An integrated model of risk and risk-reducing strategies

Journal of Business Research 59 (2006) 112 – 120

An integrated model of risk and risk-reducing strategies Jinsook Choa,*, Jinkook Leeb a

Department of Marketing, University of Wisconsin-Madison, 975 University Avenue, 3118 Grainger, Madison, WI 53706, United States b The Ohio State University, 1787 Neil Ave., Columbus, OH, Canada 43210 Received 12 January 2004; accepted 22 March 2005

Abstract We investigate the role of perceived risk in consumers’ adoption of risk-reducing strategies in the context of household investment decisions. Specifically, we examine behavioral responses intended to handle both the uncertainty and importance dimensions of perceived risk, incorporating risk propensity as another construct affecting risk-induced behaviors. We find that higher self-efficacy, greater wealth position, and risk-taking propensity lower an individual’s perceived risk for investing in the stock market. We also find that perceived risk increases both the amount of information search and transaction frequency while it lowers the proportion of assets invested in the stock market. Risk propensity, on the other hand, increases the likelihood of obtaining investment advice from professionals, as well as the proportion of assets invested in the stock market. D 2005 Elsevier Inc. All rights reserved. Keywords: Perceived risk; Risk propensity; Information search; Amount at stake; Investment decisions

1. Introduction Perceived risk plays a critical role in human behavior, particularly pertaining to decision-making under uncertainty. A considerable amount of research has been devoted to examining the role of perceived risk in different decision contexts, ranging from an evaluation of a particular brand/ product (e.g., Erdem and Swait, 2004; Dowling and Staelin, 1994) or service (e.g., Bansal and Voyer, 2000; Murray, 1991) to the adoption/trial of new products or technologies (e.g., Forsythe and She, 2003). The predominant issue in these studies concerns actions adopted by consumers to reduce or avoid risk (so-called risk-reducing strategies). High perceived risk, for instance, may lead consumers to engage in extensive information search (Dowling and Staelin, 1994; Srinivasan and Ratchford, 1991); to rely on a certain mode of communication (such as personal sources or word-of-mouth) (Bansal and Voyer, 2000); or to utilize other cues such as price, warranty, or brand name/reputation

* Corresponding author. Tel.: +1 608 265 8666; fax: +1 608 262 0394. E-mail address: [email protected] (J. Cho). 0148-2963/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2005.03.006

(Erdem and Swait, 2004). The current paper extends this line of research. In particular, we investigate the role risk plays in a decision maker’s adoption of risk-reducing strategies in the context of stock investment decisions. While complementary in its scope and nature to the aforementioned literature in risk, the current study seeks to advance the current understanding in the following aspects. First, perceived risk consists of two distinct dimensions, namely, uncertainty and significance of consequence, thus potentially entailing two different modes of behavioral responses in an attempt to lower risk (Cox, 1967; Taylor, 1974). As Taylor (1974) pointed out, ‘‘[U]ncertainty about the outcome can be reduced by acquiring and handling information. Uncertainty about the consequences can be dealt with by reducing the consequences through reducing the amount at stake’’ (p. 54). Although the latter has been recognized as an important risk-reducing strategy, it is the information search that has been the central focus of most risk-related empirical research. Casual observations, however, indicate that consumers cope with risk by making behavioral choices that will lower the impact of negative consequences (such as choosing the least expensive product,

