A RATIONAL EXPECTATIONS THEORY OF TECHNOLOGY ADOPTION: EVIDENCE FROM THE ELECTRONIC BILLING INDUSTRY

Yoris A. Au

Assistant Professor, Information Systems and Technology Management College of Business, University of Texas at San Antonio

yoris.au@utsa.edu

Robert J. Kauffman Director, MIS Research Center, and Professor and Chair

Frederick J. Riggins Assistant Professor

Information and Decision Sciences Carlson School of Management, University of Minnesota

{rkauffman, friggins}@csom.umn.edu

Last revised: April 19, 2006 ______________________________________________________________________

ABSTRACT In this paper, we draw on concepts from the rational expectations hypothesis (REH) and adaptive learning theory to introduce a new rational expectations theory of technology adoption. Although the REH and adaptive learning theory have been applied in many non-technology contexts, this research is among the first that applies rational expectations and adaptive learning theory to issues related to technology adoption. The proposed theory allows us to examine technology adoption settings where multiple parties seek to align their expectations of future value prior to making a decision to adopt. It also takes into account the learning and information sharing that generally occurs in the marketplace between multiple parties that can further influence clustered adoption and the overall adoption rate. In our effort to test our new theory, we construct several hypotheses that allow us to examine issues associated with information technology adoption that involves multiple parties (multi-partite technology adoption) and strong network externalities. We test for the existence of clustered adoption and the effects of information transmission in the electronic bill presentment and payment (EBPP) industry. We hypothesize that clustered adoption by firms will be influenced by their geographical collocation, the reach of their consumer bases, their industry sector associations, and their consensus choices of the technology vendor. Our results show preliminary evidence to support the proposed theory. KEYWORDS: Clustered adoption, economic analysis, electronic bill payment, information transmission, rational expectations theory, technology adoption _____________________________________________________________________________ ACKNOWLEDGEMENTS. The authors are grateful to Neveen Awad, Indranil Bardhan, Sanjeev Dewan, Chris Forman, Vijay Gurbaxani, Rahul Telang, Kevin Zhu, and other participants of the 2005 Workshop on Information Systems and Economics at the University of California, Irvine, where an earlier version of this paper was presented. We also thank Gordon Davis and Paul Glewwe for their helpful comments and feedback. The authors also acknowledge support from the University of Texas at San Antonio, the Management Information Systems Research Center (MISRC) at the University of Minnesota, and the third author acknowledges financial support from the 3M Corporation.

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INTRODUCTION

In this paper, we will do a general test of a proposed theory that is based on the rational

expectations hypothesis (REH) and adaptive learning theory. We call the proposed theory the

rational expectations theory of technology adoption. We will discuss how the theory allows us to

look into the issues in the technology adoption that involves multiple parties (multi-partite

technology adoption). We set up our discussion and test for the existence of clustered adoptions

and the effect of information transmission in the context of electronic bill presentment and

payment (EBPP).

Much of the adoption decision depends on a firm’s expectations about the benefits and costs

of the technology. In this research, we use the REH and adaptive learning theory and apply

them in the adoption of IT with network externalities decision making settings that require

managers (as economic agents) to have the ability to form certain levels of expectations about

the value of the technology. The REH has been applied in many non-IS/IT contexts, where

decision makers must estimate the benefits associated with different courses of managerial

action related to their perceptions about how beliefs in the economy are shaping up. Such

contexts include interest rate policy formation, financial market forecasting and money market

trading, manufacturing industry investments for the production of durable goods, and policies in

labor market wage-setting.

Our discussion and test will center on EBPP technology, which is a relatively new

technology that exhibits strong network externalities. EBPP also is an excellent example of a

technology that involves multiple parties—in this case, billers, banks, technology suppliers, and

customers—in its adoption process. In this kind of technology adoption context, there are needs

for sharing expectations of value among these disparate parties, making EBPP representative of a

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class of technology adoption problems that are more complex in nature than what occurs with

single firm technology adoption decision making in isolation.

Despite the touted benefits of ease and convenience, surveys show that the adoption of EBPP

by consumers is still relatively low. Studies by the Tower Group [2002], for example,

discovered that the percentage of households using EBPP had increased only modestly between

1998 and 2002, from 2% to 13.7%. Many analysts argue that the adoption of EBPP has been

plagued by the “chicken and egg” syndrome. Billers are not willing to adopt EBPP until a

significant portion of customers are willing to use it. However, customers are unwilling to use

EBPP until they can pay most of their bills online, that is, until most billers have already adopted

the technology [Au and Kauffman, 2001]. This points to the multi-partite adoption dependency

that exists in the market, for which we theorize that alignment in expectations of value and

willingness-to-adopt must occur.

From the perspective of the billers, there are indirect network effects with regards to the

adoption of EBPP. Indirect network effects arise when the value of a product increases as the

number or the variety of the complementary goods or services increases [Katz and Shapiro,

1985; Economides, 1996]. In the context of EBPP, indirect network externalities occur as

market-mediated effects. The more billers that adopt the technology, the more consumers are

willing to use the service. This allows each biller to realize higher benefits. One of the most

compelling reasons for adopting EBPP is the cost-savings that are available for billers (from the

ability to greatly reduce their cost for mailing paper bills). So it follows naturally that billers are

the ones that must take the initiative to adopt the technology and create the “network of billers”

to attract customers. Many billers seem to have taken this stance, evidenced by the result of an

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energy industry survey in 2000 showing that more than 90% of respondents at least had

considered offering EBPP to their customers [Brown, 2001].

EBPP systems cost billers about $1.1 million on the average. At a typical recurring cost rate

of 20% to 30% of the base investment annually, we can see why billers should not adopt EBPP

before there are enough customers who ready to use it. If they do this, then it must be because

the billers see some other convincing non-cost reasons to adopt [Gonsalves, 2003].

However, the question becomes: When should each biller adopt the technology? In the next

section, we propose that each biller should wait until the other billers are ready and so that they

all adopt the technology together—clustered in time. IT adopters may be clustered based on

geographical regions, firm size, industry, and so on. In the EBPP case, an electric utility firm in

a major city may observe a telecommunications company that serves customers in the same

geographical area to see if the company is ready to adopt the technology. By adopting together,

both firms reinforce one another’s evaluation of the market and increase the likelihood that

more customers are using the EBPP service, an externality benefit.

We have a number of specific research questions in this kind of technology adoption context.

Do we actually observe the kind of clustered adoption in the real world as suggested by our

theory of rational expectations? If so, then what kinds of clustering occur for technologies that

exhibit strong network externalities? What kinds of factors result in variance in adoption time?

What will a theory-driven conceptual model look like for this context? What facilitates

information sharing by firms related to business value and technology adoption expectations for

EBPP? What kind of empirical model can be used to test the theory?

THEORETICAL BACKGROUND

We will next review the rational expectations hypothesis (REH) and adaptive learning theory

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that constitute the foundation of the rational expectations technology adoption theory that we

propose, as well as the theory of information transmission that serves as a crucial ingredient in

our theory building. Eventually, we discuss the proposed theory that we are going to test in the

remainder of this paper.

The Theory of Rational Expectations and Adaptive Learning

Muth’s [1961] rational expectations hypothesis (REH) suggests that people are able to learn

fast to adapt to changes in economic conditions, and to anticipate what will happen in the

economic system by examining the patterns of economic activity. The REH effectively

maintains that every individual acts as an economic forecaster, using the information he or she

can collect to foretell what economic events are likely to happen. The forecasts are the

individual's rational expectations. The basic assumption of the theory is that people use of all the

information available to them efficiently. Consequently, an individual’s expectations are said to

be "rational" if she makes efficient use of all available information, allowing for the cost of the

information. Since some information can be costly and hard to obtain, expectations can be

rational but still not very accurate. However, even though rational expectations may not be very

accurate, at least they will be unbiased. This unbiasedness forms the basis of the central tenet of

the theory, that is, the average of people’s subjective expectations is equal to the true values of

the economic variables being forecast.

REH’s strong assumptions fail to consider that people have bounded rationality so although

all the information is available to them, they may not be able to process the information quickly

and accurately. Sargent [1993] suggests the theory of adaptive learning to relax some of the

strong assumptions. In adaptive learning, people are allowed some time to learn the about the

economic circumstances and update their expectations about relevant parameter values on the

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basis of newly-received information. Furthermore, unlike in the REH, in adaptive learning

boundedly-rational agents are allowed to use simple forecasting strategies—perhaps not perfect,

but at least approximately right—for a complex nonlinear world. A boundedly-rational agent

forms expectations based on observable quantities and adapts her forecasting rule as additional

information and observations become available. Adaptive learning may converge to a rational

expectations equilibrium or it may converge to an approximate rational expectations equilibrium

[Jordan and Radner, 1982], where there is at least some degree of consistency between

expectations and realizations [Evans and Honkapohja, 2001; Hommes, 2004].

The Theory of Information Transmission

For the purposes of this research, information transmission is defined as the situation where

the firm in possession of information signals or transmits the information to another firm. This is

also known as signaling [Spence, 1973; Kreps, 1990]. In other technology adoption decision

making research, the role of information transmission has been discussed mainly in the context

of information asymmetry, which refers to the situation where one firm has more information

than others [Zhu and Weyant, 2003]. In this case, information asymmetry exists and persists due

to the lack of information transmission.

As we have discussed above, information transmission is crucial to the alignment of

expectations in adaptive learning. The ability to learn what the other firms expect with regard to

the value of a new technology will allow each firm to adjust its own expectations and, possibly,

eventually reach an agreement with other firms about the business value of a technology.

Crawford and Sobel [1982] maintain that many of the difficulties associated with reaching

agreements are informational. They further suggest that sharing information makes better

agreements possible. In some cases, however, revealing all of a firm’s decision-relevant

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information to an opponent may not be the most advantageous policy [Clarke, 1983; Gal-Or,

1985]. For example, although it has been shown that information transmission among supply

chain partners may be beneficial [Mukhopadhyay et al., 1995], its effects among competitors

appear to be more tentative [Zhu, 2004]. These studies suggest that information sharing may not

always be beneficial to a firm, as has also been noted by Han et al. [2004] for the case of

Internet-based financial risk management systems; and Kauffman and Mohtadi [2004] for the

case of Internet-based supply chain management.

In the case of adoption of technologies with strong network externalities, however,

information transmission and sharing will almost certainly benefit each firm. Why? Because

their common objective is to maximize the benefit of using the technology and most of the

benefit comes from network externalities. With potential adopters in a group sharing

information over multiple periods, we can expect that they all will reach an informal consensus

about the cost and benefit of the technology, leading to an adoption decision. This idea has been

suggested by the rational expectations model discussed in Au and Kauffman [2005].

The literature on cooperative game theory and inter-firm coordination seems to support this

view as well. For example, Cooper et al. [1997] present experimental evidence that pre-play

communication resolves coordination problems in a “battle of the sexes” game. Allowing

players to communicate prior to selecting an action almost completely resolved the coordination

problems that were observed in the experimental game without pre-play communication. This

permitted them to select a desired outcome. In a similar vein, Farrell [1987] presents a model in

which the performance of two-way communication is improved if players are allowed additional

rounds in which to communicate.

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The Rational Expectations Theory of Technology Adoption

Our rational expectations theory of technology adoption suggests that under certain

conditions we can expect to observe clustered adoption, defined as the adoption of a technology

by multiple firms at about the same time. Suppose there is a group of N potential adopting

firms having to decide whether to adopt a new technology or wait until another, possibly better

technology becomes available. The technology exhibits strong network externalities. To realize

the expectations about the benefits from network externalities, a firm in the subgroup will adopt

the technology only when it has learned that the other N – 1 firms are also ready to adopt the

same technology. This is to prevent the firm from getting stranded with a technology that no

other firm would choose. As a result, we can expect that each firm in the subgroup will adopt

the technology at about the same time.

