Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda
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Digital transformation fundamentally changes SMEs' business models.
“Digital transformation is not about optimizing internal processes or incorporating new technologies, but fundamentally changing SMEs' business models (Loebbecke & Picot, 2015).”
From Digitalization, business models, and SMEs: How do business model innovation practices improve performance of digitalizing SMEs? (2019) · cites this paper as Loebbecke & Picot, 2015
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- Authors
- Loebbecke, C., & Picot, A.
- Affiliations
- University of Cologne (DE)
- Ludwig-Maximilians-Universität München (DE)
- Corresponding author
- Loebbecke, Claudia
- ORCID
- Loebbecke, Claudia
- Venue
- The Journal of Strategic Information Systems
- Publisher
- Elsevier BV
- Published
- 2015-09
- Volume
- 24
- Issue
- 3
- Pages
- 149-157
- ISSN
- 0963-8687 (ISSN-L)
- OpenAlex
- W2512365341
- Cited by
- 1,317
- References
- 104
- Citations by year
- 2026: 75 · 2025: 181 · 2024: 194 · 2023: 205 · 2022: 170 · 2021: 164 · 2020: 113 · 2019: 95 · 2018: 64 · 2017: 39 · 2016: 15 · 2015: 2
- FWCI
- 84.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Strategic Information Systems (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Digital Innovation in Industries
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Economy and Transformation
- Keywords
- digitization, big data analytics, business model transformation, societal transformation, employment effects, knowledge workers, automation, cognitive tasks, quality data, service innovation, research agenda
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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