Conference Agenda

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Session Overview
Session
19-PM1-07: ST1.1 - How Artificial Intelligence is Reshaping Business Models
Time:
Wednesday, 19/Jun/2019:
1:00pm - 2:30pm

Session Chair: Gianvito Lanzolla, Cass Business School
Session Chair: Umberto Panniello, Politecnico di Bari
Location: Amphi Curie

Session Abstract

Artificial intelligence (AI) is driving changes of business and organizational activities, as well as of the underlying processes and competencies (van der Meulen, 2018), thus attracting the interest from both scholars and practitioners due to its huge impact on processes, products, services, and business models (e.g., Bughin et al., 2017; Dean 2014).

AI refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. Among the various types of AI solutions, reactive machines and limited memory technologies can recognize objects and make predictions, with the latter using past experiences to inform future decisions (some of the decision-making functions in autonomous vehicles have been designed in this way), while self-awareness AI systems have a sense of self, consciousness and can understand their current state using this information to infer what others are feeling.

The effect of AI technologies is particularly relevant when referred to the business model unit of analysis and, in particular, on the development of new business models or on the changes introduced in existing ones. Recently, the literature seems to converge on defining business model as the “design or architecture of the value creation delivery, and capture mechanisms” of a firm (Teece, 2010). As long with refining the construct of business model and its theoretical and practical relevance (Lanzolla and Markides, 2017), both research and practice realized that business models are subject to innovation in response to changes in their competitive and industrial environment (Chesbrough, 2007; Lindgardt et al., 2009). Innovating a business model does not mean necessarily to introduce a new product, service or technology (Lindgardt et al., 2009), but rather it calls to innovate at least one of its elements, such as the value proposition or the revenue model, thus providing the firm with a new value source that can be used to create a sustainable competitive advantage (Zott and Amit, 2010). Technological change has often been associated with business model innovation and nowadays we have observed a variety of new business models patterns based on the exploitation of AI applications in different industries (e.g., IBM Watson is revolutionizing different sectors, offering novel business opportunities in healthcare, education, weather forecast, fashion, and tax preparation).

We aim at discussing about how AI systems are reshaping business models’ mechanisms, approaches and founding elements (such as organization, infrastructures, customers or value propositions). Specifically, questions include, but are not limited to:

- main managerial and organizational implications related to the adoption of AI in existing business models;

- risks and weaknesses of the adoption of AI in existing business models;

- types and archetypes of AI based business models;

- differences between AI based vs. traditional business models;

- boundary conditions enabling the adoption of AI solutions in existing business models;

- policy-based initiatives and AI based business models;

- the performance implications of adopting AI solutions in incumbents’ or new entrants’ business models;

- antecedents and consequences of the adoption of AI solutions in business models;

- characteristics of the AI solutions that mostly affect business models performance;

- governance mechanisms of business models using AI;

- resources and capabilities underlying the introduction and adoption of AI solutions in business models;

- emerging trade-offs going along with the adoption of AI solutions in business models.


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Presentations

Managing Open Innovation and IP in AI based business models

Martin A. Bader, Christian Stummeyer

Technische Hochschule Ingolstadt, Germany

Context

The research focuses on the formal and informal protection strategies of AI based business models, taking into account the challenges given by an open innovation approach.

Literature

Bader MA (2007) ‘Managing intellectual property in a collaborative environment: learning from IBM’, Int. J. Intellectual Property Management, Vol. 1, No. 3, pp.206–225.

Bonakdar A, Frankenberger K, Bader MA and Gassmann O (2017) Capturing value from business models: the role of formal and informal protection strategies, Int. J. Technology Management, Vol. 73, No. 4, pp.151–175.

Brem A, Maier M and Wimschneider, C (2016) Competitive advantage through innovation: the case of Nespresso, European Journal of Innovation Management, Vol. 19 No. 1, 2016, pp. 133-148.

EPO (2018) Patenting Artificial Intelligence. Conference summary, Munich: European Patent Office.

Stone P et al (2016) ‘Artificial Intelligence and Life in 2030.’ One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed: September 6, 2016.

WIPO (2019) WIPO Technology Trends 2019: Artificial Intelligence, Geneva: World Intellectual Property Organization.

Literature Gap

Existing literature already distinguishes between formal and informal

protection strategies and have tried to understand their influence on value

capture for different business models. However, their application to the new field of AI is still a white spot.

