Perceived Risk and Risk-Reduction Strategies For High-Technology Services

ABSTRACT - Consumer perceptions of risk in purchase decisions have been dealt with extensively in the literature, since such perceptions accompany all purchases to some degree and influence buying behavior. This study identifies risk types and risk-reduction strategies relevant to high-technology services such as online banking, online ticket reservations, and mobile education. It also compares and contrasts risk-takers and risk-avoiders in terms of their risk-handling strategies. Study results are discussed in terms of both theoretical and practical implications.


Hyun Kyung Kim, Moonkyu Lee, and Mi Jung (2005) ,"Perceived Risk and Risk-Reduction Strategies For High-Technology Services", in AP - Asia Pacific Advances in Consumer Research Volume 6, eds. Yong-Uon Ha and Youjae Yi, Duluth, MN : Association for Consumer Research, Pages: 171-179.

Asia Pacific Advances in Consumer Research Volume 6, 2005      Pages 171-179


Hyun Kyung Kim, Yonsei University, Korea

Moonkyu Lee, Yonsei University, Korea

Mi Jung, TNS Korea


Consumer perceptions of risk in purchase decisions have been dealt with extensively in the literature, since such perceptions accompany all purchases to some degree and influence buying behavior. This study identifies risk types and risk-reduction strategies relevant to high-technology services such as online banking, online ticket reservations, and mobile education. It also compares and contrasts risk-takers and risk-avoiders in terms of their risk-handling strategies. Study results are discussed in terms of both theoretical and practical implications.


In our digital age, technology is changing at a dazzling speed. New goods and services are pouring into the market at an unprecedented rate and a product developed yesterday is obsolete today (Higgins and Shanklin 1992). Every day we encounter diverse high-technology products and services that offer new and dramatic benefits (Moore 1999). Nevertheless, consumers are often reluctant to use these new technologies. Innovative goods and services can jeopardize a comfortable status quo or go against the grain of their belief structures (Lunsford and Burnett 1992; Moore 1991). In other words, consumers perceive high risks in purchasing high-tech products. Accordingly, they adopt risk-reduction strategies to diminish the possibility or the consequences of loss through a purchase. It would therefore benefit marketers of high-tech goods and services to understand the relationships among types of perceived risk, risk-reduction strategies, and consumer attitude toward risk. Such an understanding would help them enhance the attractiveness of their products/services to non-purchasers (Mitchell and Greatorex 1993) and to increase repeat purchases and loyalty by building consumer confidence (Jarvenpaa, Tractinsky, and Vitale 2000).

Undeniably, perceived risk has been discussed extensively in the literature over the last 40 years. However, most of the relevant studies are limited or out-of-date, having failed to reflect recent social, cultural, economic, and technological changes. Moreover, it is likely that consumers will adopt different risk-reducing behaviors for different perceived risks. Although some studies do address the relationship between risk perception and risk-reducing behavior, they often deal primarily with consumers’ information-search behaviors and confine themselves mostly to physical goods. The present study will focus on intangible services, discern the specific risks consumers perceive in the purchase of high-tech services, and distinguish them from the perceived risks of purchasing low-tech services. It will also examine how risk-reduction strategies differ between high-tech and low-tech purchases. Finally, the study will conduct a more segmented analysis on how risk perception and risk-reduction strategies vary with technological level of services and consumer attitude toward risk. Although this study will focus on service risks, it will first review the perceived risks involved in purchasing physical goods, which most existing literature deals with.


Perceived Risk

Definition of Perceived Risk. Consumer perceptions of risk in the context of the purchase decision-making have been widely dealt with in the past literature since they accompany all purchases to varying degrees and influence buying behavior (e.g., Bauer 1960; Bettman 1973; Cox 1967; Cunningham 1967; see Mitchell 1999 for a review). Bauer (1960) defines perceived risk as the consumer’s feeling of uncertainty about the consequences of transactions. In other word, perceived risk is a subjective concept, distinct from objective risk. Since Bauer, perceived risk has been conceptualized in various ways, although the models are generally of two sorts: (a) risk as uncertainty (Bauer 1960; Cox 1967; Taylor 1974) and (b) risk as expected loss (Bettman 1973; Cunningham 1967; Kogan and Wallach 1964; Peter and Ryan, 1976; Roselius 1971; Stone and Winter, 1987).

Dimensions of Perceived Risk. Various studies have tried to identify different dimensions of perceived risk. Jacoby and Kaplan (1972) identify five types of risk: financial risk, performance risk, psychological risk, physical risk, and social risk. Time risk is proposed as another form of perceived risk (Brooker 1984; Mitchell and Greatorex 1993; Roselius 1971; Stone and Gr°nhaug 1993; Zikmund and Scott 1977). Zikmund and Scott (1977) also suggest that loss of future opportunity is a perceived risk for certain products.

