Inf Syst Front (2017) 19:285–300

et al. 2014). Data quality concern is also another key issue as
government departments may be reluctant to release data that
they see as low quality (Conradie and Choenni 2014).
Zuiderwijk and Janssen (2014) suggest that data ownership
risks and legality concerns are major challenges of open data.
In addition, there is also the risk of privacy violation.
One of the key purposes of open data platforms is to promote
access to government data and encourage development of creative tools and applications to engage and serve the wider community through the visualisation of patterns and relationships
(Martín et al. 2015; Irani et al. 2014; Kassen 2013). In doing so,
enabling civic engagement by providing opportunity for citizens, public sector organisations, businesses and independent
developers to use systematically-updated stream of open data is
being encouraged. The governments perceive that making this
data available on the web would lead to more transparency,
participation, and innovation throughout society (Conradie
and Choenni 2014; Veljković et al. 2014; Janssen et al. 2012;
Bertot et al. 2012; Choenni et al. 2010). Governments and publishers of open data expect the users to exploit such data in as
many ways as possible for the benefit of the society
(Data.gov.uk 2015). For example, general public (nontechnical users) may use it to analyse trends in one policy area
over time, or compare how different parts of the government go
about doing their work (Sivarajah et al. 2016). On the other
hand, technical users such as software developers are encouraged to create useful applications out of the raw data files, which
can then be used by everyone benefitting the wider society.
The way people access and use open data is greatly influenced by the way data is published (Braunschweig et al.
2012). In its original, raw form, this data is often not very
useful for end users. Therefore, datasets are cleaned and customized before being published. Apart from accessing data
from these platforms, users (e.g. organisations) are also encouraged to submit useful data that can be published to the
general public. One of the challenges is making existing data
come to life, and users are encouraged to combine and reorganise existing data to offer new insights resulting in useful
visualisations of these data (Data.gov.uk 2015). Despite occasional initiatives from the government to encourage the use of
open data technologies via events such as hackathons, workshops and conferences, not much is known about the predictors which influence people’s (i.e. citizens) perceptions, willingness, ability and intention to use open data technologies.
Open data is an emerging field and the acceptance and use of
open data technologies has not received much attention in the
extant literature.

3 Research model and hypotheses development
The Diffusion of Innovation (DOI) model, derived from
Rogers’ DOI theory can suitably examine the acceptance

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and use of open data in the public sector, as it allows for
examining citizen perceptions, whilst identifying factors that
influence their acceptance decisions concerning the use of
open data. Moreover, the DOI model allows for investigating
the complex user adoption process, as the model attributes
account for up to 49 %–87 % variance in the adoption rate
(Rogers 2003). Open data is composed of varying contexts
and connotations of open datasets. These datasets are characteristically and contextually different and involve a wide array
of interdependent stakeholders with varying interests. Open
data, currently available in the raw format, can be fairly complex for users to understand and use. The DOI model focuses
on aspects such as - how relatively advantageous is the proposed solution to the existing methods, and how easy to use
technologies are important in positively influencing consumers’ use decisions. Literature extensively supports the
use of DOI attributes in measuring an innovative solution that
is aiming to attract consumer usage based on the aspects of
usefulness, ease of use, and compatibility (Moore and
Benbasat 1991; Karahanna et al. 1999; Tan and Teo 2000;
Rijsdijk and Hultink 2003; Pannell et al. 2006; Kapoor et al.
2013). This thus enhances the appropriateness of the Diffusion
of Innovation model being employed by this study to evaluate
the user perceptions of the usability of public sector open data.
According to Stokes et al. (2014), adoption studies in private sector have created a clear language and a set of frameworks for understanding innovation adoptions. Some field
experts have their reservations about these frameworks and
they identify them as being stereotypical, without sufficient
empirical support or understanding of the intricate nature of
the innovation adoption process (Stokes et al. 2014). Ongoing
research is now extending to account for not only the organizational, systemic, and contextual effects, but also the push
and pull effects of the innovators and the innovation adopters.
To the best of authors’ knowledge, there is no study analysing
the factors affecting citizens’ intentions to adopt open data.
Studies like Zuiderwijk et al. (2015) have studied the acceptance and use of open data technologies, but no study places
clear emphasis on the citizens’ intention to adopt open data
itself.
Extant literature highlights that very few studies have
attempted to empirically evaluate the performance of open data
websites. There are, however, evidences of other studies using
different measures of innovation adoption to investigate the
performance of different websites. For instance,
Wangpipatwong et al. (2008) use the Technology Acceptance
Model (TAM) alongside self-efficacy as an added measure, to
evaluate the use of an e-government website. Fang and
Holsapple (2007) focus on the navigation structure of a
website and their impact on the usability of that website by
using factors defining its usability. Wang and Senecal (2007)
used ease of use, speed, and interactivity to measure the usability of a website and its subsequent impact on user attitudes and

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