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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

This study examines how consumer information processing affects consumers' perception of risk prior to purchase. In particular, this research focuses on pre-purchase information such as brand, word-of-mouth, and customized information. The results show that customized information and word-of-mouth communication influence consumers more than do other types of information from online auctions. Consumers rely on these two factors because they are based on consumer experience and relevant to product purchase. Nevertheless, brand also has a significant effect upon consumer perceived risk. Pre-purchase information processing is directly related to reducing consumers' risk perception. In particular, information processing associated with product performance plays a crucial role in reducing consumers' perceived risk in online transactions. The results offer insights to e-marketers and e-marketing researchers about the role of pre-purchase information in management and e-commerce.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

The remarkable development of the Internet has drastically expanded the shopping space for a number of consumers. According to a Fast Company survey, Internet buyers as a percentage of all Internet users grew from just 19 percent in 1995 to 71 percent in 1999 (Kania, 2001). Not only has Internet shopping created a new marketing provider that meets consumer needs and wants, but it has changed the consumer shopping culture with “click” shopping and ease of information searches for product or service buying. Nevertheless, unlike traditional commerce, e-commerce involves various risks (e.g., product performance, credit card information) that consumers perceive (Salisbury, David, Pearson, Pearson & Miller., 2001; Tan, 1999), and thus consumers are engaging in risk reduction behaviors based on a wealth of information (Häubl & Trifts, 2000; Krishnamurthy, 2001).

Many consumers today, regardless of their intention, are exposed to a great amount of information through both off-line and on-line advertising. Providing effective information in online environments can reduce consumer search costs and lead to consumers making optimal purchasing decision. A large body of research related to information or perceived risk exists in the marketing literature. For example, Häubl and Trifts (2000) argued that interactive decision aids (e.g., recommendation) designed to help consumers in the initial screening of available products and to facilitate in-depth comparisons among selected alternatives may have highly desirable properties in terms of consumer decision making. Tan (1999) shows that consumers perceived Internet shopping to be higher risk than in-store shopping; hence only less averse consumers are more likely to use Internet shopping service. She also shows a close correlation between risk aversion and Internet shopping tendency. The several information resources – particularly brand, word-of-mouth, and customized information-act as guides that can reduce risk and facilitate consumer choice (Krishnamurthy, 2001). In particular, the information in new purchase situations is far more important for first-time customers than for existing customers. Henthorne, Latour & Williams (1993) and Tan (1999) reported that to reduce risk consumers seek out reference group appeal (e.g., particularly, the experts who relate to Internet fields, rather than the common man); users may constitute such informal influence, particularly in new buy situations in which they don't experience yet.

Despite the assumption that pre-purchase information can lower a consumer's risk (Tan, 1999), existing research on consumer behavior on the Internet has focused on Internet purchasing (Ha, 2001; Salisbury, David, Pearson, Pearson & Miller., 2001) or on information searching through the Internet (Kozinets, 1999; Rowley, 2000). There have been few studies associated with consumer perceived risk that uses this information (Tan, 1999; Ha, 2001). However, these studies have not provided useful insights for reducing consumer perceived risk because they did not consider a variety of sources of consumers' per-purchase information such as brand, word-of-mouth, and customized information. According to Deighton & Barwise (2000) and Shankar, Smith and Rangaswamy (2000), these three constructs may affect consumer risk perceptions associated with online buying. Accordingly, pre-purchase information is a crucial tool because it reduces consumer risk, increases brand comprehension, and helps consumer make a brand choice.

The goal of the study reported here was to examine how consumer perceived information affects consumer perceived risk under pre-purchase conditions. This study is closely related to that of Kivetz and Simonson (2001). Their findings indicate that missing information can affect buyers' tastes and purchase decisions made subsequently. Perhaps the findings of the current project might offer greater insight (e.g., correlation between consumer perceptions and pre-purchase information) to e-marketers and e-marketing researchers about the role of pre-purchase information in management and e-commerce.

The rest of the paper is organized as follows. We first review previous literature, set up research hypotheses, and develop a conceptual model that addresses the relationship between consumer pre-purchase information and consumer perceived risk in e-commerce environments. Subsequently, we describe the sample and measures employed in the study, then report the empirical research results. Finally, we identify limitations of the study and propose future research directions

Theoretical Background, Hypotheses, and Model

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

To the extent that a consumer cannot always be certain that all of his or her buying goals will be achieved, risk is perceived to be a factor in most purchase decisions. In fact, much of the work on risk taking indicates the perceived risk is little more than unresolved tension due to opposing vectors or forces. Risk emerges from any of the following factors (Cox, 1967a, 1967b):

  • 1
     Uncertainty as to buying goals
  • 2
     Which of several purchases (product, brand, model, etc.) best matches the buying goals
  • 3
     Possible adverse consequences if the purchase is made (or not made)

The concept of perceived risk often used by consumer researchers defines risk in terms of the consumer's perceptions of the uncertainty and adverse consequences of buying a product (or service) (Dowling & Staelin, 1994). In this study, perceived risk is defined as comprising the following components: financial, psychological, performance, time, social, and time-related risk (Stone & Gronhaug, 1993). Consumers are credited with the capacity to receive and handle considerable quantities of information and undertake extensive pre-purchase searches and evaluations. In particular, the present project investigates the perceived risks associated directly with pre-purchase information: performance risk, financial risk, psychological risk, and time risk. These may be present in any combination and in different degrees for any given purchase (Gemunden, 1985).

