six types of it-business strategic alignment

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EMPIRICAL RESEARCH

Six types of IT-business strategic alignment:

an investigation of the constructs and their

measurement

Jennifer E. Gerow1,Jason Bennett Thatcher2 and

Varun Grover2

1Economics & Business Department, Virginia

Military Institute, U.S.A.; 2Department of

Management, Clemson University, U.S.A.

Correspondence: Jennifer E. Gerow,

Virginia Military Institute,

345 Scott Shipp Hall, Lexington, VA 24450,

U.S.A.

Tel: +540-464-7278;

E-mail:[email protected]

Received: 24 October 2012

Revised: 07 February 2014

Accepted: 28 February 2014

AbstractTop management has been concerned with IT-business strategic alignment

(hereafter referred to as alignment) for the past 30 years. Consequently,

alignment researchers have developed many models to explain how alignmentgenerates value for firms. However, these models use inconsistent definitions

and measures of alignment, which has led to conflicting results and has

potentially inhibited the progress of research on this critical topic. This paper

emphasizes the importance of demarcating the six alignment types that are

sometimes confused in the literature into a single, unified model. It also reports

on the development of definitions and measures of these six types of alignment

including alignment between IT and business strategies (i.e., intellectual align-

ment), between IT and business infrastructures and processes (i.e., operational

alignment), and across these two domains such that strategies are linked with

infrastructures and processes (i.e., four types of cross-domain alignment).

Analyzing survey data collected from 140 Chief Information Officers, we found

each measure possesses desirable psychometric properties. Implications for

theory and practice are discussed.European Journal of Information Systemsadvance online publication, 20 May 2014;

doi:10.1057/ejis.2014.6

Keywords: alignment; IT-business strategic alignment; intellectual; operational;cross-domain

IntroductionIT-business strategic alignment has been studied extensively over the last

three decades. The primary focus of this literature has been assessing

whether, and how, aligning the business and IT generates value for the rm

(Celuch et al, 2007; Chan & Reich, 2007). On the one hand, cultivating

alignment between business and IT strategies could increase protabilityand generate a sustainable competitive advantage (Kearns & Lederer, 2003).

On the other hand, failure to align could result in wasted resources and

failed IT initiatives leading to adverse nancial and organizational outcomes

(Chenet al, 2010;Ravishankaret al, 2011).

One reason for this persistent concern with alignment is that IT executives

continue to identify alignment as a top-three management issue (Luftman &

Ben-Zvi, 2011). This interest is manifest in Chief Information Ofcer (CIO)

discussion boards on the topic, such as LinkedIns CIO Network group,which continually revisit alignment and label it as a major challenge that

keeps CIOs worried all the time (Shah, 2012). Due to IT executives

concerns, consulting groups such as Gartner continue surveying organiza-

tions about alignment (e.g., McKendrick, 2011), and technology groups blog

European Journal of Information Systems (2014), 127 2014 Operational Research Society Ltd. All rights reserved 0960-085X/14

www.palgrave-journals.com/ejis/
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about how to align IT with business needs and processes (e.

g.,Nituch, 2012;Ozcan, 2012).

Consistent with the practitioner literature, academics

frequently emphasize alignments positive aspects in the-

oretical frameworks and empirical research. In general,

alignment research focuses on the improvements to rm

performance (e.g., Cragg et al, 2002; Rivard et al, 2006)such as increased sales revenue (e.g., Kunnathur & Shi,2001; Kearns, 2005), improving operational efciency

(e.g., Oh & Pinsonneault, 2007), cost reductions (e.g.,

Chang etal, 2008;Johnson & Lederer, 2010), and enhance-

ments to customer value (e.g., Celuch et al, 2007). Research

suggests aligned rms more frequently leverage IT to

support overall business objectives and exploit opportu-

nities in the market such that they create a sustainable

competitive advantage and achieve supranormal prots

(e.g.,Avisonet al, 2004;Cumpset al, 2009).

While the preponderance of research emphasizes a posi-tive relationship between alignment and performance,

some research has found aligned rms report no improve-ment, or even a decline, in performance (i.e., an alignment

paradox) (e.g., Palmer & Markus, 2000; Tallon, 2003). In

these studies, researchers argue alignment creates a rigidity

that leads to stagnation, strategic inexibility, and a com-

petitive disadvantage by not responding to environmental

change (e.g., Benbya & McKelvey, 2006; Tallon, 2007a;

Chenet al, 2010). This rigidity may be caused by the time-

consuming, costly, and formal nature of alignment that

prevents quick responses to changing market conditions

(Tallon, 2007a;Chenet al, 2010). In summary, evidence of

the relationship between alignment and rm performancehas been inconsistent where most emphasizes a positive

alignmentperformance relationship but other researchindicates there may be a negative relationship.

Difculties in detecting consistent effects of alignment

may result from comparing studies that assess different

theoretical forms of alignment and utilize different align-

ment measures (Avison et al, 2004; Chen et al, 2010).

Conceptually, research has failed to converge on a consis-

tent denition of alignment (Preston & Karahanna, 2009).

For example, some indicate alignment is the linking of ITand business strategies (e.g.,Tan & Gallupe, 2006). Others

dene alignment as the t between IT and business infra-

structures and processes (e.g.,Cragget al, 2007). Still other

researchers refer to alignment as the simultaneous integra-

tion of business strategy, IT strategy, business infrastruc-

ture, and IT infrastructure (e.g., Porra et al, 2005). This

inconsistency in alignments denition is also present in

the practitioner literature. For example, practitioners may

discuss the alignment of architecture practice and deci-

sion making information (van Geel, 2011) or they might

indicateIT systems/applicationneeds to be aligned withbusiness needs (Bicocchi, 2013). These distinct concep-

tualizations of alignment imply there may be differenttypes of alignment, as originally suggested by Henderson

& Venkatraman (1993, 1999).

Inconsistent theoretical denitions of the alignment

construct present several problems. First, absent a clear,

well-specied domain for the construct, it is impossible to

assess the adequacy of alignment measures (Nunnally &

Bernstein, 1994; MacKenzie et al, 2011). Second, such

ambiguity leads to confusion about what must be included

and excluded within the domain of alignment and how to

differentiate among types of alignment (MacKenzieet al,

2011). Finally, the indicators may be decient or contami-nated since alignment is not adequately dened in a waythat differentiates it from other constructs (MacKenzie

et al, 2011).

Consequently, it is not surprising that empirical align-

ment research has failed to converge upon a set of estab-

lished scales. Of the over 175 published alignment studies

in journals, conferences, and dissertations, more than 115

employed some type of questionnaire (citation removed

for blind review). Yet, of these papers, just over 25% used

established scales like Venkatramans (1985) STROBE

(STRategic Orientation of Business Enterprises) and/orChan et als (1997) STROEPIS (STRategic Orientation of

the Existing Portfolio of Information Systems) (e.g.,Chanet al, 2006), Luftmans (2000) Strategic Alignment

Maturity Model (e.g., Khaiata & Zualkernan, 2009),

Segars & Grovers (1998) alignment items (e.g., Yayla &

Hu, 2009), or Miles & Snows (1978) categories (e.g.,

Raymond & Bergeron, 2008); none of these scales captures

the various types of alignment. The application of incon-

sistent and incomplete alignment measures presents

many problems for theory and applied research. First, if a

measure inadequately operationalizes alignment, research-

ers could derive invalid conclusions about the relation-

ships with other constructs, and the meaning of thetheory itself could be altered (Oh & Pinsonneault, 2007;

MacKenzie et al, 2011). Second, differing operationaliza-tions of alignment create inconsistent results, which cause

confusion and make it difcult for researchers to have

condence as they build upon existing research and to

compare results across studies (Dennis et al, 2001;

McKnightet al, 2002). Finally, inconsistent research nd-

ings make it more difcult for practitioners to condently

apply these ndings in a real-world setting (Denniset al, 2001).

