o'leary daniel e 2008 gartners hype cycle

8/11/2019 O'Leary Daniel E 2008 Gartners Hype Cycle

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Gartner's hype cycle and information system research issues

Daniel E. O'Leary

University of Southern California, Los Angeles, CA 90089-0441, USA

a r t i c l e i n f o a b s t r a c t

Article history:

Received 9 November 2007

Received in revised form 19 August 2008

Accepted 10 September 2008

This paperuses Gartner Group's hype cycle as a basis to analyze research

issues and opportunities in information systems. Thendings including,

where wend a technology in thehype cycle can inuencethe kinds of

research questions we can ask, the information available about that

technology and the research methods that can be employed on the

technology at that stage.

2008 Elsevier Inc. All rights reserved.

Keywords:

Research opportunities

Information systems

Accounting information systems

Research issuesHype cycle

1. Introduction

The Gartner Group (Gartner) (http://www.gartner.com/) i s a rm that specializes in providing

information about information technology to rms that subscribe to its publications. Gartner, like other

researchrms, provides client rms with research about information systems and technologies and how

those technologies are likely to inuence their organizations and which of those technologies those rms

probably should adopt. Other rms with a similar function include Aberdeen, IDC and Forrester.

As part of providing information about information technology, Gartner has generated a number of

different frameworks to help their users evaluate corresponding technologies. Although Gartner hasbecome an icon to their clients, academics apparently have paid limited attention in their research to the

company, with few exceptions, such as the SET Section.

The American Accounting Association has different special interest groups, including the Strategic and

Emerging Technologies (SET) section. One of the uses of Gartner materials has been by Andy Lymer who

developed thevalue propositionfor the SET section of the American Accounting Association (http://aaahq.

org/aiet/valueProposition.html)1 Lymer used the Gartner hype curve to indicate what kinds of issues

International Journal of Accounting Information Systems 9 (2008) 240252

Earlier versions of this paper were presented at IRSAIS International Research Symposium on Accounting Information Systems,

December 2007, in Montreal and at the American Accounting Association National Meeting, August 2008, in Anaheim, California.

E-mail address:[email protected] At the time of Lymer's analysis, the section's name was the Articial Intelligence/Emerging Technologies Section. The section voted to

change itsnametothe Strategic andEmergingTechnologies Section at the August 2008 AmericanAccounting Association National Meeting.

1467-0895/$ see front matter 2008 Elsevier Inc. All rights reserved.

doi:10.1016/j.accinf.2008.09.001

Contents lists available at ScienceDirect

International Journal of Accounting

Information Systems
http://www.gartner.com/http://aaahq.org/aiet/valueProposition.htmlhttp://aaahq.org/aiet/valueProposition.htmlmailto:[email protected]://dx.doi.org/10.1016/j.accinf.2008.09.001http://www.sciencedirect.com/science/journal/14670895http://www.sciencedirect.com/science/journal/14670895http://dx.doi.org/10.1016/j.accinf.2008.09.001mailto:[email protected]://aaahq.org/aiet/valueProposition.htmlhttp://aaahq.org/aiet/valueProposition.htmlhttp://www.gartner.com/


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members of the SET section typically addressed, in contrast to another section, the Information Systems

section. Lymer suggested,

The [SET] section researchers typically research technologies before they enter the technology

trigger stage, or at the early stages of the hype cycle.

The IS Section members are interested in research across the entire technology life cycle, but are

generally most active in exploring accounting technologies once the slope of enlightenment in a

technology's life cycle is reached and implementation of technologies are commencing and need to

be understood.

Lymer used the hype curve, because, as noted byFenn (2007, p.3)the hype curve characterizes the

typical progression of an emerging technology(italics added). In this paper, further analysis of the hype

curve provides some intriguing suggestions about research and research opportunities in informationsystems.

The purpose of this paper is to examine Gartner's hype cycle as a means of trying to understand

emerging information system research issues. In particular, the purpose of this paper is to investigate the

use of the hype cycle for categorizing technology as a basis to analyze research issues in informationsystems. Ultimately, the primaryndings of this paper include that the location of a technology on the hype

curve drives what types of research questions we can address, what research data is available and what

methodologies are available to study a technology and its uses. As a result, the location of the technology on

the hype curve impacts the research that is done regarding the technology.