  • 1
     Performance risk is defined as the loss incurred when a brand or product does not perform as expected (Horton, 1976). Performance risk occurs when the product chosen might not perform as desired and thus not deliver the benefits promised. This integrates the future quality of the product to the point of purchase.
  • 2
     Financial risk is defined as a net financial loss to a customer, including the possibility that the product may need to be repaired, replaced or the purchase price refunded (Horton, 1976). This is an extension into the future (future dollar costs) of the perceived price paid at the point of purchase (current dollar cost). Where the loss of money is an important consideration, financial risk is said to be high.
  • 3
     Psychological risk broadly describes instances where product consumption may harm the consumer's self-esteem or perceptions of self. In this study, psychological risk perception is defined as the experience of anxiety or psychological discomfort arising from anticipated postbehavioral affective reactions such as worry and regret from purchasing and using the product (Perugini & Bagozzi, 1999; Dholakia, 2001): for example, protecting privacy according to individual information exposure.
  • 4
     Time risk results when the passage of time reduces the ability of the product to satisfy wants, such as when a product rapidly becomes obsolete (Ross, 1975). In this study, the perceived cost with respect to customers' information search activities was used as a type of operational definition.

The amount of risk consumers perceive is a function of many variables, and consumer have many remedies when it comes to reducing the amount of risk they perceive associated with product purchase on the web. Search activity is entered into with the intent of lowering the person's overall perceived risk level. The nature of the search activities undertaken (and thus the amount of search) is a function of the person's acceptable risk level, the levels of the two components of perceived risk, the costs and benefits of the specific available risk-reduction activities, and the ability of the person to suffer a loss (Dowling & Staelin, 1994).

Definition of Pre-Purchase Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

Pre-purchase information will be defined as a series of data processed according to consumer-specific purposes. Consumers have special characteristics that recognize optimal information from resources and consumers act depending on their own given situation (Hoffman, 1998). In particular, the ability to collect product information and make comparisons between the different product offerings from different providers—possibly across national and currency boundaries—is often viewed as one of the main competitive challenges of e-shopping. To enhance consumer pre-purchase information processing, companies offer other information sources to consumers (Dholakia, Zhao, Dholakia & Fortin, 2000; Häubl & Trifts, 2000). Alba et al. (1997) have shown that the retailer or manufacturer on the web should provide consumer-customized information so that the consumer evaluates alternatives in the consideration sets. Accordingly, the web retailer or manufacturer must provide more appropriate information to attract, meet, and exceed consumer expectations than must in-store retailers.

The first stage in the consumer buying process is the information search. Consumers collect and evaluate information through consumer reports, magazine advertising, brand name, word-of-mouth communication, and customized information. The advertising in magazines is intended to improve brand awareness. Although consumers find much information through magazines, the main reason that they use the web is to collect optimal information. Accordingly, in this study information obtained from advertising in magazines can include components of brand and customized information. Furthermore, pre-purchase information within consumer consideration sets has an important effect on consumer buying decisions (Hoyer & Brown, 1990; Nedungadi, 1990).

Information search activity is entered into with the intent of lowering the consumer's overall perceived risk level. The important resources that influence consumer perceived risk are the following (Berthon, Hulbert & Pitt, 1999; Foxall, Goldsmith & Stephen, 1998; Harris et al., 1999; Jarvenpaa & Todd, 1997):

  • 1
     Their own experience with a product or brand
  • 2
     Recommendation (word-of-mouth) from family, friends, and colleagues
  • 3
     Previous imprinting as a result of promotion, usually in association with a specific brand

In addition to quantity of information, the quality levels of information can work as a critical factor that controls appropriateness of decision-making (Keller & Staelin, 1987). Consumers are likely to employ a phased decision process, first filtering available alternatives and then undertaking detailed comparison of the reduced consideration sets. This typical decision strategy requires quantity and quality of information. Quantity of information is important because it helps consumers form their consideration sets of alternative brands. Quality of information about brands refers to accurate and current information and is essential when consumers need to make their final choices. Particularly, quality of information refers to the usefulness of the available attribute information in aiding a decision maker to evaluate his/her true utility associated with an alternative. On the other hand, Malhotra (1984) and Jacoby (1984) disagree on the importance of the information overload paradigm, with Jacoby criticizing use of the paradigm due to his belief that the key issue is not if consumers can be overloaded, but rather will consumers be overloaded. In adopting this position, Jacoby maintains that consumers are selective in the amount and nature of the information that they obtain, suggesting that factors that impact this selectivity should be examined.

