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evaluation framework for supporting both formative and summative evaluation at various levels of detail( enabling both higher and lower level evaluation), and combining all the above methods for this purpose( both quantitative and qualitative ones).
There is long and extensive research on the evaluation of information systems( IS)( Hirschheim 1988; Smithson 1998; Willcocks 1996, 2001; Farbey 1999; Irani 2002, 2006, 2008; Gunasekaran 2006; Stockdale 2006), which has revealed its complexities and difficulties. IS offer various types of benefits, both financial and non‐financial, and also tangible and intangible ones, which differ among the different types of IS. Thus, it is not possible to formulate one generic IS evaluation method, which is applicable to all types of IS. The major outcome of this research is that a comprehensive methodology for evaluating a particular type of IS should include evaluation of both its efficiency and its effectiveness, and take into account its particular characteristics, capabilities and objectives.
Moreover, a significant part of the IS evaluation research has been focused on understanding IS acceptance. Extensive research has been conducted in order to identify the characteristics and factors affecting the attitude towards using an IS, the intention to use it and finally the extent of its actual usage, which has lead to the development of the Technology Acceptance Model( TAM) and its subsequent extensions( Davis 1989; Venkatesh 2000, 2003; Schepers 2007). This research stream on IS acceptance provides some important dimensions of IS evaluation( ease of use, usefulness, users’ intention for future use and actual use), which can be used as part of our PSI e‐infrastructures evaluation model.
Another research stream that can provide useful elements for IS evaluation is the IS success research( DeLone 1992, 2003; Seddon 1997; Rowley 2006; Sumak 2009). The most widely used IS success model has been developed by( DeLone 1992). This research stream suggests that IS evaluation should adopt a layered approach based on the above IS success measures( information quality, system quality, service quality, user satisfaction, actual use, perceived usefulness, individual impact and organizational impact).
Finally, the emergence of numerous Internet‐based e‐services( e. g. information portals, e‐commerce, e‐ banking and e‐government portals) has lead to the development of many models for their evaluation( Rowley 2006; Sumak 2009; Lu 2003; Fassnacht 2006; Saha 2011). These models suggest useful evaluation dimensions and measures either for e‐services in general, or for particular types of e‐services.( Loukis 2012) proposed an e‐services evaluation methodology, which includes a set of value dimensions and measures assessing different types of value generated by the evaluated e‐service, organized in a three‐layered value model( concerning e‐ service efficiency, effectiveness and users’ futute behaviour) and the relations among them. Our evaluation model has adopted this structure and organization of evaluation dimensions and measures proposed by the above methodology.
3. Advanced public data e‐infrastructures
The‘ traditional’ public data e‐Infrastructures have been the first step of opening public data. However, this first generation of PSI provision e‐infrastructures are characterized by data publishing in non machine‐readable formats( i. e. PDF) without providing any contextual information or linkage to other data in most of the cases. These traditional PSI e‐Infrastructures are limited to offering basic functionalities for downloading data to data users, or for uploading data by the data providers, with minor support and flexibility. They are not considering the possibility their published open data to be improved by users( e. g. through cleaning and further processing) and reused, or how they can get feedback on them by the users in order to understand better their needs. The lack of concern about public data improvement by users and reuse and its importance is being shown by the current calls for advanced service e‐infrastructures providing such capabilities, i. e. including tools that enable cleaning, analyzing, visualizing and linking datasets( Charalabidis 2011). In general, this first generation of the traditional public data e‐infrastructures has been influenced by the Web 1.0 paradigm, in which there is a clear distinction between content producers and content users.
The advent of Web 2.0, in conjunction with current research advances in the domains of Information and Communication Technologies( ICT) and E‐government, offer opportunities to exploit the full potential of PSI providing new features for open data reuse and functions that will enhance scientific research, economic growth and citizens’ trust to the governments. This gradually leads to the emergence of a second generation of more advanced PSI e‐infrastructures, which are influenced by the principles of the Web 2.0 paradigm, giving to
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