A Rational Expectations Theory of Technology Adoption Evidence ...

A RATIONAL EXPECTATIONS THEORY OF TECHNOLOGY ADOPTION:
EVIDENCE FROM THE ELECTRONIC BILLING INDUSTRY
Yoris A. Au † , Robert J. Kauffman ‡ , and Frederick J. Riggins ‡
University of Texas at San Antonio † and University of Minnesota ‡
yoris.au@utsa.edu, {rkauffman, friggins}@csom.umn.edu
I. INTRODUCTION
Many information technology (IT) adoption decisions depend on firms’ expectations about
the costs and benefits of the technology. We use the rational expectations hypothesis (REH)
and adaptive learning theory to understand the IT adoption process where strong network
externalities exist. This decision-making setting requires managers to form expectations about
future value. We develop a rational expectations theory of technology adoption and a main
conjecture: clustered adoption in time by a group of firms should occur in such settings.
We have a number of research questions: What kinds of drivers are associated with clustered
adoption? Do we 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? What facilitates
information sharing by firms related to expectations of future business value?
We test our theory and a set of related hypotheses for electronic bill presentment and
payment (EBPP). EBPP technology adoption involves multiple parties in the adoption process:
billers, banks, technology suppliers, and customers. Here, there are information sharing needs
related to the expectations of future value. EBPP is representative of a class of technology
adoption problems more complex than when a single firm contemplates adoption in isolation.
II. THEORETICAL BACKGROUND
Rational Expectations. Muth’s [1961] REH suggests that people form their expectations on
the basis of the “true” structural model of the economy in which their decisions are made. Every
individual acts as an economic forecaster, using the information 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 of rational expectations is that people use all of the information
available to them efficiently. An individual’s expectations are said to be rational if she
makes efficient use of all available information, allowing for its cost. Since some information
can be costly and hard to obtain, rational expectations may be rational but inaccurate. However,
at least they will be unbiased. This is the central tenet of the theory: 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 are boundedly-rational. So although
all the information is available to them, they may not be able to process it quickly and accurately
enough. Sargent [1993] suggests a theory of adaptive learning to relax the REH’s strong
assumptions. In adaptive learning, people learn about the economic circumstances and update
their expectations about relevant parameter values on the basis of newly-received information.
Furthermore, unlike in the REH, boundedly-rational agents are allowed to use simple forecasting
strategies—perhaps not perfect, but at least approximately right—for a complex 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, where there is at least some degree of consistency between
expectations and realizations [Evans and Honkapohja, 2001; Hommes, 2004].

Sub-Groups and Willingness-to-Pay. In the IT adoption context, suppose there is a group
of N potential adopting firms that wish to decide whether to adopt a new technology or to 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 sub-group will adopt 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 has chosen, such that little externality value flows. As a result, we
can expect that each firm in the sub-group 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 (WTP). WTP is the maximum price the
firm will pay to purchase the technology. To achieve concurrent adoption decisions, each firm
must reach a WTP level at least equal to the price set by the technology supplier. If some firms
in the sub-group have a WTP below the technology supplier’s price, then all firms will defer
adoption. Potential adopters freely share information with each other at no cost. Information
transmission is essential for alignment of expectations which, in turn, facilitates adoption.
Benchmarking, Information Sharing and Clustered Adoption. Under certain conditions,
we expect to observe clustered adoption [Au and Kauffman, 2005], the adoption of a technology
by multiple firms at about the same time (in a window of time). IT adoption decision-makers
will observe the environment and try to align their expectations with those of other decision
makers before deciding to adopt. This alignment is necessary to help shape each decisionmaker’s
expectations about the value, due to uncertainty about externality value.
In their effort to align their expectations, decision makers benchmark against each other and
share information within a targeted group. 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
in the same industry, competing in or serving the same market). In the case of the adoption
process of a new technology, however, it may not be easy for firms in the sub-group to all come
to the same conclusions about value, due to the uncertain value flows associated with the
network externalities inherent in the new technology. Through this benchmarking process, the
sub-group firms will learn from each other, by sharing information about their perceptions of the
expected value of the technology. This results in time-clustered adoption in the marketplace.
