Predicting Innovation Adoption: a Choice-Based Approach

ABSTRACT - The problem of predicting innovation adoption at the individual level is considered using a choice-based approach. The choice model incorporates information uncertainty, risk aversion, perceived product attributes, and product use norms in a utility maximization framework to predict innovation adoption. Testable hypotheses regarding innovation adoption are developed.


Soo-Jiuan Tan (1994) ,"Predicting Innovation Adoption: a Choice-Based Approach", in AP - Asia Pacific Advances in Consumer Research Volume 1, eds. Joseph A. Cote and Siew Meng Leong, Provo, UT : Association for Consumer Research, Pages: 72-78.

Asia Pacific Advances in Consumer Research Volume 1, 1994      Pages 72-78


Soo-Jiuan Tan, National University of Singapore


The problem of predicting innovation adoption at the individual level is considered using a choice-based approach. The choice model incorporates information uncertainty, risk aversion, perceived product attributes, and product use norms in a utility maximization framework to predict innovation adoption. Testable hypotheses regarding innovation adoption are developed.


The study of innovation adoption has come a long way since Roger's review of the adoption of new products by a social system (see Rogers, 1962). The last three decades have seen the proliferation of mathematical models devoted to describing innovation adoption, with most of them being variations of the simple epidemic model (Kalish, 1985). Concurrently, numerous behavioral studies have also generated different profiles of the innovative consumer, identifying characteristics such as social integration, mobility and club membership, as responsible for innovation adoption (Dickerson and Gentry, 1983). In their seminal review of diffusion research, Gatignon and Robertson (1985) refer to the mathematical modelling type of diffusion research as diffusion modelling research, and the behavioral studies as consumer diffusion research, and suggest that "an integration of the behavioral and modelling literatures on diffusion could be beneficial to both constituencies," (p. 849).

This paper attempts to provide a link between diffusion modelling and consumer diffusion research by examining the consumer's innovation adoption decision in a utility maximization context and proposes a choice model to aid in the prediction of innovation adoption. The model incorporates multiattribute preference, risk, and information uncertainty in an individual level expected utility framework.

The next section provides a perspective on the relevant literature and positions this paper accordingly. Then the theoretical framework supporting the proposed model is described, followed by a discussion on model development. The choice model of innovation adoption is then presented. Testable hypotheses regarding differences between early and late adopters in their adoption decision-making are next proposed. The paper ends with suggestions for future research.


In innovation adoption research, diffusion modellers (Gatignon and Robertson, 1985) tend to focus their investigations at the aggregate level, and produce models capable of predicting sales and price path of new products. For example, Bass's (1969) single equation diffusion model used to forecast category sales for a new durable product has been extended to include marketing variables of advertising (Horsky and Simon, 1983; Kalish, 1985), and price (Robinson and Lakhani, 1975; Bass, 1980; Dolan and Jeuland, 1981; Kalish, 1985), and other parameters such as competitive effects (Robertson and Gatignon, 1986; Gatignon and Robertson, 1989), income distribution and product uncertainty (Horsky, 1990), multi-state populations (Dodson and Muller, 1978), price expectations of consumers (Narasimhan, 1989), risk (Jeuland, 1981; Kalish, 1985), states of word of mouth (Mahajan, et al., 1984), and target market expansion (Mahajan and Petersen, 1978).

Consumer diffusion studies, on the other hand, are often conducted at the individual level, and mostly involved correlational analyses, linking individual characteristics such as demographics, psychographics, and product experience to the propensity to adopt an innovation. Studies have found that early adopters have more education, more income, and higher occupation status than do non-adopters (Adcock, Hirschman, and Goldstucker, 1977; Bell, 1963; Feldman and Armstrong, 1975; Kegerries and Engel, 1969; LaBay and Kinnear, 1981; Plummer, 1971; Robertson, 1971; Rogers and Shoemaker, 1971; Rogers and Stanfield, 1968). Innovators are also found to be more inner-directed than non-innovators (Macdonald and Jacobs, 1992), and they are driven by sensation-seeking and uniqueness-seeking motives when adopting new products (Burns and Krampf, 1992). Other studies have also linked age of adopters with product characteristics of the innovation (Adcock et al., 1977; Porter, Swerdlow and Staples, 1979; McClurg and Andrews, 1974; Lunsford and Burnett, 1992). Linkage has also been found between innovation adoption and psychological constructs such as product experience (Zaltman and Stiff, 1973), creativity (Hirschman, 1980), origence and intellectence traits (Dickerson and Gentry, 1983), and family power (Burns, 1992).

