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The Relation between IT Governance & Management Capabilities and Economies of Scale in Healthcare Organizations

Marcel Schmidt - S2347482

Healthcare Management Focus Area University of Groningen

Supervisors: Prof. Dr. Egon Berghout Dr. U. Woudstra

Co-assessor: Dr. M. L. Hage

July 2018

Word count: 12180

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Abstract

IT investments within healthcare organizations are of paramount importance to transformative healthcare that is focused on improving value for patients. The impact of IT investments on clinical decision making and the ability to improve outcomes of care are promising. This research was conducted to establish a relationship between IT governance and management capabilities, also called IT maturity factors in this paper, and organizational performance. Goal was further to find out how long it takes until IT

management and governance capabilities result in increased organizational performance. We analyzed a dataset including benchmark data from years between 2006 and 2014 of 49 Dutch hospitals provided by consultancy M&I Partners using correlation and regression analysis. Our results provide partial evidence for a positive and significant relationship between IT management and governance capabilities and organizational performance. We found tentative evidence pointing toward a time of one year between levels of IT management and governance capability and the greatest influence on organizational performance. However, the overall results of this part of the analysis are not significant and further research is needed to establish reliable conclusions. Theoretical and managerial implications are discussed.

Keywords: IT, IT management and governance capabilities, IT maturity, time lag effect, healthcare, IT Conversion Process, Business Conversion Process

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IT GOVERNANCE & MANAGEMENT CAPABILITY IN HEALTHCARE Change Management

Table of Content

Introduction and research questions……….…..4

Literature review and hypotheses………..8

Model………8

Economies of scale in business and IT……….9

Time lags, productivity, and hypotheses……….10

Method ……….15

Research context and data gathering………...15

Variables……….16

Operationalization of variables………...18

Planned analysis………..18

Concerns about validity………..19

Reliability of the data………..20

Results………..22

Factor analysis………22

Correlations……….24

Hypothesis testing………...29

Summarized results……….33

Discussion and conclusion………...36

Theoretical and managerial implications………38

Limitations and suggestions for further research………39

Conclusion………..41

References………42

Appendix………..47

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Introduction and Research Questions

In a more and more digitalized world information technology (IT) investments are of paramount importance to the future performance and survival of any organization. This is particularly the case for healthcare organizations. Beginning of last year the New York Times wrote that not the aging population but rather increasing advancement of technology is responsible for at least one third and as much as two thirds of per capita health care spending growth (Frakt, 2017). In the Netherlands the health care

expenditure was 13.2% of GDP in 2010 and will continue to increase dramatically to between 22 and 31% of GDP in 2040 (Van Ewijk, Van der Horst, & Besseling, 2013). In an article from 2012 US researchers demanded that no further investment cuts in healthcare IT should be taken. They mention the importance to invest in healthcare IT both from a financial and from a care-related viewpoint (Figlioli &

Spooner, 2012). The investments in IT are supposed to reduce government spending on healthcare in the long term, thereby saving costs. The in 2012 up-to-date implementation of electronic health records (EHR) in the USA was supposed to support new models of care delivery in the future (Figlioli &

Spooner, 2012). In the Netherlands EHR are a topic since more than two decades. Michel-Verkerke, Margreet, & Spil (2002) reported that around 25% of Dutch hospitals had adopted some form of EHR in 1999, but also outlined that it is an extended and difficult process. This was underlined by a systematic literature review on the implementation of EHR in hospitals by Boonstra, Versluis, and Vos (2014). They concluded that while EHR are anticipated to have a positive impact on the performance of hospitals the implementation is complex and often riddled with problems. Their propositions on how to improve implementation success are mainly related to managerial processes. Totten (2012) argues that one of the critical areas for investments that has the potential to lead to transformative healthcare is IT. EHR and other information technologies are important factors that are aimed at leading toward a transformation of processes and clinical practice. Other impacts of IT in healthcare are to support clinical decision making

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to ultimately improve the outcomes of care. To facilitate the transformation of care, governance and management mechanisms like aligning IT strategy with business strategy, monitoring of IT initiatives and spending, and increasing of communication and understanding of IT are necessary (Totten, 2012). These mechanisms that lie at the heart of critical transformations in healthcare organizations will be the focus of this study.

Given the importance of IT for organizations one could raise the question in what kind of IT an organization should invest for the future. Research suggests that the question regarding what kind of technology should be acquired is of less importance due to the commoditization of IT (Carr, 2003). If organizations have the same access to the same technology, what are possible sources of competitive advantage to outperform competitors? To be able to answer this question we first want to make use of the resource-based view (RBV) on organizational performance. RBV claims that organizational performance is connected to firm-specific resources and capabilities, which are non-substitutable, rare, and inimitable (Barney, 1991). Due to the commoditization of IT the technology itself is therefore not a good resource to drive firm performance. However, numerous studies suggest that IT can still be a source of competitive advantage if it is used in combination with other resources. One example of these is the finding that the complementary deployment of IT with human resources leads to improved organizational performance (Powell & Dent-Micallef, 1997). This suggests that management and other capabilities may play a big role in realizing benefits from IT investments.

Despite the importance of IT investments, it is still unknown how long it will take till an IT investment will result in increased firm performance and what mechanisms play a crucial role in this process. One of these mechanisms is IT management capability. According to Peppard (2007) IT management capability is: “(…) to understand the role that IT can play in the production of business value and to therefore manage the delivery of this value through IT” (p. 3). As a result, IT management capability is the IT staff’s ability to deliver business value through the use of IT. This shows the

complementary use of technology together with human resources to achieve organizational goals. A

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related mechanism is IT governance. According to De Haes and Van Grembergen (2009) IT governance comprises: “(…) the leadership and organizational structures and processes that ensure that the

organization’s IT sustains and extends the organization’s strategy and objectives” (p. 1). As you can see, also this mechanism is directly related to the complementarity of IT and human resources to achieve higher objectives. The interrelatedness of these two mechanisms let researchers develop a theoretical perspective on IT resources that combines management and governance capabilities to investigate how IT resources can lead to increased organizational performances (Aral & Weill, 2007; Kim et al., 2011). Kim et al. (2011) were able to demonstrate that a combination of IT management capabilities, IT personnel expertise, and IT infrastructure lead to increased organizational performance. This, again, points toward the complementary use of IT in combination with other resources to facilitate firm performance. Aral and Weill (2007) investigated how resource allocations and organizational differences explain performance variation. Their findings point towards a multifaceted view of IT in organizations in which organizations derive greater value from IT investments by having stronger organizational IT capabilities. These IT capabilities act as: “(…) a mutually reinforcing system of practices and competencies that both strengthens and broadens the performance impacts of IT” (Aral & Weill, 2007, p. 15). IT management and governance capabilities act together in influencing how IT investments are converted into

organizational performance. Wu, Straub, and Liang (2015) conducted research on this connection, showing that IT governance mechanisms and IS strategic alignment (IT management) together have a positive influence on organizational performance. Their conclusion is that IT governance mechanisms allow for a context in which IT management capabilities can be best used to increase organizational performance. All this research stresses the complementarity of IT governance and management capabilities, in which the alignment of IT resources with business practices and processes leads to superior organizational performance.

