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Innovation adoption and diffusion in the digital environment: Some research
opportunities
Article · January 2000
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eBRC 1999
eBusiness Research Center Working Paper02-1999
Innovation Adoption And Diffusion In The Digital Environment:Some Research Opportunities
Arvind RangaswamySunil Gupta
eBRC117F Technology Center Building
Research ParkUniversity Park, PA 16802-7000
Phone: (814) 863-7575Fax: (814) 865-5909
http://www.ebrc.psu.edu/
A joint venture of Penn State’s Smeal College of Business Administrationand the School of Information Sciences and Technology
Innovation Adoption and Diffusion in the Digital Environment:Some Research Opportunities
Arvind RangaswamyThe Pennsylvania State University
Sunil GuptaAcorn Information Services
October 1998Revised March 1999
Arvind Rangaswamy is Professor of Marketing, The Smeal College of BusinessAdministration, The Pennsylvania State University, University Park, PA 16802. Tel: (814)865-1907. Fax: (814) 865-3015. E-mail: [email protected]
Sunil Gupta is Executive Vice President, Acorn Information Services, 4 Corporate Drive,Shelton, CT 06484. Tel: (203) 225-7600. Fax: (203) 225-7610. E-mail:[email protected].
We thank Mr. Utpal M. Dholakia for his help with the data collection and analysisreported in the paper. We are also indebted to Professor David Reibstein for histhoughtful comments on an earlier version of the paper, which have improved thestructure and presentation of this material.
1
Abstract
The rapid growth of the Internet raises important new research questions abouthow individuals decide whether and when to adopt an innovation, and how the innovationdiffuses through the population. The digital medium will influence not only whatresearch issues we should pursue, but also how we will explore those issues, and how wedisseminate research results, insights, and techniques to a broad audience. In this paper,we take the first steps towards investigating these questions.
We start by highlighting how the digital medium is influencing adoption anddiffusion patterns of both digital and non-digital products. We then highlight severalresearch opportunities made possible by the richer data that are becoming availableonline. Specifically, we focus on opportunities for developing and testing finer-grainedmodels of innovation adoption and diffusion.
1
Innovation Adoption and Diffusion in the Digital Environment:Some Research Opportunities
1.0 Introduction
Within the past few years, a powerful new digital environment has emerged to
facilitate and support market exchanges. Although the Internet is arguably its most
visible manifestation, the digital environment includes a host of computer and
communication technologies that together are making it easier, and often better, for
buyers and sellers to find each other and complete market transactions. From the
perspective of customers, this environment supports all aspects of their purchase
(“adoption”) process, from awareness to choice, to purchase and consumption. For
digital products (e.g., software, consulting reports, music) the process can occur entirely
within the digital medium, whereas for non-digital products (e.g., automobiles, computer
hardware), the physical exchange takes place outside the medium, but the actual adoption
decisions can occur online.
The continuing growth of the digital environment offers opportunities and
challenges for us in academia to think creatively about how we model adoption behavior
and the diffusion process for new products and technologies. The study of adoption
behavior is derived from concepts and theories of individual decision making, and allows
us to segment and profile customers based on their times of adoption, or more generally,
on their propensity to adopt an innovation. Traditionally, these segments have been
categorized as “innovators,” “early adopters,” etc. (Rogers 1983). The diffusion process
explains and predicts the time path of adoption of new products and technologies in a
market, and is based on concepts and theories of communication and interaction between
customers (word-of-mouth), and the influence of marketer-controlled activities (e.g.,
2
advertising in mass media). Interestingly, the digital environment can influence both
adoption behavior and the diffusion process in significant ways. For example, this
environment can alter the quality and quantity of information that potential adopters use
in deciding whether and when to adopt an innovation. The digital environment also
facilitates both word-of-mouth and marketer-controlled communications, thereby directly
impacting the diffusion process.
The objective of this paper is to identify and articulate research opportunities
afforded by the digital medium for modeling the adoption and diffusion of new products
and technologies.1 We highlight both the salient characteristics of the new medium, and
their implications for specific research issues that can take advantage of those
characteristics. Throughout, we focus on the Internet as the key element of the digital
environment.
In Section 2, we examine the diffusion of the Internet itself and products related
to the Internet (e.g., online shopping). We then explore the characteristics of early
adopters of these products, and conclude with a set of specific propositions about how the
Internet will influence the parameters of the Bass model. In Section 3, we explore issues
related to using the Internet as a data source for parameterizing adoption and diffusion
models. Specifically, we look at Internet-based data collection that makes it feasible to
estimate word-of-mouth effects before product introduction, and to estimate enhanced
diffusion models that take advantage of the new types of online data. In Section 4, we
propose a few additional areas of research and offer some concluding remarks.
1 For readers who need a background in adoption and diffusion models, we refer them to the
excellent review by Mahajan, Muller, and Bass (1993).
3
2.0 Online Adoption and Diffusion
By any measure, the Internet is a fast growing medium. In fact, as shown in
Exhibit 1, it has grown much faster than previous media innovations (Telephone, TV, and
Radio). Its average rate of penetration in the population is more than twice that of Cable
TV, a highly successful innovation. By some estimates, the worldwide number of
Internet users is projected to be over 120 million, having grown over 50% between 1997-
98. Although the Internet population in the US is gradually becoming similar in
composition to the overall US population (see ninth GVU survey at
www.cc.gatech.edu/gvu/user_surveys), the people currently making transactions on the
net are still predominantly young, affluent, Caucasian males. In terms of psychographics,
the “Digitial Citizen Survey” sponsored by Merrill Lynch in September 1997 concluded
that the digitally connected respondents are more willing to try new products, and tend to
be “futurist, change-oriented, libertarian capitalists, with a healthy outlook toward life.”
Exhibit 1: Shows that Internet has grown faster than previous communication andentertainment media in the U.S. (Source: Morgan Stanley Dean Witter Technology Research)
0
50
100
1922
1930
1938
1946
1951
1958
1966
1974
1981
1986
1994
1996
1998
*
Use
rs(M
M)
Radio TV Cable Internet
Years to reach 50MM users:
Radio 38
TV 13
Cable 10
Internet 5*
Years to reach 50MM users:
Radio 38
TV 13
Cable 10
Internet 5*
* Morgan Stanley Dean Witter Research.