Research Questions

How can formal and informal protection strategies be applied in the field of AI based business models, taking into account an open innovation environment.

Methodology

Qualitative research based on own empirical resources and analysis of empirical reports

Empirical Material

-

Results

How to manage and balance between open source approaches and exclusivity by IP protection strategies to foster efficient innovativity and to gain and keep comparative advantages.

Contribution to Scholarship

Extension of know formal and informal IP protection strategies to the field of AI from a R&D managerial's view.

Contribution to Practice

Extension of know formal and informal IP protection strategies to the field of AI from a practitioners' view.

Fitness

Extension of know formal and informal IP protection strategies to the field of AI being relevant not only to corporations but also to SMEs and startups, reaching out for funding, investments and exit scenarios.

Bibliography

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Bader MA (2007) Managing intellectual property in a collaborative environment: learning from IBM, Int. J. Intellectual Property Management, Vol. 1, No. 3, pp.206–225.

Bader MA (2008) Managing intellectual property in the financial services industry sector: Learning from Swiss Re, Technovation, Vol. 28, pp.196–207.

Barnes J (2017) The AI-First Business Model https://www.elementai.com/news/2017/the-ai-first-business-model. Accessed 01 March 2019.

Bonakdar A, Frankenberger K, Bader MA and Gassmann O (2017) Capturing value from business models: the role of formal and informal protection strategies, Int. J. Technology Management, Vol. 73, No. 4, pp.151–175.

Brem A, Maier M and Wimschneider C (2016) Competitive advantage through innovation: the case of Nespresso, European Journal of Innovation Management, Vol. 19 No. 1, 2016, pp. 133-148.

Buxmann P, Schmidt H (2019) Grundlagen der Künstlichen Intelligenz und des Maschinellen Lernens, in: Buxmann P, Schmidt H: Künstliche Intelligenz – Mit Algorithmen zum wirtschaftlichen Erfolg, Berlin.

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Chesbrough H (2007) Business model innovation: it’s not just about technology anymore, Strategy & Leadership, Vol. 35, No. 6, pp.12–17.

Chesbrough H and Rosenbloom RS (2002) The role of the business model in capturing value from innovation: evidence from Xerox Corporation’s technology spin-off companies, Industrial and Corporate Change, Vol. 11, No. 3, pp.529–555.

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EC (2018) Artificial Intelligence: European Strategy. https://ec.europa.eu/jrc/sites/jrcsh/files/23112018-artificial_intelligence-huet_en.pdf. Accessed 01 March 2019.

EconSight (2019) Künstliche Intelligenz. Globale Entwicklungen, Anwendungsgebiete, Innovationstreiber und Weltklasseforschung, Basel: EconSight.

EPO (2013) Patents for software? European law and practice, Munich: European Patent Office.

EPO (2017) Patents and the Fourth Industrial Revolution. The inventions behind digital transformation, Munich: European Patent Office.

EPO (2018a) Patenting Artificial Intelligence. Conference summary, Munich: European Patent Office.

EPO (2018b) Guidelines for Examination: artificial intelligence and machine learning (G-II 3.3.1), Munich: European Patent Office.

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Gallié EP and Legros D (2012) ‘French firms’ strategies for protecting their intellectual property’, Research Policy, Vol. 41, No. 4, pp.780–794.

Gassmann O and Bader MA (2017) Patentmanagement: Innovationen erfolgreich nutzen und schützen, 4th edition, Berlin.

Gassmann O, Frankenberger K and Csik M (2015) The Business Model Navigator: 55 Models That Will Revolutionise Your Business, Harlow, UK.

Getsch, P (2018) Künstliche Intelligenz für Sales, Marketing und Service, Wiesbaden.

Grosz BJ and Stone P (2018) A Century Long Commitment to Assessing Artificial Intelligence and Its Impact on Society. December 2018. Communications of the ACM (CACM).

Hall BH and Ziedonis RH (2001) The patent paradox revisited: an empirical study of patenting in the US semiconductor industry 1979–1995, RAND Journal of Economics, Vol. 32, No. 1, pp.101–128.

Harabi N (1995) Appropriability of technical innovations an empirical analysis, Research Policy, Vol. 24, No. 6, pp.981–992.