Early research on the process of how, when, and why benefits occur from business investments coined the term ‘IT conversion effectiveness’ meaning: “(…) the ability of the firm to convert IT

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investments into productive outputs” (Weill, 1992, p. 4). This concept implies that there cannot be a necessary and sufficient relationship between IT spending and improved organizational performance, because a part of the investment may be wasted due to lacking quality of governance and management capabilities (Soh & Markus, 1995). The IT conversion process is therefore the process by which IT investments are translated into organizational performance. This is similar to the business conversion process in which input resources are transformed into products or outputs to create business value. Soh and Markus (1995) argued that IT management and governance capabilities influence the organization’s ability to generate economies of scale to improve the effectiveness of both the IT and business conversion process. These processes take time and IT investments do not translate directly into improved

organizational outcomes, but they may do so after a certain time lag. Brynjolfsson and Hit (2003) agree with the preceding argumentation that IT investments come in a combined effort of technology and changes in process and mechanisms that sometimes take years to implement. Related to this time lag are also scale effects. The time lag between IT investment and increase in organizational performance differs depending on the size of the organization. Small firms benefit faster from IT investments, because they can make use of generic IT that does not incorporate much firm-specificity. Larger organizations have to face a bigger time lag due to the increased firm-specificity in their IT systems. However, the competitive advantage lies with large organizations in the long run due to their individualized IT systems that cannot easily be copied by competitors (Tambe & Hitt, 2012).

This research project aims to integrate the streams of IT governance and management capability, the IT and business conversion process, as well as observations on time lags and scale effects. We assume that investing in IT governance and management capability will result in increased organizational

performance by fostering economies of scale. Hence, we raise the following research questions:

(1) To what degree are IT management and governance capabilities related to economies of scale in the IT and Business Conversion Process?

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(2) After which time lag do IT management and governance capabilities foster economies of scale in the IT and Business Conversion Process?

Literature Review and Hypotheses

Model

The research model used for this study is adapted from Soh and Markus (1995). They used a process-oriented model that used IT-expenditure as starting point, quality IT assets as discrete

intermediate outcome and improved organizational performance as discrete outcome. IT management was used as a moderator between IT-expenditure and quality IT assets so that effective conversion could only occur when the right management processes were applied. The IT use process represents that appropriate use leads to IT impacts and the competitive process represents the conversion of IT impacts into

performance. The model used in this study is also process-oriented, but greater emphasis is placed on the influence of IT governance and management capabilities on the IT and business conversion processes. IT use and competitive process of Soh and Mark’s (1995) model are combined into the business conversion process. The model is illustrated in Figure 1.

Figure 1. Conceptual Framework of IT Value Creation

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The model shows that this research project focuses on the influence of IT governance and management capabilities on both the IT conversion process and business conversion process. In this way the business conversion process can benefit from proper deployment of management and governance mechanisms during the IT conversion process. The goal is to establish a relationship between IT expenditures, scale of IT assets and ultimately organizational performance.

Economies of Scale in Business and IT

In order to establish a theoretical foundation for the hypotheses we draw on the theory of

economies of scale. The basic assumption of economies of scale is that the average cost per unit decreases as output increases (Canback et al., 2006). At a certain point, we will call it S, the economies of scale are exhausted and diseconomies of scale most likely driven by decreasing returns to management will influence the cost. After point S, the average unit cost will increase as the output increases. However, in reality this is not what is observed. McConnell (1945) and Stigler (1958) studied economies of scale in the industry and a relationship between the Average Cost and Scale is depicted in Figure 2. The Average Unit Cost (AUC) changes with the output (S) and depends on the total cost (TC) divided by the output (S). This can be written in a formula (Besanko et al., 2010):

AUC(S) = TC(S) / S

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Figure 2. McConnell/Stigler Relationship between Unit Cost and Output (Woudstra et al., 2017)

As can be observed in Figure 2, the average unit costs decrease till output level S1 is reached.

Between S1 and S2 is an inert area where no scale effects are observed and the average unit cost remains constant. If the output increases beyond level S2, diseconomies of scale will emerge and the average cost per unit will increase again (Woudstra et al., 2017).

Time Lags, Productivity, and Hypotheses

To advance the formulation of the hypotheses we will first introduce the concept of time lags and productivity in relation to this research objective. Research shows that correlations between IT

investments and outcomes such as productivity vary widely among organizations and the misfit between investments and results may possibly be due to the presence of delayed effects (Schryen, 2013). The popular paper ‘The Productivity Paradox of Information Technology’ reviewed productivity results in relation to IT investments and suggested that the supposed paradox is most likely due to flaws in measurement and methodology (Brynjolfsson, 1993). In particular, Brynjolfsson (1993) proposed four possible reasons for the productivity paradox: mismeasurement of inputs and outputs, lags due to learning and adjustment, redistribution and dissipation of profits, and mismanagement of information and

technology. While the latter two are rather pessimistic options that would suggest no benefit from IT in the long-term, the first two options point to deficiencies in research. The research on lag effects suggests that short-term results may look poor, but ultimately the pay-off will be larger due to the exploitation of IT that becomes viable through extensive learning. Brynjolfsson (1993) explains the effect like this:

“According to models of learning-by-using, the optimal investment strategy sets short term marginal costs greater than short-term marginal benefits. This allows the firm to "ride" the learning curve and reap benefits analogous to economies of scale” (p. 12). This leads us to expect a time lag effect of IT

management and governance capability on organizational performance. Research shows that the greatest

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effect of IT investment on organizational performance occurs after a time lag of three to four years (Campbell, 2012).

To our knowledge, this study is the first of its kind to examine the impact of IT management and governance capability on economies of scale in the IT conversion and business processes closely. We investigate: (1) Whether and after which time lag IT management and governance capability will lead to economies of scale in the IT Conversion Process, and; (2) Whether and after which time lag IT

management and governance capability will lead to economies of scale in the Business Conversion Process.

