Organization Structure Determinants of Information Technology Budgets

Abstract

The information technology (IT) budget of a firm is one of the key financial ratios in information systems (IS) management and a persistent issue of discussion on corporate boards. Yet, many firms determine their IT budgets by relying on simplistic industry benchmarks. With the goal of understanding what structural organization characteristics influence the IT budget, we study the associations of five constructs measuring outsourcing, control span, and globalization of the organization with the relative IT budget level. Our data from a multiyear cross-country survey provides evidence for IT outsourcing being related to lower, and business process outsourcing, control span increase, and globalization increase to higher IT budgets. Our findings advance prior research on IS financials, which has so far centered around technology and industry variables, by providing insight into the structural organization characteristics that co-determine IT budget levels. Implications for the broader IS literature and for practice are discussed.

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Organization Structure Determinants of Information Technology Organization Structure Determinants of Information Technology
Budgets Budgets
Tammo Heuzeroth
Copenhagen Business School
, th.digi@cbs.dk
Lennard Andreas Schlosser
Copenhagen Business School
, lesc20ae@student.cbs.dk
Ruben Paul Stroh
Copenhagen Business School
, rust20ab@student.cbs.dk
Till J. Winkler
University of Hagen
, till.winkler@fernuni-hagen.de
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Heuzeroth, Tammo; Schlosser, Lennard Andreas; Stroh, Ruben Paul; and Winkler, Till J., "Organization
Structure Determinants of Information Technology Budgets" (2022).
PACIS 2022 Proceedings
. 272.
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Organization Structure Determinants of IT Budgets
Pacific Asia Conference on Information Systems 2022
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Organization Structure Determinants of
Information Technology Budgets
Completed Research Paper
Tammo Heuzeroth
Copenhagen Business School,
Frederiksberg, Denmark
th.digi@cbs.dk
Lennard Andreas Schlosser
Copenhagen Business School,
Frederiksberg, Denmark
lesc20ae@student.cbs.dk
Ruben Paul Stroh
Copenhagen Business School,
Frederiksberg, Denmark
rust20ab@student.cbs.dk
Till J. Winkler
University of Hagen, Hagen, Germany
and Copenhagen Business School,
Frederiksberg, Denmark
till.winkler@fernuni-hagen.de
Abstract
The information technology (IT) budget of a firm is one of the key financial ratios in
information systems (IS) management and a persistent issue of discussion on corporate
boards. Yet, many firms determine their IT budgets by relying on simplistic industry
benchmarks. With the goal of understanding what structural organization
characteristics influence the IT budget, we study the associations of five constructs
measuring outsourcing, control span, and globalization of the organization with the
relative IT budget level. Our data from a multiyear cross-country survey provides
evidence for IT outsourcing being related to lower, and business process outsourcing,
control span increase, and globalization increase to higher IT budgets. Our findings
advance prior research on IS financials, which has so far centered around technology
and industry variables, by providing insight into the structural organization
characteristics that co-determine IT budget levels. Implications for the broader IS
literature and for practice are discussed.
Keywords: IS financial research, IT budget, IT governance, globalization, outsourcing
Introduction
Between 2005 and 2020, information technology (IT) budgets grew globally by 39.5% (Statista, 2021) and
became a crucial component within the budget allocation for the large majority of companies worldwide.
Despite the wide adoption and diffusion of IT in organizations in the era of digitalization, controlling the IT
budget is still one of the top management issues of organizations (Kappelman et al., 2020). The IT budget
of a firm—usually stated as the percentage of its annual revenue—is one of the key financial ratios for CIOs
and CFOs (Krotov, 2016). Its systematic determination is vital for companies that tend to compare their IT
efforts with their competition. However, many firms still rely on simplistic industry averages as a
benchmark for their IT budget (Kobelsky et al., 2008; Krotov & Ives, 2016).
While the downstream effects of IT investment have since been a center of gravity in the Information
Systems (IS) literature, fewer works have paid attention to the upstream determinants of IT budgets and
related metrics. The IS value stream of research has opened a broad discussion on the consequences of IT

Organization Structure Determinants of IT Budgets
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investments including the effects on firm performance and the productivity paradox (e.g., Bharadwaj,
2000; Brynjolfsson, 1993; Brynjolfsson & Hitt, 1996). Dewan et al. (1998), in contrast, were among the first
to analyze the upstream determinants of IT capital by examining the influence of the scope and scale of a
firm. Kobelsky et al. (2002) and Kobelsky et al. (2008) further pursued this stream of work by
differentiating internal and external factors influencing the IT budget level and they also analyzed how the
industry strategic IT role interacts with these factors. Other studies used similar approaches, adding further
perspectives such as static versus dynamic factors (Shen et al., 2015) and integrating insights from other
countries than the U.S. (Dixit & Panigrahi, 2013).
However, research on the upstream determinants on IS financials has two empirical and theoretical
shortcomings. Most prior studies in this string of work have used data from homogenous data sources (i.e.,
InformationWeek and Computerworld data). As a result, one shortcoming is that this literature has
concentrated on technology and industry variables (which are available in these sources) and thus neglected
other potentially relevant influences such as the structural conditions of a firm. Second, most studies have
used data from one single country (the U.S.) and thus lack generalizability to other geographies. Hence, it
is our conviction that in times of digitalization—where IT has become pivotal to business success—there is
a theoretical and a practical need to broaden our knowledge of the determinants of IT budgets (Kappelman
et al., 2020). This research, therefore, picks up a longstanding, yet under-addressed call for future research
of Dewan et al. (1998) stating that “[…] future research could also focus on the relation between IT and the
internal organization of the firm, examining issues such as centralization versus decentralization, span
of control, standardization, and the hypothesized flattening of the managerial hierarchy on account of
the adoption of IT” (p. 230).
Drawing on organization design theory (Daft, 2012; Mintzberg, 1980), we fist develop the idea that three
structural dimensions of firm organization have a determining influence on the IT budget level, understood
as all planned expenses related to a firm’s IT function: specialization, hierarchy of authority, and
centralization. We then draw on auxiliary perspectives, such as the IT outsourcing, IT governance, strategic
management and globalization literatures, to conceptualize within these three dimensions five constructs
(IT outsourcing degree, business process outsourcing degree, control span increase, globalization degree,
and globalization increase), which we hypothesize to be associated with the IT budget level.
Carefully preprocessed cross-country data from 1652 companies that participated in the global Business
and Information Technologies (BIT) survey between the years 2004 and 2011 (Mangal & Karmarkar, 2012)
provide support for four of the five hypotheses. Specifically, we find empirical support for our theoretical
arguments for why IT outsourcing is associated with lower IT budget levels, while business process
outsourcing, control span increases, and globalization increases are associated with higher IT budget levels.
Although our data is only slightly more recent than the data used in the past works on IS financials, we
argue that our results underline our key argument that IS research can fruitfully leverage an organization
design theory perspective when aiming to explain the upstream antecedents of IT financial metrics. The key
practical implication is that firms should not be agnostic to their idiosyncratic organizational structure
when determining their IT budget levels, even in times of digitalization. We also discuss limitations and
opportunities for future research, before closing with a brief conclusion.
Literature Review
At the beginning of this research, we reviewed the literature to gain an overview of prior works that
investigated IT financial metrics and to identify existing research opportunities. We focused on empirical
works that examined factors that influence a company’s IT budget level or related constructs as the
dependent variable. Relevant articles were primarily selected by scanning the AIS, IEEE and Scopus
databases using the keywords ‘IT budget’, ‘IT spending’, ‘IT expenditures’ and ‘IT expenses’ and performing
forward and backward searches from relevant papers. Our review identified only seven papers in this string
of IS financial research that date to the years between 1998 and 2015 and used datasets from 1988 until
2010. Table 1 summarizes the identified works including their research focus, the theoretical grounding,
data sources, and their key findings.
Prior IS financial research investigated different, but closely correlated constructs as dependent variables.
Dewan et al. (1998) take an asset perspective and use IT capital as the dependent variable, which is
described as “the total installed base of computer hardware, software, peripherals and services by a firm