In particular, consumer information processing in the pre-purchase context plays an important role in reducing consumer perceived risk or uncertainty (Mitchell & Boustani, 1994). In terms of purchasing a particular product, a consumer is aware of some risks such as finance, psychology, performance, and time. Social and physical risk in online commerce have less to do with consumer perceived risk. Offering optimal information, recalling brand information, and utilizing vivid word-of-mouth communication must reduce perceived risk and uncertainty and, ultimately, exert a positive effect on product purchase intentions. Accordingly, a large number of consumers participate in positive pre-purchase information collection processing in order to reduce the risk. Furthermore, pre-purchase information acquisition may alert consumers to risks and pitfalls within the product choice of which previously they had been unaware (Mitchell & Boustani, 1994). Recent research has indicated the presence of two general types of uncertainty: knowledge uncertainty (uncertainty regarding information about alternatives)and choice uncertainty (uncertainty about which alternative to choose). Choice uncertainty appears to increase the search, while knowledge uncertainty has a weaker, negative effect (Urbany, Dickinson & Willkie, 1989). Pre-purchase risk reduction essentially focuses on increasing the amount of certainty that a satisfactory product will be purchased as well as reducing the negative consequences

Brand as Information Source

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

Brand information processing is defined as the extent to which consumers allocate attention and processing resources to comprehend and elaborate on brand information in an ad. “Brand information” is defined as any executional cue designed to communicate the advertised message (Maclnnis, Deborah, Moorman, Christine & Jaworski, 1991). In the context of product information collection, brand names are particularly useful keys because the brand name becomes so closely tied to the product in the minds of consumers (Keller, 1998). They are unique and international terms. Indeed, the use and evolution of brand in the context of an international but virtual e-commerce marketplaces is a topic in itself (Berthon, Hulbert & Pitt, 1999). Consumers who purposely purchase through e-commerce usually obtain information about a specific brand through various sources (Rowley, 2000). In particular, an understanding of dimensions of perceived risk enables marketers to present their brands to instill consumer confidence (Assael, 1995). Foe example, acting as a guarantee of consistent quality, a brand reduces performance risk. However, uncertainty of information and inherent consumer risk in e-shopping produce far greater feelings of uneasiness than does in-store shopping (Tan, 1999). Potential forces regarding building a familiar brand in the online and offline marketplaces influence the amount of risk perceived in a given purchase. As more information is made easily available to consumers, and they are given opportunities to reduce the consequences of choice, the lower the perceived risk such as performance, financial, psychological, and time risk (Jarvenpaa & Todd, 1997, Mitchell, 1999). Consequently, a high possibility exists that positive information for a specific brand decreases perceived risk for that brand, whereas negative information for considered brands solidifies consumer risk perceptions more and more, and it may increase the possibility that consumers will switch from the considered brand to other competitive brands (Nedungadi, 1990; Nedungadi, Chattopadhyay & Muthukrisnan, 2000). Nevertheless, these researchers have only investigated by separating brand from risk; as yet, few researchers have studied the causal relation between brand and perceived risk. Thus, in terms of brand information, we present the following hypotheses:

  • H1a: Positive inclination towards a specific brand can lower the performance risk perception for a brand purchase by the consumer.

  • H1b: Positive inclination towards a specific brand can lower the psychological risk perception for a brand purchase by the consumer.

  • H1c: Positive inclination towards a specific brand can lower the financial risk perception for a brand purchase by the consumer.

  • H1d: Positive inclination towards a specific brand can lower the time-loss risk perception for a brand purchase by the consumer.

Word-of-Mouth as Information Source

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

Word-of-mouth is commonly defined as informal communication about the characteristics of a business or a product which occurs between consumers (Westbrook, 1987). Most importantly, word-of-mouth allows consumers to exert both informational and normative influences on the product evelautions and purchase intentions of fellow consumers (Bone, 1995; Ward & Reingen, 1990). Consumers can acquire information for buying specific products through word-of-mouth communication called ‘cyberbuzz’ on the Internet (Herr, 1991). Consumers tend to trust word-of-mouth communication with a reference group more than they do commercial information resources in estimation of brand alternatives (Hartline & Jones, 1996; Herr, Kardes & Kim, 1991; Iglesias, Belen & Vazquez, 2001; Parasuraman, Zeithaml & Berry, 1988), frequently respecting word-of-mouth as a means to reduce risk in making purchase decisions. According to Lazarsfeld (1955), word-of-mouth has a more important effect than other information resources on perceptions with respect to food and household appliance purchases. Also, a study by Parasuraman, Zeithaml & Berry (1988) showed that when consumer perceptions of service quality are high, consumers are willing to recommend the company to others. Why does information by word-of-mouth have an effect upon consumers? One of several possible explanations is the vividness of such information (Herr, Kardes & Kim, 1991; Arndt, 1967). Word-of-mouth information is fresher because first-hand experience is passed directly to other people. Accordingly, word-of-mouth communication is retrieved more easily from memory and its impact on consumers is relatively greater (Givon, Mahajan & Muller, 1995; Herr, Kardes & Kim, 1991).