Most of the concepts used in this study have been explained so far and in this research project we focus on organizations that aim to benefit from lower IT expenditures. The focus on efficient

organizations with more advanced conversion processes eliminates competitive effects so that we can focus on the relationship between IT expenditures and organizational performance. Before the hypotheses can be formulated we utilize the elementary model of productivity by Chew (1988):

Productivity = Output / Input

As a result, the productivity of the IT Conversion Process converting IT expenditures (input) into scale of IT assets (output) equals:

Productivity(ITCP) = Scale of IT assets / IT expenditures

The productivity of the IT Conversion Process will benefit from mature IT management and governance capabilities. With respect to the IT Conversion Process, we assume that its complexity would grow by the square of the scale, causing diseconomies of scale and leading to lower than average

organizational performance at a higher scale (see Scale > S2 in Figure 2). However, if these processes would be focused on business-IT alignment and structured by IT planning, investment decision, coordination and control, economies of scale could be attained (after a time lag) from mature IT management and governance capabilities (see Scale > S1; < S2 in Figure 2). We therefore hypothesize:

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(H1a) IT management and governance capability is positively associated with productivity of the IT Conversion Process.

(H1b) A time lag effect between IT management and governance capability and the point of greatest effect on the productivity of the IT Conversion Process is existent (IT process time lag).

Following we define the Business Conversion Process as the conversion of IT assets (input) into organizational performance (output). The productivity of the Business Conversion Process can be calculated like this:

Productivity(BCP) = Organizational Performance / Scale of IT assets

The productivity of the Business Conversion Process will benefit from mature IT management and governance capabilities (Weill 1992; Aral and Weill 2007; Kim et al. 2011). Aral and Weill (2007) demonstrated that firms with stronger IT capabilities derive more value per IT-Dollar invested and that IT capabilities strengthen performance effects, although the relation between IT capabilities and firm

performance failed to achieve statistical significance. Kim et al. (2011) found an indirect positive relationship between a firm’s IT capabilities and its financial performance via increased ability in changing business processes compared to competitors. As highlighted in the above discussion, the maturity of the IT management and governance capabilities plays a deterministic role (after a time lag) in the productivity of the Business Conversion Process (see Scale > S1; < S2 in Figure 2). Additionally, based on the previously established relationships between IT capabilities and firm performance we hypothesize:

(H2a) IT management and governance capability is positively associated with productivity of the Business Conversion Process.

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(H2b) A time lag effect between IT management and governance capability and the point of greatest effect on the productivity of the Business Conversion Process is existent (Business process time lag).

Research by Campbell (2012) suggests that IT investments will have the greatest effect on firm performance after a time lag of about three to four years. The organizations in his sample were all Fortune 500 companies with on average at least 35.000 employees. Tambe and Hitt (2012) found that large organizations have to face this big time lag due to increased firm-specificity in their IT systems. On the other hand they suggested that smaller firms, like the healthcare organizations included in this study, can benefit from IT investments faster because they can make use of generic IT systems that do not

incorporate much firm-specificity. Based on this reasoning we hypothesize:

(H3a) The time lag effect between IT management and governance capability and the point of greatest effect on the productivity of the IT Conversion Process (IT process time lag) will be less than 3 years.

(H3b) The time lag effect between IT management and governance capability and the point of greatest effect on the productivity of the Business Conversion Process (Business process time lag) will be less than

3 years.

According to the resource-based view (RBV) organizational performance depends on rare, imperfectly imitable, and non-substitutable resources and capabilities (Barney, 1991). Barney suggested in 1991 that management teams in organizations can represent a resource to gain competitive advantage over other firms. Powell and Dent-Micallef (1997) demonstrated that IT in combination with a firm’s complementary human and business resources leads to improved organizational performance. IT management and governance capabilities as investigated in this study are adapted from COBIT, a framework for the enterprise governance of IT. COBIT is designed as a framework to be adapted by adopting organizations (De Haes, Van Grembergen, and Debreceny, 2013), and little is known to date as

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to what components need to be retained to achieve a successful adoption. De Haes et al. (2013) outline that maturity levels of certain processes indicate potential management challenges or accomplishments.

Based on this reasoning high maturity levels of single IT maturity factors can have a positive influence on organizational performance by representing successful IT management. We therefore hypothesize:

(H4a) Single IT management and governance capabilities are positively associated with productivity of the IT Conversion Process.

(H4b) Single IT management and governance capabilities are positively associated with productivity of the Business Conversion Process.

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Method

To find answers to the research questions and to test the aforementioned hypotheses this research project utilizes data from a set that includes data on healthcare organizations, municipalities, and housing corporations in the Netherlands. Melville, Kraemer, and Gurbaxani (2004) suggest that external effects like the profitability of the market influence organizational performance substantially. By focusing on the hospital sector only we can control for the external effects that would bias the results by incorporating different markets together in one analysis. This thesis is part of a healthcare management focus area that gives students the opportunity to gain deeper insights into the mechanisms that play a crucial role in the management and innovations of the healthcare industry. The separate analysis of the different sectors makes it possible to focus the argumentation on the hospital sector while still cherishing the great value inherent in this dataset. Tentative results of the two sectors of housing corporations and municipalities are in the appendix under Table A1 and A2 for further research opportunities that could be aimed at a wider sector such as non-profit organizations.

Research context and data gathering

In this research project we draw on data collected within hospitals between 2006 and 2014. The data were obtained of 49 different hospitals of which some only participated in the benchmark for one year and others participated several years. In total 96 benchmark years were available for analysis. All the organizations are based within the Netherlands. We had to exclude data from the years 2010 and 2011 due to unsuitability to our analysis, because during these years only the total IT maturity scores of each hospital were obtained. 25 benchmark years were excluded based on missing or incomplete data. More on this matter in the section concerns about validity.

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The primary data were made available through consultancy company M&I Partners

(M&I/Partners, 2018), which is experienced in this industry. The data was gathered in the context of well- accepted definitions of the relevant categories and with agreement and in consultation with the financial departments of the organizations. All the source data has been validated by the management of the organizations and is therefore an accurate representation of the inputs, processes, and outcomes that take place within the organizations. Data were collected at the organizational level.

Variables

The IT maturity data concerning IT management and governance capability comprise the following 18 processes categorized in four domains, based on COBIT 4.1 (ISACA, 2011) and ITIL v3 (ITIL, 2017) (Table 1). ISACA is a global provider of services on information systems assurance and security, enterprise management and governance of IT, and IT-related risk and compliance. The 18 IT maturity factors representing IT governance and management capabilities are adapted from the IT processes outlined in the Process Assessment Model (PAM) designed and created by ISACA as a resource for audit, assurance, security, governance and control professionals (ISACA, 2011). The PAM defines 34 IT processes on which M&I Partners based the 18 IT maturity factors used in this study.