J. Cho, J. Lee / Journal of Business Research 59 (2006) 112 – 120

postponing a purchase, or forgoing an acquisition). Without a concurrent investigation of both behavioral responses, a complete understanding of consumers’ risk-reducing strategy cannot be achieved. Second, the extant risk literature in consumer behavior has centered on the perceived risk and ignored another risk construct, risk propensity. Risk propensity refers to one’s tendency to take or avoid risk in a decision situation involving risk (Sitkin and Pablo, 1992). The inclusion of risk propensity is necessary in linking perceived risk and risk-reducing strategies, since it influences not only behavioral choices facing risk but also the perceived level of risk itself (e.g., Forlani et al., 2002; Keil et al., 2000). Sitkin and Pablo (1992) went as far as to argue that the inconsistent empirical findings in the relationship between risk perception and risky behavior (i.e., highly risky choices even in highly risky situations) is principally attributable to the omission of risk propensity. Addressing these two issues, we examine two different behavioral modes intended to reduce perceived risk (i.e., reducing uncertainty and reducing the significance of consequences), while investigating risk propensity as another construct directing these behaviors. We also examine self-efficacy and wealth as two key antecedents of risk. All together, we propose a model that identifies the interrelationships between the proposed antecedents, two risk constructs, and their resulting behavioral responses. This model will enable us to test concurrently the linkage between risk propensity and perceived risk, and their effects on two different modes of risk-reducing strategies. Finally, we note that the analysis of our study is based on a database capturing real-life activity on a large scale. Specifically, we test the proposed model using data on consumers’ investment decisions from the 2000/2001 MacroMonitor data set constructed by SRI Inc. Investment

decisions are especially well-suited to testing the proposed construct, since they involve a relatively high level of perceived risk in general, and yet there exist varying degrees of risk perception across different individuals—a situation where risk-reducing strategies are most pronounced (Gemunden, 1985). In addition, unlike most previous literature on consumer behavior, which examines the risk construct through experiments or surveys with a limited number of subjects involving hypothetical purchasing situations, we test the model with data from a large number of subjects (2069 responses) concerning real decision situations. This allows us to corroborate relationships previously examined via small-sample surveys or experimentation and also enhances the external validity of this study.

2. Conceptual framework The model investigated in this study is depicted in Fig. 1. As seen in Fig. 1, the proposed model rests on the following propositions: (1) self-efficacy and wealth position influence one’s assessment of risk in a decision situation (perceived risk); (2) perceived risk is also affected by one’s tendency to take or avoid risk (risk propensity); and (3) both perceived risk and risk propensity influence behavior intended to manage uncertainty and significance of consequences. The following section discusses conceptual grounds for the constructs and relationships depicted in our model. 2.1. Perceived risk A decision situation is risky when a decision maker is uncertain about the consequence of a choice (Cox, 1967). Such a situation is likely when the possible outcomes

Risk-Reducing Strategy through Information Search

Amt. of Information Search Risk Propensity

Advice from Professional

Risk Perception

Risk-Reducing Strategy by Reducing the Stakes

Self-Efficacy

Wealth Position

113

Proportion of Stock Investment Transaction Frequency

Fig. 1. The model of risk and risk-reducing strategies.

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entailed by a decision vary greatly and also obtaining the desired outcomes is highly affected by chance (Libby and Fishburn, 1977). The degree of such uncertainty is evaluated and assessed differently by different decision makers (i.e., perceived risk). If an inherent uncertainty is viewed as the objective risk, the perceived risk is a subjective and biased evaluation of the objective risk. It is perceived risk, rather than objective risk, that motivates the decision maker to engage in a particular behavioral pattern (Dowling and Staelin, 1994). Perceived risk involves another dimension besides uncertainty: the significance of the consequences (or importance) (Cunningham, 1967; Taylor, 1974). Representing an individual’s perceived vulnerability to the possible adverse consequences, significance of consequences often concerns how damaging the possible financial losses associated with a negative consequence are (Mitchell, 1999). Individuals tend to define a decision situation as risky when they have a lot to lose if they make a poor decision, in particular if this loss will have a considerable impact on their financial situations. As perceived risk is an individual’s biased assessment of a risky situation, its assessment is highly dependent on one’s psychological and situational characteristics. The two variables that affect one’s risk assessment most significantly are self-efficacy (e.g., Krueger and Dickinson, 1994; Locander and Hermann, 1979) and wealth position (e.g., Goodfellow and Schieber, 1997; Grable and Lytton, 1998). Self-efficacy refers to one’s perception of how competent he or she is in organizing and executing actions necessarily to manage a prospective situation (Bandura, 1977). In other words, self-efficacy is the subjective assessment of his/her own ability to perform essential tasks involved in a decision situation. People with high self-efficacy perceive themselves as being able to analyze, process, and make accurate inferences from limited or fuzzy information. Self-efficacy thus can influence how people assess the level of uncertainty in a given situation (Krueger and Dickinson, 1994). Dulebohn (2002), for instance, found that individuals with high self-efficacy are likely to evaluate an inherent uncertainty in an investment situation as less unknown, thereby opting for a riskier alternative among investment allocation options. While self-efficacy shapes the perceived level of risk by affecting one’s assessment of uncertainty, the wealth position affects the consequence dimension of perceived risk. A person’s wealth position (such as income or household assets) determines the significance of outcomes for the individual, because wealth position dictates one’s capacity to absorb monetary loss and also to recover from it; such capacity defines a reference point from which adverse consequences are evaluated (Barsky et al., 1997). Individuals with greater wealth perceive a lower level of risk associated with a given investment than those with lesser wealth, thereby exhibiting a higher degree of risk-taking behavior (Goodfellow and Schieber, 1997; Grable and

