TY - JOUR T1 - Cloud-Based Approach for Tracking and Monitoring of Hay Bales in Smart Agriculture JF - Technology Innovation Management Review Y1 - 2021 A1 - Ilpo Pölönen A1 - Antti Suokannas A1 - Antti Juntunen KW - bale inventory KW - bale trade KW - big data KW - precision farming KW - RFID KW - round bales KW - silage KW - Smart farming AB - The introduction of new technology to agriculture has resulted in enormous amounts of data and their handling and utilization challenge. Data is typically gathered from several sources such as field sensors, machines, industrial processes, different laboratories and officials. This has led to several complicated systems that are not always compatible. Farmers are confused, unaware, and face challenges in seeing the benefits for their business in relation to the time required. This paper introduces an automatic digital tracking and monitoring system for round feed bales on farms. In this system, bale data from sensors, switches, and a GPS-device in the baling machine are collected by hardware and sent to the cloud with the bale ID read from a RFID tag attached to each bale. A digital inventory of bales forms instantly, and baling can be followed on the map application with a mobile device. Data in the cloud is utilized for the farmer's user interface. The farmer can manage and do various operations with bales. An important outcome is the yield report, showing basic statistics, quantities, and qualities of bales in a digitalized field parcel. If the farmer wants to sell bales, this can easily be done with the tool. It makes sales by connecting the farmer to an e-commerce portal. A key question and challenge to be resolved involves who owns the data. All the benefits of digitalization can be achieved only with good cooperation and mutual agreement from farmers who want to have control of their data under all circumstances. PB - Talent First Network CY - Ottawa VL - 11 UR - timreview.ca/article/1419 IS - 2 U1 - Häme University of Applied Sciences Ilpo Pölönen is an animal nutritionist, a principal research scientist in HAMK Bio Research Unit at Häme University of Applied Sciences. He has a Ph.D. in Animal Science from Helsinki University where he also holds docentship. He graduated from Helsinki University after which he continued Animal Science studies and earned a M.Sc. at Oregon State University. In nutritional research, he has specialized in the preservation of feeds, while during the last years has been involved in developing digital solutions for grass silage. He also teaches master-level students in HAMK. U2 - Natural Resources Institute of Finland Antti Suokannas is a research scientist at the Natural Resources Institute of Finland (Luke). He holds a M.Sc. in Agricultural Technology at Helsinki University. He has a long history of various research projects in forage harvesting technology and has also been involved with work safety studies in plant production. His current research interests include smart farming, automation systems, and forage harvesting processes. U3 - Häme University of Applied Sciences Antti Juntunen is a software developer (B.Sc.) in HAMK Smart research unit at Häme University of Applied Sciences. He has gained experience with many digitalization projects connected to working life. In this project, he was responsible for developing the bale inventory in the cloud and the services connected with it. ER - TY - JOUR T1 - AI-Driven Digital Platform Innovation JF - Technology Innovation Management Review Y1 - 2020 A1 - Sergey A. Yablonsky KW - Advanced Analytics KW - AI maturity KW - AI value chain KW - AI-driven platform innovation KW - Artificial Intelligence (AI) KW - big data KW - enterprise platform AB - Artificial Intelligence (AI) innovation becomes useful today when it enriches decision-making that is enhanced by applications of big data (BD), advanced analytics (AA), and some element of human interaction using digital platforms. This research aims to investigate the potential combination of AI, BD and AA for digital business platforms. In doing so, it develops a multi-dimensional AI-driven platform innovation framework with AI/BD/AA innovation value chain and related levels of AI maturity improvement. The framework can be used with a focus on the data-driven human-machine relationship and the application of AI at different levels of an AI-driven digital platform technology stack. PB - Talent First Network CY - Ottawa VL - 10 UR - timreview.ca/article/1392 IS - 10 U1 - St. Petersburg State University Sergey Yablonsky, PhD in computer science, is an Associate Professor at Graduate School of Management, St. Petersburg State University in St. Petersburg, Russia. Author of more than 200 publications. Co-creator of the Russian WordNet and the Russicon language processor and linguistic resources licensed by Adobe Systems Incorporated, Phoenix Int. (USA), Franklin Electronic Publishers (USA) etc. Engaged in 35 national and international research projects in Russia, and across Europe. Research interests include Digital Economy, Digital Business and Entrepreneurship; Multisided Platforms and Markets; Artificial Intelligence, Digital marketing; Big Data