Section 1 has introduced the paper and provided a statement of purpose. Section 2 summarizes some of

the background material.Section 3investigates how the hype curve can be used to determine appropriate

system research issues.Section 4provides some examples to illustrate the use of the concepts.Section 5

uses the results from the earlier sections to investigate some potential research strategies. Section 6

investigates how an AIS researcher might choose a technology to study. Section 7summarizes the paper,

analyzes the contribution of the paper and briey discusses some other extensions.

2. Gartner hype curve (Fenn, 2007)

Although Gartner's research likely dominates the practice side of technology, it has received at most

limited attention from academics. The hype curve was introduced by Gartner in 1995, and is used to

Fig. 1.Hype curve 1995. Source : Fenn (2005).

241D.E. O'Leary / International Journal of Accounting Information Systems 9 (2008) 240252


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characterize a typical progression of an emerging technology to its eventual position in a market or a

domain. Using consensus, Gartner analysts position technologies based on hype. Different technologies do

not move at the same speed through the hype curve.

As seen in the example hype curves for 1995 and 2005 (Figs. 1 and 2), there are ve stages of the hype

curve: technology trigger, peak of inated expectations, trough of disillusionment, slope of enlightenment

and plateau of productivity. Fig. 3 summarizes some of the kinds of information that are available about the

technologies along the hype curve and the status of the technologies as they move along those curves.

In practice, the hype curve is designed to help companies decide when they should invest in a technology.

One of the basic lessons of the hype curve is that companies should not just invest in a technology because it is

being hyped.In addition, the hype curve allows rms to see through the hype and determine likely how

many rms are employing a technology. For example, as seen in Fig. 3, at the so-called slope of

enlightenment lessthan 5% of the rms have adopted a technology. Accordingly, rms potentially can use the

hype curve to understand what their competitors are doing with a technology and determine and characterize

their own strategy with respect to particular technologies in terms of the visually insightful hype curve.

In this paper, we periodically will talk about pushing back on the hype curve. This means that at times

a technology may have new developments that might push it back from one stage on the hype curve to

another, for example, back from the slope of enlightenment to the technology trigger stage.

Fig. 2.Hype curve 2005. Source : Fenn (2005).

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3. Hype curve and information systems research

As seen in Figs.1 and 2, the hype curve has ve different stages that potentially can occur, although all of

the steps do not necessarily occur for each technology. In some cases a Rapid Growth Phaseis added at

the end of the original ve. In order to facilitate analysis, we will investigate each step separately.

Asseenin Fig.3, eachstep apparentlyhas different informationbeing promoted by the media, and different

numbers of businesses adopting it. As a result, this suggests that the information generally available to

researchers will differ throughout the cycle. In addition, because the technology is at different levels of

development in each of theve portions of the curve, the research and development of the technologies that

can be done varies at step. Accordingly, the research questions that can be asked vary across the hype curve. As

a result, the hype curve has some direct implications for information systems research.

3.1. Technology trigger

As noted by Fenn (2007, p. 4), the technology trigger occurs when a breakthrough, public demonstration,

product launch or other event generates signicant press and industry interest.

The breakthroughcould take any of a number of different formats. There might be a prototype of thetechnology that is substantial enough to capture the public's imagination. As an example, development of

expert systems was initiated in roughly the mid to late 1970's. For instance,Buchanan and Feigenbaum

(1978) had developed an expert system in chemistry, Shortliffe (1986) had developed them in medicine and

Hart et al. (1978)had developed them in geology.

It is prior to the technology trigger and at this step that basic design science research (e.g.,Hevner et al.,

2004) in the technology is made. Beyond initial development of technology, such research can include basic

models that ultimately are embedded into software, hardware design and software that provides both for

the application and integration of the application into other settings. So-called research prototypingin

the area of expert systems (e.g., O'Leary, 1988b) was an example of this kind of research activity at this

stage.

Fig. 3.Hype curve and technology information. Source : Fenn (2007).



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Because the technology has not yet been placed in an organizational setting, unfortunately, researchers

focusing on applications are not likely to readily visualize the entire scope of applications of technologies at

this step, since research is generally only being done at the development and prototype stage. Many of the

technology's advantages and disadvantages to enterprises are not likely to be visible since the technology

has only received limited attention and has not been placed in many processes or organizational contexts.