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to produce revenue” (p. 225). Later work (Dixit & Panigrahi, 2013) differs from this approach by examining
the determinants of IT investment which they define as the “sum of the spending related to the computers
and IT systems, software, IT/ITES” in their analysis (p. 16). Other studies use the annual IT budget as an
indicator for the annual (budgeted) IT expenditure (Hu & Quan, 2006; Kobelsky et al., 2008). The variable
is usually scaled by the company’s sales from the previous year. Kobelsky et al. (2008) consider IT budget
to be part of the overall firm budget and argue that this includes all planned expenditures directly associated
with a firm’s IT function. They differentiate the IT budget from the actual IT spending emphasizing that, in
practice, budget underruns and overruns can occur (Kobelsky et al., 2008).
The focus of earlier research was on both, firm-internal and firm-external (i.e., environmental) factors that
may determine IT budget levels, most often tested in the same model. With regards to external factors,
Kobelsky et al. (2008) analyzed the impact of environmental complexity and found that IT budget levels
are significantly influenced by environmental uncertainty and industry concentration. This suggests that
the fewer competitors are in a market, the higher the IT budget as expenditures are not competed away.
Shen et al. (2015) complement this research by adding both, a static and a dynamic contingency perspective
to their model. While Kobelsky et al. (2002) showed a significant moderating effect of the industry strategic
IT role (i.e., whether IT has a transformative role in an industry or not) on IT budget levels, Shen et al.
Authors
Focus
Theory
Data (years)
Key findings
Dewan et
al. (1998)
Link between firm
boundary characteristics
(scale and scope) and IT
capital
Economic
production
function
framework
Computerworld
(433 US firms,
1988-1992)
Firms with a higher degree of diversification in
related lines of business, less vertical integration,
and fewer growth options have greater IT
investments.
Kobelsky
et al.
(2002)
Determinants of
corporate IT budgets
Complexity
theory
InformationWeek
(892 US firms,
1992-1997)
The industry strategic IT role has a moderating
effect on the relationship of level of earnings,
diversification, and industry concentration on IT
budget. Firms increase IT spending more in
transformative industries to manage internal
complexity related to diversification and the level
of earnings.
Hu &
Quan
(2006)
External institutional
influence on corporate
IT budgeting processes
in the financial sector
Strategy
necessity and
institutional
theory
Computerworld &
InformationWeek
(57 US firms,
1998-1996)
The influence of previous IT budget levels is
significant to determine an annual IT budget. The
impact of competitors on the IT budget of a firm is
not significant. However, the IT budget level
becomes institutionalized by the IT budget levels
of the perceived industry leader.
Kobelsky
et al.
(2008)
The effect of
environmental,
organizational and
technological
circumstances on IT
budget levels, the
Relation of IT budgets
and firm performance
Contingency
theory
InformationWeek
(562 US firms
1991-1997)
Environmental complexity, resource availability,
sales growth, and technological factors impact IT
budget levels. IT budget levels are positively
influenced by environmental uncertainty and
industry concentration. The findings for
organizational variables (operating profit,
leverage and growth) confirm the internal
affordability argument (Kobelsky et al., 2002; Hu
& Quan, 2006).
Kobelsky
&
Robinson
(2010)
The effect of IT
outsourcing on IT
spending
No explicit
theory
InformationWeek
(647 US firms
1999-2005)
Higher IT outsourcing is associated with higher IT
spending. This impact starts immediately after the
initial IT outsourcing and continues over time.
Dixit &
Panigrahi
(2013)
Determinants of IT
investments at firm level
in India
No explicit
theory
Survey (239
Indian firms,
2007-2010)
IT capital stock and slack resources determine the
level of future investment in IT. Contradictory to
other findings, industry competitiveness seems to
have no association with the IT investments in
Indian firms.
Shen et
al. (2015)
The effect of static &
dynamic environmental,
organizational and
technological
contingencies on IT
budget
Contingency
theory
InformationWeek
and
ComputerWorld
(385 US firms,
1995-1997)
Firms in transformative industries spend
considerably more on IT as they are more likely to
obtain first-mover advantages, which increases
the investment payoff. The influence of income on
IT expenditure is significant, confirming the
affordability argument of prior research. This
effect is further reinforced in transformative
industries. The effect of structural change on IT
expenditure is marginally significant.
Table 1. Previous IS Financial Research

