A higher-order model of risk propensity

Journal of Business Research 62 (2009) 741–744

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Journal of Business Research

A higher-order model of risk propensity Dheeraj Sharma a,1, Bruce L. Alford b,⁎, Shahid N. Bhuian b,2, Lou E. Pelton c,3 a b c

Centre for Innovation Management, Athabasca University, Canada Department of Marketing and Analysis, College of Business, Louisiana Tech University, Ruston, LA 71272, United States Department of Marketing and Logistics, College of Business Administration, University of North Texas, Denton, TX 76203, United States

A R T I C L E

I N F O

Article history: Received 21 May 2006 Accepted 6 May 2008 Keywords Risk propensity Risk Risk taking Perceived risk

A B S T R A C T This study extends the paradigm of risk propensity and empirically investigates a higher-order risk propensity model. Past marketing research on risk propensity offers conflicting conceptualization and theorization. Additionally, past research viewed risk propensity as a first-order construct. The present study extends consumers' risk propensity concept, proposing and empirically assessing a higher-order model with three first-order factors, namely, risk risk-taking attitude, perceived risk, and price consciousness. In this view, the three fist-order factors are shaped by the individual's inherent risk propensity. © 2008 Elsevier Inc. All rights reserved.

1. Proposed higher-order CRP model Consumer risk propensity (CRP) is a central construct in consumer behavior. Risk propensity is defined as an individual's tendency to take or avoid risks (Sitkin and Weingart, 1995). Despite much empirical and theoretical investigation, no consensus, however, has yet been reached on the true nature of the construct (Pablo,1998). Attempts were made to use various risk attitudes and behaviors, both financial and nonfinancial, as determinants of CRP. Results are equivocal (Brendl, Higgins and Lemm, 1995; Jemison and Sitkin, 1986). Empirical works on the direct effects of CRP on various outcomes did not produce consistent results either (Bauer 1967; Grewal, Gotlieb and Marmorstein, 1994). This study attempts to conceptualize CRP as a higher-order construct representing a broad risk orientation, which is responsible for the manifestation of risk attitudes and behaviors, each of which individually represents one aspect of the general CRP. The higher-order view of risk propensity is directly supported by Nicholson et al. (2005). The authors posit risk propensity as an overarching variable. From this overarching variable are the first-order attitudinal manifestations (Nicholson et al., 2005). While Nicholson et al. did theorize the higher-order model, they did not empirically test it, which is done in the current study. Our contention also somewhat conforms to Sigmund Freud's theory of psychoanalysis. The manifest psychological traits of individuals may originate from some deeper level broad trait. The observed risk attitudes and behaviors may be reflections of a general deeper level of risk ⁎ Corresponding author. Tel.: +1 318 257 3962; fax: +1 318 257 4253. E-mail addresses: [email protected] (D. Sharma), [email protected] (B.L. Alford), [email protected] (S.N. Bhuian), [email protected] (L.E. Pelton). 1 Tel.: +1 780 418 7528; fax: +1 780 459 2093. 2 Tel.: +1 318 257 4012; fax: +1 318 257 4253. 3 Tel.: +1 940 565 3124; fax: +1 940 565 3837. 0148-2963/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2008.06.005

orientation, which is our higher-order CRP. This is perhaps the reason why existing literature is inconsistent about the influences of CRP when CRP is defined by various risk attitudes and behaviors, i.e., as a first-order construct (Jemison and Sitkin, 1986; Lopes, 1987). The authors posit that CRP gives rise to three types of consumer risk variables — consumer risk-taking attitude, perceived risk evaluation, and price consciousness. Consumer risk-taking attitude is viewed in terms of a continuum that ranges from risk averse to risk seeking. A low CRP consumer tends to be more risk averse and tends to appraise various alternative purchase options cautiously, makes choices from familiar options, and avoids new selections out of the fear of uncertainties (Das and Teng, 2001). In contrast, a high CRP consumer's risk-taking attitude tends to be risk seeking and he/she tends to choose unfamiliar options and new selections (Bromiley and Curley 1992; Das and Teng 2001). Perceived risk evaluation is a consumer's evaluation of the consequences of risky behavior along five dimensions (cf. Appendix A). This view concurs with past researchers who contend that individual's evaluate behavior along five major dimensions before making a consumption decision (Cunningham 1967;Jacoby and Kaplan 1972; Roselius 1971). What makes perceived risk evaluation different from risk-taking attitude is the assessment of the consequences of engaging in risky behavior. Price consciousness is defined as the degree to which a consumer seeks the “best price” for a particular product or service (Dickerson and Gentry 1983). Specifically, a low CRP consumer tends to be more price conscious and tends to be consistent in verifying prices of even small items, comparative shopping, and searching for the lowest possible prices. In this sense, price consciousness represents an economic motive behind a purchase decision. Existing research that considered consumer risk-taking attitude, perceived risk evaluation and price consciousness as defining components of consumer risk propensity produced equivocal results (Agarwal and Teas, 2000; Dholakia, 1997; Nicholson et al., 2005).