Hence, at the aggregate level, innovation adoption research is primarily concerned with examining how an innovation is accepted by the total population in the adoption process, without taking into considerations behavioral and perceptual characteristics of the individual consumer. At the disaggregate level, innovations adoption studies focus on the identification of who would or would not adopt an innovation, without the support of a theoretical framework outlining how the decision to adopt is arrived at. As envisaged by Gatignon and Robertson (1985), there is a need to combine the theoretical foundations and behavioral verifications available in existing diffusion research into an integrated framework, to examine innovation adoption at the disaggregate/individual level. The emphasis on individual level analysis stems from the fact that diffusion pattern at the aggregate level is an outcome of the distribution of individual adoption decisions.

This paper attempts to integrate the two stream of research by examining the individual's decision to adopt an innovation using a microeconomic framework of analysis. Unlike most consumer diffusion studies which treat the consumer's innovation adoption decision process as a 'black box', the proposed approach models innovation adoption as the outcome of a consumer's utility maximization effort. This effort is influenced by the consumer's behavioral and perceptual considerations, economic constraint, and information uncertainty. Thus the proposed choice model not only provides consumer diffusion research with the necessary microeconomic foundations, but also demonstrates how behavioral, economic, and product-related variables interact to affect the adoption decision. Multiattribute utility, information uncertainty, product use norms, interpersonal communication, and belief dynamics, are modeled borrowing from choice theory in economics, diffusion theory, and expectancy value theory. Thus the proposed model is backed by established theories and concepts.

The proposed model draws its motivation from Roberts and Urban's (1988) model on durable brand choice, but differs from that model on two aspects. First, Roberts and Urban's model aims at modelling individual brand choice probability, whereas the proposed model attempts to provide answers to the issues of how and why the innovation adoption decision occurs, as well as who are the early adopters (in terms of decision-making characteristics). Second, this model includes normative beliefs about product attributes, which are not explicitly incorporated in Roberts and Urban's model. The uniqueness of the proposed model is that it can be used to explain adoption of not only a new product category, but also a new brand for an existing product category.




In Figure 1 we describe the decision process of a consumer who is contemplating adopting an innovation.

The consumer's preference for an innovation is governed by his/her perceived utility of the innovation. This perceived utility is determined jointly by the consumer's evaluation of the product attributes (perceived product attributes) and the normative influences (product use norms) exerting on the consumer. Under conditions of risk aversion and information uncertainty, the consumer will choose to adopt the innovation with maximum expected utility, subject to the economic constraint of the maximum amount which the consumer is willing to pay for the innovation (reservation price).

Perceived Product Attributes

The importance of perceptual variables in determining purchase behaviour is well established in the marketing literature (Lehmann, 1971). In diffusion studies, perceived innovation attributes rather than personal characteristics were found to be better predictors of the rate of innovation adoption (e.g., Roger and Shoemaker, 1971; Ostlundt, 1974). Hence it is appropriate to define the utility value of a new product or brand in terms of its perceived attributes. Since the number and level of attributes for a new product or brand are uncertain, and perception is a personal concept, the perceived innovation attributes construct best captures the essence of uncertainty inherent in the valuation of a new product or brand.

Product Use Norms

Consumers use both reflective and comparative appraisal in product choices, that is, they engage in direct, verbal interaction to determine the reference group's evaluation, as well as observing the behaviour of the reference group members with regards to the decision under consideration (Moschis, 1976). Marketing practitioners have also used such reference group concepts in their efforts to persuade consumers to purchase products and brands. This reference group effect can be captured by a construct known as product use norm, similar in nature to the normative belief concept defined by Fishbein-Ajzen (1975), which means 'beliefs that certain referents think the person should or should not perform the behaviour in question,' (p.16). This product use norm is more important in some situations than others and for different individuals. For example, the product use norm may be more important in the evaluation stage of the adoption process than in other stages. It may also have more influence on the late adopters than on opinion leaders, for instance. The word-of-mouth and advertising effects on innovation adoption can thus be incorporated into the product use norms construct.

