only classical types of perceived risk in the context of online shopping with an extensive emphasis on financial risk 9 that is associated with the lack of monitoring the safety and security of sending financial information through the Internet, e.g., credit card fraud. (Lee & Turban, 2001). 7 Performance risk indicates that a product does not fulfill its function as expected, due to the inability to assess the product quality online (Harridge-March, 2006). Psychological risk reflects the concerns of control over personal information during online shopping (Forsythe & Shi, 2003). Physical risk is related to safety or health of the individuals who make online shopping (Cases, 2002). Social risk refers to the individuals perception of other people s disapproval of their online shopping behavior (Ko et al., 2004). Time risk shows the concerns about wasting time in the case of a bad purchase on the Internet (Derbaix, 1983). 8 In the context of e-commerce, Ingman et al. (2015, p. 46) define perceived risk as: a consumer s belief about the potential losses or other negative outcomes from transacting on the Internet. 9 See Featherman and Pavlou (2003), for a comprehensive description and definition of all perceived risk types.

8 2.2. Literature Review Survey Studies on Online Shopping The results of the previous literature about risk behavior during online purchasing generally provide evidence in support of the negative effects of the consumers perceived risk on online shopping behavior. For example, Shih (2004) indicated that the payment phase is one of the major concerns of consumers during online purchasing, as manifested in a questionnaire study. Likewise, Swinyard and Smith (2003) found several differences between online shoppers and non-shoppers due to risk aversion and in their empirical study, they observed that online shoppers are less fearful about financial loss resulting from online transactions. Similarly, in a survey application, Ha and Stoel (2009) found that perceived trust -as a heuristic negatively affected by perceived risk (Corritore et al., 2003) - is a predictor of consumers attitude toward online shopping. In another study, Jarvenpaa et al. (1999) indicated that trust affects online buying behavior through attitudes and risk perception 10. In addition, by using both online and paper-based surveys, Joines et al. (2003) showed that transactional privacy concerns (especially concerning credit cards) also have a detrimental effect on online shopping. In a different study, Heijen et al. (2003) conducted a survey to understand online purchase intentions and they concluded that perceived risk is an originator of negative attitudes towards online purchasing. Likewise, Hsu and Chiu (2004) used questionnaires including items for measuring perceived risk and indicated that perceived level of risk have a negative effect on attitude toward the e-service usage 11. In our study, we also conduct a survey about both general risk taking and specific risk perception on online purchasing. We found a limited negative correlation of the latter with online shopping while the former is not correlated at all. 10 The impact of trust on the use of e-commerce has been established empirically (Van Slyke et al., 2004). Ba (2001), Houston (2001) and Jarvenpaa et al. (2000) suggest that the level of trust is considered as a key factor for the sustainability of e-commerce. In addition, see Qu et al. (2015) for a study which focuses on the role of social trust in e-commerce. 11 See Crespo and Rodriguez (2008) for some other studies that observed a negative effect of perceived risk toward e-commerce.

