Open Source Software and the “Private-Collective” Innovation Model: Issues for Organization Science

Currently two models of innovation are prevalent in organization science. The "private investment" model assumes returns to the innovator results from private goods and efficient regimes of intellectual property protection. The "collective action" model assumes that under conditions of market failure, innovators collaborate in order to produce a public good. The phenomenon of open source software development shows that users program to solve their own as well as shared technical problems, and freely reveal their innovations without appropriating private returns from selling the software. In this paper we propose that open source software development is an exemplar of a compound model of innovation that contains elements of both the private investment and the collective action models. We describe a new set of research questions this model raises for scholars in organization science. We offer some details regarding the types of data available for open source projects in order to ease access for researchers who are unfamiliar with these, and als

CROSSROADS—Identifying Viable “Need–Solution Pairs”: Problem Solving Without Problem Formulation

Problem-solving research and formal problem-solving practice begin with the assumption that a problem has been identified or formulated for solving. The problem-solving process then involves a search for a satisfactory or optimal solution to that problem. In contrast, we propose that, in informal problem solving, a need and a solution are often discovered together and tested for viability as a “need–solution pair.” For example, one may serendipitously discover a new solution and assess it to be worth adopting although the “problem” it would address had not previously been in mind as an object of search or even awareness. In such a case, problem identification and formulation, if done at all, come only after the discovery of the need–solution pair. We propose the identification of need–solution pairs as an approach to problem solving in which problem formulation is not required. We argue that discovery of viable need–solution pairs without problem formulation may have advantages over problem-initiated problem-solving methods under some conditions. First, it removes the often considerable costs associated with problem formulation. Second, it eliminates the constraints on possible solutions that any problem formulation will inevitably apply

The CC Model as Organizational Design Striving to Combine Relevance and Rigor

This paper addresses the design problem of ensuring engaged research's rigorous relevance and, especially, its implications regarding the engaged researcher's role. As a theoretical background, this paper firstly uses the "role concept” from the Business Engineering discipline where "role” is a fundamental element of generic method description. Secondly, it uses the "Design Science” paradigm's generic guidelines for the assessment of research activities and results to ensure that they meet both the rigor and relevance criteria. Based on these theories, the paper finally describes and discusses a collaborative research mode of practitioners and academics called the "Competence Center Model” (CC model). This model is a useful case to study what researchers actually do when they strive to combine rigor and relevance. With the benefit of hindsight, it is possible to discuss this research practice's effectiveness and its implied benefits for and strains on the researcher's rol

Tacit knowledge and knowledge conversion: Controversy and advancement in organizational knowledge creation theory.

N onaka's paper [1994. A dynamic theory of organizational knowledge creation. Organ. Sci. 5(1) 14-37] contributed to the concepts of "tacit knowledge" and "knowledge conversion" in organization science. We present work that shaped the development of organizational knowledge creation theory and identify two premises upon which more than 15 years of extensive academic work has been conducted: (1) tacit and explicit knowledge can be conceptually distinguished along a continuum; (2) knowledge conversion explains, theoretically and empirically, the interaction between tacit and explicit knowledge. Recently, scholars have raised several issues regarding the understanding of tacit knowledge as well as the interaction between tacit and explicit knowledge in the theory. The purpose of this article is to introduce and comment on the debate about organizational knowledge creation theory. We aim to help scholars make sense of this debate by synthesizing six fundamental questions on organizational knowledge creation theory. Next, we seek to elaborate and advance the theory by responding to questions and incorporating new research. Finally, we discuss implications of our endeavor for organization science

ARTIFICIAL INTELLIGENCE-AUGMENTED DECISION MAKING IN SUPPLY CHAIN MONITORING: AN ACTION DESIGN RESEARCH STUDY

Organizations are progressively adopting hybrid human–AI systems in decision-making processes in which human decisions are augmented by AI insights. Among the promising AI applications in supply chain monitoring (SCMo) are predictive maintenance systems that predict device failures and augment maintenance decisions, allowing for timely interventions. Despite the growing use of such systems, prescriptive knowledge encompassing technical, business, social, and organizational aspects on how to design, develop, and deploy them in real operational environments remain obscure. To address this shortcoming, our action design research study designed and deployed a predictive maintenance system that predicts the failure of SCMo devices and augments the maintenance decisions of those devices. By doing so, we outline our learnings as generalizable design principles that may guide prospective predictive maintenance systems in SCMo

The making of convergence : knowledge reuse, boundary spanning, and the formation of the ICT industry

While mastering technology and industry convergence are essential for firms across a growing number of industries, convergence is often rapid and abrupt, challenging firms to develop appropriate strategic responses. Focusing on the historical convergence between information technology and communication technology, we examine the microlevel behaviors of scientists initiating and driving convergence. Analyzing a bibliometric dataset of 257 641 scientific articles, we demonstrate how industry convergence manifests in a microlevel scientific convergence, preceding industry convergence by several decades. Our article contributes to the literature on convergence by developing new bibliometric measures for scientific convergence, and by contrasting microlevel behaviors that underpin convergence. Based on our findings, we offer a set of methods and strategies to assist managers in technology-based businesses with anticipating and responding to convergence in a timely manner

Initiating private-collective innovation: The fragility of knowledge sharing

Incentives to innovate are a central element of innovation theory. In the private-investment model, innovators privately fund innovation and then use intellectual property protection mechanisms to appropriate returns from these investments. In the collective-action model, public subsidy funds public goods innovations, characterized by non-rivalry and non-exclusivity in using these innovations. Recently, these models have been compounded in the private-collective innovation model where innovators privately fund public goods innovations. Private-collective innovation is illustrated in the case of open source software development. This paper contributes to the work on this model by investigating incentives that motivate innovators to share their knowledge in an initial situation, before there is a community to support the innovation process. We use game theory to predict knowledge sharing behavior in private-collective innovation, and test these predictions in a laboratory setting. The results show that knowledge sharing is a coordination game with multiple equilibria, reflecting the fragility of knowledge sharing between innovators with conflicting interests. The experimental results demonstrate important asymmetries in the fragility of knowledge sharing and, in some situations, more knowledge sharing than theoretically predicted. A behavioral analysis suggests that knowledge sharing in private-collective innovation is not only affected by material incentives, but also by social preferences such as fairness. The results offer general insights into the relationship between incentives and knowledge sharing and contribute to a better understanding of the initiation of private-collective innovation