Value of an Innovation

Based on the foregoing constructs and using the Fishbein-Rosenberg class of expectancy models (Fishbein, 1967), the value of an innovation can be represented via the following:


Hence, for an early adopter, the weights given to product use norms may be zero, in which case the value of an innovation is determined entirely by the weighted perceived product attribute values. Alternatively, one could also envisage that at the trial stage of the new product introduction where advertising effect and word of mouth effects are minimal or zero, the potential adopter's valuation of the new product could depend entirely on his subjective evaluation of the weighted perceived attributes. Adoption of the innovation at this stage thus distinguishes the opinion leaders from the late majority.

The foregoing definition of Xj implies that the potential adopter is certain about the true value of the products, hence it is best to denote Xj by Xk , where the tilde sign represents uncertainty.

Reservation Price

Gatignon and Robertson (1985) propose that the adoption of an innovation should depend on its fit within the existing consumption system and its ability to compete for scarce resources in order to achieve a position in the consumer's priority acquisition pattern (p. 855). Thus in this study, we incorporate the effects of a consumption system fit and priority acquisition pattern on innovation adoption through an economic constraint called reservation price.

The seminal work by Gabor and Granger (1966) has suggested that because the product-selection process has certain risks, price may be taken as a quality cue to reduce the perceived risk of purchase. In the works of Gabor and Granger (1966) and Sowter, Gabor and Granger (1971), the relationship between price and quality is specified via a "limit concept". A consumer intent on purchasing a product has two price limits in mind: an upper limit above which purchase will not be made because the good is too expensive and a lower limit below which purchase will not be made because the quality of the item is suspect. This limit concept is known in the economic literature as a reservation price (Lilien and Kotler, 1983).

In this study, the reservation price is defined as the maximum amount the consumer is willing to pay for an innovation, i.e., the upper limit of the price concept discussed above. This price limit ensures that the perceived value of the innovation under consideration is compatible with those of other goods in the consumer's consumption system. The reservation price also serves as an allocation mechanism in that only innovations which are within the upper price limit will be considered by the consumer.



To facilitate model development, the following definitions and assumptions are required:

1. Innovation or new product is used interchangeably, to represent either a new product category or a new brand. This flexibility is warranted because of the use of perceived value (hence perceived product attributes) concept of an innovation mentioned in the theoretical framework discussed. The product could be a consumer durable or non-durable, since there are non-durable products where social norms are important in determining consumption behaviour (i.e., product use norms).

2. Without loss of generality, assume that the uncertain perceived value of a new product, Xj, that a consumer believes he would realize is the average value for the product plus some uncertainty associated with the product, i.e.,

EQUATION (2) - (4)

3. Hence we assume that Xj is distributed normally with mean Xj and variance o2j. This variance can be defined in terms of the total uncertainty which a consumer expects to realize and comprises:

*  subjective uncertainty about the product, i.e., o2uj , and

*  inherent product variability, o2ej, where

        o2j = o2uj + o2ej

4. The distinction is made between subjective uncertainty and inherent product variability to highlight the fact that even with perfect information (i.e., zero subjective uncertainty), there is still some uncertainty associated with the value of the product, which is beyond the consumer's control. For example, all personal computers of the same model produced in the same factory do not always have the same performance characteristics. As a result of random accidents in the production process, some substandard computers are occasionally produced and sold. The consumer has no way of knowing ahead of time whether the particular personal computer that he/she purchases is of standard quality or not.

5. Even for early adopters, we assume that all of them are risk averse, since 'introspection and observed behaviour suggest that most people are risk averse in most of their dealings,' (Henderson and Quandt, 1980). Following Currim and Sarin's (1984) suggestion, the exponential form of utility function is adopted, with degree of risk aversion r being positive and constant. The expected utility function (see Keeney and Raiffa (1976) for derivation) becomes:


where r > 0 and is held constant.

Innovation Adoption Decision

The traditional theory of consumer behaviour does not include an analysis of uncertain situations (Henderson and Quandt, 1980). Hence, we make use of the von Neumann Morgenstern expected utility theorem, the significance of which is that uncertain situations can be analyzed in terms of maximization of expected utility. Under the assumption of risk aversion, the von Neumann Morgenstern theorem postulates that a person is a risk averter to a lottery if the utility of its expected value is greater than the expected value of its utility (Henderson and Quandt, 1980).