Taken together, this review suggests the literatures fail-

ure to converge on a shared understanding of alignment or

its implications (Bergeron et al, 2001) reect theoreticaland empirical inconsistencies in denitions and measures

of alignment. Building a cumulative research tradition

requires two advances in the literature. First, we need to

further rene theoretical denitions of the alignment

types that clearly state what is, and is not, included within

each type (Chan & Reich, 2007; MacKenzie et al, 2011).

Second, we need to develop consistent measures for each

alignment type in order to ensure rigorous, comparable

investigations of alignment (Bergeron et al, 2004; Oh &

Pinsonneault, 2007). Therefore, the objective of this studyis to build upon an existing alignment framework to dene,

construct, and statistically evaluate operational measures of the

different types of alignment and their relationship with nan-

cial performance.

Six types of IT-business strategic alignment Jennifer E. Gerowet al2

European Journal of Information Systems


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To realize this objective, we draw on the Strategic

Alignment Model (SAM) (Henderson & Venkatraman,

1993, 1999) as a framework to dene and assess the

different types of alignment. Next, we discuss existing

measures and our instrument development process. We

then discuss our survey and analysis of the results. Finally,

we present the limitations of our study, discuss ourconclusions, and present implications for researchers andpractitioners.

Defining types of alignmentAlignment is the degree to which the needs, demands,

goals, objectives, and/or structures of one component are

consistent with the needs, demands, goals, objectives,

and/or structures of another component (Nadler &

Tushman, 1983, p. 119). In the IT strategy literature,

researchers suggest realizing the full potential of IT

requires aligning some or all of four business and IT

components business strategy, IT strategy, businessinfrastructure and processes, and IT infrastructure and

processes. Hence, IT-business strategic alignment refers to

the appropriate and timely t between two or more of

these components such that management of the business

and IT remain in harmony (Luftman & Brier, 1999;Chan

& Reich, 2007).

On the basis of a review of the IT strategy approaches

to alignment, Henderson & Venkatramans (1993) SAM

describes how rms aligning these four fundamentalcomponents of strategic choice help them to realize the

full potential of IT. In particular, SAM suggests rms need

to integrate business and IT components at three levels as

shown in Figure 1: strategies (i.e., external integration

or intellectual alignment), infrastructures (i.e., internal

integration or operational alignment), and strategies and

infrastructures (i.e., cross-domain integration). These

levels are discussed in the following sections.

Intellectual alignmentThe rst discussion of alignment between business and IT

components focused on the strategic, external levels (King,

1978). Kings denition focused on a one-way link where

IT strategy was designed to support the business strategy;

specically, he dened alignment as the link between the

organizations strategy set to an MIS strategy set

(p. 27). Future researchers rened Kings denition ofstrategy sets by including missions, objectives, and stra-

tegies(Pyburn, 1983, p. 3), plans/planning (Henderson &

Sifonis, 1988;Kearns & Lederer, 2003;Lee et al, 2004;Tan& Gallupe, 2006), and orientation (Chan et al, 1997; Chen,

2010). Some researchers then emphasized a two-way link

between business and IT strategies where IT strategy could

also inuence the business strategy (e.g., Teo & King, 1997;

Piccoli & Ives, 2005).

Similar to the expansion of the phrase strategy sets, the

term link in Kings original denition has grown more

nuanced since the 1970s. Some of the more recently

introduced terms that describe this link (Luftman &Ben-Zvi, 2010, p. 51) include alignment(e.g.,Sabherwal

Figure 1 Henderson & Venkatramans (1999, p. 476) strategic alignment model.

Six types of IT-business strategic alignment Jennifer E. Gerowet al 3



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& Kirs, 1994;Chanet al, 1997;Tallonet al, 2000;Kearns &

Lederer, 2003), interrelated (Tan & Gallupe, 2006), andharmony (Chen, 2010). Taken together, the focus of

Kings (and other similar researchers) work focuses on

bringing IT and business strategies into agreement. This

type of alignment is referred to as strategic or intellectual

alignment(Reich & Benbasat, 1996, 2000;Chan & Reich,2007), which is dened as the link between businessstrategy and I/T strategy reecting the external compo-

nents(Henderson & Venkatraman, 1999, p. 476).

Operational alignmentIn the early 1990s, IT strategy research expanded in scope

to consider the corresponding internal domainsof align-

ment (Henderson & Venkatraman, 1999, p. 476), which

refers to the alignment between the business and IT

infrastructures and processes (Lee & Leifer, 1992).Henderson & Venkatraman (1999) considered three

components as illustrated in Figure 1: administrativeinfrastructure/architectures, processes, and skills. Morespecically, these include policies (e.g., employee hiring

or security), procedures (e.g., customer service or work

ow), personnel (e.g., existing employees), systems (e.g.,

data center operations), structure (e.g., centralization vs

decentralization), and activities (e.g., product/IT develop-

ment) (Henderson & Venkatraman, 1999; Barua et al,

2004;Heim & Peng, 2010). Therefore, this alignment type

depends on managements ability to integrate the infra-

structures and processes of the business and IT rather than

aligning distinct sets of strategies.Researchers further renedLee and Leifers (1992)refer-

ence to alignment through the 2010s by including termssuch as coordinating(e.g.,Brown, 1999), t(e.g.,Barua

et al, 2004; Thrasher et al, 2006; Cragg et al, 2007),

integration (e.g., Kang et al, 2008; Lee et al, 2008), and

extent of adoption (e.g., Heim & Peng, 2010). Across

papers, research examined similar aspects of operationalalignment which refers to the corresponding internal

domains, namely, the link between organizational infra-

structure and processes and I/S infrastructure and pro-cesses(Henderson & Venkatraman, 1999, p. 476).

Cross-domain alignmentWhile intellectual and operational alignment examine

linkages at the same level (i.e., strategy to strategy or

infrastructure to infrastructure, respectively), cross-

domain alignment research addresses a more holistic

view of alignment by considering the strategy and infra-

structure components simultaneously (Henderson &

Venkatraman, 1999, p. 477;Sabherwalet al, 2001, p. 195).

This is important since business strategy changes may

require alignment of the business/IT infrastructure andprocesses (e.g.,Main & Short, 1989;Broadbentet al, 1999)

or IT strategy changes may require alignment of the

business/IT infrastructure and processes (e.g., Jordan &

Tricker, 1995).

Henderson & Venkatraman (1993) elaborated on four

unique cross-domain combinations of strategy and infra-

structure: strategy execution, technology transformation,

competitive potential, and service level. Strategy execution

is where the business strategy impacts the IT infrastructure

(business strategy-to-IT infrastructure cross-domain align-

ment) but is constrained by the business infrastructure(business alignment). Technology transformation is wherethe IT infrastructure is affected by the business strategy

(business strategy-to-IT infrastructure cross-domain align-

ment) but is constrained by the IT strategy (IT alignment).

