Evaluating Adoption of Emerging IT for Corporate IT Strategy:Developing a Model using a Qualitative Method

Short Title: Adoption of Emerging IT

ABSTRACT

The acquisition and evaluation of new technologies is

critical to the development of a timely IT strategy. We report

the findings of a four round Delphi study designed to elicit a

cohesive set of issues that affect an IT executive’s decision to

adopt an emerging IT into corporate IT strategy. Based on the

results, we present the Emerging Information Technology

Evaluation Model, which IT executives and academicians can use to

support a strategic adoption decision.

Keywords: Emerging information technology, Information technology

strategy, Diffusion of innovation, Delphi method

INTRODUCTION

For decades, the development of an effective information

technology (IT) strategy has consistently ranked as a key

organizational issue among surveyed IT executives (1996;

Brancheau & Wetherbe, 1987, 1990; Luftman & Kempaiah, 2008;

Luftman, Kempaiah, & Nash, 2006; Niederman & Brancheu, 1991).

Because the effectiveness of IT strategy can be affected by the

passage of time (Newkirk, Lederer, & Srinivasan, 2003; Porter,

1985), it would stand to reason that timeliness is a key element

of an effective IT strategy. Unfortunately, IT executives often

focus on integrating today’s commercially available technologies

into tomorrow’s IT strategy (Cegielski, Reithel, & Rebman, 2005).

This fact, along with the rapid evolution of IT, injects an

additional degree of complexity into the formulation and

implementation of corporate IT strategy (Davenport, 2001; Varon,

2000). Inasmuch, executives are faced with the challenge of

constructing a timely IT strategy with currently available

commercial technologies that may be obsolete by implementation

(Baldwin & Curley, 2007; Benamati & Lederer, 2001).

By focusing on currently available commercial technologies,

IT executives assume the risk of developing corporate IT

strategies that are, at best, a step behind the evolution of

technology. Given the associated costs of IT strategy planning

and implementation, the creation of an obsolete strategy serves

to facilitate future technology-related problems that may

ultimately translate into organizational inefficiencies

(Premkumar & King, 1994; Segars & Grover, 1998; Segars, Grover, &

Teng, 1998). To remedy the problem of the development of a dated

IT strategy, some technology executives focus attention on

emerging information technologies (EIT) (Gordon, 2002; Low, 2001).

Although this approach requires IT executives to evaluate

innovations earlier in the technology development life cycle, the

proactive process of examining EITs as part of planning a

comprehensive IT strategy allows technology executives to better

anticipate the future business applications of innovations.

Ultimately, a more timely (and subsequently more effective) IT

strategy may be developed (Chan, 2002; Chan & Reich, 2007;

Gottschalk, 1999).

In the research presented herein, we examine the

relationship between IT strategy and timeliness through the lens

of classical innovation diffusion theory. The question we seek

to answer is “What issues influence a corporate IT executive’s

decision to adopt an EIT into corporate IT strategy?” This

question is particularly relevant given the aforementioned

discussion regarding the importance of time and IT strategy.

Therefore, we do not limit our investigation of a specific EIT or

category of EIT. Instead, we examine the process by which EITs,

in general, are evaluated. We propose the value of the current

study lies in the identification of the issues related to any EIT

and their respective consideration for adoption into corporate

strategy given the assumption that it may preserve some

timeliness of a subsequently developed IT strategy. A more

complete understanding of the issues related to the adoption of

EITs into corporate strategy and the context in which these

issues may fit into classical diffusion theory could provide

value to both academicians seeking to expand the framework of

innovation diffusion theory in IS research and also to

practitioners by providing a reference model for framing

organizational decisions regarding said technologies.

The remainder of this article is structured as follows. We

first provide a brief literature review summarizing the salient

points of the theory of innovation diffusion as it is derived

from the reference discipline of sociology. Next, we provide an

overview of EITs and their relationship to the aforementioned

theory of innovation diffusion. Then we discuss the application

of the Delphi method employed in this research. Based on our

analysis, we present and describe the Emerging Information

Technology Evaluation Model. We close with a discussion as to how

our research contributes to both the academic field of IT

strategy and to practitioners who are concerned with obsolescence

of IT strategy.

CONCEPTUAL BACKGROUND

Innovation Diffusion

Innovation diffusion research is particularly prominent

within the discipline of Management Information Systems (MIS)

(e.g. Agarwal & Prasad, 1997; Chau, 1996; Cooper & Zmud, 1990;

Davis, 1989; Fichman & Kemerer, 1999; Lai, Guynes, & Bordoloi,

1993; Larsen, 1993; Loukis, Spinellis, & Katsigianis, 2011; Moore

& Benbasat, 1996; Premkumar, Ramamurthy, & Nilakanta, 1994;

Tornatzky & Klein, 1982; Venkatesh & Morris, 2000). Two primary

factors are thought to contribute to this prominence. First, IT,

the focus of MIS research, represents the hardware embodiment of

innovation. Second, the long linage of innovation diffusion

research provides a convenient foundation from which MIS

researchers may ground current studies. Because our study

examines factors that affect the adoption decision of emerging IT

(innovation), we find it appropriate to ground the current

research upon the innovation diffusion literature. This section

presents a brief overview of the sociological tradition of

innovation diffusion.

Rogers (2003) reports that the cumulative efforts of

thousands of scholars, in numerous fields of study who

collectively examined innovation diffusion theory, have produced

more than 3,800 published research articles. In terms of volume

of published works on a topic, innovation diffusion is among the

most widely studied aspects within the behavioral sciences.

Although the scope of the collection of innovation diffusion

research is expansive, scholars typically agree that innovation

diffusion is the process by which an innovation is communicated

through certain channels over time among the members of a society

(Katz, 1961; Katz & Levin, 1959).

An innovation is an idea, practice, or an object that is

perceived as new by an individual or organization (Rogers, 2003).