4
As a digital medium, the Internet represents many things. One way to view the
Internet is as a vast repository of information that can be dynamically organized and
retrieved in multiplicity of ways according to the needs of individual users. For example,
online users can quickly retrieve almost anything and everything related to lubricants,
something that would be very time consuming to do offline. Information, namely,
anything that can be digitized, diffuses faster, cheaper, and to more people on the Internet
than by most other media.
The Internet can also be viewed as a medium that can be used for completing
various everyday activities such as e-mail communication, chatting with friends (and
strangers), and shopping. Consider online shopping. Although only 25-40% of the
online population has concluded a transaction entirely online, according to a large-scale
survey by ActivMedia, web generated revenues will grow from $22 billion in 1997 to
around $74 billion in 1998. To understand the potential impact of the Internet on the
diffusion process, it is useful to partition products into whether they are primarily digital
in nature, and the extent to which product adoption decisions are likely to be made over
the digital medium. The following table summarizes the various possibilities2:
Medium of adoptionNature of product Digital Non-digital
Digital
Results in rapid adoptionof good products andrapid death of poorproducts (e.g., new virussoftware)
These products are at acompetitive disadvantageand may die quickly(e.g., small databasesdistributed on CD)
Non-digitalSpeeds up the adoptionprocess (e.g., Rio musicplayer)
Marginal impact ontraditional products (e.g.,a new cola drink)
2 We are indebted to an anonymous reviewer for suggesting this categorization.
5
Digital products are characterized by very low marginal costs of production and
distribution. They are ideally suited for distribution and adoption in the digital medium
and we expect competitively advantaged digital products to diffuse rapidly (see the
discussion of Netscape browser below). At the same time, competitively disadvantaged
digital products will disappear from the market more quickly (e.g., Mosaic browser).
The interesting situation arises with respect to non-digital products that can take
advantage of the digital medium to influence adoption decisions. An interesting recent
case in point is the Rio, a device to play compressed music downloaded from the Internet.
Within weeks of its introduction, it was selling over 10,000 units a day. This occurred
because of the rapid growth in the number of web sites that offered music for download,
and because of the rapid word-of-mouth both on the Net and in the traditional media. To
further articulate the impact of the digital medium on product adoption, we will explore
the diffusion of online shopping.
Exhibit 2 highlights different facets of the growth in online shopping by charting
adoption patterns for Netscape and Amazon.com. Netscape created an entirely new
product category (browser software), whereas Amazon.com shook up an existing industry
(books). The data for Netscape shows the rapid frequency with which the company
introduced new products (a new version almost every six months) and encouraged rapid
diffusion by allowing the product to be downloaded, a form of e-commerce where the
product is delivered to the customer online. As an index of the speed of diffusion on the
Internet, an interesting statistic is that the equivalent of over half of the Navigator version
1.1 users moved to version 2.0 within just one month after version 2.0 was actually
shipping. From the Amazon.com data, we can see the rapid adoption of e-commerce in a
6
category where customers make choices and purchases online, but the product is
delivered in the physical medium. For both Netscape and Amazon.com, intense
Exhibit 2: Netscape Navigator/Communicator – Number of copies (millions) downloadedeach quarter. Amazon.com – Number of new customers acquired each quarter. (Netscapedata for Dec 94 - Sep 95 are based on statements made by senior company executives. Otherdata are from publicly available company reports).
competition drives the new product development and diffusion process – Netscape
Navigator competing against Microsoft Internet Explorer in the browser market and
Amazon.com competing against Barnes & Noble in the book market.
The Internet itself represents a market for a variety of interdependent products and
technologies whose diffusion patterns are of interest to researchers (e.g., servers and
clients, network devices, software, services, etc.). How rapidly do these products diffuse
through the population? In a recent article, Bayus (1998) used empirical data from the
computer chip industry to argue that product life cycles are not getting shorter, but that
companies are introducing new products that are really based on existing technologies
0
5
10
15
20
25
No. of copies (millions)
Se
p-9
4
Ja
n-9
5
Ma
y-9
5
Se
p-9
5
Ja
n-9
6
Ma
y-9
6
Se
p-9
6
Ja
n-9
7
Ma
y-9
7
Se
p-9
7
Ja
n-9
8
Ma
y-9
8
Se
p-9
8
Month
Navigator1.0 betaavailableOct. 13
Navigator1.1 beta,March 5
Navigator3.0, April 26
Navigator 2.0,Sept. 25
IE 1.0, Aug. 24IE 2.0, Nov. 29
Navigator 4.0,Aug. 18
IE 4.0,Oct. 1
Communicator4.5, Oct
0
200
400
600
800
1000
1200
1400
1600
1800
No. of new customers
('000)
Jun
-95
Sep
-95
Dec
-95
Mar
-96
Jun
-96
Sep
-96
Dec
-96
Mar
-97
Jun
-97
Sep
-97
Dec
-97
Mar
-98
Jun
-98
Sep
-98
Dec
-98
June 10, 20-40% discountsJuly 7, Yahoo relationshipJuly 8, AOL relationship
Sept 23, features added to siteOct 20, Netscape partnershipNov 21, Deeper discountsDec 3, Geocities relationship
7
that have shorter remaining life cycles. Whether this applies to digital products sold on
the Internet remains to be explored.
For firms, the Internet represents a way to transform themselves to operate more
efficiently and effectively, and to position themselves better for the future. Some firms
have already grown rapidly by leveraging the growth of the Internet. In particular,
companies such as Dell Computers, Cisco Systems, Netscape, and Amazon are cited
frequently in the popular press as evidence for the rapid growth in e-commerce that can
be achieved in the new medium. From this perspective, the Internet and associated
technologies can be categorized as radical, rather than incremental, innovations that will
impact firms in multiple ways. They can also be characterized as process innovations
that are likely to alter how firms run their internal operations, how they transact with
various stakeholders including customers, and how they drive, anticipate, and respond to
market forces. Although there is some emerging research that has explored
organizational adoption of radical innovations (e.g., Dewar and Dutton 1986; Davenport
1993; Christensen 1997), there is very little research on how firms evaluate and adopt
Internet-based technologies, such as e-commerce (Srinivasan, 1998). A particularly
important research topic is the development of models to assess the tradeoffs between
competitive pressures to have an online presence and the fit of Internet technologies with
the business practices and strategies of the organization.