Competitive potential is where the business infrastructure

is affected by the IT strategy (IT strategy-to-business infra-

structure cross-domain alignment) but is constrained by

the business strategy (business alignment). Service level is

where the IT strategy impacts the business infrastructure

(IT strategy-to-business infrastructure cross-domain align-

ment) but is constrained by the IT infrastructure (ITalignment).

Taken together, cross-domain IT strategy research sug-gests that some rms may pursue alignment of the total

organization (Linget al, 2009) such that there is a simulta-

neous t (e.g., Chan & Reich, 2007; Chen et al, 2010),

creation (e.g.,Wijnhovenet al, 2006;Huang & Hu, 2007),

harmony (e.g., Luftman et al, 1993), collaboration

(e.g., Baets, 1996), or integration (e.g., van der Zee & de

Jong, 1999) between business strategy, IT strategy, busi-

ness infrastructures, and IT infrastructures (e.g.,Karimi &

Konsynski, 1991; Porra et al, 2005). Henderson &

Venkatraman (1993) refer to this as a recognition of

multivariate relationships (p. 477) or cross-domain align-

ment, which is the degree of t and integration among

business strategy, IT strategy, business infrastructure, andIT infrastructure(Henderson & Venkatraman, 1999;Chan

& Reich, 2007, p. 300).

Using SAM as a frameworkWhile empirical studies often reference SAM when oper-

ationalizing alignment (Ravishankar et al, 2011), many

researchers view SAM as simply a high-level conceptualmap that is weak and has no practical real-world applica-

tion (e.g.,Hu & Huang, 2006;Luftmanet al, 2008). While

drawing on assumptions of SAM (Huang & Lin, 2006;Chan & Reich, 2007, p. 303), researchers have offered

distinct operationalizations that extend SAM to many

different organizational contexts (e.g.,Broadbent & Weill,

1993; Luftman et al, 1993; Baets, 1996). As a result, the

alignment literature is littered with dozens of denitions

for the types of alignment, potentially compromising the

original organizing power of SAM.

Since case study research has shown SAM accurately

reects alignment concepts used in modern businesses

(Cooper et al, 2000; Avison et al, 2004), we seek todemonstrate SAM is empirically testable as well as pos-

sesses practical relevance. In particular, Henderson &

Venkatraman (1993) were among the rst to discuss

IT alignment as a continuous and dynamic process




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necessary for rms to adapt and change in response

to the environment (consistent with Mintzbergs (1994)

conceptualization of planning as a learning process). Their

conceptualization of cross-domain alignment directed

attention to the simultaneous development of IT strategy

with the business strategy. Since then, contemporary

researchers have emphasized this dynamic view whendiscussing alignment as a moving target (Avison et al,2004, p. 229;Ravishankaret al, 2011, p. 40) that needs to

be constantly renewed and adjusted to respond to an ever-

changing and hypercompetitive business environment

(Baker et al, 2011; McLaren et al, 2011; Guillemette &

Pare, 2012).

SAM also remains relevant to practice because it provides

rms guidance for why a exible level of alignment

enables them to take advantage of opportunities afforded

by dynamic marketplaces (Avisonet al, 2004;Ravishankaret al, 2011). To create this exibility, academics suggestthat alignment between business strategies, IT strategies,

business process, and IT processes should be coevolution-ary and simultaneous rather than rigid and sequential

(e.g., Ke & Alexandre-Leclair, 2009; Baker et al, 2011).

Two main frameworks support the importance ofexibil-

ity: the Dynamic Capabilities Framework and the Coevo-

lutionary Perspective. First, dynamic capability refers tothe rms ability to integrate, build, and recongure

internal and external competencies to address rapidly

changing environments [in order] to achieve new and

innovative forms of competitive advantage (Teece et al,

1997, p. 516). In particular, this perspective emphasizes

capabilities, such as alignment, can be developed inten-

tionally over time rather than acquired (Bakeret al, 2011).

As such, alignment can be used to maintain exibility in

the rms business and IT strategies and processes in

response to environmental changes (Schwarzet al, 2010;Bakeret al, 2011).

Second, coevolution is the joint outcome of managerialintentionality, environment, and institutional effects

[such that] change may occur in all interacting popula-

tions of organizations (Lewin & Volberda, 1999, p. 526).

In particular, the coevolution perspective emphasizes busi-

ness and IT must continually adjust to each other, result-

ing in alignment that is often complex and undened

(Peppard & Breu, 2003;Zhang et al, 2011). As such, rms

address mist between IT and business by continually

rening their business and IT strategies and processes

(Kearns & Lederer, 2003; Oh & Pinsonneault, 2007;

Tiwana & Konsynski, 2010).In summary, the Dynamic Capabilities Framework and

the Coevolutionary Perspective suggest rms need to con-stantly adjust to the changing business environment bydeveloping the capability to adapt and match strategies to

processes. By doing so, rms may realize an alignment

over time(Peppard & Breu, 2003, p. 745;Lee et al, 2008).

SAM works well with these two perspectives because SAM

(a) differentiates the external (i.e., strategy) and internal

(i.e., infrastructure) choices rms need to make and (b)

emphasizes these choices change over time and necessi-

tate subsequent responses in other domains such thatalignment is not an event but a process of continuous

Figure 2 Measure development process.

Source:Moore & Benbasat (1991).




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adaptation and change (Henderson & Venkatraman,

1999, p. 473). By distinguishing the different types of

alignment and deriving measures consistent with their

conceptualization, we can determine how dynamic align-

ment may affect nancial performance (Henderson &

Venkatraman, 1999; Tallon et al, 2000) and can strengthen

understanding of alignment and deepen understanding ofalignments impact on nancial performance (see Donget al, 2009for a similar approach), where nancial perfor-

mance is dened as the rms ability to gain competitive

advantage and therefore higher prots or stock values

(Hitt & Brynjolfsson, 1996, p. 123).

Instrument development processTo develop measures of the distinct forms of alignmentidentied in SAM, we implemented the three broad instru-

ment development stages (i.e., Item Creation, Scale Devel-

opment, and Instrument Testing) employed by Moore &

Benbasat (1991) and detailed byMacKenzie et al (2011).Figure 2 is an illustration of the specic process we

followed.

After dening our constructs, we began by creating

pools of items for the types of alignment by identifying

potentially acceptable items from existing scales as well as

creating new items that appeared to t the construct

denitions. For intellectual alignment, we adapted items

from Segars & Grover (1998) as these items have been

commonly used by other researchers studying the align-ment between IT and business strategies. For operational

alignment, we adapted items from Lee et al (2008) and

Hong & Kim (2002). We also adapted some Segars &

Grover (1998) items to apply to alignment between ITand business infrastructures and processes. For cross-

domain alignment, we adapted items from Hung et al

(2010), Gupta et al (1997), Sanchez Ortiz (2003) and

adapted the intellectual and operational alignment items

to apply to the alignment of strategies and infrastructure/

processes for all four types of cross-domain alignment.