As a result, the research questions that can be addressed at this stage relate to literally getting a technology

ready to use. There is no descriptive data about use at this time, so no research questions can address such

concerns. At this stage, expert opinion may be used to anticipate developments and applications. Thus, Delphi

studies and other approaches to gathering expert opinion may be used (e.g., Baldwin-Morgan, 1993). As a

result, research during this stage may include questionnaires or interviews with experts soliciting their

opinion as to what will happen with the technology, or how the technology might be applied.

It is at this step that many information systemsresearchers are likelyto become familiar with the technology

and some of its potential, since researchers also are consumers of information about technologies. As a result, as

information begins to diffuse about the technology, the technology trigger is likely to generate additional

research interest among researchers.

3.2. Peak of inated expectations

Fenn (2007, p. 4) describes the second stage in thehype cycle, the peak of inated expectations, as a phase

where

Over-enthusiasm and unrealistic projections, a urry of well-publicized activity by technology

leaders result in some successes, but more failures, as the technology is pushed to its limits. The

only companies making money are conference organizers and magazine publishers.

At this stage, there is still limited information about the technology, and how the technology will be

applied in organizations. Thus, the research questions that can be addressed also are limited.

Because expectations are at a high, there is likely not much discussion in the media about the limitations

of the technology. Most of the information ow about the technology is positive (things gone right). As aresult, research questions are likely to employ that kind of information. For example, Barker and O'Connor

(1989) discussed some of the benets of expert systems. Things gone right also can provide a basis for the

capture of so-called best practices, since they apparently work well, as is emphasized by the positive media

coverage. However, those best practices may be modied or augmented in later stages.

Further, since there are fewrms doing implementations, information about application can generallyonly

be garnered from one or a few rms at a time. As a result, much of the research is likely to be case-based and at

this stage, discussions about how companies have implemented the technology will be of interest. For

example,Sviokla and Keil (1988)examined DuPont's implementation strategy for articial intelligence and

Barker and O'Connor's (1989) discussed how expert systems were being used at Digital Equipment.

Accordingly, research questions are likely to be narrowed to particular company situations, aimed at beginning

to understand the technology.As seen in Fig. 3 therst generation of products is available at this step. As a result, some of therst detailed

prototypes and implementations using that technology are made during this step. As an example, the rst

expert systems in accounting probably were developed by Michaelsen (1982) and Dungan (1983). Those

systems employed software that had been derived from the early expert systems.Michaelsen (1982)used

EMYCIN which derived from the work ofBuchanan and Shortliffe (1984), while Dungan used AL/X, developed

from the work ofHart et al. (1978), and included a means of representing uncertainty with weights on the rules.

During the peak of inated expectations, students, faculty and other researchers are likely to begin to

ask and anticipate how the technology will inuence companies that they know or impact traditional

processes. For example,O'Leary and Turban (1987,p. 11) used a theory-based approach to analyze the

potential organizational impact of expert systems.Behavioral researchers may be asked to begin to assess

what needs to be done to effectively place the technology in an organization context.At this stage there is still some analysis of artifacts and their construction. For example, after the

development of the rst expert systems there was still substantial research regarding various aspects of

expert systems, e.g., weights on rules (e.g., O'Leary, 1988a), verication and validation of the systems (e.g.,

O'Keefe et al., 1987; O'Leary, 1987), and knowledge acquisition (Prerau, 1987).



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Finally, it would seem that researchers not involved with a particular technology prior to this stage,

might be seduced by the peak of expectations as much as business is, often being drawn to technologies at

this stage because of the apparent technology benets.

3.3. Trough of disillusionment

Fenn (2007, p. 4)describes the third stage, the trough of disillusionment,

Because the technology does not live up to its overinated expectations, it rapidly becomes

unfashionable. Media interest wanes, except for a few cautionary tales.

The trough of disillusionment occurs because the overinated expectations are impossible to live up to.

In the trough there is limited information, and information ow mostly concerns problems implementing

the technology (e.g.,things gone wrong). As the available information ow focuses on concerns, research

questions also are likely to focus on those limitations.

In the same sense that researchers might be seduced by the peak, they also might be turned away from

researching a technology by the trough. For example, a number of examples of technology gone wrong mightconvince researchers that this technology will not be applicable to the settings that they have examined.

In the trough, based on negative information ow, research is likely to focus on limitations of the

technology. For example, O'Leary (1990b) analyzed some of the many security limitations of expert systems.

