PAYOFF EXTERNALITIES, INFORMATIONAL CASCADES
AND MANAGERIAL INCENTIVES:
A THEORETICAL FRAMEWORK FOR IT ADOPTION HERDING
Robert J. Kauffman
Professor and Chair, Information and Decision Sciences
Co-Director, MIS Research Center
Carlson School of Management
University of Minnesota
Minneapolis, MN 55455
Email: rkauffman@csom.umn.edu
Xiaotong Li
Assistant Professor of Management Information Systems
Department of Accounting and MIS
University of Alabama, Huntsville
Huntsville, AL 35899
Email: lixi@uah.edu
Last revised: May 11, 2003
______________________________________________________________________________
ABSTRACT
We have recently observed herd behavior in many instances of information technology (IT)
adoption. This study examines the basis for IT adoption herding generated by corporate
decisionmakers’ investment decisions. We propose rational herding theory as a new perspective
from which some of the dynamics of IT adoption can be systematically analyzed and understood.
We also investigate the roles of payoff externalities, asymmetric information, conversational
learning and managerial incentives in IT adoption herding. By constructing a synthesis of the
critical drivers influencing managers’ IT investment decisions, this study will help business
researchers and practitioners to critically address the issues of information asymmetries and
incentive incompatibility in firm- and market-level IT adoption.
Keywords: Agency problem, asymmetric information, herd behavior, incentives, informational
cascades, IT adoption, network externalities, reputations, signaling games.
______________________________________________________________________________
Acknowledgements: The authors wish to acknowledge Yoris Au for helpful discussions on
related work.
______________________________________________________________________________
INTRODUCTION 1
In the recent years, there have been many instances of information technology (IT) adoption
in which we have observed “herd behavior,” as many investment decisionmakers lost touch with
their own cautious value-maximizing approaches to investment decisionmaking, and decided to
follow the advice of the many “smart cookies” in the Digital Economy. “Herd behavior,” such as
we saw during the height of the DotCom days arises in the presence of differences in the
information endowments of decisionmakers in different organizations.
Bikchandani and Sharma (2001, pp. 280-281) define herd behavior in terms of three related
aspects: (1) the actions and assessments of investors who decide early will be critical to the way
the majority will decide; (2) investors may herd on the wrong decision; and, (3) if they do make
the wrong decision, then experience or new information may cause them to reverse their
decisions, and a herd will be created in the opposite direction. Examples that we have observed
1
The above cartoon was originally published by the New Yorker Magazine in 1972 and is reproduced from
Bikhchandani, S., Hirshleifer, D., and Welch, I. (1996), “Informational Cascades and Rational Herding: An
Annotated Bibliography,” Working Paper, Anderson Graduate School of Management, University of California, Los
Angeles; Fisher College of Management, Ohio State University; and School of Management, Yale University.
Available on the Internet at welch.som.yale.edu/cascades/.
1
include the adoption of price-discriminating electronic auctions, wireless telecommunications
technologies, business-to-business electronic market solutions, and enterprise systems software,
among others. In other instances, however, considerable inertia seems to have stalled market
adoption, as senior managers ask: “Should we wait?” (Au and Kauffman, 2001). Examples
include the slow growth of electronic bill payment and presentment technologies and only
modest adoption of powerful Internet-based corporate travel reservation systems.
Herd behavior has long been studied in other fields, including Finance, Biology, Sociology
and Psychology, and especially Economics, where the literature has reached an exceptional depth
of coverage of the issues (Bikhchandani, Hirshleifer and Welch, 1996). In some cases, such as
stock market bubbles or the Internet and DotCom mania, herding is driven—in the words of
Federal Reserve Bank Chairman, Alan Greenspan—by people’s “irrational exuberance.”
Unfortunately, this can be exploited by other rational people in the economy, as Liebowitz
(2002) and Schiller (2000) point out. However, recent theoretical and empirical studies suggest
that in many other cases herd behavior is rather counterintuitively caused by the decisions of
perfectly rational people. Unfortunately, such rational decisions at the individual level
sometimes result in significant problems with information transmission, due to people’s
unwillingness to pass on information that does not match other information which they have
decided to herd on, and the associated welfare losses that arise for others in the marketplace and
the economy.