Governance; Computer linguistics and text mining; Semantic and Social Web. Courses taught: Data Governance (Bachelor Program); Digital Marketing & Digital Commerce (Bachelor programs); Digital Business (Master program); Smart Business Transformation in the Digital Age (CEMS Block Seminar); Multi-Sided Platforms and Innovation in a Global Era (CEMS Block Seminar); Digital Economy (EMBA). Visiting professor at WU (Vienna University of Economics and Business) in Austria, Stockholm Business School, Stockholm university in Sweden, Aalto University in Finland, Lappeenranta University of Technology in Finland, Hame University of Applied Sciences in Finland. ER - TY - JOUR T1 - Editorial: Insights (January 2020) JF - Technology Innovation Management Review Y1 - 2020 A1 - Stoyan Tanev A1 - Gregory Sandstrom KW - AI KW - artificial intelligence KW - B2B sales KW - big data KW - business-to-business sales KW - data-based value KW - digital solutions KW - ecosystem KW - ecosystems KW - Ethics KW - Gujarat State KW - Indian IT industry KW - innovation KW - IT clusters KW - Knowledge Innovation clusters KW - Networks Analysis KW - regional development KW - Roboethics KW - Smart robot KW - strategy KW - Systematic literature review KW - technology KW - value capture KW - value creation KW - value sales PB - Talent First Network CY - Ottawa VL - 10 UR - timreview.ca/article/1298 IS - 1 U1 - Technology Innovation Management Review Stoyan Tanev, PhD, MSc, MEng, MA, is Associate Professor of Technology Entrepreneurship and Innovation Management associated with the Technology Innovation Management (TIM) Program, Sprott School of Business, Carleton University, Ottawa, ON, Canada. Before re-joining Carleton University, Dr. Tanev was part of the Innovation and Design Engineering Section, Faculty of Engineering, University of Southern Denmark (SDU), Odense, Denmark. Dr. Tanev has a multidisciplinary background including MSc in Physics (Sofia University, Bulgaria), PhD in Physics (1995, University Pierre and Marie Curie, Paris, France, co-awarded by Sofia University, Bulgaria), MEng in Technology Management (2005, Carleton University, Ottawa, Canada), MA in Orthodox Theology (2009, University of Sherbrooke, Montreal Campus, QC, Canada) and PhD in Theology (2012, Sofia University, Bulgaria). Dr. Stoyan Tanev has published multiple articles in several research domains. His current research interests are in the fields of technology entrepreneurship and innovation management, design principles and growth modes of global technology start-ups, business analytics, topic modeling and text mining. He has also an interest in interdisciplinary issues on the interface of the natural and social sciences. U2 - Technology Innovation Management Review Gregory Sandstrom is Managing Editor of the Technology Innovation Management Review. Former Associate Professor of Mass Media and Communications at the European Humanities University and Affiliated Associate Professor at the Social Innovations Laboratory, Mykolas Romeris University in Vilnius, Lithuania. PhD from St. Petersburg State University and the Sociological Institute of the Russian Academy of Sciences, sector on Sociology of Science. Postdoctoral Research Fellow at the Lithuanian Science Council and Autonomous National University of Mexico's Institute for Applied Mathematics and Systems. Promoter and builder of blockchain distributed ledger technology systems and digital extension services. ER - TY - JOUR T1 - Editorial: Insights (October 2020) JF - Technology Innovation Management Review Y1 - 2020 A1 - Gregory Sandstrom KW - Advanced Analytics KW - AI maturity. Data science KW - AI value chain KW - AI-driven platform innovation KW - Artificial Intelligence (AI) KW - big data KW - business decision-making KW - business model components KW - business models KW - content analysis KW - data-dominant logic KW - dominant logic KW - empirical study KW - enterprise platform KW - industries KW - online communication KW - online data collection KW - organizational and managerial requirements KW - principal component analysis KW - R&D KW - research and development KW - secondary data. Sustainability KW - SMEs. Disruptive innovation KW - sustainable innovation PB - Talent First Network CY - Ottawa VL - 10 UR - timreview.ca/article/1396 IS - 10 U1 - Technology Innovation Management Review Gregory Sandstrom is Managing Editor of the TIM Review. He is a former Associate Professor of Mass Media and Communications at the European Humanities University (2012-2017), and Affiliated Associate Professor at the Social Innovations Laboratory, Mykolas Romeris University (2016-2017) in Vilnius, Lithuania. He completed a PhD from the Faculty of Sociology at St. Petersburg State University and the Sociological Institute of the Russian Academy of Sciences, sector on Sociology of Science (2010). He was a Postdoctoral Research Fellow at the Lithuanian Science Council (2013-2015), for which he conducted research visits to the Copernican Centre for Interdisciplinary Studies (Krakow), the University of Edinburgh's Extended Knowledge Project, Cambridge University's History and Philosophy of Science Department, and Virginia State University's