Smart Urban Mobility: A Positive or Negative IP Space? A Case Study to Test the Role of IP in Fostering Digital Data-Driven Innovation

2020 ◽  
pp. 143-162
Author(s):  
Giulia Priora ◽  
Caterina Sganga
Keyword(s):  
Digital Data ◽  
Data Driven ◽  
Urban Mobility

scholarly journals The Enabling Role of Digital Technologies in Sustainability Accounting: Findings from Norwegian Manufacturing Companies

Systems ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 33
Author(s):  
Olena Klymenko ◽  
Lise Lillebrygfjeld Halse ◽  
Bjørn Jæger
Keyword(s):  
Research Area ◽  
Digital Data ◽  
Manufacturing Companies ◽  
Sustainable Operations ◽  
Business Model Canvas ◽  
Automation And Control ◽  
Sustainability Accounting ◽  
And Control

Sustainability accounting is an emerging research area receiving growing awareness. This study examines the role of digital technology in manufacturing companies’ sustainability accounting. To guide the research, we use a triple layered business model canvas, which supports the accounting of a manufacturer’s performance for the economic, environmental, and social aspects of sustainability. We present an explorative case study of four Norwegian manufacturing companies representing different industries. The findings from the study indicate that while accounting for economic values is well taken care of, companies do not perform comprehensive environmental and social accounting. Furthermore, we observed a shift from a focus on sustainability issues related to the internal manufacturing process to a focus on sustainability issues for the life cycle of the product. Even though the manufacturers are at the forefront with regard to automation and control of production, with extensive use of robots giving a large amount of data, these data are not utilized towards sustainability accounting, showing that sustainability and digitalization are seen as two separate phenomena. This study sheds light on how digital data available from applied Industry 4.0 technologies could enhance sustainability accounting with limited efforts, linking sustainability and digitalization. The results provide insights for manufacturers and researchers in moving towards more sustainable operations and products.

scholarly journals Data-Driven Campaigning in the 2015 United Kingdom General Election

2017 ◽  
Vol 22 (3) ◽  
pp. 294-313 ◽  
Author(s):  
Nick Anstead
Keyword(s):  
United Kingdom ◽  
The United States ◽  
General Election ◽  
Data Driven ◽  
British Politics ◽  
Use Of Data ◽  
The Uk ◽  
Depth Interviews

While we know something of data-driven campaigning practices in the United States, we know much less about the role of data in other national contexts. The 2015 United Kingdom General Election offers an important case study of how such practices are evolving and being deployed in a different setting. This article draws on thirty-one in-depth interviews with political practitioners involved in the use of data for six major UK parties and electoral regulators. These interviews are employed to explore the perceived importance of data in contemporary British campaigns, to understand the data-based campaign techniques being used by UK parties, and to assess how data-driven practices are interacting with the preexisting institutional context of British politics. Going beyond the specifics of the UK case, this study raises questions about the comparative, theoretical, and normative dimensions of data-driven politics.

The dual drivetrain model of digital transformation: role of industrial big-data-based affordance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Liu ◽  
Wei Wang ◽  
Zuopeng (Justin) Zhang
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Data Driven ◽  
Content Type ◽  
Industrial Big Data ◽  
Technology Affordance ◽  
Effective Use ◽  
Practical Implications

PurposeTo better understand the role of industrial big data in promoting digital transformation, the authors propose a theoretical framework of industrial big-data-based affordance in the form of an illustrative metaphor – what the authors call the “organizational drivetrain.”Design/methodology/approachThis study investigates the effective use of industrial big data in the process of digital transformation based on the technology affordance–actualization theoretical lens. A software platform and services provider with more than 4,000 industrial enterprise clients in China was selected as the case study object for analyzing the digital affordance and actualization driven by industrial big data.FindingsDrawing on a revelatory case study, the authors identify three affordances of industrial big data in the organization, namely developing data-driven customized projects, provisioning equipment-data-driven life cycle services, establishing data-based trust and determining affordance actualization actions driven by technology and market. In addition, the authors reveal the underlying drivetrain mechanisms to advance industrial big data affordance and actualization: stabilizing, enriching and pioneering.Originality/valueThis study builds a drivetrain model on digital transformation by industrial big data affordance actualization. The authors also provide practical implications that can help practitioners to implement digital transformation effectively and extract value from their investment.