Thus one can liken the decision to adopt an innovation to a lottery decision. In this case, the consumer will maximize expected utility such that if the utility of the expected value of the innovation, i.e., the reservation price, is less than or equal to the maximized expected utility of the innovation (that is, the EQUATION value mentioned earlier), then the decision will be to purchase the product, i.e., innovation adoption occurs. Formally, this means adoption occurs only if:


where Pr is the reservation price.

The foregoing equation shows that the expected utility function is monotonic in:


Hence, maximizing this expression is equivalent to maximizing the expected utility of the innovation under consideration. This means that expected utility could be maximized by increasing the number of perceived product attributes and or the product use norms. This could be achieved through advertising, promotion, and word-of-mouth recommendations. Subjective uncertainty could also be reduced by receiving more information about the product. Alternatively, the manufacturers could implement product improvements or better quality control, or try to convince potential adopters that such improvements have been/will be made, to reduce product-inherent variability. Without loss of generality, it will be assumed that this product-inherent uncertainty will remain constant, in view of the 'Lemon' phenomenon mentioned earlier. Finally, by assigning heavier weights to the attributes and/or product use norms, the expected utility of an innovation could also be increased or maximized.

The foregoing also implies that negative word-of-mouth recommendation or any negative advertising or promotion effects, could similarly reduce the expected utility of an innovation. Even if there are no negative effects, if there is still alot of uncertainty about the product (especially the subjective uncertainty), then the overall attractiveness of the innovation is very much reduced due to its low expected utility.


The concept of adoption has been used in a rather limited way to refer to a single decision (Gatignon and Robertson, 1985). In reality, the consumer often face a variety of innovations, either across product categories, or within the same product category. For example, high definition televisions, VCR systems, personal computers, etc...compete for the consumer's attention in the consumer appliances category. At the same time, even within a single product category like high definition television, there are several new brands to consider. Thus we can model the choice decision via the conditional logit model (Greene, 1990), which deals with multiple choices and choice-specific attributes.

Thus the probability that an innovation i will be chosen from a set of j=1, ....., N competing innovations by the consumer is given by:


Equation (7) links the preference function that reflects perceived attributes, product use norms, information uncertainty, and product-inherent uncertainty to the probability of adopting an innovation, for an individual consumer.

Estimation of the conditional logit model is simplest by Newton's method or the method of scoring (Greene, 1990). Positive values of the coefficients increase the likelihood of adoption by the consumer for positively valued variables (e.g., the perceived product attributes and those product use norms favouring innovation adoption). Similarly, the likelihood of adoption will be decreased by the presence of negatively valued variables such as subjective uncertainty and product-inherent uncertainty, as well as unfavourable product use norms.


Besides developing the innovation adoption choice model to predict the probability of adopting an innovation, we also derive several testable hypotheses from the theoretical framework supporting the choice model. The following hypotheses relate to the differences in the adoption choice behavior of early and late adopters.

H(1): Early adopters tend to have higher reservation price than the late adopters, for the same innovation under consideration.

There are two explanations as to why the late adopters tend to have lower reservation price than the early adopters. First, late adopters are more risk averse and uncertain about the innovation than the early adopters. Hence, they may discount the value of an innovation more than what the early adopters will do. Second, they are therefore not in a hurry to adopt the innovation and do not have high waiting costs. Thus late adopters have lower willingness to pay to become an early adopter. This is in line with what we observe in the marketplace, whereby the price of a new product tend to decline as it goes through its introductory to mass marketing phase. Although cost efficiency may be a reason for the decline, the lower price could be the marketers' way of catering to the lower reservation price of late adopters.

Ostlundt's (1974) study shows that "the perceptions of innovations by potential adopters can be very effective predictors of innovativeness," (p. 28). Hence, one would expect that there are perceived attributes which prompt early adopters to adopt an innovation while there could be an alternative set of perceived attributes which prompt late adopters to adopt the innovation, albeit at a later stage. In fact, we could hypothesize that because early adopters know what they want from a new product, only salient attributes are perceived, whereas late adopters need more perceived attributes to convince them of the value of the new product for adoption. Hence,

H(2): Early adopters have fewer number of perceived attributes than those of late adopters.

We would therefore expect that in estimating the choice model, it will involve more perceived product attribute variables for late adopters than for early adopters, for the same innovation under consideration.