Lytton, 1998). We thus predict that a greater wealth position leads to a lower level of perceived risk toward the stock market, as stated in the following hypotheses: H1-1. The higher the self-efficacy concerning one’s own financial matters, the lower the perceived level of risk toward the stock market. H1-2. The greater the wealth position, the lower the perceived level of risk toward the stock market. 2.2. Risk propensity Risk propensity generally refers to a person’s willingness to take or avoid risk. Two distinct views exist, however, in conceptualizing this construct. The first view is to define risk propensity as a personality trait, thus implying that it is stable over time and across circumstances (e.g., Fischhoff et al., 1981). From this standpoint, risk propensity reflects one’s general orientation toward the risk, risk-prone or riskaverse. Risk-prone individuals enjoy risk and become uneasy and restless in stable and certain situations; they are willing to take risks with high stakes and derive pleasure from doing so. The opposite observation can be made for risk-averse individuals. An alternative conceptualization is to view risk propensity as behavioral tendency rather than a pure personality trait. From this perspective, risk propensity is affected not only by one’s risk preference but also by the judgment of whether it is worth taking risks in order to increase the probability of getting better returns (e.g., Sitkin and Pablo, 1992; Taylor et al., 1996). This view suggests that, while risk propensity is relatively stable, it can differ by a decision context and be modified based on experience and knowledge about the situation. In fact, several researchers have provided empirical support that one’s willingness to take risks varies depending on contextual and perceptual factors. MacCrimmon and Wehrung (1984), for instance, found that subjects were more willing to take risks with their firm’s resources than their own. Taylor et al. (1996) also showed that risk propensity in a given situation is affected by the outcomes of previous behavior of taking or avoiding risks in a similar situation. Actually, the extant literature provides little evidence that risk propensity is general across situations (e.g., Huff et al., 1997). The view of risk propensity as a tendency prevails in recent risk-related studies (e.g., Forlani et al., 2002; Keil et al., 2000). In this study, we conceptualize risk propensity as a behavioral tendency to take or avoid risk in investment decisions. As depicted in Fig. 1, we identify risk propensity as another construct affecting an investor’s assessment of risk with respect to the stock market. In particular, higher risktaking propensity leads to a lower level of perceived risk. Studies have argued that a person’s risk propensity influences the manner in which he or she evaluates a risky situation. In assessing risk in a situation, ‘‘[a] risk-averse