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Fig. 1. Higher-order model of risk propensity.

Studies examining their direct influences on consumer consumption decisions and behaviors could not yield consistent findings either (Agarwal and Teas, 2000; Das and Teng, 2001; Dholakia, 1997). Based on the above discussion, it is likely that consumer risk-taking attitude, perceive risk evaluation, and price consciousness are reflections of a broad construct, consumer risk propensity. The proposed model shown in Fig. 1 stipulates two outcomes of CRP, namely decisional conflict and consumption behavior. This is done to assess the predictive validity of CRP. Decisional conflict is the degree to which a consumer perceives a dilemma in engaging in any particular consumption behavior. Decisional conflict occurs primarily because of the awareness of choices and the associated risks and benefits (Ross et al., 2001). Janis and Mann (1977) contend that consumers exhibit decisional conflicts when they are faced with conflicting situations. Luce (1998) posits that negatively correlated attributes (NCA) induce decisional conflicts in consumers. Hansen and Helgeson (2001) argue that NCA generate a decision conflict when individuals perceive both risk and benefits from the decisional outcome. High CRP consumers would be more likely to engage in situations that may generate decisional conflict. Low CRP consumers tend to avoid situations that may lead to decisional conflict by making familiar choices that do not result in risky consequences. In this study, consumption behavior was viewed objectively, that is, the actual number of flights taken during the previous year determined the consumption behavior. More flights represent a higher consumption behavior. Past research indicates that anticipated losses from risky behavior inhibit buying behavior (Peter and Ryan, 1976). Additionally, the certainty/uncertainty of occurrence of negative consequences also inhibits consumption (Yeung and Morris, 2001). Consequently, individuals' with a higher CRP will undertake behaviors that those with a lower CRP would avoid. This also concurs with the past research which suggests, that a higher CRP is associated with a higher tolerance of risk (Simon, Houghton, and Aquino, 2000). Thus, a higher CRP is associated with a higher likelihood to engage in risky behavior.

For completeness of the model, following Bettman et al. (1993); decisional conflict decreases the degree of confidence that a consumer has in their consumption choices. Hence, decisional conflict may reduce the likelihood of behavior. 2. Methodology 2.1. Sample design The study was conducted in the air travel context, one of most volatile settings. Cast against the backdrop of an unprecedented terrorist attack on the U.S., the global airline industry now operates in the most turbulent market in its history. Prior to the events of September 11, 2001, the airline industry barely weathered the storm of persistent labor union disputes, rising operational costs and escalating losses. The terrorist attacks coupled with costly government-mandated security procedures have aggravated the airline industry's already dismal financial performance. At the same time, airline disasters and near-disasters continued to increase during the last decade, despite technological improvements in equipment and safety procedures. More stringent security procedures (and more publicly-scrutinized security breaches), current and impending airline bankruptcy proceedings, and capacity-strained air traffic systems continue to receive widespread media attention. When individuals are threatened by likely losses, they are likely to become uncompromising and risk averse (Staw, Sandelands, and Dutton 1981). Yet, passenger travel is at record levels despite these highly-publicized risk factors plaguing the airline industry. Surveys were distributed to 605 MBA students during classes at a large southern university. Respondents were asked to think about their airline travel purchase experiences in the past twelve months and respond to a paper and pencil survey. To ensure experience with the study context, airline travel, respondents were asked about their flying experience within the past twelve months. To be included in the study,

