The DOI matches its Crossref record; 69 of the 71 references it cites were found in a registry or an index.
Economics of Free Under Perpetual Licensing: Implications for the Software Industry
Abstract
In this paper, we explore the economics of free under perpetual licensing. In particular, we focus on two emerging software business models that involve a free component: feature-limited freemium (FLF) and uniform seeding (S). Under FLF, the firm offers the basic software version for free, while charging for premium features. Under S, the firm gives away for free the full product to a percentage of the addressable market uniformly across consumer types. We benchmark their performance against a conventional business model under which software is sold as a bundle (labeled as “charge for everything” or CE) without free offers. In the context of consumer bounded rationality and information asymmetry, we develop a unified two-period consumer valuation learning framework that accounts for both word-of-mouth (WOM) effects and experience-based learning, and use it to compare and contrast the three business models. Under both constant and dynamic pricing, for moderate strength of WOM signals, we derive the equilibria for each model and identify optimality regions. In particular, S is optimal when consumers significantly underestimate the value of functionality and cross-module synergies are weak. When either cross-module synergies are stronger or initial priors are higher, the firm decides between CE and FLF. Furthermore, we identify nontrivial switching dynamics from one optimality region to another depending on the initial consumer beliefs about the value of the embedded functionali…
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In the citing paper
“The existing literature has examined this question primarily through theoretical models analyzing featuregating versus usage-limiting strategies in isolation (Niculescu & Wu, 2014;Kato & Dumrongsiri, 2022;Shi et al., 2019)”
From Scaling a SaaS Business: The Role of Freemium Models in Converting Free Users to Paying Customers (2026) · cites this paper as Niculescu & Wu, 2014
In the citing paper
“Third, while the role of network effects in freemium strategy is theoretically well established (Boudreau et al., 2022;Niculescu & Wu, 2014), the specific mechanisms through which network effects interact with conversion pressure in SaaS contexts remain underspecified.”
From Scaling a SaaS Business: The Role of Freemium Models in Converting Free Users to Paying Customers (2026) · cites this paper as Niculescu & Wu, 2014
In the citing paper
“This finding is consistent with the theoretical logic of Niculescu and Wu (2014), who demonstrated that the optimal freemium design depends on consumer heterogeneity.”
From Scaling a SaaS Business: The Role of Freemium Models in Converting Free Users to Paying Customers (2026) · cites this paper as Niculescu and Wu (2014)
In the citing paper
“the optimal freemium design depends on consumer heterogeneity”
From Scaling a SaaS Business: The Role of Freemium Models in Converting Free Users to Paying Customers (2026) · cites this paper as Niculescu and Wu (2014)
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Metadata
- Authors
- Niculescu, M. F., & Wu, D. J.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Information Systems Research
- Published
- 2014-03
- Volume
- 25
- Issue
- 1
- Pages
- 173-199
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W3123695987
- Cited by
- 151
- References
- 71
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 9 · 2023: 9 · 2022: 11 · 2021: 13 · 2020: 17 · 2019: 19 · 2018: 12 · 2017: 14 · 2016: 13 · 2015: 5 · 2014: 3
- FWCI
- 35.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Systems Research (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Social Sciences › Business, Management and Accounting › Marketing › Copyright and Intellectual Property
- Keywords
- freemium model, software business models, bounded rationality, information asymmetry, word-of-mouth effect, experiential learning, price skimming, penetration pricing, dynamic pricing, software pricing
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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