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Modeling Alignment as a Higher Order Nomological
Framework
Rogier van de Wetering1
1 Open University of the Netherlands, Valkenburgerweg 177
6419 AT Heerlen, the Netherlands
rogier.vandewetering@ou.nl
Abstract. Achieving Business/IT-alignment (BITA) and pursuing intended
goals within organizations seems an intricate and poorly examined process. We
argue that without proper theories concerning BITA, the ‘mapping’ of
theoretical constructs onto empirical phenomena, is ambiguous. In this paper
we synthesize a higher order nomological framework for BITA with a
considerable degree of complexity, coherence and causality. We aim to extend
and generalize previous work on BITA within the healthcare domain, drawing
on principles of complexity science. Our framework explains how BITA is
related to firm performance. Using this knowledge, organizations can define
improvement activities that can be executed along five organizational
dimensions that best meets a organizations’ current and future needs; done
simultaneously and hence by an integrated management perspective. This work
contributes to academia by using a modeling approach that overcomes
acknowledged limitations of existing approaches. Doing so, the outcomes of
this study also offer many opportunities for future research.
Keywords: Business/IT-alignment, complexity science, Structural Equation
Modeling (SEM), nomological framework, firm performance
1 An acknowledged omission in scientific literature
Business/IT-alignment (hereinafter referred to as BITA) has been a major concern
for executives and IT practitioners for decades and refers to applying IS/IT in an
appropriate and timely way, in harmony (i.e. complementarity between activities)
with business strategies, goals and needs [1] and remains an enduring challenge for
firms worldwide [2]. Current literature points out that BITA remains a top priority for
business and IT executives [3]. However, strong theoretical foundations of BITA have
not been developed or tested extensively[4].
Following both recognized work and more recent studies [4-7], we argue that
achieving BITA and pursuing intended goals and objectives within organizations
seems an intricate and poorly examined process and lacks convincing theoretical
grounds. Little scientific knowledge is available about the underlying theoretical
mechanisms that govern firm performance (i.e. explanandum, and hereinafter referred
to as performance) and how BITA contributes to this as an antecedent.
Modeling Alignment as a Higher Order Nomological Framework 2
Over the past decade, the MIS community increased attention towards the
adaptive and co-evolutionary nature of IS/IT [8] and dynamic, multi-faceted, and non-
deterministic processes to align IS/IT in constantly-changing business environments
[9]. This evolutionistic and dynamic approach has its roots in nonlinear science such
as physics, biology, bio-chemistry and economy and has a profound impact on
management, strategy, organization and IS/IT studies. A number of authors have
stated that the science of complexity and complex adaptive systems (CAS) can be
considered a valuable instrument to cope with organizational and IS/IT changes in
non-linear turbulent environments [10-12].
We employ the basic thought that in order to truly understand the nature of BITA,
you will need a ‘holistic’ and ‘complex’ theoretical framework that fits the diversity
of organizational components and interactions among the many agents that are
involved in organizations. To turn this claim and perspective into a framework, a
systematic agenda is required, linking theory development with mathematical or
computational model development.
Therefore, the main objective of this paper is to develop an integrative
nomological framework to model, on the one hand, BITA, and, on the other hand, its
foundational relationship with performance. This does imply that performance is
defined as having multifactorial impacts and benefits, consistent with many studies
investigating IS/IT and performance [13, 14]. Doing so, we build upon and generalize
work done by Van de Wetering and Batenburg [7, 15, 16] that developed a holistic
approach towards alignment and maturity of IS/IT in clinical practice that extends
acknowledged limitations of the resources-based view of the firm [9].
However, they did not explicitly provide theoretical mechanisms of the holistic
and complex framework that fits the diversity of organizational components and
interactions involved with BITA. Neither did they elaborate extensively and discuss
the outcomes their results in terms of modeling BITA as the interdependency of
underlying organizational dimensions represented by a higher-order construct.
The current approach is similar to those in [4, 17] although our objective,
approach and focus is different. Defining BITA through the use of a nomological
framework occurs for instance also in Chan [17]. This work elaborates extensively on
a multitude of factors that affect BITA. Also, current work by Gerow et al. [4]
evaluate whether indirect relationships exist between BITA and firm performance.
They do so, offering a more nuanced configuration of performance constructs and
focus in their model on causal relationships between the various forms and types of
BITA. Various other alternative approaches for modeling BITA are available in
extant literature. While these contributions are valuable, they do not provide a
comprehensive foundation to operationalize theorized constructs, relations and outline
the internal logic among the various dimensions of BITA. Conceptual models,
relationships and links between constructs often described can be seen to be special
cases of a more general higher-order statistical nomological framework, which will be
the main focus of this paper.