We undertook four unique rounds of Q-sorts since they

are considered one of the best methods to assess content

validity for formative constructs (Petter et al, 2007,p. 639). Following the procedure of Moore & Benbasat

(1991) for sorting newly created and existing items intocommon pool of items that should be associated with thevarious constructs, we used fresh participants for each

round: 7 academic judges and 3 practitioner judges for

the rst round, 4 academic judges for the second round, 29

undergraduate business students for the third round, and

5 Management Ph.D. students and 3 Management Ph.D.s

as judges for the fourth round. The judges in each round

were asked to sort the randomized items based on the

construct denitions we provided but were given liberty to

either not sort the item or choose an other or n/a

classication. If the judges consistently placed an itemwithin a particular construct pool, it was considered to

demonstrate content validity (Moore & Benbasat, 1991;Churchill, 1979). Potentially, this indicates the items

demonstrate convergent validity with the related con-

struct and discriminant validity with the other two con-

structs. On the basis of participants feedback and their

apparent difculty in distinguishing the different types of

alignment, we updated the denition of cross-domain

alignment used in our stem (i.e., item lead-inphrase likeIn the following items, think about ) to differentiate it

from intellectual and operational alignment and added

denitions for business and IT alignment (other compo-

nents of cross-domain alignment) as shown inTable 1.After each round, we reworded items to make them

consistent with contemporary language used to describetechnology (e.g., IS was changed to IT in theSegars &

Grover, 1998items) as well as to make them clearer and

more internally consistent. These changes resulted in

items that differed substantially from the initial pool of

Table 1 Alignment denitions

Construct New definition

Business alignment Refers to the level of alignment in theBUSINESSand is the degree to which the higher level,

externally focused business strategies are aligned with the lower level, internally focused

business infrastructure and processes

Cross-domain alignment (business strategy to ITinfrastructure and processes)

Refers to all aspects ofBRIDGINGhigher level, externally focused strategies with lower level,internally focused infrastructure and processes. This includes how the business strategy aligns

with the IT infrastructure and processes

Cross-domain alignment (IT strategy to business

infrastructure and processes)

Refers to all aspects ofBRIDGINGhigher level, externally focused strategies with lower level,

internally focused infrastructure and processes. This includes how the IT strategy aligns with

the business infrastructure and processes

Intellectual alignment Refers to the higher level, externally focusedSTRATEGIClevel of alignment and deals with

how business strategy supports and is supported by the IT strategy

IT alignment Refers to the level of alignment inINFORMATION TECHNOLOGY (IT)and is the degree to

which the higher level, externally focused IT strategies are aligned with the lower level,

internally focused IT infrastructure and processes

Operational alignment Refers to the lower level, internally focused OPERATIONALlevel of alignment and deals with

how the business infrastructure and processes align with the IT infrastructure and processes




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items drawn from the literature (see the Appendix for the

nal set of items). The factor structure (Moore &

Benbasat, 1991, p. 201) that emerged from the fourth and

nal round demonstrated high agreement among the

judges (based on a similar high established byMoore &

Benbasat, 1991) (seeTable 2). Hence, we concluded that

the development process resulted in scales that demon-strated content validity for all three main alignment typeswith high potential to receive very good reliability

coefcients.

Item-creation strategiesAs part of our item creation, we analyzed and compared

two approaches to item creation: domain sampling

(Nunnally, 1967, p. 175) and cognitive processing (Jobe,

2003, p. 219). Domain sampling is the process of dening a

construct (i.e., establishing the domain through a concep-

tual denition) and then selecting or generating a few

individual items that will faithfully capture the established

domain (Nunnally, 1967; Davis, 1989). This is the approachused to create the alignment scales found in the literature(e.g.,Segars & Grover, 1998items presented on page 146,

where they created eight items to capture different aspects

of their construct Planning Alignment). This processfavors parsimony (the optimal number of itemsvsmaximal

accuracy), in that the fewest number of items should be

included to validly represent the domain and achieve an

acceptable reliability (Joshi, 1989;Littleet al, 1999).

By way of contrast, the cognitive processing approach

conceptualizes the thought-process between the item pre-

sentation and response through probing and cognitive

interviewing of potential participants (Jobe, 2003;

Karabenick et al, 2007). Unlike domain sampling where

researchers delineate the construct domain and thencreate a minimum set of representative, individual itemsto capture that domain (Joshi, 1989), cognitive processing

takes a gestalt approach by more closely targeting the

perceptions of potential participants and attempting to

capture elements of their environment in the items that

are used to measure the construct of interest (Karabenick

et al, 2007). By including clear construct denitions,

instructions, and examples in the stem to ensure the

options are properly understood, researchers can improve

their measurement validity and reliability with fewer, and

more similar, individual items. For example, Karabenicket al(2007)favored the item How certain are you that you

could gure out how to do a math assignment on yourown if you miss class? over How condent are you that

you can do even the hardest work in math class?because

the latter only elicited content on personal math ability

while the former captured the desired continued effort in

math.

The differences between these two approaches are

summarized inTable 3.

Table 2 Item placement ratiosfourth sorting round

Designed construct pool Participants construct pool Total Target %

IA OA CA CAb BA ITA n/a

Intellectual alignment (IA) 32 3 1 40 80%

Operational alignment (OA) 47 48 97.9%

Cross-domain alignment business Strategy to IT Infrastructure and Processes (CA) 5 33 2 40 82.5%

Cross-domain alignment IT strategy to business infrastructure and processes (CAb) 1 1 34 4 40 85%

Business alignment (BA) 1 21 24 87.5%

IT alignment (ITA) 24 24 100%

Total Item Placements = the total number of items sorted (the sum of the Total column) 216

Hits = adding all the diagonal values (i.e., sorting of the items into the designed construct pools) 191

Overall Hit Ratio = dividing the Hits (correct sorting) by the Total Item Placements (total items sorted) 88.4%

Table 3 Domain samplingvscognitive processing item-creation approaches

(Littleet al, 1999;Jobe, 2003;Lewiset al, 2005)Domain sampling Cognitive processing

Stema richness Parsimonious (e.g., one- to three-word phrases) Rich, complex, and detailed

Item meaning Imprecise where respondent interpretations may be different

based on their situation

Precise definitions and examples provided in the stem to

guide respondent thinking

Item derivation Subjective creation by the researcher Examples from practice

Item complexity Thoroughly capture the construct Simplif ied because complexity is captured in the stem

Number of items Many are needed to capture all the dimensions of a broad

construct

Few because complexity is captured in the stem

aStems are the theoretical sub-parts of dimensions and are often extracted from a combination of literature review and experience surveys in MIS

studies (Lewis et al, 2005, p. 390); in this case, it refers to the introductory verbiage that applies to the items that follow (e.g.,In the following items, think

about).




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Our pre-test of items adapted from the alignment litera-

ture (i.e., domain-sampling items) with a sample of 35

CIOs revealed cross-loadings in the conrmatory factor

analysis (i.e., convergent validity problems) and cross-

factor correlations in the correlation matrix (i.e., discrimi-

nant validity problems). Given these validity problems,

we chose to discard the adapted items created with adomain-sampling approach and elected to adopt thecognitive processing approach to create new items for

two reasons (following the approach ofKarabenick et al,

2007). First, alignment is a broad construct with many

nuances. For example, operational and cross-domain

alignment include all the rms operations, infrastruc-

tures, and processes. Since this includes a wide range of

activities (e.g., hiring, software purchases, centralization),

it is difcult to capture all the necessary components

consistently and parsimoniously in a few items. Therefore,

we created detailed stems that described specic activitiesfor the respondents to consider (i.e., before presenting the

items, we provided real-world examples at the top of thesurvey page that were designed to improve convergent

validity).

Second, alignment has not been clearly dened in the

literature or in practice. As discussed previously, each

alignment type has numerous denitions in the literature

and is discussed in multiple ways by practitioners.

As a result, it is difcult to determine how respondents

interpret items since their understanding of the

different concepts is questionable. Therefore, we provided

an illustration of SAM to highlight the construct in

question to illustrate how it is different from other align-ment types.