Science and Technology Studies program, as well as previously at the Autonomous National University of Mexico's Institute for Applied Mathematics and Systems (2010-2011). He was affiliated with the Bard College Institute for Writing and Thinking, leading student and faculty language and communications workshops, most recently (2013, 2014, 2017) in Yangon, Myanmar. His current research interests are distributed ledger technology (blockchain) systems and digital extension services. ER - TY - JOUR T1 - Selling Data-Based Value in Business-to-Business Markets JF - Technology Innovation Management Review Y1 - 2020 A1 - Tuija Rantala A1 - Tiina Apilo A1 - Katariina Palomäki A1 - Katri Valkokari KW - B2B sales KW - big data KW - business-to-business sales KW - data-based value KW - digital solutions KW - value sales AB - The purpose of this paper is to study what aspects a sales function needs to consider when selling new data-based value in business-to-business (B2B) markets. The paper combines literature on the business-to-business sales process with data-based value. The study includes altogether 29 qualitative interviews from eight companies, representing seller companies at different stages in big data utilization. In addition, the study includes customer perspectives with six interviews from four customer companies. As a result, selling new data-based value is studied from several perspectives. First, we evaluate the impacts of the generated new data-based value from the seller and the market perspective. Secondly, we study what sales representatives need to understand, both from the customer’s perspective, and in relation to data and digital solutions during the sales process. Thirdly, on the customer side, we explore the roles of “digitalist” and old-school buyers, and their effect on the sales process. Our research findings highlight the crucial understanding of customer business and knowledge about real-time data management, digital twins, and artificial intelligence (AI) when selling data-based solutions that create real-time data, recommendations, and value for a customer’s business. PB - Talent First Network CY - Ottawa VL - 10 UR - timreview.ca/article/1313 IS - 1 U1 - VTT Technical Research Centre of Finland Tuija Rantala, M.Sc. (Tech.) works as Senior Scientist at the VTT Business, Innovation and Foresight research area. For several years, she has managed and worked in innovation and risk management projects with the B2B industry. She has applied and developed qualitative risk and opportunity management methods for different contexts. Her main research interests are related to new business creation, innovation management, open innovation, and risk management. Lately, she has studied the Internet of Things (IoT), social media, Mergers and Acquisitions (M&A), and intellectual property (IP) as enablers for new business and challenges they will pose to B2B companies. U2 - VTT Technical Research Centre of Finland Dr. Tiina Apilo is a Senior Scientist at the VTT Technical Research Centre of Finland. She joined VTT in 1994 where she has gained broad experience on corporate renewal and service business acceleration. She obtained her doctoral degree from the Lappeenranta University of Technology in 2010. The title of her dissertation was "A model for corporate renewal: requirements for innovation management". Her recent research interests have focused on AI as a booster of service business, innovation ecosystems, and future ecosystemic business. U3 - VTT Research Centre of Finland Katariina Palomäki has a M.Sc. (Tech.) degree in Industrial Management and Engineering and a BA (Hons) degree in Business and Management. She has worked as a research scientist at VTT Technical Research Centre of Finland since 2010. Katariina has worked in both commercial and research projects in national and international contexts. In the area of business development and research, the key topics she has dealt with during the last few years include business model development, service business development, management of business networks, and the perspectives of sustainability and circular business. U4 - VTT Research Centre of Finland Katri Valkokari is a Research Manager working in the business, innovation, and foresight research area at VTT Technical Research Centre of Finland. She has over 15 years of experience in both research and practical development work on business networks, ecosystems, and networked business operations. She has, for example, held the post of programme manager in the large FIMECC (GP4V) and DIMECC (REBUS) research programmes, and worked for many industrial companies, large and small. Katri has published several articles, managerial guidebooks and other publications related to collaboration models, innovation, and knowledge management as well as sustainability. When it comes to ecosystems and networks, Valkokari believes versatility is the key to creating true impact. When networks are formed openly, they can be a powerful tool for solving many of society’s problems. ER - TY - JOUR T1 - Multidimensional Data-Driven Artificial Intelligence Innovation JF - Technology Innovation Management Review Y1 - 2019 A1 - Sergey