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(2015) showed that a direct effect may exist as well. This suggests that firms in transformative industries
spend more on IT as they are more likely to obtain first-mover advantages and hence increase the payoff of
the investment. Hu and Quan (2006) added another perspective to this literature stream. They argued that
IT budgeting processes have become more institutionalized since firms tend to mimic the IT budgeting
levels of their direct competitors as well as those of the perceived industry leader. They found that the
budget levels of the perceived industry leader significantly influence the budgeting of individual firms. Their
analysis, however, was limited to the financial sector (Hu & Quan, 2006).
Firm-internal factors that have been examined by prior research revolve around two main perspectives.
The first perspective is to analyze boundary firm characteristics such as the degree of diversification, vertical
integration and growth options that may impact IT budget levels. Applying this theoretical lens, Dewan et
al. (1998) showed that firms with a higher degree of diversification in related lines of business, less vertical
integration, and fewer growth options have greater IT investments as IT is used more extensively for
coordination and control. Furthermore, Kobelsky and Robinson (2010) analyzed the impact of IT
outsourcing on the IT spending, finding that against prior research suggestions, IT outsourcing is associated
with higher IT spending. The second perspective of prior research is the internal affordability perspective,
which implies that a firm’s budgeting depends on the financial performance of the previous year. Hu and
Quan (2006) showed that the IT budget decision largely depends on the level of the IT budget level of prior
years. Kobelsky et al. (2008) confirm the validity of this view by showing that operating profit is significantly
related to IT budget levels, while financial leverage and sales growth is negatively related to IT budget levels.
This means that firms “with higher operating profit, lower leverage and fewer high net present value
investment alternatives have greater resources available to invest in IT” (Kobelsky et al., 2008, p. 975).
Dixit and Panigrahi (2013) confirmed this finding by showing that the availability of slack resources is
positively associated with IT budget levels for firms in India.
A different line of work has focused on IT investment decisions. Salge et al. (2015) investigated the
motivation of IS investment decisions within hospitals and found that IS investments are not only made to
find solutions for shortfalls, but also to ensure continuity of resource allocation as well as conformity with
external norms. Dong et al. (2021) investigated the moderating effects of a general IT investment tendency
on the relationship between performance shortfalls and IT investments. They found that firms with a
general tendency to overinvest in IT also overinvest more extensively in IT as a response to performance
shortfalls. This effect can be mitigated by corporate governance mechanisms. Xue et al. (2021) investigated
the relationship between real earnings management (REM) and the commitment to IT investment plans.
They found that REM is negatively associated with the commitment to IT investment plans and that this
link can be weakened by IT decentralization and corporate governance mechanisms such as institutional
ownership and takeover threats.
Altogether, there are comparably few works that analyze the influencing factors of IT budget levels. These
studies have analyzed a number of both firm-internal and firm-external factors. However, previous studies
have focused on a single country (USA: Dewan et al. (1998), Hu and Quan (2006), Kobelsky et al. (2002,
2008), Kobelsky and Robinson (2010), Shen et al. (2015); or India: Dixit and Panigrahi (2013)). In addition,
most articles use variations of the affordability perspective (i.e., by estimating IT budget levels from
previous year budgets) to represent internal factors. Shen et al. (2015) complemented this research by
adding the perspective of structural change within a given company. Albeit this marks a first step to include
structural factors in the analysis of IT budgets, Shen et al. only included one generic factor. Thus, further
specification and a more detailed analysis of structural factors is necessary. In the following, we therefore
draw on pertinent theory to identify other structural organization factors that may influence a company’s
IT budget level. We hence follow the original call by Dewan et al. (1998) outlined above to “focus on the
relation between IT and the internal organization of the firm” (p. 230).
Hypotheses Development
In order to advance the body of knowledge in IS financial research, we draw on organization design theory
(Daft, 2012; Mintzberg, 1980) to hypothesize the influence of key structural dimensions on the IT budget.
In accordance with Kobelsky et al. (2008), we view the IT budget as all planned expenses that can be related
to a firm’s IT function. We chose to focus on the IT budget as opposed to actual expenses because of the
previously described managerial relevance of understanding what influences IT budgeting levels.

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Daft (2012) describes structural dimensions as the internal characteristics of an organization. These include
the degree of specialization, the hierarchy of authority and the degree of centralization in an organization
(Daft, 2012). Further prominent works in organizational theory have established similar concepts.
Mintzberg (1980), for example, defines so-called design parameters of organizations which include job
specialization, span of control, and unit grouping, which are closely related to Daft’s structural dimensions
specialization, hierarchy of authority, and centralization. For the purpose of our hypothesis development,
we adopt the conceptualization of Daft’s structural dimensions. Yet, it is important to note that our
hypothesis development applies equally to other central references, such as Mintzberg’s design parameters.
Specialization in an organization refers to the degree to which organizational tasks are subdivided into
separate jobs (Daft, 2012). The extent to which organizations use outsourcing is inherently related to their
degree of specialization. For example, the review by Lacity et al. (2009) found that one reason why
organizations outsource IT or business processes is to focus on their core capabilities. This allows firms to
subdivide organizational tasks into essential and non-essential, focusing on the former with the goal to
acquire specialized knowledge and skills. This motivates us to include the degrees of different types of
outsourcing within organizations as one facet of their specialization.
The hierarchy of authority within an organization is characterized by its reporting structure, often depicted
as the vertical lines on the organization chart (Daft, 2012). Daft (2012) explains the relationship between
the hierarchy of authority within an organization and the span of control of its managers. He argues that,
when the span of control of managers is narrow, the organization’s hierarchy tends to be tall, and vice versa.
Because of this relationship, it is natural to assume that an increase in the span of control has an impact on
the hierarchical structure of an organization. Hence, we see the increase or decrease of the control span as
one way to describe the structural dynamics of the hierarchy of authority within an organization. We hence
include control span increase as another variable into our research model.
Centralization within an organization describes the concentration of decision authority (Daft, 2012). When
an organization is decentralized, decision authority is distributed across multiple structural entities.
Previous research has found that globalized organizations tend to use organizational structures with a
decentralized distribution of decision authority in order to cater for locally individual requirements
(Harzing & van Ruysseveldt, 2004; Meyer et al., 2009). We hence argue that the degree of globalization of
an organization represents one facet of its decentralization. Furthermore, we see an increase in the degree
of globalization as an important facet of the structural dynamics of centralization within an organization.
We therefore include globalization increase as a separate variable into our research model.
Extending the contingency logic implied by prior IS financial research (Kobelsky et al., 2008; Shen et al.,
2015), we will in the following hypothesize the potential effects of these organization structure dimensions
on the IT budget level of firms. Our research model is depicted in Figure 1.
Outsourcing
Our first construct is the IT outsourcing degree. Lee et al. (2004) define IT outsourcing, as “the practice of
commissioning part or all of an organization’s IT assets, people, and/or activities to one or more external
providers” (p. 128). The review by Lacity et al. (2009) substantiates that the main reason for outsourcing
IT in the early 2000s has been the reduction of costs. The inherent microeconomic rationale is that
outsourcing replaces internal costs for coordinating an activity within the firm with external transaction
costs (Lacity & Willcocks, 1995). As long as these external transaction costs are lower than the internal
coordination costs associated with the activity, outsourcing that activity leads to a cost reduction.
Additionally, outsourcing leads to the division of labor thus that the internal IT staff can focus more on
business-critical activities. For example, if IT maintenance is outsourced, the remaining IT staff can focus
more thoroughly on the development of new applications and hence support innovation. This can lead both
to a better performance with regards to these critical activities and to greater efficiency since the remaining
staff can work in a more specialized and focused manner. Consequently, we hypothesize that companies
with a higher IT outsourcing degree exhibit lower IT budget levels.