Firms may initially have different levels of expectations about the value of the technology

and, consequently, different levels of willingness to pay. A firm’s willingness-to-pay for a

technology determines the maximum price the firm is willing to pay to purchase the technology.

To achieve concurrent adoption decisions, each firm must reach a level of willingness-to-pay that

is at least equal to the price set by the technology supplier. If some firms in the subgroup have a

willingness-to-pay that is below the technology supplier’s price, then all firms will defer

adoption. We assume that potential adopters are willing and able to freely share information

with each other at no cost. The information sharing is essential for the alignment of expectations

which, in turn, facilitates the adoption.

HYPOTHESES

Based on the REH and adaptive learning theory, Au and Kauffman [2005] suggest that IT

adoption decision makers will observe the environment and try to align their expectations with

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those of the other decision makers before making an IT adoption decision. This alignment is

necessary to confirm each decision maker’s own expectations about the value of the IT being

considered, due to the inherent uncertainty.

Benchmarking, information sharing and clustered adoption. In their effort to align their

expectations, decision makers “benchmark” against each other and share information within a

targeted group. Here benchmarking refers to the process in which firms evaluate various aspects

of their business processes in relation to the best practice within their own group, which consists

of firms that share similar characteristics and objectives (e.g., belong to the same industry,

competing in or serving the same market). In the case of the adoption process of a new

technology, however, it is unlikely that the so-called “best practice” exists due to the

uncertainties inherent in the new technology. Therefore, we presume that in this particular

“benchmarking” process members will learn from each other by sharing information among

themselves about their perception of the expected value of the technology prior to making an IT

adoption decision. They may eventually reach a tacit consensus, resulting in what we would

observe as time-clustered adoption in the marketplace.

The information sharing will occur in the form of informal communications through email,

telephone calls, conference presentations and panel discussions, and other informal individual

communication. This kind of communication meets the criterion that we noted earlier for cheap

talk. Although it may seem insignificant, Kim [1996] suggests that repeated interaction among

firms can enhance the credibility of cheap talk and improve the efficiency in outcomes that

would be infeasible otherwise. Furthermore, Jordan and Radner [1982] maintain that repeated

observations of the market can smooth the process by which agents construct expectations.

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We will directly consider the role of information transmission, and develop hypotheses on

how it is likely to affect the patterns of technology adoption. We will also argue that time-

clustering of adoption may occur among firms based on four critical dimensions: geographical

collocation, geographical reach, industry sector, and technology vendor. We now will develop

hypotheses around each of these as a means to test the conditions under which time-clustered

technology adoption is likely to occur. We will also consider the role of other drivers of

clustered adoption that are adapted from other theories related to technology adoption, as a

means to gauge the relative strength of the different explanations of EBBP adoption.

Direct effects of information sharing on clustered adoption. A central component of our

theoretical argument is that expectations about the appropriate time to adopt a technology will be

driven by how effectively information is transmitted and sharing among players in the

technology adoption marketplace. Although it is rarely easy to identify the extent to which

cheap talk is occurring in the marketplace, it nevertheless is possible to consider aspects of

information transmission that are likely to support cheap talk. In particular, based on the existing

theory [Seidmann and Sundararajan, 1997; Lee and Whang, 2000], we believe that the frequency

of information sharing is likely to be relevant for identifying the effectiveness of information

transmission. Moreover, it should relate to subsequent observations of adoption.

One approach to capturing this kind of information is to measure the number of conferences

that are held in the marketplace that relate to the technology under consideration. A second

possible way is to identify the number of white papers and industry surveys and studies that

relate to the given technology. There are other means to proxy for the unobservable cheap talk

and communication. Although they are imperfect, they may nevertheless provide a useful

reading on underlying conditions that support information transmission.

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Regarding conferences, in particular, the rationale is that a conference is a good opportunity

for senior managers from different firms to exchange information and ideas in an inexpensive

way. Although some information transmissions are likely to incur significant costs, we consider

conferences to be costless, relative to the other available means. This is because firms normally

set a certain budget for conference and seminar attendance, to keep up on industry and

technology developments, and so this kind of spending is probably viewed by them as a routine

cost. In addition, for EBPP at least, the conference attendance costs are insignificant relative to

the costs for acquiring and maintaining EBPP technology. Thus, in the EBPP case, we can use

the number of EBPP-related conferences conducted in the past as a proxy of information sharing

activities that occurred among potential adopters of the technology. Our first hypothesis

follows from this discussion:

• Hypothesis 1 (The Information Transmission Hypothesis). More information

transmission in the marketplace will be positively associated with the observation of

more clustered adoption.

A related argument that can be made, which also can be developed in the context of the

empirical model that we plan to estimate, is related to the observed rate of adoption over time. In

some cases, technology adoption that is observed may not meet our requirement for clustered

adoption: the time-clustering may not be tight enough, for example. Still, it may be possible to

test a weak form hypothesis for clustered adoption that examines the rate of adoption. We

indicate this in the second hypothesis:

• Hypothesis 2 (The Information Transmission Technology Adoption Rate

Hypothesis). More information transmission in the marketplace will be positively

associated with faster observed rates of technology adoption.

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Information transmission allows each firm to share its expectations about the value of the

new technology with other firms. More information transmission leads to greater information

sharing among the firms, which in turns leads to a faster learning process [Morishima, 1991;

Creane, 1995]. With faster learning, we can expect that firms will be able to align their

expectations and make an adoption decision sooner.

Geographical collocation effects. Geographical collocation is a key factor that may

increase the level of interaction among the firms, leading to time-clustered technology adoption.

The degree of interaction and information sharing among firms can be expected to be the highest

locally. This is because the transmission of new information becomes more complex and costly

with increased geographical distance. As a result, the economic activity based on technology

innovations is likely to be clustered geographically [Audretsch and Feldman, 1996]. Geographic

proximity facilitates interaction, information exchange and technological learning [Soete 1985;

Ganesh et al., 1997]. This leads us to assert our hypothesis:

• Hypothesis 3 (The Geographical Collocation Clustered Technology Adoption

Hypothesis). Firms in the same geographical region are likely to adopt at about the

same time, resulting in clustered adoption.

Geographical reach effects. Keen [1991] uses the term reach to refer to the geographical

locations and the people that a firm’s IT infrastructure is capable of connecting. Similarly, the

term geographical reach has been used to refer to the extent to which a firm has its presence and

markets its services or products [Radecki et al., 1996].

In our case, we can group the firms into two categories based on their geographical reach:

local and regional. We define local firms as firms that serve a customer base in only one state,

and regional firms as firms that serve customer bases in multiple states. We maintain that local

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firms should adopt a new technology earlier than regional firms because the latter typically go

through a more complicated decision making process due to their larger operations, causing the

learning process to take longer too. Our fourth hypothesis is as follows:

• Hypothesis 4 (The Geographical Reach Hypothesis). Similar geographical reach is

likely to result in similar underlying conditions for technology adoption, including

similar dynamics for information transmission and learning, with the result that cluster

adoption will be observed.

Industry sector effects. The rate of technology adoption varies between industries. Some

industries may find a given technological innovation more useful in their productions processes

or product lines than others [Hannan and McDowell, 1984b]. Mansfeld [1968] and Romeo

[1975] provide evidence supporting the hypothesis that the more competitive the market or

industry, the greater the rate of technology adoption and diffusion. Kamien and Schwartz [1982]

review the empirical evidence and note that technology adoption and diffusion tend to be faster

in an industry where there are fewer firms in the region and where there are similarities among

firm sizes. Forman et al. [2003] find that industries differ in their rates of adoption in Internet

technology because they differ in their use of other kinds of IT, labor costs, and industry growth

rates.

The economics literature commonly identifies information transmission and sharing among

firms as occurring mostly within the same industry [e.g., Doyle and Snyder, 1999; Christensen

and Caves, 1997] and done through mechanisms such as industry consortia, industry

conferences, and trade associations [e.g., Kirby, 1988; Vives, 1990]. Our fifth hypothesis is

based on the foregoing analysis:

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• Hypothesis 5 (The Common Industry Sector Technology Adoption Hypothesis).

Since information transmission and sharing among firms in a common industry will be

earlier to accomplish, clustered adoption by industry sector will be observed.

Technology vendor effects. In the adaptive learning process, firms may initially have only a

general idea about a new technology (e.g., the main functions of the technology, the potential

benefits the technology may offer to the firms). Over time, however, these firms add, update and

process information about many aspects of the technology, including information about specific

vendor(s). As firms share information with each other, they will narrow their focus to a certain

vendor. This will help them to be more effective in reaching a consensus on the value of the

technology being considered, which will drive the outcomes to the equilibrium point suggested

by rational expectations theory [Fryman, 1982]. This suggests that when a group of firms

reaches a consensus to adopt the technology, they are likely to have a specific vendor in mind.

Our sixth hypothesis is derived from this logic.

• Hypothesis 6 (The Competing Vendor Hypothesis). Since information transmission

and sharing among a group of firms typically focuses on a particular technology vendor,

clustered adoption by technology vendor will be observed.

Firm size effects. It is generally held that firm size is positively correlated with technology

adoption. Large firms or firms with larger market shares are more likely to adopt because they

are more likely to have the financial resources required for purchasing and installing a new

technology. In addition, they may be better able to attract the necessary human capital and other

resources. Larger firms are also more capable of spreading the potential risks associated with

new projects because they are able to be more diversified in their technology choice and are in a

position to try out a new technology while keeping the old one operating at the same time in case

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of unexpected problems. Larger firms tend to adopt new technologies sooner because they have

the “critical mass” and are able to capture economies of scale from production via the learning

curve more quickly and can spread the other fixed costs associated with adoption across a larger

number of units. The positive relationship between firm size and technology adoption has been

found to occur with reasonable consistency in a variety of empirical research settings [e.g., Link,

1980, in the chemicals and allied products industry; Kimberly and Evanisko, 1981, in the health

sector; Hannan and McDowell, 1984a, in electronic banking; Damanpour, 1992, in

manufacturing organizations; and Karshenas and Stoneman, 1993 and 1995, in the engineering

industry].

Some other literature suggests, however, that large size and market power may slow down

the rate of technology adoption. Larger firms may have multiple levels of bureaucracy that can

slow down technology adoption decision making processes. Furthermore, it may be relatively

more expensive for larger firms to adopt a new technology because they have many resources

and human capital sunk in the old technology and its architecture [Henderson and Clark, 1990].

Brynjolfsson et al. [1994] found that increases in the level of IT capital in an economic sector

were associated with a decline in average firm size in that sector. This may lead to the notion

that smaller firms are more likely to adopt a new technology earlier.

Despite the seemingly opposing views on the effects of firm size on technology adoption, we

argue that if firms of similar size display consistent technology adoption behaviors, then we

should observe clustered adoption by firm size. Our seventh hypothesis is:

• Hypothesis 7 (The Firm Size Hypothesis). Since firm size affects technology adoption,

clustered adoption by firm size will be observed.

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A conceptual model for EBPP technology adoption. Based on the foregoing development

of hypotheses, we now are in a position to offer a more general conceptual model for EBPP

technology adoption. To test our clustered adoption hypotheses, we will employ a set of cross-

sectional models with deviation from mean time-to-adopt as the dependent variable and a set of

independent variables. The values of the dependent variable are calculated based on the mean

time-to-adopt of all firms in the sample. In the other models, the values of the dependent

variable are derived based on the mean time-to-adopt of each group, stratified by region, firm

size, industry, reach, and vendor. In addition, we will use a duration model for survival analysis

involving panel data to identify the instantaneous likelihood to adopt for each firm. This

conceptual model is summarized in the following diagram. (See Figure 1.)