In the context of IT adoption, rational herding has the potential to generate several problems.
First, valuable information about new technologies is most often lost (or at least poorly
aggregated) when IT managers blindly follow the adoption decisions of others. Second, payoff
externalities-driven herding makes early adopters’ decisions disproportionately important. It
2
gives other adopters little chance to compare and experience different technologies. Third,
managers sometimes intentionally imitate others’ adoption decisions because of their career
concerns, and those reputation-motivated decisions usually fail to maximize expected IT
investment payoffs.
The widespread mimicry in IT adoption and the resultant inefficiencies motivate us to
investigate the basis for technology adoption herding generated by corporate managers’
decisions. A common and well-studied justification for IT adoption herding is positive payoff
externalities like network externalities. Recent studies have indicated that many technology
markets are subject to positive network feedback that makes the leading technology grow more
dominant (Brynjolfsson and Kemerer, 1996; Gallaugher and Wang, 2002; Kauffman,
McAndrews and Wang, 2000). Because positive network feedback makes a company’s IT
adoption return rise as more companies adopt the same technology, it usually gives managers
strong incentives to adopt the technology with the larger installed base of users. In addition to
the studies of positive payoff externalities, recent research in the area of information economics
demonstrates how rational herd behavior may arise because of “informational cascades”
(Banerjee 1992; Bikhchandani, Hirshleifer and Welch, 1992 and 1998) or managers’ career
concerns (Scharfstein and Stein, 1990; Zwiebel, 1995).
Informational cascades occur when individuals ignore their own private information and
instead mimic the actions of previous decisionmakers. Those mimetic strategies are rational
when private information is swamped by publicly observable information accumulated over
time. This is why informational cascading is sometimes referred to as “statistical herding”
(Banerjee, 1992; Ottaviani and Sorensen, 2000). Like informational cascade models, career
concerns models have information economics and Bayesian games as their theoretical
3
foundations, but they distinguish themselves by examining rational investment herding through
the lens of agency theory (Holmström, 1999). The primary implication of those models is that
managers concerned about their reputations may imitate others’ investment decisions to
positively influence others’ inferences of their professional capabilities. Although reputational
herding decisions are rational for individual managers, they are usually not in the best interests of
those companies who hire their managers to maximize investment payoffs.
Empirical evidence of herd behavior and imitative strategies has been recently documented in
financial investment decisionmaking, stock analysts’ equity recommendations, emerging
technology adoption and television programming selection (Hong, Kubik and Solomon, 2000;
Hong, Kubik and Stein, 2003; Kennedy, 2002; Walden and Browne, 2002; Welch, 2000). There
is also extensive experimental evidence of rational herding and informational cascades in the
economics literature (Anderson and Holt, 1996 and 1997; Hung and Plott, 2001). Another recent
experimental study of behavioral conformity is by Tingling and Parent (2002), who employ
senior IT and business decisionmakers instead of college students are used as subjects.
Despite the fast-growing rational herding literature and the pervasiveness of imitative
behavior in IT adoption, systematic studies of IT adoption herding are still rare in the IS
literature. By synthesizing previous rational herding models, this paper proposes an integrated
research framework based on economic theory, and within which the dynamics of IT adoption
herding can be better analyzed and understood. The next three sections discuss the underlying
theories in greater detail. We investigate the relationship between payoff externalities and IT
adoption herding in Section 2. We next demonstrate in Section 3 how the vagaries of information
transmission and observational learning can lead to information cascades in technology
investment.
4
The problem of managerial incentives in IT investment and adoption decisionmaking is the
focus of Section 4. We discuss why agency problems predispose the market to reputational
herding in IT adoption. Managerial compensation schemes designed to address those incentive
issues are also discussed. Section 5 provides a synthesis of critical theoretical drivers of IT
adoption herding and brings the ideas together into a single integrative framework. We provide
preliminary thoughts about why stakeholders to IT adoption at different levels (e.g., business
process or firm-level investor/decisionmaker, senior executive or member of the board of
directors, industry sector promoters or regulators of the economy) may have distinctly different
perspectives about , and briefly discusses its potential application. Section 6 concludes the paper
with the contributions of this work to ongoing research in IS, and ideas for further research.