A. Yablonsky KW - Advanced Analytics KW - AI maturity KW - AI value chain KW - Artificial Intelligence (AI) KW - big data KW - enterprise platform KW - innovation AB - This is a critical time for the development and adoption of Artificial Intelligence (AI). The field has existed since the 1950s and is only now emerging as viable for commercial markets. Many enterprises are placing bets on AI that will determine their future. Today AI innovation becomes useful when it enriches decision-making that is enhanced by applying Big Data (BD) and Advanced Analytics (AA), with some element of human interaction using digital platforms. This research investigates an opportunity for cross-fertilization between AI, BD, and AA with related disciplines. The paper aims to investigate the potential relationship of AI, BD, and AA with digital business platforms. In doing so, it develops a multidimensional BD-driven AI innovation taxonomy framework with an AA/BD/AA innovation value chain, related levels of BD, and analytics maturity improvement. This framework can be used with a focus on data-driven human-machine relationships, and applying AI at different levels of data driven automation maturity. PB - Talent First Network CY - Ottawa VL - 9 UR - timreview.ca/article/1288 IS - 12 U1 - St. Petersburg State University Sergey Yablonsky, PhD in computer science, is an Associate Professor at the Graduate School of Management, St. Petersburg State University in St. Petersburg, Russia. Author of more than 200+ publications. Co-creator of the Russian WordNet and the Russicon language processor, and linguistic resources licensed by Adobe Systems Incorporated, Phoenix Int. (USA), Franklin Electronic Publishers (USA) etc. Engaged in 35 national and international research projects in Russia, and across Europe. Research interests include Digital Economy, Digital Business and Entrepreneurship; Multisided Platforms and Markets; Artificial Intelligence, Digital marketing; Big Data Governance; Computer linguistics and text mining; Semantic and Social Web. Courses taught: Data Governance (Bachelor Program); Digital marketing (Bachelor Program); Digital Commerce (Bachelor Program); Digital Business (Master Program); Smart Business Transformation in the Digital Age (CEMS Block Seminar); Multi-Sided Platforms and Innovation in a Global Era (CEMS Block Seminar); Digital Economy (EMBA). Visiting professor at WU (Vienna University of Economics and Business) in Austria, Stockholm Business School, Stockholm university in Sweden, Aalto University in Finland, Lappeenranta University of Technology in Finland, Hame University of Applied Sciences in Finland. ER - TY - JOUR T1 - Uncovering Research Streams in the Data Economy Using Text Mining Algorithms JF - Technology Innovation Management Review Y1 - 2019 A1 - Can Azkan A1 - Markus Spiekermann A1 - Henry Goecke KW - big data KW - Data Economy KW - Data Ecosystem KW - Data Market KW - digital economy KW - digital transformation KW - literature review KW - Network Graph KW - Text Mining. AB - Data-driven business models arise in different social and industrial sectors, while new sensors and devices are breaking down the barriers for disruptive ideas and digitally transforming established solutions. This paper aims at providing insights about emerging topics in the data economy that are related to companies’ innovation potential. The paper uses text mining supported by systematic literature review to automatize the extraction and analysis of beneficial insights for both scientists and practitioners that would not be possible by a manual literature review. By doing so, we were able to analyze 860 scientific publications resulting in an overview of the research field of data economy and innovation. Nine clusters and their key topics are identified, analyzed as well as visualized, as we uncover research streams in the paper. PB - Talent First Network CY - Ottawa VL - 9 UR - timreview.ca/article/1284 IS - 11 U1 -
Fraunhofer Institute 
 
Can Azkan is a scientist and PhD candidate at the Fraunhofer Institute for Software and Systems Engineering ISST in Germany. He studied Mechanical Engineering at the Technical University of Dortmund and the San Diego State University, while he gained practical experience in the field of industrial engineering and digital business models in machine und plant engineering. His research at Fraunhofer ISST focuses on value co-creation in emerging data ecosystems and the management of data as a corporate asset.
U2 -
Fraunhofer Institute
 
Markus Spiekermann currently works as Head of Department "Data Business" at the Fraunhofer Institute for Software and Systems Engineering in Dortmund, Germany. He leads research projects and is active in several related advisory boards. His main research focuses on the topics of data engineering and data management, alongside on the valuation of data assets especially within data ecosystems. Before his time at Fraunhofer, he worked as IT-Professional and Software Engineer from 2008 to 2016. He obtained his Bachelor and Master of Science degree in the field of information systems with a focus on IT Management at the FOM University of Applied Sciences in Essen.
