@article {1188,
	title = {Editorial: Insights (October 2018)},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {10/2018},
	pages = {3-4},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {customer foresight, data analysis, data mining, design thinking, digital platforms, industry{\textendash}academia collaboration, machine learning, market entry, Open innovation, service design},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1188},
	url = {https://timreview.ca/article/1188},
	author = {Chris McPhee}
}
@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}
}
@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 {575,
	title = {From Stories to Evidence: How Mining Data Can Promote Innovation in the Nonprofit Sector},
	journal = {Technology Innovation Management Review},
	volume = {2},
	year = {2012},
	month = {07/2012},
	pages = {10-15},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {Being a director at a nonprofit organization often means making guesses instead of properly informed decisions. One source of the {\textquotedblleft}information fog{\textquotedblright} is fragmented funding. Nonprofit organizations have multiple types of funders, most of whom are not their direct beneficiaries. Predicting funder behaviour is therefore more of an art than a science. Planning for the future, setting goals, and making decisions all suffer in the nonprofit sector because of a lack of timely and accurate information.

This article examines the opportunities to use newly available digitized information to address this information deficit. It shows how the rich, variegated and fast-changing landscape of information available online can be collected, combined, and repurposed in order to deliver it in actionable forms to decision makers across the nonprofit sector. This information can significantly improve planning decisions and enhance the effectiveness of the sector. 

The article concludes that a cultural shift is required in order for the nonprofit sector to exploit the opportunities presented by digital information. Nonprofits and funders are enjoined to increase their numeracy and to find creative ways to use data as part of their evaluation, planning and decision making. Researchers need to be adventurous in their use of quantitative information and specifically should employ linked datasets in order to explore previously unanswerable research and policy questions. The producers of data need to collect and publish their information in ways that facilitate reuse. Finally, funders need to support a variety of projects that seek to exploit these new opportunities.
},
	keywords = {Ajah, charities, community sector, data mining, funding database, nonprofit, social innovation},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/575},
	url = {http://timreview.ca/article/575},
	author = {Michael Lenczner and Susan Phillips}
}