The above observations can be framed in the context of the parameters of the
original Bass model (1968). Consider a future in which the digital environment becomes
ubiquitous. For such an environment, we propose the following hypotheses:
8
♦ Other things equal, the market potential for an innovation, m, would be larger
online.
We expect overall market to be larger because firms would be able to reach more
customers (e.g., in foreign markets) more effectively in both the early and later stages
of the life cycle of the product.
♦ The coefficient of imitation, q, would be larger online.
We expect this because product information, marketing communications, and “word-
of-mouth” are generally cheaper online and, therefore, spread faster. Further, in the
non-digital world, word-of-mouth effects are of order n (i.e., each one of us talks to
perhaps five or 10 people about a product), whereas online, w-o-m effects could
potentially be of order n2 (each node on the network is in principle connected to all
other nodes in the relevant target group, giving rise to n(n-1) links).
♦ The coefficient of innovation, p, would be larger online.
We expect that it would be easier to try a product online (e.g., through demos,
simulations, etc.) before purchase. Also, innovators are likely to seek out more
information, and the online medium can be used to provide richer and deeper
information to them.
A larger market (m) increases total sales of the product, whereas larger values of p
and q increase the speed of adoption. Based on the above propositions, therefore, we
should expect that good products (particularly digital products) would diffuse faster
online than offline, but poor products (negative word-of-mouth) would fail faster.
Characteristics of early adopters of Internet technologies: We now turn our
attention to online adoption behavior. It is increasingly becoming clear that customer
9
decision processes online could differ systematically from decision processes offline
(e.g., Degeratu et al. 1998; Ariely and Lynch 1998). It is likely, therefore, that the digital
medium will have some influence, unknown as of yet, on consumers’ adoption decisions
for new products and technologies. We expect that the digital medium will influence
consumers’ adoption decisions both for products that are available only online (e.g.,
online auctions) and for products that are available primarily offline at present (e.g., the
movie Titanic).
What factors influence online adoption decisions? Again, we will explore this
issue with reference to the adoption of online shopping. To understand the characteristics
of early adopters of online shopping, we analyzed the data from the sixth GVU survey
completed in October 1996. These surveys began in January 1994 and are conducted
twice a year. Respondents were recruited by prominently displaying links to the survey
at such major sites as Yahoo! Over 15,000 unique respondents participated in the survey.
The questionnaire, sampling procedure, and other details about the survey are posted at
the GVU site (www.cc.gatech.edu/gvu/user_surveys).
We explored two issues using this data: (1) what underlying factors characterize
people’s perceptions of online vendors compared to offline vendors? and 2) which factors
have the most influence on overall preference (an attitudinal precursor of adoption) for
purchasing from an online vendor? All variables were measured on 1-5 Likert scales
with a higher number representing a more favorable view of online vendors3. Exhibit 3
shows the three dimensions (factors) that we identified with Varimax rotation: (1) Post-
purchase expectations, (2) Benefits of purchasing online (although timely delivery has
3 The question was: On the whole, how well do each of the following statements characterize your opinionof commercial vendors on the Web compared to other, more traditional vendors?
10
more to do with post purchase expectations for products that are not digital and cannot be
immediately delivered online), and (3) Transaction costs. The variance explained by the
three factors were 25%, 24%, and 17% respectively.
It is interesting to note that post-purchase aspects dominate people’s perceptions
of online shopping – customers do not know what will happen to their orders after they
place an order with an online vendor. Further analysis suggests that there is little
difference on the post-purchase factor regardless of whether the online user is a skeptic, a
trier, or a buyer (innovator).4 At the same time, we can see that triers and buyers believe
more strongly that online purchasing offers advantages and have more favorable
perceptions about the online transaction costs.
In examining the characteristics of innovators, 46% of them indicated that the
primary reason for using the web was shopping. The corresponding numbers for skeptics
and triers were 8% and 21% respectively. Also of interest, (a) buyers represented 48% of
those spending more than 20 hours per week on the Internet (compared to 22% and 30%
for the other two groups). (b) 38% of buyers had more than 100 bookmarks on their
browser compared to 15% and 21% for skeptics and triers respectively. In terms of
demographic characteristics, we found that on average, buyers were more likely to be
male, have higher income, work in computer-related jobs, and somewhat more likely to
be married.
4 The respondents were classified based on the number of different product categories in which they hadmade online purchases within the past six months. Of 17 product categories considered, skeptics had notbought any, triers had bought fewer than three, and buyers had bought three or more. Classifications basedon other criteria, such as dollars spent online within the past six months, yield similar results (though thesegment-level differences in terms of descriptive variables were less pronounced). Further, while thepercent of online users belonging to each group varied from survey to survey, the basic factor pattern, therelationship with overall preference for purchasing from web vendors, and the descriptive characteristics ofsegments members were remarkably stable across GVU surveys four, five, six, and seven.
