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Revising Business Model Innovation: Towards a value process framework for AI-based Offerings
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Integrated Product Development. Piab.ORCID iD: 0000-0003-3252-7299
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Advances over the last few decades in digital technologies in general and artificial intelligence (AI) technology in particular have transformed many industries. There are many successful AI use cases in industry. However, the adoption rate of AI technology by incumbent traditional industrial manufacturing firms in their offerings remains far too low compared with the big claims made about the contribution of AI to the world economy. Incumbents’ current view of AI as merely a technology resource with which to increase automation and efficiency is far too narrow and needs to be changed. Instead, AI can be a dynamic capability giving competitive advantage to incumbents if they explore AI’s value implications in their business models (BMs). Furthermore, current value discussions both generally and within BMs are too individualistic, transactional, and operational and lack the process orientation required for a more comprehensive understanding of the value potential of AI, leading to business model innovation (BMI) for incumbents.

With the overall ambition to support AI incorporation into incumbents’ offerings, this thesis proposes a process-based value framework for AI-driven BMs. For this purpose, this thesis research has produced five studies, including various methods, to understand the value processes within BMs in light of digitalization. Owing to the complex nature of the phenomenon under study, the methods used in the studies include quasi-experiments, case studies, semi-structured interviews, in-depth interviews, card sorting, longitudinal research, quantitative survey analysis, literature review, and literature mapping as required and relevant for the different studies.

The studies highlight that digital and AI technologies could potentially create new values (e.g., self-learning and intelligent offerings) for different stakeholders, provide new mechanisms for value delivery through digital servitization, and enable previously impossible value-capture techniques such as value-based dynamic pricing within BMs. It can be observed that value in digital BMI is constantly changing and hence needs to be focused on explicitly within BMs and introduced as a value-identification process. Furthermore, AI entails new value process relationships in which value creation and delivery are much more integrated, dynamic, and personalized per customer, highlighting the required emphasis on hyper-personalization.

This thesis analyzes the challenges and opportunities AI has provided within BMI in order to propose a modified value process framework for AI-enabled BMs, including value identification, value manifestation, and value capture, compared with the commonly proposed BM value processes of value creation, value delivery, and value capture. The proposed view consolidates value processes, including the individual, relational, and transactional values required by AI-based BMs, rather than just the transactional view of value covered through standard BM value processes, a view that highlights only the operational aspect of value within BMs.

Furthermore, this thesis discusses how the current approach to AI within BMI is more from a resource perspective and therefore cannot realize the full potential of AI technology. The thesis elaborates on how incumbents can utilize AI technology within BMI to create a competitive advantage by concentrating on the process view of value through the proposed new framework for handling highlighted opportunities and challenges. The new role of ecosystem stakeholders as innovation partners within BMI utilizing data/AI-driven capabilities and organizational value changes is discussed. Finally, this thesis highlights implications for BMI theory in terms of new value processes and implications for practice in terms of the BMI framework, concluding by presenting challenges and opportunities arising from the usage of AI within BMI by incumbents.

Abstract [sv]

Framsteg under de senaste decennierna inom digital teknik i allmänhet och artificiell intelligens (AI) i synnerhet har förändrat många industrier. Det finns många framgångsrika AI-användningsfall inom industrin. Men antagandet av AI-teknik av etablerade traditionella industriella tillverkningsföretag i sina erbjudanden är fortfarande alldeles för låg jämfört med de stora påståendena om AI:s bidrag till världsekonomin. De dominerande företagens nuvarande syn på AI som bara en teknisk resurs för att öka automatiseringen och effektiviteten är alldeles för snäv och måste ändras. Istället kan AI vara en dynamisk förmåga som ger konkurrensfördelar till dominerande operatörer om de utforskar AI:s värdekonsekvenser i sina affärsmodeller (BM). Dessutom är aktuella värdediskussioner både generellt och inom BM:er alltför individualistiska, transaktionsmässiga och operativa och saknar den processorientering som krävs för en mer omfattande förståelse av värdepotentialen hos AI, vilket leder till affärsmodellinnovation (BMI) för dominerande operatörer.