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However, it is worth noting that numerous academic works found that IT outsourcing attempts do not
always lead to the desired cost reductions (e.g., Barthélemy, 2001; Koh et al., 2004; Lacity & Willcocks,
1995). Additionally, Kobelsky and Robinson (2010) analyzed the impact of IT outsourcing on the IT
spending of firms—and while they hypothesized higher IT outsourcing to be associated with lower IT
spending, they found the opposite association. The authors explained their findings with goal of IT
outsourcing to enhance IT capabilities, which in turn increases costs and budgets. Our hypothesis will
therefore allow us to re-assess whether the common rationale to reduce costs through IT outsourcing
actually translates into IT budgeting reality, or not.
H1: The IT outsourcing degree is negatively associated with the IT budget level. The higher is the degree
of IT outsourcing of a firm, the lower is its IT budget level.
The cost efficiency rationale also applies to business process outsourcing (BPO) (Lacity et al., 2009).
However, the cost reductions from BPO will only manifest themselves in the budget of the respective
business area, rather than the IT budget (Lacity et al., 2011). Further, the outsourcing of business processes
will presumably increase the coordination costs between the internal IT and the external business
processes. This could be due to higher communication efforts in comparison to the previous internal
communication as a result of cultural or time-zone differences or because the outsourcing company uses
different software and standards. In accordance with this, Dewan et al. (1998) found that external processes
compared to internal ones can lead to higher costs for IT in the context of vertical integration. Thus, we
derive the following hypothesis:
H2: The BP outsourcing degree is positively associated with the IT budget level. The higher is the degree
of BP outsourcing of a firm, the higher is its IT budget level.
Span of Control
The span of control of managers in an organization is a key aspect of the organization’s hierarchy of
authority. Daft (2012) argues that organizations with a wider managerial control span exhibit a flatter
hierarchical structure. Previous work has produced inconsistent results as to whether the hierarchy of
authority is related to investment into information systems (Winkler & Wessel, 2018). Based on the analysis
of various spending indicators, Hitt and Brynjolfsson (1997) found that IT investments are broadly
associated with a work system that favors decentralized authority. Lee and Grover (1999), in contrast, did
not find any association between measures of IT and the centralization level of an organization. The
management literature, however, has generally argued that organizations with flat hierarchical structures
tend to prioritize flexibility to enable innovation (Georgsdottir & Getz, 2004) rather than focusing on
efficiency.
We draw on the IT governance literature as an auxiliary perspective to translate this rationale into the IT
context. The distribution of decision rights has been a central concern in the IT governance literature
Figure 1. Research model: Organization structure determinants of the IT budget level

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(Winkler & Wessel, 2018). It is a common rationale in the IT governance literature that firms competing on
cost and scale tend to centralize IT decisions rights compared to those competing on differentiation and
innovation (e.g., Weill & Ross, 2004). This is because in organizations that seek innovation will distribute
decision rights to ensure greater IT responsiveness to the needs of their customers and employees, which
in turn implies some level of redundancies and lower efficiency in IT. We assume the same rationale holds
for organizations that decentralize authority by widening the control span of managers. Firms which focus
on innovation rather than efficiency require a more flexible IT landscape in order to enable their innovative
capacities, which is in turn more costly. We thus argue that firms which desire to evolve to a flatter
hierarchical structure likely increase their IT budgets. On the contrary, firms which seek to evolve to a
deeper hierarchical structure and thus narrow their managers’ span of control likely decrease their IT
budgets with the goal of IT efficiency. We can posit:
H3: Control span increase is positively associated with the IT budget level.
The higher is the control span increase, the higher is the IT budget level.
Globalization
The degree of globalization of a company is one indicator of its structural decentralization. Daft (2012)
points out that globalization requires new organizational designs to achieve coordination and flexibility. In
the context of IT, this involves a globally aligned information management strategy defining data
communication infrastructures, architectures, interfaces and databases (Karimi & Konsynski, 1991). For
example, an early study by Karimi et al. (1996) found that effective IT integration within firms is a
prerequisite of firms’ willingness to leverage IT for responding to globalization. This seems logical since
subsidiaries in dispersed geographical regions need to communicate, exchange information, and share
systems with each other (Karimi & Konsynski, 1991). We argue that such coordination effort will be
permanently reflected in a higher IT budget and will grow with the number of geographically dispersed
operations a company maintains. Consequently, we hypothesize that companies with a more globalized
operational footprint exhibit higher IT budgets.
H4: Globalization degree is positively associated with the IT budget level.
The higher is the globalization degree, the higher is the IT budget level.
The strategic management literature has also looked at the resources that firms need specifically to enter
into new geographical regions (Meyer et al., 2009). A new market entry poses high coordination efforts as
new information systems, communication procedures and tools must be developed and aligned in the early
stages. More recent studies specifically highlight two types of information technology resources that are key
for companies to implement in to increase their export readiness: backbone IT systems such as advanced
manufacturing technologies (Uwizeyemungu et al., 2018; Winkler & Kettunen, 2018) as well as business
intelligence systems that provide them with insight into global markets (Li & Lakzi, 2021). Hence, when a
company strategically aims to expand globally, this may incur extra IT costs that are different from the
continual maintenance of global systems addressed by H4. We therefore argue that it is not only the actual
degree of globalization impacts a company’s IT budget, but also that a planned globalization increase that
will manifest itself in higher IT budgets. Due to these reasons, we posit:
H5: Globalization increase is positively associated with the IT budget level.
The higher is the globalization increase, the higher is the IT budget level.
In addition to the hypothesized five structural characteristics, our research model (cf. Figure 1) also
accounts for other influences by controlling for the size of the firms’ IT units, their IT intensity, customer
focus, industry, and the year of measurement. We also control for the country in which the data was
collected to account for potential geo-economic biases and make the findings from our cross-country
dataset internationally applicable.
Methods
We tested our research hypotheses with data from a cross-country survey using OLS regression methods.

