@article {1457,
	title = {Using Web Text Analytics to Categorize the Business Focus  of Innovative Digital Health Companies},
	journal = {Technology Innovation Management Review},
	volume = {11},
	year = {2021},
	month = {10/2021},
	pages = {65-78},
	publisher = {Talent First Network},
	chapter = {65},
	address = {Ottawa},
	abstract = {Categorizing the market focus of larger samples of companies can be a tedious and time-consuming process for both researchers and business analysts interested in developing insights about emerging business sectors. The objective of this article is to suggest a text analytics approach to categorizing the application areas of companies operating in the digital health sector based on the information provided on their websites. More specifically, we apply topic modeling on a collection of text documents, including information collected from the websites of a sample of 100 innovative digital health companies. The topic model helps in grouping the companies offering similar types of market offers. It enables identifying the companies that are most highly associated with each of the topics. In addition, it allows identifying some of the emerging themes that are discussed online by the companies, as well as their specific market offers. The results will be of interest to inspiring technology entrepreneurs, organizations supporting new ventures, and business accelerators interested to enhance their services to new venture clients. The development, operationalization, and automation of the company categorization process based on publicly available information is a methodological contribution that opens the opportunity for future applications in research and business practice.},
	keywords = {Digital health sector, machine learning, Market offer, topic modeling algorithm, value proposition, web analytics},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1457},
	url = {timreview.ca/article/1457},
	author = {Abdulla Aweisi and Daman Arora and Ren{\'e}e Emby and Madiha Rehman and George Tanev and Stoyan Tanev}
}
@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 {1185,
	title = {Impact of Business Intelligence Solutions on Export Performance of Software Firms in Emerging Economies},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {09/2018},
	pages = {39-49},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {The article is written with the aim of understanding how well software firms in emerging economies perform when exporting their goods. Focusing on Paraguay as a representative context, a multiple-case-study research design was adopted using different sources of evidence, including 15 in-depth interviews with founders, shareholders, and CEOs. The data were analyzed using grounded theory in order to develop patterns and categories, and to understand differences and regularities. The revised Uppsala internationalization process model was used as a theoretical framework. This article highlights the experts{\textquoteright} views of the impact of business intelligence on the export performance of software firms in Paraguay. Although only a few of the interviewees currently use business intelligence solutions to support international strategic decision-making processes, most of them reveal a desire to use them because they expect it will have a positive impact on export performance and international competitiveness. The main factors for selecting a business intelligence solution are transparency of cost and benefits, excellent client service, and an attractive pricing model. The study results apply to all stakeholders who support the impact of business intelligence systems on the export performance of software firms in emerging economies. The article fulfils an identified need and call for research to study the use and impact of business intelligence on the way an emerging country{\textquoteright}s exportation of goods actually performs, and the ability of its software firms to globalize successfully.},
	keywords = {artificial intelligence, business intelligence, emerging markets, global marketing, international business, international entrepreneurship, international management, machine learning, Paraguay, software industry},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1185},
	url = {https://timreview.ca/article/1185},
	author = {Michael Neubert and Augustinus Van der Krogt}
}
@article {1158,
	title = {The Impact of Digitalization on the Speed of Internationalization of Lean Global Startups},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {05/2018},
	pages = {44-54},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {Lean global startups need to internationalize early and fast. The digitalization of new foreign market development helps them to more efficiently identify new market opportunities in global markets. With this approach, they are saving resources while developing the most attractive markets. This article examines how lean global startups develop new foreign markets more rapidly due to digitalization. Thus, the aim is to understand the impact of digitalization on speed of internationalization of lean global startups. The study addresses a gap in the scholarly literature and a practical need to evaluate new foreign markets and business opportunities more quickly and more regularly and to understand what helps lean global startups react more quickly to opportunities and threats with respect to changing market attractiveness. Furthermore, it outlines why and how digitalization is important throughout the internationalization process. The research followed a multiple case-study design using different sources of evidence, including 73 interviews with senior managers of lean global startups. The findings reveal that digitalization allows lean global startups to increase decision-making efficiency and to optimize strategies and processes for evaluating international markets. The findings suggest that lean global startups can benefit from the use of digital technologies by applying a more efficient foreign market development process with regular reviews and a reduced workflow, by faster mediation between local market realities and strategic goals, by analyzing all foreign markets instead of just a sample of them, and by optimizing decision-making processes including the ability to make long-term, strategic decisions due to better market information. },
	keywords = {artificial intelligence, big data analytics, digitalization, global marketing, international business, international business development, international entrepreneurship, international management, lean global startup, machine learning},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1158},
	url = {http://timreview.ca/article/1158},
	author = {Michael Neubert}
}
@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 {962,
	title = {Editorial: Cybersecurity (February 2016)},
	journal = {Technology Innovation Management Review},
	volume = {6},
	year = {2016},
	month = {02/2016},
	pages = {3-4},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {cybersecurity, intrusion, licensing, literature reviews, machine learning, malware, multisided platforms, new domains, open source},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/962},
	url = {http://timreview.ca/article/962},
	author = {Chris McPhee and Dan Craigen}
}
@article {964,
	title = {Intrusion Learning: An Overview of an Emergent Discipline},
	journal = {Technology Innovation Management Review},
	volume = {6},
	year = {2016},
	month = {02/2016},
	pages = {15-20},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {The purpose of this article is to provide a definition of intrusion learning, identify its distinctive aspects, and provide recommendations for advancing intrusion learning as a practice domain. The authors define intrusion learning as the collection of online network algorithms that learn from and monitor streaming network data resulting in effective intrusion-detection methods for enabling the security and resiliency of enterprise systems. The network algorithms build on advances in cyber-defensive and cyber-offensive capabilities. Intrusion learning is an emerging domain that draws from machine learning, intrusion detection, and streaming network data. Intrusion learning offers to significantly enhance enterprise security and resiliency through augmented perimeter defense and may mitigate increasing threats facing enterprise perimeter protection. The article will be of interest to researchers, sponsors, and entrepreneurs interested in enhancing enterprise security and resiliency.},
	keywords = {adversarial learning, clustering, cybersecurity, enterprise, intrusion detection, intrusion learning, learning algorithms, machine learning, real-time analysis, resiliency, security, streaming network data},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/964},
	url = {http://timreview.ca/article/964},
	author = {Tony Bailetti and Mahmoud Gad and Ahmed Shah}
}
