@article {1419,
	title = {Cloud-Based Approach for Tracking and Monitoring of Hay Bales in Smart Agriculture},
	journal = {Technology Innovation Management Review},
	volume = {11},
	year = {2021},
	month = {02/2021},
	pages = {17-23},
	publisher = {Talent First Network},
	chapter = {17},
	address = {Ottawa},
	abstract = {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{\textquoteright}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.},
	keywords = {bale inventory, bale trade, big data, precision farming, RFID, round bales, silage, Smart farming},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1419},
	url = {timreview.ca/article/1419},
	author = {Ilpo P{\"o}l{\"o}nen and Antti Suokannas and Antti Juntunen}
}
@article {1392,
	title = {AI-Driven Digital Platform Innovation},
	journal = {Technology Innovation Management Review},
	volume = {10},
	year = {2020},
	month = {10/2020},
	pages = {4-15},
	publisher = {Talent First Network},
	chapter = {4},
	address = {Ottawa},
	abstract = {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.},
	keywords = {Advanced Analytics, AI maturity, AI value chain, AI-driven platform innovation, Artificial Intelligence (AI), big data, enterprise platform},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1392},
	url = {timreview.ca/article/1392},
	author = {Sergey A. Yablonsky}
}
@article {1298,
	title = {Editorial: Insights (January 2020)},
	journal = {Technology Innovation Management Review},
	volume = {10},
	year = {2020},
	month = {01/2020},
	pages = {3-4},
	publisher = {Talent First Network},
	chapter = {3},
	address = {Ottawa},
	keywords = {AI, artificial intelligence, B2B sales, big data, business-to-business sales, data-based value, digital solutions, ecosystem, ecosystems, Ethics, Gujarat State, Indian IT industry, innovation, IT clusters, Knowledge Innovation clusters, Networks Analysis, regional development, Roboethics, Smart robot, strategy, Systematic literature review, technology, value capture, value creation, value sales},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1298},
	url = {timreview.ca/article/1298},
	author = {Stoyan Tanev and Gregory Sandstrom}
}
@article {1396,
	title = {Editorial: Insights (October 2020)},
	journal = {Technology Innovation Management Review},
	volume = {10},
	year = {2020},
	month = {10/2020},
	pages = {3-3},
	publisher = {Talent First Network},
	chapter = {3},
	address = {Ottawa},
	keywords = {Advanced Analytics, AI maturity. Data science, AI value chain, AI-driven platform innovation, Artificial Intelligence (AI), big data, business decision-making, business model components, business models, content analysis, data-dominant logic, dominant logic, empirical study, enterprise platform, industries, online communication, online data collection, organizational and managerial requirements, principal component analysis, R\&D, research and development, secondary data. Sustainability, SMEs. Disruptive innovation, sustainable innovation},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1396},
	url = {timreview.ca/article/1396},
	author = {Gregory Sandstrom}
}
@article {1313,
	title = {Selling Data-Based Value in Business-to-Business Markets},
	journal = {Technology Innovation Management Review},
	volume = {10},
	year = {2020},
	month = {01/2020},
	pages = {45-53},
	publisher = {Talent First Network},
	chapter = {45},
	address = {Ottawa},
	abstract = {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{\textquoteright}s perspective, and in relation to data and digital solutions during the sales process. Thirdly, on the customer side, we explore the roles of {\textquotedblleft}digitalist{\textquotedblright} 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{\textquoteright}s business.},
	keywords = {B2B sales, big data, business-to-business sales, data-based value, digital solutions, value sales},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1313},
	url = {timreview.ca/article/1313},
	author = {Tuija Rantala and Tiina Apilo and Katariina Palom{\"a}ki and Katri Valkokari}
}
@article {1288,
	title = {Multidimensional Data-Driven Artificial Intelligence Innovation},
	journal = {Technology Innovation Management Review},
	volume = {9},
	year = {2019},
	month = {12/2019},
	pages = {16-28},
	publisher = {Talent First Network},
	chapter = {16},
	address = {Ottawa},
	abstract = {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.},
	keywords = {Advanced Analytics, AI maturity, AI value chain, Artificial Intelligence (AI), big data, enterprise platform, innovation},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1288},
	url = {timreview.ca/article/1288},
	author = {Sergey A. Yablonsky}
}
@article {1284,
	title = {Uncovering Research Streams in the Data Economy Using Text Mining Algorithms},
	journal = {Technology Innovation Management Review},
	volume = {9},
	year = {2019},
	month = {11/2019},
	pages = {62-74},
	publisher = {Talent First Network},
	chapter = {62},
	address = {Ottawa},
	abstract = {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\&rsquo; 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.},
	keywords = {big data, Data Economy, Data Ecosystem, Data Market, digital economy, digital transformation, literature review, Network Graph, Text Mining.},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1284},
	url = {timreview.ca/article/1284},
	author = {Can Azkan and Markus Spiekermann and Henry Goecke}
}
@article {1143,
	title = {Data Science as an Innovation Challenge: From Big Data to Value Proposition},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {03/2018},
	pages = {16-25},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {Analyzing {\textquotedblleft}big data{\textquotedblright} 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. },
	keywords = {analytics, big data, digital innovation, idea generation, innovation process},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1143},
