Validating the search, experience, and credence product classification framework

Journal of Business Research 63 (2010) 1079–1087

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Journal of Business Research

Validating the search, experience, and credence product classification framework Tulay Girard a,⁎, Paul Dion b a b

Pennsylvania State University, Altoona, Division of Business & Engineering, Altoona, PA, 16601, United States Susquehanna University, Sigmund Weis School of Business, Selinsgrove, PA, 17870, United States

a r t i c l e

i n f o

Article history: Received 1 May 2008 Received in revised form 1 November 2008 Accepted 1 December 2008 Keywords: Search Experience Credence Perceived risk Retailer attributes

a b s t r a c t Prior research identifies the influential factors for patronage intentions as product classes, retailer attributes, and risk perceptions. The Internet's ability to offer easy information search, therefore to reduce certain types of risk for products mandates evaluation of a new product classification framework called Search, Experience, and Credence. To test the nomological validity of the SEC-framework, this study investigates whether the SEC-products influence the (1) level of importance consumers place on retailer attributes, (2) level and types of risks consumers perceive, and (3) consumer patronage intentions for Internet and physical stores. The relationships between the (4) importance consumers place on retailer attributes and their risk perceptions, and (5) risk perceptions and patronage intentions for Internet and physical stores are investigated. The findings indicate while the importance of retailer attributes is equally significant across the four product classes, the SEC-products influence consumer risk perceptions and purchase-intentions for online and physical-stores. The relationship between important retailer attributes and risk perceptions is also significant. © 2009 Elsevier Inc. All rights reserved.

1. Introduction Despite increased Internet usage, the U.S. e-commerce sales grew from 3.1% in the 4th quarter of 2006 to only 3.5% in the same period of 2007 (The U.S. Census-Bureau, 2008). The reasons for the small percentage are still not well understood by either retailers or academicians. Therefore, identifying the underlying factors that may contribute to the small percentage and testing to what extend these factors influence consumer patronage-intentions for certain types of retailers is the first step toward understanding the reasons. Studies found tested only a limited number of these relationships. Furthermore, the conventional product classification frameworks, that have been used to explain consumer choices for different types of physical retail outlets (Copeland, 1923), no longer explain their choices for online retail shopping. The Internet's ability to offer easy information search and convenience for shopping for almost all products including products that are hard to find mandates evaluation of a relatively new product classification framework called Search, Experience, and Credence (SEC). Understanding how consumer risk perceptions vary in productclasses, what retailer attributes are important to consumers, and how consumer patronage intentions for different types of retailers vary across product classes is important for retailers to make strategic decisions. Therefore, this study first presents the literature that identifies the factors that influence consumer patronage intentions. ⁎ Corresponding author. E-mail addresses: [email protected] (T. Girard), [email protected] (P. Dion). 0148-2963/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2008.12.011

Identifying the gaps from the literature, the authors hypothesize whether the SEC-products significantly vary in (1) the level of importance consumers place on retailer attributes, (2) the type of risk consumers perceive, and (3) consumer patronage intentions for two retailer types—Internet and physical stores. Additionally, the relationships between (4) the importance level that consumers place on retailer attributes and their perceptions of risks in the SEC-products, and (5) consumers' perceptions of risks in the SEC-products and patronage-intentions for Internet retailers as compared to their intentions for physical stores are investigated. More specifically, the study examines the relationship between the construct of interest (SEC-products) with other constructs, and the relationships among these constructs forming a nomological network of relationships. Significant differences in the importance consumers place on retailer attributes, perceived risk, and purchase intentions for online and offline retailers across the SEC-products will provide evidence for the construct-validity of the SEC-classes. Validation of the SEC-product classification framework in a nomological network of relationships has not yet been tested in the marketing literature. Although the model proposals in this study allow for additional research objectives, this study has two main goals: to test (1) the nomological validity of the SEC-framework, and (2) the predictive power of the consumer patronage intentions model. The hypotheses appear in general terms to gain insights about the relationships among the variables of interest and test the predictive validity of the model. The results will help Internet and local retailers to design strategies to reduce risk for different products, and maintain strong customer relationships and store loyalty.