PAYOFF EXTERNALITIES: DOES ADOPTION HERDING PAY OR HURT?
One of a number of different types of payoff externalities that is commonly observed in the
IT market is “network externalities” (Economides, 1996; Katz and Shapiro, 1994; Shapiro and
Varian, 1999). Network externalities are sometimes referred to as demand-side economies of
scale. ( For additional constructs related this area of theory, see Table 1.)
5
Table 1. Key Constructs in the Payoff Externalities Theory Relative to IT Adoption
CONSTRUCT
Rational IT
adoption
herding
Network
externalities
Intrinsic and
extrinsic
network
externalities
Installed base
Path
dependencies
Tipping
equilibrium
DEFINITION
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They stem from the presence of significant technology switching costs and the benefits
associated with a large installed base of users of compatible technologies. These and other
relevant findings that characterize the Economics literature on network effects and technology
switching costs was recently surveyed by Farrell and Klemperer (2001). In the context of IT
adoption, network externalities tend to reward herding decisions by increasing the payoffs to IT
adopters who associate themselves with the majority. They also decrease the risks that an IT
adopter will be stranded in its adoption of an IT that has too small an installed base of users.
In technology markets subject to network externalities, IT diffusion processes are often
characterized by path dependencies. They represent the situational specifics of irreversible
6
managerial decisions and their impacts on the decisions of others. Many managers believe that
network externalities and technology switching costs work in tandem to justify imitative
technology adoption. In some cases, strong network effects create a “tippy” technology market
in which one technology very quickly emerges as the dominant product because of massive
adoption imitation. Under such a winner-take-all tipping equilibrium scenario, the question most
managers face is when to jump on the bandwagon, and whether to join the herd.
Although herd behavior driven by positive network feedback can be easily justified by
individual rationality, it usually leads to obvious information and welfare loss. Companies make
their IT adoption decisions mainly based on the installed user bases of competing technologies.
Consequently, managers do not concentrate on the intrinsic merits and suitableness of competing
technologies, and under many circumstances they do not even have enough time to compare all
available technology choices because the technology competition could end very quickly in favor
of a technology.
So does network externality-driven IT adoption herding pay off? Or does it hurt those firms
that adopt this way? At the individual level, each decisionmaker gains by joining the herd and
taking advantage of the positive network feedback. However, most decisionmakers lose a
chance to deliberate the associated opportunities. Very often, as some have claimed for the VHS
video format winning out over the Sony Beta format (Shapiro and Varian, 1999), the market may
end up adopting an inferior technology, which will hurt all adopters in the long run.
Payoff externalities, as a stand-alone justification for rational IT adoption herding, has its
limitations. Strong network externalities may not be so pervasive in the technology market as
many IT and business strategists expected (e.g., see Liebowitz, 2002). As a result, imitative
technology adoption strategies driven by those illusive network effects are not even individually
7
rational. Moreover, technology managers sometimes choose to adopt emerging technologies
with superior performance instead of imitating others by using the dominant technology.
Clearly, there is a tradeoff between the future potentials of superior new technologies and the
network benefits of current technologies. Adoption herding may not persist or even exist if some
firms find that the benefits of exploring new technologies outweigh those of exploiting the
dominant technology with network benefits (Lee, Lee and Lee, 2003).
It is also worth noting that payoff externalities can be either positive or negative.
Unsurprisingly, negative payoff externalities play an important role in mitigating a technology
market’s propensity to adoption herding. They are commonly seen in most competitive business
environments where downward-sloping demand curves make a company’s IT adoption payoffs
decrease as more companies adopt the same technology. Therefore, companies imitate others’
IT investment decisions may be punished by intense ex post competition in the downstream
market. Both the fiber cable network glut and the e-commerce gold rush exemplify how severely
IT adoption herding may have been penalized by negative payoff externalities. Interestingly,
adoption herding sometimes still happens in those situations where negative payoff externalities
are evidently present (Kennedy, 2002; Khanna, 1998). Because of these limitations for payoff
externalities as a justification for rational adoption herding, we need to investigate other
theoretical explanations of firm-level herd behavior in IT adoption.