Consumers are generally influenced by other people's opinions when in a purchase state of high involvement. Therefore, consumers are apt to depend more on information from word-of-mouth communication in the following situations:

  • 1
     When transparency of the product is high
  • 2
     When the product is complicated
  • 3
     When verification by objective evaluation criteria is difficult
  • 4
     When perceived risk is high

In spite of complaints about information overload, the primary reason people go online is to find information, because the Internet has become popular as a resource for gathering information about risk reductions and purchases.

There are many cases of negativity bias rather than positive effects resulting from word-of-mouth. The negative information of word-of-mouth communication exerts a stronger influence on the decision-making process than does positive information. For example, Ford Motor Company found that satisfied customers told 8 people about their cars, but dissatisfied customers told 22 people about their complaints (Dorlin, 1985). Other investigations have shown that more than half of dissatisfied consumers participate in negative word-of-mouth communication and consumers usually pay more attention to negative information than to positive information (Harrison-Walker, 2001; Mizerski, 1982; Richins, 1983). Therefore, even though the tendency of word-of-mouth communication to cause negative effects is high, this study investigates whether positive word-of-mouth communication might work as a factor in influencing consumer perceived risks (e.g., performance, finance, time, and psychology), because recent research has shown that for e-customers there is a strong correlation between word-of-mouth and consumer risk perception (Jarvenpaa & Todd, 1997; Liang & Huang, 1998; Tan, 1999).

Even though much prior research associated with word-of-mouth communication exists, no studies have explained the causal relationship between word-of-mouth communication and perceived risk. Thus, in terms of word-of-mouth information, we present the following hypotheses.

  • H2a: Positive word-of-mouth can lower the performance risk perception for a brand purchase by the consumer.

  • H2b: Positive word-of-mouth can lower the psychological risk perception for a brand purchase by the consumer.

  • H2c: Positive word-of-mouth can lower the financial risk perception for a brand purchase by the consumer.

  • H2d: Positive word-of-mouth can lower the time-loss risk perception for a brand purchase by the consumer.

Customization as Information Source

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

In this study, we define customized information as “offering optimal self-relevance information for each segmented customer based on experiences of existing or membership customers.” Most customers are likely to pay greater attention to messages that relate deeply to their own interests. More specifically, Meyvis & Janiszewski (2002) reveal that irrelevant information weakens consumers' beliefs in the product's ability to deliver the benefit. The customized information by customer segmentation in e-commerce offers optimal information to individual customers (Wind & Rangaswamy, 2001) and reduces the perceived risk associated with their purchase of a specific web brand (Häubl & Trifts, 2000). Unlike in-store purchases, on-line purchases allow companies to reduce customer perceived risks—financial, time, performance, and psychological risk—by providing customized information to each customer (Dholakia, Zhao, Dholakia & Fortin, 2000; Krishnamurthy, 2001). Krishnamurthy (2001) has noted that consumers are greatly interested in messages relevant to themselves. That is, consumers add value to the relevance of promotional messages. This factor is closely related to customization of information to each customer because if this information seems negative to the customer, there will be a loss of interest in the information or the firm. More recently, customized information is being changed by another new development in Internet technologies; namely, virtual 3-D products. For example, shoppers may rotate a 3-D image of a product, zoom in and out for inspection, animate features and functions of the product, and even change the color or contextualization with other products in different settings (Kania, 2001; Li, Daugherty & Biocca, 2001). In the context of product design, for example, the 3-D model can be adapted to resemble a customer's body shape and then dressed with clothing of interest to that customer (e.g., IC3D.com). Ha (2002) found that customized information about 3-D products or advertising directly affects performance risk and financial risk.

Such customized information is facilitated greatly by relationships between the company and the customer (Berry, 1995; Sheth & Parvatiyar, 1994). Such relationships consist of a series of repeated exchanges with both parties known to each other: they evolve in response to these interactions and to fluctuations in the contextual environment. To establish relationships with online customers, it is imperative that a firm understands the user experience and how people interact with the web (Nielsen, 1999). For instance, Amazon is able to reduce product search costs or other risks by providing product information matching customer tastes through analyzing an existing customer's purchasing tendencies when the customer re-connects.

