In 2013, the European Commission identified Social, Mobile, Analytics, Big Data
Based Simulation and Policy Modelling technologies. These technologies are on different maturity stages,
| Main Keyword(s) | Description | References |
| Augmented Reality | According to the most widely accepted definition proposed by Azuma et al. augmented reality (AR) system has three core features: it combines real and virtual objects in a real environment; it registers (aligns) real and virtual objects with each other; and it runs interactively, in three dimensions, and in real time (R. Azuma et al., 2001; R. T. A. Azuma, 1997). So, augmented reality adds virtual elements to the user’s view of the reality aiming to enrich it and provide additional information or features. That way, AR seamlessly bridges the gap between the real and the virtual (K. Lee, 2012). As AR supposes the interplay between real and virtual worlds, the technological demands and challenges in AR are higher than in pure virtual reality (VR); thus it needs longer time to mature compared to VR (Krevelen & Poelman, 2010). Any realisation of AR requires some sort of output device (usually display or projector), sensors (for input and registration), processing unit and possibly other technologies, depending on the type of AR offered (Chatzopoulos, Bermejo, Huang, & Hui, 2017; Krevelen & Poelman, 2010). While first AR prototype appeared in 1960s it took fifty years for truly mass-market technology to be developed (Tamura, 2002). AR applications are now readily available on a wide range of consumer devices, such as smartphones and portable game consoles. | References |
| Big Data | There are several definitions of big data, ranging from simple to sophisticated. According to Laney (2001) big data are “sets [characterized] by their big volume, velocity and variety”. These three characteristics often demand new technologies for data storage and analysis (Ward & Barker, 2013). This definition, while simple, is adequate for general purposes and is widely used (US Executive Office of the President, 2014). | References |
| Blockchain | Hou (2017, p.1) defines BlockChain (BC) as “a distributed ledger that maintains a continually growing list of publicly accessible records cryptographically secured from tampering and revision”. Zhang (2017) compares BC to a creation of a persistent, immutable, and ever-growing public ledger that can be updated to represent the latest state of it. It was originally used to record historical transactions of encrypted digital currencies, such as bitcoin (Zhu & Zhou, 2016). BC implementations are largely technology driven and often various combinations of technologies are needed to make the BC architecture fit for e-government applications (Engelenburg, Janssen, & Klievink, 2017). At present, the application of BC technology has been extended to five representative domains: finance, Internet of Things, public and social services, security and privacy, and reputation systems (Zheng, Xie, Dai, & Wang, 2016). | References |
| Cloud Computing | Mell and Grance, National Institute of Standards and Technology (2011, P. 2) define Cloud computing as “a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” This concept is taken a step further by other definitions that go on to say that cloud computing ”encompasses any subscription-based or pay-per-use service that, in real time over the Internet, extends its existing capabilities.” (Knorr & Gruman, 2018) or “a broad array of web-based services aimed at allowing users to obtain a wide range of functional capabilities on a pay-as-you-go’ basis that previously required tremendous hardware/software investments and professional skills to acquire” (Kepes, 2011). These definitions suggest that cloud computing is the realisation of earlier computing whilst removing the worries of deployment and the technical complexity using a pay-per-use/pay-as-you-go basis. | References |
| Community Awareness Platforms | Community awareness platforms, often found in literature as Collective Awareness Platforms for Sustainability and Social Innovation (CAPs), are defined by the European Commission as follows: "The Collective Awareness Platforms for Sustainability and Social Innovation (CAPs) are ICT systems leveraging the emerging "network effect" by combining open online social media, distributed knowledge creation and data from real environments ("Internet of Things") in order to create awareness of problems and possible solutions requesting collective efforts, enabling new forms of social innovation. The Collective Awareness Platforms are expected to support environmentally aware, grassroots processes and practices to share knowledge, to achieve changes in lifestyle, production and consumption patterns, and to set up more participatory democratic processes." (Bellini et al., 2016, p. 11). Pacini and Bagnoli (2016) also define the collective awareness platforms as “important crowdsourcing instruments that may promote cooperation, emergence of collective intelligence, participation and promotion of virtuous behaviours in the fields of social life, energy, sustainable environment, health, transportation, etc” | References |