Table 1 Sample descriptive statistics. Gender

Race Frequency

Percent

Male Female

163 137

54.3 45.7

Total

300

100.0

Caucasian White Hispanic African American/Black Asian/Pacific Islander Other Total

Flights Frequency

Percent

204 53 21 11 11 300

68.0 17.6 7.0 3.7 3.7 100.0

Frequency ≥5 ≥4 ≥3 ≥2

103 125 167 231

≥1

300

Percent 34.33 41.66 55.66 77.00 100.0

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Table 2 Confirmatory factor analysis and reliability analysis. Item Decisional conflict Collecting a lot of information before deciding I put off making decision When I face a difficult decision, I feel pessimistic about making a good decision Price consciousness I believe in checking the prices for small items A person can save a lot of money shopping around for bargains I buy the lowest priced airline tickets that will suit my needs I shop a lot on air ticket specials Perceived risk evaluation Purchase of unfamiliar brand/product leading to performance loss Purchase of unfamiliar brand/product leading to physical loss Purchase of unfamiliar brand/product leading to psychological loss Risk-taking attitude Take tremendous care before selecting from alternatives Never try new things for the fear of making mistake Safer to try familiar versus unfamiliar I am cautious about trying new things I am the kind of person who would try anything new (r) Consumption behavior

Unstandardized loading⁎

Average variance extracted

Composite reliability

Mean

SD

0.95

0.94

4.15

1.75

0.75

0.90

4.59

1.78

0.89

0.84

4.23

1.78

0.76

0.91

4.40

1.95

3.39

2.127

0.92 0.90 0.84 0.96 0.95 0.79 0.75 0.97 0.90 0.88 0.84 0.94 0.79 0.92 0.84

(r) Reversed scored item. ⁎ All loadings significant at or below the.05 level.

respondents must have taken at least one flight in the past twelve months. A total of 300 valid responses were collected (response rate 49.5%) due to missing data and lack of flying in the past twelve months. The respondents represent a multi-cultural and diverse racial background. The sample of 163 males and 137 females indicated 15 different birth countries with diverse demographic characteristics. The average number of flights taken over the past twelve months among valid respondents was 3.44. The percentage of valid respondents who have taken at least three flights or more in the past twelve months was 55.67 and the percentage of valid respondents who have taken two or more flights was 77. Participation was completely voluntary. Valid respondents' characteristics are reported in Table 1. 2.2. Measures The risk-taking attitude scale (9-items) was adopted from Raju (1980). Perceived risk evaluation was measured using three items from Jacoby and Kaplan (1972) focu0sing on physical, performance, and psychological risk. A four item scale originally used to measure the degree to which price drove the consumer's decision choice was used (Dickerson and Gentry 1983) to measure price consciousness. Decisional conflict was measured by an existing scale for decisional conflict (O'Connor 1995). The existing Decisional Conflict Scale is a 16 item scale, which measures three different domains of decision making. The scale contains three subscales that elicit certainty or uncertainty in choice (3 items), being aware and informed (9 items), and perceived quality and effectiveness of decision making (4 items) (Mitchell, Tetroe, and O'Connor 2001). The current study used the 3-item pre-established sub-scale to measure certainty or uncertainty in consumer choice (Murray et al., 2001). All scales were of Likert format ranging from strongly disagree (1) to strongly agree (7). Respondents' consumption behavior was measured as the number of flights taken in the past twelve months. 3. Analysis and results 3.1. Assessment of measures First, we undertook a confirmatory factor analysis (CFA) to evaluate the reliability, validity, and unidimensionality of the measures. The firstorder CFA consisted of the four multi-item constructs, namely, risktaking attitude, perceived risk evaluation, price consciousness, and decisional conflict; along with their 19 items. The initial fit statistics

