Consumer Learning Through Ausing@ and the Role of Prior Knowledge in the Situation of Areally New@ Products

ABSTRACT - Based on a survey of analogical learning and involvement literature, this paper identifies critical factors influencing consumer learning of Areally new@ products at the Ausing@ rather than the Apurchasing@ stage of the adoption process. The survey analysis suggests that prior knowledge may have a subsequent effect on the level of learning efforts that a consumer makes during the process of using a new product. Furthermore, the level of cognitive involvement that stems from such learning efforts may influence the level of emotional or physical involvement that a user may experience. On the basis of the survey analysis, we develop a conceptual framework proposing tentative links between prior knowledge and the physical and emotional dimensions of consumer learning through using. A set of hypotheses is generated concerning the likelihood of continued use as an outcome of the interactions among the various dimensions.


Qing Wang (2003) ,"Consumer Learning Through Ausing@ and the Role of Prior Knowledge in the Situation of Areally New@ Products", in E - European Advances in Consumer Research Volume 6, eds. Darach Turley and Stephen Brown, Provo, UT : Association for Consumer Research, Pages: 376-380.

European Advances in Consumer Research Volume 6, 2003      Pages 376-380


Qing Wang, University of Warwick, UK


Based on a survey of analogical learning and involvement literature, this paper identifies critical factors influencing consumer learning of "really new" products at the "using" rather than the "purchasing" stage of the adoption process. The survey analysis suggests that prior knowledge may have a subsequent effect on the level of learning efforts that a consumer makes during the process of using a new product. Furthermore, the level of cognitive involvement that stems from such learning efforts may influence the level of emotional or physical involvement that a user may experience. On the basis of the survey analysis, we develop a conceptual framework proposing tentative links between prior knowledge and the physical and emotional dimensions of consumer learning through using. A set of hypotheses is generated concerning the likelihood of continued use as an outcome of the interactions among the various dimensions.


New products and services represent a major source of business growth and profit. However, many novel products or services failed to reach their growth or profitable stage (Cooper, 1979; Cooper and Kleinshmidt, 1987), despite being successfully brought to the market by the first movers. Classic examples of such products include EMI’s CT scanner and Philips’ Compact Disks. Meanwhile, numerous studies (von Hippel, 1986; Griffin and Hauser 1996) show that continuous involvement with users after a new product has been introduced to the market is critical for sustaining long term profitability.

In the innovation adoption literature, two types of consumer behaviour have been distinguished, namely the "acceptance" and the "continued use" of an innovation (Robertson, 1971). Despite that both "acceptance" and "continued use" are essential for the adoption process, most studies have concentrated on examining factors leading to "acceptance" (i.e. purchase), relatively few studies have been carried out to understand factors leading to "continued use". The work on the latter can be found in a broader context of brand loyalty and relationship marketing literature. Solomon (1992) indicates that purchase decisions based on loyalty may become simplified and even habitual in nature, and this may be a result of satisfaction with the current brand. However, brand loyalty is a very complex construct, which has been studied and measured in both behavioural and attitudinal terms. Continued use may be considered as the behavioural aspect of brand loyalty, i.e. the incorporation of an innovation into the behavioural pattern (Klongglan and Coward, 1970). It is an important concept for studying durable products, as a durable product is capable of a long and useful life and survives many uses. Compared to brand loyalty, continued use is also a more appropriate concept for studying really new products. This is because in such situation, consumer learning involves the evaluation of a new product category, rather than a comparison of alternative brands (Boyd and Mason, 1999). In this paper, we intend to develop a conceptual framework for understanding consumer learning through using, and the impact of such learning on continued use, in the situation of really new products. We propose that learning acquired through the consumer-product interaction during the using process is a major indicator for continued use. The using process is defined as the process following the initial acceptance or purchase of a new product. It mobilises a series of emotional and psychological status to supplement the cognitive experience (Wilson, 1998).


Prior Knowledge, Product Comprehension and the Knowledge Transfer Process at the Purchase Stage

In consumer research literature, really new products are defined as innovations that defy straightforward classification in terms of existing product concepts (Gregan-Paxton, et al., 2002; Moreau, Markman and Lehman, 2001; Gregan-Paxton and Roedder John, 1997). Consumer learning involves developing preferences for new products that do not fit into any existing category, and are unrelated to direct or indirect consumer experience (Robertson, 1971). Researchers found that because of lack of knowledge about novel products, consumers frequently make inferences about them based on limited information (Broniarczyk and Alba 1994; Brucks, 1984), and judgements are often based on an appraisal of both potential benefits and costs of the novel products (Johnson and Payne 1985). However these findings are based on evidence at purchase, it remains unclear as to how the initial perceptions and preferences may change through the action of using As the initial goals are likely to be evaluated and adjusted while using the product, we stress that new product learning at the purchasing stage and new product learning at the using stage are interrelated.