Further, at this stage there is still limited information available. As a result, descriptive research at this

stage is likely to analyze those limited examples, possibly in the form of case analyses, or analysis of groups

of cases. For example, Cowen (2001)investigated four cases where limitations in expert system knowledge

representation occurred in different systems over the time period 19871993.

From an information and research perspective, the many cautionary tales can provide substantial

information aboutthings gone wrong, forming the basis of a research agenda of limitationsof the technology.

As one example,Waterman (1986)analyzed some of thelimitationsof expert systems, in general. As another

example,O'Leary (1990a)investigated limitations of using weights on expert system rules, based on datagathered from a few systems for which information about the weights was available at the time of his study.

Things gone wrongcan provide motivation for behavioral studies that relate to how the technology is

used or improving user interfaces. Things gone wrong can also promote a search for so-called best practices

to mitigate the problems.

3.4. Slope of enlightenment

Fenn (2007, p. 4)notes that for the slope of enlightenment,

Focused experimentation and solid hard work by an increasingly diverse range of organizations

lead to a true understanding of the technology's applicability, risks and benets. Commercial off-

the-shelf methodologies and tools ease the development process.

Generally (e.g., Fenn, 2007, p. 8) there is only a 5% adoption rate by the time the product hitsthe slope of

enlightenment. As a result, at this stage in the hype cycle, researchers are in a position to talk with and

examine a limited number of companies that actually are implementing the technology. Accordingly,

research questions can begin to address descriptive use of the technology. However, since only 5% have

adopted the product, there is still an opportunity to help design and implement the product. Further, at this

stage, since companies have implemented it, other research issues become possible, for example, auditors

could begin auditing it or at least begin assessing how they will audit it and how the information systems

group must maintain it.

It is at this stage that researchers can begin realistically to assess what went right and what went wrong,

in a range of organizational settings because there is an increased amount of data available. Unfortunately,the descriptive data available is still highly limited: only 5% of the rms have adopted the technology.

However, there are likely to be a sufcient number ofrms to begin to address settings where stock or

accounting measures are inuenced by implementation of the technology. Unfortunately, the small set of

rms at this point might arguably be a biased sample ofrms that are either willing to take the risk or need



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to take the risk to implement the technology, depending on the research question addressed. Further,

because of the small sample of adopters available at this stage, most empirical questions about the adoption

and use of the technology will result in biased results.

At this stage, there may be a call for case studies of how organizations are actually implementing the

technology, since the trend for use is becoming clearly established and best practices are becoming accepted

(Benbasat et al., 1987). An assemblage of case studies from the slope of enlightenment can begin to provide

insights for the next level, the plateau of productivity.

3.5. Plateau of productivity

Fenn (2007, p. 4)describes the plateau of productivity as where

The real-world benets of the technology are demonstrated and accepted. Growing numbers of

organizations feel comfortable with the reduced levels of risk, and the rapid growth phase of

adoption begins.

At theplateau of productivity,organizations begin to fully realize the benets of the technology. Risk

of adopting the technology has been reduced as a number ofrms have implemented the technology.Research conducted on technologies from this point on in their hype cycle generally can be descriptive,

asking questions about how the technology is used and if that use creates value, since most of the

innovation regarding the technologies will be accomplished. Most design science research, e.g., prototypes

will not be relevant, since organizations actually are implementing technologyunless developments occur

that push the whole technology or parts of it back down the hype curve. Case studies also will not be very

relevant since many companies will have already implemented the technology, and there would be a

substantial amount of case information available. Individual case studies would provide limited additional

research contributions. However, at this point case studies would address newly arising issues, e.g., in the

case of enterprise resource planning systems, upgrading a system, as developments push the technology or

parts of the technology back to earlier stages of the hype curve.

As with the slope of enlightenment, companies have implemented the technology. By the plateau ofproductivity, the technology may have slipped into traditional information systems classes and the teaching

curriculum.

In many cases, behavioral and other analysis of artifacts depends on artifact availability that may not

occur until the plateau of productivity or further in the hype cycle. In expert systems, some aspects such as

explanation were not analyzed in greater detail until full-blown artifacts were available (Arnold et al.,

2006). As a result, since full artifacts are available at this stage, any research that needs those full artifacts

can meaningfully begin at this stage.

3.6. Rapid growth phase

By the time that theRapid Growth Phaseis reached, organizations feel as though much of the risk hasbeen reduced. Many rms begin to adopt the use of the technology, and rapid growth begins.