9 Mixed-method Studies on Risk Preferences in General In addition to the pure survey studies, some empirical studies have been conducted to link survey and experimental data 12. However, to the best of our knowledge, there is no study that focuses exclusively on e-commerce. Although they do not specifically compare experimental and survey data about risk preferences, Tanaka et al. (2010) conducted experiments in order to measure risk and time preferences and used household surveys of Vietnamese villagers to see whether income and other demographic variables are correlated with these preferences. Charness and Viceisza (2011) analyzed different elicitation mechanisms (both incentivized and non-incentivized) in rural Senegal and offer results about each mechanisms advantages over each other. Note that the experiments mentioned above are field experiments. In addition, Dohmen et al. (2011) show behavioral validity of the survey method of eliciting individuals risk perceptions in an incentive-compatible field experiment. The authors used a large representative survey and a complementary experiment to examine risk attitudes and found that the question about risk-taking in general is the best predictor of risk behavior across different contexts. They suggest that there is a common underlying risk trait, but context-specific questions are also important to precisely determine risk attitudes in different contexts. Besides, Cummings et al. (2009) reported the consistency of the observations elicited from the field experiments (related with tax compliance behavior) with the survey data as an indicator of the perception of risk. Along these lines, we compare the general risk perception and experimentally elicited risk preferences and show that they are not correlated at all. Anderson and Mellor (2009) whose main interest is whether survey data can predict actual risk taking behavior in a laboratory lottery-choice task (MPL), find that - for most of the subjects - preferences are not stable across risk elicitation methods, and subjects exhibit instability in their risk preference estimates across different contexts. Additionally, Kruse and Thompson (2003) use a survey question and an experiment to elicit the value of a risk mitigation investment and then used the data to check whether there is a difference between the subjects' perceptions and actual behavior. They 12 See Lejuez et al. (2002) for a method designed to test associations between the experimental and self-reported measures of risk-related constructs. In an ongoing project, Galizzi aims to link survey and experimental data on risk preferences with a focus on health. (Available at F1CF7A4FFA99. Last accessed on April 14, 2014). In addition, see Dave et al. (2010) and Offerman et al. (2009) for reviews of pure experimental studies on risk preferences.

10 observe inconsistency across the two procedures. Moreover, Lönnqvist et al. (2011) run a laboratory experiment which compares two empirical measures of individual risk attitudes: MPL method and questionnaires. Their results suggest that there is no correlation between risk preferences elicited by using different methods. In another study that compared two different lottery tasks for measuring risk attitudes on a sample of French farmers, Reynaud and Couture (2012) observed significant relationships between risk preferences elicited using these methods. In addition, they used a questionnaire to show the context-dependency of the risk preferences, which was suggested as an explanation of the observed risk preference instability 13. Additionally, there are several studies which investigate whether experimentally elicited risk preferences can be associated with behavior in different domains. For example, Sutter et al. (2013) study time preferences, ambiguity and risk attitudes of a group of children and adolescents. They explore the relation between experimental choices and field behavior; and demonstrate that experimentally estimated risk measures are only weak predictors of field behavior (smoking, drinking, etc.) but they are significant predictors of the body mass index. Similarly, Anderson and Mellor (2008) pair an economics experiment designed to measure risk preferences (originally used by Holt and Laury (2002)) with a survey that measures health-related behaviors and test whether the elicited risk preferences are associated with the field data. They find that laboratory-measured risk aversion is negatively and significantly associated with behaviors, such as smoking, alcohol use, obesity, seat belt non-use and the likelihood of reporting of risky behaviors. In addition, Einav et al. (2012) investigate the extent to which individuals display a stable ranking in their risk preferences in making market choices over five healthrelated employer-provided insurance coverage decisions and their investment decisions. Under a stylized coverage choice model, they find that up to 30 percent of their sample makes choices that may be consistent across all domains. In line with this literature, we elicited risk perceptions/preferences via surveys and an experiment and we obtained online purchasing behavior via surveys and tested their association. We 13 See Dave et al. (2010), for another study comparing different lottery tasks to elicit risk preferences. See Isaac and James (2000), and Berg et al. (2005), for more experimental discussion on risk preference measures and stability across tasks.