The researchers cited above have studied interaction between company-customer relationships and customized information, but to date have produced no studies of causality between customized information and perceived risk. Based on the preceding research, this study presents the following hypothesis.

  • H3a: Providing customized information can lower the performance risk perception for a brand purchase by the consumer.

  • H3b: Providing customized information can lower the psychological risk perception for a brand purchase by the consumer.

  • H3c: Providing customized information can lower the financial risk perception for a brand purchase by the consumer.

  • H3d: Providing customized information can lower the time-loss risk perception for a brand purchase by the consumer.

Figure 1 shows a conceptual model path based on these hypotheses. Consumer perceived risk as a dependent variable appears to be more solidified for online purchases than for in-store purchases. Risk here can be defined as an expectation of loss (Stone & Winter, 1987). That is, the more certain one is about this expectation, the greater the risk for the individual. The perceived risk in this study can be described as an important result variable that evaluates the information for product or brand. Consumer risks within Internet environments are characterized as follows: 1) financial risk (Ha, 2001; Schaninger, 1976), expressed as financial loss1 from a buying decision made on the web site; psychological risk (Greatorex & Mitchell, 1993), a negative effect on consumer image or privacy because of a product purchase; 3) performance risk (Dowing & Staelin, 1994; Ha, 2001), when products or services purchased on the Internet do not meet consumer expectation; and 4) time-loss risk (Stone & Gronhaug, 1993; Mitchell, 1999), occurring from the need to re-purchase if the original purchase was unsatisfactory or from the time spent searching for optimal information (Ward & Lee, 2000).

image

Figure 1. Conceptual path model of hypotheses measurements.

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Method

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

The information necessary to carry out the empirical study was collected through data sampling using the 2001 Internet marketing research homepage in South Korea. Marketing research on the Internet was considered to be more useful than questionnaire, interview or experimental methodology offline. One admitted shortcoming was that the collection or participation rate was lower than it might have been using other methodologies; however, the research method was advantageous in that it offered statistically significant data at reasonable cost (Kim, 2001; Johnson, 2001; Ranchhod & Zhou, 2001). Of particular relevance is that all e-mail communications have a date, time stamp, and address. This proves to be important because researchers are able to contact the subjects and return questionnaires that were not completely filled out. There is also a time savings when effort usually spent on affixing postage, stuffing envelopes, and printing is obviated through electronic mailings. The most important advantage of Internet research is that it brings researchers closer to their sample. Because of the interaction with respondents, researchers can begin to develop a sense of the respondents' personalities and what subjects interest them (McMellon & Schiffman, 2001).

Specifically, this research was conducted on Internet auction participants whose consumer involvement and risk perception were high, because high involvement means higher perception of risk than low involvement (Zaichkowsky, 1990). A total of 128 persons participated voluntarily, resulting in 124 valid surveys. To improve the reliability of the response, a pre-test was carried out to detect any necessary changes in the wording of the items and the range to be used for item evaluation. As a result of this process, a total of 13 items were selected. The items are presented in the Appendix. All of the variables considered were measured on a 5-point Likert scale (from 1=completely disagree to 5=completely agree).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

The reliability analysis of the scales yielded favorable results. The constructs exhibited a high degree of reliability in terms of coefficient alpha. Most values exceeded the recommended value of Cronbach's alpha 0.62 (Choi, 2001; Malhotra, 1993). Table 1 presents the results of the reliability analysis (see Appendix).

Table 1.  Results of reliability analysis.
 Cronbach's alpha
Brand Information (3 items)0.75
Word-of-Mouth Information (3 items)0.62
Customized Information (3 itemss)0.64

To verify unidimensionality and evaluate the nomological validity of these multi-item constructs, the following two analyses were conducted applying the dimension divisibility method presented by Singh & Rhoads (1991). First, for each factor whose reliability had been confirmed in a former analysis, we executed factor analysis using the maximum likelihood method through direct oblimin. The evaluative criteria of factor analysis were set at over 0.3 of factor loadings and over 0.5 of variance extracted (Singh, 1991). All items satisfied these evaluative criteria (see Table 2).

Table 2.  Results of factor analysis for dimension divisibility of structural equation model.
Research ConceptItemFactor LoadingVariance Extracted
 Recognition.451 
 Goodwill.747 
Brand informationReputation.80566.79%
 Reliance.706 
 Interesting.487 
Word-of-MouthRecommendation.51556.90%
 Benefit.454 
 Attention.671 
Customization InformationInterested Item.45352.58%
 Performance.579 
 Privacy.602 
Risk PerceptionCredit Card.56057.85%
 Search Costs.563 

Next, we performed a path analysis relating each of the dimensions to the consumer pre-purchase information to evaluate the perceived risk in the web environment. As illustrated in Table 3, results obtained for this model showed an excellent fit.

Table 3.  Summary statistics of model fit.
 Structural equation model
  1. Notes

  2. 1CFI, NFI, IFI, and RFI close to 1 indicate a good fit.