INFORMATIONAL CASCADES: TOO MUCH OR TOO LITTLE INFORMATION?
The theory of payoff externalities-driven adoption herding does not sufficiently emphasize
two important features that are present in IT diffusion. The first feature is that information
asymmetries and information incompleteness are pervasive in emerging technology markets.
8
Different decisionmakers have their own judgments about the business value of a new
technology based upon their own private information, and generally no one possesses perfect
information in making an individual IT adoption decision. These information structure problems
lead to the second feature: to improve the quality of their decisions, decisionmakers keep trying
to learn valuable information by observing others’ IT adoption decisions. For those who make
their adoption decisions earlier, their actions may reveal their private information to others,
which generates information spillovers. These are often referred to as information externalities
(Zhang, 1997). (See Table 2 for constructs related to information cascades-driven herding.)
Table 2. Key Constructs in the Informational Cascades Theory Relative to IT Adoption
CONSTRUCT
Information
asymmetry
Information
completeness
Information
spillover
Informational
cascade
Observational
learning
Word-ofmouth learning
DEFINITION
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Although observational learning can facilitate information conveyance, it also can result in
an informational cascade in which most people adopt the same technology independent of their
own private information. Similar to the decisionmaking process that appears to be operative in
the judicial body depicted in the cartoon in the Introduction to this article, the reason why
informational cascades occur with IT adoption is because the information revealed through
others’ adoption actions may have accumulated enough to overwhelm a decisionmaker’s
imprecise private information (Banerjee, 1992; Bikhchandani, Hirshleifer and Welch, 1992).
The opinions of Supreme Court justices, just like the opinions that expressed in a marketplace in
which buyers make IT adoption decisions, carry substantial weight with others. In this kind of
situation (even with a single Supreme Court justice), the decisionmaker’s action may not depend
on his private information. As a result, such decisions actually become incrementally
uninformative to others. In fact, it may cause others to rationally disregard their own
information and imitate the prevailing adoption decision. The outcome is that the valuable
private information of individuals will be lost in such an informational cascade, which
simultaneously reduces efficiency because of poor information aggregation in the market. So if
informational cascades occur in the technology market, we are likely to observe inefficient
outcomes. Some of the things that we frequently observes include IT overbuilding and systems
over-investment, and the massive adoption of inferior and poorly understood new technologies.
A related intriguing question is whether informational cascades result from too little or too
much information. And, how much information is enough? Corporate decisionmakers
frequently struggle with too little information to make sound IT investment decisions. That is
why they want to gather valuable information from observing other’s adoption actions, and not
be too sure about when to apply a stopping rule and decide on their own. Paradoxically, once
10
they engage in observational learning, they may get too much accumulated information, to the
extent that it may be strong enough to swamp their private information. As a consequence, an
informational cascade will occur and most people will imitate early adopters’ decisions that are
rather unfortunately based upon limited information.
As two possible mechanisms that cause rational IT adoption herding, informational cascades
and network externalities are not mutually exclusive; in fact, they sometimes can be mutually
reinforcing (Li, 2003). Informational cascades are generally fragile because they can be stopped
or reversed by enough newly-arrived information. For example, many companies will be
observed to adopt Technology A over Technology B in a herd when their private information is
dominated in an informational cascade, even though everyone knows that the valuable
information contained in such a cascade is limited. If some credible information is revealed (by
governments or other authoritative agencies, for example) to support Technology B, the adoption
cascade can be quickly stopped or reversed. However, informational cascades are far more
resilient in the presence of network externalities. Once adoption cascades form, they will be
reinforced by later IT adopters who intentionally jump on board to reap the benefits of positive
network feedback. The interactive dynamics between the two herding mechanisms have recently
been studied by Choi (1997), Hung and Plott (2001) and Li (2003).