	url = {http://timreview.ca/article/1143},
	author = {Victoria Kayser and Bastian Nehrke and Damir Zubovic}
}
@article {1141,
	title = {Editorial: Insights (March 2018)},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {03/2018},
	pages = {3-4},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {analytics, big data, business models, closed innovation, ecosystems, emerging economies, innovation, internationalization, Internet of Things, Open innovation, technology startups, value creation, value propositions},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1141},
	url = {http://timreview.ca/article/1141},
	author = {Chris McPhee}
}
@article {1135,
	title = {Smart Farming: Including Rights Holders for Responsible Agricultural Innovation},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {02/2018},
	pages = {7-14},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {This article draws on the literature of responsible innovation to suggest concrete processes for including rights holders in the {\textquotedblleft}smart{\textquotedblright} 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{\textquoteright}s decision makers (from farmers to governance actors) and a broader audience {\textendash} anyone interested in engendering equity through innovation-led societal transitions.},
	keywords = {agriculture, automation, big data, machine intelligence, power, responsible innovation, smart agriculture, technological values},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1135},
	url = {http://timreview.ca/article/1135},
	author = {Kelly Bronson}
}
@article {1170,
	title = {A Topic Modelling Analysis of Living Labs Research},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {07/2018},
	pages = {40-51},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {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.},
	keywords = {big data, data mining, innovation, Living lab, living laboratory, research trends, text analytics, topic modeling, topic modelling},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1170},
	url = {http://timreview.ca/article/1170},
	author = {Mika Westerlund and Seppo Leminen and Mervi Rajahonka}
}
@article {1174,
	title = {Using Constructive Research to Structure the Path to Transdisciplinary Innovation and Its Application for Precision Public Health with Big Data Analytics},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {08/2018},
	pages = {7-15},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {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.},
	keywords = {adaption, big data, critical care, precision public health, resilience, transdisciplinary innovation},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1174},
	url = {https://timreview.ca/article/1174},
	author = {Carolyn McGregor}
}
@article {1067,
	title = {Big Data and Individual Privacy in the Age of the Internet of Things},
	journal = {Technology Innovation Management Review},
	volume = {7},
	year = {2017},
	month = {04/2017},
	pages = {12-24},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {The availability of {\textquotedblleft}big data{\textquotedblright} and {\textquotedblleft}smart{\textquotedblright} 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. },
	keywords = {big data, cybersecurity, data breaches, Internet of Things, IOT, privacy, smart devices},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1067},
	url = {http://timreview.ca/article/1067},
	author = {Mackenzie Adams}
}
@article {1065,
	title = {Editorial: Cybersecurity (April 2017)},
	journal = {Technology Innovation Management Review},
	volume = {7},
	year = {2017},
	month = {04/2017},
	pages = {3-4},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {anomaly detection, automation, big data, cybersecurity, exploration, Hypponen{\textquoteright}s law, Internet of Things, IOT, legislation, medical devices, privacy, real time, risk assessment, security engineering, smart devices, value proposition, vulnerabilities},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1065},
	url = {http://timreview.ca/article/1065},
	author = {Chris McPhee and Michael Weiss}
}
@article {974,
	title = {TIM Lecture Series {\textendash} Huge Memory and Collection-Oriented Programming: Less Code, More Speed?},
	journal = {Technology Innovation Management Review},
	volume = {6},
	year = {2016},
	month = {06/2016},
	pages = {41-43},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {big data, collection-oriented programming, databases, huge persistent memory, memory, object-oriented programming, programming, queries, speed, very large databases},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/974},
	url = {http://timreview.ca/article/974},
	author = {Dave Thomas}
}
@article {773,
	title = {Formulating an Executive Strategy for Big Data Analytics},
	journal = {Technology Innovation Management Review},
	volume = {4},
	year = {2014},
	month = {03/2014},
	pages = {25-34},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {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{\textquoteright}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.},
	keywords = {big data, business vision, executive strategy, IT entrepreneurship, predictive analytics},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/773},
	url = {http://timreview.ca/article/773},
	author = {Gopalakrishna Palem}
}
@article {813,
	title = {TIM Lecture Series {\textendash} The Laboratory for Analytic Sciences: Developing the Art and Science of Analysis},
	journal = {Technology Innovation Management Review},
	volume = {4},
	year = {2014},
	month = {07/2014},
	pages = {52-54},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {analysis, analytics, big data, collaboration, cybersecurity, framework, innovation, instrumentation, monitoring, prediction, strategy},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/813},
	url = {http://timreview.ca/article/813},
	author = {J. David Harris}
}