  3. 2The lower the RMISA values, the better the model is considered to be.

Chi-Square (X2)8.13
Degrees of Freedom (df)6
 X2/df1.354
 CFI10.999
 NFI10.997
 IFI10.999
 RFI10.988
 RMSEA20.054

For the path analysis of the research model, we tested the measurement models for each construct by specifying an appropriate model using AMOS 4.0 (Heo & Choi, 2000). The discriminant validity among the exogenous factors is apparent, since the largest correlation is that between word-of-mouth communication and customized information (0.14). The correlations between brand, word-of-mouth communication, and customized information are 0.12 and 0.11, respectively (see Figure 2).

image

Figure 2. Results of the structural model for interaction among measured factors.

Notes: The standardized parameters are shown. (*, **) Parameter is significant at a confidence level of p < 0.05* and p <0.01**.

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With respect to brand information, customers who were recognizing a specific brand or brand reputation showed a decreased concern with time-loss risk and product performance risk when purchasing a product from the web site. However, they still manifested awareness of psychological risk and financial risk. This finding suggests that individual fear of private information being exposed and/or credit card security being violated remains constant on Internet auction sites. Thus, H1a and H1d were supported, but H1b and H1c were rejected (see Table 4 and Figure 2).

Table 4.  Model measures for reducing consumer risk perceptions.
H1aBrand[RIGHTWARDS ARROW]Performance0.1963.04*
  1. Notes: (*, **) Parameter significant at a confidence level of p < 0.05* and p<0.01**. Non-significant path loadings are indicated with (ns).

H1bBrand[RIGHTWARDS ARROW]Privacy-0.045-0.67(ns)
H1cBrand[RIGHTWARDS ARROW]Credit Card-0.042-0.78(ns)
H1dBrand[RIGHTWARDS ARROW]Search Cost0.1882.35*
H2aWOM[RIGHTWARDS ARROW]Performance0.2343.63**
H2bWOM[RIGHTWARDS ARROW]Privacy0.0220.33(ns)
H2cWOM[RIGHTWARDS ARROW]Credit Card-0.076-1.43(ns)
H2dWOM[RIGHTWARDS ARROW]Search Cost-0.107-1.34(ns)
H3aCustomization[RIGHTWARDS ARROW]Performance0.2394.14**
H3bCustomization[RIGHTWARDS ARROW]Privacy-0.219-3.61**
H3cCustomization[RIGHTWARDS ARROW]Credit Card-0.016-0.33(ns)
H3dCustomization[RIGHTWARDS ARROW]Search Cost0.3935.41**

Word-of-mouth communication was found to create trust and confidence as part of consumer pre-purchase information perception. In particular, word-of-mouth communication was significantly related to product quality relevant to consumer purchases. Because word-of-mouth communication transmits consumers' own experiences vividly to other consumers, the pervasive effect might be that this medium rather than any other instills in consumers greater confidence in product quality. The point to keep in mind here is that marketing managers should steadily focus their interest and attention on this medium and foster its favorability, because the community or forums on specific sites convey word-of-mouth communication or cyberbuzz to a great many users in real time. However, positive word-of-mouth communication did not significantly affect consumer awareness of psychological, financial, and time-loss risk. Despite consumer experience and positive word-of-mouth communication, these risks remain in the minds of consumers. Therefore, H2a was supported but the remaining hypotheses were rejected.

In terms of customized information, the term ‘consumer’ here means that the customer in question is not a new customer or a prospective one, but is an existing consumer who has made a purchase on the site. This factor, in particular, produced results whose implication is significant. That is, customized information is an extremely useful tool as an information source for consumers. Consumers are able to trust that their personal information will remain confidential, reduce costs for information searching, and increase their confidence in their own post-purchase privacy. This finding could provide useful insights for interested parties of auction sites on the web. Perceived risks other than financial risk were all significant because customers already had purchase experience on the site. However, customized information did not significantly affect perception of financial risk. This finding suggests that consumers still recognize financial risk as the greatest of the four. Thus, H3a, H3b, H3d were supported, but H3c was rejected.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

The goal of this study reported here was to examine the ways in which consumer-perceived information affects the assessment of risk prior to a purchase situation. The results of this research support Dowling and Staelin's finding (1994) that a consumer's perception of the riskiness of a purchase situation can be decomposed into an individual-specific risk factor and a factor associated with the uncertainties and potential adverse consequences of the product-category situation. Accordingly, information processing helps a consumer reduce his/her perceived risk in purchasing a product online. Auction participants are, in addition, deeply influenced by pre-purchase information. The separation of payment and delivery and the corresponding possibilities of opportunistic behavior on the part of the seller and concern about product performance greatly increase the risk for potential buyers compared with other web purchases (Standifird, 2001). To compensate for this increased risk, the buyer must have some assurance that the seller will not take unfair advantage (Kollock, 1999).