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2. Theory and hypotheses Sheth (1983) identifies the factors that affect customer patronage preference as (a) product types and characteristics, (b) retail-outlet types and attributes, and (c) personal characteristics of shoppers, demographics and life-styles. In addition to Sheth's (1983) preference-theory, the conceptual framework of this research is also rooted in the theory of perceived risk. Although the influence of product classes on the importance that consumers place on key retailer attributes (Girard et al., 2002), risk perceptions (Chaudhuri, 1998), and purchase intentions are well-established, the retailing literature still lacks an empirical study that tests the nomological validity of product classes in a comprehensive theory of patronageintentions. Therefore, this study incorporates the suggested antecedent factors in the literature—the SEC-products, retailer attributes, and perceived risk in product classes—into a model of consumer patronage intentions for Internet and local stores. 2.1. The SEC-products The conventional product-classification framework—Convenience, Shopping, and Specialty (CSS) goods—was originally developed by Copeland (1923) and has been prominently used in the marketing literature and textbooks until the web started to become an important shopping medium. Since the SEC-product classification framework has been evolved by Nelson (1970, 1974) and Darby and Karni (1973), it has not been tested for the saliency of its operationalization in the context of online and offline shopping (Viswanathan and Childers, 1999). Ford et al., (1988, p. 239) state that “the validity of the SECframework has not been empirically tested with consumers, nor have the terms ‘search,’ ‘experience’ and ‘credence’ qualities been defined precisely.” Unlike the CSS-framework, the definitions of the SECproducts incorporate the increasing/decreasing levels of availability of information, uncertainty, and cost/difficulty consumers encounter in obtaining and evaluating the attribute information of products. For example, Search products are identified as those whose relevant attribute information (e.g., price, quality, performance, dimension, size, color, style, safety, warranty) can be easily obtained prior to use/ purchase. Experience products are those whose relevant attribute information cannot be known until the trial/use of the product/service (Nelson, 1970, 1974. Credence products are those whose relevant attribute information is not available prior to and after the use of the product/service for a considerable period of time (Darby and Karni, 1973). Klein (1998) identifies two conditions that hold for experience-products: (1) full information on dominant attributes cannot be known without direct experience, (2) information search for dominant attributes is more costly/difficult than direct experience. Therefore, the literature suggests four classes of the SEC-products: search, experience-1, experience-2, and credence (Girard et al., 2002; Girard et al., 2003; Axelsson, 2008). 2.2. Retailer attributes Eastlick and Feinberg (1999) identify some of the retailer attributes from prior literature as perceived value, convenience, order services, reputation and company responsiveness (e.g., information services). Other attributes include merchandise assortment (Januz, 1983), lower prices (Reynolds, 1974), privacy–security, salesperson interaction (Girard et al., 2002; Korgaonkar et al., 2006), and high quality products (Hansen and Deutscher, 1977–78). The relationships between product classes and retailer attributes have been established. Girard et al. (2002) find that Internet retailerattributes that are important to consumers significantly influence consumer online purchase preference for different product categories. They find that for the search, experience, and credence products, perceived value and information services are the most important attributes,

followed by convenience, reputation, order services, economic-utility, customer service, merchandise-assortment, security-privacy, and home-shopping. In addition, Lynch et al. (2001) find that the effect of such Internet retailer attributes as trust, affect/entertainment, and sitequality significantly differ across products. These relationships are tested only in the context of online shopping and the number of research testing the influence of product classes on consumer importance of retailer attributes is very limited. H1. The importance consumers place on retailer attributes differs significantly across the SEC-products. 2.3. Perceived risk The conceptualization of perceived risk starts with Bauer (1960), who recognizes that consumer behavior involves risk-taking. The perceived risk dimensions identified in the literature include financial, performance, social, psychological, physical and time/conveniencerisks (Roselius, 1971; Jacoby and Kaplan, 1972; Jarvenpaa and Todd, 1996–97; Dholakia, 1997). The types of risk perceived by consumers are mostly specific to product characteristics and the availability of information/uncertainty about a product's attributes. Chaudhuri (1998) proposes that product class determines the level of overall perceived risk in the product. Because of the decreasing availability of product/service attribute information and increasing level of uncertainty, difficulty/cost involved in obtaining the information, consumers would perceive an incrementally increasing degree of risk from search to experience to credence products. H2. The amount of overall risk consumers perceive in product classes is the lowest for search, followed by the experience-1, followed by the experience-2, and the highest for the credence products. Mitra et al. (1999) find that perceived risk increases along a continuum from search to experience to credence service purchases. Because the search products are the easiest to evaluate due to the ease of obtaining product information, the level of the six types of risks perceived (financial, performance, social, psychological, physical, and time/convenience) for the search products is expected be the lowest. H3. For the search products, the six types of risk is perceived significantly lower than for the experience-1, experience-2, and credence products. The product class definitions used in this study characterize not only an incremental decrease in the availability, and increase in complexity and cost of obtaining the relevant product attribute information, but also the need for trying/sampling products. Experience-1 products are those which consumers have a need/preference to try in order to reduce the uncertainty before making a purchase. Shoppers would need to directly examine experience-1 products mostly through their sense of touch and vision for softness, fit, smell, or taste (Li et al., 2002). Consumers are expected to spend more money, effort and time to acquire information through direct experience. Because consumers would be more willing to take time and go through aggravation to try first, then purchase what they like, their perceptions of time/convenience risk is expected to be lower. Therefore, experience-1 products may invoke perception of higher psychological, social, physical, financial, and performance risk than time loss/convenience risk. H4. For the experience-1 products, the amount of financial, performance, physical, social, and psychological risk is perceived to be significantly higher than that of time loss/convenience risk. Information search to find relevant attribute information is more costly/difficult for experience-2 products than directly experiencing

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them (Klein, 1998). That is why consumers make greater efforts to acquire the information they need than to directly experience these products. The assumption made is that consumers do not normally have the knowledge and experience of the experience-2 products. Therefore, consumers are expected to perceive higher financial, performance, physical, and time loss/convenience risks than psychological and social risks that concern their feelings of self-image and selfesteem.

purchase from a retailer type they are familiar with and had previous experience. Therefore, consumer choice of a retailer type is influenced by the product classes.

H5. For experience-2 products, the amount of financial, performance, physical, and time loss/convenience risks is perceived to be significantly higher than that of social-and psychological risks.

Mitchell (2001) points out that some studies link store attributes to consumer needs and motives; however, the studies do not test which store attributes are related to which types of risk. Prior research suggests that the level of importance perceived by consumers of certain retailer attributes may be positively related to consumer perception of certain types of risks (Prasad, 1975; Phau and Poon, 2000; Sharma et al., 1983).