It is important to note that it does not matter whether the idea,

practice, or object is new by the measure of time that has lapsed

since its discovery. The perception of newness by the potential

adopter determines the reaction to the idea, practice, or object.

Newness can be expressed as one’s knowledge regarding an

innovation (Katz, 1961). Thus, if an idea, practice, or object is

perceived as new to an individual, it may be considered an

innovation.

Emerging Information Technologies as Innovations

We define EITs as innovations that are in the early stages

of development. EITs may be in the form of hardware or software.

Regardless of the form, EITs represent an avenue for enhancing

the effective and efficient flow and utilization of information

within the organization to support business objectives and,

ultimately, firm performance. Defining characteristics of EITs

that differentiate them from other ITs include incomplete product

standardization and limited availability (i.e. beta versions of

software and prototypes of hardware). There are two distinct

categories of EITs: 1) evolutionary extensions of existing

technologies or 2) revolutionary new technologies. The literature

often considers an organization’s capability to sense and

implement revolutionary innovations to enhance organizational

performance (Christensen, 1997; O'Reilly & Tushman, 2004; Tushman

& O’Reilly, 1996). However, EITs differ from Christensen’s

description of disruptive technologies in that EITs are defined

without context to a specific firm or organization and its

existing processes (Christensen, 1997; Christensen & Raynor,

2003). Specifically, Christensen defines a disruptive technology

based upon an organization’s existing resources including people,

equipment, technologies, cash, product designs and relationships

(Lucas Jr & Goh, 2009). By this definition, what one organization

defines as a disruptive technology may not be so defined by

another organization (Christensen & Overdorf, 2000). In contrast,

EITs are technologies that are immature and in developmental

stages. The classification is made without respect to a single

organization or its processes and resources.

Regardless of how an EIT is classified, the explicit

business application of the EIT is the same: the capability of

achieving a practical purpose more effectively or more

efficiently than an existing technology. However, organizations

must not only be able to recognize new technologies, but they

must also have adequate capacity to absorb adopted technologies

(Cohen & Levinthal, 1990) and incorporate such technologies into

its governance structures and work processes (Hazen, Overstreet,

& Cegielski, 2012). In recent years, organizations have

integrated a multitude of EITs into the ordinary course of

business. Currently pervasive business applications of IT, such

as e-mail, Bluetooth, and client/server computing were, at one

time, EITs. Today, a visit to an IT tradeshow like Interop or

CeBIT will reveal a number of current EITs, such as cloud-based

enterprise applications, holographic computing interfaces, or

fractal storage devices.

EITs certainly fit the traditional sociological definition

of an innovation in that they exhibit all of the characteristics

defined in classical diffusion theory. As such, the cohesive

classical innovation diffusion theory is a particularly well

suited framework from which to position our research.

Specifically, we organize our study based on the innovation-

decision process.

The Innovation-Decision Process

The innovation-decision process, as defined within the

framework of classical diffusion theory, provides a generalized

model of the stages of innovation adoption through which a

potential innovation adopter will move as he or she evaluates the

variables that influence the innovation adoption decision.

Although the actual adoption of EITs into corporate strategy is

not the focus of this study, the innovation-decision process

provides a salient structure within which to consider potential

adopter knowledge aggregation regarding the innovation under

consideration. Potential adopters of an innovation follow a well-

defined decision process in order to arrive at an adoption

decision regarding an innovation (Rogers, 2003). This

innovation-decision process, as depicted in Figure 1, encompasses

five steps, as defined in Table 1. For most adopters, the

innovation-decision process will include common criteria, which

are defined in Table 2.

***Insert Figure 1 about here***

***Insert Table 1 about here***

***Insert Table 2 about here***

Use of diffusion of innovation theory, and specifically the

innovation-decision process, as the lens in which to view our

research problem motivated our choice of research method. The

Knowledge and Persuasion phases of the innovation-decision process

are the principle focal points of our study. However, little is

known regarding what issues are embedded within these phases of

the innovation-decision process in reference to examining EITs;

thus, we felt that a Delphi method would help us to uncover the

salient issues that influence knowledge and persuasion. In the

next section, we describe our research method.

METHOD

The Delphi method is a research technique developed by the

Rand Corporation in the early 1950s to identify future

technological and economic trends (Dalkey & Helmer, 1963). The

Delphi technique is best suited to deal with uncertainty in a

domain of imperfect knowledge (Churchman & Schainblatt, 1965).

The principal focus of this technique is to achieve a consensus

among experts regarding a specific topic (Taylor & Meinhardt,

1985). Consequently, there are no “correct” answers when using

the Delphi method. As a result, the potential applications for

the Delphi method are very broad. However, the technique is

particularly useful for controversial or multi-dimensional

subjects (Paliwoda, 1983).

Following the selection of suitable knowledge domain for the

Delphi study, the Delphi administrator must compile an initial

list of knowledgeable experts. The experts respond to a series of

linked questionnaires depicting potential future scenarios in the

knowledge domain. The initial round of the Delphi questionnaire

is open-ended (Delbecq, Van de Ven, & Gustafson, 1975). The

purpose of the first questionnaire round is to aggregate

information for subsequent ranking rounds of the study (Brancheau

& Wetherbe, 1987). In the first round, the panel of experts

contributes input that they feel is pertinent to the focus

question of the study (Nambisan, Agarwal, & Tanniru, 1999; Riggs,

1983). In the second round of the study, each expert on the

panel individually ranks, in order of perceived importance, every

issue identified in the first round of the study (Paliwoda,

1983). From the data gathered in the second Delphi round, the

study administrator scores each issue (typically using a weighted

average method) and redistributes results to the panel of experts

(Nambisan et al., 1999; Rohrbaugh, 1979). In the third round, as

well as any subsequent rounds of the study, the participating

individuals review the group rankings and re-rank the issues in

light of the aggregated response of the panel. The process ends

when there is minimal variation between rounds for the ranked

issues. At that time, a declaration of consensus exists among the

panel (Delbecq et al., 1975).