11
Factor Loading Matrix Underlying dimensions
AttributesPost-purchaseExpectations
OnlineBenefits
TransactionCosts
Ease of handling returns and refunds 0.76Customer service and after sales support 0.68Ease of canceling orders online 0.66Internet vendor’s reliability 0.66Ease of placing orders online 0.71Lowest price 0.68Quality of information about purchase choices 0.66Timely delivery of orders 0.65Security of credit information 0.84Ease of payment procedures 0.45 0.64
Prefer to buythrough web
vendor
= 0.28* (Post-purchaseexpectations)
+ 0.47* (OnlineBenefits)
+ 0.41* (TransactionCosts)
Significance
R2=0.36
.0001 .0001 .0001
Regression coefficients for each segment (numbers in parenthesis are t-values)Skeptics Triers Buyers
Post-purchase expectations 0.31 (6.27) 0.28 (4.43) 0.26 (8.41)Online benefits 0.40 (7.75) 0.45 (7.88) 0.50 (16.42)Transactions costs 0.39 (7.56) 0.40 (7.07) 0.40 (12.54)R2 0.33 0.31 0.39
Mean factor scores for each segmentPost-purchase expectations 0.00 -0.09 0.00Online benefits -0.40 -0.13 0.26Transactions costs -0.32 -0.11 0.25
Exhibit 3: The table at the top shows the factor loadings obtained from an analysis of GVUsurveys. The regression equation summarizes the impact of the factor scores (representingindependent variables) on people’s preference to buy from a web vendor. The last tablesummarizes the factor score means for three different segments of the online population.
Using another survey, we explored risk perceptions about the online medium and
their impact on users’ propensity to make online transactions and to provide personal
information. The surveys were posted between October 15 and November 15 1996 at
Techweb for one month (www.techweb.com), Yahoo! for a week, Hermes
(www.hermes.edu) for a month, and at 50 other sites for various periods of time. A total
12
of 5,974 usable responses were generated. We used partial least squares (PLS) for
analyzing this data.
Exhibit 4 summarizes the results and shows that perceived financial risk is a
major determinant of perceived overall risks online, and that when respondents have a
negative attitude towards a vendor, it affects both their willingness to buy from that
vendor and their willingness to provide information to that vendor.
Exhibit 4: A PLS model showing how perceived risks influence attitude toward onlinevendors and the propensity to purchase online. *: Significant at the 0.05 level. ***:Significant at the 0.001 level.
We measured “Innovativeness” using a multi-item scale, with items such as, “I
like to fool around with new products even if they turn out to be a waste of time,”
“Buying a new product that has not yet been proven is usually a waste of time and
money,” and “I am among the first in my circle of friends to buy a new version of
software when it is released.” “Trust” was measured on a 3-item scale containing items
such as, “Basically, most people are honest when dealing with strangers,” and “A large
Innovative
Financial Risk
Attitudetowards web-
vendorsOnline Risk
Privacy Risk
Willingness toprovide
personalinformation
Propensity to adopt
0.628***
0.116*
-.390***
0.263***
0.258***Trust
-.196***
-.145***
-.193***
-.197***
13
share of accident claims filed against insurance companies are phony.” The different
types of risks (financial, privacy, and overall) were measured on multi-item 1-7 scales
derived from items proposed by Slovic (1987, 1992). The scales combine the dimensions
of dread, knowledge, and control. We measured “Propensity to adopt” on a six-item
Likert scale (alpha reliability of 0.87). The “Attitude toward web vendors” was a
composite of the same variables used in the GVU survey (see Exhibit 3)5.
We also separately analyzed the data for the top 40 percentile of innovators (Hi)
and the bottom 40 percentile (Lo). For both Hi and Lo innovators, perceived risks
influence attitudes, but for Hi innovators, attitudes are less relevant for determining
behavior (correlation of 0.150 for Hi versus 0.375 for Lo, compared to 0.263 for the total
group).
Our results profiling the early adopters of online shopping are exploratory in
nature and need to be augmented by future research. First, we need improved ways of
selecting online samples to increase their representativeness, without increasing the costs
of the study substantially. While we took some reasonable steps to generate samples that
are representative of the online population (e.g., putting links to surveys at leading portals
like Techweb and Yahoo!), we do not know the extent to which our samples are
representative. Unlike traditional mail and telephone surveys, one cannot define a
sampling frame precisely, because there is no single list of all Internet users, nor is there
an equivalent to random digit dialing. One way to improve representativeness of online
samples is by recruiting participants from panels established by leading research
companies. Selected panel members can be recruited by sending e-mail and inviting
3 A copy of the questionnaire can be obtained by writing to the authors.
14
them to fill out questionnaires posted at a web site. We also need more research to
benchmark the profiles of online innovators against profiles of shoppers of alternative
channels, such as direct mail and retail outlets. Finally, we need to go beyond attitudinal
profiles (as was done in our studies) to characterize differences in actual behavior, such
as the actual times of adoption and the extent of purchases made by innovators.
Interestingly, we do have some comparative behavioral data on how people
respond to a new product online versus offline. As part of a research study (Degeratu,
Rangaswamy, and Wu, 1999), we tracked the purchases made by subscribers to Peapod,
an online grocery store, and compared this to purchases made by an equivalent sample of
consumers who purchased in regular stores in the same geographic area. We observed an
interesting natural experiment in this data in the margarine category, which we were
tracking. Brummel & Brown introduced a new margarine in November 1994. We
compared the purchases of this product made by our online panelists with those of the
offline panelists during the period, May 1996 to November 1997. The market share for
Brummel & Brown in Peapod was 25.9% compared to 37.9% for the offline panelists.
Further, this product represented 14.9% of the margarine purchases made by Peapod
panelists when they were browsing the electronic aisles, but 35% of margarine purchases
made when the panelists simply used their customized “personal lists” to make future
purchases. This suggests that the online market environment was not favorable for
generating trial, but once consumers put a product on their personal lists (perhaps after a
careful evaluation), they were likely to repeat purchase at a higher rate.
In summary, the digital environment raises some interesting research issues, such
as: How is the adoption and diffusion phenomenon different online versus offline? Does
15
the same new product (e.g., a new book) diffuse faster through the online population than
it does through the offline population? Which factors (product, industry, and customer
characteristics) have the highest impact on the rate of online diffusion? Are these factors
different from those having high impact offline? What is impact of Internet information
search engines on adoption behavior? What factors most influence the adoption and
implementation of diffusion models? Although we have not provided answers to these
questions here, we hope that we have articulated the context that make these questions
important to explore in future research.