It follows from hypothesis 2 that, for product attributes which are common to both the early and the late adopters, the perceived values are higher for the early adopters, i.e.,

H(2a): For the same set of product attributes, early adopters have higher perceived value than do late adopters, i.e.,


where 'n' refers to the number of perceived attributes common to both early and late adopters, for the same new product 'j' under consideration.

Early adopters are opinion leaders, who are 'people within a reference group who, because of special skills, knowledge, personality, or other characteristics, exert influence on others,' (Kotler and Armstrong, 1993, p.125). Hence, one would expect them to have less product use norms in their evaluation of the expected utility of a new product. Thus, we hypothesize that:

H(3): Early adopters have less product use norms than late adopters when evaluating new product, i.e.,


where 'n' and 'm' refer to the total number of product use norms for the early and late adopters, respectively, for the same new product 'j' under consideration.

Alternatively, the foregoing could be stated in terms of weightage given to the normative factors, i.e., early adopters are likely to pay less attention to product use norms and more attention to perceived product attributes, and vice versa for late adopters. Thus, we can hypothesize that,

H(4): Early adopters tend to give more weights to perceived product attributes than to product use norms in evaluating new product innovation, whereas late adopters give more weight to product use norms, i.e.,




At the introductory stage, most new products are not well publicized and there is alot of information uncertainty on the part of the consumers. This information uncertainty can influence the perceived attribute of the new product, because subjective uncertainty (i.e., smj) is increased. However, we would expect the early adopters to have less subjective uncertainty than the late adopters since they are more experienced in adopting new products than late adopters. It has been suggested that 'prior knowledge or experience with a product class may lead to greater ability to detect superior new products within the class and hence increase the likelihood that they will be adopted,' (Hirschman, 1980). Thus we can hypothesize that:

H(5): Early adopters have less subjective product uncertainty than late adopters, i.e.,


where the E and L subscripts represent early and late adopters, respectively.


This paper represents an attempt to provide a theoretical framework for the study of innovation adoption at the individual level. The proposed choice model of innovation adoption incorporates both behavioral and product attribute factors in a consumer expected utility maximization framework. Several testable hypotheses are developed to illustrate how the model can be used to test out the roles played by perceived attributes, product use norms, subjective uncertainty, and inherent product uncertainty in the adoption decision; and to determine whether early adopters and late adopters exhibit different behaviour in their use of decision variables. The results of hypotheses testing could contribute to our understanding of how the consumer arrives at the decision to adopt a new product/brand. They are also useful in answering important research questions like whether product attribute variables, decision-making variables, or personal characteristics are better predictors of innovation adoption. Thus the model represents an attempt to 'peep' into the decision 'black box' which most behavioral innovations adoption research do not address when predicting or explaining innovation adoption.

Theoretical development is incomplete without vigorous empirical investigation. Thus an immediate task is to carry out an empirical investigation of the model. Perhaps an experimental design involving the use of interactive computer programming could be developed to test out the model variables. A laboratory setting involving the use of interactive computer programming is required because of the need to gather perceptions and decisions of the early and late adopters concurrently as they encounter a new product or new brand. Moreover, information like reservation price, risk aversion, and perceptions of the individuals gathered after adoption are likely to be contaminated by whatever post-purchase decision dissonance there may exist. To differentiate between early and late adopters, we can make use of the socio-demographic variables such as income, education, age, social mobility, risk attitude, and social participation, which have been identified by researchers as personal characteristics of innovators (e.g., Midgley and Dowling 1978; Rogers 1983; Robertson, Zielinski, and Ward 1984). Separate estimation of the conditional logit model could then be conducted to obtain the respective coefficients of the model for testing the hypotheses proposed.

Future research could also involve relaxation of some of the assumptions used in the model, for example, those relating to risk aversion and the additive preference function. Measurement scales could also be developed to operationalize the subjective uncertainty and product-inherent variability factors in the choice model. Other personal characteristics like age and gender, and other marketing variables such as price, could also be incorporated into the model to determine new product or brand adoption.


Adcock, W.O. Jr., E.C. Hirschman, and J.L. Goldstucker (1977), "Bank Credit Card Users: An updated Profile," in Advances In Consumer Research, Vol. 4, ed. William D. Perreault, Jr., Atlanta, G.A.: Association for Consumer Research.

Bass, F.M. (1969), "A New Product Growth Model for Consumer Durables," Management Science, 2 (January), 1-18.