J. Cho, J. Lee / Journal of Business Research 59 (2006) 112 – 120

decision maker is more likely to attend to and weigh negative outcomes, thus overestimating the probability of loss relative to the probability of gain. As a consequence, a risk-averse decision maker tends to overestimate the level of risk inherent in a decision situation,’’ (Sitkin and Pablo, 1992, p. 19). The opposite argument has been made with regard to a risk-taking decision maker. Forlani et al. (2002) have found that a manager with low risk propensity tends to evaluate a new product concept as more likely to fail, thus perceiving a higher level of risk with launching a new product than a manager with high risk propensity. Keil et al. (2000) also reported that a risk-taking manager was likely to perceive a lower level of risk than a risk-averse manager in undertaking a risky business venture. We thus predict the following hypothesis in our context: H2. The higher the risk-taking propensity concerning investment decisions, the lower the perceived level of risk toward the stock market. 2.3. Risk and risk-reducing strategies As discussed previously, perceived risk can be handled using two different risk-reducing strategies. One is to reduce uncertainty through information search and the other is to reduce vulnerability by lowering the amount at stake. 2.3.1. Information search Over the last several decades, numerous studies have investigated the relationship between risk perception and information search behavior—in particular, the amount of information search undertaken. The basic argument in this relationship is that high perceived risk puts consumers in a distressed and anxious state, which in turn motivates them to engage in problem-solving activities to resolve it; consumers employ information search as a problem-solving strategy to reduce perceived risk (Dowling and Staelin, 1994). As uncertainty about the outcomes of alternatives increases, the expected returns on search are likely to increase (Srinivasan and Ratchford, 1991). High perceived risk, therefore, increases the amount of information consumers seek. We note, however, that empirical results from past studies have not always supported this link. In fact, there exist many studies rejecting the relationship between these two constructs. Gemunden (1985), for instance, examined the link between perceived risk and information search using a meta-analysis of 100 papers and found 51 contradictory results that reported no increase in information search. The results of his study, however, do not necessarily suggest the absence of a relationship between the two. Many of the decision situations examined in previous studies involved, by nature, relatively low levels of risk (such as products that were purchased routinely, were financially trivial, or had low involvement). Thus, there was little incentive to employ a risk-reducing strategy

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of any form. For decision contexts involving relatively high levels of risk such as investments, the argument that a higher perceived risk leads to more information search is likely to hold true. Perceived risk determines not only the amount of search, but also which sources of information seem most likely to satisfy the particular information needs (Cox, 1967; Locander and Hermann, 1979). In processing information, consumers attempt to economize on the cognitive effort required to reduce uncertainty. If a certain source is perceived as providing information that is sufficiently relevant and valuable, consumers will rely heavily on that source as a mode of risk reduction. External information can be classified as coming from personal sources or impersonal sources (e.g., TV, radio, magazines, or the Internet) (Murray, 1991). As perceived risk increases, consumers’ intent to use personal sources over impersonal sources tends to rise (Bansal and Voyer, 2000; Murray, 1991), and the information from personal sources becomes more influential than that from impersonal sources in making a decision (Price and Feick, 1984). Sources of external information can also be categorized as non-market-dominated (word-of-mouth from family and friends) versus market-dominated (seller-provided). Several researchers have found that consumers often seek marketdominated personal sources when facing high perceived risk, particularly when a decision requires technical knowledge and expertise (Coleman et al., 1995; Gemunden, 1985). Therefore, we posit the following hypotheses: H3-1. The higher the perceived level of risk toward the stock market, the greater the amount of information search for investment decisions. H3-2. The higher the level of perceived risk toward the stock market, the greater the likelihood to seek information from market-provided personal sources for investment decisions. Although research is limited, previous literature indicated that information search might be affected by one’s risk propensity as well. Taylor and Dunnette (1974), for instance, reported that risk-taking propensity was associated with rapid decision-making based on limited information; high risk propensity was likely to limit the amount of overall information search efforts. There is also an indication that risk propensity directs the type of information a decision maker relies on. Welsh and Young (1982) found that entrepreneurs with high risk propensity preferred personal sources of information the most, even though others might have more important and relevant information for them. In seeking information from personal sources, however, they do not reach beyond a closed circle of personal acquaintances. Welsh and Young (1982) in fact reported that entrepreneurs with high risk propensity were less likely to seek advice from professionals. We thus formulate the following hypotheses predicting the negative