The information sharing that we refer to will occur in the form of informal communications
through email, telephone calls, conference presentations and panel discussions, and other
informal interactions. This kind of communication meets the criterion for what is typically
called cheap talk [Crawford and Sobel, 1982]. Although it may seem insignificant, Kim [1996]
suggests that repeated interaction among firms makes cheap talk credible, improving the
efficiency in outcomes that may be infeasible otherwise.
Time-clustering of adoption occur due to: geographical collocation, geographical reach,
industry sector, and technology vendor. We consider the role of information transmission, and
develop hypotheses on the patterns of technology adoption. Our hypotheses are:
• H1 (Geographical Collocation Clustered Technology Adoption Hypothesis).
Firms in a geographical region will adopt EBPP technology at about the same time,
resulting in clustered adoption by geographical region.
• H2 (Geographical Reach Hypothesis). Firms located within a specific state (local
firms), as opposed to multiple states (regional firms), will adopt EBPP technology at
about the same time, resulting in clustered adoption by geographical reach.
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• H3 (Common Industry Sector Technology Adoption). Firms serving the same
industry will adopt EBPP technology at about the same time, resulting in clustered
adoption by industry sector.
• H4 (Competing Vendor). Firms competing with the same vendor will adopt EBPP
technology at about the same time, resulting in clustered adoption grouped by vendor.
• H5 (Information Transmission). More EBPP-related conferences within the region
will be positively assocated with more observed clustered adoption.
• H6 (Information Transmission Technology Adoption Rate). More EBPP-related
conferences in a region will be positively assocated with faster rates of EBPP adoption.
III. MODELS, DATA AND METHODS
Models. We use a multiplicative cross-sectional (MCS) model as the primary means for
testing the clustered adoption hypotheses. We also test an accelerated failure time (AFT) 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 specify two different
sets of dependent variables. DevMeanAdoptTime is used in our MCS model to represent deviation
of the time-of-adoption of a firm from its group mean time-of-adoption. TimeToEvent is used in
the AFT model for duration from the start of the observation period to the event. For each firm,
the event is coded with a “1” if the firm made an adoption decision, and with a “0” if otherwise.
The independent variables are almost the same in the two models. They include dummy
variables to indicate the geographical location of each firm (Northeast, Midwest, South), geographical
reach (FirmReach), EBPP vendor (Metavante), and industry (Telecom); as well as continuous
variables to represent the change in the number of EBPP-related conferences between periods
(∆Lag2QConfDensity), the percentage change of the quarterly average of Dow Jones Industrial
Average index (∆Lag2QDJIA), and the percentage change of the quarterly average of the vendor
stock prices (∆Lag2QVendorStockPrice). Some variables involve logical time lags.
Data. Our sample involves EBPP-adopting firms for U.S. utilities and telecommunications,
and two major EBPP vendors: CheckFree and Metavante. The data sources include the e-billing
vendors for corporate customers, adoption dates from Google, LexisNexis, and annual reports of
each adopting firm. Adoption date is the date when a company signed an EBPP agreement with
the technology vendor. Other variables include company location (city, state, and zip code),
size (number of customers by year), annual sales EBIT and number of EBPP conferences by
region by time. The financial data were obtained from COMPUSTAT, and the conference data
were obtained from conference organizer Web sites and interviews.
IV. RESULTS AND DISCUSSION
MCS Results. The estimation results from our MCS model follow. (See Table 1.) We use
dummy variables to represent regions (Northeast, Midwest, and South), indicating significant
differences for individual sub-groups from average time-to-adopt. We expect firms in regional
sub-groups will be clustered in their adoption time, and different from the overall average. The
FirmReach variable (equals “1,” if a firm is a regional firm) is also statistically significant,
indicating there is some clustering of regional firms. The positive coefficient suggests that
operating in more than one state will tend to adopt later than the one-state (local) firms.
The fact that Metavante is statistically significant suggests that there is a relative clustering of
Metavante-adopting firms in terms of time-of-adoption. Also ∆Lag2QConfDensity and
∆Lag2QDJIA have negative coefficients and are highly significant. This suggests an increase in
conference activities and a positive change in the economy accelerated EBPP adoption.