| Crowdsourcing | Crowdsourcing was initially defined as a business concept, which described the outsourcing of tasks to a large group of people instead of assigning such tasks to the in-house employees or contractors (Alonso & Lease, 2011). Broader definitions have also been developed according to which crowdsourcing was seen as a set of methods of soliciting solutions to tasks via open calls to large-scale communities (DiPalantino & Vojnovic, 2009; Howe, 2006) or the use of an Internet-scale community to outsource a task (Yang, Adamic, & Ackerman, 2008). Estelles-Arolas and Gonzalez-Ladron-de-Guevara (2012), after studying more than 40 definitions published between 2006 and 2011 proposed a general definition: “Crowdsourcing is a type of participative online activity in which an individual, an institution, a non-profit organization, or company proposes to a group of individuals of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a task. The undertaking of the task, of variable complexity and modularity, and in which the crowd should participate bringing their work, money, knowledge and/or experience, always entails mutual benefit.” (p.197) Today the term encompasses many practices and is used for a wide group of activities, however mostly in private sector (Liu, 2017). In relation to e-government these practices include among others content production, task competition, voting, crowdfunding (Panagiotopoulos & Bowen, 2015) and open collaboration over the web and social media (Taeihagh, 2017a). Generally in citizen-government relationships new forms of crowdsourcing are emerging (Linders, 2012) as the concept is leaning towards less institutionalized forms: like monitoring and aggregating content from open information sources (Loukis & Charalabidis, 2015; Panagiotopoulos & Bowen, 2015). Mainly driven by participatory social media technologies crowdsourcing is seen as one of the forms of e-participation and has to be considered as a complement rather than a replacement for other citizen involvement initiatives (Royo & Yetano, 2015a). Apart from providing solutions to the problems directly, crowdsourcing is also used as a research technique, particularly in case studies (Di Mauro et al., 2016; Dubey, Luo, Xu, & Wamba, 2015) as a type of online surveying. | References |
| Customised/ Personalised Public Services | Customised (or personalised) public services are about customizing or building services based on the individual citizen needs. The UK’s Prime Minister’s Strategy Unit (2007) defines such services as “tailored to the needs and preferences of citizens” (p. 33). Customised services are understood as those that are adapted to the individual as a unit of interest (Needham, 2011). Personalisation of public services can on the one hand increase the efficiency of public sector (Kim, 2017) and on the other improve the perception of public services by the citizens (Leadbeater, 2004). Research suggests that customised to individual needs services can be enabled through data analysis (Loon, 2014), citizen participation and by involving citizens in the co-creation of services (Leadbeater, 2006). | References |
| e-Signature/ e-Identity | The EU Regulation No 910/2014 of the European Parliament and of the Council of 23 July 2014 on electronic identification and trust services for electronic transactions in the internal market, defines electronic identification or eID as: “the process of using person identification data in electronic form uniquely representing either a natural or legal person, or a natural person representing a legal person” . In other words, eID is a way for citizens, businesses or administrations to prove electronically that “they are who they say they are and thus gain access to services” (European Commission, 2007). An electronic identity can take various forms. Different forms of eID include: smartcard with chip; username & password; username & password with mobile verification; software-based solution (e.g. public key infrastructure (PKI)), certificates and Mobile ID or application. Some countries already provide eIDs to their citizens for the access to public services, mostly in the form of smartcards (with a chip) but also through username and passwords. The same Regulation defines electronic signature or eSignature as “data in electronic form which is attached to or logically associated with other data in electronic form and which is used by the signatory to sign” . An eSignature can represent a person’s intent to agree to the content of a document or a set of data to which the signature relates, and a qualified electronic signature should have the same legal effect as handwritten signatures. Under the eIDAS Regulation, only natural persons can “sign” a document and therefore certificates for electronic signatures cannot be issued to legal entities. | References |
| Gamification | The first widely accepted definition for this concept was put forward by Deterding, Dixon, Khaled, & Nacke (2011), who define gamification as “…the use of game design elements in non-game contexts.” (p.10) This definition, according to Deterding et al., encompasses the following key concepts (pp.11-13): i) games, or structured situations characterized by explicit rule systems and the movement of actors towards goals or objectives; ii) elements, or ‘atoms’ of games constitute a gamified application; iii) design, or the purposeful incorporation of technical mechanisms; and iv) non-game contexts, or the application of game elements for purposes other than entertainment. When looked at from these four axes, gamification differs significantly from ‘serious games’ or the mere use of full-fledged games in non-game contexts (p.14). Taking forward the definition advanced by Deterding et al., Robson et al. (2015) define gamification to be: “...the application of lessons from the gaming domain to change behaviors in non-game situations.” (p.412) This definition may be considered important as it introduces ‘behaviour change’ as the stated objective of gamification. Seaborn and Fels (2015) go further, and state that the term gamification may be used: “…to describe those features of an interactive system based on video gaming that aim to motivate and engage end-users through the use of game elements and mechanics” (p.14) Here, the main aims of gamification are further clarified to be the motivation and engagement of individual system end-users through the application of game design elements. Huotari and Hamari (2017), through an analysis of service and marketing literature arrive at the following definition: “Gamification refers to a process of enhancing a service with affordances for gameful experiences in order to support users’ overall value creation.” (p.25) Exploring this definition, Hassan (2017) argues that the outcomes of gamification are dependent on the types of external stimuli or ‘motivational affordances’ introduced by system designers into serious contexts that affect the psychological states of system users and encourage them to act in desired ways. | References |