Med den övergripande ambitionen att stödja AI-inkorporering i etablerade företags erbjudanden, föreslår denna avhandling ett processbaserat värderamverk för AI-drivna BM:er. För detta ändamål har denna avhandlingsforskning producerat fem studier, inklusive olika metoder, för att förstå värdeprocesserna inom BMs i ljuset av digitalisering. På grund av fenomenets komplexa karaktär inkluderar de metoder som används i studierna kvasi-experiment, fallstudier, semistrukturerade intervjuer, djupintervjuer, kortsortering, longitudinell forskning, kvantitativ undersökningsanalys, litteraturgenomgång och litteratur kartläggning efter behov och relevant för de olika studierna.

Studierna visar att digitala och AI-tekniker potentiellt skulle kunna skapa nya värden (t.ex. självlärande och intelligenta erbjudanden) för olika intressenter, tillhandahålla nya mekanismer för värdeleverans genom digital servitisering och möjliggöra tidigare omöjliga värdefångande tekniker som värdebaserade. dynamisk prissättning inom BMs. Det kan observeras att värdet i digitalt BMI ständigt förändras och därför måste fokuseras på explicit inom BMs och introduceras som en värdeidentifieringsprocess. Dessutom medför AI nya värdeprocessrelationer där värdeskapande och leverans är mycket mer integrerade, dynamiska och personliga per kund, vilket lyfter fram den nödvändiga betoningen på hyperpersonalisering.

Detta examensarbete analyserar utmaningarna och möjligheterna AI har gett inom BMI för att föreslå ett modifierat ramverk för värdeprocesser för AI-aktiverade BMs, inklusive värdeidentifiering, värdemanifestation och värdefångande, jämfört med de allmänt föreslagna BM-värdeprocesserna för värdeskapande, värdeleverans och värdefångst. Den föreslagna uppfattningen konsoliderar värdeprocesser, inklusive de individuella, relationella och transaktionsvärden som krävs av AI-baserade BMs, snarare än bara den transaktionssyn på värde som täcks av standard BM-värdeprocesser, en vy som endast belyser den operativa aspekten av värde inom BMs. .

Vidare diskuterar detta examensarbete hur den nuvarande inställningen till AI inom BMI är mer ur ett resursperspektiv och därför inte kan realisera den fulla potentialen av AI-teknik. Avhandlingen utvecklar hur etablerade operatörer kan använda AI-teknologi inom BMI för att skapa en konkurrensfördel genom att koncentrera sig på processsynen om värde genom det föreslagna nya ramverket för att hantera markerade möjligheter och utmaningar. Den nya rollen för ekosystemintressenter som innovationspartner inom BMI som använder data/AI-drivna kapaciteter och organisatoriska värdeförändringar diskuteras. Slutligen belyser detta examensarbete implikationer för BMI-teori i termer av nya värdeprocesser och implikationer för praktiken i termer av BMI-ramverket, och avslutar med att presentera utmaningar och möjligheter som uppstår från användningen av AI inom BMI av dominerande operatörer.

Nyckelord: Digitalisering, Värde, Artificiell Intelligens, Affärsmodellinnovation

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2022. , p. 239
Series
TRITA-ITM-AVL ; 2022:20
Keywords [en]
Digitalization, Value, Artificial Intelligence, Business Model Innovation
National Category
Business Administration
Research subject
Machine Design
Identifiers
URN: urn:nbn:se:kth:diva-311267ISBN: 978-91-8040-234-7 (print)OAI: oai:DiVA.org:kth-311267DiVA, id: diva2:1659536
Public defence
2022-06-10, E3 / https://kth-se.zoom.us/j/65238678514, Osquars backe 14, Stockholm, 13:00 (English)
Opponent
Supervisors
Available from: 2022-05-20 Created: 2022-05-20 Last updated: 2022-06-25Bibliographically approved
List of papers
1. Edge AI Driven Technology Advancements Paving Way towards New Capabilities
Open this publication in new window or tab >>Edge AI Driven Technology Advancements Paving Way towards New Capabilities
2020 (English)In: International Journal of Innovation and Technology Management (IJITM), ISSN 0219-8770, article id 2040005Article in journal (Refereed) Published
Abstract [en]

