By Nigel Pope, Mark Brown, and Edward Forrest

In this paper we examine the issue of perceived risk and consumer intention to purchase sport products online. Three product types were investigated: sporting goods for personal training and use, sporting team merchandise and sporting event tickets. Other factors expected to influence purchase intention were product type, gender, and innovativeness. Involvement with both the product and the Internet were expected to act as significant covariates in these relationships. Using ANCOVA, significant main effects were found between gender and purchase intention, and innovativeness and purchase intention. No effect was found between product type and purchase intention. Internet involvement was found to be a significant covariate in the main effect of purchase intention, however, involvement with the product was not. Implications for the online shopping industry and directions for future research are discussed.

Research has indicated that perceptions of risk can be extended beyond the product to the shopping medium itself (Cox & Rich, 1964; Spence, Engel, & Blackwell 1970). Such concerns are likely to affect consumer behavior on the Internet and may help to explain why most consumers still use the Internet for browsing rather than purchasing (Booker, 1995; Wintrob, 1995). It may be possible, however, to discriminate between Internet users who intend to purchase online and those who do not on the basis of their perception of risk.

In this paper we examine the issue of perceived risk and consumer intention to purchase online in a specific product category. That category is sport product, and we delineate this into three of the offerings currently available online: sporting goods for personal training and use, sporting team merchandise and sporting event tickets. We commence our examination of this issue with a discussion of perceived risk, before dealing with derived elements of innovativeness, gender and involvement, each of which have been shown to relate to risk.

Perceived risk has been studied in relation to such consumer behavior constructs as information search (Gemunden, 1985; Locander & Herman, 1979; Lutz & Reilly, 1973; Zikmund & Scott, 1973), evaluation of brand and store alternatives (Hirsh, Dornoff, & Kernan, 1972; Peter & Ryan, 1976; Peter & Tarpey, 1975), purchase decision-making (Perry & Hamm, 1968), personality (Brody & Cunningham, 1968; Schaninger, 1976), and risk handling (Deering & Jacoby, 1972; Dowling & Staelin, 1994; Cox, 1967). Consumers’ mode of shopping has also been examined in terms of perceived risk (Cox & Rich, 1964; Matthews, Slocum, & Woodside, 1971; Spence, Engel, & Blackwell 1970).

A review of the extant literature concerning perceived risk reveals that there has been a lack of consensus regarding a conceptual definition of the construct. This absence of agreement represents a fundamental problem for perceived risk researchers and has resulted in diffuse research methods and difficulty in comparing results (Peter & Ryan, 1976). We therefore offer a discussion of the constituents of risk and the means by which it can be adequately measured.

The concept of perceived risk was introduced by Bauer (1960). He proposed that risk be conceived in terms of the uncertainty and consequences associated with a consumer’s actions, the results of which may or may not be pleasant. This interpretation of risk has been widely used by other researchers (e.g. Cox, 1967; Cox & Rich, 1964) and implies that the probability and outcome of a purchase situation are each uncertain (Dowling & Staelin, 1994). Bauer (1960) and Cox (1967) emphasize subjective or ‘perceived’ risk in their research. Perceived risk is not based on objective criteria but rather how a consumer subjectively feels or perceives it to be (Murphy & Enis, 1986).

Research has shown perceived risk to be a multidimensional construct consisting of a number of different types of risk including financial risk, physical risk, functional risk, psychological risk, social risk, and time-loss risk (Jacoby & Kaplan, 1972; Roselius, 1971). Financial risk stems from paying more for a product than was necessary or not getting value for the money spent (Roehl & Fesenmaier, 1992). Consumers generally address this problem by ‘shopping around’ for the most satisfactory price. Physical risk involves the potential threat to a product user’s safety or physical health and wellbeing. Functional risk, sometimes referred to as performance or quality risk, is based on the belief that a product will not perform as well as expected or will not provide the benefits desired (Bauer, 1960).

Psychological risk arises from the likelihood that a purchase will fail to reflect one’s personality or self-image. Social risk is concerned with an individual’s ego and the effect that a purchase will have on the opinions of reference groups. In the cases of social and psychological risk, a poor purchase choice can result in damage to a buyer’s social and self-image. Branding and positioning of products are closely associated with these two types of risk. Time-loss risk refers to the possibility that a purchase will take too long or waste too much time.

Clearly, many Internet users are skeptical of Internet shopping because of the potential risks involved. A question we address in this research, is:

What risk factors would discriminate between groups more or less likely to purchase sporting products via the Internet?