11 find that experimentally elicited risk preferences are significantly associated with the online purchasing behavior but survey measures are not. 3. Methodology and Design 3.1. Methodological Foundations Using survey questions to estimate risk attitudes, as in the most studies reviewed above, has both advantages and disadvantages. Surveys can be conducted at a low cost/effort, and the number of data can be increased easily. Moreover, surveys offer the possibility to measure individual attitudes directly. On the other hand, it is questionable whether these survey answers overlap with the actual behavior. Obviously, answers to survey questions reflect the perceptions of participants about what they are asked (Abeele, 1988). Many times the results of survey studies are have been criticized because they are poorly conceived and conducted, measure the wrong activity (Slater, 2001). Although survey method is commonly used in many areas of inquiry, economists are suspicious of the survey technique, especially, when the domain is measurement of risk behaviors. Because survey questions are scenariospecific and hypothetical, there is a potential to introduce an uncontrolled bias in the measurement (Kruse & Thompson, 2003). According to Camerer and Hogarth (1999, p. 8): The presence and amount of financial incentive does seem to affect average performance in many tasks, particularly judgment tasks where effort responds to incentives and where increased effort improves performance. Furthermore, this appears more likely when reviewing different risk-elicitation methods and discussing ways used to estimate risk attitudes (Holt & Laury, 2002). Risk is a multidimensional issue, and therefore, it is hard to develop a single measure -in applied research- which effectively capture risk preferences (Hudson et al., 2005). On the other hand, in the literature, a variety of experimental techniques that are mostly characterized based on their complexity has been developed to provide an appropriate way of eliciting risk preferences (Charness et al., 2013). There are various studies within different contexts which vary not only by their definition/classification of risk but also by their methods of preference elicitation. In this study, financial risk perception, which is also closely related to the psychological risk, is viewed as the main determinant of the level of uncertainty experienced by the decision maker (Bauer, 1960;

12 Cunningham, 1967). Similar assumption have been used in some previous literature (McQuivey, 2000; Strauss & Frost, 1999; Swinyard & Smith, 2003). To this end, we collect data from surveys about risk perception in general, online shopping behavior and the perceived risks associated with it. Moreover, differently from the previous studies, we also elicit financial risk preferences by using a hybrid MPL method in a laboratory experiment. MPL method is the most commonly used risk elicitation method in the literature. Matching task procedure is also another method to elicit risk preferences. Each method has its own advantages and shortfalls. To avoid the shortfalls, we propose a new hybrid methodology that basically combines these two methods. Thus, this methodology allows us not only to identify the relationship between risk perception/risk behavior and online purchasing but also to detect the inconsistency between risk perception and actual risk taking behavior, if any Experimental Design and Data This study mainly uses a laboratory experiment. We recruited a total of 64 TOBB University of Economics and Technology (TOBB ETU) undergraduate students. The experiment took place in the computer laboratory of TOBB ETU designed for experiments. The experiment included two sessions. The first session included the explanation of the structure of the experiment and a set of questions that were designed to elicit risk preferences of the subjects. In the second session, participants filled out surveys including questions about online purchasing behavior, risk perception in general and some demographics. The first session is conducted through a new hybrid method of multiple price list (MPL) and matching task procedure. Everything is computer based in all the sessions. Subjects are presented multiple price lists including the lotteries and certainty equivalents (Table 1). The agents are asked to choose one of the options by just clicking the one they prefer. The MPL part involves 15 different questions in the format of option A, option B or indifference. There is one last question asking for the participant's exact indifference point between these options based on the answers he/she gave in the previous 15 questions (this last question wants the subject to exactly match the lottery with a certain - 14 Charness and Viceisza s study (2011) shows a remarkable inconsistency in the choices made by individuals responding to the MPL questionnaire (the resulting rate of consistency is below 25 %).

13 indifference- amount which is why asking the sequence of these questions is called matching task procedure). By adding this last question, we propose a hybrid model combining the multiple price list method and the matching task procedure. This new method overcomes the weaknesses of both methods. Namely, the former method, since only interval responses are obtained from switching points, only allows estimating intervals for parameters, not exact values, and its econometrics is a little more involved. On the other hand, the latter is cognitively costly and vulnerable to usage of some undesirable rules of thumb that may bias the results, although it gives exact indifference points. This new method handles these two problems by allowing subjects to think step by step. At each table the subjects see 15 questions that include three options. One of the options is a lottery and it is the same for a given table. The other option is a certain amount that can be earned reflecting, if chosen, the preference of the agent for this certain amount over the lottery. This option starts from a low amount (lower than the expected value of the lottery) and it increases up to the higher amount in the lottery 15. The last option reflects indifference between the first two (see Table 1). The 16th (last) question in the table that appears after all 15 questions are answered asks for an exact indifference point based on the answers given in the previous 15 questions and it is in the following form: "What amount of money, x Turkish Lira (TL), if paid to you for sure would make you indifferent to the above lottery?" TL The computer program gives a warning if the entered value of x is not between the two certain payment amounts where switching occurs. Subjects work on 24 different tables and we specify different lotteries at each table by changing the probabilities and the possible amounts in the lotteries. The amounts in the lotteries are determined dynamically based on the subjects answers 16. A typical lottery is: 15 In this type of elicitation method, subjects are expected to switch from one option to the other depending on their risk appetite. In Table 1, for example, subjects are expected to start choosing option B (if not extremely risk averse) and then switch to option A at some point because in the last question, rationally, option A has to be chosen. Moreover, consistency requires no switching or at most one switching. 16 For the tables from 10 to 14, we take the indifference amount that the agent stated in table 6 and make it the higher amount (0 being the lower amount) in these lotteries. For the tables from 15 to 19, we take the amount