One of the key issues with respect to this general conceptual model will be how we handle time

related to the observation of clustered adoption. Our operational definition for clustering of

technology adoption is based on an analysis of the empirical regularities adoption of technology

in a variety of settings [Fichman, 1992; Hoppe, 2002]. We note that in the context of XML and

Web services standards [Au and Kauffman, 2003; Chen et al., 2003], technologies with similar

multi-partite adoption complexity issues, that adoption occurred in large measure over the course

of one to two years. Other examples include DVDs, Wi-Fi and camera-phones. In the case of

Wi-Fi, research firm IDC predicts that the total number of public Wi-Fi users is expected to grow

from 2.4 million in 2003 to 10.4 million in 2005, a 324% growth over a period of two years

[Infonet, 2003]. In addition, we also have seen this kind of time horizon for the main elements

of technology adoption occurring in mobile telecommunications in Europe. Gruber and

Verboven [2001] report that technology adoption was rather swift in this context also. We

further note that the multi-partite adoption issues that are likely to be present here will require a

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similar rational expectations theory interpretation of adoption-related information transmission

and sharing.

Figure 1. Conceptual Model to Test Rational Expectations-Based EBPP Adoption

Cross-sectional Models

By RegionOverall By FirmReach •DevMeanTimeToAdopt •Northeast

•Midwest •South •FirmReach •Telecom •Metavante •FirmSize •∆Lag2QConfDensity •∆Lag2QDJIA •∆Lag2QVendorStockPrice

•Northeast •Midwest •South •FirmReach •Telecom •Metavante •FirmSize •∆Lag2QConfDensity •∆Lag2QDJIA •∆Lag2QVendorStockPrice

•Northeast •Midwest •South •FirmReach •Telecom •Metavante •FirmSize •∆Lag2QConfDensity •∆Lag2QDJIA •∆Lag2QVendorStockPrice

Dependent Variables •TimeToEvent

•Northeast •Midwest •South •FirmReach •Telecom •Metavante •FirmSize •∆Lag2QConfDensity •∆Lag2QDJIA •∆Lag2QVendorStockPrice

Independent Variables By Industry

•Northeast •Midwest •South •FirmReach •Telecom •Metavante •FirmSize •∆Lag2QConfDensity •∆Lag2QDJIA •∆Lag2QVendorStockPrice

By Vendor •Northeast •Midwest •South •FirmReach •Telecom •Metavante •FirmSize •∆Lag2QConfDensity •∆Lag2QDJIA •∆Lag2QVendorStockPrice

Panel Data Model / Duration Model

Based on our brief assessment of adoption patterns for other technologies likely to have had

adoption issues caused by value flows being constrained by the adoption of different kinds of

players in the same time frame, we will argue that a one-year window of time is a reasonable

period within which to observed clustered adoption. We further note that this is consistent with

the manner in which capital budgeting for large capital projects is done in firms: they normally

must plan ahead for big investments such as EBPP, and their budgeting plans typically are done

once a year. There is no requirement that their budgets all be developed over the same set of

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months, however. Thus, we think a window of time—rather than a smaller specific period of

time—ought to make the most sense for this study.

DATA AND MEASURES

Data Sources

In this section, we will provide a description of the data set and how we collected the data as

well as the descriptive statistics for the variables in the study that we will use to test the

hypotheses discussed earlier.

Description of the Data Set

Our sample consists of EBPP adopting firms in the utilities and telecommunications industries

in the United States. We focused our analysis on two major EBPP vendors: CheckFree and

Metavante. To compile the sample, we used the list of corporate customers that CheckFree

posted on its website, as well as a list of corporate customers that we obtained directly from

Metavante. Due to the unavailability of financial information for privately-held firms, we chose

to include only public firms in our base sample set. However, in our extended sample set we

included all these private firms to perform a comparison analysis without any firm-specific

financial information. In addition, we considered companies that had the same parent company

as one company.

Since CheckFree’s list did not include the adoption dates of any of the companies, we

searched for the information on the Internet using multiple data sources, including Google,

LexisNexis, and the annual report of each company. In this case, we defined the adoption date as

the date when a company signed an EBPP agreement with the technology vendor.

Furthermore, we collected information about each company including company location (city,

state, and zip code), company size (in terms of the number of customers in the year adopted

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EBPP), and company financial performance as measured in annual sales and earnings before

interest and tax (EBIT). While information about company location and company size could be

found on each company’s web site, we used the COMPUSTAT database to obtain the company

financial performance data. For the number of EBPP conferences per quarter, we searched the

Web, LexisNexis, and conference organizer web sites. We confirmed our data by contacting

several major conference organizers.

To determine the location of regional firms whose operations span multiple states that might

fall into different regions, we used the location of the headquarters and the region where most of

the operations were taking place. Our coding of regions follows the designations specified by

the United States Census Bureau. (See Appendix 1.)

Specification of measures for the variables included in this study

Our analysis will employ two different models. The first model is cross-sectional and will

be the primary means for testing the clustered adoption hypotheses. The second is a duration

model involving panel data with time-varying covariates for identifying the instantaneous

likelihood of adoption of a particular firm at a specific point in time. Consequently, we will

specify two different sets of dependent variables to reflect the use of two different models. The

sets of independent variables are almost the same between the two models. However, as the

names imply, the first model uses cross-sectional data, whereas the second model employs panel

data. Both models include lagged variables as explanatory variables.

Table 4 summarizes the measures and definitions for the variables that are used in this

empirical study. The use of the lagged value in some of the variables is based on the idea that it

will take some time before conference activities are seen to affect any adoption decision by a

firm. Furthermore, the use of the difference in the numbers of conferences between periods

18

captures the idea about how firm adoption decisions in certain periods were affected by the

increase or decrease in the frequency of the related conference activities. Table 5 shows

descriptive statistics for variables in our data set.

EMPIRICAL MODELING

The empirical analysis in this chapter will involve two separate models. The first is a cross-

sectional model set that offers a primary test of the cluster adoption hypothesis through

econometric analysis of models that represent the five different clustering strata. The goal of the

analysis is to estimate the extent to which the different theorized effects arise for different

subgroup codings for the dependent variables, relative to deviations in time from the central

tendency of firms to adopt EBPP.

The second empirical analysis involves the specification of an accelerated failure time panel

data model, whose structure is developed to make it possible to identify the instantaneous

likelihood of adoption of a given firm in time. This analysis does not focus on deviations from

time to adopt. Nor does it include sub-sample stratifications that affect the definition of the

dependent variable. Instead, the dependent variable is the occurrence of EBPP adoption by a

firm at a specific point in time, such that the general model can capture information about the

temporal likelihood of adoption for all firms. We introduce the stratifying variables from the

first model into this panel data model, as the size of the data set allows, as categorical fixed

effects. However, we should note at the outset that it is not possible for us to include all of the

hypothesized clustered adoption conditions; the size of the data size is insufficient in statistical

power terms.

19

Table 4. Summary of Variable Definitions and Measures for This Study

VARIABLE DEFINITION ISSUES

Dependent Variables DevMeanTime ToAdopt

The deviation from group average time-to-adopt (in quarters from the beginning of the adoption timeline).

Emphasis is on differences in groups, leading to different deviations for similar observations.

TimeToAdopt The duration of time from the start of the observation period to the event. For each firm in each time period, the event is coded with a “1” if the firm made an adoption decision, and with a “0” if otherwise.

Requires information about duration, but acts only as a technical parameter in the model.

Theory-Bearing Independent Variables ∆Lag2QConf Density

The difference between the number of EBPP-related conferences held two quarters prior to the adoption period of each firm and the number of similar conferences held four quarters prior in the region.

A number of different lag specifications were examined. This two-quarter lag variable was most appropriate.

FirmSize Size of each firm, measured in terms of number of customers.

Proxy for firm size, since employees were not available.

FirmReach Dummy variable that codes for the operational span of an EBPP adopter, with 1=regional span and 0=local span only. Local span is the base case.

Span was coded based on a single state (local) or a number of states (regional). Other span definitions were possible too.

Metavante Dummy variable that codes for a vendor, with 1=Metavante, 0=CheckFree. Checkfree is the base case.

No other vendors were included in this study for a lack of public information.

Regional dummy variables

A set of dummy variables that code for four geographic regions of the U.S. and proxies for demographic similarities in the operational environment of an EBPP adopter:

• Northeast: 1=Northeast, 0=otherwise. • Midwest: 1=Midwest, 0=otherwise. • South: 1=South, 0=otherwise.

The base case is the West region.

Coding is based on regional definitions of the U.S. Census Bureau. Using the west region as the base case reflects our belief that this is the region whose EBPP growth dynamics are most well understood.

Control Variables ∆Lag2QDJIA Change in quarterly average of DJIA indices compared

to the previous quarter, measured two quarters prior to the time of adoption.

Values computed on the basis of daily market indices. Choice of lag intended to match ConfDensity variable.

∆Lag2QVendorStockPrice

Change in quarterly average of vendor stock price (CKFR for CheckFree adopters and MI for Metavante adopters) compared to the previous quarter, measured two quarters prior to the time-to-adopt.

Values computed on the basis of daily stock market prices. Choice of lag intended to match ConfDensity variable.

∆LagFirm Profit

Change of a firm’s annual EBIT in the year prior to the year-of-adoption compared with the previous year.

Single-year lag used due to financial reporting limitations, based on annual report info.

Notes: The specifications of the variables that are used in this study reflect a blend of ideal measures and pragmatic measures. The variable that codes vendor, Metavante, is an example of the former. The lag measures typically were pragmatic choices, when there were many possible measures to choose from.

20

Table 5. Descriptive Statistics

VARIABLE N MEAN STD. DEV

MIN MAX

DevMeanTimeToAdopt 80 0 3.586 -6.025 10.975 Northeast 80 0.275 0.449 0 1 Midwest 80 0.225 0.420 0 1 South 80 0.275 0.449 0 1 FirmReach 80 0.663 0.476 0 1 Telecom 80 0.188 0.393 0 1 Metavante 80 0.113 0.318 0 1 ∆Lag2QConfDensity 80 0.150 1.801 -4.000 3.000 ∆Lag2QDJIA 80 0.019 0.067 -0.060 0.453 ∆Lag2QVendorStockPrice 80 0.330 0.759 -0.664 1.541 Note: Northeast, Midwest, South, FirmReach, Telecom, and Metavante are dummy variables and therefore have a value of either 0 or 1 only.

A Cross-Sectional Clustered Adoption Model for EBPP Technology Adoption

Our main premise is that in the presence of information transmission a group of firms sharing

similar characteristics will adopt EBPP at about the same time, resulting in clustered adoption.

In practice, some of the firms will adopt either a little earlier or a little later. In other words,

clustered adoption may occur with an error term for timing, since firms with similar

characteristics will not be identical, may process information differently, and so on.

Nevertheless, we posit that the average time of adoption reflects the group’s rational expectations

about how to maximize the value associated with technology adoption by adopting at the value-

maximizing time. A firm that adopts either earlier or later than the average time shows a

deviation from the central tendency for adoption timing by members of the group. This may

diminish the firm’s ability to obtain network externality-led benefits (when adoption is too early)

or cause the firm to miss out on capturing value that is available in the marketplace (when

21

adoption occurs too late). The objective of our cross-sectional model is to explain the deviations

for operationally-defined dependent variables that reflect different possible subgroups of firms.