Hence, in this paper we address the following research questions:
RQ1: What is the role complexity science in understanding the emergent nature of
IS/IT in organizations and the dynamics of BITA in particular?
Modeling Alignment as a Higher Order Nomological Framework 3
RQ2: How can BITA and its relationship with performance be modelled as a
parsimonious nomological framework that serves both theory and practice by
capturing the complex entanglement of BITA within organizations? And finally,
RQ3: How can this framework subsequently be operationalized using a statistical
scheme that maps conceptual constructs onto empirical phenomena?
The remainder of this paper is outlined as follows. First, principle concepts are
reviewed. Subsequently, we outline our method, the applied multistep approach and
propose a nomological framework containing higher-order – i.e. the hierarchical
component model – latent constructs within the context of simultaneous equation
systems. In the last section of the paper, main findings are presented and discussed,
inherent limitations of the current study are identified and future research
opportunities are outlined.
2 Overview of principle concepts
2.1 IS/IT maturity
The concept of IS/IT maturity and adoption goes back to the early 1950s [18, 19].
The idea was proposed of considering different evolutionary levels or business
transformations of IS/IT, and what can be expected from each, outlining in that way a
number evolutionary steps that organization should follow. Since then, various
maturity models have been developed to plan and assess the evolution of IS/IT in
organizations. Within the field of information systems, Nolan and Gibson [20] are
considered the founders of the stage-based maturity perspective. They proposed a six-
stage (initially four) model representing the level of IS/IT expense for an organization
in relation to the stages of increasing the sophistication and maturity of IS/IT. This
stage-based concept has been further extended and applied to organizations by many
others [19, 21].
In general, IS/IT maturity models provide insight into the structure of elements that
represent process effectiveness of IS/IT in organizations. They also allow
organizations to define roadmaps on how to get from one level of maturity and evolve
to the next [21].
2.2 Business/IT-alignment
Strategic alignment has been a major concern for executives and IT practitioners
for decades and refers to applying IS/IT in an appropriate and timely way, in harmony
with business strategies, goals and needs [1]. Achieving BITA comes with various
performance gains, including market growth, cost control, financial performance,
increased outflow of innovation, and augmented reputation [22, 23]. Within this field
the classic Strategic Alignment Model (SAM) [24] is undoubtedly the most cited
concept and has also been extended by others [1, 25]. SAM implies that a systematic
process is required to govern continuous alignment between business and IS/IT
domains, i.e. to achieve ‘strategic fit’ as well as ’functional integration.’ The model
does, however, have its limitations. The SAM (as well as other extensions of the
Modeling Alignment as a Higher Order Nomological Framework 4
model) are also not able to monitor or measure maturity and/or performance, and
relations in the model are not operationalized or clearly defined [26]. This was
improved by Scheper [26], who extended the SAM by combining it with the MIT 90s
model [27] and defining various organizational dimensions. In contrast to the SAM,
Scheper also defined levels of incremental maturity for each of the five dimensions.
Next, he claims that alignment can be measured by comparing the maturity levels of
all five dimensions at the same time. His alignment principle is based on the idea that
organizations can mature each single domain, but only equalization among all
dimensions (i.e., alignment) will significantly improve organizations’ performances.
Scheper’s framework has been applied in various fields including Customer
Relationship Management, e-procurement, supply chain management and health
IS/IT-alignment [15, 28-30].
2.3 Complexity science and complex adaptive systems
The field of Complex Adaptive Systems (CAS) [8, 10, 31] and complexity science
has its roots in evolutionary biology, physics and mathematics. It is based on the
fundamental logical properties of the behavior of non-linear and network feedback
systems, no matter where they are found [11]. CAS are considered collections of
individual agents with the freedom to act in ways that are not always totally
predictable (non-linear), and whose actions are interconnected so that one agent’s
actions change the contexts for other agents. CAS theories presume that the
adaptation of systems to their environments emerge from the adaptive efforts of
individual agents that attempt to improve their own payoffs [12]. Commonly referred
examples include financial markets, weather systems, human immune system,
colonies of termites and organizations.
Various authors have stated that the science of complexity can be considered a
valuable instrument to cope with organizational and IS/IT changes in non-linear
turbulent environments [8, 10-12]. Both complexity science and CAS are applicable
to the field of information systems in that IS/IT act like CAS.
