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Examining hospital’s IT infrastructure capability and digital capabilities
Twenty-fourth Americas Conference on Information Systems, New Orleans, 2018 1
Examining the relationship between a
hospital’s IT infrastructure capability and
digital capabilities: a resource-based
perspective
Completed Research
Rogier van de Wetering
Open University, the Netherlands
rogier.vandewetering@ou.nl
Johan Versendaal
Open University/ HU University of
Applied Sciences, the Netherlands
johan.versendaal@ou.nl
Pien Walraven
Open University, the Netherlands
pien.walraven@ou.nl
Abstract
Hospitals increasingly make use of information technology (IT) infrastructures to enhance their
services. However, it remains unclear how IT infrastructures affect clinical and operational excellence.
We examine the relationship among hospitals’ IT infrastructure capability and their so-called digital
capabilities, i.e., IS competences regarding information processing, digitally enabled clinical decision
making, health information exchange, and telehealth. We conceptualize a research model taking a
resource-based lens, and we propose two hypotheses. First, we argue that hospitals that invest in their
IT infrastructure capability will outperform other hospitals regarding their digital capabilities.
Furthermore, as many hospitals receive financial incentives for professionalizing digital services, we
hypothesize that the strength of this particular relationship is dependent on such incentives.
Findings—based on an SEM-PLS analysis on a sample of 1143 European hospital—suggest that there
is a positive relationship between an IT infrastructure capability and digital capabilities, and,
surprisingly, financial incentives negatively affects this relationship.
Keywords
IT infrastructure capability, resource-based view (RBV), IT assets, digital capabilities, hospitals, PLS-
MGA
Introduction
Contemporary hospitals need to be able to handle a great deal of (imposed) organizational,
operational and administrative changes, while simultaneously capitalizing on emerging business
opportunities within the hospital ecosystem. Health information technology (IT) in the modern age is
crucial in order to improve clinical quality, enhance and improve service efficiency, expand access and
reduce costs (Chiasson et al. 2007; Haux 2010; Hendrikx et al. 2013; Jha et al. 2008; Ludwick and
Doucette 2009; McGlynn et al. 2003). Although IT-market analyst Gartner observes a low adoption
rate of IT in healthcare stating that “healthcare is slow to adopt IT innovations and equally sluggish in
anticipating and addressing shifting population demographics and disease demands, [...]” (Gartner
2018), they do acknowledge that ‘digitalization’ is invading all aspects of healthcare. The dramatic
shift to an intelligent real-time operations and care management/remote patient monitoring (RPM)
paradigm will occur over the next few years (Gartner 2018). Indeed more and more hospitals adopt IT
to improve the health of individuals and the performance of providers, yielding improved quality, cost
savings, and more significant engagement by patients in their health care (Blumenthal 2010; Buntin
et al. 2011). At the same time, these and other authors (Agarwal et al. 2010; Kohli and Tan 2016)
indicate that there are many social, technical and organizational challenges and problems in the
process of fully leveraging IT. Additionally, past research has asserted that it is not uncommon that IT
can also hinder and sometimes even impede organizational performance gains (Brynjolfsson and Hitt
Examining hospital’s IT infrastructure capability and digital capabilities
Twenty-fourth Americas Conference on Information Systems, New Orleans, 2018 2
2000; Overby et al. 2006; Weill et al. 2002). Therefore, a question of central importance is how the
hospital could genuinely leverage their IT investments, and enable clinical processes and create value.