were less than adequate. Based on poor loadings, high residuals, and modification indices, 4 items were eliminated from risk-taking attitude. The resulting fit statistics demonstrated a good fit: Chi square = 163.08, df = 86, CFI = .99, and RMSEA = .05. All path coefficients of all items were significant, with critical values ranging from 7.08 to 11.57. Additionally, we estimated average variance extracted (AVE) and composite reliability (CR) of each scale (Fornell and Larcker 1981; Gerbing and Anderson 1988) (cf. Table 2). The AVEs ranged from.75 to.95, indicating convergent validity (Fornell and Larcker 1981), while composite reliabilities ranged from 0.84 to 0.94. All were above the acceptable thresholds (Bagozzi and Youjae 1988). In order to check for discriminant validity, we calculated the shared variance between two constructs and compared it with the AVE for each individual construct. The shared variance was lower than the AVE suggesting that reasonable discriminant validity was achieved (Fornell and Larcker 1981). 3.2. Model results All results are shown in Table 3. We tested the proposed model using AMOS 4.0. The model achieved a good fit: Chi square = 169.52, Table 3 Results of higher-order model analyses. Criteria

Fit statistics

Chi square df CFI RMSEA

169.52 99 0.99 0.05

Risk → RTA Risk → PRE Risk → PC Risk → DEC Risk → CB DEC → CB

8.75⁎(.00⁎⁎) 11.24(.00) − 7.08(.00) 11.41(.04) 2.08(.04) − 2.02(.04)

Risk = consumer risk propensity. RTA = risk-taking attitude. PC = price consciousness. PRE = perceived risk evaluation. CB = consumption behavior. DEC = decisional conflict. ⁎ = critical ratio. ⁎⁎ = p-value.

Path coefficients

.89 1.18 − .58 1.22 .48 − .25

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df = 99, CFI = .99, and RMSEA = .05. Consumer risk propensity has significant influences on consumer risk-taking attitudes (t = 8.75, pvalue = .00), perceived risk evaluation (t = 11.24, p-value = .00), and price consciousness (t = −7.08, p-value = .00), supporting the proposed higher-order structure. Predictive validity was also supported. Consumer risk propensity is significantly related to decisional conflict (t = 11.41, p-value = .00). Consumption behavior (t = 2.08, pvalue = .04) is also significantly influenced by consumer risk propensity. Finally, decision conflict negatively influences consumption behavior (t = −2.02, p-value = .04). 4. Discussion This study proposed a higher-order view of risk propensity. The analysis shows that consumer risk-taking attitude, perceived risk, and price consciousness are significant manifestations of risk propensity. Previously, Conchar et al. (2004) viewed risk propensity as a firstorder construct, with personality factors as moderators of risk propensity. But the current study proffers risk propensity as a higher-order construct, from which personality factors emanate. In this way, attitudinal factors are shaped by the individual's inherent risk propensity and may be viewed as the manifestation of risk propensity. This view is supported by Nicholson et al. (2005), who also found risk propensity to be a higher-order construct, with personality factors as first-order constructs. The higher-order structure also agrees with Nicholson et al.'s (2005) view of risk propensity as an overarching risk propensity variable. 5. Limitations and further research Limitations of the present work should be addressed in subsequent investigations. First, generalization of findings might be limited since the MBA student sample may not be representative of the general population. Differences in the sample were very small; so, comparisons across demographic characteristics could not be performed. Future research using non-student subjects with a large variation in demographic characteristics is needed. Also, only one product category (airline) was utilized in this study. Consumers perceive different levels of risk with respect to different product types (Kim 2001). Therefore, more than one product category should be employed in future research to investigate differences across various product types. Price consciousness and attitude towards risk are first-order constructs of risk propensity. Future studies may investigate the situations and threshold at which one dominates the other. In the current study using airline travel, perceived risk reigned supreme over price goals. This was most likely due to the severe nature of loss in this study context. In settings with a lower severity of loss, price consciousness may play a larger role. Appendix A. Components along which perceived risk is evaluated

Performance The product/service did not perform as per risk expectation Physical risk The safety of consumer when using the service/ product Social risk Humiliation from the choice of product/service. What others may think of consumer's choice Financial risk The product/service was not worth the monetary value Psychological Product/service choice resulting in psychological risk grievance

Jacoby and Kaplan (1972) Jacoby and Kaplan (1972) Jacoby and Kaplan (1972) Jacoby and Kaplan (1972) Jacoby and Kaplan (1972)

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