Studies have been conducted to understand how consumers use prior knowledge to learn about a new product category and how they form perceptions of the degree of continuity of a new product. Research evidence suggests that this learning occurs through a series of stages: access, mapping and transfer (Gentner 1989; Holyoak and Thagard 1995; Gregan-Paxton and Roedder John 1997). The ease with which consumers can transform their existing category structures to accommodate the new information presented by an innovation will determine how continuous they perceive the new product to be. In their study, Moreau, Markman and Lehmann (2001) refer to this existing product category as the primary base domain, which is defined as the category most similar to the innovation in terms of the benefits provided. In the access stage, a potentially relevant base domain becomes active in a person’s memory and thus can serve as a source of information about the target. When the target shares several surface similarities (i.e. visible attributes) with a base domain, access is likely to occur spontaneously. When a domain has been accessed, consumers can compare the content and structure of the base with the target domain. These mappings then serve as paths on which additional knowledge can transfer from the base to the target. The similarity between the two domains (the base and the target) dictates the ease with which these mappings can be constructed. In turn the types of mappings constructed will influence the type and amount of knowledge that will transfer, the inferences that can be made about the product, and the extent to which consumers comprehend the new product (Moreau, Markman and Lehmann, 2001).

These studies found that prior knowledge or expertise in a primary base domain influences internal knowledge transfer process, which in turn affects consumers’ comprehension and their perceptions of relative advantages and risks. Two different types of mapping have been distinguished, namely attributes-based mapping and relation-based mapping (Clement and Gentner 1991; Gregan-Paxton and Roedder John 1997). For example, consumers using the camera base domain to understand a new digital camera can map either the attribute "button" or the relation "button opens shutter" from the base to the target. Research evidence indicates that people prefer relation-based mappings to attribute-based ones, because relational mapping enable consumers to create goal-oriented inferences about how the new product will perform. For continuous innovations, an expert in the primary base domain should be able to construct relation-based mappings between the base and the target domain, thus transfer a significant amount of useful attribute and relation-based knowledge. In contrast, a novice is unlikely to recognise the relational similarities between the two domains, and thus to rely more exclusively on attribute-based mapping such as visible product attributes. As a result, novice’s comprehension of the new product is likely to be significantly lower than that of experts.

For discontinuous innovations, or really new products, the relative level of comprehension for experts and for novices at the purchasing stage is less clear from the existing evidence. Experts who prefer relation-based mappings, are more likely than novices to recognise the relation-based dissimilarities between the base and the target as they attempt to map the two domains (Gentner, Rattermann and Forbus 1993). For example, experts will recognise that several relations in the camera domain do not map onto the digital camera domain (e.g. light no longer exposes film). Experts who want to take action shots (i.e. a base domain goal) know that with a traditional camera they can get high-quality pictures using high-speed film and short exposures (i.e. the relations among the features). The same photographers may still have the same goal when evaluating a digital camera, but the relations among the features of the primary base domain will not transfer to the target to indicate how this goal would be achieved. The difficulty even for experts to make relation-based mappings will influence their perceptions of a new product’s relative advantages and risks, as they cannot transfer the knowledge necessary to make goal-oriented inferences effectively. The above discussion of the literature shows that the level of comprehension of experts for discontinuous innovation is such that they are unable to make relation-based mappings, but nevertheless are able to comprehend the product complexity and to recognise its relation-based dissimilarities to an existing category. Therefore, we propose that experts will perceive higher learning costs involved in using the new product than novices. The level of comprehension of novices on the other hand is such that they are neither able to make relation-based mappings nor able to comprehend the product complexity/relation-based dissimilarities to any existing category. The superficiality of their new product comprehension is likely to result in lower perceived learning cost involved in using the new product. Here learning costs refer to the cognitive effort required to accumulate the knowledge necessary for effective usage (Klemperer 1987).

Prior Knowledge, Perceived Learning Costs and the Level of Learning Efforts at the Use Stage

Research in innovation adoption reveals that all the three constructs, namely (1) consumers’ comprehension of a new product, (2) their perception of the relative advantages, and (3) their perception of the risks, influence the adoption process of a new product. In the previous section, we mainly discussed the role of prior knowledge in consumer comprehension of a really new product during the knowledge transfer process and the perceived learning costs that it results at the purchase stage. In this section we will analyse how this relative level of perceived learning costs will impact on consumer learning at the using stage.