In addressing this question, an IT infrastructure capability is widely recognized as a critical element
to firm competitiveness and innovativeness (Broadbent et al. 1999; Mikalef et al. 2016; van de
Wetering et al. 2017a; Van de Wetering et al. 2017c; Weill et al. 2002). IT assets (Aral and Weill 2007;
Ross et al. 1996) and business connectivity (Broadbent et al. 1999; Weill et al. 2002) characterize a
firm’s IT infrastructure capability. This capability underpins the formation of IT-enabled capabilities
(so-called digital capabilities), and emerged as a critical competitive priority in many organizational
activities (Mikalef et al. 2016). Furthermore, the extant literature shows that IT infrastructure
capabilities facilitate IT-based competitive actions (Bharadwaj 2000). As Broadbent et al. (1999)
already argued: “an IT infrastructure is particularly important for firms in industries going through
dynamic change, for firms reengineering their business processes and for those with multiple business
units or extensive international or geographically dispersed operations.” The first three of these
characteristics (and in some cases the fourth as well) apply particularly well to hospitals. However,
there is still a limited understanding on hospitals’ IT infrastructure capability and how it relates to
relevant digital capabilities, e.g., to process clinical information, to support clinical decision making,
to electronically transfer, share and enable access to patient health data, and to enhance operational
efficiency within and between hospitals. Synthesizing from the above, we see a clear need for further
rigorous empirical examination of the relationship between a hospital’s IT infrastructure capability
and its enabled digital capabilities. Based on the above we consider a resource-based lens (Barney
1991) as an appropriate starting point for further hypothesizing considering that the targeted use of IT
assets and resources are a differentiating force within organizations (Ravichandran et al. 2005; Wade
and Hulland 2004). We develop a research model that relates hospitals’ IT infrastructure capability
and the formation of digital capabilities within hospitals. Extending prior research, we conceptualize
the IT infrastructure capability through identification of IT infrastructure business connectivity (i.e.,
hospital infrastructures reach and range, and level of standardization) (Broadbent et al. 1999; Weill et
al. 2002) and hospital IT assets (applications and their subsequent level of shareability and
integration) (Aral and Weill 2007; Ross et al. 1996). This paper’s core premise is that hospitals’
efficiency in targeting and deploying IT resources and assets influences the formation of a hospital’s
digital capabilities. This approach is in line with hospitals core business, such as improvements in
capabilities that drive clinical excellence, service to the community, and a streamlined patient journey
and overall workflow (Liedtka, 1992). Furthermore, we have noticed that many governments and
private investors have provided funding to stimulate the telehealth and digital capabilities, see, e.g.,
(Buntin et al. 2011). In this line of reasoning, we propose that the relationship between a hospital’s IT
infrastructure capability and its digital capabilities is influenced by receiving financial incentives.
Therefore, the current paper raises the following two research questions:
(1) To what extent does a hospital IT infrastructure capability influence the formation of
digital capabilities in hospitals? (2) How does the first question differ for hospitals with
or without financial incentives?
The remainder of this current study is structured as follows. We first review the theoretical
foundations of our work. Then, we propose our research model and the associated hypotheses. After
that, we discuss the applied methods including the data collection procedure from a European ehealth
benchmark and outline results. Finally, this paper concludes with a discussion on the implications of
our findings; it identifies inherent limitations and sets out an agenda for future research.
Theory and model development
This paper highlights the role of the Resource-Based View (RBV) in the development of our research
model and associated hypotheses. Further, we use a digital strategy and capability building
perspective (see the section hypotheses development) to examine the impact of IT infrastructure
capability on hospitals’ digital capabilities. Also, as will be outlined in the coming section, we highlight
the moderating role of financial incentives on hospitals’ digital capabilities. Figure 1 shows our
research model and captures the theorized relationships. The model represents both IT infrastructure
capability and (clinical) digital capabilities as reflective second-order latent constructs, in Structural
Equation Modeling (SEM) terms. First, will review the core notions of the RBV and its meaning within
IS research. We then subsequently develop the model and its hypotheses, which we test using
empirical data.