The Delphi method of research has several unique

characteristics that are worth noting.

1. Anonymity: The expert participants remain anonymous to one

another; they interact only with the Delphi coordinator.

2. Controlled feedback: All information is gathered and

redistributed via the Delphi coordinator.

3. Group response: Individuals contribute information into a

group response.

4. Expert opinion: Panelist selection is contingent on knowledge

of the field.

5. Reduced cost and time limitations: The structure of the technique

eliminates the need for the participants to arrange costly

and time-consuming face-to-face meetings.

The Delphi method has been used extensively in many facets

of business research, especially within the domain of MIS (e.g.

Fink, 1995; Garcia-Crespo, Colomo-Palacios, Soto-Acosta, & Ruano-

Mayoral, 2010; Gutierrez, 1989; Langeland & Iden, 2010;

McFadzean, Ezingeard, & Birchall, 2011; Muller, Linders, & Pires,

2010). Additionally, Delphi has been a used to identify decision

variables, as well as to populate conceptual taxonomies (Doke &

Swanson, 1995; Nambisan et al., 1999). Thus, we found the Delphi

approach to be a favorable method to explore our research

question. In this study, we used four round, web-based Delphi

process to elicit a set of cohesive issues that IT executives

consider important when evaluating EITs. Then, using diffusion of

innovation as a basis to guide our conversation, we used an

online chat session with our panel of experts to determine how

these issues combine to create an evaluation model for EIT

adoption.

Pilot Testing

A pilot test provided a basis to test the appropriateness of

the Delphi method and functionality of the web-based process.

Five local IT executives participated in the pilot study that

included three rounds of Delphi rankings regarding the

consideration of EIT and corporate IT strategy. Each of the five

executives participating in the pilot study indicated

professional experience in IT strategy planning and

implementation. Discussions with the executives following the

pilot test resulted in several technical changes to the survey

web site. None of the material content required modification.

Expert Panel

A review of the Delphi studies conducted within the MIS

discipline revealed most studies begin with less than 50

participants. Nambisan, Argarwal, and Tanniru (1999) enlisted and

maintained the participation of 11 experts through three rounds

of a Delphi. Doke and Swanson (1995), de Hann and Peters (1993),

and Couger (1988) conducted Delphi studies involving fewer than

30 participants. Subsequently, we sought to solicit enough

participants such that roughly 30 participants would be retained

throughout.

To solicit study participants, we obtained a listing of IT

executives from Fortune 1000 firms and presented each with an

opportunity to participate. Of the executives contacted, 75

completed the initial demographics survey and consented to

participation in the study. An analysis of the descriptive data

from the 75 initial study participants (Appendix A) indicated the

group to be diverse in respect to industry, organizational size,

professional experience, education, and geography. Of these

initial participants, 31 were retained throughout all four

rounds; the others were lost through attrition.

Electronic mail was the method of communication between the

Delphi administrator and expert panel in the Delphi method. For

the initial round of the Delphi, all 75 executives received an

email describing the purpose of the study, the perceived

significance with regard to IS executives, a restatement of the

definition of emerging IT from the initial contact email, a

description of the Delphi method, and a unique user

identification number and password. Additionally, the

participants received assurance of anonymity of all data

collected during the study.

During rounds two, three and four of the study, every

participating executive from the prior round received an email

message providing instructions for participation in the upcoming

round and a web site link to the survey for the stated round.

Additionally, the web page for each round of the survey contained

a restatement of the instructions provided in the pre-round email

sent to the participants.

Delphi Round One

The initial round of the Delphi study required that each

participating executive read the introductory statement that

describes the study. Additionally, the participants were provided

with the definition of an EIT used in the current study and a

brief list of previous and current EITs as a reference. The list

included technologies such as Bluetooth communications protocol;

virtual retinal display technology, and XML-based languages like

RSS. Regarding EITs in corporate IT strategy, each participating

executive submitted his or her perceptions of potential issues

regarding the integration of EITs into corporate IT strategy.

Specifically, participants were asked an open-ended question,

“What issues do you consider to be the most important when

considering the adoption and integration of emerging information

technologies into your corporate IT strategy?” Of the 75

executives that had initially agreed to participate in our study,

37 participants completed round one of the Delphi study. The

participants generated a total of 111 individual comments. These

comments were categorized into a total of 24 unique issues (Table

3), which were subsequently incorporated into round two of the

study.

***Insert Table 3 about here***

Delphi Round Two

To begin round two of the study, the 37 executives that

participated in round one received an email listing the 24

previously identified unique issues. Additionally, the email

requested that each of the 37 executive participants log into the

Delphi web site and rank (in an ordinal manner) each of the 24

issues in terms of relative importance with respect to potential

integration into corporate strategy. Per the web site

instructions, a rank of one indicated that the participant

perceived the issue to be of greatest importance whereas a rank

of 24 indicated that the executive perceived the issue to be the

least important of those issues listed. In round two, 33 of the

37 executive participants from round one completed the ranking

process.

Delphi Round Three

To start round three of the study, the 33 respondents that

participated in round two received an email that contained a list

of each of the issues identified in round one and the respective

mean rank score for each issue computed from the analysis of the

data collected in round two (Table 4). Again, the email requested

that each participant access the Delphi web site and, considering

the mean ordinal rank score derived from the group input, re-rank

the 24 issues on the perceived of importance as they pertain to

the integration of an EIT into corporate strategy. During round

three, all of the 33 executive participants from round two

completed the ranking process.

Delphi Round Four

The fourth round of the study began when the 33 participants

who completed the third study round received an email list of the

24 issues identified during round one as well as the respective

mean ordinal rank scores for each issue from rounds two and

three. The email requested that the participants review the

previous rankings and re-rank the relative importance of each

issue (Table 4). Additionally, the emailed requested that the

participants score each issue on a nine-point, Likert-type scale.

This procedure generated quantitative data, which we used for two

purposes: 1) an internal validation of the group rankings

obtained via the Delphi method and 2) a subsequent measure of

generalizability of the results beyond the expert panel.