Related to the concept of risk is that of early adoption of new products and media, in itself a potentially risky proposition. The minority of consumers who are the first to buy in a product category have been identified as the key to a successful product launch (Midgley, 1977). As a result, much research in the marketing literature has focused on the role of innovators and early adopters (e.g. Carlson & Grossbart, 1985; Hirschman, 1980; Midgley, 1977; Midgley & Dowling, 1978; Ostlund, 1974; Venkatraman & Price, 1990).

A common theme that has emerged is the identification of innovators in terms of their product adoption behavior (Venkatraman, 1991). That is, consumers who buy products earlier than others are often labeled as innovators. This view may, however, be erroneous as some researchers have argued that innovativeness and early adoption are distinct, if somewhat interrelated, constructs. Innovativeness refers to the underlying personality trait predisposing individuals to new and different experiences (Carlson & Grossbart, 1985; Hirschman, 1980; Midgely & Dowling, 1978). Adoption, on the other hand, involves the actual translation of this personality trait into the behavioral component of purchasing a new product or engaging in a new experience (Venkatraman, 1991). An individual may therefore be an innovator but not necessarily an early adopter, as the translation of innovativeness into adoption depends largely on the nature of the product itself (Gatignon & Robertson, 1985).

Innovativeness can be categorized into general or global innovativeness and domain-specific innovativeness (Flynn & Goldsmith, 1993), with the former referring to a common human personality dimension and the latter dealing with innovative attitudes or behavior within a specific domain of activity (Midgley & Dowling, 1978). Because the earliest adopters in one product category will not necessarily be early adopters in another (Flynn & Goldsmith, 1993), measures of global innovativeness are unlikely to be accurate in predicting consumer purchase behavior across product categories. Yet within a preferred domain of interest or expertise, innovators are likely to be early adopters. For this reason, the current study utilizes the domain-specific approach to measure the innovative tendencies of consumers in regard to the purchase of sporting products via the Internet.

Any examination of innovation in purchasing through a new medium must first allow for the type of product being purchased. We therefore suggest the following hypothesis in addition to our exploratory question presented above:

H1: Type of sporting product will have a significant effect on stated intention to purchase sport products via the Internet.

Similarly to the product type, the consumer type may also act in relation to risk and innovativeness. Darley and Smith (1995) report differences in gender response across different levels of perceived risk in products. As risk increases, these authors argue that women will change their response pattern to take in more objective information rather than subjective. Males however, did not change their favorability of response between risk conditions. It would appear then, that women will act with more caution as risk conditions change. So we also offer the hypothesis:

H2: Women will have a lower stated intention to purchase sport products via the Internet than men.

Having allowed for these aspects of consumer and product type, we resume our discussion of innovativeness and purchase intention. Innovativeness is generally associated with the earlier stages in the product life cycle (Alpert, 1994). The sports industry has been established for many years but with the emergence of the Internet as a new means of distribution, the role of innovativeness in the purchase of sporting products in cyberspace may assume more significance. Flynn and Goldsmith (1993) claim that despite the difficulty in distinguishing the earliest buyers as a market segment by demographic characteristics, early adopters are generally heavier purchasers of products as a whole. Due to the innovative tendencies of early adopters, it is expected that they will demonstrate a higher intention to purchase products via the relatively new distribution medium of the Internet than less innovative Internet users. Hence, it is hypothesized that:

H3: Individuals possessing higher levels of innovativeness will have a higher stated intention to purchase sport products via the Internet than those with lower levels of innovativeness.

To this point, our discussion has concentrated on the nature of risk and its elements, and other factors which might affect it. We note however, that risk is an antecedent of the involvement construct (Laurent & Kapferer, 1985; Kapferer & Laurent, 1993). Involvement is an internal state of arousal in response to a stimulus of some sort (Zaichkowsky, 1985; Andrews et al, 1990; Mittal, 1995; Poiesz & Cees, 1995). Four basic research areas have opened into the area of involvement: attention/processing; personal/situational; audience/process; and enduring/product involvement (Rothschild, 1984; Andrews et al, 1990; Celuch & Slama, 1993; Poiesz & Cees, 1995). In the context of our study, we are concerned with product involvement, although attention/processing with regard to the Internet itself needs also to be allowed for.

As yet, there is no data available regarding perceived risk of product purchase and sporting products, so it is necessary here to allow for involvement with regard to products and their use interfering with any main effects in the relationships hypothesized above:

H4: Purchase decision involvement will be a significant covariate in any main effect from type of product, gender and innovativeness to stated purchase intention.

Similarly, an individual might display levels of involvement with the medium itself. To allow for this we also hypothesize that:

H5: Internet involvement will be a significant covariate in any main effect from type of product, gender and innovativeness to stated purchase intention.