The results concerning consumers who recognize specific brands or brand reputations illustrated the usefulness of the information search and consumers' trust of product quality (Chaudhuri & Holbrook, 2001; Geisser, 2001; Tse, 1999). These results mean that brand information helps reduce consumers' performance and time-related risks associated with online purchases. In addition, most consumers expect a favored brand to provide comfort, familiarity, and trustworthiness, whether online or offline. For example, the presence of so many already familiar and reliable brands conducting business on the Internet, together with the fact that credit card issuers indemnify consumers against any online fraud, has assuaged their fears. In terms of the exposure of personal information and transaction risk on web sites, however, it appeared that brand awareness is limited in its ability to reduce customers' perceptions of risk (Hoffman, Novak & Peralta, 1998), whereas increasing brand familiarity through raising brand awareness is contingent upon reducing customer's perceived risk (Yoon, 2002). Therefore, marketers must develop new brands or web sites to provide consumers with maximum benefits in order to reduce customers' latent risks online or offline (Hartline & Jones, 1996; Selnes, 1993). For example, the Mining Company, a site where people help other people find what they need on the Internet, was renamed About.com in 1999 “to reflect its breadth of content, services, and ease of use” (Kania, 2001).

Word-of-mouth communication plays an important role in reducing consumer risk perceptions of product performance to a greater extent than any other information sources in e-commerce (Tan, 1999). With respect to reducing consumer perceived risk and uncertainty, word-of-mouth is more relied on by consumers than any other information, because it is based on consumer experience and is especially vivid. Vividly (as opposed to colorlessly) presented information tends to have a stronger influence on product judgment (Kisielius & Sternthal, 1996) and risk reduction. For example, eBay's Café, a chat room, posts a daily mix of remarks, user tips, sociable banter or complaints, good experiences, and even advice for the lovelorn. Moreover, the site is most active when consumers purchase products while in a situation of high involvement (Arndt, 1967). Consequently, marketers must participate actively in creating positive word-of-mouth, because dissatisfied customers will disseminate news of their bad experiences with the retailer (Harrison-Walker, 2001). That is, dissatisfied customers participate in negative word-of-mouth communication, and this means that new and existing customers become aware of a perceived risk or uncertainty for future purchase opportunities. Thus, marketers must effectively maintain and develop their website communities, forums, and feedback sites (Harrison-Walker, 2001; Mizerski, 1982; Richins, 1983) in order to retain satisfied customers and reduce their perceived risk. For example, the Internet makes communication less intimidating because web users can choose to be anonymous, take on another persona, or manifest their true selves from the privacy of their computers. On the other hand, as dissatisfied customers express their grievances the company, we suggest that marketing practitioners efficiently manage consumers' complaints so that negative feedback can be translated into opportunities for improvement.

The study reported here also provides useful insights into customized information. Customized information provides optimal information for each segmented customer, based on experiences of existing or membership customers. Even though they may be experienced with product purchase on the site, customers might still perceive there to be a risk for product purchase. That is, customer perceived risk for product transactions while e-shopping is still lower than the perceived risk for security. To reduce risk perception and provide effective customized information for customers, we suggest that companies first must provide detailed profiles for their products. For example, besides auction sites, existing e-stores should supply clear and accurate descriptions of their products, their return policy, comparison prices, and warranty information. However, consumers tend to dislike lengthy explanation. In terms of psychological risk, more specifically, e-marketers should routinely inform consumers when individual-specific information is collected, let them know how the information will be used, and tell them who will have access to the data. Other visible steps, such as implementing periodic consumer reviews, also would benefit both parties (Nowak & Phelps, 1997). Second, companies must commit to a warranty policy on special services and product quality for customers who have bought the product through customized information offers. The costs of keeping an existing customer are far lower than those of gaining a new customer (Krishnamurthy, 2001). In other words, the greater the loss of existing customers, the greater the costs expended on acquisition of new customers. Thus, because companies cannot provide similar customized information to all customers, they must provide efficient information to each target customer through customer segmentation. Third, marketers must identify consumer information needs and present information based on the role such information plays in the buying process. Fourth, marketers must help customers access information customized for and by themselves on their websites. At the Lands' End's website, for example, women can build 3-D virtual models based on their own physical measurements and physical features such as hair color, hair style, skin tone, and facial shape. They can then “try on” virtual outfits recommended by that Lands' End, or outfits that they have selected themselves. The model can be rotated for front, side, and back views (http://www.landsend.com). Finally, marketers must pay more attention to negative information. For example, Ostrom & Davis (1979) found that 80 percent of people weighted negative information more heavily than positive information, but 20 percent weighted positive information as more potent. Consequently, estimating how customers react to negative information from an average of all consumers' responses may not provide a full picture of consumers' reactions to negative news (Weinberger & Lepkowska-White, 2000).