Credence-products are those for which consumers cannot evaluate the attribute information confidently before and after the purchase for a considerable period of time. Mitra et al. (1999) finds that consumers perceive not only higher level of financial risk, but a higher level of social and psychological risk when making credence service purchases. Because the level of uncertainty is the highest for credence products, consumers may also perceive a higher level of time/ convenience-risk in searching for product attribute information. Therefore, the level of six types of risks perceived by consumers is expected to be the highest for credence products. H6. For credence products, the level of the six types of risks is perceived to be significantly higher than for search, experience-1, and experience-2 products. 2.4. Patronage-intentions This study contends that limitations of consumer information about product attributes have profound effects on their patronage intentions toward Internet versus physical stores. Although the number of studies on the direct effect of product classes on purchase intentions for different types of retailers is relatively small, a significant relationship between product classes and preference to shop online has been found in the literature. For instance, the findings of Girard et al. (2002, 2003) reveal that consumer patronage preference for online shopping varies across product classes. Their findings indicate that consumer preference to shop online for search products are significantly higher than for the experience products. Based on prior research, this study predicts that consumer purchase intentions for search products will be significantly higher for shopping from Internet retailers. Conversely, their purchase intentions for experience-1 products will be significantly higher for local stores because of their needs/preference for sensory information. Consumer purchase intentions for experience-2 and credence products are expected to be equally likely from both retailer types because of the increased difficulty/cost and uncertainty in evaluating the information in both product classes. To reduce risk, consumers may prefer to go to a local-retail store to talk to a salesperson and

H7. SEC-products influence consumer purchase intention for different types of retailers—internet vs. local stores. 2.5. Retailer attributes and perceived risk

H8. The importance consumers place on retailer attributes relates positively to consumer risk perceptions in product classes. 2.6. Perceived risk and patronage intentions A negative relationship occurs between perceived risk and patronage intentions in the literature (Bhatnagar et al., 2000; Forsythe and Shi, 2003). Prasad (1975) and Korgaonkar and Moschis, (1989) explain that the risks perceived in products by consumers are transferable to the retail stores and subsequently affect patronage intentions for a retailer type. Based on the findings of the prior studies, the negative influence of perceived risk on patronage intentions for Internet retailers and local stores is tested. H9. Consumer perceptions of risks in the SEC-products negatively influences patronage intentions for the two retailer types. 3. Method The data for the entire study were collected in three stages. Two sequential pretest studies were conducted to select products that are most representative of the SEC-product/service class definitions. 3.1. The first-stage-pretest data and survey The first-stage-pretest survey instrument was administered to a convenience sample of 254 undergraduate business students (18 years and older) at a state university in the southeastern region of the U.S. The respondents were asked to classify ten products/ services from a list of seventy based on the SEC-product/service class definitions drawn from the literature. Because the study tests for purchase intentions from two retailer types (Internet retailers and local retail stores), the products sold both online and in local stores are included in the first-stage survey.

Table 1 The first-stage-pretest results. Search

Freq.

Experience-1

Freq.

Experience-2

Freq.

Credence

Freq.

Microwave Fresh flowers Airplane ticket Greeting card Music-CD Concert ticket Chinaware Backpack Printer cartridge Wrist-watch

82 70 69 65 63 59 56 54 51 51

Perfume/cologne Cosmetics Automobile Sun glasses Hair-Color Digital camera Dress Shoes Mattress Wine Hearing-aids

92 78 69 65 62 61 60 60 56 43

GPS-Unit Flat-Screen-TV Mortgage loan PDA Photo-editing software Cruise travel House Notebook Computer Cell-Phone MP3-Player

56 51 51 48 47 45 40 37 36 35

Anti-wrinkle cream Herbal-Suplements Hair-growth cream Stock-mkt invest. Nicotine-patches Teeth-whitener Auto insurance Ionic-air-purifier Lawn fertilizer Golf-club mship.

111 106 101 96 80 73 63 62 55 44

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To avoid order bias, the product/service definitions were presented in a random order—credence, experience-1, experience-2, and search. The ten products that were listed most frequently in each product/service definition were used in the second-stage pretest survey instrument (Table 1). The ten products listed were consistent with previous research. For example, Girard et al. (2002) uses MusicCD as an example for search product, perfume/cologne for experience-1, cellular-phone for experience-2, and vitamins (herbal-supplements in this research) for credence products. 3.2. The first-stage pretest sample The first-stage pretest sample (N = 254) was comprised of approximately 45% males and 55% females. According to an online report by eMarketer, females made up 51.6% of the U.S. online population (eMarketer, 2005). Therefore, the percentage of the gender distribution was consistent with that of online users in the U.S. The majority of the respondents were between 18 and 36 years-old with a college education and a full-time job. Sixty-percent of the respondents were single and 29% were married. Thirty-three percent of the respondents had an annual household-income of $25,000 to $50,000. Most of the respondents were White (61%), followed by African-Americans (18%), followed by Spanish/Hispanic/Latinos (14%), and held various occupations.

Table 2 The second-stage pretest results. Agree

Disagree

Search

Percent

Count

Percent

Count

Airplane ticket Music-CD Printer cartridge Greeting card

96% 95% 95% 93%

106 105 104 102

4% 5% 5% 7%

4 5 6 8

Experience-1 Perfume/Cologne Automobile Cosmetics Mattress

77% 76% 69% 69%

69 68 62 62

23% 24% 31% 31%

21 22 28 28

Experience-2 House Flat-screen-TV GPS-Unit Mortgage loan

83% 63% 49% 46%

96 73 57 53

17% 37% 51% 54%

20 43 59 63

Credence Anti-wrinkle cream Herbal-supplements Hair-growth cream Stock-market investment