Examining hospital’s IT infrastructure capability and digital capabilities
Twenty-fourth Americas Conference on Information Systems, New Orleans, 2018 3
Figure 1. Research model
Resource-based view and its notion in Information Systems research
The RBV is widely acknowledged as one of the most prominent and influential theories of strategic
management, innovation, and IT-business value research. The RBV explains how firms achieve and
sustain a competitive advantage as a result of the resources they own or have under their control
(Barney 1991). This theory is grounded in foundational economic scholarship concerned with firm
heterogeneity and imperfect competition (Chamberlin 1937). The notion of resource in contemporary
research was subsequently further split to encompass the processes of resource-picking and
capability-building, two distinct facets central to the RBV (Makadok 2001). Amit and Schoemaker
(1993) define firm’s resources as tradable and non-specific firm assets, and capabilities as non-
tradable firm-specific abilities to integrate, deploy, and utilize other resources within the firm. Thus,
resources represent the input of the production process, while a firm’s capability is the capacity to
deploy these particular (IT) resources, aiming to improve productivity. The RBV theory provides
valuable ways for information systems (IS) research to think about how IT contributes to firm
performance and how to create value (Wade and Hulland 2004). In fact, the RBV has gained
considerable research interest over the past twenty years. The theory has been used extensively in the
IS context through the notion of single IT resources, sets of IT resources and IT capabilities
(Bharadwaj 2000; Wade and Hulland 2004; Wang et al. 2012). These recent studies acknowledge that
the process of leveraging IT resources in combination with other organizational resources is a source
of competitive advantage and value creation (Pavlou and El Sawy 2006). Scholars also argue that to
develop a robust IT capability it is necessary for a firm to have invested in all the required resources
(Wade and Hulland 2004). Failure to invest in resources—that are valuable, rare, inimitable, and non-
substitutable—may cause the collapse of the value of resources and capabilities, making it necessary to
place equal importance to each (Mikalef et al. 2014; Van de Wetering 2016). Wade et al. (2004) divide
IT resources broadly into IT assets and IT capabilities. Moreover, the RBV perspective implies that IT
assets (e.g., infrastructure) are the most accessible resources for competitors to copy (Teece et al.
1997) and can, therefore, be considered a fragile source of sustainable competitive advantage for a
firm (Wade and Hulland 2004). Furthermore, drawing from main RBV arguments, studies have
postulated that the targeted use of IT assets and resources is a differentiating force within a firm
(Ravichandran et al. 2005; Wade and Hulland 2004), contrary to past studies that assumed direct
effects of IT resources on firm-level performance. Therefore, the RBV acknowledges that the single
investment in IT is not a necessary and sufficient condition for improving firm performance. Soh and
Markus (1995) already argued that the particular value of IT needs better understanding and
subsequently proposed that IT investments should be converted into IT assets such as IT
infrastructure and applications.
RBV and IS research in healthcare environments
Until recently it was not common practice to apply the RBV-lens to public organizations. Only in 2007
Bryson et al. (2007) explicitly indicated the usage of the RBV in public organizations. In 2014,
Szymaniec-Mlicka (2014) provided a literature overview on the application of the RBV noting that
more and more, though still a limited number of, public organizations use an RBV lens to identify and
leverage their resources (both assets and capabilities). She concludes that “implementing the logic of
the resource-based view in the management of public organizations in a turbulent environment seems
to be the right strategy” (p. 26). It is remarkable that in Szymaniec-Mlicka’s overview IT infrastructure
and digital capabilities is very limitedly mentioned, and only in the context of ‘technical skills’ or
indirectly as ‘ability to access information’ and ‘knowledge sharing’ competences. The body of
knowledge on the application of the RBV lens in healthcare, with a focus on IT infrastructure
Examining hospital’s IT infrastructure capability and digital capabilities
Twenty-fourth Americas Conference on Information Systems, New Orleans, 2018 4
capability and digital capabilities, is rather sparse, and mostly provided through case studies in the
particular healthcare domain, see, e.g., Gordon and Tarafdar (2007). Therefore, we consider it
relevant to explore this area further.
Hypotheses development
Firms within business ecosystems invest in IT resources and capabilities that simultaneously shape
the digital strategic posture of a firm, i.e., the degree to which a firm is active in a given strategic
dimension relative to an industry norm (Mithas et al. 2013). We follow these authors definition of a
digital business strategy, i.e., “the extent to which a firm engages in any category of IT activity.”