***Insert Table 4 about here***

DATA ANALYSIS

Evaluation of Consensus

In addition to the identification of domain-specific issues,

group consensus is the desired product of a Delphi process (Okoli

& Pawlowski, 2004). Kendall’s coefficient of concordance (W) was

the first measure used to assess the relative strength of

consensus among the participants. Kendall’s W is a measure of the

degree to which a set of ranked scores agree (Siegel, 1956). A

significant W (p < .05) indicates that the participants applied

essentially the same standard when judging the importance of the

issues. Kendall’s W was computed following each of the Delphi

rounds, and was found to be significant after the third round of

the study. However, a fourth round was necessary for further data

collection. For the final round of the Delphi study, Kendall’s W

= 0.5919 and is statistically significant (p < .001).

An analysis of the percentage of the participants who ranked

each issue comparably to the group rank served as a secondary

means to assess the strength of group consensus (Table 4). During

round four, no less than 50% of the respondents ranked 23 of the

24 issues equal to the group rank. Given the significance of

Kendall’s W and the high occurrence of group association,

additional Delphi rounds were unnecessary.

Internal Validation

For those engaged in Delphi-based research, the developers

of the method suggest the use of some form of internal validation

to substantiate the obtained results (Riggs, 1983). One common

technique used to validate Delphi rankings is the application of

an alternative measurement scale (Malhotra, Steele, & Grover,

1994). Specifically, the aforementioned Likert-type survey

provided the data to assess, through another dimension, the

consistency applied by the panel members while assigning ranks to

issues.

A comparison of the Likert survey rankings to those of the

final Delphi rankings revealed a considerable number of

consistencies (Table 5). It is particularly noteworthy that each

set of rankings, while not in the exact order, included 10 of the

same issues in the top half of the ranking schema. Additionally,

several issues received identical rankings across methods.

Finally, empirical assessment (W = .6103, p < .001) indicated

congruence between the Delphi rankings and the rankings derived

via the Likert scale score.

***Insert Table 5 about here***

External Validation

Some methodologists question whether the findings of a

Delphi process are generalizable beyond the expert panel from

which the results emanate. To abate the aforementioned concern

regarding the current method, an additional group of 131 IT

executives, drawn from the membership of two professional

information systems organizations, received an email requesting

participation in an IT survey. The email provided each recipient

with: 1) our definition of emerging IT, 2) a link to the survey

web site, and 3) a request for participation. Via the same web-

based, 9-point Likert-type instrument utilized during the final

round of the Delphi process, 99 of the 131 executives

participated in the evaluation of the 24 issues identified during

the Delphi process. The profile of the confirmatory survey

respondents was similar to the general profile formulated of the

respondents who participated in the Delphi process (Appendix B).

Because the purpose of the quantitative survey was to add a

degree of generalizability to the findings of the Delphi study,

there was no intention to solicit additional issues that may

affect the integration of EITs into corporate strategy from the

respondents.

Overall, the results of the external confirmatory survey

generally reflect the ranking achieved through the Delphi process

(Table 6). Four of the top 12 issues received the same rank from

both groups while six of the lower 12 issues were consistent

among the survey groups. Alignment of 10 of the 24 issues between

the internal and external groups indicates a degree of

generalizability.

***Insert Table 6 about here***

DISCUSSION WITH EXPERTS AND MODEL DEVELOPMENT

In lieu of our research team drawing conclusions based

solely on our independent evaluation of the findings, we

solicited the thoughts of our expert panel to make sense of the

data. We hosted free-flowing, unstructured online chat session

with participants and began the conversation with the question,

“Given the results of this study, what would you say are the

overarching themes regarding the evaluation of emerging

information technologies for your corporate IT strategy?” Based

on the input of these experts and the innovation-decision process

encompassed by innovation diffusion theory, we created an

Emerging Information Technology Evaluation Model that explains

our findings while providing direction for future research. In

this section, we describe our conversation with the expert panel,

the process in which we created our model, and the major

constructs presented within the model.

Qualitative feedback provided via the online chat session

with the Delphi expert panel revealed that the overwhelming

majority of the participating IT executives believe that the

adoption decision of EITs is stratified into two distinct but

related assessment areas: “business alignment issues” and

“technical alignment issues.” Interestingly, alignment, in

numerous facets, is a key issue in IT strategy identified in

several previous research studies (Grover & Segars, 2005).

According to a CIO from a global IT firm whose sentiments were

widely supported by the group, the two areas differ in that,

“business issues address the general ways and means that a

particular technology will support an organization’s objectives”

while technical alignment issues focus on “the nuts and bolts of

a particular technology like compatibility with existing

systems.” According to the study group, the business alignment

issues reflect concerns that are universal to all organizations –

competitive advantage, customer relationship management, and

organizational fit. Interestingly, the study participants defined

all of these issues in qualitative assessment measures.

Conversely, the technical alignment issues are firm specific and,

as the study group described, tend to focus on very quantifiable

aspect of a technology. While the group did feel that both areas

are “equally important,” a consensus formed among the executives

during the chat session that the integration decision regarding

EITs must focus initially on business alignment issues in order

to ensure support for organizational objectives.

These business alignment issues and technical alignment

issues can be decomposed into additional components, based on the

issues identified in our Delphi study. Based on the feedback

garnered by our discussion with the expert panel, these

components were used to formulate the Emerging Information

Technology Evaluation Model (Figure 2). In the remainder of

this section, we describe the components of this model, using our

study’s findings and the input of the expert panel as

justification for each construct.

***Inset Figure 2 about here***

Business Alignment Issues

Each of the issues described in this section related

specifically to higher order business concepts such as business

process design, supply chain relationships, and customer

relationship management. Therefore, the participants suggested

that these issues be classified together under the umbrella of

business alignment. Per the definitions presented in Table 2 that

summarize the classical diffusion theory, the three areas

identified as business alignment issues could be stratified into

two principle innovation-decision variables – relative advantage and

compatibility. In the model presented in Figure 2, these business

alignment issues are therefore framed accordingly as relative

advantage and compatibility in relation to the grounding theory

for this study.