We base our model on the multiplicative model which takes the form of:

εβ βββ KiKiii xxxy ,,2,10

21 K=

where k = counter for k = 1 to K independent variables

yi = deviation from mean time-to-adopt for EBPP adopter firm i among

i = 1 to I firms in the sample

x1,i, x2,i, …, xK,i = explanatory variable k for firm i

ε = normally-distributed error term with 0 mean

The multiplicative model represents interactions among the independent variables. For

example, the deviation from mean time-to-adopt might be a combination effect of the location of

the firm and its size.

The above multiplicative form can be rewritten in the log-linear form using the variable

names specified in the previous section:

εβββ

βββββ

ββββ

ln)Pr2ln()2ln()2ln(

)Prln()ln(Re

ln)ln(

11

109

87

654

3210

+∆+∆+∆+

∆+++++

+++=

iceckQVendorStoLagQDJIALagtyQConfDensiLag

ofitLagFirmFirmSizeMetavanteTelecomachFirm

SouthMidwestNortheasteToAdoptDevMeanTim

(The Basic Cross-Sectional Model) (1)

This basic cross-sectional model includes all the independent variables. The value of the

dependent variable for each observation (in other words, each row in the regression) is calculated

based on the deviation from the mean time-to-adopt of all observations in the sample.

22

For computational purposes, to avoid an invalid operation with the logarithmic function in

the model because of a negative or zero value, we add a constant (10) to each of the dependent

variable DevMeanTimeToAdopt value as well as each ∆Lag2QConfDensity value. Similarly, we

add 1 to ∆LagFirmProfit, ∆Lag2QDJIA as well as ∆Lag2QVendorStockPrice. These constants

will be taken care of when we calculate the marginal effect of the respective coefficients later on.

In addition, we also estimate four other models based on four different clustering strata:

region, reach, vendor, and industry. The model based on the region stratifier excludes the

Northeast, Midwest, and South dummy variables, with the Western United States region as the

base case. This is because the values of those variables are reflected in the values of dependent

variable, which is calculated based on the difference in mean time-to-adopt of observations per

region.

εβββ

ββββββ

ln)Pr2ln()2ln()2ln(

)Prln()ln(Reln)ln(

11

109

87

6540

+∆+∆+∆+

∆+++++=

iceckQVendorStoLagQDJIALagtyQConfDensiLag

ofitLagFirmFirmSizeMetavanteTelecomachFirmeToAdoptDevMeanTim

(The Regional Stratification Model) (2)

Similarly, we have a model based on the firm reach stratifier that will exclude the FirmReach

variable and whose dependent variable values are calculated based on the difference in the mean

time-to-adopt per reach group (regional and local).

)Pr2ln()2ln()Prln()2ln(

)ln(ln)ln(

1110

97

865

3210

iceckQVendorStoLagQDJIALagofitLagFirmtyQConfDensiLag

FirmSizeMetavanteTelecomSouthMidwestNortheasteToAdoptDevMeanTim

∆++∆+∆+

+++++++=

ββββ

βββββββ

(The Firm Geographical Reach Stratification Model) (3)

The next model is based on the industry stratifier and it will exclude the Telecom variable.

23

The dependent variable values are calculated based on the difference in mean time-to-adopt per

industry group (utilities and telecommunications).

)Pr2ln()2ln()Prln()2ln(

)ln(Reln)ln(

1110

98

764

3210

iceckQVendorStoLagQDJIALagofitLagFirmtyQConfDensiLag

FirmSizeMetavanteachFirmSouthMidwestNortheasteToAdoptDevMeanTim

∆+∆+∆+∆+

++++++=

ββββ

βββββββ

(The Industry Stratification Model) (4)

The last cross-sectional model is based on the vendor stratifier and it will exclude the

Metavante variable. The dependent variable values are calculated based on the difference in

mean time-to-adopt for firms by vendor group for Metavante and CheckFree.

)Pr2ln()2ln()Prln()2ln(

)ln(Reln)ln(

1110

98

754

3210

iceckQVendorStoLagQDJIALagofitLagFirmtyQConfDensiLag

FirmSizeTelecomachFirmSouthMidwestNortheasteToAdoptDevMeanTim

∆+∆+∆+∆+

++++++=

βββββββ

ββββ

(The Vendor Stratification Model) (5)

A Panel Data Model for EBPP Technology Adoption Likelihood

We analyze our data using a duration model for survival analysis (Greene, 2002; Hosmer and

Lemeshow, 1999; Hougaard, 2000). The data consist of a response variable that measures the

duration of time until a specified event occurs, as well as a set of independent variables

associated with the adoption time variable. The response variable is also known as the event

time, failure time, or survival time variable. The purpose of survival analysis is to model the

underlying distribution of the event time variable and to assess the dependence of the event time

variable on the independent variable.

We analyze our panel data using an accelerated failure time (AFT) model, also known as the

accelerated time model or the ln(time) model [Cleves et al., 2002; Lee and Wang, 2003]. The

24

typical form of the model is )ln()ln( ixii xt τβ += , where ti is time-to-adopt of firm i and τ i is the

residual. The word “accelerated” refers to the fact that in such a model, the effect of a change in

one of the independent variables increases with time. This is something we expect to see in

many technology adoption scenarios, including EBPP adoption. This is especially true in the

context of our theory, where we expect the potential adopters to go through a learning process

for a while to reinforce their understanding and expectations about the technology. In addition,

technologies have underlying elements that exogenously improve. As a result, as EBPP matures

over time, we should observe a more profound effect of the independent variables, leading to the

greater likelihood of adoption of the technology by firms.

Our basic panel data model is as follows:

)ln(Pr222

PrReRe)ln(

11109

876

543210

i

i

iceckQVendorStoLagQDJIALagtyQConfDensiLagofitLagFirmachFirmMetavante

TelcomachFirmSouthMidwestNortheastt

τβββ

βββββββββ

+∆+∆+∆+

∆++++++++=

(The Basic Panel Data Model) (6)

In the AFT model, exponentiated coefficients have the interpretation of time ratios for a one-

unit change in the corresponding covariate. For an EBPP adopting firm i with covariate

values , ),,,( 21 ki xxxx K= ikki xxxt τβββ )exp( 2211 +++= K

ikk xxx τβββ })1(exp{ 2211 ++++ K

. If the value of for the firm

increases by 1, then , and the ratio of to will be

1x

*itit

* =

)1

it

exp(β .

Results of the Basic Cross-Sectional Model

The estimation results from our basic cross-sectional model are summarized in the following

table. (See Table 6.) We check for the presence of multicollinearity by calculating the variance

inflation factor (VIF). Each VIF value in the table is less than two, suggesting there is no

25

multicollinearity issue with our data. The values of R2 and adjusted R2 (60.4% and 54.0%,

respectively) both indicate a relatively good model fit. The value of F-statistic (9.44) is highly

significant (p-value < 0.0001), suggesting that we can reject the hypothesis that all coefficients of

the explanatory variables all equal to zero.

Table 6. Results of the Basic Model

VARIABLES COEFFICIENT (STD. ERROR)

P-VALUE

Constant (β0) 4.521*** (0.660) 0.000 Northeast (β1) 0.199* (0.101) 0.053 Midwest (β2) 0.176* (0.100) 0.083 South (β3) 0.250** (0.097) 0.012 FirmReach (β4) 0.182** (0.081) 0.028 Telecom (β5) -0.075 (0.109) 0.491 Metavante (β6) 0.228* (0.121) 0.064 FirmSize (β7) 0.007 (0.030) 0.816 ∆LagFirmProfit (β8) 0.029 (0.247) 0.908 ∆Lag2QConfDensity (β9) -1.168*** (0.197) 0.000 ∆Lag2QDJIA (β10) -2.151*** (0.638) 0.001 ∆Lag2QVendorStockPrice (β11) 0.094* (0.056) 0.102 R2 (Adj. R2) 60.4% (54.0%) F-statistic 9.44*** White’s Test 57.89 (p-value = 0.72) Notes: Model: Basic Cross-Sectional Model (Equation 1). Dependent variable is ln(DevMeanToAdopt). Sample size N = 80. The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. Each VIF value is less than 2, suggesting there is no multicollinearity issue with our data.

By observing the p-value of each coefficient, we can see that the majority of the variables are

statistically significant. Three variables—Telecom, FirmSize, and ∆LagFirmProfit—are clearly

insignificant. Another variable, ∆Lag2QVendorStockPrice, is marginally significant. This gives

us some idea about which variables to exclude in our revised models, taking into account the key

variables needed for testing our theory.

26

To provide us with a better idea about our data, we next performed the estimations based on

the stratification models. The results, which are summarized in Table 7, show that the three

variables—Telecom, FirmSize, and ∆LagFirmProfit—are not statistically significant in any of

the models. Our revised models will exclude these three variables.

Table 7. Results of the Basic and Stratification Models

STRATIFICATION MODEL VARIABLES BASIC MODEL Regional FirmReach Industry Vendor

Coefficient (Std.

Error)

Coefficient(Std.

Error)

Coefficient (Std.

Error)

Coefficient (Std.

Error)

Coefficient(Std.

Error) Constant (β0) 4.521***

(0.660) 4.230*** (0.619)

4.643***

(0.622) 4.947*** (0.659)

4.752*** (0.620)

Northeast (β1) 0.199** (0.101)

N/A 0.131 (0.095)

0.142 (0.103)

0.202** (0.100)

Midwest (β2) 0.176* (0.100)

N/A 0.100 (0.094)

0.202** (0.101)

0.173* (0.098)

South (β3) 0.250*** (0.097)

N/A 0.179** (0.091)

0.212** (0.099)

0.266*** (0.093)

FirmReach (β4) 0.182** (0.081)

0.159** (0.078)

N/A 0.123 (0.082)

0.150** (0.076)

Telecom (β5) -0.075 (0.109)

-0.104 (0.101)

-0.085 (0.101)

N/A -0.041 (0.104)

Metavante (β6) 0.228** (0.121)

0.249** (0.115)

0.212** (1.109)

0.133 (0.120)

N/A

FirmSize (β7) 0.007 (0.030)

0.027 (0.028)

0.003 (0.231)

-0.027 (0.028)

-0.004 (0.028)

∆LagFirmProfit (β8)

0.029 (0.247)

0.026 (0.229)

0.097 (0.231)

-0.061 (0.249)

0.069 (0.242)

∆Lag2QConf Density (β9)

-1.168*** (0.197)

-1.082*** (0.183)

-1.134*** (0.184)

-1.081*** (0.199)

-1.191*** (0.192)

∆Lag2QDJIA (β10)

-2.151*** (0.638)

-2.064*** (0.603)

-1.713*** (0.595)

-2.086*** (0.664)

-2.016*** (0.623)

∆Lag2QVendorStockPrice (β11)

0.094* (0.056)

0.101* (0.053)

0.070 (0.053)

0.059 (0.057)

0.086 (0.055)

R2 60.4% 56.0% 54.5% 54.1% 59.5% Adjusted R2 54.0% 51.1% 47.9% 47.4% 53.6% F-statistic 9.44*** 11.31*** 8.28*** 8.13*** 10.12*** White’s Test 57.89

(p = 0.72) 45.30

(p = 0.26) 56.95

(p = 0.44) 48.08

(p = 0.77) 51.40

(p = 0.68)

27

Notes. Model: Basic Cross-Sectional Model (Equation 1); Regional Stratification Model (Equation 2); Firm Reach Stratification Model (Equation 3); Industry Stratification Model (Equation 4); Vendor Stratification Model (Equation 5). Dependent variable in each model is ln(DevMeanToAdopt). Sample size in each model is N = 80. The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01.

Revised Estimation Models

The results of the basic cross-sectional models presented earlier show that three variables—

Telecom, FirmSize, and ∆LagFirmProfit—are not statistically significant although we have

initially expected the opposite. This suggests that adoption times are not clustered by industry.

In addition, firm characteristics (especially firm size and profitability) appear not to affect the

time of adoption in the EBPP context.