From a research perspective, with all of the rms adopting the technology, it is at this point that there is

sufcient data so that descriptive empirical analysis can be done. At this point there is less concern for

sample bias in research using accounting measures or stock price-based information. At this stage, event

studies are likely, since sufcient disclosures and rms are available to analyze the market's response.

In this stage there is little interest in design science solutions since commercial solutions are being rapidly

adopted. Behavioral research also is unlikely, since many of the major problems have already been identied.

Of course, all of this changes if there is a development that pushes the technology back to earlier phases of the

hype cycle.

3.7. Research activities and the hype cycle

As we have worked through the analysis, it appears that the hype cycle suggests rst that different kinds

of information and opportunities are associated with different parts of the hype cycle. Second, as a result,

different kinds of research can be conducted at different times in the life cycle. For example, nancial-based



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empirical research is not likely to be a part of research until or after the plateau of enlightenment since not

enough companies have implemented the technology to gather meaningful samples of data. As another

example, potential design science research will be possible and is possibly the only feasible kind of research

at the trigger point in the hype cycle, other than expert opinion. Although there are likely to be some design

science issues beyond the technology trigger, into the peak of inated expectations and onto the trough of

disillusionment, they will rarely go beyond those two stages. One exception is when a completely eshed

out artifact is required to really analyze a particular characteristic.

Through the plateau of productivity, where 5% of the market is reached, the likelihood of getting sufcient

sample sizes for descriptive empirical research is limited. Two potential exceptions are with the peak of

inated expectations: there may be a number of stories, implementations, orrms where there is information

about things gone well, and through the trough of disillusionment, there may be a number of stories,

implementations for rms where things gone wrong.These two areas can provide information for things

gone right and things gone wrong, as a precursor to research that can be done for the slope of enlightenment.

Case study research is really most relevant in the peak of inated expectations or the trough of

disillusionment. Prior to those times few organizations have really implemented the technology. After that,

many companies have already done the implementation of the technology and case issues have begun to be

addressed. Although cases may be well-received in the slope of enlightenment, they are clearly of decreasinginterest, as more and morerms implement the technology.

Interestingly, behavioral research can be done across the entire cycle, although it also is most likely to be

done early on. At or before the technology trigger stage, behavioral research can establish how the human

computer interface or human use issues can be best designed. In the Peak and Trough, behavioral research

can be done to nd or test what worksand what does not. However, behavioral research also can wait

until artifacts are established and then analyze them.

Use of a technology in an early stage generally is more expensive and risky than the same technology at

a later stage. As a result, accounting and stock price numbers are likely to be heavily affected by those

differential costs and risks. Empirical research based on stock price or nancial accounting indicators

generally must be done at a stage where there are a sufcient number of companies affected. Typically, this

means that it cannot be done before the technology is at the slope of enlightenment.Further, the hype cycle illustrates that empirical research done too early in the hype cycle is likely to

have a biased sample of companies. It is only after the technology has reached the slope of enlightenment

Fig. 4.Research by hype curve stage.



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that a large enough and mixedenough sample ofrms will be found. Interestingly, this suggests that in

some situations, accepted empirical research ndings, with appropriate sample sizes, may have generated

biased ndings, because of sample characteristics.

This seems to be a critical differentiator of technology-based research: the life cycle inuences the kind

of research that is most feasible and of greatest interest. Some of these issues are summarized in Fig. 4.

4. Summary analysis of the expert systems example

Development of expert systems was initiated in roughly the mid to late 1970's. For example, Buchanan

and Feigenbaum (1978) had developed an expert system in chemistry, Shortliffe (1986) had developed them

in medicine andHart et al. (1978)had developed them in geology. The rst expert systems in accounting

probably were developed by Michaelsen (1982) and Dungan (1983). Those systems employed software that

had been derived from the early expert systems.Michaelsen (1982)used EMYCIN which derived from the

work ofBuchanan and Shortliffe (1984), while Dungan used AL/X, developed from the work ofDuda et al.

(1979), and included a means of representing uncertainty with weights on the rules. After the rst

accounting systems there was still substantial research regarding various aspects of expert systems, e.g.,

regarding the weights (e.g., O'Leary,1988a), verication and validation of the systems (e.g., O'Keefe et al.,1987;O'Leary, 1987), knowledge acquisition (Prerau, 1987) and explanation (Arnold et al., 2006). Barker and

O'Connor (1989) presented a discussion about XCON, one of therst well-accepted expert systems in practice.