3.0 The Digital Environment as a Data Source for Models
From a research perspective, the most significant aspect of the digital medium is
that it offers a favorable stratum for recording and retrieving information about the
activities that take place on the medium. For example, the entire set of web pages at a
site can be recorded and archived, the discussions of a chat group can be stored for future
retrieval, or the movements of visitors to a site can be tracked and summarized. Standard
recording protocols used by web servers (e.g., common log formats or enhanced log
formats) can be supplemented with data from online and offline surveys. Further, visitors
to some sites are required to register (under a pseudonym or with proper authentication),
which provides potentially useful additional data. These aspects of the Internet are
particularly relevant for studying word-of-mouth (w-o-m) effects on the diffusion of
innovations.
Measuring w-o-m: Many "online communities" now dot the Internet landscape,
where members with common interests chat about issues of relevance to them, including
potential products that meet their needs. Electronic "user-groups" have been popular in
16
the computer industry for several years. In these user groups, purchasers and potential
purchasers of a particular software or hardware can trade information about such aspects
as product use, troubleshooting tips, and related products. Today, there is an online
community for almost every conceivable topic. These groups often bring together a
spectrum of people and experience that would ordinarily not be possible in the physical
world. Some of these communities arrange periodic online conferences of interest to
their members.
Online communities can exert a powerful influence on a product’s adoption, both
unfavorable and favorable. Members of these communities transcend temporal,
geographic, or positional (based on their official titles) limitations in their ability to
influence others. Antilla (1992) provides an interesting early example. When an
investment expert provided advice on Prodigy's Money Talk, a number of other
subscribers immediately pointed out errors and inconsistencies in the advice, forcing the
expert to publicly acknowledge the errors and change his recommendations.6 Likewise,
Andrew Grove, CEO of Intel, was forced to issue an apology on the Internet to soothe
irate customers who were unconvinced about the steps the company was taking to resolve
concerns about a possible flaw in the Pentium chip. Various chat groups at that time
were discussing both the flaw and the solutions being proposed by Intel (Wall Street
Journal, November 29, 1994). Yet another example is the story of what happened to
PackRat, a top personal information management software sold by Polaris Software
(Vadlamudi 1995). In a hurry to follow-through on an expensive pre-launch marketing
campaign, the company released a buggy version. Many of their customers participating
6 It is important to note that it may be nearly impossible to identify the true source of an electronic message.Consequently, information obtained in this manner may not necessarily be authentic.
17
in a product support forum on CompuServe, quickly turned against the product. They
started asking each other about what product to switch to, and most decided it was a
product called Ecco. Many switched, told their friends, and PackRat was nearly killed
(its market share dropped from 27% to 10% in a year). The company President had to
issue an apology to forum members.
Most of the product-related w-o-m at online communities is positive. In one
study of postings containing product recommendations in Usenet newsgroups, only 8 out
100 product-related messages recommended against buying specific brands.
Nevertheless, it is becoming increasingly important for companies to monitor word-of-
mouth about their products on the Internet. Some companies, such as Saab and Harley
Davidson, are using the Internet to carefully cultivate their online communities to
reinforce and enhance their image (Muniz 1997). More broadly, Hagel and Armstrong
(1997) document the many ways in which a company can benefit by establishing and
nurturing an online community of its customers and prospects.
The Internet is becoming a vehicle that provides focus and strength to the
opinions of innovators (lead users, early adopters, or folks who are just plain interested
in, or knowledgeable about a product or technology). An important aspect of Internet
w-o-m is that discussions can be archived in searchable databases, making the opinions of
innovators accessible to a large number of people in the future. This could intensify
online w-o-m effects (either positive or negative) and make them stronger than would be
the case in the physical world, or even in other electronic media like TV.
Consider the following example from the movie industry. At pathfinder.com,
people meet to discuss various new movies being released. The Appendix summarizes a
18
sample of comments about the movie, Titanic, both before and after the movie was
released. One can see the transition from a mixed evaluation before the movie was
released to the generally more positive comments after the movie was released. If one
had doubts about Titanic’s market success before it was released, the strong positive
comments after its release would have convinced even a hardened skeptic! Although
discussions at pathfinder.com may not be statistically representative of the true w-o-m
about the movie, it can nevertheless provide valuable information in much the same way
as focus groups. An interesting research challenge is to identify ways to transform verbal
protocols at these discussion groups into parameters of diffusion models – to quantify and
model word-of-mouth effects (e.g., strength and direction) and use this to predict future
performance of the new product (see, for example, Urban, Hauser, and Roberts 1990).
Another potentially useful way to operationalize pre-release opinions into
diffusion model parameters is suggested by sites such as the Hollywood Stock Exchange
(www.hsw.com). Any interested participant can subscribe to this free service (possibly
under a pseudonym), receive $2 million in “Hollywood dollars” for trading in movie
stocks and bonds for movie stars. A movie is offered as an IPO on this exchange anytime
from three years to two weeks prior to its release in theaters. Participants trade in these
movie stocks, with current stock prices updated daily (see Exhibit 5). Then, four weeks
after the movie's release in theatres, HSX delists that stock at a price that corresponds to
the exact amount of money the movie made. If a stock trades at $20, it means that people
expect that the movie would earn $20 million in the first four weeks of its release. Star
bonds are rated like real bonds, with AAA for the highest rated bonds and C for the
lowest rated bonds.
19
Exhibit 5: A Hollywood Stock Exchange listing of bond prices of Hollywood stars and stockprices of forthcoming movies (on September 7, 1998).
In August 1998, HSX had over 120,000 registered traders in 100 different
countries who trade about 200 million shares a day! Interestingly, the Hollywood dollar
is also becoming a currency that can be exchanged for (promotional) goods, much like
frequent flier miles. An interesting research issue here is to use participant sentiments as
expressed in pre-release stock prices to calibrate diffusion models. In particular, it should
be possible to forecast movie sales from measured w-o-m on a given day based on news
and TV coverage about the movie and that day’s stock price for the movie.
Data for enhanced diffusion models: Increasingly, adoption and diffusion models
being developed accommodate more complex phenomena (Mahajan, Muller, and Bass
1993). However, to test these models, we would need richer data that include one or
more of the following: (1) the actual times at which individuals adopt an innovation; (2)
the characteristics of those who adopt and those who do not; (3) the market context (e.g.,
competition, advertising expenditures) in which adoption decisions are made; (4) the
process by which customers decide whether and when to adopt an innovation.
