Extending private-collective innovation: a case study

The private-collective innovation model proposes incentives for individuals and firms to privately invest resources to create public goods innovations. Such innovations are characterized by non-rivalry and non-exclusivity in consumption. Examples include open source software, user-generated media products, drug formulas, and sport equipment designs. There is still limited empirical research on private-collective innovation. We present a case study to (1) provide empirical evidence of a case of private-collective innovation, showing specific benefits, and (2) to extend the private-collective innovation model by analyzing the hidden costs for the company involved. We examine the development of the Nokia Internet Tablet, which builds on both proprietary and open source software development, and that involves both Nokia developers and volunteers who are not employed by the company. Seven benefits for Nokia are identified, as are five hidden costs: difficulty to differentiate, guarding business secrets, reducing community entry barriers, giving up control, and organizational inertia. We examine the actions taken by the management to mitigate these costs throughout the development period.

Enabling knowledge creation through outsiders: towards a push model of open innovation

Open innovation is increasingly being adopted in business and describes a situation in which firms exchange ideas and knowledge with external participants, such as customers, suppliers, partner firms, and universities. This article extends the concept of open innovation with a push model of open innovation: knowledge is voluntarily created outside a firm by individuals and organisations who proceed to push knowledge into a firm’s open innovation project. For empirical analysis, we examine source code and newsgroup data on the Eclipse Development Platform. We find that outsiders invest as much in the firm’s project as the founding firm itself. Based on the insights from Eclipse, we develop four propositions: ‘preemptive generosity’ of a firm, ‘continuous commitment’, ‘adaptive governance structure’, and ‘low entry barrier’ are contexts that enable the push model of open innovation.

Sampling in Open Source Software Development: The case for using the Debian GNUILinux Distribution

Research on open source software (OSS) projects often focuses on the SourceForge collaboration platform. We argue that a GNU/Linwr distribution, such as Debian, is better suited for the sampling ofprojects because it avoids biases and contains unique information only available in an integrated environment. Especially research on the reuse of components can build on dependency information inherent in the Debian GNU/Linux packaging system. This paper therefore contributes to the practice of sampling methods in OSS research and provides empirical data on reuse dependencies in Debian.

How Firms Make Friends: Communities in Private-Collective Innovation

When firms contribute to open source projects, they in fact invest into public goods which may be used by everyone, even by their competitors. This seemingly paradoxical behavior can be explained by the model of private-collective innovation where private investors participate in collective action. Previous literature has shown that companies benefit through the production process providing them with unique incentives such as learning and reputation effects. By contributing to open source projects firms are able to build a network of external individuals and organizations participating in the creation and development of the software. As will be shown in this doctoral dissertation firm-sponsored communities involve the formation of interorganizational relationships which eventually may lead to a source of sustained competitive advantage. However, managing a largely independent open source community is a challenging balancing act between exertion of control to appropriate value creation, and openness in order to gain and preserve credibility and motivate external contributions. Therefore, this dissertation consisting of an introductory chapter and three separate research papers analyzes characteristics of firm-driven open source communities, finds reasons why and mechanisms by which companies facilitate the creation of such networks, and shows how firms can benefit most from their communities.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

On an Application of Microsoft Excel’s Evolutionary Solver to the Resource-Constrained Project Scheduling Problem RCPSP

The Solver Add-in of Microsoft Excel is widely used in courses on Operations Research and in industrial applications. Since the 2010 version of Microsoft Excel, the Solver Add-in comprises a so-called evolutionary solver. We analyze how this metaheuristic can be applied to the resource-constrained project scheduling problem (RCPSP). We present an implementation of a schedule-generation scheme in a spreadsheet, which combined with the evolutionary solver can be used for devising good feasible schedules. Our computational results indicate that using this approach, non-trivial instances of the RCPSP can be (approximately) solved to optimality.

An MILP-based heuristic for staff scheduling problems with acceptance levels

We present a real-world staff-assignment problem that was reported to us by a provider of an online workforce scheduling software. The problem consists of assigning employees to work shifts subject to a large variety of requirements related to work laws, work shift compatibility, workload balancing, and personal preferences of employees. A target value is given for each requirement, and all possible deviations from these values are associated with acceptance levels. The objective is to minimize the total number of deviations in ascending order of the acceptance levels. We present an exact lexicographic goal programming MILP formulation and an MILP-based heuristic. The heuristic consists of two phases: in the first phase a feasible schedule is built and in the second phase parts of the schedule are iteratively re-optimized by applying an exact MILP model. A major advantage of such MILP-based approaches is the flexibility to account for additional constraints or modified planning objectives, which is important as the requirements may vary depending on the company or planning period. The applicability of the heuristic is demonstrated for a test set derived from real-world data. Our computational results indicate that the heuristic is able to devise optimal solutions to non-trivial problem instances, and outperforms the exact lexicographic goal programming formulation on medium- and large-sized problem instances.