Prior literature documents that modern digital business strategies focus on capability-building and
leveraging IT investments (Bharadwaj et al. 2013; Sambamurthy et al. 2003; Setia et al. 2013; Van de
Wetering et al. 2017b). Moreover, strategic investments in the firm’s IT infrastructure and resources
are deemed necessary to develop firm-wide capabilities to gain performance enhancements and IT
business value (El Sawy and Pavlou 2008; Mithas et al. 2012). Extant literature shows that IT can be
beneficial for hospitals regarding patient value, hospital performance gains and enhancements
(Blumenthal 2010; Buntin et al. 2011). Therefore, strategic investments are especially necessary for
healthcare. In practice, however, we see that IT implementations in healthcare are often multi-year
projects and are subject to constant (scope) changes. These conditions imply the need for clinical
digital capabilities to achieve efficiency and quality gains. Digital capabilities include IS competencies,
and hospital focused systems in a provider’s setting (Boonstra and Broekhuis 2010; Jaana et al. 2012).
Given the above, it seems that these capabilities frame the hospital’s business strategy and clinical
practice. Although the RBV perspective may provide some critical insights on the role of
complementary IT assets and resources that firms must own or have under its control (Powell and
Dent-Micallef 1997), it is not well developed into this theory (Wade and Hulland 2004). Moreover, an
obvious limitation of the conventional RBV theory is that it assumes that resources are always applied
in their best uses, neglecting the way both resources and assets collectively should be leveraged to
derive value from them (Melville et al. 2004). Therefore, a growing amount of research directed its
path toward identifying a firm’s ability to leverage its IT assets and resources, in combination with
other organizational resources and capabilities, to address rapidly changing business environments
(Mikalef et al. 2016). Moreover, recent scholarship argues that—instead of trying to determine what
combinations of IT resources and IT competencies firms should aim for—it is more pertinent to
identify those (digital) capabilities that IT should target to enable or strengthen (Kohli and Grover
2008; Mikalef et al. 2016; Van de Wetering et al. 2017c). We see that firms’ IT infrastructure is
considered to be a major resource for competitive advantage (Overby et al. 2006; Sambamurthy et al.
2003). Typically IT infrastructures are referred to by their physical components, including, e.g.,
hardware and software, networks, data sources, and IT-enabled services. Firm’s IT infrastructure
capability can be considered an integrated set of reliable IT assets, resources and services available to
support both existing applications and new initiatives (Weill and Vitale 2002). Past studies have
defined and subsequently refined key characteristics of such a capability. Among these is the extent to
which the infrastructure is shareable and reusable across the firm (Duncan 1995), and the degree to
which standards and policies have been established and how applications connect and interoperate
with each (Weill and Ross 2005). Also, the level of integration of applications (Broadbent and Weill
1997; Broadbent et al. 1999), and the degree to which infrastructure services are directed toward the
re-engineering of business processes (Duncan 1995) are considered key quality attributes of this
capability. IT infrastructure capabilities can also be assessed through their level of business
connectivity (Broadbent and Weill 1997; Broadbent et al. 1999). The business connectivity
characteristic of a firm’s IT infrastructure capability can be defined regarding their reach and range
(Broadbent et al. 1999). As reach refers to connected locations through the firm’s infrastructure, the
range typically refers to the level of encapsulated functionality (i.e., information and transaction
processing) or services that share automatically. As each of the individual qualities and dimensions of
an IT infrastructure may to some extent strengthen a hospital’s digital options, it is not likely that they
will drive digital clinical capabilities in isolation. We foresee that the combinative effect of the
underlying dimensions of IT infrastructure capability enables hospitals to develop the digital
capabilities to improve clinical quality, enhance and improve service efficiency, expand access and
reduce costs. Based on the preceding, we define:
Hypothesis 1. An IT infrastructure capability is positively associated with hospitals’ digital
capabilities.
Examining hospital’s IT infrastructure capability and digital capabilities
Twenty-fourth Americas Conference on Information Systems, New Orleans, 2018 5
The specific conditions under which an IT infrastructure capability is expected to add value has been a
subject of much debate. Although an IT infrastructure capability is argued to enhance the formation of
digital capabilities, the value-adding properties may be amplified through moderation. Of particular
interest is whether or not, financial stimulation influences this relationship. In practice such financial
programs are often initiated with the aim to improve clinical quality, enhance service efficiency,
expand access and reduce costs through increased digitalization; see, e.g., (Buntin et al. 2011), and the
EU Health Programme
1
contributing to healthcare innovation and citizens healthcare access.