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effect of risk propensity on both the amount of information search and the likelihood of seeking investment advice from professionals. H4-1. The higher the risk-taking propensity concerning investment decisions, the smaller the amount of information search for investment decisions. H4-2. The higher the risk-taking propensity concerning investment decisions, the lower the likelihood to seek information from market-provided personal sources for investment decisions. 2.3.2. Amount at stake In addition to information search, risk can be handled by reducing the amount at stake or the vulnerability associated with the adverse outcome. Such behavior aims to reduce the impact of negative consequences resulting from a decision. In the context of our study, the key behavior in this regard is to reduce the proportion of financial assets invested in the stock market. Individuals perceiving a high level of risk toward the stock market will have a smaller portion of their financial assets invested in stocks. We also identified short-term holdings (i.e., frequent transactions) as another behavioral response to manage consequences. It has been observed that people investing in a risky, emerging market tend to have very frequent transactions (Dorn and Huberman, 2002). A high level of uncertainty with such a market would make it difficult for investors to have long-term commitments. It may also be that investors with high risk perceptions are motivated to diversify times of investment as well as their portfolio. Instead of making a few transactions with significant amounts, they tend to purchase smaller portions at different points of time. We thus formulate the following hypotheses: H5-1. The higher the perceived level of risk toward the stock market, the lower the proportion of financial assets invested in the stock market. H5-2. The higher the perceived level of risk toward the stock market, the more frequent the stock transactions. Risk propensity is expected to influence risk-related behavioral outcomes as well. As discussed, high risk propensity is likely to outweigh the significance of a potential return, thus leading to risk-seeking behavior by increasing the involvement with a risky situation. As risk propensity increases, the proportion of assets in the stock market will be likely to increase, since such behavior has the potential to bring more significant returns. Risk propensity may also influence transaction frequency. Fellner and Maciejovsky (2002) reported that risk-taking investors tend to exhibit aggressive investment strategies, engaging in more frequent turnovers of their portfolio than risk-averse investors. Wood and Zaichkowsky (2004) indicated that a positive attitude toward taking investment risk increased the

frequency of stock trading. We thus formulate the following hypotheses: H6-1. The higher the risk-taking propensity concerning investment decisions, the higher the proportion of financial assets invested in the stock market. H6-2. The higher the risk-taking propensity concerning investment decisions, the more frequent the stock transactions.

3. Methodology 3.1. Data The 2000 –2001 MacroMonitor data set was employed to examine the conceptual framework proposed. The Consumer Finance Decision section of SRI Consulting Co. collected the MacroMonitor data. This database contains information about consumer perceptions, attitudes, and behaviors concerning financial services and products. Participants are recruited via two-stage random sampling, with a stratified disproportionate random sample at the first stage, followed by a simple random sample of all households at the second. Detailed information about the sampling methodology can be found at http://www.sricbi.com/CFD. To eliminate response bias due to structural differences in households, we included responses only from households with both female and male heads. A total of 2650 households met this criterion and were therefore included in this study. After deleting the responses with missing variables on key constructs, the final sample size was reduced to 2069. In each household, the individual most responsible for household investment decisions is asked to participate in the study. Approximately 55% of these respondents were male. Nearly half of the respondents aged 35 to 50, had completed either some college or a 4-year college degree, and had household incomes between $30,000 and $80,000. About 45% of the respondents had assets between $20,000 and $80,000 invested in the stock market, and had made an average of 3.5 stock transactions during the last 12 months. 3.2. Measures Measures for our study were constructed as follows. First, the extent of information search for investments refers to the effort that an individual devotes to the search for information before making an investment decision. A number of researchers have adopted a composite measure of the extent of information search: the number of various search activities engaged in, which counts all the external sources of information used (e.g., Punj and Staelin, 1983; Srinivasan and Ratchford, 1991). Following this approach, the present study measures the extent of information search

J. Cho, J. Lee / Journal of Business Research 59 (2006) 112 – 120

by counting the number of sources of information on investments a respondent used. In addition, a consumer’s likelihood of obtaining professional advice for investments is identified on a 4-point Likert scale to measure the extent to which this particular information source has been utilized. The second form of risk-reducing strategy (behavioral responses to lower vulnerability) is measured using the following two variables: (1) the proportion of the total financial assets invested in the stock market (calculated by dividing the amount of money invested in stock mutual funds and individual stocks by the money invested in all financial products) and (2) the number of stock transactions made during the last 12 months (both stock mutual funds and individual stocks). Third, sets of questions were identified as items measuring each of the following constructs: perceived risk, risk propensity, and self-efficacy. All the items were assessed on 4-point Likert scales ranging from ‘‘mostly agree’’ to ‘‘mostly disagree.’’ Because we use secondary data, our measures are limited to the questions asked in the survey. Although these measures are well accepted in many surveys regarding financial matters (e.g., the Survey of Consumer Finances commissioned by the Federal Reserve Board), we carefully examined the validity of the proposed constructs. Detailed information regarding the measurement testing and specific statements of items is presented in the results section. Finally, household net worth was used to measure wealth position. Net worth is operationalized as the sum of total financial and non-financial (i.e., primary house and other residential/commercial real estates) assets minus the total household liabilities (such as mortgage, margin and car loans, credit card debt, etc.).