| Gaming-based Simulation | Becu et al. (2016) expanded on the widely accepted definition of Gaming-based Simulation (GS) by Duke (1974): “[GS is a] gestalt communication mode (today we would call it a [participatory simulation] workshop), which combines a game-specific language (e.g. game rules), appropriate communication technologies (communication channel by which participants transmit and receive messages) and the multilogue interaction pattern (the multiple, simultaneous dialogue among members of a group that takes place during a gaming/simulation workshop).” (p.10) GS includes an operating model of central features of real or proposed systems or processes. Scenarios are developed, roles are defined in interacting systems, and players are given goals, resources, and rules. Then, they work out the simulations, trying out alternative roles and strategies within the system constraints defined (Tanwattana & Toyoda, 2018). | References |
| Internet of Things | Gubbi et al. (2013) define the Internet of things as “Interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework, developing a common operating picture for enabling innovative applications. This is achieved by seamless large scale sensing, data analytics and information representation using cutting edge ubiquitous sensing and cloud computing.” (p. 4) This definition captures the three “visions” of IoT realisation as described by Atzori et al. (2010): internet-oriented (middleware), things-oriented (sensors) and semantic-oriented (knowledge). It also underlines the intrinsic connections of IoT to the other concepts relevant for defining Government 3.0: cloud computing, sensors, (big) data analytics and smart cities. | References |
| Linked Data | Linked Data is defined as: “a set of design principles for sharing machine-readable data on the Web for use by public administrations, business and citizens (ISA, 2016). It is presented as the evolution from a “document-based web” of a “Web of interlinked data” (Heath & Bizer, 2011). It has to follow certain Web standards such URl and RDF, SPARQL and vocabulary standards (RDFS, OWL, SKOS). Basically, Data can be linked to URIs from other data sources, using open standards such as RDF without being publicly available under an open licence. Linked data is about applying the principles of the web to sharing data, and doing so at a deeper level than just publishing a full document or a file. | References |
| Machine Learning | Chui et al (2017) defines machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed”. The definition captures four types of machine learning: (i) un-supervised learning; (ii) supervised learning; (iii) semi-supervised learning; and (iv) reinforcement learning. Fundamentally, none of the data is labelled in type (i), the training/testing samples are labelled in type (ii) and there are many unlabelled data and few labelled data in type (iii) (Chui T. K. et al, 2017). Various problems can be tackled using machine learning algorithms, some examples are: classification, regression, clustering, dimensionality reduction, structured prediction, face detection, decision making, speech recognition, signal de-noising, anomaly detection, deep learning and reinforcement learning (Chui et al., 2017). | References |
| Natural Language Processing | Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech to do useful things (Noble, 1988). In 1963 Reed C. Lawlor surmised that computers would one day become able to analyse and predict the outcomes of judicial decisions (Lawlor, 1963). According to Lawlor, reliable prediction of the activity of judges would depend on a scientific understanding of the ways that the law and the facts impact on the relevant decision-makers, i.e., the judges. More than fifty years later, the advances in Natural Language Processing (NLP) and Machine Learning (ML) provide us with the tools to automatically analyse legal materials, so as to build successful predictive models of judicial outcomes, (Aletras et al., 2018). | References |
| Once Only Principle | Once only principle (OOP) is defined by the European Commission in EU eGovernment Action Plan 2016-2020 (European Commission, 2016) as one of the principles that should be observed by the initiatives within the Action Plan: “Once only principle: public administrations should ensure that citizens and businesses supply the same information only once to a public administration. Public administration offices take action if permitted to internally re-use this data, in due respect of data protection rules, so that no additional burden falls on citizens and businesses” | References |
| Open Data | Open Data Handbook (2017) provides the following definition of Open Data: “Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike” Simply, the process of putting at disposal and making the data available free of charges results in creation of Open Data. The ultimate aim is to allow a free, open access to the available data without any restrictions or mechanism of control. The Open Data is following the same rational of the open source and all the other open system (open hardware, open content, open government, etc.). | References |