Many recent studies are concerned with the perceived risks associated with online shopping. These studies show that consumers perceive higher risks online than offline because the Internet is open and complex in nature and the technology beyond the control of users (Rose, Khoo, and Straub 1999). Jarvenpaa and Todd (197) identify personal and privacy risks specific to online shopping (e.g., improper disclosure and use of private information). Cases (2002) identifies delivery risk regarding product delivery, payment risk concerning disclosure of credit card information on the Internet, and source risk related to the credibility of the website.

Several other studies have examined resistance to innovation, i.e., consumer unwillingness to purchase products of a newly developed technology. According to Ram and Sheth (1989), barriers to innovation diffusion are of two sorts, functional and psychological. In a similar vein, Higgins and Shanklin (1992) identify four major types of perceived risk that are specific to technology and can prevent purchase: (a) fear of technical complexity, (b) fear of rapid obsolescence, (c) fear of social rejection, and (d) fear of physical harm. In addition, Robertson and Gatignon (1986) propose that consumers are reluctant to purchase innovative products because of their lack of knowledge about the products and because of high expected costs and risks. Mick and Fournier (1998) find through surveys and in-depth interviews that consumers have mixed reactions to new technologies, believing that these technologies offer more control, new benefits, and greater efficiency, but at the same time that they cause chaos and are prone to obsolescence. For the purposes of our study, these "paradoxes of technology" can be viewed as a type of perceived risk, in the sense that they involve consumer expectations of loss.

Risk-Reduction Strategies

Consumers are known to rely on risk-reduction strategies to reduce perceived risks when the purchase decision is very important but the information available to them is incomplete. Roselius (1971) describes a risk-reduction strategy as a tool used by a seller or a consumer to reduce the consumer’s perceived risk. He identifies 11 such strategies: endorsements, brand loyalty, brand image, private testing, store image, free samples, money-back guarantees, government testing, shopping, expensive models, and word-of-mouth communications. Other studies also indicate such strategies as extensive search for more information (Akaah and Korgaonkar 1988), endorsement (Schiffman and Kanuk 1998), purchase of a cheaper brand, special offers, and use of information on the package or consumer magazines (Greatorex and Mitchell 1994).

Sheth and Venkatesan (1968) argue that a consumer’s choice of risk-reduction strategy depends on the uncertainty regarding the product or on the consumer’s experience with the brand. Mick and Fournier (1998) identify strategies consumers use to cope with their contradictory attitudes to technology (or so-called "paradoxes of technology"), with the stresses associated, and consequently, with purchasing and using high-tech goods and services. The risk-reduction strategies they cite include (a) pre-acquisition avoidance strategies, (b) pre-acquisition confrontative strategies, (c) consumption avoidance strategies, and (d) consumption confrontative strategies.

Dimensions of Perceived Risk and Risk-Reduction Strategies

Roselius (1971) identifies four types of loss: (a) hazard loss, (b) ego loss, (c) financial loss, and (d) time loss. He examines their relationships with the 11 risk-reduction strategies listed earlier. He finds that there is a clear preference order in risk-reduction strategies. For all four types of loss, brand loyalty and major brand image are the most effective in reducing consumer perception of risk. In addition, Locander and Hermann (1979) examine the relationships between information-seeking behaviors and five products that involve different levels of performance risk and social risk. They find that consumer level of confidence has a significant influence on information-searching behaviors for all products. In Mitchell and Boustani’s study (1993), non-purchasers appear to perceive fewer risks than purchasers do; however, no significant difference is found between the two groups in terms of risk-perception factors or the usefulness of risk-reduction strategies.

Derbaix (1983) also examines the relationships between risk dimensions and risk-reduction strategies, finding that it varies with product type. Brand loyalty is the preferred strategy for non-durable experience goods, money-back guarantee and store image for durable experience goods, and shopping for search goods. In the case of financial loss, consumers prefer the money-back guarantee for non-durable experience goods and reputation for search goods. In a similar vein, Greatorex and Mitchell (1994) examine the usefulness of 14 risk-reduction strategies for services in terms of four types of loss, and find that brand loyalty is the most effective, the recommendation of celebrities and the advice of salespersons the least effective. They also find that risk-reduction strategies can vary depending on the type of service. Their study has a limitation, however, in the sense that it analyzes the priorities of risk dimensions and of risk-reduction strategies separately; thus, it fails to indicate which risk-reduction strategy is the most effective for a particular risk dimension. Mick and Fournier (1998) find that pre-acquisition avoidance strategies, such as ignoring information about certain technological products, refusing to buy a specific technological product, and delaying the purchase of a product, appear to be useful in handling risks. But they suggest that the effectiveness of risk-reduction strategies may vary across the types of technological paradox.