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Data Sources and Preparation
Raw data was obtained from the Business and Information Technologies (BIT) survey network. The BIT
survey was initiated after the turn of the new millennium by researchers at UCLA Anderson and partnering
institutions as a global study of the changes in business practices driven by IT (Mangal & Karmarkar, 2012).
The standardized survey instrument contained a broad set of questions intended to track the adoption and
outcomes of the organizational use of information technology. The primary group of respondents were the
Chief Information Officers (CIOs) and managers in similar IT leadership positions in their respective
organizations. Between 2004 and 2011, a total of ten surveys had been conducted by participating
researchers in eight countries. While this data was acquired more than ten years ago, the research team
obtained a unique access to this data only recently. We will discuss to which extent our insights from this
data can still be of relevance in today’s times of digitalization.
Although the BIT surveys used a standardized instrument, the local data collections employed different
survey administration methods (e.g., mail, online, and phone) resulting in different levels of data quality
and completeness. We first had to engage in a several steps of data preparation and validation. The
unprocessed dataset consisted of 3600 entries. First, all entries with a missing value for the IT budget
variable were removed. After that, we built our relative measure for the IT budget level. The IT budget was
asked to be stated as a percentage of the annual sales in the surveys. Several respondents (498), however,
erroneously stated the IT budget as an absolute number. We divided the respective IT budgets by the annual
sales included in the survey and checked the result for plausibility. Some implausible entries with IT budget
values smaller than or equal to zero and above 100 had to be removed. Since the relative IT budget level has
a right-skewed distribution that would produce non-normally distributed residuals in an OLS regression,
we calculated the logarithm of the relative IT budget value. Finally, we removed outliers from the log-
transformed IT budget level by applying the 1.5 IQR rule (Zhao et al., 2013).
In the next step, we replaced missing values where reasonably possible. For some of the items, we used the
logical default of the respective ordinal scale. For example, if it was asked to what extent the respondent
agrees or disagrees to a certain statement, the logical default would be neutral in the middle of the scale.
For the industry classification, we transformed classifications other than the requested SIC codes and
textual descriptions into the eight top-level groups of SIC codes and filled complete missing values using a
multinomial regression model based on industry dichotomy questions. All data preprocessing, imputation,
and statistical analyses were performed in Python using statsmodels, scikit-learn and other packages.
The final dataset consisted of 1652 entries from 8 different countries with observations at 15 different points
in time. To prepare for the OLS regression, we transformed the categorical country and industry values to
dummy variables (country reference category: USA; industry reference category: Public Administration).
The year values (2004-2011) were re-scaled from 0-7 to avoid skewed coefficients in the regression model.
Table 2 shows an overview of the resulting country datasets included in our analysis after preprocessing.
Country
Chile
Columbia
Germany
India
Korea
Spain
Taiwan
USA
Samples
211
106
201
110
220
348
365
91
Years
2005
2007/08
2006/11
2004/06
2005/06
2005/07/09
2005/09
2005
Table 2. Overview of country samples
Construct Operationalization
Since the standardized BIT survey was designed to assess a broad set of IT and organization-related issues,
rather than measuring psychometrically validated research constructs, we engaged in substantial data
exploration efforts to identify factors that would fit our theoretical model. Specifically, we conducted
explorative factor analyses (EFA) using a principal component analysis (PCA) as the extraction method and
varimax as the rotation method to identify clear-cut factors in the question blocks of the survey. Only items
with loadings above 0.6 on one factor and less than 0.3 on other factors were considered for the further
analysis. Similar thresholds have been used by previous research (Chin et al., 1997; Wulf et al., 2015). As a
result of this procedure, we operationalized the organization structure constructs as follows, see Table 3.

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Pacific Asia Conference on Information Systems 2022
9
The IT outsourcing degree was measured through 5 items that capture the extent to which several IT sub-
functions are sourced out by a company (cf. Table 3). Analogously, the business process outsourcing degree
was measured through 3 items describing the extent to which different business processes are sourced out
(cf. Table 3). The control span increase of firms was measured by two items, describing the span of control
increase as such and whether an organizational structure is becoming flatter (cf. Table 3). We argue these
items as reflective indicators, since they measure a similar matter and the question outcomes are caused by
the underlying constructs (MacKenzie et al., 2011).
The globalization degree of a given company can be thought of as its spread across the globe (Asmussen et
al., 2007). Thus, we measured the globalization degree and the globalization increase as composites, where
each of 10 global regions (cf. Table 3) is a formative indicator. Globalization degree items asked whether a
company already operates in a certain region (binary scale: yes/no) while the globalization increase items
Constructs
Description
Item(s)
Item scale level
Dep. var.
IT budget level
IT budget level as a
percentage of the annual
sales of a given firm
IT budget level
Metric (Percentage)
Independent variables
IT outsourcing (ITO)
degree
Degree of IT outsourcing
of a given firm
Programming; data center
operations; network management;
data management; customer IT
support
3-point-scale (not
outsourced, partially
outsourced, outsourced)
Business process
outsourcing (BPO)
degree
Degree of business
process outsourcing of a
given firm
Accounting; finance; order fulfillment
3-point-scale (not
outsourced, partially
outsourced, outsourced)
Control span increase
Increase of the control
span of managers in a
given firm
Span of control is increasing;
organizational structure is becoming
flatter
5-point-Likert (strongly
disagree, disagree,
neutral, agree, strongly
agree)
Globalization degree
Degree of globalization of
a given firm
USA; Canada and Mexico; Latin
America; Western Europe; Central
and Eastern Europe; Africa; Middle
East; SE Asia; South Asia; East Asia
Binary (have / not have
operations in region)
Globalization increase
Planned geographical
expansion of a given firm
USA; Canada and Mexico; Latin
America; Western Europe; Central
and Eastern Europe; Africa; Middle
East; SE Asia; South Asia; East Asia
Binary (expansion
planned / expansion not
planned)
Control variables
IT unit size
Size of the firm‘s IT unit
in terms of employees
Total number of IT employees
Metric
IT intensity
Number of technologies
adopted by a given firm
ERP; SCM; BI; Productivity;
Collaboration; E-Commerce; RFID;
Biometrics; etc. (19 overall)
Binary (have / not have
technology)
Customer focus
Degree to which a
company focuses on their
customers through
technology-mediated
channels
Phone Text Messaging; Automated
phone response; Phone-computer
technology integration; Website;
Screen pop; Online intermediary
Binary (have / not have
customer channel)
Industry
Industry sector of a given
firm according to SIC
code groups
Agriculture; Construction; Finance;
Insurance & real estate;
Manufacturing; Mining; Public
administration; Retail trade;
Wholesale trade*
Categorical
(dummy coded)
Year
Year of the data collection
Year (scaled to 0-7)
Metric (integer)
Country
Country where the data
was collected and the firm
had a headquarter
Chile; Columbia; Germany; India;
Korea; Spain; Taiwan; USA*
Categorical
(dummy coded)
* Reference categories without dummy coding
Table 3. Overview of research constructs