We will estimate the following revised econometric models by excluding those three

variables:

εβββββ

ββββ

ln)Pr2ln()2ln()2ln(Re

ln)ln(

1110

964

3210

+∆+∆+∆+++

+++=

iceckQVendorStoLagQDJIALagtyQConfDensiLagMetavanteachFirm

SouthMidwestNortheasteToAdoptDevMeanTim

(The Revised Cross-Sectional Model) (7)

Similarly, our revised panel data model will also exclude the three variables.

)ln(Pr222Re)ln(

11

1096

43210

i

i

iceckQVendorStoLagQDJIALagtyQConfDensiLagMetavanteachFirmSouthMidwestNortheastt

τβββββββββ

+∆+∆+∆++

++++=

(The Revised Panel Data Model) (8)

In the following sections we will present the results of these models.

Results of the Revised Models

The results from the revised basic model are shown in Table 8 below. Now all coefficients

are statistically significant and the adjusted R2 value is slightly higher compared with that of the

28

original basic model (55.7% versus 54.0%). The F-statistic is highly significant (13.40, p <

0.0001). All of the VIF values are below two, suggesting that there is no indication of

multicollinearity.

Table 8. Results of the Revised Basic Model

VARIABLES COEFFICIENT (STD. ERROR)

P-VALUE

Constant (β0) 4.585*** (0.444) 0.000 Northeast (β1) 0.187** (0.093) 0.048 Midwest (β2) 0.186* (0.096) 0.057 South (β3) 0.249*** (0.093) 0.009 FirmReach (β4) 0.170** (0.074) 0.026 Metavante (β6) 0.208* (0.111) 0.064 ∆Lag2QConfDensity (β9) -1.143*** (0.188) 0.000 ∆Lag2QDJIA (β10) -2.163*** (0.604) 0.001 ∆Lag2QVendorStockPrice (β11) 0.088 (0.054) 0.105 R2 (Adjusted R2) 60.2% (55.7%) F-statistic 13.40 (0.000) Notes. Model: Revised Cross-Sectional Model (Equation 7). Dependent variable is ln(DevMeanToAdopt). Sample size is N = 80. The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. Each VIF value is less than 1.8, suggesting there is no multicollinearity issue with our data.

In addition to the revised basic model, we performed estimations for the revised stratification

models. The results are summarized in Table 9. Notice that industry is not one of the stratifiers

any more since we now exclude the Telecom variable. The results show generally consistent

estimates in terms of the sign and the significance level of each coefficient across the different

models. For example, ∆Lag2QConfDensity and ∆Lag2QDJIA are all negative and highly

significant, and FirmReach is positive and significant.

29

Table 9. Results of the Revised Basic and Stratification Models

OVERALL STRATIFICATION MODELS VARIABLES MODEL Regional FirmReach By Vendor

Coefficient (Std. Error)

Coefficient (Std. Error)

Coefficient (Std. Error)

Coefficient (Std. Error)

Constant (β0) 4.585*** (0.444)

4.575*** (0.421)

4.646*** (0.399)

4.682*** (0.428)

Northeast (β1) 0.187** (0.093)

N/A 0.123 (0.088)

0.200** (0.090)

Midwest (β2) 0.186** (0.096)

N/A 0.111 (0.090)

0.178* (0.095)

South (β3) 0.249*** (0.093)

N/A 0.174** (0.088)

0.261*** (0.090)

FirmReach (β4) 0.170** (0.074)

0.156** (0.072)

N/A 0.139** (0.069)

Metavante (β6) 0.208* (0.111)

0.199*** (0.104)

0.206** (0.099)

N/A

∆Lag2QConf Density (β9)

-1.143*** (0.188)

-1.063*** (0.178)

-1.094*** (0.174)

-1.168*** (0.183)

∆Lag2QDJIA (β10)

-2.163*** (0.604)

-1.942*** (0.575)

-1.756*** (0.569)

-2.078*** (0.593)

∆Lag2QVendor StockPrice (β11)

0.088* (0.054)

0.092* (0.051)

0.066 (0.051)

0.087 (0.053)

R2 60.2% 55.2% 53.4% 59.3% Adj. R2 55.7% 52.1% 49.5% 55.3% F-statistic 13.40*** 18.21*** 12.05*** 14.97*** Notes. Model: Revised Cross-Sectional Model (Equation 7). The other three models are stratification models similar to Equations 2, 3, and 5 but without the Telecom, FirmSize, and ∆LagFirmProfit variables on the right-hand side. Sample size is N = 80. Dependent variable is ln(DevMeanToAdopt). The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01.

The following Table 10 shows the size of the marginal effect of each coefficient and its

actual impact on the dependent variable. The marginal effect of the Northeast dummy variable

can be calculated as follows. Let be the value of the dependent variable in the base case

(i.e., West region), and be that of the Northeast region. If (where B

represents the sum of the values on the right-hand side of the cross-sectional model for the base

Basey

Northeasty BBase ey =

30

case), then . The marginal effect of the coefficient β1β+= BNortheast ey

yNortheast

1 on the dependent

variable is given by: . Table 10 shows the value

of for each model. We can derive a similar marginal effect for the other dummy variable

coefficients (Midwest-β

)1( 11 −=−=− + ββ eeeey BBBBase

11 −βe

2, South-β3, FirmReach-β4, and Metavante-β6).

1− 16 −βe)10ln()10 9βe−∆+

1)1ln( −∆+ 1ln(11βe

Table 10. Coefficient marginal effects on the dependent variable

OVERALL STRATIFICATION MODELS VARIABLES MODEL Regional FirmReach By Vendor

Coefficient Marginal

Effect

Coefficient Marginal

Effect

Coefficient Marginal

Effect

Coefficient Marginal

Effect Northeast (β1) 0.206 N/A 0.131 0.221 Midwest (β2) 0.204 N/A 0.117 0.195 South (β3) 0.283 N/A 0.190 0.298 FirmReach (β4) 0.185 0.169 N/A 0.149 Metavante (β6) 0.231 0.220 0.229 N/A ∆Lag2QConf Density (β9)

-0.007 -0.008 -0.008 -0.007

∆Lag2QDJIA (β10)

-0.021 -0.019 -0.017 -0.020

∆Lag2QVendor StockPrice (β11)

0.001 0.001 0.001 0.001

Note: To avoid an invalid operation with the logarithmic function in the model because of a negative or zero value, we added a constant (10) to each ∆Lag2QConfDensity value, and 1 to ∆Lag2QDJIA as well as ∆Lag2QVendorStockPrice. As a result, each constant shows up in the marginal effect for the respective coefficient as shown above in this table. The marginal effects of β1, β2, β3, β4, and β6 are calculated using , e , e ,

, and respectively. The marginal effect of β11 −βe 12 −β 13 −β

4βeln(9βe

10βe

9 is determined using , with ∆ = 1. The marginal effects of β10 and β11 are calculated using

and , respectively, with ∆ = 1%. 1) −∆+

The results in Table 9 show that both ∆Lag2QConfDensity and ∆Lag2QDJIA have a negative

coefficient and they are both significant at 0.01 level. This suggests that an increase in

conference activities and a positive change in the economy in general have accelerated the

31

adoption of EBPP. This supports the Information Transmission Technology Adoption Rate

Hypothesis (H2). The difference in the values of the dependent variable caused by a change in

the number of conferences of ∆ is given by

)( 10ln)10ln(

10ln)10ln(10ln)10ln(*

99

9999

ββ

ββββ

eee

eeeeeeyyB

BBBBBase

−=

−=−=−∆+

∆++∆++

Therefore, the marginal effect is given by , where ∆ is the actual difference in

the number of EBPP conferences as defined for ∆Lag2QConfDensity. (Note: The constant 10

appears in the calculation above since we added the number 10 to every ∆ prior to estimating our

cross-sectional model to avoid an invalid operation with the logarithmic function in the model

because of a negative or zero value.) Since β

10ln)10ln( 99 ββ ee −∆+

007.010ln143.1 −=−

9 is negative, a positive ∆ will result in a negative

marginal effect, which means earlier adoption. For example, in the overall model case, if ∆ = 1,

then the marginal effect is e . )110ln(143.1 −+− e

For ∆Lag2QDJIA (β10), the difference in the values of the dependent variable caused by a

change in the DJIA stock market index ∆ is given by:

)1( )1ln(

0)1ln(1ln)1ln(*

10

101010

−=

−=−=−∆+

∆++∆++

β

βββ

ee

eeeeeeyyB

BBBBBase

Therefore, the marginal effect is given by coefficient of ∆Lag2QDJIA (β10) is given by

, which also means earlier adoption for a positive change in the stock index. (Note:

Similar to the number of conferences case, we added the constant 1 to every ∆ prior to estimating

our cross-sectional model to avoid an invalid operation with the logarithmic function in the

model because of a negative or zero value.) For example, in the overall model case, if ∆ = 0.01

1)1ln(10 −∆+βe

32

(which means the stock market index increases by 1%, then the marginal effect

is e . 021.01)01.01ln(163.2 −=−+−

The other variables have a positive coefficient and are statistically significant at various

levels. The ∆Lag2QVendorStockPrice variable has a positive coefficient and is statistically

significant in two of the revised models. The positive sign is counter-intuitive since we would

expect that a positive change in the vendor stock performance should accelerate the adoption of

the technology; instead, the result shows the opposite.

The FirmReach variable has a positive coefficient and is statistically significant at 0.05 level.

FirmReach is set to “1” if a firm is a regional firm. The fact that the coefficient is significant

suggests that there is a relative clustering of regional firms in terms of time of adoption because

holding all other things equal, each regional firm will adopt at the same time. The positive

coefficient suggests that regional firms adopted later than the base case (i.e., the local firms).

Therefore, the Geographical Reach Hypothesis (H4) is supported. The marginal effect of the

FirmReach coefficient for the overall model is 0.185. (See Table 10.) This means that holding

all other things equal, regional firms’ deviation from the overall mean time-to-adopt is smaller

than local firms’ by 0.185 times. Since the deviation from the overall mean time-to-adopt of the

base case is negative, we conclude that on the average regional firms adopted later than the local

firms.

The Metavante variable has a positive coefficient and is statistically significant at 0.01, 0.05,

or 0.10 level, depending on which model we look at. Metavante is set to “1” if a firm is a

Metavante adopter. Again, the fact that the coefficient is significant suggests that there is a

relative clustering of Metavante adopting firms in terms of time of adoption. The positive

coefficient suggests that Metavante adopters adopted later than the base case (i.e., the CheckFree

33

adopters). This supports the Competing Vendor Hypothesis (H6). The marginal effect of the

Metavante coefficient for the overall model is 0.231. (See Table 10.) This means that holding all

other things equal, Metavante firms’ deviation from the overall mean time-to-adopt is smaller

than Checkfree firms’ by 0.231 times. Since the deviation from the overall mean time-to-adopt

of the base case is negative, we conclude that on the average Metavante firms adopted later than

Checkfree firms.

The Northeast, Midwest, and South variables all have a positive coefficient and are

statistically significant at different levels (except in the FirmReach stratification model where

Northeast and Midwest are not significant). This suggests that there is clustered adoption in each

region, supporting the Geographical Collocation Clustered Technology Adoption Hypothesis

(H3). The positive sign suggests that firms in each of these regions adopted later than the base

case, which is the Western firms. The marginal effect of the Northeast coefficient for the overall

model is 0.206. (See Table 10.) This means that holding all other things equal, Northeast firms’

deviation from the overall mean time-to-adopt is smaller than West firms’ (the base case) by

0.206 times. Since the deviation from the overall mean time-to-adopt of the base case is

negative, we conclude that on the average Northeast firms adopted later than West firms.