In the context of online shopping, on the other hand, Cases (2002) examines the dimensions of perceived risk and risk-reduction strategies. She identifies four types of information source (i.e., product, remote transaction, Internet, and website), and analyzes the relationships between eight dimensions of perceived risk and 15 risk-reduction strategies in terms of the types of information source. She finds that privacy risk, source risk, and payment risk related to the security of the Internet and to the credibility of websites are all perceived to be high; the most effective risk-reduction strategy appears to be the provision of payment security. To cope with performance risk, consumers tend to rely on exchange and money-back guarantees; to handle social risk, they depend on word-of-mouth communications and exchange guarantees.


The findings of the past studies on the relationships between perceived risk dimensions and consumer risk-handling strategies seem to be confusing, and sometimes contradictory to each other. Therefore, the main idea of this study is threefold: (a) to find out what types of risk consumers perceive in making purchase decisions and how they cope with the risks, (b) to determine what types of risk-handling strategy are the most effective for reducing what types of risk, and (c) to examine service differences and individual differences in the relationships between risk types and risk-reduction strategies. As stated earlier, this study focuses on services rather than on physical goods. There are two moderating variables that are examined in the study. The first one is a service characteristic. Specifically, the technological level of service (i.e., high-technology versus low-technology services) are investigated. The second one is consumer attitude toward risk (i.e., risk-taking versus risk-averse). Thus, the present research attempts to find out how the relationships between risk dimensions and effective risk-coping strategies vary across high-tech vs. low-tech services and across risk-takers vs. risk-avoiders.


Pilot Study

To determine categories of high-tech service for analysis, and to develop questionnaire items that would measure perceived risks and risk-reduction strategies for those services, we conducted in-depth interviews with 20 respondents ranging in age from 20 to 50.

Selection of Service Categories. Three categories of service were selected by a pretest a la Higgins and Shanklin (1992) and O’Regan & Ghobadian (2003): (a) online vs. conventional banking, (b) online vs. conventional ticket reservations, and (c) mobile vs. conventional education services. Online and mobile services were classified as high-tech services, and conventional services as low-tech.

Developing Measurement Items. Jacoby and Kaplan’s work (1972) is probably one of the most widely accepted models of perceived risk. However, it is clear now that consumers of high-tech services would perceive risks which are new to their model; these perceptions might underlie consumer resistance to innovation and paradoxical attitudes towards technology. We conducted in-depth interviews, therefore, to identify aspects of high-tech and low-tech services from which to extract meaningful risk dimensions. Aspects identified include the advantages and disadvantages of using the service, expected risks of purchase, reasons for reluctance to purchase, and negative feelings. Interviewees were also asked, for both high-tech and low-tech services, about risk-reduction strategies; in other words, what strategies they would use to reduce a given risk. The results of the pretest, together with the results of past research, shaped the main study.

Main Study

Data Collection. A survey was conducted with a convenient sample of 402 respondents, ranging in age from their 20’s through their 50’s. A total of 381 valid questionnaires were used in the final analysis. Fifty-seven percent of respondents were males, 43% females. Respondents in their 20’s constituted 57% of the sample; 18.1% were in their 30’s, 3.6% in their 40’s, and 9.7% in their 50’s or over.

Research Procedure. Respondents were asked to read brief instructions about a specific service and, with that service in mind, to assess statements about perceived risks and risk-reduction strategies. The first part of the questionnaire consisted of items for a manipulation check of technology-level variables. The second part included six items to measure attitude toward risk. These items were drawn from Griffin, Babin, and Attaway (1996) and checked and modified by marketing professionals. The third part consisted of questions to identify and assess risk-reduction strategies. Each respondent answered questions for two services.


Reliability and Manipulation Checks

A manipulation check was performed prior to analysis. The reliability coefficient of the three items proposed to measure technology level was 0.93. As expected, responses for high-tech services scored significantly higher than those for low-tech services (M=4.85 vs. 2.40, respectively; t=19.95, p<.01). Measurement of attitude toward risk also proved to be reliable, with a coefficient of 0.80. Thus, a median split was used in dividing the sample into two groups. Specifically, 179 respondents with a mean rating higher than 3.5 were classified as risk-takers, and 202 with a mean of 3.5 or less were categorized as risk-avoiders.

Dimensions of Perceived Risk

The perceived risks involved in purchasing services were measured with 38 questions. The responses were not well classified in the first factor analysis, so a second analysis was run after exclusion of four questions that showed similar loadings in all factors, had loadings that were too low, or were impossible to interpret when involved in certain factors. The second procedure adopted principal component analysis. Eight factors were identified as a result: operational risk, privacy risk, socio-psychological risk, performance risk, communication risk, financial risk, obsolescence risk, and cognitive risk. All eight factors showed a reliability coefficient greater than 0.72. Table 1 shows the results of the analysis.