14 "0 TL with probability 0.1; 300 TL with probability 0.9" We change the probabilities with 0.1 increments for the first 9 tables. The increments are 0.2 for the rest of the lotteries. We employ the Becker-DeGroot-Marschak (BDM) incentive mechanism to determine how and what amount the subjects would be paid. After the subjects answered all 16 questions at each table, one of the tables is randomly drawn using a randomization device. Then, one of the 16 questions is randomly drawn and based on the subject's answer; he/she is either paid the certain amount or plays the lottery. If one of the first 15 questions is drawn, then the agent gets the certain amount if it is chosen. If the lottery is chosen, it is implemented by the randomization device. If the last question is drawn from the chosen table, we again use the BDM mechanism to determine the payment as follows: Suppose that the lottery is "0 TL with probability 0.1; 300 TL with probability 0.9" where the subject is asked what amount he would require to make her indifferent between that amount and this lottery. Suppose her answer is 220 TL. Then, a random number is drawn between 0 and 300. If it is smaller than 220, the lottery is played. If it is greater than or equal to 220, then the amount drawn is paid to her immediately. The purpose of this random draw is to make subjects reveal truthful answers by attaching monetary incentives to them, hence to prevent random responses from the subjects. It is a weakly dominant strategy to report the true indifference value in the BDM mechanism (Noussair et al., 2004). At the end of the experiment, we randomly choose 8 participants and make the payments as described above. The payment range is 95 TL TL (One dollar was approximately 1.5 TL at the time of the experiment). The amount the subjects earn is paid in cash privately if they are entitled to payment 17. In the second part, we ask subjects to "fill out surveys". These surveys were obtained from various online resources and involved simple multiple choice or free form questions about the subjects' Internet usage, online shopping behavior, risk perception etc. In addition to what they earn (if any, from the previous set of questions and make it the lower amount (300 being the higher amount) in these lotteries. For the last 5 questions, we make the answers given at table 12 and 17 the amounts of the lotteries. 17 No subject earned less than the 5 TL show up fee. Moreover, both determination of the earnings and actual payments are done at the end of the experiment to prevent potential wealth effect.

15 other than the show up fee) from the first session, subjects are also promised to be paid a fixed amount (40 TL) only if they complete the surveys. Table 1 MPL table (screenshot from experiment software) Alternatives Option A (Certain payment) Indifference Option B (Lottery) TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob TL 0 TL with prob. 0.1; 300 TL with prob. 0.9 All participants complete the surveys and are paid cash at the end of the experiment. During this part, to be more specific, we ask binary questions such as whether they have ever engaged in online purchasing, whether they have credit cards and whether they prefer to use them. Moreover, we ask, by using a typical five-level (or seven-level) Likerd scale, whether they agree on statements such as I don t want to give out my credit card number to a computer, I worry about my credit card number being stolen, Buying things on the Internet scares me, I just don t trust Internet retailers, etc. To be able to get a measure of how individuals perceive themselves in terms of risk-taking in general, we asked whether they agree on the following statement I am a risk-taker in general, by using a seven-level Likerd scale (7 being completely agree and 1 being completely disagree ). This answer is used as a proxy for the subjects own risk perceptions and compared appropriately to the risk parameters obtained in the experimental part. Moreover, we collected some demographic information (age, gender, income

