@article {1410,
	title = {Editorial: Insights (December 2020)},
	journal = {Technology Innovation Management Review},
	volume = {10},
	year = {2020},
	month = {12/2020},
	pages = {3-3},
	publisher = {Talent First Network},
	chapter = {3},
	address = {Ottawa},
	keywords = {5G technology; Bitcoin, blockchain, business model, business model innovation, crypto assets, cryptocurrencies, Diem, Diem Association, digital currency, distributed ledger technology, Facebook, fiat currencies, financial inclusion, Financial industry, front-end, ideation, innovation, Libra, local currencies, Management Model, not-for-profit, research center, strategic foresight, strategy, technology firms; project portfolio management, tokenization; product innovativeness, visioning},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1410},
	url = {timreview.ca/article/1410},
	author = {Stoyan Tanev and Gregory Sandstrom}
}
@article {1245,
	title = {A Management Innovation Approach to Project Planning},
	journal = {Technology Innovation Management Review},
	volume = {9},
	year = {2019},
	month = {06/2019},
	pages = {4-13},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {Innovation is viewed as indispensable to the economic and social development of countries, and the subject has been widely researched. The initial research focused on the development of new products and services by firms applying technological initiatives. The concept has expanded to cover many domains and features of innovation that led to innovation in non-technological areas, currently referred to as {\textquotedblleft}management innovation{\textquotedblright}. Many tools were developed by management specialists and gurus such as strategic planning, vision and mission statements, benchmarking, customer-satisfaction measurement, and outsourcing to target performance improvements in firms. The output of this article is a project development planning model that integrates management innovation tools related to the field of project planning as a novel approach tested by a consulting firm over several applications. The article has implications for managers, consultants, and professionals involved in the design, planning, and implementation of a varied range of development projects.},
	keywords = {innovation, management innovation, management innovation tools, scenario planning, strategic foresight},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1245},
	url = {https://timreview.ca/article/1245},
	author = {Haven Allahar}
}
@article {1189,
	title = {Strategic Foresight of Future B2B Customer Opportunities through Machine Learning},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {10/2018},
	pages = {5-17},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {Within the strategic foresight literature, customer foresight still shows a low capability level. In practice, especially in business-to-business (B2B) industries, analyzing an entire customer base in terms of future customer potential is often done manually. Therefore, we present a single case study based on a quantitative customer-foresight project conducted by a manufacturing company. Along with a common data mining process, we highlight the application of machine learning algorithms on an entire customer database that consists of customer and product-related data. The overall benefit of our research is threefold. The major result is a prioritization of 2,300 worldwide customers according to their predicted technical affinity and suitability for a new machine control sensor. Thus, the company gains market knowledge, which addresses management functions such as product management. Furthermore, we describe the necessary requirements and steps for practitioners who realize a customer-foresight project. Finally, we provide a detailed catalogue of measures suitable for sales in order to approach the identified high-potential customers according to their individual needs and behaviour. },
	keywords = {action research, B2B industries, customer base analysis, customer foresight, customer knowledge, customer profile, data mining, machine learning, strategic foresight},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1189},
	url = {https://timreview.ca/article/1189},
	author = {Daniel Gentner and Birgit Stelzer and Bujar Ramosaj and Leo Brecht}
}
