Search results

The growing turbulence of the external environment has progressively led to the necessity by organizations of exploiting new opportunities provided by data-driven approaches for supporting the even more complex decision-making processes. The new digital environment has led to the development and adoption of innovative approaches; also in the urban context which has always been characterized by different, interconnected, and dynamic dimensions. Urban governance models have been enhanced by smart technologies, which act as enablers of advanced services and foster connections between citizens, public and private organizations, and decision-makers. In this context, the objective of this chapter is to examine the role of data-driven approaches in the urban context during the chaotic and high variable circumstances related to the diffusion of the Coronavirus disease 2019 (Covid-19). Thanks to the adoption of the co-evolutionary perspective, a cycle in urban governance decision-making approach based on digital technologies is depicted and its contribution for managing the ongoing Covid-19 is traced. The results of the analysis highlight how the data-driven approach supports urban decision-making process and shed light on the co-evolutionary perspective as heuristic device to map the interactions settled in the networks between local governments, data-driven technologies, and citizens. In this sense, this chapter offers interesting insights, potentially capable of generating useful implications for both researchers and professionals in the public sector.

Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.

Because of the use of digital technologies in smart cities, municipalities are increasingly facing issues related to urban data management and are seeking ways to exploit these huge amounts of data for the actualization of data driven services. However, only few studies discuss challenges related to data driven strategies in smart cities. Accordingly, the purpose of this study is to present data driven approaches (architecture and model), for urban data management needed to improve smart city planning and design. The developed approaches depict how data can underpin sustainable urban development.

Design science research is adopted following a qualitative method to evaluate the architecture developed based on top-level design using a case data from workshops and interviews with experts involved in a smart city project.

The findings of this study from the evaluations indicate that the identified enablers are useful to support data driven services in smart cities and the developed architecture can be used to promote urban data management. More importantly, findings from this study provide guidelines to municipalities to improve data driven services for smart city planning and design.

Feedback as qualitative data from practitioners provided evidence on how data driven strategies can be achieved in smart cities. However, the model is not validated. Hence, quantitative data is needed to further validate the enablers that influence data driven services in smart city planning and design.

Findings from this study offer practical insights and real-life evidence to define data driven enablers in smart cities and suggest research propositions for future studies. Additionally, this study develops a real conceptualization of data driven method for municipalities to foster open data and digital service innovation for smart city development.

The main findings of this study suggest that data governance, interoperability, data security and risk assessment influence data driven services in smart cities. This study derives propositions based on the developed model that identifies enablers for actualization of data driven services for smart cities planning and design.

This study explores the enablers of data driven strategies in smart city and further developed an architecture and model that can be adopted by municipalities to structure their urban data initiatives for improving data driven services to make cities smarter. The developed model supports municipalities to manage data used from different sources to support the design of data driven services provided by different enterprises that collaborate in urban environment.

The purpose of this paper is to explore the epistemological tensions embedded within big data and data-driven technologies to advance a socio-political reconsideration of the public dimension in the assessment of their implementation.

This paper builds upon (and revisits) the European Union’s (EU) normative understanding of artificial intelligence (AI) and data-driven technologies, blending reflections rooted in philosophy of technology with issues of democratic participation in tech-related matters.

This paper proposes the conceptual design of sectorial and/or local-level e-participation platforms to ignite an ongoing discussion – involving experts, private actors, as well as cognizant citizens – over the implementation of data-driven technologies, to avoid siloed, tech-solutionist decisions.

This paper inscribes the EU’s normative approach to AI and data-driven technologies, as well as critical work on the governance of these technologies, into a broader political dimension, suggesting a way to democratically and epistocratically opening up the decisional processes over the development and implementation of these technologies and turn such processes into a systemic civic involvement.

Digitalization accelerates the need of tourism and hospitality ecosystems to reframe business models in line with a data-driven orientation that can foster value creation and innovation. Since the question of data-driven business models (DDBMs) in hospitality remains underexplored, this paper aims at (1) revealing the key dimensions of the data-driven redefinition of business models in smart hospitality ecosystems and (2) conceptualizing the key drivers underlying the emergence of innovation in these ecosystems.

The empirical research is based on semi-structured interviews collected from a sample of hospitality managers, employed in three different accommodation services, i.e. hotels, bed and breakfast (B&Bs) and guesthouses, to explore data-driven strategies and practices employed on site.

The findings allow to devise a conceptual framework that classifies the enabling dimensions of DDBMs in smart hospitality ecosystems. Here, the centrality of strategy conducive to the development of data-driven innovation is stressed.

The study thus developed a conceptual framework that will serve as a tool to examine the impact of digitalization in other service industries. This study will also be useful for small and medium-sized enterprises (SMEs) managers, who seek to understand the possibilities data-driven management strategies offer in view of stimulating innovation in the managers' companies.