Table 1. IT Maturity Factors representing IT Management and Governance Capability (adapted from ISACA, 2011)

Domains Planning and

Organization

Acquisition and Implementation

Delivery and Support

Monitoring and Governance IT Maturity Factors

IT performance and

capacity management Change

management Assistance for end-users

(helpdesk) Evaluation of effectiveness

Control of work and

project management Supplier

management Education and training of end-users Safety and risk

management

Professionalizing procedures

Availability of IT IT management and

planning Incident- and problem management

Architecture Identification and allocation of costs

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Security

Configuration management

Usage of service level agreements to end-users

The maturity level of IT management and governance capability is according to COBIT 4.1 defined in the following six levels (ISACA 2011; De Haes and van Grembergen 2009) (Table 2). The PAM defines the levels as maturity levels that each IT process can be rated on and the levels are adapted from the International Organization for Standardization (ISO). The process maturity system implies that the minimum total IT Maturity score a hospital can achieve is 0 and the maximum is 90 for any given year.

Table 2: Definition of Maturity Levels each Factor can reach (ISACA, 2011; De Haes and van Grembergen, 2009)

Maturity Level Definition

0 Non-existent Complete lack of any recognizable processes. The enterprise has not even recognized that there is an issue to be addressed.

1 Initial There is evidence that the enterprise has recognized that the issues exist and need to be addressed. There are, however, no standardized processes; instead there are ad hoc approaches that tend to be applied on an individual or case-by-case basis. The overall approach to management is disorganized.

2 Repeatable but

Intuitive Procedures have been standardized and documented, and communicated through training. It is, however, left to the individual to follow these processes, and it is unlikely that deviations will be detected. The procedures themselves are not sophisticated but are the formalization of existing practices.

3 Defined Process It is possible to monitor and measure compliance with procedures and to take action where processes appear not to be working effectively. Processes are under constant improvement and provide good practice. Automation and tools are used in a limited or fragmented way.

4 Managed and

Measurable It is possible to monitor and measure compliance with procedures and to take action where processes appear not to be working effectively. Processes are under constant improvement and provide good practice. Automation and tools are used in a limited or fragmented way.

5 Optimized Processes have been refined to a level of best practice, based on the results of continuous improvement and maturity modeling with other enterprises. IT is used in an integrated way to automate the workflow, providing tools to improve quality and effectiveness, making the enterprise quick to adapt.

The main variables of the conceptual framework are defined as follows. Scale of IT Assets is

measured as the number of workstations in the specific organization. This is a commonly used measure to estimate the amount of deployed IT (IDC, 2007; Gartner, 2007). IT Expenditure was measured by the total annual IT cost of an organization. The total annual IT spending was computed based on Maanen and Berghout’s (2002) Total Cost of Ownership (TCO) model. This model determines hardware, software, and costs for human resources per TCO-entity. The model is known for its high accuracy and detail as it

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also takes hidden costs such as depreciation into account. IT Expenditure was obtained by adding the values of depreciation and exploitation costs, whereby the actual investment costs were included in the depreciation values obtained by M&I Partners. Organizational Performance was measured by the annual revenue in Euro.

Operationalization of variables

Independent variables. The IT management and governance capability is operationalized by the sum score of the 18 IT maturity factors (total IT Maturity). To test the six hypotheses under 1 to 3 the sum of the single IT maturity factors is used for the analysis. For the two hypotheses belonging to 4 the single IT maturity factors are used for the analysis to determine the isolated effect of the IT maturity factors on the dependent variables.

Dependent variables. The productivity of the IT Conversion Process is operationalized as 10.000 times the number of workstations (Scale of IT Assets) divided by IT Expenditure. The productivity of the Business Conversion Process is operationalized as the revenue divided by the number of workstations times 10.000. Due to the variable Scale of IT Assets being in the denominator of the variable Productivity of the Business Conversion Process the standardized beta coefficients of the regression outputs have to be interpreted by multiplying them with -1 to directly compare them to the IT Conversion Process. Lower standardized beta coefficients mean a higher influence on the productivity of the Business Conversion Process.

Planned analysis

In the first step of the analysis we will subject the 18 single IT maturity factors to a principal components analysis (PCA) using SPSS version 25 as outlined in Pallant’s (2011) SPSS survival manual.

The PCA is chosen to explore interrelationships between the variables. We do this because there is no research yet that proves that the IT processes outlined in the PAM (ISACA, 2011) are grouped in domains

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In our analysis we make use of quantitative research methods as outlined by Cohen, Cohen, West, and Aiken (2003). We plan to test hypotheses H1a and H2a by using linear regression analysis of the predictor total IT maturity per hospital per year on the dependent variables productivity of the IT and Business Conversion Process respectively. This way we can test the relationship between the IT maturity of the hospitals and the outcome variables to make statements that help us in answering the research questions and to how IT governance and management capabilities are related to economies of scale in the IT and Business Conversion Process.

Hypotheses H1b, H2b, H3a, and H3b, are related to the process time lag of the IT and Business Conversion Process. For these hypotheses we are going to use linear regression analyses of the predictor total IT maturity per hospital per year on the dependent variables productivity of the IT and Business Conversion Process. The time lag is analyzed as follows: For a time lag of 1 year we use the total IT maturity in year X as a predictor on the dependent variable productivity of the IT and Business

Conversion Process of year X + 1, for a time lag of two years we use the total IT maturity in year X on the productivity of the IT and Business Conversion Process of year X + 2, et cetera.

To test hypotheses H4a and H4b were are going to use linear regression analysis of the single IT maturity factors to determine their isolated influence on the dependent variables. For the single maturity factors we are going to use the single IT maturity scores per hospital per year. The technique we are going to use to analyze the time lag is the same as before.

Concerns about validity

The time-series data used to calculate the process time lag of the IT- and Business Conversion Process has several missing observations within series of measurements. This means for example that for hospital X there are data available for years 2006, 2007, 2008, 2009, 2012, and 2013. In this example there are no data available for the years 2010 and 2011. In the dataset used in this study there are 29 of these observations missing, while 91 observations are present. In comparison to a complete dataset which would consist of 120 observations the percentage of missing observations is 24%. Rubin (1976)

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distinguished between three different types of missing data. Missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR). We concluded that the missing data in our dataset are MCAR, meaning that the probability to be missing is not related to any factor of the dependent or independent variables. Rubin suggests that data that is MCAR can be ignored without having much influence on the outcome of the analysis due to the independence from the relevant variables. However, we acknowledge that the random error of the process time lag analysis will be increased due to the missing data. As a possible solution we tried using simple imputation techniques such as mean substitution, but concluded that this biases the results too strongly. Furthermore we took multiple imputation techniques such as multiple imputation pooling into account, but arrived at the conclusion that these techniques cannot be used due to the complete instead of partial absence of data for the missing years.

