Richard M. Burton

Equity and the Technology Transfer Strategies of American Research Universities

American universities are experimenting with new mechanisms for promoting the commercialization of academic research and generating revenue from university intellectual property. This paper discusses mechanisms available to universities in managing the commercialization of intellectual property, considering equity as a technology transfer mechanism that offers advantages for both generating revenue and aligning the interests of universities, industry and faculty. Employing data from a national survey of Carnegie I and Carnegie II institutions, we document the recent rise in university equity holdings. We present and estimate a model that considers the universitys use of equity to be a function of behavioral factors related to the universitys prior experiences with licensing, success relative to other institutions, and the organization of the technology transfer office, as well as structural characteristics related to university type.

Organizational Structure as a Determinant of Academic Patent and Licensing Behavior: An Exploratory Study of Duke, Johns Hopkins, and Pennsylvania State Universities

This paper examines the influences of university organizational structure on technology transfer performance. The analysis treats the organizational structure of the technology-transfer office as an independent variable that accounts, in part, for measured differences in inter-institutional patenting, licensing, and sponsored research activities. We derive and investigate three hypotheses that link attributes of organizational form – information processing capacity, coordination capability and incentive alignment – to technology transfer outcomes. A detailed analysis of three major research universities – Johns Hopkins University, Pennsylvania State University, and Duke University – provides evidence of the existence of alternative organizational structures. The data also suggest that these organizational capabilities result in differences in technology transfer activity.

Strategic Organizational Diagnosis and Design

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The validity of computational models in organization science: from model realism to purpose of the model

price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. R.M. Burton, B. Obel Strategic Organizational Diagnosis and Design

Virtual Teams: What are their Characteristics, and Impact on Team Performance?

Computational models are widely applied to address fundamental and practical issues in organization science. Yet, computational modeling in organization science continues to raise questions of validity. In this paper, we argue that computational validity is a balance of three elements: the question or purpose, the experimental design, and the computational model. Simple models which address the question are preferred. Non-simple, imbalanced computational models are not only inefficient but can lead to poor answers. The validity approach is compared with well-known validity criteria in social science. Finally we apply the approach to a number of computational modeling studies in organization science, beginning with Cyerts. They were pioneering and are examples of well designed computational models.

Designing efficient organizations : modelling and experimentation

To date, efforts to understand virtual teaming have been largely anecdotal and atheoretical. Therefore, drawing from the extant research in the groups domain, we attempt to ground the definition of a virtual team in well-established group-level constructs, and design a simulation study to investigate the impact of different virtual team characteristics on team performance. Essentially, we argue that the virtual team is defined by three key characteristics—the virtual team context, the virtual team composition, and the virtual team structure. Using the VDT computational discrete event simulation model as our experimental platform, we simulated different virtual team models, and examined their impact on various team performance dimensions. We found that virtual team characteristics have different effects on different aspects of team performance. The virtual context team had a lower rework volume but higher coordination volume and longer project duration than the virtual composition team. Interestingly, we also found that the virtual structure team performed better than the software development team baseline model in all aspects of team performance. Based on these results, we proposed strategies to improve performance in different types of virtual team. Specifically, we propose (1) increasing the ease of communication and availability of routines in the virtual context team; (2) clarifying role expectations and fostering a team culture in the virtual composition team; and (3) implementing a lateral structure in the virtual team. Our results also suggest that firms should consider situational demands, specifically tolerance for errors and coordination volume, when considering the design of virtual teams.

Organizations and Complexity: Searching for the Edge of Chaos

Organization and the Research Approach. Modelling the Organization. Research Design and Analysis. Appendix: Nonparametric Statistical Methods. The Hierarchical Form of Organization. Information and Coordination. Reward Systems and Incentives. Summing Up to Begin Anew. Bibliography. Index.

Computational Laboratories for Organization Science: Questions, Validity and Docking

Traditional organizational theory advocates increased differentiation and horizontal integration for organizations in unstable environments or with uncertain technologies. This paper seeks to develop a better understanding of the relationship of group structure and the level of interdependency between individuals on group performance under various task complexities. Complexity theory in general, and NK models in particular, are introduced as theoretical frameworks that offer an explanation for group performance. Simulation models are developed, based on the communication network research of Bavelas (1948) and Leavitt (1952), to explore the effects of decentralization and interdependence. The simulation model developed here shows general consistency with previous human subject experiments. However, contrary to predictions, not all decentralized group structures perform well when undertaking complex task assignments. Structures that are highly connected (actors communicating with all others) perform much worse than those with a lower level of connection. Further experiments varying both the number of actors and the degree of interdependence between them find evidence of the “edge of chaos.” This research advances our understanding of organizations beyond earlier models by suggesting that there is an optimal range of interconnectedness between actors or tasks that explains the variation in performance. An intriguing result is that this optimal level of interdependence is fairly low, regardless of the size of the group.

A Computer Simulation Test of the M-Form Hypothesis.

A computational laboratory is a “place” where we can: ask a question about an organization and its processes, build a computational experiment, design and conduct an experiment, and answer or comment on the question. The questions can be: what is, what might be, and what should be.Validation is a fundamental concern in science; the validity of a laboratory and model depends upon the question being addressed. A laboratory for a descriptive what is question may not be valid for a what should be design question.Docking—the alignment of two models—goes beyond validity. Docking juxtaposes two models to investigate whether they proceed in like manner or yield similar results. I argue that docking provides a guide in the use of different laboratories to address organization questions; and, further computational and non computational models can be docked to deepen and broaden our understanding of organization science.

The multilevel approach to organizational issues of the firm--A critical review

This research began in the summer 1978 with the support of the Danish Social Science Research Council. Thanks go to Ms. Margaret Figueroa, the first authors research assistant. A grant from the Duke University Research Council supported her efforts. This research tests Williamsons M-form hypothesis which posits an information imperative of organizational form instead of a technology imperative. The experimental design is a two-by-two factorial design. Two levels of decomposability of technology are the values for the first variable. The multidivisional form (M-form) and the unitary (U-form) are the values for the second variable. The data are generated from a perturbed decomposed mathematical programming model that is coordinated by a Dantzig-Wolfe pricing approach. The data are analyzed using the Kruskal-Wallis nonparametric approach. The results substantiate Williamsons hypothesis that the M-form of organization is superior to the U-form organization alternative. For each of the two levels of decomposability of technology, the M-form of organization yields higher profit solutions than the U-form.