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4. Data analysis and results The main data analysis consisted of two steps. First, a confirmatory factor analysis (CFA) with LISREL was used to check the validity and reliability of the measurement items. To examine the structural relationships in the model, we then employed full-information structural equation modeling with all the measurement items finalized (SEM). For both analyses, three types of fit indices were used in addition to the chi-square test to assess the overall model fit: the comparative fit index (CFI), the goodness-of-fit index (GFI), and the root mean square error of approximation (RMSEA). In general, model fit is considered to be adequate if CFI and GFI are larger than 0.90 and RMSEA is smaller than 0.08 (Jaccard and Choi, 1996). T-test, magnitudes and standard errors of factor loading, and modification indices were used to check the significance of a particular path. 4.1. Measurement model Table 1 summarizes the results of CFA for measurement items. The chi-square of the measurement model was 110.06 with 18 degree of freedom (df). The fit indices indicated that the model has a good fit (CFI = 0.99, GFI = 0.99, and RMSEA= 0.049). As presented in Table 1, all items had completely standardized factor loading (CSL) greater than 0.60 as suggested by Bagozzi and Yi (1988). To check the reliabilities of latent variables, composite reliability (CR) and average variance extract (AVE) were calculated. All scales exhibit acceptable reliabilities that exceed recommended cutoff values: CR > 0.7 and AVE > 0.5. These results

Table 1 Confirmatory factor analysis of the measurement model Items Self-Efficacy I am very organized in my approach to financial matters. I sometimes feel stupid when I ask questions about financial matters (reverse coded). I do a very good job of keeping my financial affairs in order. Often I am not sure whether the financial decisions I’ve made are the right ones (reverse coded). Risk Propensity I am willing to take substantial risks to realize substantial financial gains from investments. I am willing to put some proportion of savings in uninsured investments to get a high yield. I am willing to accept some risk of losing money if an investment is likely to come out ahead of inflation in the long run. Perceived Risk The stock market is too risky for me. Off-Diagonal Phi Coefficient: Self-Efficacy  Risk Propensity = 0.06 Self-Efficacy  Perceived Risk = 0.13 Risk Propensity  Perceived Risk = 0.38

Mean

S.D.

CSL

Composite reliability

Average variance extracted

1.83 2.18

0.76 0.87

0.75 0.67

0.84

0.58

2.08 1.84

0.82 0.76

0.67 0.88

2.66 1.85

0.95 0.80

0.64 0.76

0.81

0.51

2.36

0.96

0.66

2.97

0.94

1.00

N/A

N/A

Fit indices: chi-square = 110.06 with 18 df, CFI = 0.98, GFI = 0.99, RMSEA= 0.049. CSC: Completely Standardized Factor Loading.

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indicate satisfactory construct validity and reliability of the measures. To examine the discriminant validity, the final measurement model was compared to alternative models with a fixed covariance matrix between two latent factors at 1 (i.e., pairwise comparison) (Bollen, 1989). We then examined a chi-square difference to determine whether the restriction in the covariance matrix deteriorated the model. First, the final measurement model was compared to the one with the covariance between perceived risk and risk propensity set to 1. The results showed that such a constraint significantly degraded the model fit (v 2 = 800.25 with 19 df, CFI = 0.86, GFI = 0.80, RMSEA= 0.16; Dv (1)2 = 690.19, p = 0.000). We applied the same procedure to the other sets of two constructs. The results indicated that the covariance constraint in any sets of two constructs significantly deteriorated the model fit (Dv (1)2 = 505.86 with a constraint between self-efficacy and risk propensity, p < 0.000; Dv (1)2 = 609.43 with a constraint between self-efficacy and perceived risk, p < 0.000). We thus conclude that the measurement model has satisfactory discriminant validity. 4.2. The risk-behavior model As indicated, the overall efficacy of the model and statistical significance of the structural relationships were examined with full-information structural equation modeling. The chi-square of the model was 737.40 with 77 df. The fit indices showed that the model has a good fit (CFI = 0.94, GFI = 0.95, and RMSEA= 0.065). The values of completely standardized LISREL estimation of each path are presented in Table 2. First, the path from self-efficacy to risk perception was negative and significant (k = .10, t = 4.64, p < 0.005). Table 2 Structural relationships in the model Model fit