Fortunately, the digital medium offers the potential to gather all of these types of data.
20
Some of the data can only be collected with the cooperation of participating companies
(e.g., online advertising budget; time of adoption of a particular customer), while other
types of information could be obtained from online information brokers (e.g., Millward
Brown; Binary Compass). In some cases, the digital medium can be used to advantage in
collecting “observational data” through autonomous agents. For example, we can get
data on firms’ adoption of the “online transaction model” by designing and deploying
search agents to automatically poll selected web sites at regular intervals and report to us
when any given site starts online transactions. We can also separately track these
adoptions by categories of firms, Fortune 500 firms, firms in specific SIC codes, etc.
We hasten to point out that the online medium is still not mainstream, and that
data collected online will be limited by self-selection bias compared to the total
population. Nevertheless, with richer data that is either already available on the Internet
(some of it essentially for free) or can be obtained online, it becomes easier to empirically
test more sophisticated models of adoption and diffusion.
We highlight below some of the modeling enhancements that will become more
amenable for testing and implementation in the new medium.
• Interdependencies among innovations. For example, the adoption of Internet radio
depends on the adoption of software such as RealAudio, and vice versa. Bayus
(1989) provides a model and an example application for the case of the simultaneous
diffusion of compact-disc players (primary product) and CD’s (secondary product).
Diffusion models that incorporate interdependencies do not currently require any new
types of data for their testing. However, it is important to note that the digital
medium, by its nature, is creating a number of research opportunities for formulating
21
and testing such models because of the interdependencies that exist in the adoption of
hardware, software, and standards.
• Multistage decision process for adoption. The traditional binary construct of
adoption-non-adoption is giving way to more graded decision structures that include
intermediate stages such as awareness of product category, knowledge of product
attributes, etc. To operationalize such diffusion models, we would need more
detailed information about how potential adopters transition from one stage to the
next, something that is more feasible to track online than offline.
• Effects of marketing mix variables. Past research has focused mainly on
incorporating the effects of price and advertising on the parameters of the diffusion
model. Even for these models, there is a lack of empirical validation (Mahajan and
Wind 1986). The digital medium offers two important benefits in this regard: (1) it
should (eventually) make it easier to do empirical tests by helping us track marketing
mix variables in this medium, and (2) it allows for easier manipulation of marketing
mix variables (e.g., product design options, prices, promotional inducements), which
make it possible to more precisely characterize the effects of the marketing mix on
product adoption and diffusion.
• Effects of competitive strategies. The dynamics of competitive interactions is critical
for understanding the growth of a product category. Categories that attract a number
of competitors may grow faster, and categories growing faster may attract new
competitors. The original diffusion model (Bass 1969) was developed to forecast
sales at the level of a product category, and ignored strategic interactions between
individual brands. Since then, a few models have considered equilibrium behavior of
22
competitors in setting pricing and advertising policies over the long run, mostly in
duopoly settings.
Mahajan, Muller, and Bass (1993) summarize the key results in this area, and
call for more research on this topic. In particular, there is a need for empirical
research to validate the theoretical propositions (e.g., equilibrium paths) derived from
these models. The digital environment provides a rich context for exploring the
effects of strategic and competitive factors on the diffusion of innovations. The
effects of factors such as the order of entry, price leadership, competitive advertising,
channel leadership, multiple generations of products, and discrete market events can
be explored in greater depth than has been possible in previous research. By
facilitating observations of competitive interactions and by facilitating tracking and
measurement, the online environment provides a fertile ground in which to explore
these issues.
• Effects across media. One of the interesting marketing aspects of the digital medium
is that companies are trying to integrate their online presence with their overall
operations. This raises important issues about how online adoptions impact offline
adoptions, and vice versa. Consider, for example, how Doubleday promoted a recent
book by John Grisham. The company put out full page ads in leading newspapers
containing half the first Chapter of his new book, The Street Lawyer, and telling
readers to get the rest of that Chapter from an e-mail address provided in the ad.
Likewise, some music CD manufacturers try to create a “buzz” on the Internet by
providing some samples from the CD. They hope that the interest generated this way
fill feed print stories, which in turn, will create further incentives to sample the CD
23
online. As more firms exploit these types of synergies, it becomes important to
develop models that capture such inter-media dynamics in predicting how a new
product would diffuse through the population. We believe that this provides a fertile
area for research.
• First purchase and repeat purchase. The diffusion model is essentially a model of
first purchase, namely, predicting when potential adopters will actually adopt. To
predict total sales, a few studies have superimposed repeat purchases and replacement
purchases within the framework of the basic diffusion models (see, for example,
Bayus 1991). However, several other aspects of the impact of first purchase on repeat
purchase need to be explored carefully in future research. For example, do early
adopters become heavy users? If yes, what factors mediate this relationship? Does
size of first purchase increase loyalty in future purchases? Is the relationship between
satisfaction and repeat purchase higher for imitators (later adopters) than for
innovators (early adopters)? To address these and related questions, we need
longitudinal data at the individual level – something that is increasingly becoming
feasible online with user registrations and frequency programs.
• Effects of discrete “market events.” Online behavior is more prone to be influenced
by discrete market events. Recall how online trading further exacerbated market
decline during the stock market crash of October 19, 1987. Likewise, the recent anti-
trust lawsuit against Microsoft may have influenced the diffusion of Netscape’s
browser. Unfortunately, diffusion models typically ignore these types of effects,
although hazard rate modeling (described below) can potentially incorporate such
effects.
• Heterogeneity in adoption behavior. Most existing diffusion models use aggregate
data on the number of people who adopt a product in a given period, and ignore
information on who adopted when. However, by incorporating such data, we should
be able to more carefully articulate individual characteristics that influence the timing
of adoption – e.g., what (observable) characteristics are associated with early
adoption of a particular innovation?