3 Toward a nomological framework for BITA
For this study an incremental development process was employed following the
initial stages of the design science research methodology approach [32]. In this
approach, knowledge is produced by constructing and evaluating artifacts which are
subsequently used as input for a better awareness of the problem [33]. Doing so, the
current study pays considerable attention to link the articulation of the theoretical
position with existing baseline and empirical work. This study focuses on designing a
generalized nomological framework (the artifact) to statistically model BITA and its
foundational relationship with performance. To ensure quality and validity of the
framework, we followed analytical design evaluation methods, guidelines and a
systematic process [32, 33]. Figure 1 provides an overview of the four interrelated
process steps that were conducted, within the scope of this study, using a process-
deliverable diagram (PDD) [34]. A PDD is a meta-modeling technique based on UML
activity and class diagrams. Within such a model, the activities and processes are
Modeling Alignment as a Higher Order Nomological Framework 5
modeled on the left-hand side and deliverables on the right-hand side (see Figure 1).
In this diagram we have included only the main (simplified) deliverables.
Fig. 1. Nomological framework development process
During the initial step we did an extensive literature review concerning the
research topic and synthesized the ‘problem’. Based on this review and the
researcher’s own field experience, the research field vocabulary was subsequently
captured, initial concepts were synthesized and designed (i.e. step 2). Additionally,
we defined what the artifact should accomplish. Hence, (1) we acknowledged that it is
import to define and explain the rationale underlying the functional form of the
artifact, i.e. the nomological framework (including dependent and independent
variables) [35] and (2) devote attention to the nature and direction of relationships
Step 2: Suggestion
Step 3: Design
Step 1: Initiation
Define key concepts
Design initial
framework
Review framework
Review literature
Synthesize problem
Define research scope
Capture vocabulary
Define artifact
objectives
Finalize enhanced
framework
Step 4: Conclusion
Identify improvement
opportunities
FINAL FRAMEWORK
Finalize framework
Review framework
critically
Is basis for
IMPROVEMENT
OUTCOME
VOCABULARY
INITIAL FRAMEWORK
Is basis for
ENHANCED
FRAMEWORK
Else
Framework
complete
Is basis for
Is basis for
LITERATURE
OVERVIEW
Is basis for
Is basis for
Is basis for
RESEARCH AGENDA
KEY CONCEPT
FRAMEWORK
ADDITIONS
Modeling Alignment as a Higher Order Nomological Framework 6
between the various constructs [36]. Taking these concepts as a basis, an initial
nomological framework was designed – step three – which subsequently was
enhanced and extended based on critical reflections and comparisons with previous
empirical baseline work. Step 4 is the conclusion. In this final step we once more
critically reviewed the framework, identified improvement opportunities and possible
future work.
3.1 A multistep approach toward designing a nomological framework
Based on extensive work and previous statistical analyses [7], a generalized
conceptual nomological framework was designed combining three central elements:
(1) BITA, (2) maturity (for each of the five dimensions) and (3) performance as the
explanandum. For each of these five dimensions,
(1) Strategy and Policy (S&P),
(2) Organization and Processes (O&P),
(3) Monitoring and Control (M&C),
(4) Information Technology (IT) and
(5) People and Culture (P&C),
modelled as independent variables (constructs), distinctive maturity levels and
associated indicators have previously been defined [7]. Subsequently, distinctive
maturity levels can be successively labeled for O&P3, O&P4 and O&P5; IT3, IT4 and
IT5; and so on. Maturity levels 1 and 2 – are currently omitted for pure practical
reasons.
Next, we formalize performance, following the same steps, logic and balanced
evaluation perspective [37], as a multifactorial (dependent) construct to be measured
in terms of a complementary I. External construct (i.e. subdivided into I. Customer
and II. Financial) and an II. Internal construct (i.e. subdivided into I. Internal
processes and II. Organizational capacity). This is also in accordance with studies
evaluating IS/IT performance from a rich and diverse understanding of outcomes [13,
14]. The performance construct, as developed, enables a more diverse understanding
of outcomes from various perspectives.
Hence, we apply a multistep approach using path modeling to hierarchically
construct latent variables for all latent constructs of our nomological framework. With
regard to the independent part of the framework, we model:
1. The first-order exogenous constructs as represented the different maturity
levels (labeled SP3–SP5, OP3–OP5, MC3–MC5, IT3–IT5, PC3–PC5) and
relate each of them to their respective manifest variables: SP3: MV1 & MV2;
SP4: MV3 & MV4; SP5: MV5 & MV6; OP3: MV7 & MV8; IT4: MV21 &
MV22; etc.;
2. The second-order construct as the five organizational dimensions,
constructed by relating the blocks of the underlying first-order latent
constructs (i.e. step 1);
3. The third-order construct, labeled as BITA, as related to the underlying
second-order constructs (i.e. step 2).