The paper reinterprets value creation practices in business models through the lens of data-driven approaches. In this way, this paper offers a new (conceptual and empirical) perspective to investigate how the hospitality sector at large can use the massive amounts of data available to foster innovation in the sector.

The study queries the drivers of innovation management in contemporary data-driven organizations/companies. It is argued that data-driven organizations that integrate a strategic orientation grounded in data, human abilities and proactive management are more effective in triggering innovation.

Research reported in this paper employs constructivist grounded theory, Gioia methodology, and the abductive approach. The data collected through semi-structured interviews administered to 20 Italian start-up founders are then examined.

The paper identifies the key enablers of innovation development in data-driven companies and reveals that data-driven companies may generate different innovation patterns depending on the kind of capabilities activated.

The study provides evidence of how the combination of data-driven culture, skills' enhancement and the promotion of human resources may boost the emergence of innovation.

The purpose of this paper is to present the applicability of data-driven solvers to computationally demanding three-dimensional problems and their practical usability when using real-world measurement data.

Instead of using a hard-coded phenomenological material model within the solver, the data-driven computing approach reformulates the boundary value problem such that the field solution is directly computed on raw measurement data. The data-driven formulation results in a double minimization problem based on Lagrange multipliers, where the sought solution must conform to Maxwell’s equations while at the same time being as close as possible to the available measurement data. The data-driven solver is applied to a three-dimensional model of a direct current electromagnet.

Numerical results for data sets of increasing cardinality verify that the data-driven solver recovers the conventional solution. Additionally, the practical usability of the solver is shown by using real-world measurement data. This work concludes that the data-driven magnetostatic finite element solver is applicable to computationally demanding three-dimensional problems, as well as in cases where a prescribed material model is not available.

Although the mathematical derivation of the data-driven problem is well presented in the referenced papers, the application to computationally demanding real-world problems, including real measurement data and its rigorous discussion, is missing. The presented work closes this gap and shows the applicability of data-driven solvers to challenging, real-world test cases.

The peer-to-peer perspective on open data is an interesting topic to research, taking into account that data-driven innovations and related startups are often developed independently by civic and private stakeholders in a highly collaborative manner and are tentatively beginning to directly compete with traditional e-government solutions, providing arguably better services to citizens and businesses. In this regard, the paper aims to further debate on the potential of such independent data-driven collaboration not only to transform the traditional mechanisms of public sector innovations but also provide more democratic ways to ensure greater transparency of government and its responsibility before the society.

The research is based on a cross-country case study, resorting to the content analysis of three demonstrative cases in the development of open data-driven projects, which specifically promote peer-to-peer communication between its stakeholders. In this regard, the case study itself relies heavily on the analysis of rich empirical data that the author collected during his field studies in the Northern European region in 2015–2017, particularly in Estonia, Finland and Sweden. The practical research itself consists of three major parts, which reflect peer-to-peer perspectives of correspondingly civic, public and private stakeholders through manifested examples of related independent projects in the area.

The paper's results demonstrate that the use of peer-to-peer mechanisms in advancing related public sector reforms allows to transform the traditional understanding of e-government phenomena in a conceptually new way. E-government or its last more political interpretation – from the perspective of its peers could be regarded not necessarily as a platform to provide digital public services but as a source of raw material for various third party projects in, respectively, civic, government and business peer-to-peer dimensions of such reforms. As a result, open data provides an interesting playground to change the very nature of public sector innovations in the area.

The choice of countries for research was motivated by purposive and convenience sampling because all these countries are situated in one region, have both similarities and differences in historical, political and socioeconomic backgrounds and, therefore, provide an ideal playground to investigate open data as a context dependable phenomenon. In this regard, the unique political and socioeconomic contexts of these countries provide an interesting playground to debate on the potential of social democracy, egalitarian society and social equality, i.e. public values that are deeply embedded into the fabric of societies there, to benefit the open data movement in a fundamental manner.

This paper reports on unique practical approaches for peer-to-peer collaboration and cooperation in advancing open data-driven platforms among stakeholders. The results of the case studies in three Nordic countries, which are currently among global leaders in advancing the concept of open government, are presented in an intrinsically illustrative manner, which could help practitioners and policymakers to understand better the potential of such a peer-to-peer perspective on open data. In this regard, the models proposed, of citizen-to-citizen, business-to-business, government-to-government interactions, could be interesting to a wide audience of e-government stakeholders in many nations.

The paper also enters into philosophical debates about societal implications of digital peer-to-peer data-driven communication among people. Recent efforts to digitize almost every part of social life, starting from popularization of solutions for distant work and ending to online access to various public services, incentivize individual members of civil society to communicate in an inherently peer-to-peer way. This fact will definitely increase the demand for related digital services. Social distancing in a digital context will allow to paradoxically emancipate technically savvy and entrepreneurial people in creating new services, including using open data, which could meet the demand.

