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Innovation adoption and diffusion in the digital environment: Some research

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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: arvindr@psu.edu

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:sgupta@acornis.com.

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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