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Pacific Asia Conference on Information Systems 2022
10
asked whether a company is planning to expand to a new global region in the coming three years (same
scale). These items are formative indicators since the regions a company operates in, and plans to expand
to, collectively determine the company’s globalization degree and globalization increase, respectively
(MacKenzie et al., 2011). The scores for these two composites were calculated from a PCA following Petter
et al. (2007) who suggest using “principal components analysis (rather than common factor analysis) to
examine the item weightings for measures” (p. 642) in order to validate formative constructs.
In terms of control variables, IT unit size was measured by the log-transformed number of IT employees.
IT intensity was measured as a count of information technologies adopted by the firm. Customer focus
refers to the degree to which a company employs technology-mediated channels to communicate with their
customers and was measured as composite of six possible channels (binary scale: have / not have). The
industry was measured by the eight top-level groups of the SIC codes and used in dummy coded format.
The year of the respective survey was stated by an integer scaled to 0-7. And lastly, the country of a firm
was stated as a categorical symbol and coded as nine dummies.
Table 3 provides an overview of the research constructs used in this study, including their description, their
items, and their scale levels. Table 4 shows the loadings and weights of the reflective and formative
indicators for their respective constructs (only hypothesized constructs are shown).
Loadings of reflective indicators
Weights of formative indicators
Question item
1
2
3
Question item
4
5
Programming
.830
USA
.372
.300
Data center operations
.897
Canada & Mexico
.312
.264
Network management
.885
Latin America
.304
.301
Data management
.898
Western Europe
.383
.226
Customer IT support
.801
Central/Eastern Europe
.332
.381
Accounting
.914
Africa
.239
.289
Finance
.940
Middle East
.296
.268
Order fulfillment
.898
SE Asia
.349
.383
Increasing control span
.835
South Asia
.268
.356
Flattening of structure
.835
East Asia
.277
.354
Variables: 1=IT outsourcing degree; 2=business process outsourcing degree; 3=Control span increase; 4=Globalization degree;
5=Globalization increase
Table 4. Loadings and weights of constructs
To test our stated hypotheses, we estimated the following model (including transformations):
log (IT budget level) = 𝛽0 + 𝛽1 * ITO degree + 𝛽2 * BPO degree + 𝛽3 * Control span increase
+ 𝛽4 * Globalization degree + 𝛽5 * Globalization increase + 𝛽6 * log (IT unit size) + 𝛽7 * IT intensity
+ 𝛽8 * Customer focus + ∑𝛽9+𝑖
8
𝑖=0 * 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖+ 𝛽17 * (Year – 2004) + ∑𝛽18+𝑗
7
𝑗=0 * 𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑗 𝛽𝑗 * + ε
Results
We first describe the sample characteristics and analytically establish the validity of our measures before
we present the hypotheses test results.
Measurement Validity
Table 5 shows the descriptive statistics of all model variables excluding categorical variables (industry and
country) along with quality indicators and cross-correlations. On average, the companies of our sample
allocated 6.51 percent of their annual sales for their IT budgets and had 50.8 IT employees. Internal
consistency of the factors with reflective indicators (ITO degree, BPO degree, control span increase) is
supported by sufficient levels of composite reliability (cr > 0.70) (Hair et al., 2011).
For the composites with formative indicators (globalization degree, globalization increase, customer focus)
variance inflation factor (VIF) values are far below the common threshold of 10 (Belsley et al., 1980),
indicating no issues with multicollinearity between their indicators (cf. Table 5). As some variables are
highly correlated (cf. Table 5), we also computed the VIF on a model level. The highest VIF score in the non-
categorical variables is 2.16 (ITO), hence, multicollinearity does not seem to be an issue on the model level.

