00620nas a2200193   4500008004100000022001400041245008100055210006900136260004200205300000800247490000700255653002800262653001500290653001800305653002000323653003400343653001900377856003000396       2021                        eng d  a1927-032100aCall for Papers: Distributed Ledger Technologies for Smart Digital Economies0 aCall for Papers Distributed Ledger Technologies for Smart Digita  aOttawabTalent First Networkc02/2021  a1-10 v1110aartificial intelligence10ablockchain10acybersecurity10adigital economy10adistributed ledger technology10asmartification  utimreview.ca/article/142202080nas a2200217   4500008004100000022001400041245011300055210006900168260004200237300001000279490000700289520137500296653002801671653002601699653002901725653002001754100002001774700002101794700001701815856003001832       2021                        eng d  a1927-032100aCan Artificial Intelligence be a Critical Success Factor of Construction Projects? Practitioner perspectives0 aCan Artificial Intelligence be a Critical Success Factor of Cons  aOttawabTalent First Networkc12/2021  a17-320 v113 aThe construction sector has not been altogether successful in adopting automated systems. Related research on artificial intelligence has mainly been confined to the development of software models for a specific subset of construction work. This study aims to identify whether artificial intelligence is a potential critical success factor for construction project success. Data were collected through semi-structured interviews and analyzed using content analysis. The interviewees were selected on the basis of convenience and included highly experienced project managers from the global community with expertise in project management working on large construction projects. Our research shows that senior project managers perceive artificial intelligence as different from information technology and advanced project management software. Major drawbacks of artificial intelligence were found to be (i) lack of soft skills, (ii) lack of intelligence to interpret things in various ways like human beings, and (iii) lack of human relationship capabilities, including the ways people manage projects. The interviewees believe that artificial intelligence is still years away from becoming self-aware. This study improves the understanding of artificial intelligence as a success factor for construction projects and provides future directions for research in this field.10aartificial intelligence10aConstruction Projects10aCritical Success Factors10aProject Success1 aKumar, Virender1 aPandey, Amrendra1 aSingh, Rahul  utimreview.ca/article/147102253nas a2200313   4500008004100000022001400041245014100055210006900196260004200265300001000307490000700317520124900324653002801573653001501601653002001616653002101636653003401657653002501691653002301716653002301739653002501762653002301787653002101810653001901831653002001850653001601870100002301886856003001909       2021                        eng d  a1927-032100aDistributed Ledger Technologies and Social Machines: How to “smartify” the economy with blockchain-based digital extension services?0 aDistributed Ledger Technologies and Social Machines How to smart  aOttawabTalent First Networkc06/2021  a42-570 v113 aThis paper examines the broad impact of digitalization on economic development. More specifically, it addresses the computer science-derived notion of "social machines", along with the invention of distributed ledger technologies (DLTs) (or blockchain), as potential signposts on the pathway to "smart(er) digital economies". The paper investigates blockchain-based ecosystems as examples of social machines that assist in economic "smartification" and development. It looks at distributed ledger-based communities (DLCs) that provide examples of functioning social machines for a variety of business and personal network communications purposes. It then analyses the scaleup of DLT-based social machines by comparison with "extension services", largely in education and agriculture, which are currently undergoing processes of digitalization. Overall, this conceptual study examines the general horizons and potential impact of blockchain and social machines on the provision of online products and services, across a range of sectors and industries. The paper offers interpretative assistance to managers, entrepreneurs, technology experts, and academics with lingering questions about blockchain in and for business and economic development.10aartificial intelligence10ablockchain10adigital economy10adigital platform10adistributed ledger technology10aeconomic development10aextension services10aextension thinking10ainnovation diffusion10aInternet of Things10aledger community10asmartification10asocial machines10aweb