Bass, F.M. (1980), "The Relationship Between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations," Journal of Business, 53 (March), 851-867.

Bell, W.E. (1963), "Consumer Innovators: A Unique Market for Newness," in Toward Scientific Marketing, ed. Stephen A. Greyser, Chicago: American Marketing Association, 90-93.

Boone, L.E. (1970), "The Search for the Consumer Innovator," Journal of Business, 43 (April), 135-140.

Burns, D.J. (1992), "Husband-Wife Innovation Consumer Decision Making: Exploring the effect of Family Power," Psychology and Marketing, 9 (May/June), 175-189.

Burns, D.J. and R.F. Krampf (1992), "Explaining Innovative Behavior: Uniqueness-Seeking and Sensation-Seeking," International Journal of Advertising, 11, 227-237.

Currim, I.S. and R.K. Sarin (1984), "A Comparative Evaluation of Multiattribute Consumer Preference Models," Management Science, 5 (May), 543-561.

Dickerson, M.D. and J.W. Gentry (1983), "Characteristics of Adopters and Non-adopters of Home Computers," Journal of Consumer Research, 10 (September), 225-235.

Dodson, J.A. and E. Muller (1978), "Models of New Product Diffusion Through Advertising and Word of Mouth," Management Science, 15 (November), 1568-1578.

Dolan, R.J. and A.P. Jeuland (1981), "Experience Curves and Dynamic Demand Models: Implications for Optimal Pricing Strategies," Journal of Marketing, 45 (January), 52-62.

Feldman, L.P. and G.M. Armstrong (1975), "Identifying Buyers of a Major Automotive Innovation," Journal of Marketing, 39 (January), 47-53.

Fishbein, M. (1967), Readings in Attitude and Measurement, New York: John Wiley and Sons.

Fishbein, M. and I. Ajzen (1975), Belief, Attitude, Intention and Behavior, MA: Addison-Wesley.

Gabor, A. and C.W.J. Granger (1966), "Price as an indicator of Quality: Report on an Inquiry," Economica, Vol. 33, No. 129 (February), 43-70.

Gatignon, H. and T.S. Robertson (1985), "A Propositional Inventory for New Diffusion Research," Journal of Consumer Research, 11 (March), 849-867.

Gatignon, H. (1989), "Technology Diffusion: An Empirical Test of Competitive Effects," Journal of Marketing, 53 (January), 35-49.

Green, P.E. and F.J. Carmone (1970), Multidimensional Scaling and Related Techniques in Marketing Analysis, Boston: Allyn and Bacon, Inc.

Greene, W. (1990), Econometric Analysis, New York: Macmillan Publishing.

Henderson, J.M. and R.E. Quandt (1980), Microeconomic Theory: A Mathematical Approach, Singapore: McGraw-Hill, Inc.

Hirschman, E.C. (1980), "Innovativeness, Novelty Seeking, and Consumer Creativity," Journal of Consumer Research, 7 (December), 283-295.

Horsky, D. (1990), "A Diffusion Model Incorporating Product Benefits, Price, Income and Information," Marketing Science, 4 (Fall), 342-365.

Horsky, D. and L.S. Simon (1983), "Advertising and Diffusion of New Products," Marketing Science, 2 (January), 1-18.

Jeuland, A.P. (1981), "Parsimonious Models of Diffusion of Innovation: Part A and B," Working Paper, University of Chicago.

Johnson, R.M. (1971), "Market Segmentation: A Strategic Management Tool, " Journal of Marketing Research, 8 (February), 13-18.

Kalish, S. (1985), "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, 12 (December), 1569-1585.

Keeney, R.L. and H. Raiffa (1976), Decision Making with Multiple Objectives: Preferences and Value Tradeoffs, New York: John Wiley and Sons.

Kegerreis, R.J. and J.F. Engle (1969), "The Innovative Consumer: Characteristics of the Earliest Adopters of a New Automotive Service," in Marketing Involvement in Society and the Economy, ed. Philip R. McDonald, Chicago: American Marketing Association, 357-361.

Labay, D.G. and T.C. Kinnear (1981), "Exploring the Consumer Decision Process in the Adoption of Solar Energy Systems," Journal of Consumer Research, 8 (December), 271-278.