Ability to Gain and Sustain Competitive Advantage

Practitioners as well as researchers hold the popular

opinion that competitive advantages derived from using IT are

often short-lived because of the ability of competitors to

replicate, and subsequently eliminate, the advantage (Clemons &

Row, 1991). The participating executives agreed that the ability to

gain/sustain competitive advantage via the integration of an EIT into

strategy is a paramount concern regarding the EIT evaluation

process (e.g. issues A, B, and W from Tables 4, 5, and 6).

Interestingly, most of the participants expressed competitive

advantage not in terms of a single application of technology that

produces a benefit for a finite amount of time, but rather as a

continuous effort to manage the integration of technologies as

they develop. Given this information, we have modularized

competitive advantage into sequential components shown in the

model, which fall under the relative advantage component of the

innovation-decision process.

Appropriateness of Technology for Partners/Clients

According to the executive respondents, the second business

alignment issue to consider when evaluating an EIT is the

appropriateness of the technology for use entities and partners/clients (e.g. issues

D, I, J, and L from Tables 4, 5, and 6). This falls under

compatibility in reference to the innovation-decision process. The

former CIO of an international women’s fashion retailer explained

his perception of appropriateness of technology for their

customers with this example.

“We evaluated an e-commerce plan. It was a comprehensive

plan we had on the table and it would have been costly. We

learned from preliminary market studies that our customers

would not purchase our clothes online. For them, there is a

need to see and feel a garment before they buy. They like to

come into the store and try it on, see it in the mirror, and

get some opinion on how it makes them look. Because of that,

we decided not to incorporate an e-commerce platform in our

web presence. I’ll also tell you that watching all of our

competition move to e-commerce platforms made us all worry.

We wondered if we had made the right decision. Five years

after, we know our assessment of our customers was right on.

I know that our competition hasn’t experienced the kind of

return on their sites that they expected. We know it all

boils down to our customer’s preferences.”

Support Current/Future Business Operations

Within the domain of business alignment, the subject group

defined support of current and future business operations and processes (which

encompasses such issues as M, O, and T from Tables 4, 5, and 6)

as another issue to consider when evaluating an EIT. One CIO

suggested that by appropriately evaluating the compatibilities of

an EIT with current and future organizational operations, an IT

strategist can avoid the difficult lesson learned by so many

adopters of ERP systems in the late 1990s: integrating technology

for the sake of technology is a poor approach to developing an IT

strategy. The overriding theme expressed by the group regarding

EITs and operations compatibility is that it is of paramount

importance to ensure that the operations of an organization are

not altered solely to allow for the integration of a technology.

While all agreed that it is completely acceptable to utilize

technology to facilitate organizational change, attempts to

conform organizational processes around an EIT are tantamount to

placing the cart in front of the horse.

Technical Alignment Issues

According to the study group, while an IT strategist

assesses the business alignment issues regarding an EIT, he or

she may also find it appropriate to explore the relevant issues

of the EIT as it would relate to compatibility with existing

systems, support and licensing, and standards. The group

classified the following issues into the category of technical

alignment. Each of the technical alignment issues seem most aptly

described as representing issues with compatibility, as encompassed

by the innovation-decision process. All three of the technical

issues grouped by the study participants seem to fit within the

context of the definition of compatibility (Table 2) with needs,

functions, or expectations. Therefore we have indicated this

grounding in our model presented in Figure 2.

Current/Future Uses of Technology

Initially, the analysis of an EIT should focus on the current

and future uses of the EIT within the organization (e.g. issues E and F

from Tables 4, 5, and 6). The CIO of an international paper

products manufacturer explained the analysis of potential uses of

an EIT as a “proactive” activity.

“I have a good handle on our current [inventory] systems. I

know what we are capable of doing and what we are not.

Spec’ing out a new product means I have to know how, when,

where, and what the technology is going to be used for.

Fixes to problems today can create problems tomorrow. I

evaluate all of the technology that we are looking at by

using a life cycle chart. It helps me to find ways to use a

technology and also an idea of a useful timeline for it.”

Several of the group participants suggested that identifying

future uses of an EIT is very difficult because of the nature of

technology evolution, which underscores the importance of

compatibility in reference to the innovation-decision process.

“One problem with anticipating future uses of technologies

is that developments can take place like an explosion but it

usually takes time for support to catch up. Going way back,

look at ISDN. The technology was developed long before the

rules were ever worked out. By the time everybody finally

agreed on the standards, ISDN was an afterthought for

broadband…”

Although the identification of future uses of an EIT requires

some forecasting, most of the executives in the study indicated

that the key to future use assessment of an emerging technology

is the analysis of the wealth of information available from

technology developers.

Technical Performance Aspects

The remaining assessment areas in the EIT evaluation model

are components of technical alignment that have to do with

technical performance and compatibility issues. Technical

performance aspects (e.g. issues C, G, and H from Tables 4, 5, and

6), per the subject group, collectively reflect the myriad of

technology specifications of an EIT. For example, many of the

participating executives were particularly concerned about the

technical security factors associated with new technologies.

However, performance issues also include other technical areas

such as product reliability, particularly with respect to

hardware.

Technical Compatibility with Existing Information Systems

General technical compatibility issues center on the specifics of

integrating an EIT into an organization’s existing IT

infrastructure and encompass compatibility issues that are not

captured elsewhere in the model (e.g. issues N, S, and R from

Tables 4, 5, and 6). Most of the systems compatibility issues

focused on specific areas such as cross-platform connectivity,

software application integration, and deployment.