Differences in risk perception in relation to the technological level of service (high-tech vs. low-tech) and consumer attitude toward risk (risk-taker vs. risk-avoider) are presented in Table 2. Consumers perceived higher operational, privacy, performance, communication, and cognitive risks for high-tech than for low-tech services. Risk-avoiders perceived higher operational risk than risk-takers did; no significant difference between the two groups was detected in regard to other types of risk.

Risk-Reduction Strategies

Analysis was conducted of risk-reduction strategies in purchasing high-tech services. Results showed that the preferred strategies were major brand name (M=5.55), brand loyalty (M=5.25), ease of exchange and refund (M=5.25), retention of receipts (M=5.23), credibility of stores or websites (M=5.21), purchase of goods or services that suit purchasing goals (M=5.19), word-of-mouth (M=5.14), easy access to stores (M=4.98), frequent use by acquaintances (M=4.93), learning how to use (M=4.83), and limiting price range (M=4.82). Although 30 items pertaining to risk-reduction strategies were included in the survey, only those 11 strategies that reached statistical significance are cited in the results.

There were some differences in risk-reduction strategies in relation to technological level and attitude toward risk. For high-tech services, credibility of stores or websites, learning how to use, and high Internet security were preferred strategies, while easy access to stores was strongly preferred for low-tech services. Not surprisingly, risk-avoiders used risk-reduction strategies more than risk-takers did. However, the reliance on easy access to stores was applied to the two groups in the similar way (see Table 3).

Relationships Between Risk Dimensions and Risk-Reduction Strategies

Pearson correlation coefficients were calculated to analyze the relationships between risk dimensions and risk-reduction strategies. Only the 11 meaningful items were examined for risk-reduction strategies. The results are shown in Table 4. For operational, performance, and financial risks, ease of exchange and refund showed the highest correlation, while credibility of stores or websites and major brand name were the most heavily used strategies for privacy and obsolescence risks.

With regard to low-tech services, frequent use by acquaintances showed the highest correlation with operational and privacy risks, while brand loyalty (or choosing a credible provider) was the most frequently used strategy for reducing financial and obsolescence risks. For high-tech services, diverse risk-reduction strategies were used for all risk dimensions (see Table 5).

Finally, risk-takers use a wider variety of risk-reduction strategies than risk-avoiders do. Table 6 presents risk-handling strategies that risk-takers and risk-avoiders typically use in an effort to cope with diverse types of risk.


This study analyzed the associations between perceived risks and risk-reduction strategies, as moderated by technological level and consumer attitude toward risk. The results show that consumers perceive eight dimensions of risk: operational risk, privacy risk, socio-psychological risk, performance risk, communication risk, financial risk, obsolescence risk, and perceptional risk. Moreover, risk dimensions and risk-reduction strategies vary across the technological level of the service and the consumer attitude toward risk.

The results of the current study have good research implications. As mentioned earlier, existing literature has some limitations in the sense that risk-reduction strategies and risk dimensions were analyzed separately and important mediators ignored. This study provides a comprehensive model of consumers’ risk perceptions and their coping behaviors, which will serve as a solid basis for future studies in this field.

In addition, the findings of the study have some practical implications. If the dimensions of risk perceived by consumers and their risk-reduction strategies can be understood and predicted, marketers will be able to establish far more effective plans, and will be able to maximize profits through successful resource allocation. Furthermore, customized marketing strategies, based on consumers’ perceptions of risk and corresponding risk-reduction strategies, will not only stimulate initial purchases but also induce more repurchases. Additionally, as this study addresses both service and consumer characteristics, it provides useful guidance for future segmentation studies or positioning strategies, thus helping marketers to develop effective communication strategies for each market segment and to establish an intimate long-term relationship with consumers.

The current research opens up several avenues for future studies in this area. More sophisticated quantitative analyses should follow up on this exploratory study. Future research should identify and examine moderating variables other than technological level and attitude toward risk. An immediate extension of the study would be a comparison analysis between physical goods and intangible services in terms of risk types and risk-reduction strategies. Also, more research is needed on the mediating processes by which risks are handled and reduced by certain strategies.

Future research should be conducted in an effort to enhance the generalizability of the current findings. Since the data were collected from a convenience sample in this study, the study results could be applicable only on a limited basis. Respondents of diverse characteristics should be selected in a more systematic way in future studies for a higher level of external validity of results.














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Hyun Kyung Kim, Yonsei University, Korea
Moonkyu Lee, Yonsei University, Korea
Mi Jung, TNS Korea


AP - Asia Pacific Advances in Consumer Research Volume 6 | 2005

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