The strength of informational cascade theory as an explanation for rational IT adoption
herding is its emphasis on social learning under information asymmetries. However, social
learning can sometimes mitigate a market’s propensity to be influenced by informational
cascades. Most informational cascade models assume that decisionmakers can only infer
information from observing others’ actions. This assumption exacerbates the information
aggregation problem of rational herding. In a simple world where every decisionmaker
11
truthfully tells public his private information, no valuable private information will be lost and the
information aggregation problem disappears. In fact, prior innovation diffusion studies have
recognized the significant role played by word-of-mouth learning in affecting technology
diffusion, as noted by Rogers (1995).
Nevertheless, the effectiveness of conversational information sharing in preventing
informational cascades should not be overestimated. The major obstacle for effective word-ofmouth learning under many IT adoption scenarios is that each individual decisionmaker’s
incentive for truthful information revelation. Potential adopters can benefit from talking with
early adopters if what they are told is credible, but who can guarantee the truthfulness of the socalled “cheap talk” (Crawford and Sobel, 1982; Farrell and Rabin, 1996)? In competitive
business environments where most IT adoptions occur, individuals may have strong incentives to
misinform others through strategic lying or signal jamming, as pointed out by Crawford (2003)
and Fudenberg and Tirole (1986). That’s why most researchers who believe that “actions speak
louder than words” emphasize observational learning and downplay conversational learning in
their informational cascade models.
At the market level, informational cascades are more likely to occur when the incentive
problems associated with information revelation block credible conversations. At the firm level,
decisionmakers’ incentives sometimes cause agency problems that provide another explanation
for rational IT adoption herding.
MANAGERIAL INCENTIVES IN ADOPTION: PROFITS OR REPUTATION?
Since a herd involves a group of decisionmakers, it is natural for researchers to concentrate
on understanding the market-level interactive dynamics, such as payoff and information
12
externalities. However, significant developments in agency and incentive theory (Laffont and
Martimort, 2002) over the last three decades have nourished a stream of research on rational
herding that explores the role of managerial incentives in fostering investment herding. (See
Table 3 for definitions of reputational herding and incentive compatibility, two key constructs
that figure importantly in the managerial incentives theory literature.)
Table 3. Key Constructs in the Informational Cascades Theory Relative to IT Adoption
CONSTRUCT
Reputational
herding
Incentive
compatibility
DEFINITION
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Traditional capital budgeting theory suggests that profit-maximizing companies primarily
look at the expected investment payoffs when they make their IT adoption decisions. However,
corporate managers hired by a company’s owners or shareholders may have incentives to deviate
from the company’s goals and to pursue their own interests when they make their IT adoption
decisions. The conflicts of interest, coupled with information incompleteness, can lead to many
inefficient outcomes. Some of these are frequently referred to as market-for-lemons problems,
adverse selection or moral hazard.
In a seminal paper in Economics, Holmström (1999) showed that reputation-concerned
managers are very likely to make inefficient investment decisions in the absence of effective
mechanisms to align their own interests with those of their companies. In some cases where
managerial incentive problems are present, managers may herd solely for reputational purposes
in investment decisionmaking. In the influential reputational herding model presented by
13
Scharfstein and Stein (1990), the authors make the case that managers tend to intentionally
imitate others’ investment decisions. Intentional herding comes from the belief that managers
do not wish to run the risk to be associated with those who are not identified as being in the
highly-talented group. In a more specific context, reputation and career concerns are found to
be important in promoting security analysts’ herd behavior commonly seen in their stock
recommendations or earning forecasts (Graham, 1999; Hong, Kubik and Solomon, 2000).
We believe that reputational herding theory has its distinctive advantages in helping us to
understand IT adoption herding. Like other important corporate investment decisions, IT
adoption decisions are usually made by senior managers. Because their adoption decisions are
not immune to agency and incentive problems, they will imitate others’ decisions to enhance
their professional reputations if the situation warrants. But unlike most other investment
decisions, IT adoption decisions—especially strategic IT adoption decisions—are more
susceptible to reputational herding. This is not because a good decisionmaking reputation is
more valuable to IT managers like CIOs than to other mangers. Instead, it is because the
informational problems are usually more severe. Under many IT adoption scenarios, managers
have to make their IT adoption decisions quickly with very limited information. Because IT
adoptions are highly specialized tasks that involve a lot of technical details, there are also
significant information asymmetries between the decisionsmakers (IT managers) and their
supervisors (the firms’s owners or board). Furthermore, the economic payoffs of many IT
investments are notoriously difficult to observe or measure in the short run, which gives
managers more room reputational gain at the expense of their companies.
