Customer behavior and adoption · Longitudinal observational study using data from a SaaS vendor and a linear mixed model to analyze measures intended to improve continued usage over time.
Usage Continuance in Software-as-a-Service
Elias Baumann, Jana Kern, Stefan Lessmann (2022). Usage Continuance in Software-as-a-Service. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-10065-w.
Plain-English take
Several proactive activation measures positively affect usage penetration, while software and client characteristics do not display significant estimates; the authors emphasize proactive activation initiatives to raise usage penetration. For your marketing: Use it as a product, platform or behavior mechanism reference rather than a direct forecast for a SaaS funnel.
How much weight to give this
2.5/5 Evidence needs cautious interpretation
The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.
Longitudinal observational study using data from a SaaS vendor and a linear mixed model to analyze measures intended to improve continued usage over time.
A SaaS vendor's application usage over time, including vendor activation measures and software/client characteristics; the publisher abstract does not report the number of customers or observations.
Based on the publisher-described design and comparison; see the article for complete analysis.
Publisher record was fetched directly; full text was not assumed open.
Double-blind? Blinding was not established from the retrieved publisher record.
Research question
What the authors investigated
Which vendor activation measures and software/client characteristics increase continued usage (usage penetration) of SaaS applications after initial adoption?
Design
Longitudinal observational study using data from a SaaS vendor and a linear mixed model to analyze measures intended to improve continued usage over time.
Sample and setting
A SaaS vendor's application usage over time, including vendor activation measures and software/client characteristics; the publisher abstract does not report the number of customers or observations.
Reported result
What the study found
Several proactive activation measures positively affect usage penetration, while software and client characteristics do not display significant estimates; the authors emphasize proactive activation initiatives to raise usage penetration.
This is a paraphrase of the source. Check the original paper for definitions, estimates, statistical uncertainty and analysis details.
Scope and transfer
Limitations to keep in view
Not stated in the publisher abstract.
Possible founder application
Use it as a product, platform or behavior mechanism reference rather than a direct forecast for a SaaS funnel.
Evidence access
Publisher abstract/metadata retrieved; consult the linked paper for full methods and tables.
Springer's abstract says the study is longitudinal, uses data from a SaaS vendor and a linear mixed model, and finds that proactive activation initiatives raise usage penetration. Direct publisher-page retrieval was recorded on 2026-10-09; profile summarizes the accessible record and does not claim independent review of inaccessible full text.
If only an abstract or metadata was available, the profile is limited to claims supported there. A paper's paywall does not change its evidentiary status, but it limits what we can substantiate from accessible text.