LIMITATIONS

The current study utilizes a novel qualitative research

methodology as a means to elicit a rich contextual perspective on

a traditional IS research theme. We believe that this type of

research approach is critical for 1) the development of

exploratory analysis that may lead to the development of new

research directions in the existing common body of knowledge and

2) a deeper level of understanding regarding complex unstructured

organizational processes that are not well suited for empirical

analysis. However, we do understand that there are several

limitations in the current study that should be addressed. First,

our sample consisted of a relative homogenous group of

participants. Each subject was an IT executive with a Fortune

1000 firm from the United States. As such, the data collected may

not reflect concerns that may have been expressed by those IT

executives that are not employed by large American companies.

Another limitation is presented by our exclusive use of IT

executives. We understand that strategy development is a cross-

functional activity and involves employees from many different

areas of an organization. Thus, our data may present a

perspective that is inherently limited to an IT point-of-view. In

addition, while our sample was sufficiently large, we did

experience some subject attrition through the rounds of data

collection. This attrition may have also limited the scope and

perspective of the data and subsequent conclusions. Future

research that operationalizes our proposed Emerging Information

Technology Evaluation Model, in part or in its entirety, will

serve to allay the aforementioned limitations and enhance our

understanding of EITs in IT strategy. Similarly, our study is

focused generically on EIT. Although our results can be of use to

scholars and practitioners, future research may wish to examine

specific types or categories of EIT in order to provide more

particular guidance. Using our results as a starting point,

additional research may be able to offer a more refined version

of our Emerging Information Technology Evaluation Model for

specific categories of EIT.

CONCLUSIONS

In today’s global business environment, timeliness is

important. The rapid evolution of technology utilized in the

ordinary course of business leaves little doubt that such

technological advancement is placing great pressure on those who

are charged with the responsibility of developing timely IT

strategies to support their respective businesses. Given the rate

of technological change, the exclusive use of currently available

commercial technologies often creates a rapidly outdated IT

strategy. Because strategic IT planning is a critical component

of success (Heckman, 2003), practitioners should give some

consideration to the appropriateness of EITs as potential pieces

of corporate IT strategy. The sentiments of academicians are

echoed by executives engaged in IT strategy development. Thus, we

engaged in the current research in an attempt to answer the

question “What issues influence a corporate IT executive’s

decision to adopt an EIT into corporate IT strategy?” By

answering this question we hope to have created 1) better

understanding of the innovation-decision process for adopting

EITs as part of corporate IT strategy and 2) a means through

which IT executives engaged in the process of developing IT

strategy can prioritize and evaluate the strategic fit of EITs

for their respective businesses and ultimately develop a more

timely IT strategy.

Our research has identified two overarching factors that

consolidate many issues related to the evaluation and subsequent

adoption of EITs into corporate IT strategy. First, the

participants in the current study identified business alignment

as those issues that are related to the higher order of business

functions such as business process management, customer

relationship management, and supply chain management. The second

overarching factor that the group proposed was technical

alignment. The issues that underlie this factor are much more

granular in nature and relate to the EIT and its potential

compatibly and performance with existing infrastructure. Both

factors identified in this research can be characterized as

specific contextual expressions of relative advantage and

compatibility, as seen through the lens of innovation diffusion

theory.

We consolidated each of the aforementioned factors and

issues into the Emerging Information Technology Evaluation Model.

Using the model presented herein, IT executives may be able to

work toward the creation of a more timely IT strategy by quickly

assessing the potential fit of EITs within a firm-specific

context. As a result, forward-looking IT strategies that

capitalize on early innovation are developed. The net effect is a

longer useful life for an IT strategy.

REFERENCES

Agarwal, R., & Prasad, J. (1997). The role of innovation

characteristics and perceived voluntariness in the

acceptance of information technologies. Decision Sciences, 28(3),

557-582.

Baldwin, E., & Curley, M. (2007). Managing IT innovations for business

value: Practical strategies for IT and business managers. Santa Clara, CA:

Intel Press.

Benamati, J., & Lederer, A. L. (2001). Coping with rapid changes

in IT. Communications of the ACM, 44(8), 83-88.

Brancheau, J. C., Janz, B. D. J., & Wetherbe, J. C. (1996). Key

issues in information systems management: 1994-1995 SIM

Delphi results. MIS Quarterly, 20(2), 121-121.

Brancheau, J. C., & Wetherbe, J. C. (1987). Key issues in

information systems management. MIS Quarterly, 11(1), 23-45.

Brancheau, J. C., & Wetherbe, J. C. (1990). The adoption of

spreadsheet software: Testing innovation diffusion theory in

the context of end-user computing. Information Systems Research,

1(2), 115-143.

Cegielski, C. G., Reithel, B. J., & Rebman, C. M. (2005).

Emerging information technologies: developing a timely IT

strategy. Communications of the ACM, 48(8), 113-117.

Chan, Y. E. (2002). Why haven't we mastered alignment? The

importance of the informal organization structure. MIS

Quarterly Executive, 1(2), 99-112.

Chan, Y. E., & Reich, B. (2007). IT alignment: What have we

learned? Journal of Information Technology, 22(4), 297-315.

Chau, P. Y. K. (1996). An empirical assessment of a modified

technology acceptance model. Journal of Management Information

Systems, 13(2), 185-204.

Christensen, C. M. (1997). The innovator's dilemma: When new technologies

cause great firms to fail. Boston, MA: Harvard Business School

Press.

Christensen, C. M., & Overdorf, M. (2000). Meeting the challenge

of disruptive change. Harvard Business Review, 78(2), 66-76.

Christensen, C. M., & Raynor, M. E. (2003). The innovator's solution:

Creating and sustaining successful growth. Boston, MA: Harvard

Business School Press.

Churchman, C. W., & Schainblatt, A. H. (1965). The researcher and

the manager: A dialectic of implementation. Management

Science, 11(4), 69-87.

Clemons, E. K., & Row, M. C. (1991). Sustaining IT advantage: The

role of structural differences. MIS Quarterly, 15(3), 275-292.

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A

new perspective on learning and innovation. Adminstrative

Science Quarterly, 35(1), 128-152.