Most reputational herding studies use signaling (or signal jamming) games in which
managers try to positively influence their supervisors’ and the labor market’s posterior beliefs on
14
their capabilities and reputations through their investment decisions. Because firms’ owners or
the labor market usually lack concrete evidence to indicate whether an individual IT project will
be successful or unsuccessful in the short run, IT managers’ professional reputations will heavily
depend on the market consensus that is reached by peer managers or short-term reactions in the
stock market. As a result, IT managers that are concerned with their reputations are more likely
to exhibit herd behavior in their IT adoption decisions than those who are not.
Furthermore, when IT managers are concerned about their career prospects, then imitating
the IT adoption decisions of other will be fully rational, if doing so will result in a better
reputation. The potential inefficiency and welfare loss stem from the conflicting interests among
different parties. Therefore, the key to preventing inefficient reputational herding is to address
the issue of incentive compatibility. By offering managers appropriate compensation contracts,
firms can provide them with explicit incentives to maximize investment returns. Ideally, firms
should make their managers’ compensation contingent on the returns of their investment
projects, including those of IT managers who make decisions about IT project investments.
Two difficulties arise in the context of IT adoption, however. First, it is usually hard to
quantify IT investment payoffs, at least in the short run. Incentives from ambiguously designed
performance-based contracts are easily subjugated by the implicit incentives from managers’
career concerns. For example, compensation contracts based on short-term stock price could
actually exacerbate the efficiency loss caused by rational investment herding (Brandenburger and
Polak, 1996). Second, long-term performance-based compensation, like an option on stock, is
thought to be more effective in solving agency problems. However, many IT investments play
an instrumental role in strengthening companies’ long-term competitiveness. So the quality of
an IT manager’s decision, unlike a chief executive officer’s decision, generally will have less
15
impact on a firm’s overall performance. So long-term performance, when it is used as a basis for
compensation, must be significant enough to dominate a manager’s gains from reputation
building. However, this usually makes these compensation schemes very expensive to
implement. This problem deserves the attention of IS researchers, who should study how to
overcome these difficulties in designing optimal incentive contracts for managers who make IT
adoption decisions.
A SYNTHESIS OF CRITICAL DRIVERS IN ADOPTION HERDING
One of the primary determinants of the value of the body of theoretical knowledge that we
have proposed for explaining observed IT adoption phenomena is the extent to which we are able
to identify when its application becomes effective.
In this section of the paper, we provide the
basis for a framework that permits us to evaluate the applicability and the quality of the insights
that the theory offers for herding in different IT adoption settings. Our framework cuts the
alternative theoretical perspectives with levels of analysis that represent the possible stakeholders
in different IT adoption settings.