Hazard rate modeling offers a promising way to incorporate the effects of factors
indicated above (e.g., the marketing mix elements, discrete market events, effects of
alternate media, and customer characteristics) on the diffusion process. In particular, the
proportional hazard rate model or its variants would be a good place to start. This
modeling approach allows us to incorporate various “covariates” that influence the
hazard rate, i.e., the likelihood that an individual who is a non-adopter until time t,
becomes an adopter at time t. We briefly describe this modeling approach and its
applicability to Internet data.
Let the time to adoption be a random variable having some probability density
function f(t), with cumulative density represented by F(t). Let h(t) > 0 be the likelihood
that adoption occurs at time t, given that it has not occurred in the time interval (0,t). h(t)
> 0, is referred to as the hazard rate. In the original Bass model, h(t) is equal to:
where m (> 0) is
time t (= mF(t)),
innovation and c
h t pq
F t( ) ( )= +
q
24
(1)
the market size, S(t) is the cumulative number of adopters (sales) until
and p and q are constants (> 0) representing the coefficients of
oefficient of imitation respectively. Because S(t) is nondecreasing in t,
)()( tSm
pth +=
25
h(t) is also nondecreasing in t. Although the Bass model has been used successfully, its
hazard rate given in (1) is just one of many ways to specify the hazard rate. Other
specifications of f(t), such as Exponential, Weibull, Gamma, generalized Gamma, and
Gompertz, have been used in other fields (e.g., biometrics) to specify more flexible ways
to estimate the hazard rates from data. These functions can be used to specify increasing,
constant, or decreasing hazard rates.
From a practical perspective, a particularly useful modeling approach is the
proportional hazards model, originally suggested by Cox (1972). Here, we specify the
hazard rate to be a multiplicative function of two components: a base hazard rate and a
nonnegative component, φ, that proportionately adjusts the base hazard rate up or down,
depending on a vector of covariate values, xt (x1t, x2t, …xkt) and their impact (β).
For further details about hazard rate models, see Helsen and Schmittlein (1993). In this
framework, it is clear that the Bass (1969) model is a proportional hazards model with
h0(t) being a linear function of the number of previous adopters and φ(.) = 1 (more
generally, a constant). β in equation (2) is estimated by maximum likelihood techniques.
Further details about these estimation techniques and related modeling issues can be
found in standard reference sources such as Heckman and Singer (1985).
There have been a few applications of hazard rate modeling to offline adoption
data. Sinha and Chandrashekaran (1992) develop a “split hazard” model to explain
factors that influence the timing of adoption of ATM machines by banks.
Chandrashekaran and Sinha (1995) develop a “Split Population Tobit” model to identify
factors that influence the timing and volume of purchases of PCs among a random sample
h t x h t xt t( | ) ( ) ( , )= 0 φ β (2)
26
of US firms. Weerahandi and Dalal (1992) combine a diffusion model with a binary
choice model to forecast the penetration of fax machines in different market segments.
Haldar and Rao (1998) study the effects of such covariates as age, income, and
employment on the timing of adoption of five durable goods by a panel of about 300
households, who were interviewed about every 6 months over a 12-year period regarding
their adoption of these products.
All four studies have some data limitations. In the first three studies, either we do
not have a reasonably good set of covariates, or the covariates were not observed
throughout the period of interest – covariate values are based on observations either at the
end or at the beginning of the modeling period. Although the last study (Haldar and Rao,
1998) uses a good set of covariates, it is based on “grouped data’ in which adoption
decisions are noted only in periodic intervals, which means we do not have the exact
times of adoption. The Weerahandi and Dalal (1992) study also uses only grouped data.
We hope that these and other limitations of studies based on offline data, would be
overcome with the newer data sets that are becoming available online. As a result, we
should get a better understanding of the drivers of adoption behavior.
4.0 Conclusion
Rarely do we get an opportunity that creates both a meaningful context for
research, while at the same time, providing a rich medium for collecting data for those
research studies. The emergence of the digital medium represents such a “discontinuous”
opportunity for researchers.
In this paper we explored new developments in the digital medium and their
implications for adoption and diffusion research. So far, we highlighted research
27
opportunities that extend the traditional frameworks of adoption and diffusion modeling
by leveraging the developments in the digital medium. We summarize below a few other
research topics that have gained increased prominence due to the growth of the digital
medium.
Effects of global access on product adoption: The Internet has no geographic
boundaries, and this presents interesting opportunities and challenges for global
businesses. Gatignon, Eliashberg, and Robertson (1989), among others, have shown that
there are substantial differences between countries in the timing of product adoption
(since product introduction). They also identify systematic patterns in the diffusion
process that depend on the characteristics of individual markets. Mahajan, Muller, and
Kalish (1995) explore the conditions that influence the sequence in which products
should be introduced across markets, by allowing the adoption of a product in one market
(the lead market) to influence adoption in another market (the follower market). They
show that a “sprinkler strategy” involving simultaneous entry in both markets makes
more sense in competitive global markets. This occurs because a benefit of sequential
entry, namely, the positive influence of adoptions in the lead market on adoptions in the
follower market, could be outweighed by lower profits and market shares when these
markets are competitive.
With the growth of the digital medium and intensifying global competition, there
is greater information transfer across countries. This was brought home in a salient way
to Hewlett Packard's chemical analysis group. Like many technology companies, HP has
a strategy of introducing new products first in highly profitable countries (customers with
high reservation prices), followed by entry in other countries at later stages of the product
28
life cycle. However, HP is increasingly finding that (informed) overseas customers, who
browse its web pages in the U.S., want to purchase products that have not yet been
introduced in their countries. Such customers may postpone purchases in anticipation of
the release of the new product. Will the expectations of such customers cause companies
to launch new products on a global basis? If products are launched globally, what can
companies do to sustain discriminatory pricing? These and related issues have been
explored in past research (e.g., Narasimhan (1989); Padmanabhan (1993)), but have
gained increased importance with the growth of the Internet.
