A Reference Model for Data-driven Business Model Innovation Initiatives in Incumbent Firms
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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ECIS 2022 proceedings. AIS eLibrary, 2022. 1873 (ECIS 2022 research papers; No. 156).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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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 -