Cooper, R. B., & Zmud, R. W. (1990). Information technology

implementation research: A technological diffusion approach.

Management Science, 36(2), 123-139.

Couger, J. D. (1988). Key human resource issues in IS in the

1990s: Views of IS executives versus human resource

executives. Information & Management, 14(4), 161-174.

Dalkey, N., & Helmer, O. (1963). An experimental application of

the Delphi method to the use of experts. Management Science,

9(3), 458-467.

Davenport, T. (2001). The new, new IT strategy. CIO 14(14), 58-59.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use,

and user acceptance of information technology. MIS Quarterly,

13(3), 319-340.

de Haan, J., & Peters, R. (1993). Technology: Toys or tools?

Results of a Dutch Delphi study. Information & Management,

25(6), 283-289.

Delbecq, A. L., Van de Ven, A. H., & Gustafson, D. H. (1975).

Group techniques for program planning: A guide to nominal group and Delphi

processes. Glenview, IL: Scott-Foresman.

Doke, E. R., & Swanson, N. E. (1995). Decision variables for

selecting prototyping in information systems development: A

Delphi study of MIS managers. Information & Management, 29(4),

173-182.

Fichman, R. G., & Kemerer, C. F. (1999). The illusory diffusion

of innovation: An examination of assimilation gaps.

Information Systems Research, 10(3), 255-275.

Fink, D. (1995). IS security issues for the 1990s: Implications

for management. Journal of Systems Management, 46(2), 46-50.

Garcia-Crespo, A., Colomo-Palacios, R., Soto-Acosta, P., & Ruano-

Mayoral, M. (2010). A qualitative study of hard decision

making in managing global software development teams.

Information Systems Management, 27(3), 247-252.

Gordon, M. (2002). How to succeed in strategic planning. CIO,

15(11), 46-47.

Gottschalk, P. (1999). Implementation predictors of strategic

information systems plans. Information & Management, 36(2), 77-

91.

Grover, V., & Segars, A. H. (2005). An empirical evaluation of

stages of strategic information systems planning: patterns

of process design and effectiveness. Information & Management,

42(5), 761-779.

Gutierrez, O. (1989). Experimental techniques for information

requirements analysis. Information & Management, 16(1), 31-43.

Hazen, B. T., Overstreet, R. E., & Cegielski, C. (2012). Supply

chain innovation diffusion: Going beyond adoption.

International Journal of Logistics Management, 23(1).

Heckman, R. (2003). Strategic information technology planning and

the line manager's role. Information Systems Management, 20(4),

16-21.

Katz, E. (1961). The social itinerary of social change: Two

studies on the diffusion of innovation. Human Organization,

20(2), 70-82.

Katz, E., & Levin, M. (1959). Traditions of research on the diffusion of

innovation. Paper presented at the American Sociological

Association, Chicago, IL.

Lai, V. S., Guynes, J. L., & Bordoloi, B. (1993). ISDN: Adoption

and diffusion issues. Information Systems Management, 10(4), 46-

52.

Langeland, L., & Iden, J. (2010). Setting the stage for a

successful ITIL adoption: A Delphi study of IT Experts in

the Norwegian Armed Forces. Information Systems Management,

27(2), 103-112.

Larsen, T. J. (1993). Middle managers' contribution to

implemented information technology innovation. Journal of

Management Information Systems, 10(2), 155-176.

Loukis, E., Spinellis, D., & Katsigianis, A. (2011). Barriers to

the adoption of B2B e-marketplaces by large enterprises:

Lessons learned from the Hellenic aerospace industry.

Information Systems Management, 28(2), 130-146.

Low, L. (2001). Co-evolving business and IT strategies. CIO

perspectives @ 25—Different voices, shared visions: Getting

alignment right., from

www2.cio.com/conferences/october2001/coverage33_content.html

.

Lucas Jr, H. C., & Goh, J. M. (2009). Disruptive technology: How

Kodak missed the digital photography revolution. The Journal of

Strategic Information Systems, 18(1), 46-55.

Luftman, J., & Kempaiah, R. (2008). Key issues for IT executives.

MIS Quarterly Executive, 7(2), 99-112.

Luftman, J., Kempaiah, R., & Nash, E. (2006). Key issues for IT

executives 2005. MIS Quarterly Executive, 5(2), 27-45.

Malhotra, M. K., Steele, D. C., & Grover, V. (1994). Important

strategic and tactical manufacturing issues in the 1990s.

Decision Sciences, 25(2), 189-214.

McFadzean, E., Ezingeard, J.-N., & Birchall, D. (2011).

Information assurance and corporate strategy: A Delphi study

of choices, challenges, and developments for the future.

Information Systems Management, 28(2), 102-129.

Moore, G., & Benbasat, I. (1996). Integrating diffusion of

innovations and theory of reasoned action models to predict

utilization of information technology by end-users. In K.

Kautz & J. Pries-Heje (Eds.), Diffusion and Adoption of Information

Technology. London: Chapman and Hall.

Muller, R. M., Linders, S., & Pires, L. F. (2010). Business

intelligence and service-oriented architecture: A Delphi

study. Information Systems Management, 27(2), 168-187.

Nambisan, S., Agarwal, R., & Tanniru, M. (1999). Organizational

mechanisms for enhancing user innovation in information

technology. MIS Quarterly, 23(3), 365-395.

Newkirk, H. E., Lederer, A. L., & Srinivasan, C. (2003).

Strategic information systems planning: too little or too

much? The Journal of Strategic Information Systems, 12(3), 201-228.

Niederman, F., & Brancheu, J. C. (1991). Information systems

management issues for the 1990s. MIS Quarterly, 15(4), 475-500.

Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a

research tool: An example, design considerations and

applications. Information & Management, 42(1), 15-29.

O'Reilly, C. A. & Tushman, M. L. (2004). The ambidextrous

organization. Harvard Business Review, 82(3), 1-9.

Paliwoda, S. J. (1983). Predicting the future using Delphi.