science1 aSandstrom, Gregory  utimreview.ca/article/144901826nas a2200217   4500008004100000022001400041245010300055210006900158260004200227300000900269490000700278520111400285653002801399653003101427653002701458653002201485653003301507100001701540700002101557856003001578       2021                        eng d  a1927-032100aReinvigorating the Discourse on Human-Centered Artificial Intelligence in Educational Technologies0 aReinvigorating the Discourse on HumanCentered Artificial Intelli  aOttawabTalent First Networkc05/2021  a5-160 v113 aThe increasing relevance of artificial intelligence (AI) applications in various domains has led to high expectations of benefits, ranging from precision, efficiency, and optimization to the completion of routine or time-consuming tasks. Particularly in the field of education, AI applications promise immense innovation potential. A central focus in this field is on analyzing and evaluating learner characteristics to derive learning profiles and create individualized learning environments. The development and implementation of such AI-driven approaches are related to learners' data, and thus involves several privacies, ethics, and morality challenges. In this paper, we introduce the concept of human-centered AI, and consider how an AI system can be developed in line with human values without posing risks to humanity. Because the education market is in the early stages of incorporating AI into educational tools, we believe that this is the right time to raise awareness about the use of principles that foster human-centered values and help in building responsible, ethical, and value-oriented AI.10aartificial intelligence10adesign for value approach.10aeducational technology10ahuman-centered AI10aintelligent tutoring systems1 aRenz, André1 aVladova, Gergana  utimreview.ca/article/143802562nas a2200205   4500008004100000022001400041245010700055210006900162260004200231300001000273490000700283520188600290653002802176653003202204653003002236100002002266700001902286700002102305856003002326       2020                        eng d  a1927-032100aCorrelation between Entrepreneurial Orientation and Implementation of AI in Human Resources Management0 aCorrelation between Entrepreneurial Orientation and Implementati  aOttawabTalent First Networkc04/2020  a72-790 v103 aThis paper develops the concept of adopting artificial intelligence (AI) in human resources management (HRM) through a research questionnaire and reports the results of a study designed to investigate the perception of adopting and introducing AI in HRM processes. In addition, it investigates the correlation between entrepreneurship orientation (EO) and AI in HRM processes. A survey was conducted with a sample of 310 firm members in the HR Section Romande, as well as a literature review on the adoption of new technologies. The results indicate a perceived positive value of introducing AI in HRM and a correlation between the level of a company's EO and the introduction of AI in HRM. This means that the more a company is entrepreneurially oriented, the more it tends to implement or include already implemented AI projects and tools in HRM processes. The perceived value of AI in HRM was evaluated by comparing answers to research questions involving the introduction of AI in HRM tools, and expectations of widely implementing AI in the next five years. The main barrier of adopting AI in HRM appeared to be a lack of skills and training. In addition, potential features of implementing AI in HRM were identified as potential steps toward introducing AI as a new technology. Questions regarding the evaluation of EO were based on a research Colvin Slevin (1989). It is important for SMEs to invest in information technology to set the basis for further development. Owing to intensified competitive pressures and the necessity of entering global markets, SMEs are incrementally employing Information Technology (IT) to create substantial benefits. Most prior research has focused more on IT adoption in large organizations, yet when regarding the limited resources of SMEs, the IT adoption process is considerably different. (Ghobakhloo, Sabouri, Hong and Zulkifli, 2011).10aartificial intelligence10aentrepreneurial orientation10aHuman Resource Management1 aBaldegger, Rico1 aCaon, Maurizio1 aSadiku, Kreshnik  utimreview.ca/article/134801178nas a2200445   4500008004100000022001400041245003900055210003600094260004200130300000800172490000700180653000700187653002800194653001400222653001300236653003100249653002100280653002200301653001400323653001500337653001100352653001800363653002300381653001500404653001600419653003400435653002200469653002500491653001500516653001600531653001300547653003300560653001500593653001800608653001900626653001600645100001800661700002300679856003000702       