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Hypotheses Tests
Table 6 shows the results of our OLS regression model using a simplified notation for the categorical
variables industry and country. Hypotheses were tested for significance at the p<.05 level based on one-
tailed tests.
Our data supports the hypothesis that the IT outsourcing degree is associated with the IT budget level (H1).
The higher the IT sourcing degree, the lower the IT budget and vice versa (β = -.139; p < .010); H1 is
supported. The business process outsourcing degree is also associated with the IT budget level, yet—as
hypothesized (H2)—in the opposite direction. The higher the business process outsourcing degree, the
higher is the IT budget (β = .150; p < .010); H2 is supported, too.
Our data further confirms that a control span increase is positively associated with the IT budget level (H3).
This means that flattening organizations have a higher IT budget, while organizations becoming steeper in
terms of hierarchy have a lower IT budget (β = .067; p < .050); H3 is supported.
We find no support for the globalization degree being associated with the IT budget level (H4). The
coefficient is close to zero and not in the hypothesized direction (β = -.043; p < .20); H4 is not supported.
We do find support, however, for the association of globalization increase with the IT budget level (H5).
The higher the planned globalization increase of a firm, the higher is its IT budget. This variable has the
strongest effect of all hypothesized associations (β = .256; p < .010); H5 is supported.
The control variables in our model are partially significant. IT unit size exhibits a positive coefficient and is
significant, indicating that firms with more IT employees tend to have higher relative IT budget levels. The
IT intensity of a firm is positively but not significantly related to the IT budget, corroborating that increased
technology adoption may drive IT budgets up. Interestingly, the customer focus is negatively and
significantly related, suggesting that firms with more technology-mediated customer interfaces actually
tend to have lower IT budgets. Most of the industry dummies are not significant in our sample except
Mining (negative effect). The year of measurement has a negative coefficient and is significant as well,
indicating that IT budget levels generally decreased in the observation period. The country coefficients show
a heterogeneous distribution with three country dummies being significantly related to the IT budget level,
indicating that the country a firm has its headquarters in can be a significant influence for its IT budget
level.
The R2 of our model is 20.40% (adjusted R2: 19.20%), which is similar to models from previous research
that did not include the IT budget of the previous year as an explanatory variable (Kobelsky et al., 2002,
2008). This relatively small R2 score indicates that structural factors itself explain only some portion of the
IT budget level of a firm and that other factors outside our nomological model influence the IT budget as
well.
Va
mean
std
min
max
cr/νc
1
2
3
4
5
6
7
8
9
1b
6.51
14.1
.025
100
-
2
0
1
-1.59
2.35
.936
.029
3
0
1
-1.59
3.42
.941
.067**
.675**
4
0
1
-3.48
2.19
.821
.065**
.111**
.101**
5
0
.92
-.627
2.50
2.95
-.01
.156**
.054*
.065**
6
0
.42
-.164
2.96
2.02
.092**
.095**
.089**
.087**
.081**
7
50.8
292
0
7000
-
.171**
.118**
.016
.023
.219**
.043
8
8.96
3.46
0
19
-
.031
.213**
.129**
.089**
.259**
.056*
.393**
9
0
.719
-.532
1.91
2.19
.059*
.075**
.036
.084**
-.03
.081**
.172**
.067**
10b
2007
1.834
2004
2011
-
-.142**
.088**
.102**
-.09**
.135**
.017
-.037
.203**
-.149**
a Variables: 1=IT budget; 2=IT outsourcing degree; 3=Business process outsourcing degree; 4=Control span increase; 5=Globalization degree;
6=Globalization increase; 7=IT unit size; 8=IT intensity; 9=Customer focus; 10=Year
b Variable descriptives before transformation; c composite reliability for reflective factors and highest VIF value for formative composites
*Correlation is significant at .05 level (two-tailed test); **Correlation is significant at .01 level (two-tailed test); n=1652
Table 5. Descriptive statistics, quality criteria, and cross-correlations.

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Discussion
This research built on the idea that the structural dimensions of organizations, including their
specialization, the hierarchy of authority, and centralization, are potentially relevant determinants of the
IT budget level, beyond the already known technology and industry contingencies. With this, we followed a
longstanding, yet unaddressed call by Dewan et al. (1998) to investigate the relation between the internal
organization of the firm and IT-related indicators. We drew on organization design theory (Daft, 2012;
Mintzberg, 1980) and auxiliary perspectives to derive two constructs for the specialization dimension (IT
outsourcing degree, business process outsourcing degree), one construct for the hierarchy of authority
(control span increase), and two constructs capturing spatial decentralization of a firm (globalization
degree, globalization increase). We hypothesized associations of these five constructs with the IT budget
level and tested our hypotheses with carefully preprocessed data from a multi-country, multi-year dataset
obtained from the global BIT network (Mangal & Karmarkar, 2012). Our research model additionally
controlled for some of the technology and industry characteristics that have been the focus of prior research,
next to other potential biases inherent to our method.
Our finding that four of the five constructs are significantly related to the IT budget level is, first of all, an
extension of the sparse string of IS financial research. Our results provide new insights into the structural
organization characteristics that co-determine IT budget levels. With this, we extend the literature that
focuses on the upstream determinants of IT financials (e.g., Dewan et al., 1998; Kobelsky et al., 2008;
Kobelsky & Robinson, 2010). Although our data is only slightly more recent than the data in comparative
studies in this string of research (cf. Table 1), we believe our research makes a case for why structural
characteristics of organizations should generally be taken into account when determining IT budgets levels.
Under the assumption that the demonstrated relationships still hold in times of digitalization, we discuss
the contributions of this research to the auxiliary literatures in the following.
In terms of firm specialization, our twofold findings regarding the degree of IT outsourcing (H1) vis-à-vis
business process outsourcing (H2) contribute to the literature on outsourcing in IS. The outsourcing
literature has primarily focused on the decision for, and the outcomes of, outsourcing (Lacity et al., 2010).
Although the desire for cost reduction has repeatedly been reported as the primary motive for IT
outsourcing, there is surprisingly little evidence on whether such cost saving outcomes are actually achieved
and thus reflected in IT budgets (ibid). The cost saving effects of IT outsourcing have, in fact, remained
controversial in the academic and practitioner literature (Barthélemy, 2001; Ho & Atkins, 2010; Koh et al.,
2004). Kobelsky and Robinson (2010), who hypothesized that IT outsourcing leads to lower IT spendings,
found the opposite and concluded that IT outsourcing is primarly used for enhancing IT capabilities. In
contrast to Kobelsky and Robinson (2010), our findings suggest that a greater degree of IT outsourcing, at
Hyp.
Variable
β-coefficient
Std. Error
t-value
p-value
Result
-
Const
-.068
.661
-.103
.918
-
H1
IT outsourcing degree
-.139
.052
-2.67
.004*
Sup.
H2
BP outsourcing degree
.150
.049
3.06
.001*
Sup.
H3
Control span increase
.067
.037
1.80
.036*
Sup.
H4
Globalization degree
-.043
.045
-.955
0.170
N/S
H5
Globalization increase
.256
.088
2.93
.002*
Sup.
-
IT unit size
.162
.028
5.69
.000*
-
-
IT intensity
.013
.011
1.18
.119
-
-
Customer focus
-.222
.056
-3.94
.000*
-
-
Industry
Services(+), Agricult(+), Construct(-), Manufact(+),
Transport(+), Fin(+), Retail(+), Wholesale(+), Mining(-)*
-
-
Year
-.104
.025
-4.16
.000*
-
-
Country
Chile(-), Columbia(+)*, Germany(-), Korea(+)*, India(-), Spain(+),
Taiwan(-)*
-
R2
.204
Adjusted R2
.192
N
1652
*=significant at p < .05; for categorical variables: (+) = positive coefficient, (-) = negative coefficient
Table 6. OLS regression results

