Special Issue "Technology Driven Innovation, Research Management and Policy Making"

Special Issue Editors

Guest Editor
Prof. Miltiadis D. Lytras

1. School of Business, Deree—The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi Athens, Greece
2. Effat University, Jeddah, Saudi Arabia
Website | E-Mail
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management
Guest Editor
Prof. Anna Visvizi

1. School of Business, Deree—The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi Athens, Greece
2. Effat University, Jeddah, Saudi Arabia
Website | E-Mail
Interests: smart cities; political economy of European integration, politics, economy, and security in Central Europe, Visegrad (V4) countries; global safety and security, including transatlantic relations, and theoretical dimensions of these processes

Special Issue Information

Dear Colleagues,

The postindustrial era is characterized by a continuous quest for innovation. The evolution in ICTs and the latest developments in policy making recognize innovation as a complex construct where advanced management, sophisticated ICTs and sustainable policy making, are converging in order to justify a unique value proposition.

The purpose of this Special Issue is to disseminate recent and sound recent on technology driven innovation. In the focus of the Special Issue is on the emerging technologies and applications of our times, including:

  • Social networking technologies
  • Cloud computing
  • Internet of things
  • Virtual reality
  • Artificial intelligence
  • Machine learning
  • Big data and analytics
  • 3D printing
  • Unmanned vehicles and drones
  • Sensors and 5G networks
  • Medical informatics
  • Smart cities technologies
  • Location-aware services

The relevant discussion is organized under a critical policy making lenses, favoring topics related to:

  • International innovation networks
  • International technology transfer
  • Research and development policies (at academia, industry, and business)
  • Regional studies on innovation policy design and implementation
  • Innovation as a social process
  • Innovation as enabler of sustainable social inclusive economic growth
  • Innovation management in innovation and research centers
  • Policies for sustainable impact of research and innovation
  • Cases studies of R&D projects
  • Critical technologies management

Prof. Dr. Miltiadis D. Lytras
Prof. Dr. Anna Visvizi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Open Innovation: Technology, Market, and Complexity is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 650 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Innovation
  • Innovation management
  • Emerging technologies
  • International technology transfer
  • Innovation networks
  • Research
  • research management
  • Critical technologies
  • Smart cities
  • Innovation clusters
  • Smart education
  • Social inclusive economic growth

Published Papers (3 papers)

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Research

Open AccessArticle Ethical Framework for Designing Autonomous Intelligent Systems
J. Open Innov. Technol. Mark. Complex. 2019, 5(1), 18; https://doi.org/10.3390/joitmc5010018
Received: 29 January 2019 / Revised: 24 February 2019 / Accepted: 5 March 2019 / Published: 13 March 2019
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Abstract
To gain the potential benefit of autonomous intelligent systems, their design and development need to be aligned with fundamental values and ethical principles. We need new design approaches, methodologies and processes to deploy ethical thought and action in the contexts of autonomous intelligent [...] Read more.
To gain the potential benefit of autonomous intelligent systems, their design and development need to be aligned with fundamental values and ethical principles. We need new design approaches, methodologies and processes to deploy ethical thought and action in the contexts of autonomous intelligent systems. To open this discussion, this article presents a review of ethical principles in the context of artificial intelligence design, and introduces an ethical framework for designing autonomous intelligent systems. The framework is based on an iterative, multidisciplinary perspective yet a systematic discussion during an Autonomous Intelligent Systems (AIS) design process, and on relevant ethical principles for the concept design of autonomous systems. We propose using scenarios as a tool to capture the essential user’s or stakeholder’s specific qualitative information, which is needed for a systematic analysis of ethical issues in the specific design case. Full article
(This article belongs to the Special Issue Technology Driven Innovation, Research Management and Policy Making)
Open AccessArticle Operational Decision Model with Carbon Cap Allocation and Carbon Trading Price
J. Open Innov. Technol. Mark. Complex. 2019, 5(1), 11; https://doi.org/10.3390/joitmc5010011
Received: 18 December 2018 / Revised: 18 February 2019 / Accepted: 18 February 2019 / Published: 20 February 2019
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Abstract
This paper considers a carbon emission cap and trade market, where the carbon emission cap for each entity (either government or firm) is allocated first and then the carbon trading price is decided interdependently in the carbon trading market among the non-cooperative entities [...] Read more.
This paper considers a carbon emission cap and trade market, where the carbon emission cap for each entity (either government or firm) is allocated first and then the carbon trading price is decided interdependently in the carbon trading market among the non-cooperative entities which make their production decision. We assume that there are n entities emitting carbon during the production process. After allocating the carbon (emission) cap for each participating entity in the carbon cap and trade market, each participant makes a production decision using the Newsvendor model given carbon trading price determined in the carbon trading market and trades some amount of its carbon emission, if its carbon emission is below or above its own carbon cap. Here, the carbon trading price depends on how carbon caps over the entities are allocated, since the carbon trading price is determined through the carbon (emission) trading market, which considers total amount of carbon emission being equal to total carbon caps over entities and some fraction of total carbon emission should be from each entity participating in the carbon cap and trade market. Thus, we can see the interdependency among the production decision, carbon cap and carbon trading price. We model this as a non-cooperative Stackelberg game in which carbon cap for each entity is allocated in the first stage and each entity’s production quantity is decided in the second stage considering the carbon trading price determined in the carbon trading market. First, we show the monotonic property of the carbon trading price and each entity’s production over the carbon cap allocation. In addition, we show that there exists an optimality condition for the carbon cap allocation. Using this optimality condition, we provide various results for carbon cap and trade market. Full article
(This article belongs to the Special Issue Technology Driven Innovation, Research Management and Policy Making)
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Open AccessArticle Patterns of Learning in Dynamic Technological System Lifecycles—What Automotive Managers Can Learn from the Aerospace Industry?
J. Open Innov. Technol. Mark. Complex. 2019, 5(1), 1; https://doi.org/10.3390/joitmc5010001
Received: 26 October 2018 / Revised: 15 December 2018 / Accepted: 21 December 2018 / Published: 28 December 2018
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Abstract
Not only with respect to the common overlaps within the market of urban air mobility, but also in terms of their requirement profile with regard to the systemic core, all mobility industries are converging. This article focuses on the required patterns of learning [...] Read more.
Not only with respect to the common overlaps within the market of urban air mobility, but also in terms of their requirement profile with regard to the systemic core, all mobility industries are converging. This article focuses on the required patterns of learning in order to cope with these changes, and what automotive managers can learn from the aerospace industry in this context. As organizational learning is the central parameter of economic evolution, and technology develops over trajectory shifts, companies are, at the very least, cyclically forced to learn ambidextrously, or are squeezed out of the market. They have to act and react as complex adaptive systems in their changing environment. Especially in these dynamics, ambidextrous learning is identified to be a conditio sine qua non for organizational success. Especially the combination of efficiency-oriented internal exploitation with an explorative and external-oriented open innovation network turns out to be a superior strategy. By combining patent data, patent citation analysis and data on the European Framework Programs, we show that there are temporal differences, i.e., position of the product in the product, technique, technology, and industry life cycle. Furthermore, we draw a conclusion dependent on the systemic product character, which enforces different learning requirements concerning supply chain position and, as an overarching conclusion, we identify product structure to be decisive for how organizational learning should be styled. Full article
(This article belongs to the Special Issue Technology Driven Innovation, Research Management and Policy Making)
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J. Open Innov. Technol. Mark. Complex. EISSN 2199-8531 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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