Measurement of the independent variables was inconsistent over time through the assessment of 18 IT maturity factors in 2006 and 2007 and 22 IT maturity factors in the remaining years. We decided to exclude the four variables from the analysis. This decision means that we had to exclude 19 benchmark years gathered in 2010 and 2011 from the analysis, because for these two years only the total IT maturity scores per hospital were obtained. Having only the total score available made it impossible for us to take out the 4 variables from these years that were not assessed in 2006 and 2007. We made this decision to ensure consistent measurement of the independent variables.

Inclusion of adequate control variables was considered. The only control variable that was identified according to presence in the data set and literature was size of hospital in terms of number of employees. However, the variable was not available for all hospitals and all years and was therefore not included. Additionally, performing an ANCOVA was not suitable to test our stated hypotheses.

Reliability of the data

The data obtained by M&I Partners relies on self-reports. As with all self-reported data the

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consultancy M&I Partners is focused on the healthcare and governmental sector with a specialization on ICT and operates since three decades. Its benchmarks give hospitals an overview of their IT

price/performance ratios to allow better planning, improvement and comparison between other hospitals (M&I/Partners, 2018). We think that the data obtained via the benchmarks by M&I Partners are reliable and accurate and therefore allow drawing reliable conclusions.

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Results

25 benchmark years were excluded from the analysis due to missing data for one or more of the dependent and/or independent variables. We removed two benchmark years based on an outlier analysis (Figure A1 and A2). The Scale of IT Assets exceeded the 1.5 times IQR distance for one benchmark year and also did not fit in logically with the remaining years for this hospital. The productivity of the IT Conversion Process exceeded the 1.5 times IQR distance for one hospital and was more than three times as high as for comparable hospitals for the same year. In total there were 69 complete benchmark years available from 36 different hospitals. Graphical representation and the Kolmogorov-Smirnov test

suggested that the independent variables were normally distributed. Descriptive statistics of the variables used in this study are outlined in Table 3.

Table 3. Descriptive Statistics

Variables N Mean Std. Deviation Skewness Kurtosis

IT Expenditure 69 8241281.59 3736038.08 0.30 -0.56

Revenue 69 186849806.85 82053612.68 0.22 -0.87

Scale of IT

Assets 69 2030.42 919.90 0.18 -0.52

Total IT Maturity

69 49.15 10.56 -0.14 -0.83

IT Conversion

Process 69 2.48 0.56 0.64 0.68

Business Conversion Process

69 9.46 1.64 -0.44 -0.95

Note. Skewness and Kurtosis measures are all below 2, indicating that the values in our sample stem from a normally distributed population (Duncan, 1997).

Factor analysis

The 18 single IT maturity factors adapted from the PAM of COBIT4.1 (ISACA, 2011) were subjected to principal components analysis (PCA) using SPSS 25. Before we performed PCA we tested

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the data for suitability. Examination of the correlations matrix revealed the presence of many coefficients of at least 0.3 or higher. The Kaiser-Myer-Olkin value was .77, exceeding the recommended value of .6 (Kaiser, 1970) and Bartlett’s Test of Sphericity (Bartlett, 1954) reached statistical significance, supporting the factorability of the correlation matrix.

PCA revealed the presence of six components with an eigenvalue exceeding 1, explaining 33.81%, 10.37%, 8.02%, 6.49%, 6.13%, and 5.58% of the variance respectively. An inspection of the screeplot revealed a clear break after the first component, with a smaller break after the second component. Using Catell’s (1966) scree test, we decided to keep two components for further

investigation. This decision was further supported by the results of Parallel Analysis, which showed two components with eigenvalues exceeding the corresponding criterion values for a randomly generated data matrix of the same size.

The resulting two-component solution explained a total of 44.18% of the variance, with

Component 1 contributing 33.81% and Component 2 contributing 10.37%. To allow better interpretation of these two components, oblimin rotation was performed. The rotated solution revealed the presence of simple structure (Tucker, 1955), with both components showing a number of strong loadings and the majority of variables loading substantially on only one component. The interpretation of the two components was not consistent with the domains outlined in the PAM of COBIT4.1. No research has been conducted on the factorability of IT governance and management capabilities. The results suggest 2 main underlying factors as can be seen in Table 4. The composition of these two factors is not related to the four domains Planning and Organization, Acquisition and Implementation, Delivery and Support, and Monitoring and Governance as outlined in the PAM (ISACA, 2011) and adapted by M&I Partners.

Therefore, we will refrain from testing the influence of the aforementioned domains on the productivity of the IT and Business Conversion Process.

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Table 4. Pattern and Structure Matrix for PCA with Oblimin Rotation of TWO Factor Solution of IT Maturity Factors

Item Pattern Coefficients Structure Coefficients Communalities Component 1 Component 2 Component 1 Component 2

Security 0.739 -0.085 0.706 0.201 0.505

Architecture 0.704 -0.130 0.653 0.142 0.441

Safety and risk

management 0.695 -0.192 0.621 0.077 0.417

Availability of IT 0.694 0.124 0.742 0.392 0.564

Education and training of end-users

0.668 -0.023 0.659 0.235 0.435

IT management and planning

0.631 0.198 0.707 0.441 0.533

Identification and

allocation of costs 0.561 0.023 0.570 0.240 0.326

Change

management 0.472 0.300 0.588 0.482 0.422

Evaluation of

effectiveness 0.453 0.131 0.504 0.307 0.268

Supplier management

0.384 0.276 0.491 0.424 0.306

Professionalizing

procedures -0.231 0.795 0.076 0.706 0.544

Incident- and problem

management -0.064 0.768 0.233 0.743 0.556

Configuration

management -0.007 0.746 0.282 0.743 0.552

Assistance for end- users (helpdesk)

0.187 0.718 0.465 0.791 0.655

IT performance and

capacity management 0.356 0.546 0.568 0.684 0.576

Usage of Service Level Agreements to end-users

0.295 0.415 0.456 0.530 0.355

Data management

(quality of data) 0.322 0.342 0.454 0.467 0.306

Control of work and

project management 0.237 0.288 0.348 0.380 0.192

Note. Major loadings for each item are bolded; Rotation Method: Oblimin with Kaiser Normalization.

Correlations

In reporting the strength of the correlations we use Cohen’s (1988) guidelines suggesting that a small correlation is .1 < r < .3, a medium correlation is .3 < r < .5, and a large correlation is r > .5. Our results show a medium to large significant correlation between year and IT maturity. This demonstrates

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years, indicating that the healthcare organizations in our sample are steadily investing in IT and acknowledge the importance of IT within healthcare (Figure 3). IT maturity is represented as the total score of IT maturity per hospital per year. The relation between the variables IT Expenditure and Scale of IT Assets is large and significant, indicating that hospitals who invest more in IT also tend to have more workstations (Figure 4). This suggests a relationship between size of the hospital and IT-Dollar spent, because the number of workstations is an indicator of the number of employees. Based on this reasoning, the dependent variable Productivity of the IT Conversion Process seems a viable estimate of how well hospitals convert IT investments into IT assets.