58% 58% 57% 50%

63 63 62 54

42% 42% 43% 50%

45 45 46 54

3.3. The second-stage pretest data and surveys

3.4. The second-stage pretest samples

The second-stage pretest was comprised of four online surveys that were administered to a total of 1000 (4 × 250) randomly selected adult U.S. online shoppers by Zoomerang.com. The response rates for the four surveys were 44% (n = 110), 36% (n = 90), 40% (n = 108) and 46% (n = 116) for the search, experience-1, experience-2, and credence products, respectively. Each of the four second-stage pretest survey contained a total of fifteen questions. Fourteen demographic questions were identical in the four surveys. Five demographic questions asked respondents' Internet usage and shopping experience to make sure that the samples used in this study consisted of Internet shoppers rather than Internet nonshoppers. Nine demographic questions included gender, education, annual household income, ethnicity, age, marital status, zip-code resided, work status, and occupation. Each survey differed by the product/service definition and the ten products/services listed under each definition. The respondents were asked on a dichotomous-scale whether they agree/disagree with the classification of the ten products in each class that came from the firstpretest study. The most frequently selected four products/services in each product/service class were used as examples to accompany the product/service class definitions in the third-stage survey to enhance the generalizability and content validity of the definitions of each product/service class (Table 2). Search products had the highest and experience-1 products had the second highest percents of agreements. For experience-2 products, global-positioning-system (GPSunit) and mortgage loan had slightly more disagreements than agreements. The respondents in the second-stage study were experienced online shoppers who purchased products frequently on the Internet. Given the fact that experienced online shoppers would be efficient in searching for information on the Internet, they might not have perceived as difficult/costly searching for product attribute information for a GPS-unit and mortgage loan. Consumers, who are not familiar with these products and do not know what to search for, may perceive a GPS-unit and mortgage loan as more representative of experience-2 products. A similar explanation applies to a stockmarket investment as a credence product. Online shoppers can easily obtain investment information anytime online although performance of a stock may not be known for a considerable period of time. Therefore, these products were considered as appropriate to be used as examples in the third-stage study.

All of the respondents in the four surveys were frequent online shoppers. For example, the approximate total dollar amounts they spent in the last 12-months ranged from $801 to $1000 and were evenly distributed across the four samples. Majority of the respondents had more than 4 years online purchase experience. Most of the respondents in all surveys were white and female, had an age between 35 and 54 years-old with college education, earned between $50,000 and $75,000, and hold a full-time job in mostly managerial/ administrative occupations. 3.5. The third-stage data and surveys A total of 3589 randomly selected adult U.S. online shoppers were invited by Zoomerang.com to participate in the four surveys. The response rates after a few drop-outs were 48% of 898 invitees (n = 432) for search, 52% of 899 invitees (n = 471) for experience-1, 50% of 898 invitees (n = 445) for experience-2, and 54% of 894 invitees (n = 486) for credence product. The third-stage surveys included the same fourteen-demographic questions as in the secondstage pretest surveys. Each of the four survey instruments provided one of the four product/service class definitions with four examples that were selected in the second-stage pretest study. The main part of the survey assessed overall-risk in product/ service class was measured on a 5-point scalel (1 = Not risky at all, 2 = A little risky, 3 = Risky, 4 = Very risky, 5 = Extremely risky) by asking, “How risky do you feel it would be for you to purchase products/services from Product/Service Condition-1? Each product/ service condition corresponded to each product class definition (i.e., 1 = Search, 2 = Experience-1, 3 = Experience-2, 4 = Credence). This question was used to test H2. The six perceived-risk types were measured on a 5-point scale (1 = very unlikely to 5 = very likely) by asking “How likely do you feel purchasing products/services from Product/Service Condition-1 (i.e., 1, 2, 3, 4) would lead to a loss because of (1) financial risk involved; (2) the risk of product/service performance failure; (3) your significant others may think less highly of you; (4) a mismatch between the product/service and your selfimage; (5) the risk of the product/service being unsafe, and (6) the time involved in solving problems with any of the product/service features?”

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The twenty-two statements that measured seven dimensions of retailer attributes are: Perceived Value (3), Convenience (3), Merchandise Assortment (3), Order Services (3), Retailer Reputation (3), Security-privacy (3), and Information Services (4). A 5-point scale (1 = Very Unimportant to 5 = Very Important) was used to measure each statement. Two purchase intention questions asked, “What is the probability that you will purchase products/services from Product/Service Condition-1 (i.e., 1, 2, 3, 4) on the Internet and in local retail stores. The respondents were asked to indicate “to what extent the following features of retailers are important to you when you purchase products/services from Product/Service Condition (1, 2, 3, 4) on the Internet and/or in local retail stores?” A 0 to 100 probability-scale was used to measure purchase intentions adopted from Haley and Case (1979).