Although the Gartner hype curve was not developed until 1995, we can attempt to retrot at least part of

the expert systems hype curve. The technology trigger probably was in the mid to late 1970's after the rst

expert systems demonstratedthe ability to capture human expertise. The rst accounting expert systems

were developed using existing software models and probably helped push the hype curve up to the peak of

inated expectations. There were at least two articles in the Wall Street Journaldetailing issues associatedwith

expert system implementations (Kneale, 1986; Rose, 1988). The rst of those articles which talked about a

clever approach to capturing knowledge, was probably on the peak of inated expectations. The second of

those articles, that discussed some limitations of expert systems, could have signaled the beginning of the

trough of disillusionment. Further, sometime around the mid 1980's there was a trend to replace the termexpert system with knowledge-based system (e.g., Hayes-Roth,1984; Davis,1986). Most of the eshing out

of expert systems as a technology took place over the time frame 1985 to 1995, but is still going on. Research

continues throughout and beyond the hype cycle. Fully understanding a technology can require that a full-

blown system be investigated as seen in Arnoldet al.(2006). Based on Fig.1, in 1995,knowledge-basedsystems

were on theplateau of productivity.

5. Career research strategies

There are at least three contrasting pure" research strategies that a faculty member might employ

based on discussion of the hype curve.

Strategy 1. Choose a technology and stay with that technology through the hype or cycle.

As an example of this strategy, a researcher might follow expert systems through the entire hype cycle,

potentially using different methodologies at different stages. This strategy has the advantage of making the

researcher well-rounded in terms of knowledge of methodologies and depth of knowledge of a particular

technology. However, the researcher is dependent on the particular ebbs andows of that specic technology.

For example, is there continued interest in that technology or are there changes to that technology that are

pushing it back on the hype curve?

Following a single technology builds deep understanding and expertise of the technology. Continuing to

do work in a single technology can build a reputation for the researcher in research circles for that

technology. However, if that technology is seen as dated, then the researcher also can be seen as dated.Finally, theresearcher may be seen as rigid and non changingby continuingto focus on the same technology.

The primary disadvantage of this research strategy is that, in order to stay with a single technology

across its life cycle, and do research across the life cycle typically would require knowledge and use of

multiple research methods. Few researchers consistently make use of a wide range of research methods.



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Strategy 2. Choose a research methodology and stay with it by analyzing different technologies at similar

stages in the hype cycle.

As an example of this strategy, the researcher might employ empirical nancial methods to analyze a

range of technologies in the slope of enlightenment. The primary efciency of this strategy is that the

researcher does not need to learn a number of different research methods. However, the inefciency in this

strategy is that the researcher needs to learn about a lot of different technologies. Further, they have to hope

that the specic methodology that they use is amenable to the particular technologies.

Unfortunately, this strategy is not likely to be equally applicable for all research approaches. For example,

design science approaches generally are applicable only in the technology trigger and pre-technology trigger

stages.

Strategy 3. Choose a research methodology and technology and stay with it.

As an example of this stage, a researcher might start with expert systems and continue doing research in

expert systems, even if it is through the entire hype cycle. In a world that did not change, this would be the

strategy: learn a technology and learn a research methodology. This strategy is likely to be useful in at leastthree settings: if the researcher has a relatively short career, the technology has a long run or variations or

testable artifacts of the technology keep emerging. Unfortunately, the last two rationales are beyond control of

the researcher.

6. Choosing technologies to research?

If a researcher wants to be a part of the technologyat the trigger stage or earlier in the hype cycle, then it

is important that they choose a technology for which they are prepared to contribute at that early stage. If

the researcher is in accounting information systems (AIS) then that typically means that they choose a

technology that has a basis in one of the functional areas of accounting, e.g., nancial accounting (e.g.,

XBRL) or management accounting (e.g., Business Process Management), but also occurs on a hype curveearly in the life of the technology. Generally, AIS researchers would chose a technology for which there is a

match to an accounting problem or accounting sub-discipline.