Reults of the present study indicate that most consumers learn about product quality through brand reputation, word-of-mouth communication, and customized information. Their perceived risk related to product quality is low, whereas consumers still feel uncomfortable about using a credit card for the product purchase. To reduce this limitation on e-commerce, we suggest that companies should develop an absolutely safe verification system through partnership with well-known warranty firms, improve company-customer relationships, and foster customer satisfaction programs. Companies also must understand contextual marketing environments and continually improve point of contact management with their customers (Kenny & Marshall, 2000). Understanding the ubiquity of the Internet might increase contact with customers and thus enable the company to implement more effective customer targeting for market segmentation on the basis of the customer information.

In sum, the results of this study show that collecting and understanding a consumer's online pre-purchase information processing plays a crucial role in reducing consumer perceived risk or uncertainty. In particular, consumers appear most perceptive of risk when it comes to post-purchase product performance. The characteristics of online marketplace based on the separation of payment and delivery and the risk perception about product performance indicate that e-consumers perceive risk regarding their specific purchases. Thus, the perception of an appropriate pre-purchase information might reduce consumers' perceived risk and help clarify their purchase intentions. Finally, similarly to the findings of Dowling & Staelin (1994), when the perceived benefits are high relative to the perceived risks, it may be that the benefits completely dominate the risks.

Limitations and Future Research

The research reported here has admitted limitations. Because the study was focused on consumers' perceived information about Internet auction sites, the findings may not be generalizable to the many types of e-stores in e-commerce. Also, consumers often carry out information searches for themselves to supplement pre-purchase information search such as brand reputation, word-of-mouth, and customized information (Rowley, 2001). In particular, we overlooked other sources regarding risk perceptions: namely, communities, bulletin boards, 3-D advertisings, and so forth. Many customers may be interested in them and join voluntarily. Through these experiences, customers can perceive risks regarding the purchase of products or services. Finally, interaction impacts that can influence consumer perceptions such as satisfaction, trust, and loyalty are insufficient because they can significantly affect consumer perceived risk. Nevertheless, despite its limitations, this empirical research has shown that consumer perceived risk is closely affected by consumer pre-purchase information associated with product purchase through e-commerce.

Future research should focus on how interaction between pre-purchase information and post-purchase information influences consumer purchase decisions because post-purchase information processing based on the purchase performance may differ considerably from pre-purchase information processing. Theoretically, having found differences in the pre- and post-purchase information processing stages, this provides encouragement to investigate in more detail changes in risk perception and reduction throughout the whole consumer decision process (Mitchell & Boustani, 1994). Without a doubt such interaction will have a profound effect over time on brand success and consumer satisfaction with the e-commerce experience. Accordingly, it is hoped that future research will produce a theoretical framework for the relationships among information, perceived risk, and online consumer behavior.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References

We thank Vincent-Wayne Mitchell, the two anonymous JCMC reviewers and Co-Editor Margaret McLanghlin for their insightful comments on the initial draft of this article.

Appendix

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References
VI. Brand Information
  • V1.1: I know information about brand XXX.

  • V1.2: Brand XXX gives me a feeling of goodwill.

  • V1.3: Brand XXX is a well-known brand.

V2. Word-of-Mouth
  • V2.1: I trust specific word-of-mouth associated directly with my purchase.

  • V2.2: I am interested in information I receive through word-of-mouth communication.

  • V2.3: I can believe information I acquire through friends' or colleagues' recommendations.

V3. Customized Information.
  • V3.1: XXX site provides useful information to me.

  • V3.2: XXX site provides specific information relevant to me.

  • V3.3: I pay attention to specific information sent by e-mail from XXX site.

  • V4: I feel at low risk for product performance/quality on XXX site.

  • V5: I feel at low risk for privacy exposure on XXX site.

  • V6: I feel at low risk for the use of credit cards on XXX site.

  • V7: I do not spend much time searching for products on XXX site.

  • Note: All items refer to the product category of auction sites.

Footnotes
  • 1

    For security in B2C transactions, most consumers and firms prefer payment by credit card to individual check or other payments. Unlike the U.S. or U.K., all e-auction firms in South Korea absolutely prefer payment by credit card to ensure the safety of transactions for their customers. Further, according to the KNP Survey (2001), most customers perceive a direct risk in C2C transactions.

  • 2

    Chronbach's alpha of 0.6 is not a valid cut-off (Nunnally, 1978).

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  2. Abstract
  3. Introduction
  4. Theoretical Background, Hypotheses, and Model
  5. Definition of Pre-Purchase Information
  6. Brand as Information Source
  7. Word-of-Mouth as Information Source
  8. Customization as Information Source
  9. Method
  10. Results
  11. Discussion
  12. Acknowledgments
  13. Appendix
  14. References
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