Similar arguments hold for Midwest as well as South, which marginal effects are 0.204 and

0.283, respectively. Again, the fact that each of these coefficients is significant shows that there

is a relative clustering of adoption time per region.

Estimations with an Extended Sample Size

Since we have removed all firm-specific variables (i.e., FirmSize and ∆LagFirmProfit), we

can now include all the non-public firms that were initially taken out of the original sample set.

This is because we no longer have the need to include values of variables that would only be

34

typically reported by publicly-held firms. This will allow us to work with a larger sample set

(118 firms versus 80 originally). Our extended sample set consists of 92 utilities and 26 telecom

firms. There are now 26 Metavante firms (versus 9 in the original sample set), and 92 Checkfree

firms (versus 71 in the original sample set).

Although there are more observations, the results of the estimations using the extended

sample set do not give us better support for our hypotheses. (See Table 11). Although there are

more telecommunications firms in the extended sample set, we still do not have a statistically

significant coefficient for the Telecom variable. Indeed, all three regional variables (Northeast,

Midwest, and South) are now statistically insignificant. We suspect that non-public firms may

behave differently than public firms in terms of deciding when to adopt a technology.

Table 11. Results of the Revised and Stratification Models with Extended Data Set

STRATIFICATION MODELS VARIABLES BASIC MODEL Regional FirmReach Industry Vendor

Coefficient (Std.

Error)

Coefficient(Std.

Error)

Coefficient (Std.

Error)

Coefficient (Std.

Error)

Coefficient(Std.

Error) Constant (β0) 4.747***

(0.386) 4.800*** (0.397)

4.670***

(0.392) 4.538*** (0.359)

4.696*** (0.357)

Northeast (β1) 0.108 (0.097)

N/A 0.145 (0.097)

0.076 (0.092)

0.091 (0.090)

Midwest (β2) 0.029 (0.101)

N/A 0.077 (0.100)

0.058 (0.096)

0.026 (0.094)

South (β3) 0.105 (0.084)

N/A 0.112 (0.085)

0.074 (0.080)

0.086 (0.076)

FirmReach (β4) 0.158** (0.075)

0.187** (0.075)

N/A 0.105 (0.070)

0.152** (0.064)

Telecom (β5) -0.028 (0.084)

-0.036 (0.086)

0.014 (0.083)

N/A -0.032 (0.074)

Metavante (β6) 0.121 (0.094)

0.155** (0.095)

0.043 (0.088)

0.053 (0.084)

N/A

∆Lag2QConf Density (β9)

-1.193*** (0.175)

-1.199*** (0.178)

-1.130*** (0.176)

-1.074*** (0.163)

-1.148*** (0.163)

∆Lag2QDJIA (β10)

-2.165*** (0.683)

-1.839*** (0.692)

-2.451*** (0.682)

-2.286*** (0.648)

-2.080** (0.637)

35

∆Lag2QVendorStockPrice (β11)

0.033 (0.055)

0.033 (0.056)

0.039 (0.056)

0.016 (0.052)

0.028 (0.051)

R2 52.9% 48.6% 50.9% 50.9% 52.8% Adj. R2 49.0% 45.8% 47.3% 47.3% 49.3% F-statistic 13.49*** 17.45*** 14.13*** 14.13*** 15.22*** Notes: Model: Basic Cross-Sectional Model (Equation 1); Regional Stratification Model (Equation 2); Firm Reach Stratification Model (Equation 3); Industry Stratification Model (Equation 4); Vendor Stratification Model (Equation 5); all without FirmSize and ∆LagFirmProfit. Dependent variable in each model is DevMeanToAdopt. Extended sample size in each model is N = 118. The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01.

Panel Data Model Results

We used Stata 8.0’s STREG command to estimate our panel data model. The STREG

command allows the analysis of the panel data using the accelerated failure time model.

Alternate Parametric Functional Forms for the AFT Model

There are five parametric models that can be used: exponential, Weibull, generalized gamma,

log-normal, and log-logistic. Briefly, the different parametric models permit the representation

of somewhat different assumptions about ixii tx )exp( βτ −= in applied settings involving the

adoption of IT. With an exponential parametric model, it is assumed that

)}{exp(~ 0βτ lExponentiai , i.e., iτ is distributed as exponential with mean exp( )0β . With a

Weibull parametric form, it is assumed that )p,(~ 0Weibulli βτ , i.e., iτ is distributed as Weibull

with parameter ( ),0 pβ . The generalized gamma parametric form implies that

),,(~ 0 σκβτ Gammai , i.e., iτ is distributed as generalized gamma with parameter ),,0( σκβ .

Finally, the assumption for the log-normal regression model is that that )~ ,( 0 σβτ Lognormali ,

i.e., iτ is distributed as log-normal with parameter ),( 0 σβ . In contrast, with the log-logistic

regression model, the assumption is that ),0(log~ γβτ isticLogi , i.e., iτ is distributed as log-

36

logistic with parameter ),( 0 γβ . The log-normal and the log-logistic parametric forms differ in

that they increase and decrease (in this case, the log-logistic hazard increases and decreases

if 1<γ ). As a result, they may be more appropriate to represent settings in which the hazard rate

is going up and then going down, a phenomenon that we would observe in many technology

adoption settings where adoption is initially slow then going at a faster rate but eventually

slowing down after some time. Taken together the empirical modeling choices offered by the

parametric forms of the accelerated failure time model constitute a strong basis for studying a

variety of IT adoption phenomena.

)ck +AIC

The time option of the STREG command specifies that the model is to be estimated in the

accelerated failure-time metric rather than in the log relative-hazard metric. This option is only

valid for the exponential and Weibull models since they have both a hazard ratio and an

accelerated failure-time parameterization. For the other three models, the STREG command

always estimates the accelerated failure time model.

Empirical Model Selection Procedure

To determine which model to use, we followed the recommendation made by Akaike [1974].

This paper suggests penalizing each model’s log-likelihood to reflect the number of parameters

being estimated and then comparing them. Although the best-fitting model is the one with the

largest log-likelihood, the preferred model is the one with the lowest value of the Akaike

information criterion (AIC). For parametric survival models, the AIC is defined as:

(2ln2 L +−=

where k is the number of model covariates and c the number of model-specific distributional

parameters [Cleves et al., 2002]. The values of c for the different distributions are shown in

Table 12.

37

Table 12. AIC’s c Value for Various Distributions

DISTRIBUTION C

Exponential 1 Weibull 2 Generalized gamma 3 Log-normal 2 Log-logistic 2

We first performed the accelerated failure time estimation using the basic panel data model.

Table 13 shows the log-likelihood and AIC values of each distribution model. We can see that

the log-logistic model has the lowest AIC value. The Weibull and generalized gamma functional

forms were roughly tied for second place, with some distance in terms of the AIC score that

separated the log-logistic form. As a result, we selected the log-logistic model. These results

practically conform with what the different parametric forms have to offer. As discussed earlier,

only the log-normal and log-logistic forms are non-monotonic, which is more in line with our

EBPP adoption pattern. And despite the similarities between log-normal and log-logistic, the

latter allows it to increase at a slower rate initially with the appropriate value of γ (in this case,

STATA reported using a value of γ of 0.138).

Table 13. The AIC’s c Value for Various Distributions (Basic Panel Data Model)

DISTRIBUTION LOG LIKELIHOOD

K C AIC

Exponential -233.24943 11 1 490.49886 Weibull -99.06118 11 2 224.12236 Generalized gamma -98.717149 11 3 225.43430 Log-normal -101.67105 11 2 229.34210 Log-logistic -98.379405 11 2 222.75881

38

Full and Revised Panel Data Model Results for the Log-Logistic AFT Model

Table 14 shows the estimation results of the basic panel data model using the log-logistic

model.

Table 14. Results of the Basic Panel Data Model

VARIABLE COEFFICIENT (STD. ERROR) Z

Constant (β0) 2.757*** (0.062) 44.43 Northeast (β1) 0.030 (0.057) 0.53 Midwest (β2) -0.051 (0.057) -0.90 South (β3) -0.006 (0.056) -0.10 FirmReach (β4) 0.101** (0.050) 2.03 Telecom (β5) -0.010 (0.066) -0.16 Metavante (β6) 0.004 (0.068) 0.06 FirmSize (β7) 3.18e-09 (0.000) 1.05 ∆LagFirmProfit (β8) 0.021 (0.045) 0.48 ∆Lag2QConfDensity (β9) -0.030*** (0.011) -2.61 ∆Lag2QDJIA (β10) -0.887** (0.376) -2.36 ∆Lag2QVendorStockPrice (β11) -0.057** (0.028) -2.08 Log-likelihood -98.379 LR(χ2) 33.15 Prob > χ2 0.0005*** Notes: Model: Basic Panel Data Model (Equation 6). Dependent variable in each model is Time-to-Adopt (t). Sample size in each model is N = 80. The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. LR(χ2) is the likelihood ratio χ2.

Several variables have a statistically significant coefficient. They are FirmReach,

∆Lag2QConfDensity, ∆Lag2QDJIA, ∆Lag2QVendorStockPrice. In the revised panel data

model, we eliminated the insignificant variables from the model. However, we still retained

Northeast, Midwest, South, and Metavante to enable us to compare the results with the revised

cross-sectional model. Table 17 lists the AIC value of each distribution of the revised panel data

model. Again, log-logistic is the preferred distribution based on the AIC criterion. In addition,

the Weibull functional form performs almost as well.

39

Table 17. AIC’s c Value for Various Distributions (Revised Panel Data Model)

DISTRIBUTION LOG LIKELIHOOD

K C AIC

Exponential -233.71317 8 1 485.42634 Weibull -100.06237 8 2 220.12474 Generalized gamma -99.77483 8 3 221.54966 Log-normal -102.53552 8 2 225.07104 Log-logistic -99.510098 8 2 219.02020

In Table 18, we report the results of the revised panel data model. We can see that the same

variables are significant.

Table 18. Results of the Revised Panel Data Model

VARIABLE COEFFICIENT (STD. ERROR) Z

Constant (β0) 2.757*** (0.061) 44.98 Northeast (β1) 0.051 (0.055) 0.92 Midwest (β2) -0.054 (0.057) -0.94 South (β3) -0.002 (0.056) -0.03 FirmReach (β4) 0.122*** (0.046) 2.62 Metavante (β6) 0.003 (0.066) 0.04 ∆Lag2QConfDensity (β9) -0.030*** (0.011) -2.64 ∆Lag2QDJIA (β10) -0.892** (0.374) -2.38 ∆Lag2QVendorStockPrice (β11) -0.061** (0.028) -2.21 Log-likelihood -99.510 LR(χ2) 30.89 Prob > χ2 0.0001*** Notes. Model: Revised Panel Data Model (Equation 8). Dependent variable in each model is Time-to-Adopt (t). Sample size in each model is N = 80. The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. LR(χ2) is the likelihood ratio χ2.

The marginal effects of the coefficients that are statistically significant are as follows:

• FirmReach (β4): e0.122 = 1.130

• ∆Lag2QConfDensity (β9): e–0.030 = 0.970

• ∆Lag2QDJIA (β10): e–0.892 = 0.410

40

• ∆Lag2QVendorStockPrice (β11): e–0.061 = 0.941

A marginal effect that is smaller than 1 means that the variable has an accelerating effect,

whereas greater than one means the variable has a decelerating effect on the time-to-adopt.

Assessing Parameter Heterogeneity and Stability via Pseudo-Replicate Data

Sets

To estimate the accuracy of our sample statistics, we perform the jackknifing procedure.