16 etc.) as well (see Appendix A). A summary of the statistics of the variables can be seen in Appendix B. Appendix C shows the correlation coefficients given by the Spearman matrix. 4. Analyses and Results In this section, we first analyze the data collected from the conducted experiment in which a hybrid method is used to elicit risk preferences. A von Neumann-Morgenstern utility function with constant coefficient of relative risk aversion (CRRA) can be estimated by using this data. The estimation is standard and based on the presented lotteries and certainty equivalents that the subjects report for each lottery. If the agent's certainty equivalent value is smaller than the expected value of the lottery, then the agent is risk-averse and vice versa. We use the following CRRA utility function in our estimations: ()= 1 where r is the coefficient of relative risk aversion (negative values of r refer to risk-loving, value of zero means risk neutrality and values between zero and one refer to risk aversion). By using the data, we estimate r : ()=(1)+(1 )(2) () =(1) 1 1 +(1 ) (2) 1 For a typical lottery such as "0 TL with probability 0.1; 300 TL with probability 0.9", p=0.1, reward1=0, reward2=300 and ce is the certainty equivalent that the subject reports for this lottery. We fit the collected data from 64 subjects to the mentioned utility function to find their risk parameters (r values). The analyses are performed in EViews by using least squares method. The parameters of 12 subjects turn out to be insignificant. For the rest, the values of r are as expected: most of the subjects exhibit risk aversion. Twelve of the subjects (18%) can be characterized as risk-loving and the rest is risk-averse. If looked closely, seven of the subjects can be categorized as risk-neutral since their values are not significantly different from zero (Histogram of all r values can be seen in Figure 1).

17 One of the disadvantages of the MPL method is the subjects possible misunderstandings of the tables. Normally, subjects are expected to switch from choosing the lottery to the certain amount as they proceed in the table (or vice versa depending on the probability - reward combination) and in some cases no switching behavior may be observed (for example, extremely risk-averse subjects may start from the certain amount and show no switching behavior and extreme risk lovers may always choose the lottery in Table 1 except the last choice). Thus, if some inconsistent switching behavior is observed different from these patterns, we can conclude that subjects potentially misunderstand the task. In the whole data, we observed 4 subjects who exhibit unusual switching patterns and we excluded them from the data 18. Out of 52 r values that are significant at 10% level, 49 of them are significant at 5% level. Three out of 52 subjects were excluded due to inconsistent switching. Two of these subjects were excluded from the group whose r values are significant at 5% level Risk Perception and Risk Behavior The relationship between risk perception and actual risk behavior is of interest to experimental researchers. For example, as mentioned in previous sections, the results of Anderson and Mellor (2009), and Kruse and Thompson (2003) are in line with our observations that indicate the differentiation between risk perception and actual behavior; however Dohmen et al. (2011), and Cummings et al. (2009) disagree with our results because they found association between different procedures. In this section, in order to make a contribution to the above-mentioned discussion on the risk perception - risk behavior relationship, we analyze whether commonly used risk measure in the literature obtained from the question about risk-taking in general is correlated with actual financial risk taking. Figure 1 shows histograms of estimated CRRA coefficients and risk perceptions. Lower values of CRRA coefficient r and higher scores of risk perception (certainly risk taking 7, certainly not risk taking 1) refer to higher risk taking. Note that r value being zero and risk perception value being four (midpoint of one to seven scale) mean risk neutrality. Thus, in both histograms, from left to right, risk aversion 18 Actually, there were a total of eight subjects who switched inconsistently. We excluded four of them. Two of them switched inconsistently in only one out of 24 tables so that we accepted these as mistakes and included them in the analysis. The other two subjects chose the indifference option several times consecutively; probably because they regard the amounts as very close to each other (we used the average of these indifference points as the actual indifference amount in our analysis). We included these in the analysis as well.

