Adopt early, prescribe often?

Editor’s note: Michael Latta is executive director of YTMBA, a Wilmington, Del., research and consulting firm.

Getting new products adopted, including those with obvious advantages, is many times difficult. As Machiavelli (1513) famously wrote: “There is nothing more difficult to plan, more doubtful of success, nor more dangerous to manage than the creation of a new order of things. Whenever his enemies have the ability to attack the innovator they do so with the passion of partisans, while the others defend him sluggishly, so that the innovator and his party alike are vulnerable.”

Since Machiavelli, adoption of new products has been studied extensively. Even the relationship between diffusion of innovation and the adoption process has been explored. Here, diffusion is concerned with the spread of a new product or technology from an innovative manufacturer to the end user, while adoption refers to the sequence of psychological stages the end user progresses through from becoming aware of the new product or technology to final acceptance and adoption.

Rogers (1962) first outlined his innovation adoption process with five stages: knowledge, persuasion, decision, implementation, confirmation.

However, Beal, Rogers and Bohlen (1957) validated five stages empirically which were known as: awareness, information, application, trial, adoption.

These labels are very similar to current marketing models including the product life cycle.

Rogers (2003) has also postulated there are individual members of a social system who are predisposed to be innovative and will adopt an innovation sooner than those who are not. The tendency of members of a social system to adopt innovations was classified into five categories according to the amount of time passing from innovation availability to adoption:

Innovators (2.5 percent);

Early Adopters (13.5 percent);

Early Majority (34.0 percent);

Late Majority (34.0 percent);

Laggards (16.0 percent).

The proportion of members of a social system falling into each of these categories appears in parentheses above. At one end are the risk takers or pioneers who adopt innovations early while at the other end are those who resist adopting innovations for a long time if they ever adopt.

Adoption of pharmaceuticals

Adoption of pharmaceuticals has been studied by academics who are interested in testing theories about product adoption and by marketing research practitioners who are employed by pharmaceutical companies to maximize the adoption rate of new products to increase return on investment.

Empirical research on adoption of pharmaceuticals began in 1954 with the Columbia University drug diffusion study of tetracycline. This field study sponsored by Pfizer was done among 125 general practitioners, internists and pediatricians in Bloomington, Galesburg, Peoria and Quincy, Ill. An additional 128 physicians who were colleagues of these physicians were included as members of the social system. The results indicated that medical journal ads, detailing by sales representatives, providing physicians with peer-reviewed journal articles and sampling created awareness and knowledge of product attributes and benefits among members of the medical community, but were insufficient to persuade the average physician to adopt tetracycline. Peer-to-peer communications which are personal, targeted and trustworthy were found to be powerful determinants of adoption of the new drug. Today pharmaceutical marketers spend many promotional dollars on peer influence groups and symposia to influence both key opinion leaders and community physicians.

More recent academic research on diffusion of pharmaceuticals has focused on trial and repeat use and diffusion of new drugs into developing nations. Other studies of pharmaceuticals have described the timing of prescription writing by individual physicians to determine the effects of face-to-face selling on physician prescribing.

On the practitioner side, commercial primary marketing research on new pharmaceutical adoption has focused on practical questions such as:

- What do you like about this drug?

- What do you dislike about it?

- If it were to become available tomorrow, what is the likelihood you would prescribe it?

- What patients would you prescribe it for?

- What patients would you not prescribe it for?

- What drugs currently in use might it replace?

Historically, there has been an impressive array of academic research studies done on Rogers’ adoption model. However, to date no one has attempted to develop a measure of the predisposition to adopt new products to use in both academic and commercial new product research.

The implication of Rogers’ adoption theory is that there are members of a social system known as Innovators and Early Adopters who begin using new products as soon as they are available or shortly thereafter and who then influence other members of their social system to try and adopt the innovation. These members of the medical social system participate in the promotion of new pharmaceuticals during peer influence groups, convention exhibit discussions and word of mouth. Thus, development of such a psychometric scale of a predisposition to adopt new products would be helpful in identifying how many and who the Innovators and Early Adopters are, before launch.

Versions were pretested

As a start, a version of the adoption categories worded to reflect the medical environment surrounding pharmaceutical adoption was created. Throughout the year 2000 different versions of the original scale were pre-tested with medical professionals to check the face validity, the content validity and to refine the wording.

One change made early on was the substitution of Traditionalist for Laggard as the name of the final adopter status category. This change is consistent with Rogers (1995) approach and was more in line with medical practice and customary use.

Participants in medical marketing research studies typically fill one of the following four decision maker roles. A physician, nurse or other medical professional like a dietician who either: makes the final decision alone; decides with others; evaluates options; recommends to others who decide.