Jackknifing uses a number of pseudo-replicate data sets, each of which contains all but one of

the original data elements [Efron, 1979]. Variations on this approach also permit the analyst to

iteratively drop out one, then two, then three observations, etc., up to the point where it becomes

impossible to establish coefficient estimates due to the lack of data. In our case, we do three

types of jackknifing that we have conceptualized for the purposes of this analysis: backward,

forward, and one-period-at-a-time jackknifing:

• In backward jackknifing, we start with the full data set, then go backward in time and

iteratively take out observations from the latest period, and estimate the remaining

sample each time, until the sample becomes too small to provide any meaningful

results. With this approach, fewer and fewer more recent observations of technology

adoption will be included. (See Table 19.)

• Forward jackknifing is the opposite of backward jackknifing. In forward

jackknifing, we start with the full data set, then go forward in time and iteratively

remove observations from the earliest period, and estimate the remaining sample each

time, until the sample becomes too small for further useful analysis. With this

41

approach, fewer and fewer earlier observations of technology adoption will be

included. (See Table 20.)

• One-period-at-a-time jackknifing involves an analysis process in which we take away

one period of observations from the overall sample each time and iteratively estimate

the model’s coefficients on the remaining parameters. With this approach, we have

an opportunity to remove observations from middle periods in the panel of data, while

preserving data from the other periods with which to run the estimation model. (See

Table 21.)

Table 19. Results of the Panel Data Backward Jackknifing

VARIABLE T ≤ T-1 ≤ T-2 ≤ T-3 ≤ T-4 Constant (β0) 2.757*** 2.693*** 2.627*** 2.561*** 2.627*** Northeast (β1) 0.051 0.109** 0.031 0.090* 0.031 Midwest (β2) -0.054 0.002 0.006 0.065 0.006

South (β3) -0.002 0.052 0.020 0.075 0.020 FirmReach (β4) 0.122*** 0.116*** 0.148*** 0.140*** 0.148*** Metavante (β6) 0.003 0.016 0.090 0.105** 0.090

∆Lag2QConfDensity (β9) -0.030*** -0.029*** -0.039*** -0.039*** -0.039*** ∆Lag2QDJIA (β10) -0.892** -0.803** -0.616* -0.539** -0.616*

∆Lag2QVndrStckPrc (β11) -0.061** -0.051* -0.026 -0.015 -0.026 Log-Likelihood -99.510 -93.564 -72.936 -66.072 -61.715

Sub-sample No. of Firms 80 79 76 75 74 LR(χ2) 30.89*** 28.70*** 28.75*** 29.34*** 27.53***

Notes. Model: Revised Panel Data Model (Equation 8). Dependent variable in each model is Time-to-Adopt (t). The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. Information on R2, adjusted R2, and F-statistic omitted, since model fit is

generally already known. LR(χ2) is the likelihood ratio χ2.

42

Variable ≤ T-5 ≤ T-6 ≤ T-7 ≤ T-8 ≤ T-9 Constant (β0) 2.564*** 2.569*** 2.458*** 2.448*** 2.581*** Northeast (β1) 0.046 0.015 0.101* 0.081 0.045 Midwest (β2) 0.035 0.032 0.061 0.084 0.015

South (β3) 0.073 0.049 0.145** 0.123** 0.055 FirmReach (β4) 0.119*** 0.100** 0.130*** 0.083* 0.054 Metavante (β6) 0.115** 0.130** 0.102 0.125* 0.021***

∆Lag2QConfDensity (β9) -0.048*** -0.054*** -0.063*** -0.098*** -0.273*** ∆Lag2QDJIA (β10) -0.428 -0.298 -0.082 -0.099 -0.131

∆Lag2QVndrStckPrc (β11) -0.002 0.014 0.031 0.036 0.061* Log-Likelihood -56.978 -51.252 -41.395 -27.926 8.275

Sub-sample No. of Firms 72 70 65 57 43 LR(χ2) 25.46*** 26.07*** 28.04*** 36.68*** 84.41***

Notes. Model: Revised Panel Data Model (Equation 8). Dependent variable in each model is Time-to-Adopt (t). The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. Information on R2, adjusted R2, and F-statistic omitted, since model fit is

generally already known. LR(χ2) is the likelihood ratio χ2.

Table 19 above shows that FirmReach (β4), ∆Lag2QConfDensity (β9), ∆Lag2QDJIA (β10)

are consistently significant. Metavante (β6) is also significant but shows some inconsistency.

We suspect that this is due to the unbalanced sample (there are much fewer Metavante than

Checkfree observations).

Table 20. Results of the Panel Data Forward Jackknifing

VARIABLE ALL ≥ T-5 ≥ T-6 ≥ T-7 ≥ T-8 Constant (β0) 2.757*** 2.780*** 2.804*** 2.871*** 2.882*** Northeast (β1) 0.051 0.034 0.018 -0.017 -0.023 Midwest (β2) -0.054 -0.070 -0.083 -0.116** -0.124** South (β3) -0.002 -0.019 -0.036 -0.074 0.085* FirmReach (β4) 0.122*** 0.105** 0.088** 0.038 0.033 Metavante (β6) 0.003 -0.012 -0.025 -0.083 -0.107** ∆Lag2QConfDensity (β9) -0.030*** -0.028*** -0.026** -0.019** -0.015 ∆Lag2QDJIA (β10) -0.892** -0.876** -0.981*** -0.539 -0.332 ∆Lag2QVndrStckPrc (β11) -0.061** -0.064** -0.068*** -0.083*** -0.089*** Log-Likelihood -99.510 -94.186 -88.984 -74.824 -59.844 Sub-sample No. of Firms 80 79 78 74 70 LR(χ2) 30.89*** 30.24*** 31.26*** 27.74*** 30.26*** Notes. Model: Revised Panel Data Model (Equation 8). Dependent variable in each model is

43

Time-to-Adopt (t). The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. Information on R2, adjusted R2, and F-statistic omitted, since model fit is generally already known. LR(χ2) is the likelihood ratio χ2.

VARIABLE ≥ T-9 ≥ T-10 ≥ T-11 ≥ T-12 ≥ T -13 Constant (β0) 2.893*** 2.899*** 2.890*** 2.897*** 2.968*** Northeast (β1) -0.031 -0.031 -0.018 -0.019 -0.036 Midwest (β2) -0.123** -0.102*** -0.113*** -0.099** -0.114*** South (β3) -0.081* -0.075** 0.063* -0.067*** -0.077* FirmReach (β4) 0.023 0.005 0.017 0.022 0.001 Metavante (β6) -0.113** 0.130*** -0.141*** -0.158*** -0.145*** ∆Lag2QConfDensity (β9) -0.011 -0.005 -0.005 -0.003 0.008 ∆Lag2QDJIA (β10) -0.934*** -1.469*** -1.117*** -0.662 -0.129 ∆Lag2QVndrStckPrc (β11) -0.106*** -0.108*** -0.101*** -0.112*** -0.129*** Log-Likelihood -43.908 -17.025 -12.523 -11.704 -0.667 Sub-sample # of Firms 67 59 55 52 37 LR(χ2) 45.10*** 61.32*** 55.30*** 49.25*** 43.97*** Notes. Model: Revised Panel Data Model (Equation 8). Dependent variable in each model is Time-to-Adopt (t). The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. Information on R2, adjusted R2, and F-statistic omitted, since model fit is generally already known. LR(χ2) is the likelihood ratio χ2.

Table 20 shows results similar to those in Table 19. In addition, Midwest (β2) and South (β3)

are also significant pretty consistently when we take out observations from the earlier periods.

There may be outliers in those two regions that may sway the results.

44

Table 21. Results of the Panel Data One-Period-at-a-Time Jackknifing

VARIABLE ALL T=3 OUT T=5 OUT T=7 OUT T=8 OUTConstant (β0) 2.757*** 2.780*** 2.782*** 2.825*** 2.766*** Northeast (β1) 0.051 0.034 0.034 0.016 0.046 Midwest (β2) -0.054 -0.070 -0.067 -0.087 -0.060 South (β3) -0.002 -0.019 -0.019 -0.040 -0.010 FirmReach (β4) 0.122*** 0.105** 0.104** 0.071 0.118*** Metavante (β6) 0.003 -0.012 -0.011 -0.057 -0.017 ∆Lag2QConfDensity (β9) -0.030*** -0.028*** -0.028*** -0.023** -0.025** ∆Lag2QDJIA (β10) -0.892** -0.876** -0.992*** -0.424 -0.763** ∆Lag2QVndrStckPrc (β11) -0.061** -0.064** -0.065*** -0.077*** -0.066** Log-Likelihood -99.510 -94.186 -94.737 -87.651 -88.707 Sub-sample # of firms 80 79 79 76 76 LR(χ2) 30.89*** 30.24*** 31.34*** 24.67*** 29.19*** Notes: Model: Revised Panel Data Model (Equation 8). Dependent variable in each model is Time-to-Adopt (t). The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. Information on R2, adjusted R2, and F-statistic omitted, since model fit is generally already known. When a time period is not indicated as having been dropped out, it means that there were no observations of adoption occurring in that period. LR(χ2) is the likelihood ratio χ2.

VARIABLE T=9 OUT T=10 OUT

T=11 OUT

T=12 OUT

T=13 OUT

Constant (β0) 2.785*** 2.782** 2.734*** 2.753*** 2.771*** Northeast (β1) 0.039 0.051 0.075 0.054 0.063 Midwest (β2) -0.060 -0.035 -0.060 -0.039 -0.043 South (β3) -0.007 0.000 0.019 0.000 -0.011 FirmReach (β4) 0.104* 0.100* 0.144*** 0.131*** 0.147*** Metavante (β6) -0.020 -0.022 -0.002 0.009 0.081 ∆Lag2QConfDensity (β9) -0.024** -0.022** -0.031*** -0.029** -0.020 ∆Lag2QDJIA (β10) -1.231*** -1.237*** -0.582 -0.689 -1.115** ∆Lag2QVndrStckPrc (β11) -0.084*** -0.065** -0.049* -0.066** -0.062* Log-Likelihood -88.616 -86.391 -95.799 -99.171 -94.010 Sub-sample # of firms 77 72 76 77 65 LR(χ2) 39.49*** 30.52*** 29.16*** 27.22*** 23.85*** Notes. Model: Revised Panel Data Model (Equation 8). Dependent variable in each model is Time-to-Adopt (t). Sample size in each model is N = 80. The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. Information on R2, adjusted R2, and F-statistic omitted, since model fit is generally already known. LR(χ2) is the likelihood ratio χ2.

45

VARIABLE T=14

OUT

T=15

OUT

T=16

OUT

T=17

OUT

T=18

OUT

Constant (β0) 2.730*** 2.765*** 2.701*** 2.763*** 2.766*** Northeast (β1) 0.069 0.062 0.094 0.044 0.033 Midwest (β2) -0.055 -0.060 -0.052 -0.056 -0.080 South (β3) -0.013 -0.008 0.046 -0.010 -0.000 FirmReach (β4) 0.170*** 0.137*** 0.158*** 0.117** 0.110** Metavante (β6) 0.065 -0.011 -0.010 0.006 0.003 ∆Lag2QConfDensity (β9) -0.046*** -0.030** -0.034*** -0.034*** -0.039*** ∆Lag2QDJIA (β10) -1.059*** -0.990** -0.819** -0.837** -0.864** ∆Lag2QVndrStckPrc (β11) 0.018 -0.090*** -0.060** -0.061** -0.054* Log-Likelihood -92.758 -92.996 -93.991 -97.580 -94.289 Sub-sample # of firms 66 72 75 78 77 LR(χ2) 30.31*** 35.56*** 34.50*** 30.13*** 30.10*** Notes. Model: Revised Panel Data Model (Equation 8). Dependent variable in each model is Time-to-Adopt (t). The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. Information on R2, adjusted R2, and F-statistic omitted, since model fit is generally already known. LR(χ2) is the likelihood ratio χ2.

