When to make proprietary software open source

Caulkins, Jonathan P.; Feichtinger, Gustav; Grass, Dieter; Hartl, Richard F.; Kort, Peter M.; Seidl, Andrea

doi:10.1016/j.jedc.2013.02.009

Cited by 28 publications

(11 citation statements)

References 25 publications

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“…Then either Proposition 5 or 6 can be used to extend the model to determine the effect of an increase in the instantaneous variance of the change in the post-adoption capital stock on the adoption decision when the second-stage control problem has an infinite planning horizon. Indeed, these three propositions can be applied just as readily to the two-stage optimal control models of workplace reorganization of Valleé and Moreno-Galbis (2011) and closed-versus open-source software distribution of Caulkins et al (2013), to draw similar qualitative conclusions.…”

Section: Further Applicationsmentioning

confidence: 84%

Optimal plans and timing under additive transformations to rewards

Caputo

Förster

2016

Oxf. Econ. Pap.

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The nature and role of additive transformations to rewards are elucidated for a general class of deterministic, nonautonomous, optimal control problems with many state and control variables. Conditions relating to the optimal choice of initial and terminal times and initial and terminal values of state variables are identified such that additive transformations affect optimal plans. General comparative static results are derived and the framework is extended to cover two common classes of stochastic control problems. Three applications are presented: the canonical adjustment cost model of a firm, a stochastic extension of an irreversible pollution accumulation problem with regime switching and an extension of a lifecycle model of retirement in which an agent's retirement wealth evolves stochastically.

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Section: Further Applicationsmentioning

confidence: 84%

Optimal plans and timing under additive transformations to rewards

Caputo

Förster

2016

Oxf. Econ. Pap.

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“…A related challenge is determining when software should be contributed (Wnuk et al 2012). Several studies have attempted to model and identify an optimal timing (Haruvy et al 2008;Kort and Zaccour 2011;Caulkins et al 2013). Caulkins et al (2013) for example, identify costs related to the development and adapting the business model, along with the software quality as factors affecting when software should be released as OSS.…”

Section: Costs and Risks Of Sharing Software As Ossmentioning

confidence: 99%

“…A related complexity is that of the commoditization process and the concerned software artifact (CC4), which is also brought up in literature (Van der Linden et al 2009;Wnuk et al 2012;Haruvy et al 2008;Kort and Zaccour 2011;Caulkins et al 2013;Linåker et al 2018). By being early, an organization can get a first-mover advantage, gain bigger influence, and potentially steer the development, e.g., within a community through a new feature (Linåker et al 2019a;Munir et al 2018a), or within an industry through a new standard or platform (West and Gallagher 2006) (see e.g., contribution objective CO8 and CO9).…”

Section: Contribution Complexitiesmentioning

confidence: 99%

What to share, when, and where: balancing the objectives and complexities of open source software contributions

Linåker¹,

Regnell²

2020

Empir Software Eng

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Context: Software-intensive organizations' rationale for sharing Open Source Software (OSS) may be driven by both idealistic, strategic and commercial objectives, and include both monetary as well as non-monetary benefits. To gain the potential benefits, an organization may need to consider what they share and how, while taking into account risks, costs and other complexities. Objective: This study aims to empirically investigate objectives and complexities organizations need to consider and balance between when deciding on what software to share as OSS, when to share it, and whether to create a new or contribute to an existing community. Method: A multiple-case study of three case organizations was conducted in two research cycles, with data gathered from interviews with 20 practitioners from these organizations. The data was analyzed qualitatively in an inductive and iterative coding process. Results: 12 contribution objectives and 15 contribution complexities were found. Objectives include opportunities for improving reputation, managing suppliers, managing partners and competitors, and exploiting externally available knowledge and resources. Complexities include risk of loosing control, risk of giving away competitive advantage, risk of creating negative exposure, costs of contributing, and the possibility and need to contribute to an existing or new community. Conclusions: Cross-case analysis and interview validation show that the identified objectives and complexities offer organizations a possibility to reflect on and adapt their contribution strategies based on their specific contexts and business goals.

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“…A closely related study is Llanes and Elejalde (2013) Among the dynamic models studied, Yıldırım (2006) considers the free-rider problem when the incentive to induce others to contribute by contributing more is present. 7 Caulkins et al (2013) He characterizes the conditions under which supporting is optimal and also shows that a larger open source programmers' community does not necessarily increase the welfare. 6 Assuming the product packages are composed of a base module, an extension module and a complementary service, they consider pure and mixed business models.…”

Section: Literature Reviewmentioning

confidence: 99%