U3 -
German Economic Institute
Since 2017 Dr. Henry Goecke has been head of the Research Group "Big Data Analytics" at the German Economic Institute. Previously he worked at the German Economic Institute as scientific assistant of the Director, at the IW Consult as Senior Economist, at the TU Dortmund University as research and teaching assistant as well as lecturer at the University of Cologne and the Hochschule Fresenius. He studied Economics at the TU Dortmund University, Strathclyde University of Glasgow, and the Leuphana University of Lüneburg. His research interests are on the impact of social media, artificial intelligence, big data, and data economy.
ER - TY - JOUR T1 - Data Science as an Innovation Challenge: From Big Data to Value Proposition JF - Technology Innovation Management Review Y1 - 2018 A1 - Victoria Kayser A1 - Bastian Nehrke A1 - Damir Zubovic KW - analytics KW - big data KW - digital innovation KW - idea generation KW - innovation process AB - Analyzing “big data” holds huge potential for generating business value. The ongoing advancement of tools and technology over recent years has created a new ecosystem full of opportunities for data-driven innovation. However, as the amount of available data rises to new heights, so too does complexity. Organizations are challenged to create the right contexts, by shaping interfaces and processes, and by asking the right questions to guide the data analysis. Lifting the innovation potential requires teaming and focus to efficiently assign available resources to the most promising initiatives. With reference to the innovation process, this article will concentrate on establishing a process for analytics projects from first ideas to realization (in most cases: a running application). The question we tackle is: what can the practical discourse on big data and analytics learn from innovation management? The insights presented in this article are built on our practical experiences in working with various clients. We will classify analytics projects as well as discuss common innovation barriers along this process. PB - Talent First Network CY - Ottawa VL - 8 UR - http://timreview.ca/article/1143 IS - 3 U1 - Ernst and Young Victoria Kayser is a Data Scientist in Ernst & Young’s Advisory Organization. Her research is focused on the intersection of analytics and innovation management. Her PhD examined the contribution of text mining to foresight and future planning. She has worked in the fields of innovation research and strategy development as well as in the automotive sector. She holds a Master of Science degree in Information Engineering and Management. U2 - Ernst and Young Bastian Nehrke is a Manager with Ernst and Young’s Advisory in Stuttgart. He specializes in developing organizational analytics capabilities and supports clients in setting up their own analytics hubs and CoEs as well as innovation and data thinking methods. He is a certified Project Manager, Business Analyst, and Requirements Engineer and studied International Management and Innovation and Technology Management in Frankfurt and Heilbronn. U3 - Ernst and Young Damir Zubovic leads Ernst and Young’s Data and Analytics Practices in Germany, Switzerland, and Austria as Partner. With 15 years of professional experience in leading business intelligence, analytics and big data initiatives, he is responsible for business development, specializing in analytics applications in the automotive and life sciences sectors and in consumer and retail products. His extensive experience in the field makes him an experienced mentor who also acts as coach, lecturer, and keynote speaker. ER - TY - JOUR T1 - Editorial: Insights (March 2018) JF - Technology Innovation Management Review Y1 - 2018 A1 - Chris McPhee KW - analytics KW - big data KW - business models KW - closed innovation KW - ecosystems KW - emerging economies KW - innovation KW - internationalization KW - Internet of Things KW - Open innovation KW - technology startups KW - value creation KW - value propositions PB - Talent First Network CY - Ottawa VL - 8 UR - http://timreview.ca/article/1141 IS - 3 U1 - Technology Innovation Management Review Chris McPhee is Editor-in-Chief of the Technology Innovation Management Review. Chris holds an MASc degree in Technology Innovation Management from Carleton University in Ottawa, Canada, and BScH and MSc degrees in Biology from Queen’s University in Kingston, Canada. He has nearly 20 years of management, design, and content-development experience in Canada and Scotland, primarily in the science, health, and education sectors. As an advisor and editor, he helps entrepreneurs, executives, and researchers develop and express their ideas. ER - TY - JOUR T1 - Smart Farming: Including Rights Holders for Responsible Agricultural Innovation JF - Technology Innovation Management Review Y1 - 2018 A1 - Kelly Bronson KW - agriculture KW - automation KW - big data KW - machine intelligence KW - power KW - responsible innovation KW - smart agriculture KW - technological values AB - This article draws on the literature of responsible innovation to suggest concrete processes for including rights holders in the “smart” agricultural revolution. It first draws upon historical agricultural research in Canada to highlight how productivist values drove seed innovations with particular consequences for the distribution of power in the food system. Next, the article