Financial incentives concerning IT adoption and development of (clinical) digital capabilities in a
hospital setting have been investigated in various studies. For example, Otto and Nevo (2013),
examine the adoption of an Electronic Health Record (EHR) with regard to a lack of financial
incentives, taking a simulation approach. Lluch (2011) performs a literature review on the barriers to
health IT adoption. Outcomes suggest that the application of financial incentives under certain
conditions could support maturing digital capabilities. Despite these studies, much ambiguity remains
about the influence of financial incentives, which encourages us to define the following hypothesis:
Hypothesis 2. Receiving financial incentives from health plans and other organizations
that are tied to the types of IT the hospital adopts, influence the strength of this particular
relationship.
Methods
Data and sampling procedure
To test our research model, we use survey data from the European Hospital Survey: Benchmarking
deployment of e-Health services (2012-2013). Within this broad study, an extensive survey was
carried out in about 1,800 hospitals across thirty countries in Europe. The survey measures the level
of deployment and take-up of IT and eHealth applications in acute care hospitals in Europe. This
benchmark focused purposefully on European acute hospitals, which guaranteed coherence and
comparability with the previously executed e-Business W@tch12
2
. This survey was carried out by PwC
EU Services, in cooperation with Global Data Collection Company. The survey covered a wide range of
relevant topics ranging from a) IT infrastructure, b) hospital IT applications, c) health information
exchange and d) security and privacy. Based on the constructs that we use in this current study and to
improve the quality of the data, we conservatively removed 608 cases based on particular missing
values. We can attribute these cases can mainly to the digital capabilities a) information processing
and b) clinical decision support. Also, we removed cases based on ‘don’t know’ entries as it is
challenging to generate meaningful insights from these values. Removed cases primarily were from
the countries Bulgaria, France, Poland, Italy, and Germany. These relatively large countries
collectively represent more than half of the removed cases. In total 1143 hospitals were included in the
final analyses, representing most of the European countries.
Measurements and indicators
The derivation of IT dimensions from existing IT infrastructure research contributions are both
comprehensive and manifold (Byrd and Turner 2000). Therefore, each of the included constructs in
this study is inspired based on past empirical and validated work (Byrd and Turner 2000; Tiwana et
al. 2010). Following established work, we measure hospital’s IT infrastructure capability through two
latent constructs, i.e., (1) business connectivity and (2) IT assets. We measure business connectivity
through the level of I) standardization—referring to the degree to which a firm’s standards and
policies establish how applications connect and interoperate with each other is a crucial quality (Weill
and Ross 2005)—and II) the infrastructures range and reach. Following Broadbent et al. (1997; 1999)
we operationalized this indicator as the product of hospitals’ reach of a computer system (from only
personal computers that are not part of a hospital-wide system to systems are part of a regional or
national network as reach refers to locations) and a broad range of services the hospital is managing.
We measured the latent IT assets construct through two indicators, i.e., I) the variety of critical
applications (i.e., business intelligence, document management system, critical care information
1
https://ec.europa.eu/health/funding/programme_en
2
e-BusinessW@tch (2006) was conducted by Ipsos for the EC's Directorate-General Enterprise under
the direction of Empirica
Examining hospital’s IT infrastructure capability and digital capabilities
Twenty-fourth Americas Conference on Information Systems, New Orleans, 2018 6
system, tele-homecare, appointment booking system, service order placing, transmission of clinical
results and health events reporting system) and II) the degree to which applications within the
hospital are integrated. Taken together, IT assets and hospital’s business connectivity provide a
practical measure of IT infrastructure capability. We operationalized digital capabilities through four
latent constructs, i.e., (I) information processing—containing 17 measurements including medication
list, lab and radiology results, medical history, allergies, immunizations and ordered tests—(II)
clinical decision support—containing the measurements clinical guidelines, drug-drug interactions,
drug-allergy alerts, drug-lab interactions, contraindications, alerts to a critical laboratory value—(III)
health information exchange—containing 12 measurements including interaction with patients,
appointments , transfer prescriptions and exchange patient medication lists and finally (IV)
telehealth—containing the measurements training, consultations with other healthcare practitioners,
consultations with patients, and monitoring patients remotely. All the above items were measured on
or rescaled to a Likert scale from 1 to 5. We measure financial incentives using a binary scale.