| Policy Modelling | Ruiz Estrada (2011, p.524) defines policy modelling as “an academic or empirical research work, that is supported by the use of different theories as well as quantitative or qualitative models and techniques, to analytically evaluate the past (causes) and future (effects) of any policy on society, anywhere and anytime”. The authors goes on to review 1501 papers published in the Journal of Policy Making to provide a classification of the policy modelling research conducted so far with respect to the field in which it contributes. In particular, Estrada identifies 12 different categories of policy making research including (I) domestic and international trade policy modelling; (ii) energy, communications, infrastructure and transportation policy modelling; (iii) environmental and natural resources management policy modelling; (iv) fiscal and government spending policy modelling; (v) institutional, regulation and negotiation policy modelling; (vi) labour, employment and population policy modelling; (vii) monetary, banking and investment policy modelling; (viii) production and consumption policy modelling; (ix) technological and R&D policy modelling; (x) welfare and social policy modelling; (xi) economic growth and development policy modelling; (xii) miscellaneous policy modelling (Estrada, 2011). | References |
| Service co-Creation | Co-creation is active involvement of end-users in various stages of production process (Prahalad & Ramaswamy, 2000; Vargo & Lusch, 2004). In the case of public service production, it refers to the active involvement of the citizens in different stages of public service production. This involvement can be voluntary or involuntary and may happen during public services’ design, management, delivery and/or evaluation (Osborne, Radnor, & Strokosch, 2016). This definition clearly delimits co-creation from more general citizen participation, which may involve also passive participation (W. Voorberg, Bekkers, Timeus, Tonurist, & Tummers, 2017). In the literature the term co-creation is commonly used together (Vargo & Lusch, 2004) or even interchangeably (Gebauer, Johnson, & Enquist, 2010) with co-production. Co-creation and co-production are the terms used very often to describe the modern reforms in public service provision, especially in the times of austerity (Osborne et al., 2016; W. H. Voorberg, Bekkers, & Tummers, 2015). In the report of the European Commission (Hubert, 2010, p. 30) the co-production is presented as a necessary part of social innovation: “social innovation [..] mobilizes each citizen to become an active part of the innovation process”. | References |
| Service Modules | Grönroos (2011) has presented a view of managing a company's service offering in which a service comprises a basic service package and the subsequent augmentation of this package. The augmentation, in turn, comprises four parts: the core solution, the enabling service, the enhancing services, and the user interface (UI). Such argument according to Tuunanen et al. (2011) can help achieve a better conceptualization of information systems and information technology-enabled services. It can be argued that core, enabling, and enhancing services should be considered as service modules (Johansson and Lahtinen, 2012) that include aspects such as infrastructure, deployment, and user interface. | References |
| Smart City | Dameri and Benevolo, (2016) define smart cities (SC) as “a recent but emerging phenomenon, aiming at using high technology and especially information and communications technology (ICT) to implement better living conditions in large metropolises, to involve citizens in city government, and to support sustainable economic development and city attractiveness. The final goal is to improve the quality of city life for all stakeholders.” (p. 1) In a more technical perspective, Costa and Santos (2016) state that “Smart Cities are known for their human dynamics, which makes recurrent use of permanently connected devices, frequently known as Internet of Things (IoT). Consequently, since these new cities generate a vast volume of data with significant variety and velocity, they have the potential to be one of the richest and challenging systems to generate Big Data and to benefit from its adequate storage, processing, analysis and public availability.” (p. 1247) Smart city is also defined as innovative (not necessarily but mainly ICT-based) solutions that enhance urban living in terms of people, governance, economy, mobility, environment and living (Anthopolous & Reddick, 2016). | References |
| Virtual Reality | Virtual reality is a simulation, in which computer graphics are used to create a realistic-looking world, which is dynamic and a user can interact with it using certain input methods (Burdea & Coiffet, 2003). It is usually presented to a user through a head-mounted device with screen(s), which allow the user to immerse into the completely artificial environment, instead of augmenting the reality as in case of AR. As early as 1993 researchers have recognized the potential of virtual reality for training and education. It is seen as a valuable technology in the spheres were real world practical training would be expensive or difficult, like surgical procedures (Satava, 1993) or aircraft piloting (Bricken & Byrne, 1992). Ideally virtual reality can simulate real situations and prepare the trainees to dealing with real problems and complications. | References |