Modeling Alignment as a Higher Order Nomological Framework 7
With regard to the dependent part of the nomological framework (i.e.
performance), we model:
4. First-order exogenous constructs and relate them to their respective manifest
variables as defined (Customer: MV31 & MV32; Financial: MV33 & MV34;
Internal processes: MV35 & MV36; Organizational capacity: MV37, MV38);
5. The second-order constructs (External construct and Internal construct), as
related to the block of the underlying first-order latent constructs (see step
4);
6. The third-order construct, labeled as Performance, as related to the
underlying second-order constructs (i.e. step 5).
Thus, we develop a nomological framework that combines three concepts:
(1) BITA, describing the interdependency and synergetic mechanisms of five
underlying organizational dimensions (i.e., independent constructs), (2) maturity,
defined as the level of incremental maturity for each of the five dimensions; thus a
classification according to a stage of development, and finally (3) performance as the
multifactorial impacts and benefits (i.e. dependent constructs) to be measured in terms
of a complementary external construct and an internal construct.
3.2 Operationalizing using the Structural Equation Modeling notation
BITA as part of the nomological framework is modeled as a third-order latent
construct, whereas the second-order constructs represent the organizational
dimensions to be co-aligned and the first-order constructs represent the IS/IT maturity
levels. This type of modeling is statistically appropriately captured by a pattern of
covariation, which coincides with the concept of (co-)alignment [38].
Our framework follows the central concept of internal logic among the various
dimensions, since it is in accordance with the theories of complexity science and CAS
outlined previously. Our framework is a more parsimonious presentation of the
underlying factors gleaning interdependency of complex constructs [39]. The co-
alignment as covariation approach is, therefore, preferred over other common
alignment schemes (e.g. leveling, gestalt, moderator, mediator, etc.) since the
operationalization of their optimal profiles – with numerical scores along a set of
underlying areas of resource allocations – is difficult [38].
The operationalization of our nomological framework can be performed most
accurately using Structural Equation Modeling, SEM [40]. SEM (or ‘causal
modeling’) is a second generation data analysis family of statistical models that seeks
to explain complex relationships among multiple observable and latent constructs in
models. The application of SEM fits a mode of integrative thinking about theory
construction, measurement problems and data analysis. It enables stating the theory
more exactly, testing the theory more precisely and yielding a more thorough
modeling/understanding of empirical data about complex phenomena and
relationships [41]. In interconnecting the principle concepts of our framework, i.e.
BITA, maturity and performance (see previous section), we propose a reflective
Modeling Alignment as a Higher Order Nomological Framework 8
construct model (molecular, principal factor model, common latent construct),
through which the manifest variables are affected by the latent variables (in contrast
to the formative constructs) [42]. Our factor model, i.e. higher order reflective
construct model, is specified as an alternative to a mode in which latent constructs are
modelled using patterns of correlations.
Within PLS, higher-order constructs can be constructed using repeated indicators
(i.e. the hierarchical component model) [42]. That is, all indicators of the first-order
constructs are reassigned to the second-order construct, as second-order models are a
special type of PLS path modeling that use manifest variables twice for model
estimation. The same patterns are applicable to subsequent higher-order constructs. A
prerequisite for this model approach is that all manifest variables of the first-order and
higher-order constructs should be reflective [36].
Figure 2 portrays our nomological framework using the SEM notation. It captures
the theorized relationships between the maturity levels (i.e. first-order construct),
organizational dimensions (i.e. second-order construct) and BITA (i.e. third-order
construct), on the one hand, and its impact on performance (i.e. third-order construct),
on the other hand. The framework fits the diversity of organizational components and
interactions among the many agents involved in turbulent environments. This is in
accordance with the complexity science lens.
4 Discussion and conclusion
This study demonstrates that that BITA can be represented, in the context of a
higher order nomological framework, as the interdependency of five underlying
organizational dimensions, each containing maturity levels that in their turn can be
represented by manifest variables, i.e. measurable indicators. Likewise, performance
follows this parsimonious concept and can be represented by a higher-order construct.
Also, we presented how the three imperative theoretical concepts of BITA,
maturity and performance coincide with covariation (or co-alignment) using higher-
order latent structures as an operationalized statistical scheme within SEM. Doing so,
we acknowledged the importance of the rationale underlying the functional form of
constructs (dependent and independent constructs) [35] and devote attention to the
nature and direction of relationships between the various constructs [36].
