PAYOFF EXTERNALITIES, INFORMATIONAL CASCADES AND MANAGERIAL INCENTIVES A THEORETICAL FRAMEWORK FOR IT ADOPTION HERDING

Kauffman Professor and Chair, Information and Decision Sciences Co-Director, MIS Research CenterCarlson School of ManagementUniversity of MinnesotaMinneapolis, MN 55455Email: rkauffman@c

A THEORETICAL FRAMEWORK FOR IT ADOPTION HERDING

Robert J Kauffman

Professor and Chair, Information and Decision Sciences

Co-Director, MIS Research CenterCarlson School of ManagementUniversity of MinnesotaMinneapolis, MN 55455Email: rkauffman@csom.umn.edu

Xiaotong Li

Assistant Professor of Management Information Systems

Department of Accounting and MISUniversity of Alabama, HuntsvilleHuntsville, AL 35899Email: 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

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Acknowledgements: The authors wish to acknowledge Yoris Au for helpful discussions on

related work

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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/

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 insignificant 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

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

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 ofthose companies who hire their managers to maximize investment payoffs

Empirical evidence of herd behavior and imitative strategies has been recently documented infinancial 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 recentexperimental 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 informationtransmission and observational learning can lead to information cascades in technology

investment

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 paperwith 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.)

Table 1 Key Constructs in the Payoff Externalities Theory Relative to IT Adoption

Rational IT

adoption

herding

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxNetwork

externalities

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxIntrinsic and

extrinsic

network

externalities

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxInstalled base xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxPath

dependencies

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxTipping

equilibrium

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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

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 mostmanagers 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 mayend 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

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

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 problemslead 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

Information

asymmetry

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxInformation

completeness

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxInformation

spillover

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxInformational

cascade

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxObservational

learning

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxWord-of-mouth

learning

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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) Theopinions 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

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 adoptioncascade 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 recentlybeen 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

truthfully tells public his private information, no valuable private information will be lost and theinformation 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 mouth learning under many IT adoption scenarios is that each individual decisionmaker’s

word-of-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 so-called “cheap talk” (Crawford and Sobel, 1982; Farrell and Rabin, 1996)? In competitive business environments where most IT adoptions occur, individuals may have strong incentives tomisinform 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

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

Reputational

herding

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xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxIncentive

compatibility

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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 deviatefrom 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

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

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 amongdifferent 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 impact