Data-driven Personalization of Physiotherapy Care Pathway: Case Posture Scanning (Preprint)

2020 ◽  
Author(s):  
Olli Korhonen ◽  
Karin Väyrynen ◽  
Tino Krautwald ◽  
Glenn Bilby ◽  
Anna Broers ◽  
...  
Keyword(s):  
Decision Making ◽  
Care Pathway ◽  
Human Movement ◽  
Digital Data ◽  
Data Driven ◽  
Care Process ◽  
Root Cause ◽  
Care Models ◽  
The Impact

BACKGROUND Advanced sensor, measurement and analytics technologies enable entirely new ways to deliver care. Increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making, or even automating some parts of decision making in relation to the care process. OBJECTIVE The aim of this study is to analyze how digital data acquired from posture scanning can enhance physiotherapy and enable more personalized delivery of physiotherapy. METHODS A Case study is conducted with a company that has designed a Posture Scan Recording System (PSRS), which is an Information System (IS) that can record, measure and report human movement digitally to be used in physiotherapy. Interviews are used to explore the viewpoints of different stakeholders involved in physiotherapy. The data is analyzed thematically. RESULTS As the result of our thematic analysis, we identified three different support types the posture scanning can provide to enable more personalized delivery of physiotherapy. The types are: (1) Modeling the condition, which is about the use of posture scanning data for detecting and understanding the healthcare user’s condition and the root cause of the possible pain. (2) Visualization for a shared understanding, which is about the use of posture scanning data to inform and involve the healthcare user in more collaborative decision-making regarding care. (3) Evaluating the impact of the intervention, which is about the use of posture scanning data to evaluate the care progress and impact of the intervention. CONCLUSIONS Current care models in healthcare emphasize the importance to put the healthcare user at the center of the care. However, physiotherapy has lacked data driven solutions to inform and involve the healthcare user in care in a person-centered manner. The present study analyzes how posture scanning can enhance physiotherapy and presents three different types of support that posture scanning can provide for data-driven personalization of physiotherapy.

scholarly journals Data-driven innovation processes within federated networks

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aya Rizk ◽  
Anna Ståhlbröst ◽  
Ahmed Elragal
Keyword(s):  
Innovation Process ◽  
Digital Data ◽  
Data Driven ◽  
Innovation Networks ◽  
Digital Innovation ◽  
Content Type ◽  
Growing Domain ◽  
Multiple Case ◽  
Innovation Processes

PurposeWithin digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks.Design/methodology/approachA multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes.FindingsEvidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network.Originality/valueThe paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion.

scholarly journals Incremental Digital Twin Conceptualisations Targeting Data-Driven Circular Construction

Buildings ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 554
Author(s):  
Pedro Mêda ◽  
Diego Calvetti ◽  
Eilif Hjelseth ◽  
Hipólito Sousa
Keyword(s):  
Built Environment ◽  
Construction Industry ◽  
Digital Transformation ◽  
Digital Data ◽  
Data Driven ◽  
Three Solutions ◽  
Digital Twin ◽  
Multiple Challenges ◽  
Environmental Goals

The construction industry faces multiple challenges, where transition to circular production is key. Digitalisation is a strategy to increase the sector’s productivity, competitiveness, and efficiency. However, digitalisation also impacts environmental goals, such as those concerning more eco-friendly solutions, energy efficiency, products recycling, and sustainability certifications. These strategies rely on data, understood as digital, interoperable, incremental and traceable. Data related concepts, such as digital data templates (DDT) and digital building logbooks (DBL), contribute to “good data”. Despite some research focused on each one, little importance has yet been given to their combination. Relevant relationships and overlaps exist, as they partially share the exact same data through the built environment life cycle. This research aims to provide improved understanding on the role of these concepts and their contribution to a more circular industry. The review develops conceptualisations where DDT and DBL are complementary and framed within an incremental digital twin construction (DTC). Misconceptions or confrontations between these three solutions can therefore stand down, for the benefit of a data-driven priority. To increase understanding and reduce misconceptions, our study developed the “Digital data-driven concept” (D3c). This concept contribution is the ability to structure, store, and trace data, opening way to streamlined digital transformation impacting circular built environment concerns.

scholarly journals Data-Driven Personalization of a Physiotherapy Care Pathway: Case Study of Posture Scanning

10.2196/18508 ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. e18508
Author(s):  
Olli Korhonen ◽  
Karin Väyrynen ◽  
Tino Krautwald ◽  
Glenn Bilby ◽  
Hedwig Anna Theresia Broers ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Information System ◽  
Health Care Professionals ◽  
Digital Data ◽  
Digital Tools ◽  
Data Driven ◽  
Care Process ◽  
The Impact