Kerns J (2017) What’s the Difference Between Weak and Strong AI? https://www.machinedesign.com/robotics/what-s-difference-between-weak-and-strong-ai. Accessed 01 March 2019.

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Lepak DP, Smith KG and Taylor MS (2007) Value creation and value capture: a multilevel perspective, Academy of Management Review, Vol. 32, No. 1, pp.180–194.

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Mainzer K (2019) Künstliche Intelligenz – Wann übernehmen die Maschinen, Berlin 2019

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The user is allowed to reproduce, distribute, adapt, translate and publicly perform this publication, including for commercial purposes, without explicit permission, provided that the content is accompanied by an acknowledgement that WIPO is the source and that it is clearly indicated if changes were made to the original content.

Disclaimer: “The Secretariat of WIPO assumes no liability or responsibility with regard to the transformation or translation of the original content.”

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trade_secrets.htm. Accessed 01 March 2019.

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Mapping Artificial Intelligence as a Research Topic in Management Literature

Johannes Dahlke, Bernd Ebersberger

University of Hohenheim, Germany

Context

Academic publications on AI have increased seven-fold since 1996, as well as there was a five-fold increase of AI patents issued from 2004 to 2014 (Shoham et al., 2018). This parallel development of academia and businesses raises the question how management science is discussing the implications of AI.

Literature

Some publications have already examined pressing issues such as AI’s effects on human labor (see Huang & Rust, 2018; Fleming, 2019) and the way humans and AI may work together (see Jarrahi, 2018; Shukla et al., 2017). Yet, other questions remain under-explored. Cockburn et al. (2017) ask how AI will influence the productivity of R&D teams, how data pooling activities might create monopolistic structures or how the democratization of AI as a service might level the playing field between smaller companies and multinational corporations.

Literature Gap

Given this dynamic field of research, we aim to contribute a more complete picture of the discourse about AI specific to management science in order to help guide future research on this topic.

Research Questions

We ask how the discourse about AI in management science be characterized? To find an answer, we ask what is being talked about in connection to A, how the discourse evolved over time and who the driving forces behind the discourse are?

Methodology

The method of science mapping has been shown to uncover (thematic) academic networks and trace their evolution through a spatial display of interrelations between publications (see Herrera-Viedma et al., 2018; B¨orner et al., 2005; Small, 1999). Following Herrera-Viedma et al. (2018, p.1278), a nine step procedure is conducted: (1) Data retrieval from Scopus and Web of Science (WoS); 1 (2) data cleaning; (3) selection of units of analyses; (4) building of bibliographic network; (5) normalization of data; (6) employing clustering algorithms to layout academic networks (over time); (7) network analysis; (8) network visualization; (9) interpretation of results.

Empirical Material

Our bibliometric analysis is based on a distinct dataset of peer-reviewed AI publications in the area of management science from 1996 to 2019. It comprises 478 publications (Scopus) with a young median publication year of 2013. The U.S. (authors’ affiliations) is leading all other countries in the amount of AI research being carried out in management science (257 papers) and, by citation paths, constitutes the biggest influence on most of the top 20 countries. Some European countries (e.g. Germany, Netherlands) also exhibit a long history of research. Chinese management scholars have only started to participate in the international discourse after 2015. Emerging nations include Romania, Iran, Finland and Lithuania. The top three journals by number of AI publications in management science include Journal of the Operational Research Society, Knowledge Based Systems and Decision Support Systems.

Results

First, against the backdrop of technological innovation systems (TIS) (Carlsson & Stankiewicz, 1991; Bergek et al., 2008), a higher focus on interdisciplinary publications that are combining a technical and social perspective might be helpful to bridge the gap between academic clusters in management science. Second, the dialogue regarding the social implications of AI still needs to receive more attention by management scholars. Especially, it seems like AI is still widely associated with automated robots and less understood as the general purpose technology it promises to become as outlined by Cockburn et al. (2017). Third, the academic discourse is spilling over to smaller nations. While China has only recently begun to internationally publish its scholars’ efforts, it might constitute one of the most important nations to drive future academic progress.

Contribution to Scholarship

Given this dynamic field of research, we aim to contribute a more complete picture of the discourse about AI specific to management science in order to help guide future research on this topic. We identify which apparent gaps within the fragmentized discourse about AI in management science need to bridged in order to reach a more inclusive discussion. Specifically, we show that a convergence of technical and implicative arguments in literature is needed. These topical chasms are also evident by linking journals and world regions to certain types of arguments.