uses document analysis to suggest that a similar value framework is motivating public investment in smart farming innovations. The article is of interest to smart farming’s decision makers (from farmers to governance actors) and a broader audience – anyone interested in engendering equity through innovation-led societal transitions. PB - Talent First Network CY - Ottawa VL - 8 UR - http://timreview.ca/article/1135 IS - 2 U1 - University of Ottawa Kelly Bronson is an Assistant Professor in the Faculty of Social Sciences and the Institute for Science, Society, and Policy at the University of Ottawa, Canada. She is a social scientist studying science–society tensions that erupt around controversial technologies and their governance – from GMOs to big data. Her research aims to bring community values into conversation with technical knowledge in the production of evidence-based decision-making. She has published her work in regional (Journal of New Brunswick Studies), national (Canadian Journal of Communication), and international journals (Journal of Responsible Innovation, Big Data and Society). ER - TY - JOUR T1 - A Topic Modelling Analysis of Living Labs Research JF - Technology Innovation Management Review Y1 - 2018 A1 - Mika Westerlund A1 - Seppo Leminen A1 - Mervi Rajahonka KW - big data KW - data mining KW - innovation KW - Living lab KW - living laboratory KW - research trends KW - text analytics KW - topic modeling KW - topic modelling AB - This study applies topic modelling analysis on a corpus of 86 publications in the Technology Innovation Management Review (TIM Review) to understand how the phenomenon of living labs has been approached in the recent innovation management literature. Although the analysis is performed on a corpus collected from only one journal, the TIM Review has published the largest number of special issues on living labs to date, thus it reflects the advancement of the area in the scholarly literature. According to the analysis, research approaches to living labs can be categorized under seven broad topics: 1) Design, 2) Ecosystem, 3) City, 4) University, 5) Innovation, 6) User, and 7) Living lab. Moreover, each topic includes a set of characteristic subtopics. A trend analysis suggests that the emphasis of research on living labs is moving away from a conceptual focus on what living labs are and who is involved in their ecosystems to practical applications of how to design and manage living labs, their processes, and participants, especially users, as key stakeholders and in novel application areas such as the urban city context. PB - Talent First Network CY - Ottawa VL - 8 UR - http://timreview.ca/article/1170 IS - 7 U1 - Carleton University Mika Westerlund, DSc (Econ), is an Associate Professor at Carleton University in Ottawa, Canada. He previously held positions as a Postdoctoral Scholar in the Haas School of Business at the University of California Berkeley and in the School of Economics at Aalto University in Helsinki, Finland. Mika earned his doctoral degree in Marketing from the Helsinki School of Economics in Finland. His research interests include open and user innovation, the Internet of Things, business strategy, and management models in high-tech and service-intensive industries. U2 - Aalto University Seppo Leminen is an Adjunct Professor of Business Development at Aalto University in Helsinki, Finland, and an Adjunct Research Professor at Carleton University in Ottawa, Canada. He holds a doctoral degree in Marketing from the Hanken School of Economics in Finland and a doctoral degree in Industrial Engineering and Management from the School of Science at Aalto University. His research and consulting interests include living labs, open innovation, innovation ecosystems, robotics, the Internet of Things (IoT), as well as management models in high-tech and service-intensive industries. He is serving as an associate editor in the BRQ Business Research Quarterly, on the editorial board of the Journal of Small Business Management, as a member of the Review Board for the Technology Innovation Management Review, and on the Scientific Panel of the International Society for Professional Innovation Management (ISPIM). Prior to his appointment at Aalto University, he worked in the ICT and pulp and paper industries. U3 - South-Eastern Finland University of Applied Sciences XAMK Mervi Rajahonka, DSc (Econ), works as an RDI Advisor at the Small Business Center (SBC), currently a part of the South-Eastern Finland University of Applied Sciences XAMK, Finland, and she is an Adjunct Research Professor at Carleton University in Ottawa, Canada. She has been working at the SBC for about 10 years. She earned her doctoral degree in Logistics from the Department of Information and Service Economy at Aalto University School of Business in Helsinki, Finland. She also holds a Master’s degree in Technology from the Helsinki University of Technology and a Master’s degree in Law from the University of Helsinki. Her research interests include sustainable logistics and supply chain management, business models, service modularity, and service innovations. Her research has been published in a number of journals in the areas of logistics, services, and operations management. ER - TY - JOUR T1 - Using Constructive Research to Structure the Path to Transdisciplinary Innovation and Its Application for Precision Public Health with Big Data Analytics