As shown by our survey in the Appendix, the cognitiveprocessing approach presents far richer stems to describe

the complex alignment concept as opposed to the simpler

items presented in domain sampling. While the respon-

dent has to take more time reading the extensive deni-

tions and examples in the cognitive processing approach

(i.e., these are longer than the 1-to-3 word stems from

domain sampling), items generated from domain sam-

pling would involve too many concepts and require toomany items to capture the full construct and still maintain

an acceptable validity and reliability (Little et al, 1999;

Karabenicket al, 2007).

Survey administrationWe contracted with a reputable data collection service to

survey a sample of CIOs with veried identities and job

responsibilities. We then analyzed the reliability and

validity (i.e., discriminant and convergent) of the items,

tested for predictive validity including mediation tests,

and analyzed our measures inuencevis a vis well-estab-lished control variables to situate types of alignment

within the broader nomological net (i.e., internal validity).

We provide detail on this process in the following

paragraphs.

Research designResearch Now, a national market research rm, adminis-

tered our survey. Research Now provides access to respon-

dents whose roles within organizations are veried. This

data collection approach is used in management research

(e.g.,Porter & Donthu, 2008) and MIS research (e.g.,Sun,

2012). We usedResearch Nowto elicit responses from CIOs,because the CIO plays a key role in alignment decisions

regarding IT, has an eye on the external environment due

to an upper-level management position, and is well posi-tioned to assess the rms alignment level (Carter et al,

2011). Research Nows CIO panel is comprised of almost

2000 members fairly evenly spread across the United States

(the Southern region has about 200 more members than

the other regions). Reecting the diversity in titles held by

the most senior IT professionals in companies, the CIO

panel includes respondents with titles such as Director of

IT, Vice President of IT, and Chief Technology Ofcer

(Preston & Karahanna, 2009;Bankeret al, 2011).

The survey was sent to 1077 CIOs in the Research NowCIO panel. Of these, 218 panelists clicked on the e-mail to

the survey link page. Eighteen respondents chose not to

enter the survey. In addition, the screening question

eliminated 36 respondents. A total of 140 questionnaires

were completed, resulting in a response rate of 13%. While

this response rate is low, this is consistent with research

conducted on CIOs where response rates typically range

from 7 to 20% (Preston et al, 2006; Oh & Pinsonneault,

2007). Respondents demographic statistics are shown in

Table 4.

Analyses

Reliability, convergent validity, and discriminant validityanalysesWe assessed unidimensionality of the six alignment types

by estimating a conrmatory factor analysis. Like the pre-

test, we used SmartPLS 2.0 (Ringleet al, 2005) to run this

analysis.Table 5shows satisfactory factor analysis results

since all items loaded higher on their hypothesized con-

structs than on other constructs.We also estimated the Cronbachs , skewness, and

kurtosis (Noar, 2003) to verify the normal distribution of

each construct.Table 6reveals satisfactory levels and nonon-normality problems. Taken together, these results

indicate that the six alignment types and nancial perfor-

mance are normally distributed such that the statistical

tests we used are appropriate in this study.

For convergent validity, we evaluated the loading of

each item onto their specied factor (Chin & Frye, 1996).

First, we compared the coefcients for the indicators with

the standard errors, where the loadings should be at least

twice as much as the standard error (Anderson & Gerbing,

1988). Second, a t-statistic of 1.65 or higher suggests theitem loading is signicant at 0.05 (n=140). All the load-

ings are signicant as illustrated inTable 7.

Then, we evaluated discriminant validity. We entered all

rst-order factors in a correlation matrix. Then, we




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compared cross-factor correlations against the square root

of the average variance extracted (AVE) of each factor

(Chin & Frye, 1996). If the cross-factor correlation exceeds

the square root of the AVE, there may be a lack of

discriminant validity. The correlation matrixes illustrated

inTable 8 indicate that there are some high cross-factor

correlations between cross-domain alignment and opera-

tional alignment, suggesting that there may be discrimi-

nant validity issues between these two constructs.

However, our Q-sorts and pre-tests indicated that cross-domain alignment was conceptually distinct from the

other types of alignment. Also, given that our AVE was

higher than the inter-construct correlations, evidence

suggests that the constructs were discriminant (Chin,

1998). Across our factor analysis, reliability calculations,

and item-level discriminant validity tests, we found evi-

dence that our measures were discriminant.

Predictive validityPredictive validity, subsumed in construct validity, ensuresthat our constructs demonstrate relationships consistent

with theory and correlate with or predict a given outcome

variable(Straubet al, 2004, p. 398). To establish predictive

validity, we used nancial performance as the dependent

Table 5 Conrmatory factor analysis results

BA CA CAb IA ITA OA PERF

Business alignment

BA1 0.89 0.52 0.50 0.37 0.48 0.50 0.52

BA2 0.73 0.39 0.39 0.23 0.35 0.33 0.40

BA3 0.82 0.45 0.42 0.34 0.42 0.44 0.46

BA4 0.80 0.44 0.41 0.31 0.42 0.40 0.32

BA5 0.79 0.41 0.38 0.24 0.38 0.39 0.35

BA6 0.85 0.46 0.43 0.34 0.40 0.48 0.49

Cross-domain alignment (business Strategy to IT infrastructure and

processes)

