02228nas a2200277   4500008004100000022001400041245010200055210006900157260004200226300001000268490000700278520139300285653002601678653002101704653001701725653002901742653002201771653001801793100002001811700001701831700001701848700001901865700001801884700001801902856003001920       2021                        eng d  a1927-032100aUsing Web Text Analytics to Categorize the Business Focus  of Innovative Digital Health Companies0 aUsing Web Text Analytics to Categorize the Business Focus of Inn  aOttawabTalent First Networkc10/2021  a65-780 v113 aCategorizing 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.10aDigital health sector10amachine learning10aMarket offer10atopic modeling algorithm10avalue proposition10aweb analytics1 aAweisi, Abdulla1 aArora, Daman1 aEmby, Renée1 aRehman, Madiha1 aTanev, George1 aTanev, Stoyan  utimreview.ca/article/145700710nas a2200253   4500008004100000022001400041245003900055210003600094260004200130300000800172490000600180653002300186653001800209653001600227653002000243653002200263653003800285653002100323653001700344653002000361653001900381100001800400856003800418       2018                        eng d  a1927-032100aEditorial: Insights (October 2018)0 aEditorial Insights October 2018  aOttawabTalent First Networkc10/2018  a3-40 v810acustomer foresight10adata analysis10adata mining10adesign thinking10adigital platforms10aindustry–academia collaboration10amachine learning10amarket entry10aOpen innovation10aservice design1 aMcPhee, Chris  uhttps://timreview.ca/article/118802499nas a2200277   4500008004100000022001400041245010800055210006900163260004200232300001000274490000600284520159900290653002801889653002601917653002101943653002101964653002701985653003502012653002902047653002102076653001302097653002202110100002102132700003002153856003802183       2018                        eng d  a1927-032100aImpact of Business Intelligence Solutions on Export Performance of Software Firms in Emerging Economies0 aImpact of Business Intelligence Solutions on Export Performance   aOttawabTalent First Networkc09/2018  a39-490 v83 aThe 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’ 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’s exportation of goods actually performs, and the ability of its software firms to globalize successfully.10aartificial intelligence10abusiness intelligence10aemerging markets10aglobal marketing10ainternational business10ainternational entrepreneurship10ainternational management10amachine learning10aParaguay10asoftware industry1 aNeubert, Michael1 aVan der Krogt, Augustinus  uhttps://timreview.ca/article/118502640nas a2200265   4500008004100000022001400041245009400055210006900149260004200218300001000260490000600270520177400276653002802050653002302078653001902101653002102120653002702141653003902168653003502207653002902242653002402271653002102295100002102316856003702337       2018                        eng d  a1927-032100aThe Impact of Digitalization on the Speed of Internationalization of Lean Global Startups0 aImpact of Digitalization on the Speed of Internationalization of  aOttawabTalent First Networkc05/2018  a44-540 v83 aLean 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. 10aartificial intelligence10abig data analytics10adigitalization10aglobal marketing10ainternational business10ainternational business development10ainternational entrepreneurship10ainternational management10alean global startup10amachine learning1 aNeubert, Michael  uhttp://timreview.ca/article/115802071nas a2200289   4500008004100000022001400041245008600055210006900141260004200210300000900252490000600261520120700267653002001474653001901494653002701513653002301540653002301563653002101586653001601607653002101623653002401644100002001668700002001688700001901708700001601727856003801743       2018                        eng d  a1927-032100aStrategic Foresight of Future B2B Customer Opportunities through Machine Learning0 aStrategic Foresight of Future B2B Customer Opportunities through  aOttawabTalent First Networkc10/2018  a5-170 v83 aWithin 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. 10aaction research10aB2B industries10acustomer base analysis10acustomer foresight10acustomer knowledge10acustomer profile10adata mining10amachine learning10astrategic foresight1 aGentner, Daniel1 aStelzer, Birgit1 aRamosaj, Bujar1 aBrecht, Leo  uhttps://timreview.ca/article/118900682nas a2200253   4500008004100000022001400041245004500055210004200100260004200142300000800184490000600192653001800198653001400216653001400230653002300244653002100267653001200288653002500300653001600325653001600341100001800357700001700375856003600392       2016                        eng d  a1927-032100aEditorial: Cybersecurity (February 2016)0 aEditorial Cybersecurity February 2016  aOttawabTalent First Networkc02/2016  a3-40 v610acybersecurity10aintrusion10alicensing10aliterature reviews10amachine learning10amalware10amultisided platforms10anew domains10aopen source1 aMcPhee, Chris1 aCraigen, Dan  uhttp://timreview.ca/article/96201883nas a2200313   4500008004100000022001400041245006200055210006100117260004200178300001000220490000600230520100200236653002501238653001501263653001801278653001501296653002401311653002301335653002401358653002101382653002301403653001501426653001301441653002701454100001901481700001701500700001601517856003601533       2016                        eng d  a1927-032100aIntrusion Learning: An Overview of an Emergent Discipline0 aIntrusion Learning An Overview of an Emergent Discipline  aOttawabTalent First Networkc02/2016  a15-200 v63 aThe 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.10aadversarial learning10aclustering10acybersecurity10aenterprise10aintrusion detection10aintrusion learning10alearning algorithms10amachine learning10areal-time analysis10aresiliency10asecurity10astreaming network data1 aBailetti, Tony1 aGad, Mahmoud1 aShah, Ahmed  uhttp://timreview.ca/article/964