Lehmann, D.R. (1971), "Television Show Preference: An Application of a Choice Model," Journal of Marketing Research, 8 (February), 47-55.

Lilien, G.L. and P. Kotler (1983), Marketing Decision Making: A Model-Building Approach, New York: Harper and Row, Publishers Inc.

Macdonald, C.G.R. and L.W. Jacobs (1992), " The Importance of Inner/Other-Directed Personality Factors upon Innovative Product Acceptance," International Journal of Advertising, 11 (3), 239-247.

Mahajan, V. and R.A. Petersen (1978), "Innovation Diffusion in a Dynamic Potential Adopter Population," Management Science, 15 (November), 1589-1597.

Mahajan, V., E. Muller and R. Kerin (1984), "Introduction Strategy for New Products with Positive and Negative Word of Mouth," Management Science, 12 (December), 1389-1404.

McClurg, J.M. and I.R. Andrews (1974), "A Consumer Profile Analysis of the Self-Service Gasoline Customer," Journal of Applied Psychology, 59 (February), 119-121.

Midgley, D.F. and G.R. Dowling (1978), "Innovativeness: The Concept and Its Measurement," Journal of Consumer Research, 4 (March), 229-242.

Moschis, G.P. (1976), "Social Comparison and Informal Group Influence," Journal of Marketing Research, 13, 237-244.

Narasimhan, C. (1989), "Incorporating Price Expectations in Diffusion Model," Management Science, 8 (Fall), 343-357.

Ostlundt, L.E. (1974), "Perceived Innovation Attributes as Predictors of Innovativeness," Journal of Consumer Research, 1 (September), 23-29.

Plummer, J.T. (1971), "Lifestyle Patterns and Commercial Bank Credit Card Usage," Journal of Marketing, 35 (April), 35-41.

Roberts, J.H. and G.L. Urban (1988), "Modeling Multiattribute Utility, Risk, and Belief Dynamics for New Consumer Durable Brand Choice," Management Science, 34 (February), 167-185.

Robertson, T.S. (1971), Innovative Behavior and Communications, New York: Holt, Rinehart and Winston.

Robertson, T.S., J. Zielinski and S. Ward (1984), Consumer Behavior, Glenview, IL: Scott, Foresman.

Robertson, T.S. and H. Gatignon (1986), "Competitive Effects on Technology Diffusion," Journal of Marketing, 50 (July), 1-12.

Robinson, V. and C. Lakhani (1975), "Dynamic Price Models for New Product Planning," Management Science, 21 (June), 1113-1132.

Rogers, E.M. (1962), Diffusion of Innovations, New York: The Free Press.

Rogers, E.M. (1983), Diffusion of Innovations, New York: The Free Press.

Rogers, E.M. and F.F. Shoemaker (1971), Communications of Innovations, New York: The Free Press.

Rogers, E.M. and D.J. Stanfield (1968), "Adoption and Diffusion of New Products," in Applications of the Sciences in Marketing Management, eds., Bass, Frank, King, C.W. and Pessemier, E.A., New York: John Wiley.

Sowter, A.P., A. Gabor and C.W.J. Granger (1971), "The Effect of Price on Choice," Applied Economics, 3, 167-181.

Zaltman, G. and R. Stiff (1973), "Theories of Diffusion," in Consumer Behavior: Theoretical Sources, eds. Scott Ward and Thomas S. Robertson, Englewood Cliffs, NJ: Prentice Hall, 416-468.



Soo-Jiuan Tan, National University of Singapore


AP - Asia Pacific Advances in Consumer Research Volume 1 | 1994

Share Proceeding

Featured papers

See More


‘Family Tech-Support’: Consequences for Family Assemblages and Non-Purchase Decision Technology Adoption

Pao Franco, University of Melbourne, Australia

Read More


Emotional Volatility and Cultural Success

Jonah Berger, University of Pennsylvania, USA
Yoon Duk Kim, University of Pennsylvania, USA
Robert Meyer, University of Pennsylvania, USA

Read More


Effortful but Valuable: How Perceptions of Effort Affect Charitable Gift Choice and Valuations of Charity

Haesung Annie Jung, University of Texas at Austin, USA
Marlone Henderson, University of Texas at Austin, USA

Read More

Engage with Us

Becoming an Association for Consumer Research member is simple. Membership in ACR is relatively inexpensive, but brings significant benefits to its members.