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attribute items that loaded on one factor with loadings ranging from .763 to .903 were used in hypothesis testing. The total variance explained was 71%, and the Cronbach's alpha-coefficient was 0.932. 4.3. Hypotheses testing H1 predicts that the consumer importance of retailer attributes would significantly differ across product classes. The factor scores of the seven retailer attributes were tested for significant differences across the four product classes using an ANOVA with Tukey–Kramer Post-Hoc-test. The factor scores did not significantly differ across the four product classes (F(3,1739) = 1.8, p N 0.05). Therefore, the findings do not support H1. In H2, differences in overall-risk perception across the four SECproducts were tested using a One-Way-ANOVA procedure. The results revealed a significant F-test (F(3,1739) = 280.5, p b 0.01). The searchproduct class had the lowest overall-risk perception (mean = 1.59) as expected. Overall-risk perception in experience-2 products was significantly higher (2.78) than that in search products (p b 0.01). Overall-risk perception in experience-1 products (3.35) was significantly higher than that in experience-2 products (p b 0.01). Finally, overall-risk was the highest for the credence products (3.45) as expected. Because the overall-risk was measured with a separate statement, to cross-validate these results, the factor score of all six perceived risk variables were also tested for differences using an ANOVA with Tukey–Kramer Post-Hoc-test. All of the differences between the product categories were significant (p b 0.01), with risk increasing from search to experience-2 to experience-1 to credence (F(3,1739) = 180, p b 0.01). These findings are consistent with those of the overall-risk variable. Because the experience-1 product class was not expected to carry higher risk than the experience-2 product class, H2 is partially supported. To test H3, whether the six individual risk types are perceived significantly lower for search products than the other product classes, an ANOVA was performed. The mean values of the six perceived risks were compared across the four product classes. For the search products, the level of the six types of risk was perceived significantly lower than the experience-1, experience-2, and credence products (Table 3). All of the mean differences between perceived risk variables for the search products were significant (p b 0.01). Therefore, H3 is supported. In H4, to find out whether the amount of financial, performance, physical, social, and psychological risk for experience-1 products is perceived to be significantly higher than that of time loss/convenience risk, paired-sample t-tests were performed. The mean of time loss/ convenience risk (3.69) was significantly higher than the financial (3.51), physical (3.07), psychological (2.69), and social (2.20) risk types as opposed to what was hypothesized (Table 4). All of the pairwisedifferences except for the performance (3.64) and time risk variables were significantly different (p b 0.01). Therefore, H4 is not supported. To test H5, the differences in the means of the six types of perceived risk for experience-2 products, paired-sample t-tests were performed. The means of the social (2.02) and psychological (2.30)

3.6. The third-stage samples Only those respondents who were frequent Internet shoppers were included in the analysis. The gender distribution in the thirdstage study included an equal number of male and female respondents in the four samples, which is consistent with the latest GVU online shopper demographics in 1998 (GVU, 1998) and a report by eMarketer (2005). Most of the respondents in all surveys were married, whites between 25 and 64 years-old with college education and earnings ranged from $25,000 and $150,000, and hold full-time jobs in mostly managerial occupations. 4. Results Before testing the hypotheses, all metric variables were used to diagnose multivariate-outliers with the Mahalanobis-D2 measure. Thirty-five observations were found to be significantly different/ unique in combination; therefore, they were removed from the data. Next, the multivariate-measurement model—perceived risk and retailer attributes—in the four product class datasets were assessed for discriminant validity using PCA. Convergent validity was assessed by testing the reliabilities by examining Cronbach's alpha-coefficients. 4.1. Discriminant and convergent validity of the perceived risk measurements After the multivariate-outliers were removed, all six perceived risk variables loaded on one factor and the factor loadings ranged from .787 to .857, and with 63% total variance explained. Cronbach's alphacoefficient was .881. 4.2. Discriminant and convergent validity of the retailer attribute measurements The first step in the analysis involved determining whether the twenty-two variables captured the seven constructs of retailer attributes. The analysis did not produce an overall clean factor structure with items loading on the appropriate components. Seven retailer Table 3 Descriptive statistics and F-test results for H3. Dependent variable

F-test

Time-risk Performance-risk Financial-risk Physical-risk Psychological-risk Social-risk

F = 103.4 F = 157.4 F = 155.3 F = 129.8 F = 64.9 F = 44.9

a

p b 0.01.

Search N = 409

Experience-1 N = 445

Experience-2 N = 427

Credence N = 462

Meana

SD

Meana

SD

Meana

SD

Meana

SD

2.56 2.42 2.28 2.07 1.93 1.61

1.05 .957 .960 .961 1.00 .89

3.69 3.64 3.51 3.07 2.69 2.20

1.12 1.03 1.05 1.16 1.16 1.19

3.46 3.30 3.27 2.81 2.30 2.02

1.09 .99 1.03 1.07 1.04 1.03

3.68 3.78 3.65 3.48 2.87 2.44

1.07 1.03 1.04 1.10 1.08 1.16

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Table 4 T-test results for H4.

Table 6 Post-hoc-test results for H6.

Experience-1 (N = 445,df = 444)

Mean diff.

SD

Std-Error

Time-risk–Financial-risk Time-risk–Performance-risk Time-risk–Social-risk Time-risk–Psychological-risk Time-risk–Physical-risk

.184 .052 1.49 1.00 .618

.999 .939 1.37 1.23 .967

.047 .045 .065 .058 .046

a

t 3.89 1.16 22.96 17.15 13.48

Sig.

Dependentvariable

Product-class (A)

.000a .246 .000a .000a .000a

Financial-risk

Credence (3.65), Search (2.28) N = 462 N = 409 Experience-1 (3.51) N = 445 Experience-2 (3.27) N = 427

1.38

Credence (3.78)

1.36

p b 0.01.

risk variables were significantly lower than the means of the financial (3.27), performance (3.30), physical (2.81), and time (3.46) risk types (p b 0.01) (Table 5). Therefore, H5 is supported. To test H6, whether the means of the six types of perceived risks for credence products are significantly higher than for search, experience-1, and experience-2 products, an ANOVA with Tukey– Kramer Post-Hoc-test was performed. The levels of six risks perceived for credence products were significantly higher than for search, experience-1 and experience-2 products (Table 6). However, for financial, performance, and time loss/convenience risk, the difference between credence and experience-1 products was not significant (p N 0.05). Therefore, H6 is partially supported. To test H7, whether consumer patronage intentions for Internet and local stores are significantly influenced by the SEC-products, an ANOVA was performed using the two patronage intention variables (ProbInt, ProbLoc) as dependent variables and product class as the categorical independent variable (Table 7). Patronage intentions for Internet and local stores varied significantly (p b 0.01) across the SECproducts with F-values (F(3,1739) = 375.84, F(3,1789) = 147.51, respectively). The means of patronage intentions for both Internet retailers and local stores appeared from the highest to lowest in the order of search, experience-2, experience-1, and credence products. These results are consistent with those found in testing H2. As the risk increases from search to experience-2, to experience-1 to credence products, the purchase intentions for these products from either retailer type decreases in the same order. The Tukey–Kramer Post-Hoc-test results were examined to determine between which product classes each dependent variable had significant differences. Purchase intentions for Internet retailers were not significantly different between experience-1 and credence products (Table 8). Purchase intentions for local retailer stores were not significantly different between experience-1 and experience-2 products. Overall, product classes explained a significant proportion of the variance in consumer patronage intentions between an Internet and local store. Therefore, H7 is supported. 4.4. Multivariate-testing of H8 and H9 A multivariate analysis was performed to further test the significance of the relationships in the hypothesized model including H8–H9 using Structural-Equation-Modeling (Fig. 1). The sample data