Contribution to Practice

We connect specific business fields (departments) to certain clusters in the academic discourse about AI. Through this, it becomes apparent, that advances in AI are mainly concentrated on a limited spectrum of business activities (such as finance, marketing and logistics). However, the potential of AI as a general purpose technology with innovative and strategic capabilities is widely ignored.

Fitness

We discuss the very nature of the discourse about AI in management science and how the dialogue can be enhanced by bridging topical clusters in literature. This macro-perspective is needed in order to guide future research on AI towards multidisciplinarity and increased consciousness of social implications.

Bibliography

Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., & Rickne, A. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37(3), 407–429. doi: doi.org/10.1016/ j.respol.2007.12.003

Börner, K., Chen, C., & Boyack, K. W. (2005). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179–255. doi: doi.org/10.1002/aris.1440370106

Carlsson, B., & Stankiewicz, R. (1991). On the nature, function and composition of technological systems. Journal of Evolutionary Economics, 1(2), 93–118. doi: doi.org/10.1007/BF01224915

Cockburn, I. M., Henderson, R., & Stern, S. (2017). The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis. In Nber conference on research issues in artificial intelligence (pp. 1–38). Toronto. Retrieved from https://www.nber.org/chapters/c14006

Fleming, P. (2019). Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job. Organization Studies, 40(1), 23–38. doi: doi.org/10.1177/0170840618765568

Herrera-Viedma, E., Moral-Munoz, J. A., Mart´ınez, M. ´A., Guti´errez-Salcedo, M., & Cobo, M. J. (2018). Some bibliometric procedures for analyzing and evaluating research fields. Applied Intelligence, 48(5), 1275–1287. doi: doi.org/10.1007/s10489-017-1105-y

Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. doi: doi.org/10.1177/1094670517752459

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. doi: doi.org/10.1016/j.bushor.2018.03.007

Shoham, Y., Perrault, R., Brynjolfsson, E., Clark, J., Manyika, J., Niebles, J. C., . . . Bauer, Z. (2018, dec). The AI Index 2018 Annual Report (Tech. Rep.). Stanford, CA: AI Index Steering Committee, Human-Centered AI Initiative, Stanford University. Retrieved from http://cdn.aiindex.org/2018/AIIndex2018AnnualReport.pdf

Shukla, P., James, H., Alter, A., & Lavieri, D. (2017). Machine reengineering: Robots and people working smarter together. Strategy and Leadership, 45(6), 50–54. doi: doi.org/10.1108/SL-09-2017-0089

Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799–813. doi: doi.org/10.1002/(SICI)1097-4571(1999)50:9h799::AID-ASI9i3.0.CO;2-G

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. doi: doi.org/10.1007/s11192-009-0146-3

van Eck, N. J., & Waltman, L. (2014). CitNetExplorer: A new software tool for analyzing and visualizing citation networks. Journal of Informetrics, 8(4), 802–823. doi: doi.org/doi.org/10.1016/j.joi.2014.07.006



What is the user value of AI? A taxonomy based on AI startups in France in 2019

Caroline Nowacki, Angéla Martin

Altran, France

Context

AI can industrialize cognitive tasks, which opens up a wide range of possibilities for companies to grow. A survey of 3000 executives showed that 30% of early AI adopters had achieved revenue increases by using AI to gain market share or expand their services (Bughin et al., 2017).

Literature

However, the fit of the technology in the company’s business model is essential. The technology comes second to a strong customer value proposition (Johnson, 2018).

To build this customer value proposition, designers identify the customer’s activities, pains and gains. (Norman, 2013; Farrel, 2017). Human-centered design ensures that end user’s needs are met by the solution developed by the company (Norman, 2013).

Starting from the customers’ needs is also categorized as a case of “market pull”. However, technology companies sometimes start with the technology and search for the problems it can answer, a case categorized as a “technology push” (Osterwalder et al., 2014). Artificial intelligence seems to be in the second case of “technology push”, with famous applications such as IBM Watson, being developed to demonstrate technological superiority, and not to respond to customers' needs. This results in AI typologies focused on technical performance and not on users' needs.

Literature Gap

There is no extensive cross-company study that can support our assessment that AI-based start-ups follow a “technology push” strategy. Besides, we lack an understanding of what AI product-solution fit looks like, and if AI is particularly adept at answering a typology of customer needs.