The sample for the research was selected from an undergraduate business class of 253 students from an Australian, east-coast university. This group provided a total of 189 useable responses. Males constituted 98 of the useable respondents and females 91. Mean age of the group was 22.3 years.

Instrument and Administration.

The instrument was a collation of several pre-existing scales relating to risk, involvement and purchase intention.

Overall risk and the six risk dimensions (financial, performance, physical, psychological, social, and time-loss) were measured using a modified version of the Stone and Grønhaug (1993) multi-item scale. Modifications were based upon a pre-test of the scale adapted to the Internet as a distinct mode of shopping.

 

Involvement with the Internet was measured using McQuarrie and Munson’s Revised RPII (1991) while involvement with different types of sporting goods was measured by Mittal’s PDI (1989).

 

Purchase intention was measured using the scale of Whitlark, Geurts and Swenson (1993). This item is ranked on a scale of 1 to 5 with the lower score representing a higher intention to purchase. Rather than reverse score this item, we left it as a negative so as to be in keeping with the risk items which are also negative constructs.

 

For consistency, all these items were measured on a 5 point Likert type scale.

Product type was treated as a categorical variable of three dimensions: sport tickets, sporting equipment and team merchandise. Different questionnaires represented different categories so that the sample was effectively divided into three groups. To avoid between group differences a paired sample method was used. The total group was first given a general knowledge test relating to the Internet, and asked further questions relating to sport viewing and participation rates. Respondents were then allocated to one of the three groups in a manner that ensured even distribution of individual types and gender in each.

The entire questionnaire was presented to these groups with a random allocation of product category.

Given the large range of different scales used, we first conducted factor analyses of the risk elements. The data were obliquely rotated and factor loading set at .50 in accordance with the recommendations of Hair, Anderson, Tatham and Black (1998). After a check for nomological consistency, the extracted factors were then subjected to an item reliability analysis and items deleted where necessary to achieve a Cronbach’s alpha of .70 (Nunally, 1978). Remaining items were then summated within the factors to present unique constructs of risk (Spector, 1992). Overall risk was treated separately and as a different construct.

Scales of involvement and innovation were also subjected to item reliability analysis with Cronbach’s alpha again set at .70. The mean of the summated scale of innovation was established and those above it identified as high innovation respondents and those at or below as low innovation respondents.

Hypotheses 1 to 5 were tested using a single ANCOVA, with dependent variable of purchase intention and independent variables of product type, gender and high or low innovation. Purchase decision involvement and Internet involvement were treated as covariates with the main effect.

The exploratory question of what risk factors discriminate between purchasers and non-purchasers of Internet sold products was investigated through discriminant analysis against the defined groups established in the tests of hypotheses 1 to 5.

Factor analysis of the risk items (excluding overall perceived risk) obtained six constructs. These were examined nomologically and appear to be semantically logical. These factors and their constituent parts are presented at Table 1.

<<Insert Table 1 about here>>

As can be seen from Table 1, these six factors of value for money, social opinion, stress, time loss, security and physical damage accounted for 69.6 percent of the total variance. We subjected the items within each factor to an item reliability analysis with the exception of Factor 5 (security) which had only two items. Results showed a Cronbach’s alpha of .88 for Factor 1 (value for money), .80 for Factor 2 (social opinion), .87 for Factor 3 (stress), .84 for Factor 4 (time loss), and .84 for Factor 6 (physical damage).

Reliability tests were also conducted on the Internet involvement scale, purchase decision involvement scale and the innovation scale. They achieved Cronbach’s alphas of .89, .71 and .70 respectively. Mean score for innovativeness was found to be 2.4, and high innovators were identified as being those with a higher score than this.

Hypotheses 1 to 5 were tested using ANCOVA. Results appear at Table 2. As Table 2 shows, significant main effects were found between gender and purchase intention, and innovativeness and purchase intention. No effect was found between product type and purchase intention, so Hypothesis 1 is rejected. Nor were any interactions found.

<<Insert Table 2 about here>>

An examination of the means showed that women displayed a lower intention to purchase (mean = 4.65 on a scale of 1 to 5, 5 being the lesser intent) than men (mean = 4.30). We therefore accept Hypothesis 2. The means also showed that those who exhibited a high level of innovativeness were more likely to purchase sporting products via the Internet than those who had a low level of innovativeness (means of 4.25 and 4.77, respectively). Hypothesis 3 is also accepted.

