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intentions. Wang et al. (2001) empirically study consumers’
satisfaction of a website dealing with digital products and services using beliefs and attitudinal constructs.
According to Zuiderwijk et al. (2015), the acceptance and
use of Information Technology has been significantly important from the IS research and practice perspectives. The DOI
model is one of the most used models for examining the acceptance and use of Information Technology. The five perceived attributes of innovations (Relative advantage,
Compatibility, Complexity, Observability, and Trialability)
from the DOI model have been extensively investigated and
found to explain about half of the variance in users’ technology acceptance rates (Rogers 2003). This theory is regarded as
a principal theoretical perspective on technology adoption,
offering a conceptual framework for discussing adoption at a
global level (Kapoor et al. 2013). Rogers (2003) has synthesized sixty years of innovation-adoption research in developing this theory. His model has been well received in the world
of innovative solutions, and is one of the most used theories in
the field of innovation diffusion (Kapoor et al. 2013). The
concept of trialability is most suited in cases where a
product/service is available for limited period for consumers
to try, prior to making an adoption decision. Since open data is
freely available for citizens to access and use as desired, without any concept of cost or usage bond/contract associated with
it, this attribute has been omitted from this study.
In addition to attributes from the DOI model, there is one
another aspect that tends to become the topic of concern when
discussing open data. The aspect of associated risk is a topic
that open data experts deal with on a regular basis. The risk of
data being analysed or interpreted incorrectly, and that of open
data being used against the publisher (Dodds 2015). The
Diffusion of Innovations (DOI) model has undergone a minor
modification to suit the context of this study, and a component
of risk has been introduced in this case to account for security
concerns associated with the use of open data (Fig. 1). More
justification on the inclusion of risk as an additional factor can
be found within section 3.1.5 below.
The effects/influences of relative advantage, compatibility,
complexity, observability, and risk will be individually measured across users’ behavioral intentions (Fig. 1). These five
characteristics are expected to significantly impact users’ intentions towards the usage of open data platforms. The positive or negative correlations that will surface post the empirical evaluations will then be logically reasoned and analyzed
for their significance in influencing users’ intentions towards
using open data.
3.1 Hypotheses development
As justified in the earlier section, the modified and extended
DOI model will now be further discussed for its attributes and
their probable effects on users’ intention to use open data. The

Fig. 1 Modified and extended DOI research model

four attributes from the DOI model were aimed at exploring
different aspects spread across the functional value of using
open data (relative advantage, complexity), its usability (compatibility), the stereotype perceptions associated with its use
(observability), and the associated security concerns (risk).
Hypotheses for each attribute have been individually
discussed in the following parts of this section.
Behavioural intention, or use intention, or intention to use
is one of the most frequently used attributes in the innovation
related studies (Taylor and Todd 1995; Lu et al. 2008; Akturan
and Tezcan 2010; Kapoor et al. 2013). As defined by Ajzen
and Fishbein (1980), behavioural intention measures the likelihood of an individual being involved in a given behaviour.
The behaviour of an individual, that is, their decision to accept
or reject a technological innovation, is determined by their
intention to perform that behavior, that is, their intention to
use that technological innovation (Fishbein and Ajzen 1975).
All hypotheses proposed in this study are aimed at examining
the influences of the aforementioned five variables on behavioral intentions of the study’s respondents. Since this study is
interested in both adopters and non-adopters of open
data, behavioural intention (as opposed to ‘adoption’)
will be used as a dependent variable to account for both
adopters and non-adopters.
3.1.1 Relative advantage
Relative advantage will help assess if the information available as open data is relatively better across multiple aspects in
comparison to the same data that a citizen can access via other
physical offices and platforms. In measuring the advantages of
a new service, users tend to evaluate the pluses and minuses of
using that service. Relative advantage is known to determine
the ultimate rate of most innovation adoptions in the long run

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