Research Questions

What are the types of customer value propositions of AI-based start-ups in France?

How do these types relate to industries and types of AI technology?

Do French AI-based start-ups use a market-pull or a technology-push strategy in their industries?

Methodology

We systematically review customer value propositions of AI-based start-ups in France, and match them with a list of core added values from the literature. New core added values might be added based on a qualitative bottom-up assessment of the CVP. Correlation between those core added values and start-ups' industry and AI technology will also be tested. Finally, industry CVP will be compared to industry trending needs, as identified in industry-wide trend reports and confirmed by interviews with specialists.

Empirical Material

To answer these questions, we characterize the customer value proposition (CVP) of 335 AI-based start-ups in France using the database of the “France is AI” program.

The first step of the analysis is to describe succinctly the CVP of each start-up.

The second step of the analysis matches the CVP with “core added values” for the customers. There is no existing list of “core added values” for the customer. We therefore built a preliminary list based on the literature as follows (Rzepka & Berger, 2018):

- Instantaneity

- Choice

- Personalization

- Entertainment

- Ease (lower cognitive load)

The list coming from the literature will be improved and completed following a bottom-up approach. Several researchers will review the classification to reduce bias.

The third step compares the “core added values” with the characteristics of the start-ups studied, notably their industry and the type AI technology they use, to find potential correlations.

Finally, the customer value propositions of start-ups are compared to the trending needs of their industries, to assess the degree to which strategies of techno-push and market-pull are used. Trending needs will come from industry reports and be confirmed through interviews with specialists.

Results

The analysis described above will lead to a typology of “core added value” that characterizes the customer value propositions of AI-based start-ups in France. In addition, the distribution of these “core-added values” among French AI start-ups, and per industry and type of AI technology will be available. Finally, we’ll present the degree to which start-ups in each industry respond to a market-pull or create a techno-push.

Contribution to Scholarship

This study will contribute to better understand what AI can bring in terms of value proposition to the customer and propose a unique list of “core added value” that AI-based start-ups can bring. It will propose a human-centered approach instead of a functional or technical one to the analysis of AI. This approach is in line with calls in the scholarship on business models and innovation to anticipate customers’ unmet needs as a key requirement to succeed in uncertain environments.

Contribution to Practice

Starting from the customers’ needs and matching AI's added value with those will contribute to make AI more relatable to customers, which is particularly needed given the fears that customers have expressed in surveys.

Second, the “core added value” can be a guide for start-ups interested in comparing themselves to competitors or thinking about what their unique value is.

Finally, the analysis of the fit between start-ups’ value proposition and trends in industries’ needs will give an idea of the type of markets in which AI-based start-ups operate in France.

Fitness

By bringing the perspective of the customers back into the study of what AI can bring to companies/start-ups, we match this year’s conference theme of bridging research, industry and society.

Bibliography

Bughin J., McCarthy B., Chui M. 2017. A Survey of 3,000 Executives Reveals How Businesses Succeed with AI. Harvard Business Review, https://hbr.org/2017/08/a-survey-of-3000-executives-reveals-how-business....

Chao, C.-Y., Chang, T.-C., Wu, H.-C., Lin, Y.-S., and Chen, P.-C. 2016. "The Interrelationship between Intelligent Agents’ Characteristics and Users’ Intention in a Search Engine by Making Beliefs and Perceived Risks Mediators," Computers in Human Behavior (64), pp. 117-125

Farrell, S. 2017. “UX Research Cheat Sheet”, available at: https://www.nngroup.com/articles/ux-research-cheat-sheet/

Johnson, M. 2018. Digital Growth Depends More on Business Models than Technology. Harvard Business Review. Available at: https://hbr.org/2018/12/digital-growth-depends-more-on-business-models-than-technology

Norman, Don. 2013. “The Design of Everyday Things”. Basic Books: 217–219

Osterwalder, A. ; Pigneur, Y. ; Bernarda, G. ; Smith, A. 2014. Value Proposition Design: How to Create Products and Services Customers Want. John Wiley & Sons

Rzepka, C., Berger, B. 2018. "User Interaction with AI-enabled Systems : A Systematic Review of IS Research", Thirty Ninth International Conference on Information Systems, San Francisco.



 
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