2020                        eng d  a1927-032100aEditorial: Insights (January 2020)0 aEditorial Insights January 2020  aOttawabTalent First Networkc01/2020  a3-40 v1010aAI10aartificial intelligence10aB2B sales10abig data10abusiness-to-business sales10adata-based value10adigital solutions10aecosystem10aecosystems10aEthics10aGujarat State10aIndian IT industry10ainnovation10aIT clusters10aKnowledge Innovation clusters10aNetworks Analysis10aregional development10aRoboethics10aSmart robot10astrategy10aSystematic literature review10atechnology10avalue capture10avalue creation10avalue sales1 aTanev, Stoyan1 aSandstrom, Gregory  utimreview.ca/article/129801123nas a2200385   4500008004100000022001400041245004000055210003700095260004200132300000800174490000700182653003100189653003200220653002800252653002900280653001700309653003200326653002300358653002100381653002200402653001500424653002600439653001600465653001400481653002400495653002200519653003900541653003000580653001900610653001800629653001900647100001800666700002300684856003000707       2020                        eng d  a1927-032100aEditorial: Insights (November 2020)0 aEditorial Insights November 2020  aOttawabTalent First Networkc11/2020  a3-40 v1010aAI innovation and maturity10aand diaspora entrepreneurs.10aartificial intelligence10aconfidential information10acriminal law10adigitally enhanced teamwork10aeconomic espionage10aentrepreneurship10ahealth technology10aimmigrants10aInnovation management10aliving labs10amigration10aMultidisciplinarity10asituated practice10asmall and medium-sized enterprises10astakeholder participation10asustainability10atrade secrets10atransnationals1 aTanev, Stoyan1 aSandstrom, Gregory  utimreview.ca/article/140401741nas a2200229   4500008004100000022001400041245008900055210006900144260004200213300001000255490000700265520103800272653002801310653002301338653002601361653000701387653002001394653002301414100001901437700002501456856003001481       2020                        eng d  a1927-032100aThe Effect of Machine Learning on Knowledge-Intensive R&D in the Technology Industry0 aEffect of Machine Learning on KnowledgeIntensive RD in the Techn  aOttawabTalent First Networkc03/2020  a88-980 v103 aThe impact of such current state-of-the-art technology as machine learning (ML) on organizational knowledge integration is indisputable. This paper synergizes investigations of knowledge integration and ML in technologically advanced and innovative companies, in order to elucidate the value of these approaches to organizational performance. The analyses are based on the premise that, to fully benefit from the latest technological advances, entity interpretation is essential to fully define what has been learned. Findings yielded by a single case study involving one technological firm indicate that tacit and explicit knowledge integration can occur simultaneously using ML, when a data analysis method is applied to transcribe spoken words. Although the main contribution of this study stems from the greater understanding of the applicability of machine learning in organizational contexts, general recommendations for use of this analytical method to facilitate integration of tacit and explicit knowledge are also provided.10aartificial intelligence10aexplicit knowledge10aknowledge integration10aML10atacit knowledge10atechnological firm1 aViberg, Daniel1 aEslami, Mohammad, H.  utimreview.ca/article/134001906nas a2200205   4500008004100000022001400041245004200055210003900097260004200136300001000178490000700188520137700195653000701572653002801579653001101607653001501618653001601633100002101649856003001670       2020                        eng d  a1927-032100aAn Ethical Framework for Smart Robots0 aEthical Framework for Smart Robots  aOttawabTalent First Networkc01/2020  a35-440 v103 aThis article focuses on “roboethics” in the age of growing adoption of smart robots, which can now be seen as a new robotic “species”. As autonomous AI systems, they can collaborate with humans and are capable of learning from their operating environment, experiences, and human behaviour feedback in human-machine interaction. This enables smart robots to improve their performance and capabilities. This conceptual article reviews key perspectives to roboethics, as well as establishes a framework to illustrate its main ideas and features. Building on previous literature, roboethics has four major types of implications for smart robots: 1) smart robots as amoral and passive tools, 2) smart robots as recipients of ethical behavior in society, 3) smart robots as moral and active agents, and 4) smart robots as ethical impact-makers in society. The study contributes to current literature by suggesting