We employed a systematic agenda linking theory development with a
computational model, thereby overcoming the acknowledged limitations of existing
approaches. The adoption of complexity science – and specifically CAS – as both a
theoretical and practical lens opens up a whole new vista of perspectives, approaches
and techniques.
Following the frameworks’ logic, organizations can define improvement activities
– with accompanying investments – that can be executed along the five organizational
dimensions that best meets an organizations’ current and future needs; done
simultaneously and hence by an integrated management perspective. Hence, a set of
measurements can then be defined which are organized into projects that take into
account the risks involved, investment costs, critical success factors and benefits. In
the course of the execution of all improvement activities, the level of alignment
Modeling Alignment as a Higher Order Nomological Framework 9
between the five organizational dimensions should monitored in managing
similarities, overlap and synergy between the improvement projects.
Fig. 2. Theoretical SEM notation for the nomological framework (δi/εi = measurement errors, ζ
= disturbance terms for higher-order constructs, λi = first-order factor loadings, γi = factor
loading coefficients for higher-order constructs, β = estimated value for the path relationship in
the structural model)
The current study has several limitations. Obviously, empirically applying our
nomological framework is needed to further enhance and validate it. Yet, we have
undertaken considerable efforts to ensure that we synthesized a framework for BITA
with a considerable degree of complexity, coherence and causality. Comparing results
across industries, countries and distinct groups might also contribute to the
generalizability of our findings. Next, critical readers might argue that we did not
explicitly address the mediating and/or moderating impact of environmental dynamics
and e.g. collaborative network organizations (CNO’s) in the context of our
Strategy
and
policy
Monitoring
and
control
Organization
and
processes
People
and
culture
BITA Performance
S&P3
S&P5
S&P4
O&P3
O&P5
O&P4
P&C3
P&C5
P&C4
MV1
MV2
δ1
δ2
MV3
MV4
δ3
δ4
MV5
MV6
δ5
δ6
MV7
MV8
δ7
δ8
MV9
MV10
δ9
δ10
MV11
MV12
δ11
δ12
MV13
MV14
δ13
δ14
MV15
MV16
δ15
δ16
MV17
MV18
δ17
δ18
MV25
MV26
δ25
δ26
MV27
MV28
δ27
δ28
MV29
MV30
δ29
δ30
ζ
ζ
ζ
ζ
ζ
ζ
ζ
ζ
ζ
ζ
ζ
ζ
ζ
ζ
β
λ1
λ2
λ3
λ4
λ5
λ6
λ7
λ8
λ9
λ10
λ11
λ12
λ13
λ14
λ15
λ16
λ17
λ18
λ25
λ26
λ27
λ28
λ29
λ30
γ1
γ2
γ3
γ4
γ5
γ6
γ7
γ8
γ9
γ13
γ14
γ15
γ21
γ22
1st order 2nd order 3rd order 1st order
γ16
γ17
γ19
γ18
2nd order3rd order
M&C3
M&C5
M&C4
ζ
ζ
ζ
Information
technology
IT3
IT5
IT4
MV19
MV20
δ19
δ20
MV21
MV22
δ21
δ22
MV23
MV24
δ23
δ24
ζ
ζ
ζ
ζ
λ23
λ19
λ20
λ21
λ22
λ24
γ10
γ11
γ12
γ20
Customer
External
construct
MV31
MV32
ε31
ε32
ζ
ζ
λ31
λ32
Financial
MV33
MV34
ε33
ε34
ζ
λ33
λ34
γ23
γ24
Internal
processes
Internal
construct
MV35
MV36
ε35
ε36
ζ
ζ
λ35
λ36
Organizational
capacity
MV37
MV38
ε37
ε38
ζ
λ37
λ38
γ25
γ26
Independent part Dependent part
Modeling Alignment as a Higher Order Nomological Framework 10
framework. Albeit, we argued that the co-alignment approach in the context of this
study is the preferred operationalization scheme.
Future research could also apply BITA into the field of CNO’s including business
ecosystems, long-term and goal-oriented networks, virtual communities and breeding
environments [43]. Following this perspective and field, relevant research questions
include the following ‘How can partners within an CNO’s align different IS/IT
resources, capabilities and competences to enable I. co-creation, II. business
evolvability, III. sustainable business performance and IV. the management and
governance of multi-direction alignment effects?’, see also [44].
To conclude, the developed nomological framework is therefore designed for
further empirical research. In practice, our framework can be applied as an useful
checklist for organizations to systematically identify BITA improvement areas using
the central concepts of our framework.
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