Model specification and validation
We use Structural Equation Modeling, Partial least squares (PLS)-based, to simultaneously assess our
research model’s measurement (‘outer’) and the structural (‘inner’) model (Hair Jr et al. 2016). In
interconnecting the above first-order latent constructs within our research model, we propose a
reflective construct model, through which the manifest variables are affected by the latent variables
(Chin 1998; Wetzels et al. 2009). Within this scheme, the first-order constructs represent the
underlying dimensions of these particular model aspects. Thus, the second-order latent construct
explains and encompasses the first-order constructs in a more parsimonious way. For the estimation
of relevant parameters for both the inner and the outer model, we use SmartPLS version 3.2.7. (Ringle
et al. 2015), which is an SEM application using PLS. To compute the level of the significance of the
regression coefficients, we employed a non-parametric bootstrapping procedure (Tenenhaus et al.
2005). For this study, we used 500 replications to obtain stable results and to interpret their
significance. Regarding sample size requirements, the 1143 responses received exceeds all minimum
requirements concerning the measurement and structural model.
Measurement model
We assessed the psychometric properties of our research model on satisfactory levels of validity and
reliability. We assessed reliability at both the construct and item level. At the construct level, we
examined Composite Reliabilities (CR) (Tenenhaus et al. 2005) and established that their values were
above the threshold of 0.70. After running the PLS algorithm to obtain the indicator loadings (to
check for construct-to-item loadings), we removed all variables with a loading less than 0.6
3
from our
model (Bagozzi and Yi 1988). Hence, we removed several indicators from the first-order digital
capabilities. To obtain acceptable loadings and constructs, we removed eight measurements from
information processing, one from the clinical decision support construct, six from health information
exchange, and finally, we removed one indicator from the telehealth construct. To assess convergent
validity, we computed the average variance extracted (AVE), i.e., the average variance of measures
accounted by the latent construct.
1
2
3
4
5
6
1. Business connectivity
0.770
2. IT assets
0.209
0.788
3. Clinical decisions support
0.296
0.333
0.746
4. Health information exchange
0.440
0.322
0.414
0.851
5. Information processing
0.172
0.446
0.342
0.389
0.734
6. Telehealth
0.199
0.280
0.404
0.324
0.157
0.801
AVE
0.594
0.725
0.621
0.557
0.539
0.642
Composite reliability
0.744
0.840
0.891
0.882
0.913
0.842
Table 1. Assessment of reliability, convergent and discriminant validity of reflective constructs
3
An even more liberal threshold is a loading value of 0.4 for exploratory studies, see (Hulland 1999).
Examining hospital’s IT infrastructure capability and digital capabilities
Twenty-fourth Americas Conference on Information Systems, New Orleans, 2018 7
All obtained values exceed the lower limit of 0.50 (Fornell and Larcker 1981), with the lowest observed
value being 0.539. We, then, assessed discriminant validity by assessing the extent to which
constructs are genuinely distinct from other constructs by empirical standards (Hair Jr et al. 2016).
Therefore, we checked for cross-loadings on other constructs (Farrell 2010). Also, we investigated if
the square root of the AVEs is larger than the inter-construct correlations (Chin 1998). Table 1 shows
that all square root scores of the AVEs are higher than the shared variances of the constructs with
other constructs in the model. We found, based on the above outcomes, adequate convergent and
discriminant validity for all constructs. The outcomes of the measurement model suggest that the
constructed PLS model is valid and reliable.
Analysis
Outcomes of the structural analyses using SmartPLS found support for our first hypothesis. There was
a significant positive impact of IT infrastructure capability on digital capabilities (β =.493; t = 20.296;
p < .0001). PLS—in contrast to covariance-based structured equation modeling—does not optimize
any global scalar function, so it naturally lacks an index that can provide the user with a global
measure of fit (e.g., χ2 statistic and various indices based on CBSEM) (Tenenhaus et al. 2005).