Figure 3. Increase of IT Governance and Management Capability over the Years

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Figure 4. Relationship between the Scale of IT Assets and IT Expenditure

The results also show a small to medium significant correlation between the amount of money invested in IT and the total IT maturity of the corresponding hospital (Figure 5). This suggests that hospitals that spend more money on IT tend to have higher levels of IT management and governance capabilities. In addition to that, the significant correlation between the number of workstations in a hospital and its IT governance and management capabilities has roughly the same strength (Figure 6).

This is another indication for the relation between IT investments and the amount of IT assets within Dutch hospitals.

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Figure 5. Relation between IT Expenditure and IT Governance and Management Capability

Figure 6. Relation between Scale of IT Assets and IT Governance and Management Capability

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Our results show a large significant correlation between the organizational performance of a hospital and its number of workstations (Figure 7). The relationship between revenue and scale of IT assets is valid and this indicates that the dependent variable Productivity of the Business Conversion Process is a good indicator of how well a hospital converts IT assets into organizational performance. The relationship between revenue and IT management and governance capabilities is not significant and smaller than the previously discussed relationships, because small hospitals with lower revenue can still have a high level of IT management and governance capability (Figure 8). This suggests that also smaller hospitals in our sample acknowledge the importance of IT within healthcare.

Figure 7. Relation between Scale of IT Assets and Revenue

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Figure 8. Relation between Revenue and IT Governance and Management Capability

Hypothesis testing

The first hypotheses to be tested were H1a and H1b. We did not find evidence supporting hypotheses H1a and H1b. The results of the regression analysis of the predictor total IT maturity on the dependent variable productivity of the IT Conversion Process can be seen in Table 5. Although the standardized beta coefficient of a 1 year time lag between a certain total IT maturity level and the point of greatest effect on the productivity of the IT Conversion Process was increased, the regression analysis failed to achieve statistical significance.

We did find statistical evidence at the α = 10% level supporting H2a, indicating that IT management and governance capability may be positively associated with productivity of the Business Conversion Process (Table 5). Total IT maturity did significantly predict productivity of the Business Conversion Process, β = -.22, t(68) = -1.86, p = .07. We did not find significant evidence supporting H2b, indicating that there is no statistically significant time lag effect between a certain total IT maturity level

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and the point of greatest effect on productivity of the Business Conversion Process (Table 5). However, our results point towards a time lag effect of one year, β = -.28, t(32) = -1.62, p = .12.

Our results provided statistically non-significant evidence for both hypotheses H3a and H3b. We found that the time lag effect between a certain level of total IT maturity and the point of greatest effect on productivity of the IT Conversion Process may be one year (Table 5). The time lag effect between a certain level of total IT maturity and the point of greatest effect on productivity of the Business

Conversion Process may be also one year (Table A7). However, the two strongest beta coefficients at a time lag of one year each both did not reach statistical significance.

Our results did not significantly support H4a indicating that in our sample there is no statistically significant relation between single IT maturity factors and productivity of the IT Conversion Process (Table 6). We found evidence supporting 4Hb, indicating that 6 single IT maturity factors are significantly related to the Business Conversion Process (Table 6). Specifically, we found that

Availability of IT significantly predicted productivity of the Business Conversion Process, β = -.33, t(68)

= -2.87, p < .01. Configuration Management also significantly predicted productivity of the Business Conversion Process, β = -.25, t(68) = -2.12, p = .04. IT Management and Planning significantly predicted productivity of the Business Conversion Process, β = -.24, t(68) = -2.06, p = .04. Incident and Problem Management significantly predicted productivity of the Business Conversion Process, β = -.31, t(68) = - 2.67, p < .01. The two single IT maturity factors Architecture, β = -.23, t(68) = 1.96, p = .06, and Assistance for End-users, β = -.23, t(68) = -1.9, p = .06, significantly predicted productivity of the Business Conversion Process at the α = 10% level.

Post-hoc analysis furthermore revealed a time lag effect of 4 single IT maturity factors on

productivity of the IT Business Conversion Process and a time lag effect of 6 single IT maturity factors on productivity of the Business Conversion Process (Table 6). The results are illustrated in Figure 10.

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Table 5. Regression Analysis Independent

Variable Dependent Variable N Standardized

Beta Coefficient Test Statistic P Value

TITM PITCP 69 .01 .09 .93

1 Year ITPTL 33 .18 1 .32

2 Year ITPTL 17 .08 .32 .76

TITM PBCP 69 -.22 -1.86 .07*

1 Year BPTL 33 -.28 -1.62 .12

2 Year BPTL 17 -.16 -.62 .55

Note. TITM: Total IT Maturity; PITCP : Productivity of the IT Conversion Process; PBCP: Productivity of the Business Conversion Process; ITPTL: IT Process Time Lag; BPTL: Business Process Time Lag; *** 1% level; **

5% level; * 10% level

Table 6. Regression Analysis (Single IT Maturity Factors) Independent Variable Dependent

Variable N Standardized

Beta Coefficient Test Statistic P Value

Architecture PITCP 69 .16 1.32 .19

1 Year ITPTL 33 .17 .96 .34

2 Year ITPTL 17 .02 .08 .94

Architecture PBCP 69 -.23 -1.96 .06*

1 Year BPTL 33 -.15 -.85 .4

2 Year BPTL 17 -.21 -.84 .42

Control of Work and Project

Management PITCP 69 .07 .59 .56

1 Year ITPTL 33 .03 .18 .86

2 Year ITPTL 17 -.1 -.38 .71

Control of Work and Project

Management PBCP 69 .06 .46 .65

1 Year BPTL 33 .03 .16 .87

2 Year BPTL 17 .36 1.51 .15

Availability of IT PITCP 69 .1 .82 .41

1 Year ITPTL 33 .29 1.66 .11

2 Year ITPTL 17 .28 1.12 .28

Availability of IT PBCP 69 -.33 -2.87 .005***

1 Year BPTL 33 -.45 -2.77 .009***

2 Year BPTL 17 -.46 -2.03 .06*

Security PITCP 69 .01 .09 .93

1 Year ITPTL 33 -.2 -1.13 .27

2 Year ITPTL 17 -.07 -.28 .78

Security PBCP 69 .01 .05 .96

1 Year BPTL 33 .21 1.18 .25

2 Year BPTL 17 .28 1.15 .27

Configuration Management PITCP 69 -.02 -.13 .9

1 Year ITPTL 33 .15 .87 .39

2 Year ITPTL 17 -.03 -.1 .92

Configuration Management PBCP 69 -.25 -2.12 .04**

1 Year BPTL 33 -.3 -1.77 .09*

2 Year BPTL 17 -.09 -.34 .74

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Education and Training of End-users PITCP 69 -.06 -.48 .63