Chi-square df CFI GFI RMSEA

Path

Completely t-value standardized coefficient

Latent Paths H1-1: Self-EfficacyYPerceived Risk H1-2: Wealth PositionYPerceived Risk H2: Risk PropensityYPerceived Risk H3-1: Perceived RiskYInformation Search H3-2: Perceived RiskYAdvice f/ Professionals H4-1: Risk PropensityYInformation Search H4-2: Risk PropensityYAdvice f/ Professionals H5-1: Perceived RiskYProportion in Stocks H5-2: Perceived RiskYTransaction Frequency H6-1: Risk PropensityYProportion in Stocks H6-2: Risk PropensityYTransaction Frequency *p < 0.05; **p < 0.005.

0.10 0.03 0.64 0.07 0.02 0.06 0.25 0.17 0.12 0.37 0.02

737.40 77 0.94 0.95 0.065

4.64** 1.74* 24.78** 3.08** 0.66 0.62 6.69** 5.54** 3.48** 10.84** 0.55

Household wealth position was also found to negatively influence risk perception (k = 0.03, t = 1.74, p < 0.05). We thus support both H1-1 and H1-2. Second, the results indicated that risk propensity had a negative and significant effect on risk perception (b = 0.64, t = 24.78, p < 0.005), supporting H2. Third, we found that perceived risk increased the number of information sources consulted (b = 0.07, t = 3.08, p < 0.05), supporting H3-1. However, the effect of perceived risk on the likelihood of seeking professional advice was not significant, so we reject H3-2. Fourth, the results indicated that risk propensity increased, rather than decreasing, the likelihood of seeking professional advice (b = 0.25, t = 6.69, p < 0.005). Further, risk propensity did not have a significant effect on the number of information sources consulted. These results reject H4-1 and H4-2. Fifth, risk perception was found to have a negative and significant influence on the proportion of financial assets invested in stocks (b = 0.17, t = 5.54, p < 0.005), supporting H5-1. The results also supported H5-2, indicating that risk perception increased the frequency of stock transactions (b = 0.37, t = 10.84, p < 0.005). Lastly, we found that risk propensity had a positive and significant effect on the proportion of financial assets invested in stocks (b = 0.37, t = 10.84, p < 0.005). The effect of risk propensity on transaction frequency, however, was insignificant. We thus support H6-1, but reject H6-2. Lastly, we checked the possibility that risk propensity is a moderator rather than a predictor. We split the sample into high (n = 992) and low (n = 1077) risk propensity groups based on the median value, and ran the split-sample analysis. In particular, we conducted the invariance test to see if the relationship between perceived risk and the outcome variables would notably differ across groups. While some variances existed in specific path coefficients between the groups, the results indicated that the structural invariance could be supported (v 2 = 198.05 with 52 df, GFA= 0.94, CFA= 0.04, RMSEA= 0.050). We thus conclude that the moderating role of risk propensity will not jeopardize the validity of the proposed model.

5. Discussion and implications The current study investigates interrelationships between the antecedents of risk perception, two risk constructs, and risk-induced behavioral responses in the context of household investment decisions. The results of this study entail the following implications. 5.1. Antecedents of perceived risk First, the results indicate that perceived risk is negatively related to self-efficacy (a perceptual factor affecting uncertainty) and wealth position (a situational factor affecting the significance of consequences). These findings confirm that perceived risk is shaped not only by how