18 rises (In the risk perception histogram, the x axis is reversed to make both figures comparable). Further note that average r value (0.18) is positive referring to risk aversion, on the other hand, average risk perception (4.97) is higher than four referring to risk-loving. This implies that risk perceptions and r parameters reflecting actual financial risk behavior are not compatible with each other. This finding can be interpreted in line with the study of Corbitt et al. (2003) that used a survey to collect data from Internet users and identified a number of factors related to trust in the B2C context. They found no direct negative relationship between perceived risk/trust and between perceived risk/online buying experience. Fig. 1. Histograms of CRRA coefficients and risk perceptions To further examine this relationship, we plot the risk perception of individuals versus their estimated risk parameters in Fig. 2. Since lower values of r and higher scores of risk perception refer to higher risk taking, a negative relationship is expected between these two risk measures. However, there seems to be no relationship between them. Moreover, Spearman s rank correlation coefficients are very small and insignificant (p=0.946 when whole data is used and p=0.66 when only significant risk parameters are used). Thus, this result is yet another evidence showing that self-reported risk attitudes in general are not correlated with the actual financial risk behavior elicited through lottery choices. An interesting observation comes from the segregation of the actual risk behavior as risk-averse and risk-loving and analyzing their corresponding risk perception scores. The average of the risk perception score of the ones who are risk-loving is 4.83, which is higher than the mid score 4 (one sample t test, p=0.012). This means that the ones who show risk-loving behavior actually see themselves as risk-loving. However, the average of the risk perception score of the ones who are risk-averse is 5,

19 which is higher than the mid score 4 (one sample t test, p=0.001). This score is even higher than that of risk lovers (but this difference is not statistically significant, two sample t test, p = 0.711). This implies that the ones who are actually risk-averse tend to see/report themselves as similarly risk-loving as the ones who exhibit risk-loving behavior 19. Fig. 2. Plot of CRRA parameters and risk perceptions In addition, we calculated Cronbach's alpha as an estimate of the reliability of the survey. As it can be seen in Table 2, the lowest item-test correlation was found for riskperception (0.2883). However, since item-test correlation may not be adequate to detect the item that fit poorly, we checked this result by calculating the item-rest correlation of riskperception ( ). Here the riskperception item does not seem to fit well in the scale in all respects (the only item with a negative item-rest correlation coefficient). The average inter-item correlation increases substantially by removing riskperception; apparently, it does not correlate strongly with the other items. To sum up, the test scale Cronbach s alpha will increase from to if the riskperception item is dropped. The case is the opposite for the rest of the items, except returnhassle. However, it is ignorable, because no substantial difference is observed in the absence of it (only ). Finally, the overall alpha value of the test could be interpreted as acceptable (0.6313). Of course, there are various reasons why Cronbach s alpha could be low for even a perfectly valid test, such as reverse coding and multiple factors. However, the alpha 19 As observed in the literature, the majority of the subjects in our sample is risk-averse (approximately 80%). Thus, when we segregate the data, the data is divided asymmetrically. Moreover, since the sample size is relatively small in this study, further in-depth research (both lab and field) with larger data sets is necessary for reliable results. Nonetheless, this study stands as an important first step in exploring this promising topic.

20 results and the level of correlation between riskperception and the scale that is formed by all other items can be evaluated as a confirmation of the tendency of subjects to report their perception of risk different from their actual behavior, which is in line with our existing findings. Thus, it is possible to accept that the other items are better measures of risk perception (relatively than riskperception). Table 2 Reliability indices for items Item Obs Sign item-test correlation item-rest correlation average interitem covariance alpha riskperception auctionsites merchandise Ccgive ccstolenfear buyingscare returnhassle Notrust Test scale Regression Results The maximum likelihood estimation results of six logit models are presented in Table 3. The dependent variable is dummyusage (simply, 1 for internet users, 0 otherwise) for all equations. Each column shows a different specification of the equation. All columns use one r-related variable 20. The first column presents the basic equation with largest data capture (number of observations: 59; control variable: r); the second one checks the effect of the exclusion of inconsistent switchers (number of observations: 55; control variable: r-consistent); the third one uses the r values that are significant at %10 level (number of observations: 48; control variable: r-significant10); the fourth one uses the r values that are significant at %10 level by excluding inconsistent switchers (number of observations: 45; control variable: r-significant10consistent); the fifth one uses the r values that are significant at %5 level (number of observations: 45; control variable: r-significant5); and the last one uses the r values 20 r, r-consistent, r-significant10, r-significant10consistent, r-significant5, r-significant5consistent