Framework Preliminaries
We need to address two issues in developing our theoretical framework for IT adoption
herding. First, almost all the rational herding models in the literature study investment herding
in generic investment settings. Although studying herd behavior in generic investments
increases the generality of results, it sometimes fails to capture the distinctive features of certain
types of investment like IT adoption. So our framework emphasizes the uniqueness of IT
investments to provide more in-depth and relevant analysis of adoption herding. Table 4 shows
how certain features of IT investments can increase the chances for rational investment herding
to occur. [Insert Table 4 here] Second, our integrated framework recognizes the difference and
16
relationships among the three explanations for rational investment herding. As one major goal of
our framework is to allow IT adoption herd behavior to be studied from appropriate theoretical
perspective, it is important for us to understand the strength and weakness of each explanation
under different IT adoption scenarios. We provide a comparison of the three theoretical
explanations for IT adoption herding in table 5. [Insert Table 5 here]
o 2nd paragraph: discussion of the stakeholder levels, motivation for that, what leverage
looking at IT adoption problems at these levels of analysis creates for insights, why
stakeholder level enable us to focus on different strategy perspectives relative to the
different herding related theories
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An Evaluative Framework for Assessing Herding Theories of IT Adoption
□
3 paragraphs for this section
o 1st paragraph: introduce the framework with a brief couple sentences and then give
the table
o 2nd paragraph: explain the contrasts that the framework depicts, answer question
about why this framework provides a basis for figuring out which of the herding
behavior theories is the most appropriate to use in the interpretation of the observed
adoption phenomena in real world settings
o 3rd paragraph: explain the structure of a mini-case analysis in general terms using the
framework; provides an opportunity to tell reader how to think about our “results”—
how we will sell the quality of the framework dimensions in terms of the insights it
offers
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Table 6. An Evaluative Framework for the Alternative IT Adoption Herding Theories
THEORETICAL
PERSPECTIVE
Payoff externalities
Informational
Cascades
Managerial
incentives
BUSINESS PROCESS
LEVEL/ IT
ADOPTER,
INVESTOR
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FIRM LEVEL /
BOARD OF
DIRECTORS,
SENIOR MGMT
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INDUSTRY SECTOR /
STANDARDS GROUP
ECONOMY LEVEL /
REGULATOR
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Four Applications of the Evaluative Framework for IT Adoption Herding Behavior
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(1) Payoff Externalities-Focused IT Adoption: The Case of Wi-Fi Hotspot Adoption.
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(2) Informational Cascades-Influenced IT Adoption: ICQ and Microsoft Messenger.
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(3) Managerial Incentives-Influenced IT Adoption: Morgan/Reuters and Riskmetrics.
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(4) A Mixed Theoretical Interpretation: Micropayment Solutions on the Internet. xxxxx
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Discussion
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CONCLUSION
This study provides a new theoretical framework for rational IT adoption herding as basis for
understanding some observed forms of IT adoption and the related fundamentals of managerial
decisionmaking for IT investments. Herding, as a type of human imitative behavior, tends to
result rather naturally from an individual’s imperfect reasoning and cognitive capabilities with
respect to the costs, benefits and risks associated with an IT investment. Au and Kauffman
(2003) have argued that many inefficient IT adoption decisions can be attributed to the adopters’
bounded rationality. Thus, it will be helpful to explore some of the potential behavioral
justifications of observed investment decision herding under certain circumstances (Schiller,
1995). We also believe that the rational herding theory synthesized in our framework is likely to
be very useful in many settings where bounded rationality plays a significant role. In fact, many
problems associated with bounded rationality can be studied in fully rational models with
imperfect information and vice versa (Conlisk, 1996). By formally analyzing IT adoption
herding in a somewhat simplified rational world, we are able to pay attention to the relevant
information and incentive problems, without sacrificing the real world applicability of most of
the managerial insights that will be generated.
26
The reader should not interpret our exposition in this article as a basis for rationalizing the
herd behaviors that are commonly observed in IT investment decisionmaking. Nevertheless, our
rational herding framework suggests payoff externalities, informational cascades and managers’
career concerns as three interrelated explanations for the kinds of imitative decisionmaking
behaviors that are observed in IT adoption. We investigated network externalities, observational
learning and managerial incentives as critical drivers influencing managers’ IT investment
decisions. By doing so, we demonstrated the relevance and the importance of our proposed
framework to academic researchers, business strategists and IT investment managers. Instead of
introducing various rational herding models as standalone theories, instead we emphasized their
inherent relationships in an attempt to acquire more distinctive insights for IT adoption herding.
For example, we showed why an adoption herd is much more likely to form in a market for an
emerging technology market, where information asymmetries and network effects come into
play. We also demonstrated why reputational herding is more common in IT adoption processes
where agency problems are compounded by severe information problems.
This paper presents a research framework within which IT adoption herding can be
systematically studied. We believe that future studies within this framework will not only
enhance our understanding of herd behavior in IT investment, but also offer insights and
contributions to the rational herding literature in Economics. Most informational cascade models
downplay the importance of conversation and information sharing across firms because of the
credibility issue. However, word-of-mouth communications are usually very useful in IT
diffusion and social learning in general (Ellison and Fudenberg, 1995; Rogers 1995), and the
growing literature on “cheap talk” in Economics also sheds light on the effectiveness of
conversational learning in strategic interactions (Crawford and Sobel, 1982; Farrell and Rabin,
27
1996). So future studies that explore the role of conversation in IT adoption herding have the
potential to inform the herding literature by generalizing the prior informational cascade models.
