A Reference Model for Data-driven Business Model Innovation Initiatives in Incumbent Firms

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

A Reference Model for Data-driven Business Model Innovation Initiatives in Incumbent Firms. / Rashed, Faisal; Drews, Paul; Zaki, Mohamed.
ECIS 2022 proceedings. AIS eLibrary, 2022. 1873 (ECIS 2022 research papers; No. 156).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Rashed, F, Drews, P & Zaki, M 2022, A Reference Model for Data-driven Business Model Innovation Initiatives in Incumbent Firms. in ECIS 2022 proceedings., 1873, ECIS 2022 research papers, no. 156, AIS eLibrary, 30th European Conference on Information Systems - ECIS 2022, Timisoara, Romania, 18.06.22. <https://aisel.aisnet.org/ecis2022_rp/156/>

APA

Rashed, F., Drews, P., & Zaki, M. (2022). A Reference Model for Data-driven Business Model Innovation Initiatives in Incumbent Firms. In ECIS 2022 proceedings Article 1873 (ECIS 2022 research papers; No. 156). AIS eLibrary. https://aisel.aisnet.org/ecis2022_rp/156/

Vancouver

Rashed F, Drews P, Zaki M. A Reference Model for Data-driven Business Model Innovation Initiatives in Incumbent Firms. In ECIS 2022 proceedings. AIS eLibrary. 2022. 1873. (ECIS 2022 research papers; 156).

Bibtex

@inbook{0c31e1c06b124ef28a240564d707d8be,
title = "A Reference Model for Data-driven Business Model Innovation Initiatives in Incumbent Firms",
abstract = "In the past decade, we have witnessed the rise of big data analytics to a well-established phenomenon in business and academic fields. Novel opportunities appear for organizations to maximize the value from data through improved decision making, enhanced value propositions and new business models. The latter two are investigated by scholars as part of an emerging research field of data-driven business model (DDBM) innovation. Aiming to deploy DDBM innovation, companies start initiatives to either renovate their existing BM or develop a new DDBM. Responding to the recent calls for further research on design knowledge for DDBM innovation, we developed a reference model for DDBM innovation initiatives. Building upon a design science research approach and the Work System Theory as a kernel theory and a set of design principles, we propose a reference model comprising a static and a dynamic view. Our results are based on a research study with empirical insights from 18 companies, 19 cases and 16 expert interviews as well as theoretical grounding from a systematic literature research on key concepts of DDBM innovation. The developed reference model fills a gap mentioned in the DDBM innovation literature and provides practical guidance for companies.",
keywords = "Business informatics, Chatbots, Conversational Agents, Education, Learning, Literature Review, Pedagogical Conversational Agents",
author = "Faisal Rashed and Paul Drews and Mohamed Zaki",
year = "2022",
language = "English",
series = "ECIS 2022 research papers",
publisher = "AIS eLibrary",
number = "156",
booktitle = "ECIS 2022 proceedings",
address = "United States",
note = "30th European Conference on Information Systems - ECIS 2022 : New horizons in digitally united societies, ECIS 2022 ; Conference date: 18-06-2022 Through 24-06-2022",
url = "https://ecis2022.eu/",

}

RIS

TY - CHAP

T1 - A Reference Model for Data-driven Business Model Innovation Initiatives in Incumbent Firms

AU - Rashed, Faisal

AU - Drews, Paul

AU - Zaki, Mohamed

N1 - Conference code: 30

PY - 2022

Y1 - 2022

N2 - In the past decade, we have witnessed the rise of big data analytics to a well-established phenomenon in business and academic fields. Novel opportunities appear for organizations to maximize the value from data through improved decision making, enhanced value propositions and new business models. The latter two are investigated by scholars as part of an emerging research field of data-driven business model (DDBM) innovation. Aiming to deploy DDBM innovation, companies start initiatives to either renovate their existing BM or develop a new DDBM. Responding to the recent calls for further research on design knowledge for DDBM innovation, we developed a reference model for DDBM innovation initiatives. Building upon a design science research approach and the Work System Theory as a kernel theory and a set of design principles, we propose a reference model comprising a static and a dynamic view. Our results are based on a research study with empirical insights from 18 companies, 19 cases and 16 expert interviews as well as theoretical grounding from a systematic literature research on key concepts of DDBM innovation. The developed reference model fills a gap mentioned in the DDBM innovation literature and provides practical guidance for companies.

AB - In the past decade, we have witnessed the rise of big data analytics to a well-established phenomenon in business and academic fields. Novel opportunities appear for organizations to maximize the value from data through improved decision making, enhanced value propositions and new business models. The latter two are investigated by scholars as part of an emerging research field of data-driven business model (DDBM) innovation. Aiming to deploy DDBM innovation, companies start initiatives to either renovate their existing BM or develop a new DDBM. Responding to the recent calls for further research on design knowledge for DDBM innovation, we developed a reference model for DDBM innovation initiatives. Building upon a design science research approach and the Work System Theory as a kernel theory and a set of design principles, we propose a reference model comprising a static and a dynamic view. Our results are based on a research study with empirical insights from 18 companies, 19 cases and 16 expert interviews as well as theoretical grounding from a systematic literature research on key concepts of DDBM innovation. The developed reference model fills a gap mentioned in the DDBM innovation literature and provides practical guidance for companies.

KW - Business informatics

KW - Chatbots

KW - Conversational Agents

KW - Education

KW - Learning

KW - Literature Review

KW - Pedagogical Conversational Agents

UR - https://aisel.aisnet.org/ecis2022_rp/18/

UR - https://www.mendeley.com/catalogue/267dc3b6-024d-30de-b84b-f8ab93a1c683/

M3 - Article in conference proceedings

T3 - ECIS 2022 research papers

BT - ECIS 2022 proceedings

PB - AIS eLibrary

T2 - 30th European Conference on Information Systems - ECIS 2022

Y2 - 18 June 2022 through 24 June 2022

ER -

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