CA1 0.46 0.82 0.70 0.56 0.60 0.66 0.26

CA2 0.40 0.80 0.63 0.44 0.56 0.64 0.27

CA3 0.52 0.86 0.68 0.52 0.65 0.75 0.40

CA4 0.46 0.81 0.65 0.50 0.51 0.67 0.33

CA5 0.44 0.85 0.68 0.48 0.61 0.78 0.28

CA6 0.43 0.85 0.70 0.48 0.57 0.75 0.34

Cross-domain alignment (IT strategy to business infrastructure and

processes)CAb1 0.37 0.66 0.83 0.55 0.64 0.73 0.26

CAb2 0.38 0.61 0.79 0.46 0.63 0.66 0.15

CAb3 0.48 0.67 0.83 0.60 0.60 0.70 0.34

CAb4 0.39 0.67 0.80 0.53 0.63 0.70 0.27

CAb5 0.42 0.71 0.83 0.49 0.70 0.73 0.18

CAb6 0.50 0.68 0.87 0.52 0.60 0.71 0.34

Intellectual alignment

IA1 0.34 0.52 0.54 0.82 0.44 0.51 0.19

IA2 0.44 0.60 0.58 0.85 0.51 0.59 0.32

IA3 0.16 0.38 0.42 0.72 0.35 0.43 0.14

IA4 0.25 0.43 0.47 0.84 0.39 0.49 0.25

IA5 0.27 0.41 0.49 0.73 0.37 0.41 0.21

IA6 0.19 0.43 0.49 0.72 0.36 0.44 0.08

IA7 0.23 0.37 0.49 0.77 0.39 0.46 0.19IA8 0.35 0.55 0.56 0.82 0.49 0.57 0.23

IT alignment

ITA1 0.42 0.51 0.60 0.40 0.84 0.60 0.18

ITA2 0.44 0.64 0.65 0.48 0.85 0.66 0.21

ITA3 0.44 0.56 0.61 0.41 0.82 0.58 0.19

ITA4 0.46 0.59 0.62 0.50 0.83 0.66 0.20

ITA5 0.40 0.64 0.66 0.47 0.86 0.72 0.14

ITA6 0.33 0.58 0.66 0.43 0.81 0 .64 0.15

Operational alignment

OA1 0.41 0.67 0.72 0.50 0.66 0.81 0.21

OA2 0.40 0.59 0.64 0.42 0.61 0.73 0.18

OA3 0.50 0.70 0.68 0.55 0.58 0.82 0.33

OA4 0.34 0.66 0.69 0.51 0.62 0.78 0.20

OA5 0.40 0.76 0.69 0.47 0.60 0.84 0.29

OA6 0.48 0.75 0.74 0.60 0.71 0.87 0.25

Financial performance

PERF1 0.51 0.38 0.32 0.24 0.23 0.33 0.87

PERF2 0.40 0.29 0.28 0.18 0.19 0.26 0.85

PERF3 0.39 0.31 0.26 0.21 0.25 0.23 0.82

PERF4 0.43 0.27 0.22 0.20 0.05 0.19 0.80

PERF5 0.38 0.26 0.19 0.20 0.14 0.22 0.70

PERF6 0.47 0.33 0.28 0.22 0.12 0.29 0.87

PERF7 0.44 0.32 0.29 0.29 0.22 0.24 0.80

PERF8 0.38 0.30 0.31 0.27 0.22 0.26 0.73

The bold values highlight the loadings on the correct construct.

Table 4 Demographic statistics (n=140)

Characteristic Frequency

Gender Male 112

Female 20

Unreported 8

College Education Average=5.42 yearsIndustry CIO IT

Experience


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variable in the four dominant alignment perspective mod-els: strategy execution, technology transformation, com-

petitive potential, and service level (Henderson &

Venkatraman, 1999). These models follow a specic logic,

with relationships that are unidirectional in nature: strat-

egy execution is driven by business alignment, which

leads to nancial performance through operational align-

ment; technology transformation is driven by intellectual

alignment, which leads to nancial performance through

IT alignment; competitive potentialis driven by intellec-

tual alignment, which leads to nancial performancethrough business alignment; service levelis driven by IT

alignment, which leads to nancial performance through

operational alignment (Henderson & Venkatraman,

1999). These four models address the dysfunctionalcon-

sideration of bivariate relationships and recognize the

importance of cross-domain relationships in addition to

intellectual and operational alignment (Henderson &

Venkatraman, 1999, p. 477).

Theory suggests rms with higher levels of alignment

should realize higher long-term protability, availability of

nancial resources, and sales growth than rms with lower

IT alignment (Cragg et al, 2002; Croteau & Raymond,

2004). In addition, the level of performance may shiftdepending on the alignment perspective the rm chooses

(Henderson & Venkatraman, 1999). As such, we included

Table 6 Reliability and normality test results

Construct Reliability

()aMean Standard

deviation

Skewness

(error)bKurtosis

(error)b

Intellectual alignment 0.91 31.29 4.97 1.61 (0.21) 6.27 (0.41)

Operational alignment 0.90 23.56 3.50 0.54 (0.21) 1.63 (0.41)

Cross-domain alignment business strategy to IT infrastructure andprocesses

0.91 23.47 3.72

0.53 (0.21) 1.30 (0.41)

Cross-domain alignment IT strategy to business infrastructure and

processes

0.91 23.48 3.59 0.48 (0.21) 1.09 (0.41)

Business alignment 0.90 23.11 4.96 0.99 (0.21) 2.60 (0.41)

IT alignment 0.91 23.99 3.84 0.74 (0.21) 1.57 (0.41)

Financial performance 0.92 29.21 7.34 1.26 (0.21) 2.98 (0.41)

aHighly reliable >0.8 or 0.9 (Nunnally, 1978;Cronbach & Shavelson, 2004).bSevere nonnormality issues: skewness >2 and kurtosis >7 (Fabrigaret al, 1999).

Table 7 Convergent validity testa

Item t-value (STERR) Item t-value (STERR) Item t-value (STERR)

Cross-domain alignment (business strategy to IT infrastructure and processes) Operational alignment ITalignmentCA1 21.17 (0.04) OA1 16.51 (0.05) ITA1 12.97 (0.06)

CA2 18.46 (0.04) OA2 10.18 (0.07) ITA2 15.74 (0.05)

CA3 34.36 (0.03) OA3 25.27 (0.03) ITA3 10.54 (0.08)

CA4 18.08 (0.04) OA4 13.34 (0.06) ITA4 11.14 (0.07)

CA5 22.86 (0.04) OA5 26.19 (0.03) ITA5 13.89 (0.06)

CA6 24.30 (0.03) OA6 30.12 (0.03) ITA6 10.60 (0.08)

Cross-domain alignment (ITstrategy to business infrastructure and processes) Business Alignment

CAb1 19.76 (0.04) BA1 29.56 (0.03)

CAb2 14.42 (0.05) BA2 9.18 (0.08)

CAb3 25.67 (0.03) BA3 13.97 (0.06)

CAb4 18.92 (0.04) BA4 12.49 (0.06)

CAb5 20.71 (0.04) BA5 10.11 (0.08)

CAb6 29.93 (0.03) BA6 25.01 (0.03)

Intellectual alignment Financial performanceIA1 7.40 (0.11) PERF1 26.12 (0.03)

IA2 8.38 (0.10) PERF2 21.82 (0.04)

IA3 4.51 (0.16) PERF3 16.66 (0.05)

IA4 7.15 (0.12) PERF4 12.71 (0.06)

IA5 7.11 (0.10) PERF5 9.99 (0.07)

IA6 4.37 (0.16) PERF6 36.83 (0.02)

IA7 6.35 (0.12) PERF7 15.31 (0.05)

IA8 7.18 (0.11) PERF8 11.21 (0.06)

aAll significant at 0.05; STERR= Standard Error.




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nancial performance in our model to (a) establish that

our measure predicts nancial performance and (b) evalu-

ate whether the relationship between the types of align-

ment and nancial performance varies. The results showninFigures 36demonstrate that our measures signicantly

predict nancial performance, where the competitive

potential perspective has the strongest relationship with

nancial performance (R2=28.1%) and the technology

transformation has the weakest (R2=5.8%). Given the

signicant alignmentperformance relationship and the

varying relationship between the alignment perspectives

and nancial performance, we found evidence that our

measures have predictive validity.

Sobel test for mediationTo further test for predictive validity and fully test SAM, we

also usedSobels (1982)test for mediation. To test for the

mediation of the six alignment types, we calculated Sobels

(1982) test for mediation using the formula shown in

Equation (1). The results shown in Table 9 indicate

that business, intellectual, and IT alignment do have

mediation effects through operational, IT, and businessalignment in their given models. Therefore, we con-

clude that business alignment has an indirect effect on

nancial performance through operational alignment in

the strategy execution alignment perspective, intellectual

alignment has an indirect effect on nancial perfor-

mance through IT and business alignment in the

technology transformation and competitive potential

alignment perspectives, respectively, and IT alignment

has an indirect effect on nancial performance through

operational alignment in the service-level alignment

perspective.

z - value a*bffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

b2*s2a + a2*s2b

q (1)

Table 8 Construct correlation matrixa,b,c

BA CA CAb IA ITA OA PERF

Business alignment 0.80

Business strategy to IT infrastructure and processes 0.54 0.83

ITstrategy to business infrastructure and processes 0.51 0.81 0.82

Intellectual alignment 0.36 0.60 0.64 0.79IT alignment 0.49 0.71 0.77 0.53 0.84

Operational alignment 0.49 0.84 0.86 0.62 0.78 0.81

Financial performance 0.51 0.38 0.32 0.27 0.22 0.29 0.81

aDiagonals = Square-root AVE.bOff-diagonals = Correlations.cCross-factor correlations that exceed the AVE (potential lack of discriminant validity).