In the cognitive learning literature, leaning-cost inferences follow from the nature of high-complexity products, i.e. they are more difficult to use and understand. Since knowledge acquisition requires cognitive effort, attributes of high-complexity products should be associated with high learning cost (Farrell and Shapiro 1988). Research on information overload suggests that individuals are vigilant about the mental effort required to process information (Keller and Staelin 1987). Thus anticipated cognitive effort in the form of learning cost is likely to be a negative inference made about the novel products. Research in analogical learning also indicates that compared to novices, experts in the primary base domain are likely to have more unresolved goals, as they are able to recognise relation-based dissimilarities. These unresolved goals will be encoded as risks because the experts are unable to predict whether the new product can favourably satisfy them (Moreau, Markman and Lehmann, 2001).

These findings indicate a negative relationship between the level of prior knowledge and the perceived advantage of a really new product. However, these studies did not take into consideration factors such as confidence and self efficacy, which may also influence the kind of inferences they make about a novel product. Previous research on expertise indicates that confidence in one’s ability to learn may motivate learning efforts (Bandura 1977). As the amount of given information increases, a decision maker’s confidence in his performance increases (Oskamp 1965). Research evidence also indicates that this positive effect of confidence on learning may be retarded by experts’ tendency towards over-confidence in their existing knowledge, and over-confidence may lead to complacency (Wood and Lynch, 2002). However, over-confidence is unlikely to occur if an expert is aware of the relation-based dissimilarities. In this situation, the confidence of experts in their learning ability is likely to generate positive motivational effect on new product learning. For example, a camera expert who wants to take action shots with digital camera will be more motivated to learn how to obtain a good quality action shot even with a camera that does not have either film or lens. He will experiment and practice until he is satisfied with the results. If self-learning is not enough, he may take a training course to improve his knowledge and skills. Whereas a novice who is less motivated to achieve high level performance will either give up taking action shots (because it is 'too complicated’), or do it incorrectly and result in poor quality picture. Therefore, we propose that despite the higher perceived learning costs, experts may still evaluate the new product favourably due to higher motivation to learn. Consequently, higher motivation to learn is likely to result in higher learning efforts from the experts than from the novices during the subsequent use stage. Therefore, we predict that greater prior knowledge at the purchase stage will have a subsequent impact on individual’s motivation to learn at the use stage. On the contrary, novices who perceived lower learning costs based on attribute-related similarities in the purchasing decision stage may be less motivated to learn during the using stage.

Dimensions of Involvement and Continued Use of Really New Products

Cognitive learning effort represents one of the several dimensions of product involvement. Celsi and Olson (1988) define product involvement as the perceived personal relevance of a product. They suggest that product involvement be determined by the activated cognitive structure of means-end associations that link people’s knowledge about product attributes and benefits with their self-knowledge about important needs, goals, and values. A product is self-relevant to the extent a consumer sees it as instrumental in achieving important consequences or values. When means-end knowledge is activated from memory or formed in a situation, the person perceives the product to be personally relevant and feels involves with it. Thus, means-end knowledge structures are the cognitive basis for involvement. Mulvey, et al (1994) found that low involvement consumers have a simple means-end chain associated with attributes. In contrast, high involvement consumers have a more complex chain of meaning associated with attributes. Although their study was cross-sectional, i.e. it did not explore the development over time, they speculate that the knowledge structures of highly involved consumers have evolved to the point where most concepts are interrelated. Involved consumers appreciate the rather subtle implications between many product attributes and functional consequences (e.g. feel, control, and comfort). These studies imply that the means-end knowledge structure of some consumers may develop from simple to complex. The complex knowledge structure defined by Mulvey et al. is similar to the concept of enduring involvement, which is defined by Rothchild (1979) as to represent an on-going concern with a product. It is found that consumers with high enduring product involvement conduct ongoing information search and are expected to have greater knowledge (Lichtenstein et al., 1988). For example, a beginner of Internet may initially set the goal of using Internet as purely functional, i.e. to search for a particular piece of information, or experiential, i.e. to see how it is like. As he starts using the Internet, his or her skill may increase with practice. As the skill level increases, the individual may adjust the goal to on-line shopping or entertainment, which may set off a spiral pattern of means-end knowledge structure development from simple to complex.