Consequently, different wording was developed depending upon whether or not the individual medical professional makes decisions on use of new products alone (category 1) or the individual plays some other role in the process (categories 2 to 4 for junior physicians in a group setting, nurses, dieticians, etc.).

The final scale definition for a physician who is the final decision maker is shown in Example 1.

If the medical professional involved was a junior physician in a group practice, nurse, dietician or some other specialty not in a final decision-making role, but in an influence role, the wording was modified to reflect their status in decision making within a physician practice, as shown in Example 2.

Hypotheses

Two obvious hypotheses can be stated as follows:

Does the distribution of medical professionals in the adopter categories first specified by Rogers conform to the expected values specified?

Does the distribution of medical professionals in the adopter categories vary a great deal or are they similar?

Methodology

Data were collected from participants in 10 primary marketing research projects concerned with new product development, trial and use. Eight of the 10 samples came from qualitative projects, with six involving individual depth interviews (IDIs) and two involving focus groups. The two quantitative projects involved self-administered surveys conducted at conventions. The samples, the medical specialty of the participants and the project type appear in Table 1.

Results

A graph of the theoretical and observed distributions of adopter status for all samples combined appears in Figure 1. The distributions show a higher percentage of medical personnel in the Innovator, Early Adopter and Early Majority than would be expected according to Rogers’ theory. A chi-square test shows the two distributions are significantly different from each other (p < .0001).

The distributions of medical professionals across the five adopter categories are presented in Table 2 for each sample separately and for all samples combined. The last row of the table shows Rogers’ theoretical values.

All of these 10 samples yielded a distribution of adopter status that is statistically significantly different (p < .05) from the expected values suggested by Rogers (1962) adoption theory.

Discussion

Clearly, the first hypothesis specifying that the distribution of medical professionals in the adopter categories conform to Rogers’ expected values is not supported. More medical professionals defined themselves as innovators, early adopters or early majority than is expected.

A second finding is that the distributions of medical professionals vary from one sample to another. For example, in three samples (prostate cancer with oncologists, chronic kidney disease with dieticians and HPV vaccine with nurses, GPs and IMs) there were no self-described Traditionalists. This finding is not surprising given the methodology exposed the participants to new product profiles prior to measuring their predisposition to adopt new products of that kind.

In addition the following situational factors are relevant to the lack of Traditionalists in these three samples.

  • The product described to oncologists is indicated for late-stage prostate cancer where bone metastases are common. This particular product strengthens bones and there are data suggesting it has the potential to prevent bone metastases, making it attractive for early adoption.
  • The product class described to dieticians is used with virtually every dialysis patient. The new product was developed to move the mode of administration for this class of drugs from injection to an oral medication, providing both quality of life and economic benefits making it also attractive for early adoption.
  • Finally, the product described to nurses, GPs and IMs was shown in clinical data to be 100-percent effective against HPV infections, which can cause cervical cancer, making it extraordinarily attractive for early adoption.

Conclusions and implications

The strength of field studies is their external validity or ability to reveal real-world relationships between theory and practice. Thus, a strength of the present study is exposing practicing physicians, nurses and dieticians to a new-product profile as well as measuring adoption tendencies. However, a weakness of field studies is their lack of internal validity or the ability to control extraneous sources of relationships, thereby allowing alternative explanations of the results that cannot be attributed to a relationship between adoption theory and adoption behaviors.

Two such sources of alternative explanations are demand characteristics of the measurement process and lack of randomization (Orne, 1962). Thus, the resulting distribution of responses on the adoption measurement scale proposed here might be expected given the self-selection bias in primary marketing research studies concerning new product development and adoption. Another possible reason for this distribution is that medical professionals define a segment of the population that just happen to behave in a way consistent with Rogers’ definition of Innovators, Early Adopters and Early Majority.

Ways to support using Rogers’ theory as a viable explanation of the results reported here include exploration of other professions and populations to validate these results. In addition, future research is needed to see if the adoption scale proposed here is related to actual new product adoption behaviors.

References

Beal, G. M., Rogers, E. M., and Bohlen, J. M. (1957), “Validity of the Concept of Stages in the Adoption Process.” Rural Sociology, 2, 166-168.

Rogers, E. Diffusion of Innovation (1st ed. 1962, 5th ed. 2003), New York: The Free Press.

Machiavelli, N. The Prince (1st ed. 1513, translation 1980 by W. K. Marriott), London: J. M. Dent and Sons, pp. 117-131.

Orne, M. T. (1962), “The Social Psychology of the Social Psychological Experiment: With Particular Reference to Demand Characteristics and their Implications.” American Psychologist, 17, 776-783.