21 that are significant at %5 level by excluding inconsistent switchers (number of observations: 43; control variable: r-significant5consistent). All models turn out to be statistically significant at 1% level. The analysis suggests that the r- related variable is consistently relevant to explain online shopping decision in all of the specifications (at the level of 1% significance) 21. The coefficients for relative risk aversion show that a lower r-related value (implying more risk-loving) increases the probability of making online shopping. Thus, it appears that risk parameters obtained by experimental methods are good predictors of online shopping behavior 22. The general conclusion is that the relative risk aversion parameter has a strong and robust impact on online buying decisions (more risk taking increases the tendency to shop online). By comparing different specifications, it is possible to say that the results are fairly robust across models. Table 3 Logit regression estimates Model Variables r *** r-consistent *** r-significant *** r-significant10consistent *** r-significant *** r-significant5consistent *** Constant Observations Prob > chi Pseudo R Note: 1. Dependent variable is dummyusage for all models. 2. ***Significant at %1 21 All equations are re-estimated by probit analysis as well but we do not present them here because the results are almost the same. 22 In addition, we used gender as an additional control variable in all columns and found that male subjects differ significantly from female subjects in the third, fourth and fifth columns. However, these findings may not be reliable, because the ratio of male to female students was about 77:23 (see Appendix A). So the gender effects need to be further investigated in future work. See Garbarino and Strahilevitz (2004) for a detailed research of gender differences in the perceived risk of buying online.

22 5. Discussion and Conclusion The contribution of e-commerce to the global economy, with its increasing trend of online buying, is noteworthy. However, the concept of risk regarding online shopping as a multidimensional issue is an outgrowth of society's concern about buying on the Internet. Therefore, in order to understand the fundamentals of consumers attitudes towards e-commerce, much effort has recently been directed to analyze the effects of risk perception/behavior regarding online purchasing. Our research was motivated by the notion that existing methods lack a well-formatted framework to provide an unbiased insight into the nature of the association between the risk perception and the risk behavior during online buying process. Previously, the influence of general risk perception on the attitude towards online shopping was mostly analyzed with data obtained from surveys. Additionally, data coming from using experimental methods are also used but the number of studies is very limited and their contexts are different. Until now, to the best of our knowledge, there has been no attempt to take advantage of both methods at the same time in this manner and within the context used in this paper. Our study contributes to the electronic commerce research by presenting a hybrid methodology which combines these two approaches in the context of online shopping. The application of this methodology leads to interesting observations. Firstly, self-reported risk measures are not correlated with the actual risk parameters elicited experimentally. This finding is mostly in line with previous research documenting inconsistency across different risk elicitation methods in various contexts. There are some studies in the literature which confirms the discrepancy between the responses elicited from different techniques (e.g., Anderson & Mellor, 2009; Berg et al., 2005; Isaac & James, 2000; Kruse & Thompson, 2003; Lönnqvist et al, 2011). For example, Anderson and Mellor (2009) results are compatible with our conclusion that there is no significant association between the risk indicator defined from the survey questions and the risk preference elicited from the MPL method See Deck et al. (2008) for a similar study that considers an MPL task, a Deal or No deal game, a job gamble and an inheritance gamble. In general, they find that risk aversion of the same respondent may vary across elicitation techniques as we observed in our study. To measure the risk attitudes, they use a psychometric scale which was presented by Weber et al. (2002) and partially explain the variation by individual personality traits.