JF - Technology Innovation Management Review Y1 - 2018 A1 - Carolyn McGregor KW - adaption KW - big data KW - critical care KW - precision public health KW - resilience KW - transdisciplinary innovation AB - New approaches to complex societal challenges require a diverse mix of resources and skillsets from different disciplines to create solutions that are of a transdisciplinary innovation nature. The constructive research method enables the purposeful creation of methods, modules, tools, and techniques that have applicability well beyond the case study that motivated their creation. This research presents a bottom-up approach that follows a structured path to transdisciplinary innovation. A method is presented that demonstrates how a set of innovative research collaborations progress from disciplinary innovation to multidisciplinary innovation and ultimately onto interdisciplinary innovation. Anchored in overlapping computer science concepts, drawing on the constructive research methodology for purposeful synthesis and integration between the projects, a greater transdisciplinary goal can emerge. This method is demonstrated through a case study involving a set of big data analytics research projects involving diverse disciplines such as computer science, critical care medicine, aerospace, tactical operations, and public health. The resultant collective vision for transdisciplinary innovation that has resulted offers new approaches to maintaining individual wellness within communities across their entire lifespan on earth and in space. PB - Talent First Network CY - Ottawa VL - 8 UR - https://timreview.ca/article/1174 IS - 8 U1 - University of Ontario Institute of Technology Carolyn McGregor AM is the Canada Research Chair (Alumni) in Health Informatics based at the University of Ontario Institute of Technology in Oshawa, Canada. She received her BAScH in Computer Science (1st class) degree and her PhD degree in Computer Science from the University of Technology Sydney in Australia. Dr. McGregor has led pioneering research in big data analytics, real-time stream processing, temporal data mining, patient journey modelling, and cloud computing. She now progresses this research within the context of critical care medicine, mental health, astronaut health, and military and civilian tactical training. ER - TY - JOUR T1 - Big Data and Individual Privacy in the Age of the Internet of Things JF - Technology Innovation Management Review Y1 - 2017 A1 - Mackenzie Adams KW - big data KW - cybersecurity KW - data breaches KW - Internet of Things KW - IOT KW - privacy KW - smart devices AB - The availability of “big data” and “smart” products are credited with advancing solutions to complex problems in medicine, transportation, and education, among others. However, with big data comes big responsibility. The collection, storage, sharing, and analysis of data are far outpacing individual privacy protections, whether technological or legislative. The Internet of Things (IoT), with its promise to create networks of networks, will magnify individual data privacy threats. Recent data breaches, exposing the personal information of millions of users, provide insight into the vulnerability of personal data. Although seemingly expansive, there are core individual privacy issues that are central to current big data breaches and anticipated IoT threats. This article examines both big data and the IoT using examples of data privacy breaches to illustrate the impact of individual data loss. Furthermore, the article examines the complexity of tackling technological and legislative challenges in protecting individual privacy. It concludes by summarizing these issues in terms of the future implications of the IoT and the loss of privacy. PB - Talent First Network CY - Ottawa VL - 7 UR - http://timreview.ca/article/1067 IS - 4 U1 - SOMANDA Inc. Mackenzie Adams is Co-Founder and Creative Director at SOMANDA Inc., and she is a recent graduate of the Technology Innovation Management (TIM) program at Carleton University in Ottawa, Canada. As an avid learner and serial entrepreneur, Mackenzie is always seeking new challenges to continue evolving and expanding her interests, knowledge base, and skills. Her interests span the fields of artificial intelligence, quantum computing, EdTech, and FinTech. Her passion is to find and cultivate the next generation of innovators in underserved communities. ER - TY - JOUR T1 - Editorial: Cybersecurity (April 2017) JF - Technology Innovation Management Review Y1 - 2017 A1 - Chris McPhee A1 - Michael Weiss KW - anomaly detection KW - automation KW - big data KW - cybersecurity KW - exploration KW - Hypponen’s law KW - Internet of Things KW - IOT KW - legislation KW - medical devices KW - privacy KW - real time KW - risk assessment KW - security engineering KW - smart devices KW - value proposition KW - vulnerabilities PB - Talent First Network CY - Ottawa VL - 7 UR - http://timreview.ca/article/1065 IS - 4 U1 - Technology Innovation Management Review Chris McPhee is Editor-in-Chief of the Technology Innovation Management Review. Chris holds an MASc degree in Technology Innovation Management from Carleton University in Ottawa, Canada, and BScH and MSc degrees in Biology from Queen's University in Kingston, Canada. He has nearly 20 years of management, design, and content-development experience in Canada and Scotland, primarily in the science, health, and