In addition to the cognitive involvement, the action of using may intensify a consumer’s emotional or physical involvement. The likelihood of continued use results from not only the user’s cognitive development over time, but also the degree of emotional and physical pleasure that the user derives from sensory stimulation while using the product. For example, an individual or organisation may decide to install the Internet or Internet-embedded software, based on the perception of the importance and usefulness of the services (i.e. e-mail, file transfer, news, on-line financial services, shopping and multimedia services and project management software), the level of expertise with computers and the motivation for using the services (Barbara, Sultan and Henrichs, 2000). Once installed and while using the services, many additional factors will influence the experience, and in turn the likelihood of continued use. These factors include the attractiveness of the website or software design, the content of the websites, the effectiveness of the search engine, the response time, the capacity and specification of the personal computer and the critical mass of users. A study of the early adopters of Web Groupware found that the problem of lacking regular users of the system meant that the expected benefit of the system, namely the ability to interact with anybody anywhere at any time, could not be realised (Dennis, Pootheri and Natarajan, 1998). Consequently this affects the emotional and physical pleasure that users could enjoy with the system, which in turn became a major reason for discarding it.

In our research, the need for communicating with other users and the sense of belonging is considered to represent the emotional dimension of involvement in the using process. The emotional dimension of involvement can be defined as the degree of psychological identification and affective, emotional ties the consumer has with the product category or specific brand (Martin, 1998). In addition to the sense of belonging, factors in the emotional dimension may also include the sense of trust and security and the sense of freedom and enjoyment. The physical interactiveness of the consumers with a product or system, on the other hand, represents the physical dimension of involvement. The degree of physical involvement is affected by factors such as the operational efficiency, the user interface and the product design features.


Linking the discussion of the emotional and physical dimensions of involvement with our earlier analysis of the role of prior knowledge at the using stage, in this section we develop a conceptual framework as shown in figure 1 below. The objective is to build initial conceptual links between prior knowledge and the physical and emotional dimensions of product involvement at the use stage.

H1: A user with higher prior knowledge in an existing product category is more likely to make higher cognitive learning efforts while using the new product.

H2: Higher cognitive learning efforts are likely to have a positive effect on a user’s physical involvement with the new product, moderated by the user’s emotional involvement with the new product.

H3: Higher cognitive learning efforts are likely to have a positive effect on a user’s emotional involvement with the new product, moderated by the user’s physical involvement with the new product.

H4: Higher cognitive learning efforts are unlikely to have a positive effect on the level of product involvement when a user’s involvement with the new product is low in both emotional and physical dimensions.

H5: When a user achieves a high level of involvement with a new product in all three dimensions (regardless of the sequence), his or her knowledge of th new product is likely to increase and so is the likelihood of continued and effective use of the new product.


As illustrated in the conceptual framework, in this paper we posit that the use stage can be studied in terms of a user’s involvement with a new product in the physical, cognitive and emotional dimensions. The likelihood of continued use is the outcome of the interactions among these three dimensions. We focus our analysis of the cognitive dimension on the role of prior knowledge and the level of learning efforts it results. We propose that interaction between the dimensions may occur if a user is initially highly involved with a new product in any one particular dimension. For instance, a user’s high cognitive learning efforts may enable him to master the physical subtleties of the new product, which in turn increases his or her cognitive understanding of the relations between the product’s attributes and benefits. However, we propose that this positive and interactive effect between the cognitive and physical dimensions of new product learning will only take place when the user feels a certain degree of emotional attachment to the new product. Such emotional attachment may be generated through advertising or other external or internal stimuli. The interaction between the cognitive dimension and the physical dimension will intensify the emotional dimension of the user involvement as the skill level of the users approaches the challenging level of the new product, and ultimately lead to the state of "flow" for some users (see Csikszentmihalyi, 1975).



In a situation where a user is either naturally skilled in mastering physical subtleties or the design of a new product is simple to operate and user-friendly, the user may develop an instantaneous emotional liking or attachment to the new product without the need for high cognitive learning efforts. Such interaction between the physical dimension and the emotional dimension of a user’s involvement with a product commonly occurs in so called relationship-prone consumer products. It can also be achieved through purposeful marketing strategies and user friendly design emphasising operational simplicity in high-tech durable products. The operational simplicity and the emotional attachment with a new product at the early stage of use process provide the users the opportunity for acquiring deeper cognitive understanding of the product attributes and benefits, as their experience of using the new product develops subsequently.


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Qing Wang, University of Warwick, UK


E - European Advances in Consumer Research Volume 6 | 2003

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