Financial-risk–Social-risk Financial-risk–Psychological-risk Performance-risk–Social-risk Performance-risk–Psychological-risk Physical-risk–Social-risk Physical-risk–Psychological-risk Time-risk–Social-risk Time-risk–Psychological-risk a

p b 0.01.

Social-risk

Credence (2.44)

Psychological- Credence (2.87) risk

Mean-diff. Std- Sig. (A–B) Error

Search (2.42)

.069

.000⁎⁎⁎

.148

.068

.129

.382

.069

.000⁎⁎⁎

.068

.000⁎⁎⁎

Experience-1 (3.64) Experience-2 (3.30)

.143 .479

.067 .067

.139 .000⁎⁎⁎

Search (1.61) Experience-1 (2.20) Experience-2 (2.02)

.826 .235 .418

.073 .072 .072

.000⁎⁎⁎ .006⁎⁎⁎ .000⁎⁎⁎

Search (2.87)

.936

.073

.000⁎⁎⁎

Experience-1 (2.69) Experience-2 (2.30)

.178 .568

.071 .072

.062⁎ .000⁎⁎⁎

Physical-risk

Credence (3.48)

Search (2.07) Experience-1 (3.07) Experience-2 (2.81)

1.410 .404 .668

.073 .072 .072

.000⁎⁎⁎ .000⁎⁎⁎ .000⁎⁎⁎

Time-risk

Credence (3.68)

Search (2.56) 1.127 Experience-1 (3.69) − .006 Experience-2 (3.46) .225

.074 .072 .073

.000⁎⁎⁎ 1.000 .001⁎⁎⁎

⁎p b 0.10. ⁎⁎p b 0.05. ⁎⁎⁎p b 0.01.

fit the hypothesized model well. The Chi2-value (.075) approximated the degrees-of-freedom (2) with Probability = .963. The absolute and incremental fit-indices were greater than .95, and RMSEA value less than .08. The model explained 52% of the variance in purchase intentions for Internet retailers, and 21% of the variance in purchase intentions for local stores. All coefficients were significant (p b 0.01). However, the relationship between SEC-products and retailer attributes was not significant, which was consistent with the results in H1. The results for H8, which stated a positive relationship between the importance consumers place on retailer attributes and consumer risk perceptions in product classes, was supported with significant standardized-beta-coefficients (β = .11, t = 5.2, p b 0.01), supporting H8 (Table 9). H9 states negative relationships between perceived risk and purchase intentions for both Internet and local retailers. The relationships were negative and significant (p b 0.01) with β = − .428 and − .217, and t = − 22.9 and − 9.4, respectively, supporting H9. Although it was not originally hypothesized, the relationship between purchase intentions for Internet and local retailers was found to be positive and significant (β = .282, t = 15.1, p b 0.01) (Table 9). Table 7 F-test and descriptive statistics for H7.

Table 5 T-test results for H5. Experience-2 (N = 427,df = 426)

Performancerisk

Product-class (B)

Mean diff. 1.25 .972 1.28 1.00 .787 .508 1.44 1.159

SD 1.31 1.25 1.27 1.19 1.21 1.09 1.41 1.29

Std-Error .063 .060 .061 .058 .059 .053 .068 .063

t 19.77 16.07 20.86 17.29 13.44 9.63 21.09 18.54

Sig.

Product-class

Mean SD

N

F-Sig.

R2

Purchase intentions for Internet

Search Experience-1 Experience-2 Credence Total

78.48 28.09 36.63 25.00 41.19

21.91 27.60 27.75 26.89 33.64

409 445 427 462 1743

375.8 (.000)

.52

Purchase intentions for local-store

Search Experience-1 Experience-2 Credence Total

74.11 60.07 61.41 35.54 57.19

25.12 31.07 26.79 28.08 31.24

409 445 427 462 1743

147.5 (.000)

.21

a

.000 .000a .000a .000a .000a .000a .000a .000a

T. Girard, P. Dion / Journal of Business Research 63 (2010) 1079–1087 Table 8 Post-hoc-test results for H7.

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Table 9 Standardized-regression-weights and T-test results for H8 and H9.

Dependent-variable

Product-class (A)

Product-class (B)

Meandiff.(A–B)

StdError

Sig.

Relationships

β

StdError

t-values

Sig.