that there are two underlying ethical and moral dimensions behind these perspectives, namely the “ethical agency of smart robots” and “object of moral judgment”, as well as what this could look like as smart robots become more widespread in society. The article concludes by suggesting how scientists and smart robot designers can benefit from a framework, discussing the limitations of the present study, and proposing avenues for future research.10aAI10aartificial intelligence10aEthics10aRoboethics10aSmart robot1 aWesterlund, Mika  utimreview.ca/article/131201878nas a2200265   4500008004100000022001400041245008500055210006900140260004200209300000900251490000700260520107400267653001801341653001601359653002801375653001801403653002601421653001901447100002201466700002401488700002801512700002101540700002101561856003001582       2020                        eng d  a1927-032100aIntegrated AI and Innovation Management: The Beginning of a Beautiful Friendship0 aIntegrated AI and Innovation Management The Beginning of a Beaut  aOttawabTalent First Networkc11/2020  a5-180 v103 aThere is a growing consensus around the transformative and innovative power of Artificial Intelligence (AI) technology. AI will transform which products are launched and how new business models will be developed to support them. Despite this, little research exists today that systematically explores how AI will change and support various aspects of innovation management. To address this question, this article proposes a holistic, multi-dimensional AI maturity model that describes the essential conditions and capabilities necessary to integrate AI into current systems, and guides organisations on their journey to AI maturity. It explores how various elements of the innovation management system can be enabled by AI at different maturity stages. Two key experimentation stages are identified, 1) an initial stage that focuses on optimisation and incremental innovation, and 2) a higher maturity stage where AI becomes an enabler of radical innovation. We conclude that AI technologies can be applied to democratise and distribute innovation across organisations.10aAI innovation10aAI maturity10aartificial intelligence10aIMS ISO 5600210aInnovation management10amaturity model1 aYams, Nina, Bozic1 aRichardson, Valerie1 aShubina, Galina, Esther1 aAlbrecht, Sandor1 aGillblad, Daniel  utimreview.ca/article/139901885nas a2200205   4500008004100000022001400041245007900055210006900134260004200203300001000245490000600255520125800261653001501519653002801534653002301562653002001585653002401605100002001629856003001649       2019                        eng d  a1927-032100aThe 3S Process: A Framework for Teaching AI Strategy in Business Education0 a3S Process A Framework for Teaching AI Strategy in Business Educ  aOttawabTalent First Networkc12/2019  a36-420 v93 aA gap has emerged in teaching artificial intelligence (AI) in business education, where a style of curriculum based on strategy is missing. This article presents a new framework, the 3S Process, as a method for teaching leaders how to strategically adopt AI within their organizations. At a high-level, the 3S Process consists of three stages (Story, Strategy, and Solution), which are described in detail in the article. Stage 1: Story in the process is inspired by the Harvard Case Method to provide context for a problem. Stage 2: Strategy uses Design Thinking to produce candidate solutions. The substage of Empathy in Design Thinking plays a crucial role to reduce bias in designing AI. Virtualization technology is a tool for students to experience hands-on learning in prototype development. Stage 3: Solution is where students advocate for their conceptual AI solution in the context of the case study. AI is a type of complex system; therefore, students should consider feedback loops and the potential for unintended biases to enter a deployed solution. The presentation of the 3S Process in this article is conceptual. Further empirical studies, including evaluations of the 3S Process in classroom settings, will be considered in the future.10a3S Process10aartificial intelligence10aBusiness Education10adesign thinking10aHarvard Case Method1 aBhalla, Navneet  utimreview.ca/article/129002715nas a2200217   4500008004100000022001400041245005400055210005400109260004200163300000900205490000600214520213200220653000702352653002902359653002802388653001202416653001502428653000802443100001602451856003002467       2019                        eng d  a1927-032100aArtificial Intelligence for Innovation in Austria0 aArtificial Intelligence for Innovation in Austria  aOttawabTalent First Networkc12/2019  a5-150 v93 aIt has been claimed that Artificial Intelligence (AI) carries enormous potential for service and