The research is intrinsically of an empirical character because recent e-government reforms in the public sector in many countries, including in the open data area, provide rich practical knowledge to test the limits of new technologies to advance society in socioeconomic and, more importantly, political development. In this regard, this paper provides the first research in analyzing open data from a unique peer-to-peer perspective with an ultimate goal of the whole investigation to draw the attention of other e-government scholars and initiate debates on the collaborative nature of the phenomena to empower civil society and ensure transparency of government.

This new paper aims to extend the authors’ previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data–driven strategy. After feeding back the output signal to the input and introducing one feedback controller in the adding feedback loop, two parts, i.e. unknown aircraft flutter model and unknown feedback controller, exist in this closed-loop aircraft flutter system, simultaneously, whose input and output are all corrupted with external noise. Because of the relations between aircraft flutter model parameters and the unknown aircraft model, direct data–driven identification is proposed to identify that aircraft flutter model, then some identification algorithms and their statistical analysis are given through the authors’ own derivations. As the feedback controller can suppress the aircraft flutter or guarantee the flutter response converge to one desired constant value, the direct data–driven control is applied to design that feedback controller only through the observed data sequence directly. Numerical simulation results have demonstrated the efficiency of the proposed direct data–driven strategy. Generally, during our new information age, direct data–driven strategy is widely applied around our living life.

First, consider one more complex closed loop stochastic aircraft flutter model, whose input–output are all corrupted with external noise. Second, for the identification problem of closed-loop aircraft flutter model parameters, new identification algorithm and some considerations are given to the corresponding direct data–driven identification. Third, to design that feedback controller, existing in that closed-loop aircraft flutter model, direct data–driven control is proposed to design the feedback controller, which suppresses the flutter response actively.

A novel direct data–driven strategy is proposed to achieve the dual missions, i.e. identification and control for closed-loop aircraft flutter test. First, direct data–driven identification is applied to identify that unknown aircraft flutter model being related with aircraft flutter model parameters identification. Second, direct data–driven control is proposed to design that feedback controller.

To the best of the authors’ knowledge, this new paper extends the authors’ previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data–driven strategy. Consider the identification problem of aircraft flutter model parameters within the presented closed loop environment, direct data–driven identification algorithm is proposed to achieve the identification goal. Direct data–driven control is proposed to design the feedback controller, i.e. only using the observed data to design the feedback controller.

This new paper aims to combine the recent new contributions about direct data driven control and other safety property to form an innovative direct data driven safety control for aircraft flight system. More specifically, within the framework of direct data driven strategy, the collected data are dealt with to get the identified plant and designed controller. After reviewing some priori information about aircraft flight system, a closed loop system with the unknown plant and controller simultaneously is considered. Data driven estimation is proposed to identify the plant and controller only through the ratios of two correlation functions, computed from the collected data. To achieve the dual missions about perfect tracking and safety property, a new notion about safety controller is introduced. To design this safety controller, direct data driven safety controller is proposed to solve one constrain optimization problem. Then the authors apply the Karush–Kuhn–Tucker (KKT) optimality conditions to derive the explicit safety controller.

First, consider one closed loop system corresponding to aircraft flight system with the unknown plant and feed forward controller, data driven estimation is used to identify the plant and feed forward controller. This identification process means nonparametric estimation. Second, to achieve the perfect tracking one given transfer function and guarantee the closed loop output response within one limited range simultaneously, safety property is introduced. Then direct data driven safety control is proposed to design the safety controller, while satisfying the dual goals. Third, as the data driven estimation and direct data driven safety control are all formulated as one constrain optimization problem, the KKT optimality conditions are applied to obtain the explicit safety controller.

Some aircraft system identification and aircraft flight controller design can be reformulated as their corresponding constrain optimization problems. Then through solving these constrain optimization problems, the optimal estimation and controller are yielded, while satisfying our own priori goals. First, data driven estimation is proposed to get the rough estimation about the plant and controller. Second, data driven safety control is proposed to get one safety controller before our mentioned safety concept.

To the best of the authors’ knowledge, some existing theories about nonparametric estimation and tube model predictive control are very mature, but few contributions are applied in practice, such as aircraft system identification and aircraft flight controller design. This new paper shows the new theories about data driven estimation and data driven safety control on aircraft, being corresponded to the classical nonparametric estimation and tube model predictive control. Specifically, data driven estimation gives the rough estimations for the aircraft and its feed forward controller. Furthermore, after introducing the safety concept, data driven safety control is introduced to achieve the desired dual missions with the combination of KKT optimality conditions.