Purchase-intentions for Internet

Search

Experience-1 Experience-2 Credence Experience-2 Credence Credence

50.39 41.86 53.48 − 8.54 3.09 11.63

1.79 1.81 1.78 1.78 1.74 1.76

.000a .000a .000a .000a .286 .000a

Experience-1 Experience-2 Credence Experience-2 Credence Credence

14.04 12.70 38.57 − 1.34 24.53 25.86

1.91 1.93 1.89 1.89 1.85 1.87

.000a .000a .000a .894 .000a .000a

Product-class → Perceived-risk Product-class → Purchase Intent-Internet Product-class → Purchase Intent-local-stores Retailer-attributes → Perceived-risk Retailer-attributes → Purchase intent-Internet Perceived-risk → Purchase-intent-Internet Perceived-risk → Purchase-intent-local-stores Purchase intent-local-stores → Purchase intentInternet

.393a − .206 − .328 .113 −.064 −.428 −.217 .282

0.02 .576 .647 .022 .564 .628 .724 .02

18.0 − 10.8 − 14.2 5.2 − 3.84 − 22.9 − 9.4 15.1

.000b .000b .000b .000b .000b .000b .000b .000b

Experience-1 Experience-2 Purchase intentions for local-store

Search

Experience-1 Experience-2 a

p b 0.01.

5. Conclusion The findings of H1–H9 support the conclusion that the SEC-product classification is a salient framework to study patronage intentions in the online shopping context. Because of the prominent differences in the four SEC-product definitions, this study finds significant differences in consumer risk perceptions in the SEC-products, and patronage intentions for Internet and local retailers. First, the importance of retailer attributes does not vary across product classes (H1). This means that whether the product is from the search, experience-1, experience-2, or credence class, retailer attributes carry the same amount of importance. This is an important finding. The individual retailer attributes might have varied across product classes as found in prior studies (Girard et al., 2002; Korgaonkar et al., 2006). However, testing the individual relationships was not one of the objectives of this research. Second, besides the overall-risk, the levels of the six types of risk were perceived significantly lower for search products than the other product classes. Another important finding was that the perceivedrisk increased from search to experience-2 to experience-1 to credence product classes (H2–H3). This result can be explained with the result found in testing H4. In H4, the time risk was higher than the other risk types for the experience-1 products, and the overall-risk was found to be the highest for the credence products in H6. This means that consumers perceive higher time loss/convenience risk for experience-1 and credence products than for search and experience-2 products. Therefore, time risk perceived by consumers makes it more challenging for retailers to sell experience-1 and credence products. While the financial, performance, physical, and time risk were perceived the highest for credence products, experience-1 was the second-highest.

Fig. 1. Structural-equation-model of the relationships.

a b

Standardized-regression-coefficient. p b 0.01.

For experience-2 products, financial, performance, physical, and time risks were perceived higher than psychological and social risks as expected (H5). This means consumers perceive that information search to find relevant attribute information is indeed more costly/ difficult for experience-2 products than directly experiencing them (Klein, 1998). The assumption that consumers do not normally have the knowledge and experience of the experience-2 products is thus confirmed. Consumers perceive higher financial, performance, physical, and time loss/convenience risks than psychological and social risks that concern their feelings of self-image and self-esteem. Third, consumer risk perceptions have a negative relationship with purchase intentions for Internet and local retailers (H9). The findings of other studies provide additional support that perceived risk is significant in explaining shopping intentions of Internet shoppers. For example, although Forsythe and Shi (2003) do not integrate product classes in their study, they find that perceived financial risk is the most consistent while time loss/convenience-risk is a significant predictor of Internet patronage behavior. Consistent with the findings of Bhatnagar et al. (2000), perceived risk is negatively related to patronage intentions. Fourth, consumer patronage intentions for Internet and local retailers were found to differ significantly across the SEC-products as hypothesized in H7. A further insight gained from this finding was that patronage intentions for Internet and local retailers were the highest for search products, followed by experience-2, followed by experience-1, and lowest for credence products. These findings are parallel with those of consumer risk perceptions but in the negative direction (i.e., lowest risk for search, then experience-2, then experience-1, and highest for credence). The purchase intentions to buy experience-1 and credence products from Internet retailers did not significantly differ, which also coincide with the results of H6. Consumers perceived the same amount of financial, performance, and time risk for experience-1 and credence products. Purchase intentions to buy experience-1 and experience-2 products from local stores were not significantly different (Table 7). When it involves visiting physical stores to buy these products, consumers perceive certain types of risk, especially the time risk as was confirmed in testing H4. Finally, a positive association exists between importance placed on retailer attributes and risk perceptions (H8). This can be interpreted as positive correlations rather than a causal relationship. The lack of examination of the individual relationships between retailer attributes and risk perceptions in product classes still remains a huge gap in the literature and warrants further attention. A preliminary theoretical effort was made by Mitchell (2001) by pointing out that some studies link store attributes to consumer needs and motives. In addition, the findings of research (Prasad, 1975; Phau and Poon, 2000; Sharma et al., 1983) suggest a positive relationship with consumer perception of different types of risks. Therefore, the studies testing which store attributes are related to which types of risk are needed.