education sectors. As an advisor and editor, he helps entrepreneurs, executives, and researchers develop and express their ideas. U2 - Carleton University Michael Weiss holds a faculty appointment in the Department of Systems and Computer Engineering at Carleton University in Ottawa, Canada, and is a member of the Technology Innovation Management program. His research interests include open source, ecosystems, mashups, patterns, and social network analysis. Michael has published on the evolution of open source business, mashups, platforms, and technology entrepreneurship. ER - TY - JOUR T1 - TIM Lecture Series – Huge Memory and Collection-Oriented Programming: Less Code, More Speed? JF - Technology Innovation Management Review Y1 - 2016 A1 - Dave Thomas KW - big data KW - collection-oriented programming KW - databases KW - huge persistent memory KW - memory KW - object-oriented programming KW - programming KW - queries KW - speed KW - very large databases PB - Talent First Network CY - Ottawa VL - 6 UR - http://timreview.ca/article/974 IS - 3 U1 - First Derivatives FD Labs Dave Thomas is Chief Scientist/CSO, First Derivatives FD Labs. He is also Founder and Chairman of the YOW! Australia and Lambda Jam conferences, he is a GOTO Conference Fellow, and he is an ACM Distinguished Engineer. With a unique ability to see the future and translate research into competitive products, he is known for his contributions to object technology including IBM VisualAge and Eclipse IDEs, Smalltalk, and Java virtual machines, and more recently, he has been a proponent for the use of applied functional programming. He holds close links to the R&D community as an Adjunct Research Professor at Carleton University in Canada, and he has held past positions at UQ, QUT, and NICTA in Australia. While a professor at Carleton, he formed the Object-Oriented Research Group and established Ottawa's leadership in object-oriented technology. Dave has been a business and technical advisor to many technology local and international technology companies. And, among his past roles, he was Co-Founder and Chairman of Bedarra Research Labs (BRL), Founder and CEO of Object Technology International (OTI), becoming CEO of IBM OTI Labs after its sale to IBM. ER - TY - JOUR T1 - Formulating an Executive Strategy for Big Data Analytics JF - Technology Innovation Management Review Y1 - 2014 A1 - Gopalakrishna Palem KW - big data KW - business vision KW - executive strategy KW - IT entrepreneurship KW - predictive analytics AB - The recent surge in big data technologies has left many executives, both of well-established organizations and emerging startups, wondering how best to harness big data. In particular, the analytics aspect of big data is enticing for both information technology (IT) service providers and non-IT firms because of its potential for high returns on investment, which have been heavily publicized, if not clearly demonstrated, by multiple whitepapers, webinars, and research surveys. Although executives may clearly perceive the benefits of big data analytics to their organizations, the path to the goal is not as clear or easy as it looks. And, it is not just the established organizations that have this challenge; even startups trying to take advantage of this big data analytics opportunity are facing the same problem of lack of clarity on what to do or how to formulate an executive strategy. This article is primarily for executives who are looking for help in formulating a strategy for achieving success with big data analytics in their operations. It provides guidelines to them plan an organization's short-term and long-term goals, and presents a strategy tool, known as the delta model, to develop a customer-centric approach to success with big data analytics. PB - Talent First Network CY - Ottawa VL - 4 UR - http://timreview.ca/article/773 IS - 3 U1 - Gopalakrishna Palem is a Corporate Technology Strategist specialized in distributed computing technologies and advanced predictive analytics solutions. During his 12-year tenure at Microsoft and Oracle, he helped many customers build their executive strategy for various technology initiatives, driving the brand-name promotions and improved revenue targets. He offers consultations for C-level executives in technology management strategy and is actively engaged in guiding researchers and entrepreneurs in knowledge modelling systems, algorithmic information theory, and systems control and automata. He can be reached at gopalakrishna.palem.in ER - TY - JOUR T1 - TIM Lecture Series – The Laboratory for Analytic Sciences: Developing the Art and Science of Analysis JF - Technology Innovation Management Review Y1 - 2014 A1 - J. David Harris KW - analysis KW - analytics KW - big data KW - collaboration KW - cybersecurity KW - framework KW - innovation KW - instrumentation KW - monitoring KW - prediction KW - strategy PB - Talent First Network CY - Ottawa VL - 4 UR - http://timreview.ca/article/813 IS - 7 U1 - Laboratory for Analytic Sciences J. David Harris is the inaugural Director of the Laboratory for Analytic Sciences in Raleigh, North Carolina, where the aim is to develop a science of analysis and analytic methodology. During nearly 25 years service with the U. S. Department of Defense, David has worked in a variety of technical and leadership positions in areas of research and development, technology transfer, and operations. ER -