Dear sir, I need the answers for all five questions from Assignment document, using the Roger's five factors, and E-books cases... Thank you!
Instructions Use the E-books case and your reading on Roger's five factors to answer the following questions: 1. What do you expect the long-run total adoption of e-books to be (i.e., the parameter m in the Bass model)? The total market size, m, should be the total number of potential consumers for e-books as opposed to the total number of e-book purchases/downloads. There is no "right" answer, but I want you to explain the logic behind your estimate. 2. What values of p and q should we use to model the adoption of E-books? Justify your answer using the values the case provides for other markets. 3. Consider two of Rogers five factors that you think most apply to E-book adoption. Use these factors to explain how you might adjust your values of p and q. 4. Using your estimate of m p and q, create an adoption forecast using the Bass model. 1. What are your expected cumulative sales in year 5? 2. In what year do sales peak? 5. Consider the more advanced model in which two segments (influentials and imitators) can influence themselves, but imitators can also be influenced by influentials (but not the other way around). In this model, your sales estimate is the sum of two Bass models. The first is identical to the traditional model, but the second includes an additional parameter w that determines the degree to which imitators are influenced by influentials (in addition to themselves). The model for each is: n1 = [p1+q1(N1/m1)][(m1-N1)] and n2 = [p2+q2[w(N1/m1)+(1-w)(N2/m2)]][m2-N2]. Assuming m1 = 100,000 and m2=1,000,000, what values of p1,p2,q1,q2 and w, will produce the kind of "Chasm" we discussed in class? Note on Innovation Diffusion: Rogers' Five Factors 9-505-075 R E V : A P R I L 1 7 , 2 0 0 6 ________________________________________________________________________________________________________________ Professor John T. Gourville prepared this note as the basis for class discussion. It is intended to be used with HBS Case No. 502-045, “Four Products: Predicting Diffusion,” but can also be used as an independent learning vehicle. Copyright © 2005 President and Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1-800-545-7685, write Harvard Business School Publishing, Boston, MA 02163, or go to http://www.hbsp.harvard.edu. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the permission of Harvard Business School. J O H N T . G O U R V I L L E Note on Innovation Diffusion: Rogers' Five Factors Managers have long been interested in the diffusion of innovations. The thinking being, if one can better understand how innovations diffuse, one can better predict and manage that diffusion. Toward this end, great focus and effort have been put toward understanding the impact of “people differences” on diffusion. Both Everett Rogers and Geoffrey Moore,1 for instance, place potential innovation adopters into one of five categories—innovators, early adopters, early majority, late majority, and laggards—with one’s propensity to adopt decreasing as one moves from innovators to laggards. Further, each argues the likelihood of an individual falling into any one of these groups can be captured by a bell curve, as reflected in Figure A. In both Rogers’ and Moore’s frameworks, diffusion typically starts among innovators, moves to early adopters, then the early majority, and so on. However, while Rogers envisions a relatively smooth transition from group to group, Moore argues there is a significant gap between early adopters and the early majority, resulting in a “chasm” that many firms have difficulty overcoming. A second people-based framework for considering the diffusion of innovations is offered by Frank Bass. 2 He argues for two types of individuals—innovators and imitators—and argues that the rate of innovation diffusion will be governed by the relative size of these two groups and their respective propensity to innovate or imitate.3 Figure A The Categorization of Innovation Adopters Early Majority Late Majority Laggards Early Adopters Innovators Early Majority Late Majority Laggards Early Adopters Innovators Source: Adapted from Everett Rogers, Diffusion of Innovations (1995), p. 262. 1 For more details, see Everett Rogers, Diffusion of Innovations (New York, NY: The Free Press, 1995); and Geoffrey Moore, Crossing the Chasm (New York, NY: HarperCollins, 1999). 2 For more details on the Bass model, see Elie Ofek, “Forecasting the Adoption of a New Product,” HBS Case No. 505-062. 3 Users of the Bass model, however, do make allowances for the type of product being promoted when considering the size of the two groups. For the exclusive use of R. Grandhe, 2021. This document is authorized for use only by Ram Grandhe in GSMPR 625 (SU21) Portland taught by Jameson Watts, Willamette University from May 2021 to Oct 2021. 