Future studies of IT adoption herding can also provide additional insights related to the theory of
reputational herding. In most reputational herding models that we have seen to date, the
outcomes of managers’ investment decisions are observable ex post in a mechanistic manner. So
the labor market and firm owners can infer managers’ capabilities through Bayesian updating.
However, as we pointed out earlier, many IT projects are strategically instrumental to the firms
that invest in them, and yet their payoffs are hard to measure in the short run. This complicates
the inference process that leads to managerial decisions about IT investments, and thus requires
the extension of reputational herding models so that they will yield more useful insights in this
managerial context.
Finally, we believe that the framework proposed in our study can also make contributions to
the IS literature in some contexts other than IT adoption. For example, we frequently observe
herd behavior in IS curriculum design, research topic selection, consumer online shopping
patterns, IT firms’ advertising campaigns, R&D spending in developing certain kinds of
information systems, and so on. The applications are varied and interesting it turns out. Our
study suggests that IS researchers ought to pay close attention to a number of related issues when
they study a decisionmaker’s behavior in those contexts. The first issue is payoff
interdependence. It is important to try to figure out whether a decisionmaker’s decision will hurt
or benefit others who make the same decision. The second issue is information gathering
through learning. In this case, it is useful to understand whether a decisionmaker can learn
information from the similar decisions of others, and whether the information gathered is
credible. The third issue is the incentives of different stakeholders. Here, we want to know
28
whether there are conflicts of interest among the different stakeholders, and if so, whether a
specific decisionmaker will have sufficient incentive to pursue her own interests at the expenses
of other stakeholders. Our future research will take up some of these issues.
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31
Table 4. Rational Herding in Generic Investments and IT Investments
Payoff Externalities
Driven Herding
Informational Cascades
(Statistical Herding)
Reputational Herding
Generic Investments
IT Investments
Investment herding may arise when
there are strategic
complementarities among investors
who make similar decisions.
However, negative externalities
may curb investment herding in
many competitive environments.
Decision-makers imitate others’
investment decisions when their
private information is overwhelmed
by the information acquired through
observational learning.
Many IT markets are subject to
network externalities, a type of positive
payoff externalities. Network effects
and significant IT switching costs are
the driving forces behind many
instances of IT adoption herding.
Instead of helping their companies
to maximize investment returns,
Managers imitate others’ decisions
to build their reputations and to
maximize their own human capital
returns.
Information incompleteness and
information asymmetries in the IT
market make observational learning
important for decision-makers, and
thus increase the possibility of an
informational cascade.
The financial returns of most IT
investments are hard to measure in the
short run, which creates more implicit
incentives for managers to engage in
investment herding when situations
warrant.
Table 5. A Comparison of Three Explanations for IT Adoption Herding
Network-effect-driven
Herding Models
Informational Cascade
Models
Reputational Herding
Models
Theoretical
Foundation
Payoff interdependency and
strategic complementarities
Information economics and
Bayesian learning
Aspects of
IT Adoption
emphasized
Model
Strength
IT switching costs, network
externalities and technology
compatibilities.
Many IT markets are
subject to network
feedback. These herding
models are generally more
intuitive and robust.
Model
Weakness
Hard to explain herding in
situations where network
externalities are weak or
negative payoff externalities
are strong.
Information externality and
social learning in IT
adoption
These models rigorously
demonstrate how herding
arise because of
Information asymmetries
and the associated learning
problems.
Simplified assumptions of
market information structure
and learning processes
make these models
unrealistic in some settings.
Information economics,
contracting and agency
theory
Managers’ implicit
incentives and career
concerns in IT adoption
These models show that
herding may be caused by
incentive problems, which
builds a bridge between the
agency theory and the
rational herding theory.
The conditions that lead to
herding are more complex
and less obvious. Most
models only deal with very
simple investment settings.