Background Advanced sensor, measurement, and analytics technologies are enabling entirely new ways to deliver health care. The increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making or even by automating some parts of decision making in relation to the care process. Objective The aim of this study was to analyze how digital data acquired from posture scanning can enhance physiotherapy services and enable more personalized delivery of physiotherapy. Methods A case study was conducted with a company that designed a posture scan recording system (PSRS), which is an information system that can digitally record, measure, and report human movement for use in physiotherapy. Data were collected through interviews with different stakeholders, such as health care professionals, health care users, and the information system provider, and were analyzed thematically. Results Based on the results of our thematic analysis, we propose three different types of support that posture scanning data can provide to enhance and enable more personalized delivery of physiotherapy: 1) modeling the condition, in which the posture scanning data are used to detect and understand the health care user’s condition and the root cause of the possible pain; 2) visualization for shared understanding, in which the posture scanning data are used to provide information to the health care user and involve them in more collaborative decision-making regarding their care; and 3) evaluating the impact of the intervention, in which the posture scanning data are used to evaluate the care progress and impact of the intervention. Conclusions The adoption of digital tools in physiotherapy has remained low. Physiotherapy has also lacked digital tools and means to inform and involve the health care user in their care in a person-centered manner. In this study, we gathered insights from different stakeholders to provide understanding of how the availability of digital posture scanning data can enhance and enable personalized physiotherapy services.

scholarly journals Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4076
Author(s):  
Martin Kueppers ◽  
Christian Perau ◽  
Marco Franken ◽  
Hans Joerg Heger ◽  
Matthias Huber ◽  
...  
Keyword(s):  
Renewable Energy Sources ◽  
Energy Systems ◽  
Energy System ◽  
Fine Tuning ◽  
Data Driven ◽  
Spatially Resolved ◽  
Grid Topology ◽  
Data Driven Approach

The decarbonization of energy systems has led to a fundamental change in their topology since generation is shifted to locations with favorable renewable conditions. In planning, this change is reflected by applying optimization models to regions within a country to optimize the distribution of generation units and to evaluate the resulting impact on the grid topology. This paper proposes a globally applicable framework to find a suitable regionalization for energy system models with a data-driven approach. Based on a global, spatially resolved database of demand, generation, and renewable profiles, hierarchical clustering with fine-tuning is performed. This regionalization approach is applied by modeling the resulting regions in an optimization model including a synthesized grid. In an exemplary case study, South Africa’s energy system is examined. The results show that the data-driven regionalization is beneficial compared to the common approach of using political regions. Furthermore, the results of a modeled 80% decarbonization until 2045 demonstrate that the integration of renewable energy sources fundamentally changes the role of regions within South Africa’s energy system. Thereby, the electricity exchange between regions is also impacted, leading to a different grid topology. Using clustered regions improves the understanding and analysis of regional transformations in the decarbonization process.

Data-Driven Personalization of a Physiotherapy Care Pathway: Case Study of Posture Scanning (Preprint)

2020 ◽  
Author(s):  
Olli Korhonen ◽  
Karin Väyrynen ◽  
Tino Krautwald ◽  
Glenn Bilby ◽  
Hedwig Anna Theresia Broers ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Information System ◽  
Health Care Professionals ◽  
Digital Data ◽  
Digital Tools ◽  
Data Driven ◽  
Care Process ◽  
The Impact

BACKGROUND Advanced sensor, measurement, and analytics technologies are enabling entirely new ways to deliver health care. The increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making or even by automating some parts of decision making in relation to the care process. OBJECTIVE The aim of this study was to analyze how digital data acquired from posture scanning can enhance physiotherapy services and enable more personalized delivery of physiotherapy. METHODS A case study was conducted with a company that designed a posture scan recording system (PSRS), which is an information system that can digitally record, measure, and report human movement for use in physiotherapy. Data were collected through interviews with different stakeholders, such as health care professionals, health care users, and the information system provider, and were analyzed thematically. RESULTS Based on the results of our thematic analysis, we propose three different types of support that posture scanning data can provide to enhance and enable more personalized delivery of physiotherapy: 1) modeling the condition, in which the posture scanning data are used to detect and understand the health care user’s condition and the root cause of the possible pain; 2) visualization for shared understanding, in which the posture scanning data are used to provide information to the health care user and involve them in more collaborative decision-making regarding their care; and 3) evaluating the impact of the intervention, in which the posture scanning data are used to evaluate the care progress and impact of the intervention. CONCLUSIONS The adoption of digital tools in physiotherapy has remained low. Physiotherapy has also lacked digital tools and means to inform and involve the health care user in their care in a person-centered manner. In this study, we gathered insights from different stakeholders to provide understanding of how the availability of digital posture scanning data can enhance and enable personalized physiotherapy services.

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