product innovation. Policy makers world-wide nowadays aim to foster environments conducive for AI-based innovation. This paper addresses the current lack of empirical data for evidence-based innovation policies and the management of AI-based innovation. It focuses on &ldquo;AI and innovation management&rdquo; in addressing the question whether innovation that is based on new AI technology requires a management approach different from other forms of IT innovation. We present results from a study of Austrian companies on the degree of use and implementation of AI, and on challenges related to AI-based innovation management. This study used a keyword-list approach to define &ldquo;Artificial Intelligence&rdquo; and to find AI-based innovation projects in research databases. These projects facilitated the identification of experts from organisations developing AI-based innovation. In total, eleven experts were interviewed about their AI-based innovation activities. The results show that AI is a very fast emerging technology that is being applied in many sectors. A broad range of innovative solutions are being developed and some have already reached the market. Specific AI business models are, however, less clear and still developing. Companies are facing multiple challenges from regulation to human resources and data collection. Managing AI-based innovation will be particularly difficult for smaller enterprises, where problems are often more pronounced than in larger industries. Explicit challenges for managing AI-based innovations include the necessary attention to managing expectations and ensuring historic metadata expertise essential for many AI-based solutions. Policies to support AI-based innovation therefore should focus on human aspects. This includes increasing the availability of AI experts, but also concerns the development of new job profiles, such as experts in AI training. AI innovators also require clear AI regulation and research investments in key challenges, such as explainable AI.10aAI10aAI innovation management10aartificial intelligence10aAustria10ainnovation10aSME1 aPrem, Erich  utimreview.ca/article/128704471nas a2200289   4500008004100000022001400041245010400055210006900159260004200228300001000270490000600280520359100286653002803877653002103905653001603926653002003942653001503962653002503977653001504002653001804017653002204035100002204057700002304079700003004102700001904132856003004151       2019                        eng d  a1927-032100aConnected Health Innovation:  Data Access Challenges in the Interface of AI Companies and Hospitals0 aConnected Health Innovation Data Access Challenges in the Interf  aOttawabTalent First Networkc12/2019  a43-550 v93 aThe purpose of this paper is to explore the challenges and potential solutions regarding data access for innovation in the realm of connected health. Theoretically, our study combines insights from data management and innovation network orchestration studies, taking thereby a new approach into issues that have emerged in these research streams. Empirically, we study these issues in the context of a development endeavor involving an AI-driven surgery journey solution in collaboration with hospitals and companies. Our study indicates that the challenges and solutions in data access can be categorised according to the level where they emerge: individual, organisational, and institutional. Depending on the level, the challenges require solutions to be searched from different categories. While solutions are generally still scarce, organizational level solutions seem The purpose of this paper is to explore the challenges and potential solutions regarding data access for innovation in the realm of connected health. Theoretically, our study combines insights from data management and innovation network orchestration studies, taking thereby a new approach into issues that have emerged in these research streams. Empirically, we study these issues in the context of a development endeavor involving an AI-driven surgery journey solution in collaboration with hospitals and companies. Our study indicates that the challenges and solutions in data access can be categorised according to the level where they emerge: individual, organisational, and institutional. Depending on the level, the challenges require solutions to be searched from different categories. While solutions are generally still scarce, organizational level solutions seem to hold wide-ranging potential in addressing many challenges. By discussing these dynamics, this paper provides new knowledge for academics and practitioners on the challenges and solutions for data access and management in networked contexts. The greatest challenges among healthcare providers and