However, the variance explained by the model (R2)—the coefficient of determination—values of the
endogenous constructs are commonly used to assess model fit. The structural model explains 24.3% of
variance for digital capabilities (R2 = .243). The model’s predictive power thus represents moderate to
substantial predictive power and accuracy (Hair Jr et al. 2016). In the analyses of the structural
model, we also look at predictive relevance (Stone-Geisser Q2). By performing a blindfolding
procedure, outcomes suggest that the Q2 value for the dependent construct is above the threshold
value of zero for both cross-validated redundancy and cross-validated communality. Thereby the
outcomes indicated the overall model’s predictive relevance (Hair Jr et al. 2016).
To test hypotheses two, we performed a non-parametric multi-group analysis (PLS-MGA) and divided
our sample into two groups following the binary moderator variable (Hair Jr et al. 2016), financial
incentives. We, then, estimated the models separately for each respective data set. We do so, based on
the approach suggested by Henseler et al. (2009). Thus, we performed separate bootstrap analyses,
and these particular outcomes serve as a basis for the hypothesis tests of group differences. Group
differences within this MGA analysis are significant at the 5% probability of error level if the obtained
p-value is ≤ .05 or ≥ .95 for specific path coefficients. Hence, we obtained statistically significant
results between the two groups (p = .993). IT infrastructure capability exerted a stronger impact on
digital capabilities in subgroup 2—the group that is not receiving financial incentives (N = 772)—(β =
.535, t = 19.732, p < .001) than in subgroup 1 (N = 371)—the group that received financial incentives
from health plans and other organizations that are tied to the types of IT the hospital adopts—(β =
.398, t = 8.121, p < .001). Also, the structural model for subgroup 2 explains 28.6% of variance for
digital capabilities (R2 = .286) in comparison 15.9% (R2 = .159) for subgroup 1. These analyses confirm
hypothesis 2.
Discussion and conclusion
IT infrastructure capabilities have been extensively researched in past work. However, scholarly
contributions concerning the value of IT infrastructure capabilities within hospitals remain very
modest. Following leading academic insights, we argued that the development of digital capabilities in
clinical practice require a high-level of sophistication in terms of resource allocation. We proposed
that tied financial incentives influence the relationship between hospital’s IT infrastructure capability
and digital capabilities. Our results show that there is a significant positive relationship between
hospitals’ IT infrastructure capability and digital capabilities. Moreover, we see, surprisingly, that
financial incentives—as a moderating variable—negatively impacts the strength of this particular
relationship. This particularity obviously requires further investigation. Scholars have advocated
financial incentives for digital capabilities, see (Lluch 2011). Extant literature also suggests to take into
account additional conditions and interventions next to financial incentives, see, e.g. (Gans et al.
2005; Vishwanath and Scamurra 2007). Possible directions to explain the confirmation of hypothesis
2 could be to investigate whether or not, hospital personnel is intrinsically motivated to adopt IT, or
that other factors play an essential role, apart from financial incentives. It might also be the case that,
depending on which digital capability is enhanced with IT, the role of financial incentives may vary. It
is, therefore, worth investigating in detail how financial incentives are tied to the digital capabilities
and what the impact is on, e.g., patient service management, and clinical benefits. Future research
may also wish to investigate the relationship between digital capabilities and hospital performance.
Recent research demonstrates that mediating models—that include organizational (or IT-enabled)
Examining hospital’s IT infrastructure capability and digital capabilities
Twenty-fourth Americas Conference on Information Systems, New Orleans, 2018 8
capabilities as mediating constructs between organizational and IT resources and performance—
better explain the added value of IT resources than direct effect models without these particular
capabilities (Liang and You 2009; Mikalef et al. 2016). Finally, it would also be interesting to
investigate the individual contribution of the digital capabilities to specific outcome measures (e.g.,
hospital discharge, length of stay, care delivery effectiveness). Then, more details can be unfolded
concerning the limits and conditions to which digital capabilities and their configurations add value.
Also, comparing results across hospital types and countries might also contribute to the
generalizability of our findings. To conclude, hospitals could benefit from our research, taking a
strategy also to mature their IT infrastructure capability while striving for increasing their digital
capabilities.
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