As industries hold the opportunity to embrace artificial intelligence (AI) driven innovation, their success to a significant extent will depend on the value the new technology generates for different business stakeholder groups. This is in turn dependent upon how management can embrace these techniques and change as companies will frequently need to transform both internal processes and offerings to customers in order to reap the benefits of AI. AI is a growing research area currently concentrated around technology and modeling of techniques and yet only few examples and limited research are available, on how AI technology enables new capabilities that can impact the value delivered as well as radically transform it. We thus need to understand what new capabilities these technologies bring about and how they are used. Based on three concrete empirical quasi-experiments, interviews conducted with start-ups and a Swedish industrial manufacturing firm dealing with outdoor power products (like grass-cutters, chain-saws, concrete-saws, etc.) for professional and consumer use and using an analytical framework derived from the Resource Based View, this paper explores capabilities enabled through Edge AI and the competitive advantage these may offer. Specific capabilities (self-calibration, enhanced-sensing, selective-capture and reputation) are identified and implications for theory are discussed, pointing out the importance to consider this type of technology not only as a resource, but rather as a dynamic capability in itself.

Place, publisher, year, edition, pages
World Scientific, 2020
Keywords
Artificial intelligence, dynamic capabilities, edge AI, resource based view, value
National Category
Economics and Business
Identifiers
urn:nbn:se:kth:diva-290829 (URN)10.1142/S0219877020400052 (DOI)000599361100001 ()2-s2.0-85097891839 (Scopus ID)
Note

QC 20220425

Available from: 2021-03-23 Created: 2021-03-23 Last updated: 2022-06-25Bibliographically approved
2. Value-capture in digital servitization
Open this publication in new window or tab >>Value-capture in digital servitization
Show others...
2022 (English)In: Journal of Manufacturing Technology Management, ISSN 1741-038X, E-ISSN 1758-7786, Vol. 33, no 5, p. 986-1004Article in journal (Refereed) Published
Abstract [en]

This paper intends to explore the perception of value delivered in digital servitization in a business-to-business context of incumbent manufacturing firms. We investigate how individual entrepreneurial orientation (IEO) influence and affect the adoption of such digital servitization strategies. The observations are made through a survey and empirical assessment across a couple of large industrial organizations interested in servitization and digitalization. Findings contribute to the existing literature on digital servitization and business model innovation by suggesting that IEO influence perceived value in delivering digital service offers, whereas functional affiliation does not. Further observations suggest that digital capabilities can become a crucial enabler for the perception of value delivered in digital business models by providing swift access to data for affected stakeholders.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Marketing
National Category
Business Administration
Identifiers
urn:nbn:se:kth:diva-305975 (URN)10.1108/jmtm-05-2021-0168 (DOI)000751513800001 ()2-s2.0-85123837808 (Scopus ID)
Note

QC 20220425

Available from: 2021-12-13 Created: 2022-04-20 Last updated: 2023-10-16Bibliographically approved
3.
The record could not be found. The reason may be that the record is no longer available or you may have typed in a wrong id in the address field.
4.
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5. Perception of value delivered in digital servitization
Open this publication in new window or tab >>Perception of value delivered in digital servitization
2021 (English)In: Industrial Marketing Management, ISSN 0019-8501, E-ISSN 1873-2062, Vol. 99, p. 167-174Article in journal (Refereed) Published
Abstract [en]

This paper intends to explore the perception of value delivered in digital servitization in a business-to-business context of incumbent manufacturing firms. We investigate how individual entrepreneurial orientation (IEO) influence and affect the adoption of such digital servitization strategies. The observations are made through a survey and empirical assessment across a couple of large industrial organizations interested in servitization and digitalization. Findings contribute to the existing literature on digital servitization and business model innovation by suggesting that IEO influence perceived value in delivering digital service offers, whereas functional affiliation does not. Further observations suggest that digital capabilities can become a crucial enabler for the perception of value delivered in digital business models by providing swift access to data for affected stakeholders.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Marketing
National Category
Business Administration
Identifiers
urn:nbn:se:kth:diva-305975 (URN)10.1016/j.indmarman.2021.10.011 (DOI)000720546200001 ()2-s2.0-85119074094 (Scopus ID)
Note

QC 20220421

Available from: 2021-12-13 Created: 2021-12-13 Last updated: 2024-03-15Bibliographically approved

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