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13
large, goes along with lower IT budget levels (H1). Hence, it supports the view that long-term IT efficiency
gains have been realized after outsourcing. A possible explanation for the deviation from the results of
Kobelsky and Robinson (2010) could be the different time of measurement. As our data was collected at a
later period of time, firms may have experienced a learning curve on how to outsource IT more successfully.
Our support for the hypothesis that business process outsourcing is associated with IT budget levels (H2)
taps into new territory of outsourcing research in IS. The majority of works that addressed the outsourcing
of entire business processes have remained tacit regarding the financial implications of business process
outsourcing for IT (Lacity et al., 2011). Bardhan et al. (2006), who investigated the effect of IT investments
on the outsourcing of production processes, found a positive relationship, arguing that high IT investments
enable firms to outsource business processes. Our causal reasoning was reverse, since we hypothesized that
business process outsourcing may lead to cost increments for providing and running underlying inter-
organizational information systems that integrate company operations with their business process
outsourcing partners. However, our measured (positive) association between BP outsourcing and IT
budgets is consistent with Bardhan et al.'s (2006) findings. Further, our reasoning is also consonant with
Dewan et al. (1998) who concluded that less vertical integration of a firm requires more external
coordination and thus leads to higher IT investments since IT is used for communication and exchange with
other value chain participants. Together, our findings regarding the specialization dimension add to the
outsourcing literature in IS by highlighting the distinct IT budget implications of IT outsourcing and
business process outsourcing.
Our finding regarding the hierarchy of authority dimension (H3) can be seen as a contribution to IT
governance research in IS. Prior research has produced inconsistent results regarding the relationship
between decision making structures and investment in information systems (e.g., Hitt and Brynjolfsson,
1997; Lee and Grover, 1999). We found empirical support for our argument that an organization’s tendency
towards a widening span of managerial control is significantly related to increasing IT budget levels due to
some companies’ desire to prioritize IT innovation over efficiency (Georgsdottir & Getz, 2004). However,
while prior IT governance literature has widely argued for the correlation of decentralized governance with
greater responsiveness and innovation goals, there has been a lack of evidence of whether such goals also
actually translate into higher IT budget levels. Our H3 finding therefore substantiates that greater
innovation through decision rights decentralization seems to have its ‘price tag’ in terms of higher IT budget
levels.
The third organization design dimension employed in this research was (de)centralization. Two facets of
centralization were assessed, the spatial globalization degree and the planned geographical expansion. Here
our nuanced findings imply that it is not the degree of global operations per se that drives relative IT budget
levels (H4 not supported), but the strategic intent to drive globalization forward that leads firms to make
higher investments in IT (H5 supported). Companies face IT budget surpluses for each additional global
region that they enter. The finding regarding H5 is a contribution to the strategic management literature
that has focused on firm resources required for global market expansion. This literature lacks an
understanding of the crucial role of information technology resources for globalization (Karimi et al., 1996;
Karimi & Konsynski, 1991). Few recent works in IS have advanced the literature by highlighting the role of
backbone systems and market analytics in preparing a successful global market entry (Li & Lakzi, 2021;
Uwizeyemungu et al., 2018). Our novel contribution is to provide evidence that technology investments for
global expansions are actually reflected in increased IT budget levels. Taking our findings on the
(de)centralization dimension (H4 and H5) together, our results suggest that, once IT investments for the
expansion are made, globally operating firms gain additional efficiency in IT and manage to revert to normal
IT budget levels. In other words, it is not about being global, but about going global what drives IT budgets
up.
Practical Implications
The key practical implication of this research is that executive boards need to take into account their specific
firm structure when making strategic decisions about their IT budget levels, all the more in the era of
globalization and digitalization. Our results might be of particular interest for IT consultancy firms that
advise companies in benchmarking and determining their IT budget levels (e.g., Gartner, McKinsey,
Accenture). While such benchmarking approaches typically consider size and industry influences, our work
provides that degrees of outsourcing, hierarchy, and globalization are additionally relevant influences that

Organization Structure Determinants of IT Budgets
Pacific Asia Conference on Information Systems 2022
14
co-determine industry-typical IT budget levels.—Whether such refined benchmarks are then considered to
be adequate for the specific company is another question that CIOs and their boards need to tackle.
Limitations and Future Work
The following limitations merit consideration. First, due to the collection methods of the BIT survey, data
quality for some of the variables (industry) was moderate, which may have influenced the few significant
findings regarding this control variable. Second, since our data is from a 2004-2011 timeframe, IT budget
levels and their relative structural influences may have changed until today. For example, the increased
sourcing of services from cloud vendors, which are contracted directly from business departments, may
alter the influence of the IT outsourcing level on IT budgets. Third, although our data stems from different
socio-economic geographies that we controlled for through country dummies, generalizations still need to
be made with care since our data cannot necessarily be regarded as a globally representative sample. Lastly,
our statistical analyses only ascertain association, not the causation inherent in our theoretical arguments.
Future work can extend the sparse string of IS financial research in multiple compelling directions. For
example, authors might want to collect more recent data to track how IT budget and spending levels have
changed in times of digitalization. Especially fruitful would be research designed to unveil the under-
researched discrepancies between planned IT budgets and actual IT spending. In this context, research also
needs to take into account the hard-to-measure budgets for ‘shadow IT’ and ‘business-managed IT’ run in
and paid for by business departments. Another promising area for IS financial research could be to correlate
IT budgets with the ‘technical debt’ that companies accumulate at the cost of keeping their IT assets up to
date. IT business value research in IS has predominantly used a ‘soft’ understanding (and accordingly latent
measures) of IT value and its antecedents. Overall, we believe that this soft perspective can be strengthened
by more research on hard measures of popular management ratios such as IT budgets, IT investment, and
IT spending levels, all of which are of high relevance to practice still today.
Conclusion
Motivated by the idea that organization structure is an under-researched co-determinant of key IS financial
ratios, this research investigated the relationships between facets of specialization, hierarchy, and
centralization aspects and the relative IT budget level of the firm. Comprehensive data from a cross-country
survey provide evidence for our theoretical arguments that IT outsourcing decreases IT budgets, while
business process outsourcing, control span increases, and global expansions increase IT budget levels. Our
findings contribute to the outsourcing, IT governance, and strategic management literatures by highlighting
the explanatory value of the established, but underappreciated organization design theory dimensions for
determining key managerial variables such as the IT budget level. The key practical implication of this
research is that companies need to take into account the idiosyncratic structural characteristics of their
organization when determining their IT budget levels. We hope that future research can build on our
organization design theory perspective to strengthen the fine but important line of financial research in IS.
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