The link between rate of technology change and the rate of adoption: In “Internet
time,” everything changes rapidly. Several companies have made more than five major
overhauls to their web sites within the past two years. Major enhancements to browsers
and related software are being made every six months. Such frequent introductions of
new products may be “too much” for some customers. In such environments, customers
have to constantly make decisions on whether to adopt a technology today or to leapfrog
to a future technology later. While some research (see, for example, Dhebar 1994, 1996)
has explored these issues, we need better understanding of the optimal rate of new
product introductions, based on improved articulation of costs and benefits to customers
of waiting versus adoption.
Two-way interactions between companies and customers: Traditional diffusion
modeling has explored the effects of one-way communication (e.g., advertising) from the
firm to its customers. Increasingly, the Internet is facilitating two-way and multi-way
communications between the firm, its customers, and possibly third parties that can
significantly influence adoption decisions, particularly among innovators. Consider how
29
Marshall Industries (marshall.com) now encourages adoption of new electronic
components. A customer or prospect who is considering a new Digital Signal Processor
(DSP’s) can participate in live or canned “net seminars” hosted by the company. This
puts the customer in direct contact with engineers who helped design the product. In the
seminars, these engineers address individual questions, help overcome objections, and
provide hand holding to encourage adoption of the product. The seeding of the diffusion
process through these types of interactions present opportunities for modeling and
evaluation, particularly for specifying the timing of “product take off.”
Increasing the adoption of diffusion models: Although diffusion models have had
a long history in academia, their application in industry is not commensurate with their
potential. Although there have been several successful applications, there is much
potential for increasing the adoption and use of diffusion models (Lilien, Rangaswamy,
and Van den Bulte, 1999).
One reason for the relative scarcity of applications is that many practitioners are
unfamiliar with diffusion models. Another reason is that there are not many easy-to-use,
prepackaged software for calibrating these models. This situation would improve if those
in academia take more responsibility for diffusing their research output to practitioners.
There is some indication that the digital medium is beginning to make this happen. For
example, the diffusion modeling software included with this book facilitates the
application of these models in real decision situations. Soon, it is likely we will have
implementations of the Bass model and its variants that can be run over the Internet using
standard browsers. With such software, anyone with a browser would be able to run
30
these models from anywhere in the world, th+ereby enabling our collective research
efforts to diffuse faster into practice.
The online availability of diffusion models should also help address several
important research issues identified by Mahajan, Muller, and Bass (1993). In particular,
it should be easier to track the factors that enhance model use, conduct experiments to
identify conditions under which the model makes good forecasts, and compare the value-
in-use of alternative models. Such research would then have a reverse diffusion effect,
namely, practice influencing model development.
In conclusion, we note that some researchers seem to believe that diffusion
modeling is a mature area, with few “exciting” opportunities. We hope that this paper
contributes in some measure to dispel such a notion. We have ahead of us a number of
research opportunities, to contribute to both theory and practice, by firmly positioning our
research on innovation adoption and diffusion within the context of the digital
environment.
31
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Appendix
This following is a selection of postings about the movie “Titanic” at thewww.pathfinder.com site. This selection is not representative of the total set of postingsat this site. Titanic was one of 413 movies released in the U.S. in 1997.
Sampling of comments before the movie was released
At first, I would have said Titanic was a bomb from the drawing boards. I mean, a fifth ofa billion on a MOVIE? That was until I saw the actual preview for the movie, it actuallylooks pretty good! If Spielberg's ludicrous Jurassic Park sequel can rake in $250 millionat the box office, Titanic shouldn't have a problem being a hit (though it might not makeit to $200 million, that is a big risk).
This movie has a 50/50 shot. On one hand, we have awesome looking effects, LeonardoDecaprio (who I don't know why became a sex symbol in boring R&J), James Cameron(that can be good or bad), and a whole lotta' hype…. Personally, I'll spend my money onTomorrow Never Dies and maybe spend matinee price on Titanic.
I just saw the preview to Titanic last night and what I saw looked incredible. I guess all Ireally noticed was the special effects. I think the special effects is what is really going todraw the crowds in. I know it did for me. As for the acting I can't tell at this time if it willbe good or not. I do have faith in Leonardo in doing a good job in this movie.
I'm Japanese woman. I've already watched "Titanic" in Tokyo National Film Festival onNov.1. This movie is very great! It will be the best 1 movie in 1997. All of us weredeeply impressed with it. By the way, James Cameron and Leonardo DiCaprio appearedand greeted before us! I'm sorry for my poor English. Thanks.
Tomorrow Never Dies will sink Titanic's financial ship. No doubt. There are two waysTitanic will make money during the December 19th weekend: 1) women who love lovestories and hate action will see it, and 2) after all of the screen for TND fill up,disappointed moviegoers will settle for Titanic.
Titanic looks good. But let's face the facts. What would you rather go see? A movie inwhich you already know the ending (for all you people who don't know, the ship sinks),or would you rather go see Pierce Brosnan save the world as JAMES BOND 007 in'Tomorrow Never Dies'? No contest. Leonardo...the name's Bond. James Bond. AndTitanic's gonna sink faster than, well, James can down a martini.
Comments after the movie’s release (first few weeks after introduction)
Titanic rocks! Very simple folks. The all time top grossing movie list is ready for a newqueen. Her name is Titanic. Long live the queen!
35
I thought Titanic was awesome. The only things I didn't like were for one, Kate lookedway older than Leo and for two, the scene in the water after the boat sinks, when he istelling her to stay alive, they were trying too hard to be cold. I mean Kate's jaw wasbouncing up and down like a ping pong ball and Leo was just a little too stiff. Other thanthose two things, I totally loved the movie and I plan to see it for the 3rd time.
I just want to say that Titanic is the best movie that I have ever seen. I have never beforegone to see a movie purposely a second time, and this one I did. I will probably be backfor a third time as well because I just can't get enough of it.
I always was afraid of death, but not anymore. . . . I just realized that I am ready to riskmy life for someone I love. I think this is a great discovery.
This is the movie that I will compare all other movies to from now on. It is the first movieI have ever cried at. I am 17. I wasn't just teary-eyed. I was all-out sobbing for the lasthour and a half.
I just got back from my 11th viewing and when I got there the line was out the door.
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