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individuals see themselves being able to handle uncertainty, but also by how individuals interpret the impact of possible negative outcomes (i.e., having a different risk tolerance of the possible negative outcomes). As argued by Dowling and Staelin (1994), ‘‘While many perceived-risk constructs incorporate implicit wealth effects in their measures (e.g., importance of the loss component of compositional measures), few researchers attempt to explicitly model this aspect of a personal disposition’’ (p. 133). We explicitly recognize this variable and find that the (in)ability to absorb a monetary loss is indeed a significant factor in shaping perceived risk. Besides self-efficacy and wealth position, risk propensity is found to be another key determinant of perceived risk. Specifically, a decision maker with a higher risk-taking tendency in his/her investment decisions perceives a lower level of perceived risk associated with the stock market. The link between risk propensity and perceived risk has been neglected in risk-related consumer research. This finding highlights the importance of incorporating risk propensity into understanding perceived risk. While supporting the link between the two, however, our data are insufficient to verify the cognitive mechanisms argued for this relationship in the previous literature. A future study would examine the detailed perceptual processes accounting for the relationship between perceived risk and risk propensity in the investment context. 5.2. Behavioral responses to perceived risk First, the model proposes that risk perception influences both the amount of information search and the likelihood to seek information from market-provided personal sources (i.e., information acquisition from investment professionals). The results indicate that as perceived risk increases, a greater number of information sources are consulted before investing. We thus conclude that the link between perceived risk and the extent of information search (a link that has often been rejected in the extant literature) can be established in the investment context. However, the path from risk perception to information acquisition from investment professionals is insignificant. This finding may be attributable to that with many market-provided sources offering investment advice online, respondents are able to find investment advice of their own; risk perception may not necessarily increase the likelihood for seeking information from market-provided personal sources. Few studies have indicated that the amount and the pattern of information search are influenced by risk propensity. In particular, risk propensity decreases the extent of information search and the likelihood of seeking professional advice. Our results, however, show that high risk propensity does not necessarily decrease the overall extent of information search. Moreover, we find that as risk propensity increases, the likelihood of seeking professional advice increases. This apparent discrepancy may exist

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because the subjects in past research were entrepreneurs who were competent in making their own decisions; they may not necessarily want to admit to professionals that they need help (Welsh and Young, 1982). Such tendencies are believed to be less significant for our subjects. Our results would imply that, while risk-taking consumers appreciate the potential value of information search (i.e., reducing uncertainty), they may not be inclined to take time to collect a large amount of information from many different sources. They may rather try to obtain information efficiently by consulting professionals who can sort out and integrate a large amount of information, and offer more decisionrelevant information. The results suggest that when facing a high level of perceived risk, consumers make behavioral choices that will lower their vulnerability to potentially negative outcomes. In particular, consumers with higher perceived risk toward the stock market have significantly lower percentages of their financial assets invested in stocks. We also find that higher risk propensity directs investment decision in a way to favor riskier choices. Specifically, consumers with a high level of risk propensity tend to allocate larger proportions of their financial assets to riskier investments. The results also indicate that frequent stock transactions are the results of high perceived risk rather than high risk propensity; it is a behavioral response to cope with high perceived risk (i.e., risk-reducing strategy) rather than an attempt to take risk to obtain better returns. In fact, investing a proportion of money at different points in time (such as dollar-cost-average) is often suggested as a way to cope with the risk associated with high market fluctuations, while it may not lead to the best possible return (Kennon, 2005). 5.3. Limitations and future study directions First, the results should be generalized with the context specificity in mind. The proposed model concerns risk and risk-reducing strategies regarding consumers’ investment decisions. The findings will be best applied to consumer behavior in a similar context. Second, although items representing proposed constructs showed desired psychometric constructs, the measurement items could be improved. Scholars have in fact noted the lack of a risk measure appropriately reflecting the specific context of a decision situation (Mitchell, 1999). Primary research devoted to developing or validating risk measures in financial matters is encouraged, as it will help academic, business, and public policy-makers delineate a better picture of risk-related consumer behavior with respect to financial products/services. Third, the current study, by design, is static (i.e., correlational data) in that it does not focus on the changes in the individual’s views and decisions as a result of previous actions. For instance, it is possible that people may modify their perspectives as a result of previous behavioral choices. A future study could track how individuals’ risk

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propensity and risk perception have changed over time as their psychological and situational factors change.

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