Table 2 also shows that involvement with the Internet and Purchase Decision Involvement were found to be significant covariates in the main effect. A further exploration of this revealed that of the two covariates, only Internet involvement had a positive relationship with intention to purchase sporting goods. Hypothesis 4, relating to a relationship between Purchase Decision Involvement and purchase intention is rejected. Hypothesis 5, suggesting that Internet involvement is a significant covariate in the main effect of purchase intention is accepted. The direction of the slope, however, (Beta is positive and the construct is negative) indicates that the relationship with purchase intention is negative. The amount of variance accounted for is only four percent, so any relationship would seem to lack meaningfulness, despite being statistically significant.

<<Insert Table 3 about here>>

We also explored the perceived risk factors which might discriminate between men and women and high and low innovators. Results of this analysis with regard to gender is presented at Table 4. Two factors emerged: value for money and summated total of overall risk. An examination of the means showed that males had a lesser fear of not getting value for money (mean of 2.93, as opposed to 3.19) yet were greater in their perception of overall risk (mean of 3.39, as opposed to 3.33). In our analysis of factors discriminating between high and low innovators only one item emerged, the summated scale for security risk. This showed high innovators as having a lower overall level of security risk than low innovators (4.25 as opposed to 4.77).

<<Insert Table 4 about here>>

<<Insert Table 5 about here>>

Our research sought to identify the nature of perceived risk as it applies to online purchase of sporting products. Previously, Stone and Grønhaug (1993) attempted to measure how much of overall perceived risk was explained by the six risk dimensions (i.e. financial, physical, performance, psychological, social, and time-loss). Using multiple items to measure each dimension, they were successful in capturing over 90% of the variance. The researchers argue it is likely that the near 10% remaining may not have been due to measurement error alone. It may be that there are factors which contribute to overall perceived risk that are not accounted for within the six dimension model. This view appears to be supported by our study, in which the variance captured by the six risk dimensions was nearly 70%. Note also that when we carried out our factor analysis, we found a different set of six dimensions. The argument that our understanding of perceived risk is poor is further strengthened when one considers the results of the Jacoby and Kaplan (1972) study wherein more than one third of the variance was unaccounted for.

However, it is clear that although the six dimensions of perceived risk are not entirely comprehensive, they still have some bearing on overall perceived risk. Indeed, it would appear that perceived risk may well vary from product group to product group, and this is an aspect that future researchers may well choose to explore. Given the above, it is interesting that we found that the type of sporting product had no effect on stated intention to purchase online. Our explanation for this is that the nature of risk might be different for various product categories but is channelled into other areas, namely, on this data, gender and innovativeness.

Our discovery that gender is a significant factor in online purchase intention seems to confirm the work of Darley and Smith (1995) regarding the response of women to risk factors in purchase situations. This should not be an issue of great concern to online purveyors since, as familiarity with the medium grows, the gender difference should reasonably be expected to disappear. This would seem to be confirmed by the lack of any interaction between gender and innovativeness, the other significant finding in our testing of our five hypotheses. That innovators should be more willing to purchase sport products online we had expected, and is not a terribly surprising finding.

When we explored the factors which discriminated between these groups (gender and high versus low innovators), we were seeking to identify the nature of risk perceived by consumers in purchasing sport products online. It is interesting that high innovators are not as concerned with the security aspect of online purchase, and we suggest that this augurs well for the future of online shopping. Our basis for this suggestion is that it would seem to be similar to other means of providing credit card and personal details for commercial purposes (e.g., the facsimile and telephone) which, having found initial consumer resistance, are now normal methods of operation.

The findings regarding gender are more interesting. That men perceived greater overall risk yet were more willing to purchase reinforces the Darley and Smith (1995) findings about male/female behavior. This becomes even more informative when we see that men are not as fearful of not getting value for money when purchasing sporting goods online. This presents a fascinating possibility for future research and serves to reinforce our earlier comments about the lack of understanding we currently have about the nature of risk.

Our research, while raising interesting matters regarding risk in online purchase of sporting goods, was subject to limitations. The sample was young and from a student base. This has implications as to attitudes toward the Internet itself and to innovativeness. Indeed, it may well account for the lack of real significance in our covariate of Internet involvement. That said, there is some justification for using a student sample in this case, because of the predominance of tertiary educated users in the Internet community.

Our feeling is that our research is predominantly exploratory and has asked more questions than were answered. The field is now ripe for a furtherance of this research into the questions we have posed, and answers to those questions should assist management in Internet trading generally as well as in the sport industry specifically.

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Table 2 Analysis of covariance results

Table 3 Within cells regression analysis for covariates

Table 4 Stepwise Discriminant Analysis Comparing Gender and Perceived Risk

Table 5 Stepwise Discriminant Analysis Comparing High and Low Innovativeness and Perceived Risk