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6. Implications A few implications are drawn from the conclusions. First, because differences in the importance of retailer attributes were not significant to online shoppers across the SEC-products, retailers should pay equal attention to their attributes when they sell products from different classes. Retailers with good reputations for practicing important attributes can reduce consumer risk perceptions in product classes as confirmed with the results of H8. More specifically, they can make it easy to exchange purchases, return merchandise for fullcredit, sell dependable products, protect personal information against identity-theft, keep personal information confidential, make purchasing with a credit card safe, and provide information about features of products/services. Second, overall-risk perceived was the lowest for search and the highest for credence products as expected; however, it was higher for experience-1 than for experience-2 products. That may be because having to directly experience a product to be able to evaluate its relevant attribute information (e.g., quality, performance, price, dimensions, size, color, style, warranty) was perceived significantly riskier than obtaining the relevant attribute information through more costly/ difficult mental processes. This finding suggests that placing experience2 products before experience-1 products on a continuum may be more appropriate to capture the increasing level of uncertainty, cost/difficulty in evaluating the products prior to purchase. In addition, because credence products were found to carry the highest risk level, followed by the risk level for experience-1 products (that require testing/sampling the product prior to purchase), reducing perceptions of time as well as financial, performance, and physical risk becomes necessary for retailers. For example, providing information through efficient and effective customer service when a problem arises will help reduce the time risk. According to a report by eMarketer, online shoppers increasingly “…expect more from the online shopping experience. Retail websites must have powerful search and navigation, quality product information, simple checkout and cross-channel shopping options” (Grau, 2008). A web-analytics report by Pixel-fx.com indicates that the average time spent on a website is 60 seconds. Internet retailers are advised to give customers response options of one-hour, two-hours, six-hours or 24-hours. “In the absence of an expectation . . . people expect an immediate response” (Riedman and Cuneo, 1999, p.32). Delivering products to consumers in a timely manner also reduces time loss/conveniencerisk by Internet retailers. Retailer reputation becomes more important in selling credenceproducts because credence product attributes are the hardest to evaluate and cannot be evaluated for a considerable period of time. Retailers can provide customer ratings and evaluations of these products to reduce these risk perceptions. Especially, a less known retailer that has not built reputation can provide third-party reviews and/or customer-testimonials regarding product/service performance of the credence and experience-1 products, design its website to build a professional image, and ensure the website has easy navigation and usability. These retailers face the challenge of not only matching but exceeding their competitors' offerings. For experience-2 products (for which finding the attribute information is more difficult/costly), because it is now safe to assume that consumers do not normally have the knowledge and experience with them, retailers should sell dependable products and make the relevant product and/or service information be easier and less costly to obtain. Third, although consumer patronage intentions for Internet and local retailers were found to be significantly influenced by the SECproducts, patronage-intentions for Internet retailers were not significantly different between experience-1 and credence products (Table 8). That may be because a disadvantage of purchasing products from an Internet retailer is the inability to touch or experienceproducts prior to purchase. Because experience-1 products require

“more tactile cues for their evaluation” (Citrin et al., 2003) and the uncertainty is the highest for the credence products, consumer intentions to purchase these products online not only are lower for these products but they do not differ significantly (Table 7). The inability to experience and evaluate the experience-1 and credence products may also be the reason for consumers to perceive the same magnitude of time loss/convenience risk (Tables 4 and 6). These findings suggest that Internet retailers should provide salespeople to consult with while shopping online in addition to providing information about features of products/services and evaluations from other shoppers to reduce perceived performance risk. Internet retailers must also provide the ability to compare prices between retailers to reduce financial and time loss/convenience risk. This strategy necessitates that Internet retailers offer price-matching guarantees in order to compete with other Internet and local retailers. Similarly, consumer patronage intentions for local stores were not significantly different between experience-1 and experience-2 products (Table 8). That may be because of the advantage that local retailers can provide customer service for exchange/return of products easier than an Internet retail store can. 7. Limitations and suggestions Because the findings are generalizable only to the U.S. online shoppers, no implications can be drawn for offline shoppers. What is currently lacking in the retailing literature is research that examines the differences between online and offline shoppers in their importance of retailer attributes, risk perceptions in product classes, and patronage intentions for different types of retailers to purchase different types of products. The same relationships can be tested with mall shoppers in various malls in the U.S. to bridge this gap in the literature. Additionally, because of the space limitations, this study does not attempt to test specific and detailed relationships between the antecedent factors—retailer attributes and perceived risk—of patronage intentions. It only validates the SECproduct classification as a salient framework to study in a theory of patronage intentions. The model's predictive power was found to be significantly high and the relationships were in the expected directions. Using this framework in future research will suggest better methods for product and customer relationship management in the online and offline retail industry than the conventional product classification framework. References Axelsson K. Exploring relationships between product characteristics and B2Cinteraction in electronic-commerce. J Theoretical Appl Elect Commerce Res 2008;3(20):1-17. Bauer R. Consumer behavior as risk-taking. In: Hancock RS, editor. Dynamic mark changing worldChicago: Amer Mark Assoc; 1960. p. 389–98. Bhatnagar A, Misra S, Rao RH. On risk, convenience, and Internet-shopping behavior. Comm ACM 2000;43(11):98-105. Chaudhuri A. Product-class-effects-on-perceived-risk: the-role-of-emotion. Intl J Res Mark 1998;15:157–68. Citrin AV, Stern DE, Spangenberg ER, Clark MJ. Consumer need for tactile input: an internet retailing challenge. J Bus Res 2003;56(11):915. Copeland MT. Relation of consumers' buying habits of marketing methods. Harv Bus Rev 1923;1:282–9 April. Darby MR, Karni E. Free competition and the optimal amount of fraud. J Law Econ 1973;16:67–86 (April). Dholakia UM. An investigation of the relationship between perceived risk and product involvement. Adv Consum Res 1997;24:159–67. Eastlick MA, Feinberg RA. Shopping motives for mail catalog shopping. J Bus Res 1999;45:281–90. Ford GT, Smith DB, Swasy JL. An empirical test of the search, experience and credence attributes framework. In Adv Consum Res 1988;15:239–43. Forsythe SM, Shi B. Consumer patronage and risk perceptions in Internet shopping. J Bus Res 2003;56(11):867–75. Georgia Tech Univ 2001. GVU's 10th WWW Survey. http://www.gvu.gatech.edu/ user_surveys/survey-1998-10/. Girard T, Silverblatt R, Korgaonkar P. Influence of product class on preference for shopping on the Internet. J Comp-Mediated Comm 2002;8 October.

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