health technology companies lay on uncertainties and interpretations concerning regulation, data strategy, and guidelines. Creating guidelines for data use and access in a hospital can be a first step to creating connected health innovations in collaboration with AI companies. For their part, these companies need to put effort into gaining in-depth knowledge and understanding of the processes and standards in healthcare context. Our paper is one of the first to combine data management and innovation network orchestration literatures, and to provide empirical evidence on data access related issues in this setting.to hold wide-ranging potential in addressing many challenges. By discussing these dynamics, this paper provides new knowledge for academics and practitioners on the challenges and solutions for data access and management in networked contexts. The greatest challenges among healthcare providers and health technology companies lay on uncertainties and interpretations concerning regulation, data strategy, and guidelines. Creating guidelines for data use and access in a hospital can be a first step to creating connected health innovations in collaboration with AI companies. For their part, these companies need to put effort into gaining in-depth knowledge and understanding of the processes and standards in healthcare context. Our paper is one of the first to combine data management and innovation network orchestration literatures, and to provide empirical evidence on data access related issues in this setting.10aartificial intelligence10aconnected health10aData access10adata management10agovernance10ainformation mobility10ainnovation10aorchestration10apatient- centered1 aKemppainen, Laura1 aPikkarainen, Minna1 aHurmelinna-Laukkanen, Pia1 aReponen, Jarmo  utimreview.ca/article/129101222nas a2200445   4500008004100000022001400041245004000055210003700095260004200132300000800174490000600182653002800188653002600216653001800242653001800260653001300278653001700291653001900308653003100327653003100358653002100389653003100410653001400441653001500455653003500470653001500505653002600520653001400546653001500560653002200575653002000597653001400617653000900631653001300640653001700653653003500670100001800705700002300723856003000746       2019                        eng d  a1927-032100aEditorial: Insights (November 2019)0 aEditorial Insights November 2019  aOttawabTalent First Networkc11/2019  a3-40 v910aartificial intelligence10acompetitive advantage10acybersecurity10adeep learning10aDeepfake10adesign rules10adigitalization10aentrepreneurial ecosystems10aentrepreneurial university10aentrepreneurship10aentrepreneurship education10afake news10ainnovation10ainternational entrepreneurship10aleadership10aLearning Capabilities10amarketing10amotivation10anew venture teams10aquadruple helix10asanctions10aSMEs10ateamwork10atriple helix10auniversity business incubation1 aTanev, Stoyan1 aSandstrom, Gregory  utimreview.ca/article/127801856nas a2200205   4500008004100000022001400041245005100055210004600106260004200152300001000194490000600204520129800210653002801508653001801536653001801554653001301572653001401585100002101599856003001620       2019                        eng d  a1927-032100aThe Emergence of Deepfake Technology: A Review0 aEmergence of Deepfake Technology A Review  aOttawabTalent First Networkc11/2019  a40-530 v93 aNovel digital technologies make it increasingly difficult to distinguish between real and fake media. One of the most recent developments contributing to the problem is the emergence of deepfakes which are hyper-realistic videos that apply artificial intelligence (AI) to depict someone say and do things that never happened. Coupled with the reach and speed of social media, convincing deepfakes can quickly reach millions of people and have negative impacts on our society. While scholarly research on the topic is sparse, this study analyzes 84 publicly available online news articles to examine what deepfakes are and who produces them, what the benefits and threats of deepfake technology are, what examples of deepfakes there are, and how to combat deepfakes. The results suggest that while deepfakes are a significant threat to our society, political system and business, they can be combatted via legislation and regulation, corporate policies and voluntary action, education and training, as well as the development of technology for deepfake detection, content authentication, and deepfake prevention. The study provides a comprehensive review of deepfakes and provides cybersecurity and AI entrepreneurs with business opportunities in fighting against media forgeries and fake news.10aartificial intelligence10acybersecurity10adeep learning10aDeepfake10afake news1 aWesterlund, Mika  