Artificial Intelligence as a Growth Engine for Health Care Startups: Emerging Business Models

Garbuio, Massimo; Lin, Nidthida

doi:10.1177/0008125618811931

Cited by 193 publications

(107 citation statements)

References 2 publications

Supporting

Mentioning

102

Contrasting

Unclassified

Order By: Relevance

“…Future study also needs to exemplify different types of emerging business model archetypes. Different delivery models such as platform, SaaS (Software as a Service), and PaaS (Platform as a Service) can be discussed [10,53] in the context of AI-based business model innovation. Quantitative empirical studies are subsequently recommended, taking more organizations into account and providing another dimension of insights.…”

Section: Resultsmentioning

confidence: 99%

“…Regretfully, however, studies investigating the direct impact of emerging technologies on business model innovation are few and far between. A few studies have only recently explored AI technology meticulously in consideration of business model innovation (e.g., [10,11]). For instance, Garbuio and Lin [10] identified seven emerging business model archetypes in their timely and critical analysis of AI-driven health care startups.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence

Lee

Suh

Roy

et al. 2019

Journal of Open Innovation: Technology, Market, and Complexity

231

View full text Add to dashboard Cite

Artificial intelligence (AI) has been altering industries as evidenced by Airbnb, Uber and other companies that have embraced its use to implement innovative new business models. Yet we may not fully understand how this emerging and rapidly advancing technology influences business model innovation. While many companies are being made vulnerable to new competitors equipped with AI technology, this study attempts to focus on the proactive side of the use of AI technology to drive business model innovation. Describing AI technology as the catalyst of business model innovation, this study sheds light on contingent factors shaping business model innovation initiated by the emerging technology. This study first provides a brief overview of AI, current issues being tackled in developing AI and explains how it transforms business models. Our case study of two companies that innovated their business models using AI shows its potential impact. We also discuss how executives can create an innovative AI-based culture, which rephrases the process of AI-based business model innovation. Companies that successfully capitalize on AI can create disruptive innovation through their new business models and processes, enabling them to potentially transform the global competitive landscape.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence

Lee

Suh

Roy

et al. 2019

Journal of Open Innovation: Technology, Market, and Complexity

231

View full text Add to dashboard Cite

show abstract

“…10) Analyses of populations that are underrepresented in existing entrepreneurship research (e.g., populations from outside of Western, educated, industrialized, rich, and democratic countries (WEIRD) or superstar entrepreneurs or entrepreneurial personalities in political leadership; . 11) Business model processes relevant for entrepreneurial organizations and growth (Chen et al 2017;Garbuio and Lin 2019;Hartmann et al 2016). 12) Stress processes, well-being, health, and social behavior of entrepreneurial people and teams (e.g., using smartphone methods; Harari et al 2017;Uy et al 2010).…”

Section: ) a Practical Introduction Into Relevant Methods Technologmentioning

confidence: 99%

Artificial intelligence and big data in entrepreneurship: a new era has begun

Obschonka¹,

2019

View full text Add to dashboard Cite

While the disruptive potential of artificial intelligence (AI) and Big Data has been receiving growing attention and concern in a variety of research and application fields over the last few years, it has not received much scrutiny in contemporary entrepreneurship research so far. Here we present some reflections and a collection of papers on the role of AI and Big Data for this emerging area in the study and application of entrepreneurship research. While being mindful of the potentially overwhelming nature of the rapid progress in machine intelligence and other Big Data technologies for contemporary structures in entrepreneurship research, we put an emphasis on the reciprocity of the co-evolving fields of entrepreneurship research and practice. How can AI and Big Data contribute to a productive transformation of the research field and the real-world phenomena (e.g., "smart entrepreneurship")? We also discuss, however, ethical issues as well as challenges around a potential contradiction between entrepreneurial uncertainty and rule-driven AI rationality. The editorial gives researchers and practitioners orientation and showcases avenues and examples for concrete research in this field. At the same time, however, it is not unlikely that we will encounter unforeseeable and currently inexplicable developments in the field soon. We call on entrepreneurship scholars, educators, and practitioners to proactively prepare for future scenarios.

show abstract

“…It can be seen that the feelings expressed in UGC can be analyzed with Neural Connection Analysis of groups of interacting users; Textual Analysis, which is the analysis of words and their sentiments to determine key factors; Time, which is the analysis of the time over which this UGT takes place, and the analysis of patterns of sentiments in social networks; Hashtags, URLs, or Mentions, which is the analysis of labels of user groups; Topic, which is the analysis of sentiments of content categories with the same topic; and finally, Classification of Information, which is a feature of Sentiment Analysis that uses information keywords with the sample [6,32,33]. Table 2 shows the main characteristics of Sentiment Analysis.…”

Section: Sentiment Analysis With Social Network Analysismentioning

confidence: 99%

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

2019

View full text Add to dashboard Cite

The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.

show abstract

Artificial Intelligence as a Growth Engine for Health Care Startups: Emerging Business Models

Cited by 193 publications

References 2 publications

Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence

Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence

Artificial intelligence and big data in entrepreneurship: a new era has begun

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

Contact Info

Product

Resources

About