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Table 1. Results of the Multiplicative Cross-Sectional (MCS) Model for EBPP Adoption
VARIABLES COEF.(STD. ERR.) p-VALUE VIF
Constant 4.585 (0.444) 0.000 *** 0.000
Northeast 0.187 (0.093) 0.048 ** 1.733
Midwest 0.186 (0.096) 0.057 * 1.620
South 0.249 (0.093) 0.009 *** 1.721
FirmReach 0.170 (0.074) 0.026 ** 1.241
Metavante 0.208 (0.111) 0.064 * 1.223
∆Lag2QConfDensity -1.143 (0.188) 0.000 *** 1.337
∆Lag2QDJIA -2.163 (0.604) 0.001 *** 1.311
∆Lag2QVendorStockPrice 0.088 (0.054) 0.105 1.160
R 2 (Adj.-R 2 ): 0.60 (0.56) F-statistic:13.40 ***
Notes. Model: Multiplicative cross-sectional (MCS). Dependent var.: ln(DevMeanToAdopt).
Sample size is N = 80. Signif.: * p < .10, ** p < .05, and *** p < .01.
AFT Model Results. The MCS model is the primary means for testing the clustered
adoption hypotheses. We use an accelerated failure time (AFT) model for identifying the
instantaneous likelihood of adoption of EBPP by firms over time. An AFT model captures the
essence of the information structure of the EBPP adoption setting: increasing likelihood of
adoption over time due to the maturing qualities of the technology solution [Hougaard, 2000].
We determined that the appropriate distributions for the underlying hazard function were the
Weibull and the log-logistic distributions. 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. (See Table 2.)
Table 2. Results of the Accelerated Failure Time (AFT) Model for EBPP Adoption
VARIABLE COEFF. (STD. ERROR) Z (SIGNIFICANCE)
Constant 2.757 (0.061) 44.98 ***
Northeast 0.051 (0.055) 0.92
Midwest -0.054 (0.057) -0.94
South -0.002 (0.056) -0.03
FirmReach 0.122 (0.046) 2.62 ***
Metavante 0.003 (0.066) 0.04
∆Lag2QConfDensity -0.030 (0.011) -2.64 ***
∆Lag2QDJIA -0.892 (0.374) -2.38 **
∆Lag2QVendorStockPrice -0.061 (0.028) -2.21 **
Log-likelihood: -99.510 LR(χ 2 ): 30.89 Prob > χ 2 : 0.001 ***
Notes. Model: Accelerated failure time (AFT) model. Dependent var.: Time-to-adopt (t).
N = 80. Signif.: * p < .10, ** p < .05, and *** p < .01. LR(χ 2 ) is the likelihood ratio χ 2 .
The AFT model variables have a similar interpretation to those in the MCS model. A positive
coefficient for FirmReach suggests that regional firms adopted later than local firms. A negative
coefficient for ∆Lag2QConfDensity implies that an increase in conference activities accelerates
the adoption of EBPP. The results generally corroborate the MCS findings.
Extensions. Our clustered adoption theory can be extended to other technologies that exhibit
strong network externalities and complex information sharing-based adoption decision-making.
Such technologies include RFID, Wi-Fi, and B2B procurement systems. Before making a
decision to adopt, firm decision-makers should collect and utilize all available information. Each
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IT adoption involves choosing the right standards, designs, and infrastructures. Since network
externalities will be involved across multiple players, we expect to see clustered adoption.
V. CONCLUSION
This research applies rational expectations and adaptive learning theory to technology
adoption issues. This “ratex theory” allows us to examine issues in technology adoption that
involve multiple parties who seek to align their expectations of value prior to making a decision
to adopt. It further enables us to offer an alternative perspective by factoring in the complex
interactions among different firms over multiple periods. This view takes into account learning
and information sharing that occur over many periods in the marketplace. Although the
alignment of expectations by multiple firms with similar information will never be perfect due to
bounded rationality and the costs associated with processing information, the theory provides a
useful view of the underlying dynamics that occur relative to the time-clustered IT adoption.
The resulting technology adoption theory suggests that, due to network externalities, it is in
the best interest of each firm within a sub-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 proposal about clustered
adoption, an alternative view to those based on the rational herding behavior theory of
Bikchandani et al. [1992, 1998]. Our theory differs in that it assumes that decision-makers are
willing to collect and utilize the available information efficiently before deciding to adopt. This
research contributes to the IS/IT literature by offering a new theoretical perspective on multipartite
technology adoption. Further studies in different industries and technology settings will
enable us to confirm the soundness of the theory we have proposed.
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