505-075 Note on Innovation Diffusion: Rogers' Five Factors 2 In contrast to this focus on “people differences,” far less attention has been paid to “product differences”—that is, those factors intrinsic to the good or service being offered. By almost any measure, this is a great oversight. Research by Rogers and his colleagues over the past four decades suggests that 49% to 87% of the variance in the rate of new product adoption can be explained by five product-based characteristics.4 In other words, the reason one product diffuses rapidly and another slowly largely can be found in a small handful of product characteristics, or “factors.” In turn, managers who understand the role of these factors can (1) better anticipate the likely adoption of an innovation, (2) proactively develop a product so as to increase the likelihood of product adoption, and/or (3) tailor one’s marketing activities to leverage those factors on which the innovation does well and overcome those factors on which the innovation does poorly. In the remainder of this note, we will take a closer look at “Rogers’ Five Factors.” An Introduction to Rogers’ Five Factors5 Many technologists believe that advantageous innovations will “sell themselves” and diffuse rapidly. Reality suggests this rarely is the case, with innovative new products failing far more often than they succeed.6 And those that eventually do succeed often take far longer than expected. One merely needs to look at the telephone, a product we now take for granted. The telephone was invented in 1876 by Alexander Graham Bell. At the time, people and businesses delivered information either in person, by messenger, or perhaps by telegraph. In retrospect, the telephone seems like a natural solution to the problems of time and distance that existed in the late 1800s. Yet, by 1900, 25 years after its invention, the telephone could be found in only 10% of the households of the United States. By 1935, 60 years after its invention, it could be found in only about 30% of households. And it was not until the 1980s that the telephone reached 90% of American households. This slow rate of diffusion highlights the struggles any innovation faces in the marketplace, even one as seemingly foolproof as the telephone. But, why do some products diffuse easily and rapidly, while others diffuse slowly, or not at all? As early as the 1960s, Rogers suggested five product-based factors that largely governed the rate of innovation diffusion. These five factors were: Relative advantage—the degree to which a product is better than the product it replaces Compatibility—the degree to which a product is consistent with existing values and experiences Complexity—the degree to which a product is difficult to understand and use Trialability—the degree to which a product may be experimented with on a limited basis Observability—the degree to which product usage and impact are visible to others In considering these five factors, as we will do shortly, at least two issues are important to consider. First, perceptions matter! For instance, while one could analyze the objective “relative advantage” of one product over another, what really matters is the “relative advantage” as perceived by the potential adopter. Therefore, a manager’s responsibility is to manage not only the objective characteristics of an innovation, but the characteristics of that innovation as perceived by individuals. 4 For details, see Chapter 6, “Attributes of Innovations and Their Rate of Adoption,” in Rogers, Diffusion of Innovations (1995, 4th Edition). 5 Much of what follows has been adapted from Rogers, Diffusion of Innovations (1995, 4th Edition). 6 For evidence of this, refer to Glen Urban and John Hauser, Design and Marketing of New Products (Englewood Cliffs, NJ: Prentice Hall, 1993). Also see Peter Golder and Gerard Tellis, “Pioneering Advantage: Marketing Logic or Marketing Legend?” Journal of Marketing Research (May 1993). For the exclusive use of R. Grandhe, 2021. This document is authorized for use only by Ram Grandhe in GSMPR 625 (SU21) Portland taught by Jameson Watts, Willamette University from May 2021 to Oct 2021. Note on Innovation Diffusion: Rogers' Five Factors 505-075 3 Second, one needs to think of an innovation as any product or idea that is perceived as new by a given set of people. Indeed, a product that is well entrenched in one culture could be viewed as highly innovative in another culture if that product is perceived as new by that second culture. Rogers’ Five Factors Now let us look at Rogers’ Five Factors in detail. Factor #1: Relative advantage Relative advantage is “the degree to which an innovation is