Management Decision, 21(1), 31-38.

Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior

performance. New York, NY: Free Press.

Premkumar, G., & King, W. R. (1994). The evaluation of strategic

information system planning. Information & Management, 26(6),

327-340.

Premkumar, G., Ramamurthy, K., & Nilakanta, S. (1994).

Implementation of electronic data interchange: An innovation

diffusion perspective. Journal of Management Information Systems,

11(2), 157-186.

Riggs, W. E. (1983). The Delphi technique: An experimental

evaluation. Technological Forecasting and Social Change, 23(1), 89-94.

Rogers, E. (2003). Diffusion of innovations (5th ed. ed.). New York,

NY: Free Press.

Rohrbaugh, J. (1979). Improving the quality of group judgment:

Social judgment analysis and the Delphi technique.

Organizational Behavior and Human Performance, 24(1), 73-92.

Segars, A. H., & Grover, V. (1998). Strategic information systems

planning success: An investigation of the construct and its

measurement. MIS Quarterly, 22(2), 139-163.

Segars, A. H., Grover, V., & Teng, J. T. C. (1998). Strategic

information systems planning: Planning system dimensions,

internal coalignment, and implications for planning

effectiveness. Decision Sciences, 29(2), 303-341.

Siegel, S. (1956). Nonparametric statistics for the behavioral sciences. New

York: McGraw-Hill Book Company.

Taylor, R. E., & Meinhardt, D. J. (1985). Defining computer

information needs for small business: A Delphi method.

Journal of Small Business Management, 23(2), 3-9.

Tornatzky, L., & Klein, K. (1982). Innovation characteristics and

innovation adoption-implementation: A meta-analysis of

findings. IEEE Transactions on Engineering Management, 29(1), 28-45.

Tushman, M. L. & O'Reilly, C. A. (1996). Ambidextrous

organizations: Managing evolutionary and revolutionary

change. California Management Review, 38(4), 8-30.

Varon, E. (2000). Be nimble, be quick. CIO, 13(17), 84-85.

Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to

ask for directions? Gender, social influence, and their role

in technology acceptance and usage behavior. MIS Quarterly,

24(1), 115-139.

TABLE 2Adopter evaluation criteria for an innovation

Criteria Definition Impact on Adoption Decision

Relative Advantage

The degree to which anindividual perceives aninnovation to be better than apreviously-accepted idea.

Innovations perceived to havea greater degree of relativeadvantage are more likely tobe adopted.

Compatibility

Perception of an innovation asconsistent with existing norms,values, experiences, and needsof the potential adopter.

Innovations that conflict withan existing social system areless likely to be adopted.

TABLE 3Issues identified in round one

1. Cost of the technology to deploy2. Security of the technology3. Integration of the technology with organizations outside the firm4. Acceptance of the technology by end-users5. Ability to support the technology with current IT staff6. Acceptance of technology by customers/clients7. Cost to maintain the technology8. Current uses for the technology9. Perceived future uses of the technology10. Ability to gain competitive advantage through the use of

technology11. Reliability of the technology12. Commercial access to the technology13. Standardization of the technology14. Compatibility of technology with current business operations15. Ability to sustain competitive advantage using technology16. Training for users of technology

TABLE 4Ranking of issues from rounds 2,3, & 4

Round 2 Round 3 Round 4

Issues Rank

MeanRank

% ofParticipantsWho Rankedthis Issue

Equal to theGroup Rank

Rank

MeanRank

% ofParticipantsWho Rankedthis Issue

Equal to theGroup Rank

Rank

MeanRank

Participants

Equal to the

A Ability to gain competitive advantage usingtechnology 1 3.96 48.48% 1 3.40 60.61% 1 2.80

B Ability to sustain competitive advantage using technology 2 4.12 39.39% 2 4.20 48.48% 2 3.54

C Security of the technology 3 4.18 33.33% 4 4.20 51.52% 3 4.14

D Acceptance of technology by customers/clients 4 4.24 36.36% 3 4.00 36.36% 4 3.66

E Current uses for the technology 5 4.32 48.48% 5 6.00 63.64% 5 6.04F Perceived future uses of the technology 6 5.48 51.52% 6 7.75 51.52% 6 7.78

G Reliability of the technology 7 5.63 30.30% 7 9.50 57.58% 7 10.08

H Performance aspects of technology 8 5.95 33.33% 8 9.75 63.64% 8 10.48

I Compatibility of technology with current business operations 9 6.03 27.27% 9 10.2

5 45.45% 9 10.38

J Compatibility of technology with future business operations 10 6.15 39.39% 10 10.5

0 63.64% 10 10.55

TABLE 5A comparison of internal confirmatory survey rankings to final

Delphi round rankings

Issues

InternalConfirmatory SurveyIssue Rank

Round4 FinalRank

A Ability to gain competitive advantage through the use of technology

1

B Ability to sustain competitive advantage using technology

2

C Security of the technology 5D Acceptance of technology by customers/clients 3E Current uses for the technology 4F Perceived future uses of the technology 7G Reliability of the technology 6H Performance aspects of technology 8I Compatibility of technology with current business operations

11

J Compatibility of technology with future business operations

9

K Cost to maintain the technology 13L Integration of the technology with organizations 10

TABLE 6A comparison of external confirmatory survey rankings to internal

confirmatory rankings

Issue

ExternalSurveyRank

InternalSurvey

A Ability to gain competitive advantage through the use of technology

1

B Ability to sustain competitive advantage using technology

2

C Security of the technology 6D Acceptance of technology by customers/clients 3E Current uses for the technology 4F Perceived future uses of the technology 5G Reliability of the technology 10H Performance aspects of technology 11I Compatibility of technology with current business operations

12

J Compatibility of technology with future business operations

7

K Cost to maintain the technology 13

TABLE 1The five levels of adopter understanding regarding an innovation

Level The point in the adoption decision process when an individual……learns of the existence of an innovation and accumulates some