The bold values highlight the loadings on the correct construct.

Figure 3 Predictive validity results for strategy execution.




12/27

Internal validity analysis: control variable resultsTo more rigorously assess predictive validity, we included

variables that capture noise in the broader nomological

net surrounding business-IT alignment and performance

relationship. By including rm demographics such as rm

age (number of years since founded), rm size (measured

in terms of employees and revenues), andrm type (publicvs private) (Powell, 1992; Armstrong & Sambamurthy,

1999; Cragg et al, 2002; Chan et al, 2006) and IT

department demographics such as IT department size (the

number of IT employees), IT spending (the percentage of

revenue spent on IT), and IT department (number of years

since founded) (Karimiet al, 2000;Teo et al, 2003;Li et al,

2006), we could isolate our measures ability to predict

alignments relationship with performance. Even when

these control variables were entered, we found thatthe alignmentperformance relationship was largely

unchanged (less for rm size (revenue) and rm type) as

Figure 4 Predictive validity results for technology transformation.

Figure 5 Predictive validity results for competitive potential.




13/27

shown inTable 10. This provides further evidence of the

robustness of our alignment measures.

LimitationsAs with all research, it is important to note the limitations

of our work. First, we followed a cognitive processingapproach to developing our items. While the items use

broad terminology (i.e., strategies, infrastructures, pro-

cesses), the stems were designed to target specic concepts

(e.g., business strategy = business competing in the

market; business infrastructure and processes = internal

business policies, procedures, personnel, and structure). By

doing so, we ensured that respondents interpreted items in

the way we intended. However, cognitive sampling

requires more extensive reading and reection on the

itemsmeaning than for simpler constructs such as ease ofuse; this can lead to fatigue, as well as increase common-

method bias. Therefore, to avoid respondent fatigue, we

recommend future researchers collect data using the align-

ment measures most germane to their investigation.

Second, we only surveyed CIOs. Studies often cite their

use of single respondents as problematic due to common

source bias (e.g., Lai et al, 2009). Although this concern can

be addressed by using multiple respondents in the same

rm (Teo & King, 1996), collecting data from two sources

at the executive level is quite difcult (Chan et al, 1997)

and could compromise the anonymity of the question-naire (Podsakoffet al, 2003;Kearns & Sabherwal, 2006). In

addition, subjectivity and measurement error are still apossibility even for matched pairs (Tallon, 2007b). None-

theless, while we are condent in our measures, we believe

future work that assesses their psychometric properties

using matched pairs and in diverse populations may

provide additional insight into their usefulness.

Like all behavioral research studies, common-method

bias threatens the validity of our study (Podsakoff et al,

2003). Following the techniques described byPodsakoffet

al (2003), we tried to control for common-method bias

through the design of our studys procedures and through

statistical controls. Procedural remedies help control com-

mon-method bias by identifying the connection between

the measures of the predictor and dependent variables and

then eliminating or minimizing these common character-

istics by carefully designing the study (Podsakoff et al,

2003). We employed two procedural remedies to try to

control the inuence of common-method bias: psycholo-

gical separation of measurement (i.e., our cover letter did

not tie alignment to performance and we physically

separated the alignment and performance measures in the

survey) and protecting respondent anonymity/reducingevaluation apprehension (i.e., we never identied the

respondents by name or company).

We also conducted two statistical remedies to control for

common-method bias. First, we used a Harman one-factor

Figure 6 Predictive validity results for service level.

Table 9 Sobel test

Alignment perspective z-value P-value a b s a sb

Strategy execution 3.27


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test (Harman, 1976; Malhotra et al, 2006) and found no

single factor accounted for a majority of the covariance

(the factors accounted for 70.42% of the variance with the

rst factor accounting for 34.97%), which suggests com-

mon-method bias might not pose a severe threat to the

validity of our study (Harman, 1976). In addition, we used

Lindell & Whitneys (2001)correlation marker technique,

which uses a marker variable (i.e., a theoretically unrelatedvariable) to adjust the correlations of the models core

constructs (Lindell & Whitney, 2001). We did not nd

high correlations between the common-method bias

variable and any of the models core constructs and found

our signicant correlations remained signicant. Having

used these procedural and statistical controls for common-

method bias, we posit that common-method bias was not

particularly problematic in our study even though we

acknowledge that there are limitations to the techniques

we used to test for common-method bias (seeChin et al,

2012 for details on the problems associated with latent

method construct approaches).

Finally, although we attempted to develop scales forcross-domain alignment, two cross-domain alignment

measures (i.e., Business Strategy to IT Infrastructure and

Processes and IT Strategy to Business Infrastructure and

Processes) were confounded with the other types of align-

ment. While the sorting exercises and pre-tests indicated

that these two cross-domain alignment types were con-

ceptually distinct from the other types of alignment, and

while their scales have good reliability, the factor analysis

from our pre-test grouped these two cross-domain align-

ment types with intellectual and operational alignment.Furthermore, analysis of the correlation tables indicates

that these two cross-domain alignment types had poten-

tial discriminant validity issues with intellectual and

operational alignment in the pre-test and with operational

alignment in the nal study (as shown in Table 8). How-

ever, we note that the other two types of cross-domain

alignment (i.e., business and IT alignment) exhibit satis-

factory reliability and validity in all tests. We discuss this

further in the next section.

Discussion and implications

Contributions to practiceThis study offers three implications for practice. First,

cross-domain alignment is associated with higher levels of

nancial performance as indicated by the signicant cor-

relations between IT/business alignment and nancial

performance for three of the four alignment perspectives

(all but Service Level as shown in Figure 6). This suggests

that rms pursuing alignment of strategies and infrastruc-

tures may experience supranormal prots and may be

able to achieve a sustainable competitive advantage.

Second, aligning business and IT strategies (i.e., intellec-tual alignment) and business strategies and infrastructure

(i.e., business alignment) is more closely associated with

higher levels of nancial performance than other align-

ment combinations examined in this study (as indicated

by the Competitive Potential results in Figure 5). This

suggests that rms may not need to place as much

emphasis on aligning their IT infrastructure with the

other three alignment components to see higher levels of

nancial performance. Finally, aligning business and IT

strategies (i.e., intellectual alignment) alone is not asso-ciated with higher levels of nancial performance

(as indicated by the results from the Sobel test). This

suggests rms may need to consider how they can aligntheir infrastructure with their strategy to see improve-

ments in their performance.

Contributions to researchIn addition to the contributions to practice, our ndings

also have implications for research. First, cross-domain

alignment has been largely neglected in the literature withregard to denitions and measurement. WhileHenderson

& Venkatraman (1993) dened intellectual and opera-

tional alignment with a fair amount of precision, theygave cross-domain alignment a broad denition to capture

anything that crosses the strategy-infrastructure domains.

As such, the four unique types of cross-domain alignment

were not dened and research since that time has not

clearly differentiated these alignment types. We found 9

studies that empirically examined cross-domain align-

ment (of the 175 alignment studies mentioned earlier). Of

these studies, none considered business or IT alignment.