1 Year ITPTL 33 .02 .11 .91

2 Year ITPTL 17 .11 .44 .67

Education and Training of End-users PBCP 69 .05 .36 .7

1 Year BPTL 33 .06 .32 .75

2 Year BPTL 17 -.07 -.29 .78

Evaluation of Effectiveness PITCP 69 .05 .37 .71

1 Year ITPTL 33 .19 1.07 .29

2 Year ITPTL 17 .22 .88 .4

Evaluation of Effectiveness PBCP 69 .05 .42 .68

1 Year BPTL 33 .03 .19 .85

2 Year BPTL 17 -.04 -.15 .89

Usage of Service Level Agreements to

End-users PITCP 69 -.03 -.21 .84

1 Year ITPTL 33 .34 1.99 .06*

2 Year ITPTL 17 .05 .19 .85

Usage of Service Level Agreements to

End-users PBCP 69 -.1 -.81 .42

1 Year BPTL 33 -.38 -2.25 .03**

2 Year BPTL 17 -.25 -.98 .34

Data Management PITCP 69 .05 .43 .67

1 Year ITPTL 33 -.14 -.77 .45

2 Year ITPTL 17 -.09 -.36 .72

Data Management PBCP 69 -.14 -1.15 .26

1 Year BPTL 33 0 0 1

2 Year BPTL 17 -.17 -.65 .53

Assistance for End-users PITCP 69 -.07 -.6 .55

1 Year ITPTL 33 .08 .46 .65

2 Year ITPTL 17 -.42 -1.77 .01

Assistance for End-users PBCP 69 -.23 -1.9 .06*

1 Year BPTL 33 -.3 -1.75 .09*

2 Year BPTL 17 -.08 -.3 .77

IT Management and Planning PITCP 69 .02 .17 .86

1 Year ITPTL 33 .31 1.83 .08*

2 Year ITPTL 17 .11 .42 .68

IT Management and Planning PBCP 69 -.24 -2.06 .04**

1 Year BPTL 33 -.46 -2.88 .007***

2 Year BPTL 17 -.49 -2.17 .046**

IT Performance and Capacity Management

PITCP 69 .06 .5 .62

1 Year ITPTL 33 .12 .69 .5

2 Year ITPTL 17 -.07 -.27 .79

IT Performance and Capacity

Management PBCP 69 -.2 -1.64 .11

1 Year BPTL 33 -.33 -1.95 .06

2 Year BPTL 17 -.32 -1.32 .21

Identification and Allocation of Costs PITCP 69 .05 .41 .68

1 Year ITPTL 33 .03 .16 .87

2 Year ITPTL 17 -.2 -.79 .44

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1 Year BPTL 33 .02 .12 .9

2 Year BPTL 17 .22 .88 .39

Incident and Problem Management PITCP 69 .08 .67 .5

1 Year ITPTL 33 .11 .63 .54

2 Year ITPTL 17 -.21 -.84 .41

Incident and Problem Management PBCP 69 -.31 -2.67 .009***

1 Year BPTL 33 -.42 -2.56 .02**

2 Year BPTL 17 -.33 -1.35 .2

Supplier Management PITCP 69 -.15 -1.26 .21

1 Year ITPTL 33 -.14 -.77 .45

2 Year ITPTL 17 -.3 -1.22 .24

Supplier Management PBCP 69 -.12 -1 .32

1 Year BPTL 33 .01 .06 .95

2 Year BPTL 17 .11 .42 .68

Professionalizing Procedures PITCP 69 -.16 -1.29 .2

1 Year ITPTL 33 -.29 -1.67 .11

2 Year ITPTL 17 -.46 -2.01 .06*

Professionalizing Procedures PBCP 69 .01 .12 .91

1 Year BPTL 33 .04 .24 .81

2 Year BPTL 17 .2 .78 .45

Safety and Risk Management PITCP 69 .09 .77 .45

1 Year ITPTL 33 .33 1.91 .07*

2 Year ITPTL 17 .14 .54 .59

Safety and Risk Management PBCP 69 -.14 -1.13 .26

1 Year BPTL 33 -.07 -.38 .7

2 Year BPTL 17 .13 .5 .63

Change Management PITCP 69 -.19 -1.6 .12

1 Year ITPTL 33 .07 .37 .71

2 Year ITPTL 17 -.02 -.08 .94

Change Management PBCP 69 -.13 -1.06 .29

1 Year BPTL 33 -.19 -1.09 .28

2 Year BPTL 17 -.2 -.79 .44

Note. PITCP : Productivity of the IT Conversion Process; PBCP: Productivity of the Business Conversion Process;

ITPTL: IT Process Time Lag; BPTL: Business Process Time Lag; *** 1% level; ** 5% level; * 10% level

Summarized results

We summarized the effect of total IT maturity on the dependent variables in Figure 9 by showing how the strength of the standardized beta coefficients changes with increasing time lag. The values of the coefficients are identical with Pearson correlations in our analysis. We summarized the results of the hypotheses tests in Table 7. Partially supported hypotheses are supported by statistical evidence at the α = 10% level and/or only a certain number of relationships between single IT maturity factors and the dependent variables were found to be significant.

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Figure 9. Effect of Total IT Maturity on Dependent Variables changes with Time Lag

Table 7. Summary of Hypotheses Tests

Hypotheses Results

H1a: IT management and governance capability is positively associated with productivity

of the IT Conversion Process Not supported

H1b: A time lag effect between IT management and governance capability and the point of greatest effect on the productivity of the IT Conversion Process is existent (IT process time lag).

Not supported

H2a: IT management and governance capability is positively associated with productivity

of the Business Conversion Process. Partially

supported H2b: A time lag effect between IT management and governance capability and the point of

greatest effect on the productivity of the Business Conversion Process is existent (Business process time lag).

Not supported

H3a: The time lag effect between IT management and governance capability and the point of greatest effect on the productivity of the IT Conversion Process (IT process time lag) will be less than 3 years.

Not supported

H3b: The time lag effect between IT management and governance capability and the point of greatest effect on the productivity of the Business Conversion Process (Business process time lag) will be less than 3 years.

Not supported

H4a: Single IT management and governance capabilities are positively associated with productivity of the IT Conversion Process.

Partially supported H4b: Single IT management and governance capabilities are positively associated with

productivity of the Business Conversion Process. Partially

supported

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The summarized results of the effects of the single maturity factors on the dependent variables are displayed in Figure 10. We only included single IT maturity factors for which significant results were found. We also only included time lag effects if they were significant. Dashed lines indicate a significant relation at the α = 10% level and normal lines indicate a significant relation at the α = 5% and 1% levels.