6.1. XBRL

XBRL is an XML-based (extensible mark-up language) derivative designed to facilitate nancial

reporting. AIS researchers have been involved with XBRL at least since Debreceny and Gray (2001). In 2005,

XBRL was in the trough of disillusionment, but in 2006, the Securities and Exchange Commission awarded a

$5.5 million contract to XBRL U.S. (www.xbrl.us) to develop a taxonomy for U.S. generally accepted

accounting principles for nancial accounting. From the late 1990's through the current day, AIS researchers

are actively involved in research regarding XBRL, including issues such as the development of standardstaxonomies. Recently, a leading XBRL researcher indicated the need for case studies (Debreceny, 2008).

6.2. Business process management

Business process management (BPM), also referred to as business performance measurement and many

other names, has been given many denitions, including the following denition byMicrosoft (2006).

BPM is the use of an integrated set of key performance indicators that are used to monitor an

organizationalprocess in realtime.Business process management (BPM) is a managementdiscipline that

combines a process-centric and cross-functional approach to improving how organizations achieve their

business goals. A BPM solution provides the tools that help make these processes explicit, as well as thefunctionality to help business managers control and change both manual and automated workows.

In 2005, as seen in Fig. 2, aspects of business process management, those of business process networks and

business process suites were included on the hype curve. Business process management is almost a merger of

http://www.xbrl.us/http://www.xbrl.us/


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management accounting and accounting systems. BPM is about managing a process, but it is also about

choosing the right key performance indicators. Interestingly, apparently there has been little research by

academics in the area of BPM. O'Leary (2007) provides a BPM design science structure for a particular

accounting process. Based on discussion earlier in the paper we are likely to nd some different kinds of

research as we move through the hype cycle, for example, things gone right or wrong, based on information

ows about positive or negative events. Case studies might be developed illustrating some of the

implementation issues. Behavioral research might examine design issues at rst and then move to analysis

of implemented systems. As the technology matures, we might see research about how stock market price or

accounting measures are inuenced by disclosures about BPM.

7. Caveat, summary, contributions and extensions

This paper has described general trends as tied to the hype curve. However, this does not imply that all

research and researchers strictly follow this scheme. Instead, it provides an analysis of a theory that can

provide an overview into general trends in the research process.

In this paper we have focused on Gartner and some of the tools that Gartner has developed. We examined

how those tools might be used to understand and anticipate research issues in accounting informationsystems. It was found that different portions of the hype cycle have different information resources and

different research and development opportunities. As a result, we also suggested different research strategies

for researchers based on the use of the hype cycle.

Apparently, there has been limited integration of research from rms such as Gartner into academic

research. This research used the hype cycle to investigate what research methods can be used at different

points in the life cycle of a technology.

The key contribution of this paper is that the notion that the type of research that can be done is tied to

where the technology is in the hype curve, and that as a result, researchers have different strategies in doing

research in technologies.

This research could be extended in a number of different ways. First, additional examples could be

examined to further illustrate and substantiate the discussion. For example, we might take real time auditingor XBRL and map it back to the hype curve.

Fig. 5.Hype curve and accounting applications.



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Second, the framework itself could be analyzed. Unfortunately, there has been limited research

examining the so-called hype cycle itself to see how well it ultimately denes technology change. We have

taken that framework as given and used it as the basis of analysis, however, future research could directly

examine the framework. For example, the framework could be analyzed empirically by actually generating

a history on the many technologies that have been analyzed over the years by Gartner.

Third, the hype curve framework could be integrated into accounting applications. One approach to

integrating the hype cycle into accounting applications is given inFig. 5. In particular, the hype cycle is

placed on one axis and the area of accounting is placed on another. Then technologies that might occur at

each step in the hype cycle are examined to see how they map into accounting disciplines.

Interestingly, the hype curve and its tie to different research methodologies suggests why in some cases

it is difcult to chose a topic that meets the requirements of multiple researchers in a joint research project.

Imagine that a design science researcher examines a topic such as XBRL, and suggests joint research with a

colleague in another area, e.g., nancial or behavioral analysis of that same technology. As we have seen

here, the research projects and available information are not likely to be in alignment and limit, in some

cases, potential collaboration.

The hype curve could also be used to evaluate when and where particular types of accounting research

can and should be done. As an example, consider the well-known SarbanesOxley (SOX) legislation. SOXlikely was at equivalent of the trigger stage when it was rst passed. As the legislation went through early

adopters, there likely were peaks where the legislation was praised and troughs where it was criticized.

There were not enough rms to do descriptive empirical research until a few years after the legislation had

taken affect, perhaps in the equivalent of the plateau of productivity.

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