utimreview.ca/article/128202525nas a2200265   4500008004100000022001400041245011500055210006900170260004200239300001000281490000600291520170600297653002802003653002602031653000902057653001702066653002702083100002002110700001202130700001902142700001702161700002602178700001802204856003702222       2018                        eng d  a1927-032100aConvergent Innovation in Food through Big Data and Artificial Intelligence for Societal-Scale Inclusive Growth0 aConvergent Innovation in Food through Big Data and Artificial In  aOttawabTalent First Networkc02/2018  a49-650 v83 aInclusive innovation has not yet reached societal scale due to a well-entrenched divide between wealth creation and social equity. Taking food as the initial test bed, we have proposed the convergent innovation model to address such challenges still facing 21st century society by bridging sectors and disciplines around an integrated goal on both sides of the social-economic divide for innovations that target wealth creation with an upfront consideration of its externalities. The convergent innovation model is empowered by two key enablers that integrate an advanced digital infrastructure with leading scientific knowledge on the drivers of human behaviour in varying contexts. This article discusses the structure, methods, and development of an artificial intelligence platform to support convergent innovation. Insights are gathered on consumer sentiment and behavioural drivers through the analysis of user-generated content on social media platforms. Empirical results show that user discussions related to marketing, consequences, and occasions are positive. Further regression modelling finds that economic consequences are a strong predictor of consumer global sentiment, but are also sensitive to both the actual price and economic awareness. This finding has important implications for inclusive growth and further emphasizes the need for affordable and accessible foods, as well as for consumer education. Challenges and opportunities inspired by the research results are discussed to inform the design, marketing, and delivery of convergent innovation products and services, while also contributing to dimensions of inclusion and economic performance for equitable health and wealth.10aartificial intelligence10aconvergent innovation10afood10asocial media10auser-generated content1 aDubé, Laurette1 aDu, Pan1 aMcRae, Cameron1 aSharma, Neha1 aJayaraman, Srinivasan1 aNie, Jian-Yun  uhttp://timreview.ca/article/113902499nas 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/115802261nas a2200265   4500008004100000022001400041245012800055210006900183260004200252300001000294490000600304520139900310653001401709653002801723653002601751653002101777653002401798653003001822653002901852100001801881700001801899700002301917700001801940856003701958       2017                        eng d  a1927-032100aUsing Artificial Intelligence and Web Media Data to Evaluate the Growth Potential of Companies in Emerging Industry Sectors0 aUsing Artificial Intelligence and Web Media Data to Evaluate the  aOttawabTalent First Networkc06/2017  a25-370 v73 aIn this article, we describe our efforts to adapt and validate a web search and analytics tool – the Gnowit Cognitive Insight Engine – to evaluate the growth and competitive potential of new technology startups and existing firms in the newly emerging precision medicine sector. The results are based on two different search ontologies and two different samples of firms. The first sample includes established drug companies operating in the precision medicine field and was used to estimate the relationship between the firms’ innovativeness and the extent of online discussions focusing on their potential growth. The second sample includes new technology firms in the same sector. The firms in the second sample were used as test cases to determine whether their growth-related web search scores would relate to the degree of their innovativeness. The second part of the study applied the same methodology to the real-time monitoring of the firms’ competitive actions. In our findings, we see that our methodology reveals a moderate degree of correlation between the Insight Engine’s algorithmically computed relevance scores and independent measures of innovation potential. The existence of such correlations invites future work in attempting to analyze company growth potential using techniques founded in web content scraping, natural language processing, and machine learning.10aanalytics10aartificial intelligence10abusiness intelligence10aentrepreneurship10aonline textual data10aprecision medicine sector10astartup growth potential1 aDroll, Andrew1 aKhan, Shahzad1 aEkhlas, Ehsanullah1 aTanev, Stoyan  uhttp://timreview.ca/article/1082