VARIABLE T=19 OUT T=20 OUT T=22 OUT T=24 OUT Constant (β0) 2.757*** 2.716*** 2.695*** 2.693*** Northeast (β1) 0.051 0.090 -0.027 0.109** Midwest (β2) -0.054 -0.016 -0.052 0.002 South (β3) -0.002 0.036 -0.032 0.052 FirmReach (β4) 0.122*** 0.118*** 0.154*** 0.116*** Metavante (β6) 0.003 0.012 0.076 0.016 ∆Lag2QConfDensity (β9) -0.030*** -0.031*** -0.042*** -0.029*** ∆Lag2QDJIA (β10) -0.892** -0.857** -0.728** -0.803** ∆Lag2QVndrStckPrc (β11) 0.061** -0.055** -0.036 -0.051* Log-Likelihood -99.510 -96.181 -79.987 -93.564 Number of firms 79 79 79 LR(χ2) 30.89*** 29.82*** 34.43*** 28.70*** Notes. Model: Revised Panel Data Model (Equation 8). Dependent variable in each model is Time-to-Adopt (t). Sample size in each model is N = 80. The estimated parameter significance levels are: * = p < .10, ** = p < .05, and *** = p < .01. LR(χ2) is the likelihood ratio χ2.

77

46

Tables 19, 20, and 21 show relatively consistent results for the coefficients in terms of their

magnitudes, signs, and significance levels. This confirms the model robustness and the

parameter stability of the primary estimate of our sample.

DISCUSSION

We have examined clustered adoption using deviation from group mean time-to-adopt as the

dependent variable in cross-sectional models. We have also employed the accelerated failure

time model using panel data econometrics. In this section, we will discuss the results in greater

detail, and try to wrap up the case for the observation of clustered adoption relative to our

proposed rational expectation theory of technology adoption.

As we hypothesized, conference activities have a significant effect on the adoption timing of

the firms. As the results of the cross-sectional and panel data model show, an increase in the

number of EBPP conferences decreases the time-to-adopt. In this case, we use a two-quarter lag

variable, which measures the difference between the number of EBPP-related conferences held

two quarters prior to the adoption period of each firm and the number of similar conferences held

four quarters prior in the region where the firm is located. The use of lagged values makes sense

because we believe it will take some time before information sharing facilitated by the

conferences would take effect.

The variable FirmReach is significant and has a positive sign, indicating that there is relative

clustering with regard to the geographical reach of the firms, thus supporting our hypothesis. In

this case, regional firms adopted later than local firms. Based on the adaptive learning

perspective, we can argue that regional firms require more learning time due to the fact that they

may need to align their expectations with more firms that serve the many regions in which they

operate. Another possible explanation is that relative to local firms, regional firms probably need

47

to involve more constituents inside their own organization in their technology adoption decision

making, causing the process to take more time.

Contrary to our hypothesis, FirmSize does not affect time-to-adopt. We suspect it is because

EBPP is still within the affordability range of small to medium firms. So firms of any size can

adopt the technology whenever they feel the technology is worth adopting, i.e., whenever they

think the benefits would outweigh the costs. Furthermore, firms of all size categories can be

found in each region. If large and small firms tend to cluster by region, then it will be difficult to

expect that they will also cluster by size.

However, we did not find evidence for clustered adoption by industry. We suspect it is

because there are fewer telecommunications firms (compared with the number of utilities firms)

in our sample set. The reason is that the majority of the telecommunications market nationwide

is served by just a few major companies (e.g., AT&T/SBC, Verizon, Sprint, and MCI). Another

possible explanation is that while most of the telecommunications firms in our sample are

national/regional firms, there are a few that are local, creating an imbalance in the sample.

In addition to the theory-bearing variables, we included several control variables in our

models to help us explain some of the variation in the dependent variables that is not otherwise

explained by the theory-bearing variables. Our results show that the general economic condition

(represented by the change in Dow Jones Industrial Average index) significantly impacts the

adoption times. A positive increase in the DJIA index pulls the deviation away from the group

mean time-to-adopt to the left, meaning that firms adopt earlier in a positive economic condition,

holding all other things equal. It suggests that a positive economic condition eases and

accelerates technology adoption decision making.

48

The variable ∆LagFirmProfit is not statistically significant although we had initially

expected that firms that are profitable will be more likely to adopt a new technology earlier. We

believe that the reason why the profitability of adopting firms does not matter in our case is

because EBPP is considered a strategic necessity by many firms. Our claim is supported by

interviews conducted by Celent [2002] (a Boston-based consulting firm) which revealed that

most firms cite competitive pressures and strategic necessity as their primary motivators for

offering e-services. We argue that because EBPP is a strategic necessity, it will be adopted by the

firms when the other factors tell them to adopt, without really considering whether they have

been profitable recently or not. In fact, we could argue that some firms might consider EBPP as

a means for cutting costs, offering the potential for them to be profitable or become more

profitable. In addition, competitive pressures—cited as the other factor by Celent—prompt firms

to constantly benchmark themselves against each other in their comparison group, thus

conforming to the information sharing and learning process that we have described to support

our theory.

Our results also show that ∆Lag2QVendorStockPrice is not consistently significant, in the

sense that the results of the different models do not consistently show that this particular variable

is statistically significant. We argue that this is because the observation period (1997-2002) is a

period when stock prices were very volatile and it would be difficult for the potential adopting

firms to base their decisions on the ups and downs of the vendor stock prices.

We next discuss the results of the panel data model in the log-logistic regression. Several

variables have statistically significant coefficients. They are FirmReach, ∆Lag2QConfDensity,

∆Lag2QDJIA, ∆Lag2QVendorStockPrice. Although they apply to the time-to-adopt, these

variables have a similar interpretation as in the cross-sectional model. Therefore, the positive

49

coefficient of FirmReach means that regional firms adopted later than the base case (i.e., the

local firms), whereas the negative coefficients of ∆Lag2QConfDensity, ∆Lag2QDJIA,

∆Lag2QVendorStockPrice mean that a positive change in the value of each of these variables

will reduce the time-to-adopt, meaning earlier adoption.

As discussed earlier, the cross-sectional model is the primary means for testing the clustered

adoption hypotheses, whereas the duration model involving panel data is for identifying the

instantaneous likelihood of adoption using a specific parametric model. Although the results are

slightly different due to the different models employed, we see consistencies in some of the

variables such as FirmReach and ∆Lag2QConfDensity.

Overall, our results show some evidence for the rational expectations theory of technology

adoption that we propose in this thesis.

CONCLUSION

In this section we will discuss the main findings and theoretical contributions of this research

related to our application of rational expectations theory and thinking, as well as our

conceptualization of clustered adoption. In addition, we will discuss contributions to practice

and insights for managers.

Main Findings and Theoretical Contributions

This research is among the first that applies rational expectations and adaptive learning

theory and thinking to technology adoption issues. The theory allows us to look into the issues

in technology adoption that involve multiple parties (multi-partite technology adoption) who

seek to align their expectations of value prior to making a decision to adopt. The theory further

enables us to offer an alternative perspective by factoring in the complex interactions among

50

different firms over multiple periods, beyond the typical approach that involves modeling only

two firms in two periods. More specifically, it takes into account the learning and information

sharing that occur between multiple entities over many periods, a phenomenon that generally

occurs in the marketplace. Although the alignment of expectations by multiple firms in the

presence of the same information will never be the same due to bounded rationality and costs

associated with processing information, the theory provides a useful characterization of the

underlying dynamics that occur in the market relative to the time-clustered adoption of a

technology.

The resulting technology adoption theory derived from rational expectations and adaptive

learning suggests that due to network externalities, it is in the best interest of each firm within a

group sharing similar characteristics and/or serving similar markets to adopt simultaneously (up

to the point at which bounded rationality and information processing costs become influential).

This leads to our conceptualization of the clustered adoption hypotheses, which are an alternative

view to those based on the rational herding behavior theory of Bikchandani et al. [1992, 1998].

Our clustered adoption theory differs in the fact that it assumes that decision makers are willing

to collect more information over time and utilize all available information efficiently before

making a technology adoption decision. We believe this is more in line with the basic

assumption of firm value maximization.

The representation of the dependent variable for empirical analysis in the cross-section

models deserves special mention. Although we could have alternatively used the actual period

number of the time-to-adopt of each firm as the dependent variable, we believe that using the

deviation from the mean time-to-adopt allows us to better illustrate clustered adoption, since it is

very easy to see how much each firm has deviated from the group mean time-to-adopt. We also

51

contribute to the IS/IT literature by showing how we can use the forward, backward, and drop-

one jackknifing methods to assess the robustness and the stability of the estimation models’

results, in view of their relatively small sample size.

We used the multiplicative model in our cross-sectional analysis since we believe there are

interactions among the independent variables, which are comprised of binary/dummy and

continuous variables. The dummy variables allow us to see if there are effects of the categories

on the deviation from the group mean time-to-adopt. For example, if a dummy variable for a

region has a coefficient of a certain magnitude that is statistically significant, then we can argue

that there is a relative clustering in that particular region. This is indeed very similar to the idea

of using dummy variables to identify whether there are relative groupings of incomes of lawyers,

doctors, professors, etc. A statistically significant coefficient of the dummy variable that

represents the lawyer category would indicate that lawyers have a certain level of income and

each lawyer’s income is expected to be clustered around that level.

Contributions to Practice and Insights for Managers

IT adoption is an important responsibility of IS and other managers in a firm and

expectations about the benefit and cost of the technology being considered always play an

essential role in the adoption decision making process. The REH offers a unique perspective by

suggesting that in setting their expectations, managers should not base their decisions on the

results of the past beyond the point where past information serves as an input for forming

expectations about the future. We can see why this perspective is appropriate if we consider the

ever-changing nature of information technologies. Technologies that worked in the past may not

be relevant anymore today, let alone in the future.

52

For the EBPP technology vendors, the findings in this research suggest that it may be useful

for them to identify which firms belong to which groups or subgroups. This is because we

believe that each firm tries to learn about a new technology by communicating with and

observing the other firms within the same group. This creates an opportunity for a vendor to

eventually sign up most, if not all, of the firms in the group in a relatively short period of time.

Our clustered adoption theory can be extended to some other nascent industries in which the

technologies exhibit strong network externalities characteristics similar to EBPP. Such

technologies include voice over Internet protocol (VoIP) and radio frequency identification

(RFID). Relative to each of these cases, we would again point out that before making a decision

to adopt the technology, firm decision makers will collect information over time and utilize all

available information efficiently. And since there are network externalities involved, we should

observe clustered adoption to some extent.

In closing, we believe that this research contributes to the IS/IT literature by offering a new

theoretical perspective on multi-partite technology adoption. Further studies in different

industries and technology settings will enable us to confirm the soundness of the theory we have

proposed.

53

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58

Appendix 1 United States Census Bureau Regions

REGION 1

(NORTHEAST) REGION 2

(MIDWEST) REGION 3 (SOUTH)

REGION 4 (WEST)

Connecticut Maine Massachusetts New Hampshire New Jersey New York Pennsylvania Rhode Island Vermont

Illinois Indiana Iowa Kansas Michigan Minnesota Missouri Nebraska North Dakota Ohio South Dakota Wisconsin

Alabama Arkansas Delaware District of Columbia Florida

Georgia Kentucky Louisiana Maryland Mississippi North Carolina Oklahoma South Carolina Tennessee Texas Virginia West Virginia

Alaska Arizona California Colorado Hawaii Idaho Montana New Mexico Nevada Oregon Utah Washington Wyoming

Source: U.S. Census Bureau (available at www.census.gov/geo/www/us_regdiv.pdf).