We also could not nd any studies that specically dened

or measured the unique cross-domain types from businessstrategy to IT infrastructure or IT strategy to business

infrastructure; instead, they used a broad denition of

cross-domain alignment (i.e., aligning strategies and infra-

structures or processes). As such, this is the rst study that

Table 10 Control variable results

Model (alignment perspective) coefficient

Firm age F irm s ize (employees) Firm size (revenue) Firm type IT age IT spending IT size

Strategy execution 0.11 0.03 0.19* 0.22* 0.09 0.10 0.01

Technology transformation

0.11

0.01 0.20* 0.23* 0.09 0.13

0.05Competitive potential 0.04 0.06 0.26* 0.16* 0.09 0.07 0.09

Service level 0.11 0.03 0.19* 0.22* 0.09 0.10 0.02

*significant at 0.05.




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has attempted to demarcate these four cross-domain align-

ment types.

Through our item development process, we were able to

clarify and rene the denitions of the types of alignment.

The development steps showed that the existing align-

ment denitions made it hard to conceptually distinguish

between the different types such that it was confusing todetermine what was and was not included in the deni-tion of each type of alignment. As a result, we expanded

the denitions to specify the level of analysis. For example,

we created more precise denitions that established intel-

lectual alignment as the higher level, externally focused

STRATEGIC level of alignment as opposed to the lower

level, internally focused, OPERATIONAL level for opera-

tional alignment. We also ensured that each denition

clearly specied the components that were being aligned.

For example, business alignment includes the alignment

of business strategy and business infrastructure/processes.Therefore, our denition makes reference to both of these

boxes in that the higher level, externally focusedbusinessstrategies are aligned with the lower level, internally

focusedbusiness infrastructure and processes. These changes

highlight the focus of each type of alignment such that

one can clearly determine which components of align-

ment are under consideration.

By creating comprehensive denitions that clearly spe-

cify the components of each type of alignment, future

researchers will be able to utilize items that clearly cover

the domain of interest, can test the adequacy of each

alignment measure, have a strong foundation on which

to build future alignment research, and can compareresults across studies since they can be condent that they

are examining the same construct. Specically, we recom-mend that future researchers consider the context of their

study and then pick the type of alignment that is most

suited to that situation. For example, operational align-

ment may be more important in manufacturing rms

while IT alignment may be more critical in technology

rms. Since we found public or private rm type was a

signicant control variable in our models, it may be

interesting to compare the strength of the alignment

performance relationship based on public or private sector,

specic industries, or performance goals.

Second, the study contributes to the literature by devel-

oping an overall instrument to measure the various types

of alignment. The instrument development process

included surveying known existing instruments, adapting

appropriate items, creating new items using the cognitiveprocessing approach, and undertaking an extensive scale

development process based on the procedures employedbyMoore & Benbasat (1991)and described byMacKenzieet al (2011). Due to the rigor of our process, our measures

demonstrated a high degree of content and construct

validity as illustrated in Table 11 (Moore & Benbasat,

1991; MacKenzie et al, 2011). Specically, our study

yielded a 38-item instrument that measures 6 alignment

types, all with acceptable levels of reliability. To establish

their predictive validity, we examined how the different

alignment types inuence nancial performance. To

further an understanding of alignments nomological net,

we recommend future researchers employ our measures toexamine other dimensions of rm performance such as

productivity or customer benet since different industriesor rms often have different performance goals (Deming,1986) such that manufacturers may be more interested in

productivity whereas retailers may consider customer ben-

ets more important.

In addition, it is also important to consider other

constructs in alignments nomological network. First,

environmental turbulence (i.e., uncertainty) may impact

a rms ability to align (e.g., Bergeron et al, 2001;Changet al, 2008;Huang, 2009). Second, IT investments may be

necessary to support and enable alignment of IT with the

business (e.g., Kearns & Lederer, 2004; Byrd et al, 2006;Schwarzet al, 2010). Third, alignment may be contingent

upon the rms strategy (e.g., Palmer & Markus, 2000;Chan et al, 2006; Raymond & Croteau, 2006). Fourth,

governance structure (e.g., authority structure) may facil-

itate alignment success (e.g., Bergeron et al, 2001; Oh &

Pinsonneault, 2007;Lee et al, 2008;Yayla, 2008). Finally,

people, commitments, and communication (i.e., social

alignment) may be the foundation for establishing align-

ment (e.g., Kearns & Lederer, 2003; Celuch et al, 2007;

Taipala, 2008). Taken together, it may be useful to evaluatean omnibus model that integrates all the constructs in

alignments nomological network in addition to the six

Table 11 Construct development test results

Analysis Test Test result

Content validity Four Q-sorts High agreement among the judges Established

Reliability Cronbachs Established (over 0.8 and 0.9)

Normality Skewness, kurtosis Normal (skewness >2, kurtosis >7)

Convergent

validity

Factor analysis;t-statistic Satisfactory loadings (>2 times standard error) and significant

Discriminant

validity

Cross-factor correlation Cross-domain and operational alignment correlations high but otherwise

established

Predictive validity Sobel test Mediation exists

Modeled with dependent and control

variables

Established




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presented by Henderson & Venkatraman (1999) and

nancial performance. By testing these models, we found

these four dominant alignment perspectives are positively

related to nancial performance. As such, future research-

ers should consider the four types of cross-domain

alignment in addition to intellectual and opera-

tional alignment when examining the relationship

between alignment and performance; in addition, they

may nd that SAM is a useful model for including all six

alignment types.

About the Authors

Jennifer E. Gerow is an Assistant Professor in the Econom-

ics and Business department at Virginia Military Institute.She holds a B.S. in Biological Sciences with a minor in

Secondary Education, an M.B.A., and a Ph.D. in Manage-

ment (Concentration: Information Systems) from Clem-

son University. Her research interests are power and

politics in the workplace, IT-business strategic alignment,

and drivers of IT use/resistance. She has previously pub-

lished in the European Journal of Information Systems,Computers in Human Behavior, Journal of Information Tech-

nology Theory and Application, theJournal of Service Science &

Management, and the proceedings of various conferences.

Jason Bennett Thatcherdirects the Social Analytics Insti-

tute and is a Professor of Information Systems in the

Department of Management at Clemson University.

He holds B.A.s in History (Cum Laude) and Political Science

(Cum Laude) from the University of Utah as well as a

M.P.A. from the Askew School of Public Administration

and Policy and a Ph.D. in Business Administration from

the College of Business at Florida State University. His

work appears in MIS Quarterly,Journal of the AIS,Journal of

Applied Psychology, Organizational Behavior and Human

Decision Processes, Journal of Management Information

Systems, European Journal of Information Systems, andIEEE Transactions on Engineering Management. He lives in

Greenville, South Carolina where he reads Garden and

Gun, sips bourbon, and smokes BBQ.

Varun Grover is the William S. Lee (Duke Energy) Distin-

guished Professor of Information Systems at Clemson

University. He has published extensively in the informa-

tion systems eld, with over 200 publications in major

refereed journals. Nine recent articles have ranked him

among the top four researchers based on number ofpublications in the top Information Systems journals, as

well as citation impact (h-index). He is Senior Editor for

MISQ Executive, and Senior Editor (Emeritus) for MIS

Quarterly, the Journal of the AIS and Database. He is

currently working in the areas of IT value, system politics,

and process transformation and recently released his third

book (with M. Lynne Markus) on process change. He is

recipient of numerous awards from USC, Clemson, AIS,

DSI, Anbar, PriceWaterhouse and so on for his research

and teaching and is a Fellow of the Association forInformation Systems.

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six types of it-business strategic alignment

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