Figure 10. Significance of Relation between Single IT Maturity Factors and Dependent Variables including IT and Business Process Time Lag

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Discussion and conclusion

The results of this study provide tentative evidence for the relation between IT governance and management capabilities and both the IT and Business Conversion Process. Our results show a positive and significant association between IT governance and management capability and productivity of the Business Conversion Process, although the evidence exists only at the α = 10% level. This finding can be regarded as an extension of Aral and Weill’s (2007) and Kim et al.’s (2011) research on the relation between IT capabilities and organizational performance, because in this research project we could establish a direct link between IT management and governance capabilities and organizational performance. However, Aral and Weill (2007) and Kim et al. (2011) based their measurements of IT capability on constructs that have more empirical support than the IT governance and management capabilities used in this study. Also the way the data were obtained is more transparent in the mentioned studies and deserves greater detail than available in this research project. The results of the principal components analysis should serve as a preliminary starting point to further empirical investigation of underlying factors among the IT maturity factors outlined in COBIT. This is in line with the research opportunities outlined in a paper exploring the use of COBIT in future research activities (De Haes, Van Grembergen, and Debreceny, 2013). More extensive empirical research can strengthen COBIT’s position as framework for enterprise governance of IT.

Furthermore, our results are pointing toward a time lag effect of one year between a certain maturity level of IT management and governance capability and the point of greatest effect on both the IT and Business Conversion Process (Figure 9). This is indicated by the increase in strength of the

standardized beta coefficients, however the relationships are not statistically significant and therefore only indicative of a possible effect. These results can be regarded as in line with Tambe and Hitt’s (2012) suggestion that medium sized organizations face a shorter time lag compared to the Fortune 500 companies due to faster realization of benefits from IT. However, the analysis regarding the time lag

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effects suffered from a relatively small sample size in our study. A larger sample could provide further insights into the relation between IT capabilities and time lag effects. Furthermore was it planned to investigate a business and IT process time lag of up to five years. The data were only sufficient to conduct statistical analyses of a process time lag for two years. This limitation makes it difficult to compare the results to research that studied time lag effects of up to five years (Campbell, 2012).

The results of the factor analysis suggest that the four domains Planning and Organization, Delivery and Support, Acquisition and Implementation, and Monitoring and Governance outlined in the PAM of COBIT (ISACA, 2011) cannot be empirically confirmed in our sample. The 18 single IT maturity factors load on two major components, whereby the composition of these two components remains unrelated to the allocation of factors in the four proposed domains. Further exploratory factor analysis can help to uncover the underlying structure of a large set of variables like the IT maturity factors used in this study and would strengthen COBIT’s position as a framework for enterprise governance of IT (De Haes, Van Grembergen, and Debreceny, 2013).

We explored the influence of a part of the IT maturity factors on the IT and Business Conversion Process further by looking at the influence of single IT maturity factors. We found that six single IT maturity factors are significantly related to productivity of the Business Conversion Process. These factors are Architecture, Availability of IT, Configuration Management, Assistance for End-users, IT Management and Planning, and Incident and Problem Management. None of the single IT maturity factors were found to be significantly related to productivity of the IT Conversion Process, which is defined as the conversion of IT investments into IT assets. Therefore, in our study the IT management and governance capabilities are not significantly related to the ability of a hospital to transform IT investments into IT assets. However, IT capabilities seem to be positively related to the ability of hospitals to convert IT assets into revenue. More detailed statements about the effects of single IT maturity factors remain problematic due to the unknown nature of the content of the single IT maturity factors and the exact way the scores were obtained.

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We observed that there seems to be a business process time lag for six single IT maturity factors indicated by the increase of their effect on productivity of the Business Conversion Process after a time lag of one or two years. These single IT maturity factors are Availability of IT, Configuration

Management, Usage of Service Level Agreements to End-users, Assistance for End-users, IT

Management and Planning, and Incident and Problem Management. This means that the effect of these IT maturity factors on a hospital’s ability to convert IT assets into revenue increases after a time lag of one or two years. We further found that there seems to be an IT process time lag for four single IT maturity factors indicated by the increase of their effect on productivity of the IT Conversion Process after a time lag of one or two years. These IT Maturity factors are Usage of Service Level Agreements to End-users, IT Management and Planning, Professionalizing Procedures, and Safety and Risk Management. The effect of these IT Maturity factors on a hospital’s ability to convert IT investments into IT assets is stronger after a time lag of one or two years.

Theoretical implications

This research project adds to the existing literature evidence of the direct relation between IT management and governance capabilities and organizational performance. This relation was previously only indirectly observed through moderation and mediation effects, or in research that relied on matched samples (e.g.: Bharadwaj (2000); Santhanam & Hartono, (2003). Our results indicate that there is a direct link between a hospital’s IT management and governance capability and how well the hospital converts IT assets into revenue.

Our results further add to the existing literature that time lag effects between IT management and governance capability and the influence of single IT management and governance capabilities on

organizational performance exist. It is also suggested by the data that the point of greatest effect of IT capabilities on organizational performance occurs after a time lag of one year.

This study contributes to research on the use of COBIT as a framework for enterprise governance

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research is needed to consolidate COBIT’s position in the academic literature as an empirically proved framework for organizations and researches to implement IT management practices and to conduct research aimed at improving theoretical knowledge of IT management and governance capability.

Managerial implications

Managerial implications that can be drawn from the results of our research project are directed toward realization of the importance of IT management and governance capabilities for the organizational performance of a hospital. Our results show that IT management and governance capabilities are

positively related to how well the hospital converts IT assets into revenue. Hence, we advise managers that no IT budget cuts should be undertaken and to take a closer look at the single IT management and governance capabilities and how they are related to business practices to achieve optimized organizational performance. Furthermore, the presence of delayed effects of IT management and governance capabilities on both the IT and Business Conversion Process should be considered when making strategical

investment decisions to increase benefits realization and ultimately improve organizational performance.

Limitations

A limitation of this research is the availability of data. Planned was to do an analysis that covers a time lag effect of up to five years. The data in our sample were hardly sufficient to conduct an analysis covering a time lag effect of up to two years. It was also a limitation that a little more than half of the hospitals participated in the benchmark for only one year during the covered time period of 9 years. This is particularly limiting the analysis of the IT and business process time lag. For a process time lag of two years the number of benchmarked years barely covers the minimum observations needed to perform the analysis, resulting in unreliable and underpowered results. A further limitation is the absence of

qualitative information about the data set. It is not known why more than half of the hospitals only participated in the benchmark for one year. Additionally it is not known why some hospitals missed

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The author would also like to thank the Lieuwe Lei, Hans van den Broek, Martijn Wilpshaar, who together with Luuk Groet Koerkamp collaborated in this project as student assistents