Sangyoon Yi

Analysis of keyword networks in MIS research and implications for predicting knowledge evolution

New concepts and ideas build on older ones. This path dependence in knowledge evolution has promoted research to identify important knowledge elements, research trends, and opportunities by analyzing publication data. In our study, keyword networks formed from published academic articles were analyzed to examine how keywords are associated with each other and to identify important keywords and their change over time. Based on MIS publication data from 1999 to 2008, our analysis provided several notable findings. First, while the MIS field has changed rapidly, resulting in many new keywords, the connectivity among them is highly clustered. Second, the keyword networks show clear power-law distribution, which implies that the more popular a keyword, the more likely it is selected by new researchers and used in follow-on studies. In addition, a strong hierarchical structure is identified in the network. Third, the network-based perspective reveals interdisciplinary keywords which are different from popular ones and have the potential to lead research in the MIS field.

The organization of scientific knowledge: the structural characteristics of keyword networks

The understanding of scientific knowledge itself may promote further advances in science and research on the organization of knowledge may be an initiative to this effort. This stream of research, however, has been mainly driven by the analysis of citation networks. This study uses, as an alternative knowledge element, information on the keywords of papers published in business research and examines how they are associated with each other to constitute a body of scientific knowledge. The results show that, unlike most citation networks, keyword networks are not small-word networks but, rather, locally clustered scale-free networks with a hierarchic structure. These structural patterns are robust against the scope of scientific fields involved. In addition, this paper discusses the origins and implications of the identified structural characteristics of keyword networks.

Inertia in Routines: A Hidden Source of Organizational Variation

Traditionally, routines have been perceived as a primary source of inertia, which slows down organizational change and hinders organizational adaptation. Advancing prior research on routine dynamics, this study examines how inertia in routines influences the process of organizational adaptation, both in the absence and presence of endogenous change of routines. Contrary to conventional wisdom, our analysis suggests an overlooked mechanism by which routine-level inertia may help, rather than hinder, organization-level adaptation. We demonstrate this mechanism by using a simple theoretical model in which the organization is characterized as a configuration of interdependent routines and study the process by which this configuration adapts to cope with its task environment. We find that inertia in routines may engender potentially useful variation in the process of organizational adaptation because reduced rates of routine-level changes may lead to temporal reordering when these changes are implemented. In our nuanced perspective, inertia is not only a consequence of adaptation or selection as perceived in prior research, but also a source of variation that turns out to be useful for adaptation. This logic is helpful to better understand why apparently inertial organizations keep surviving and from time to time exhibit outstanding performance. We conclude by discussing how this advanced understanding of the role of routines in organizational adaptation helps elaborate the theory of economic evolution.

Garbage Can in the Lab

We develop an experimental setting where the assumptions and predictions of the garbage can model can be tested. A careful reconstruction of the original simulation model enables us to select parameters that leave room for potential variations in individual behavior. Our experimental design replicates these parameters and thereby facilitates comparison of human behavior with the behavioral assumption of the original model. We find that the majority strategy of human subjects is consistent with the original model, but exhibits some extent of behavioral diversity. What is notable is that, although slightly outperforming the simulation model, human subjects failed to learn at the group level to improve performance. This is because human subjects exhibit fluid diverse behaviors that improve coordination in the face of uncertainty, but hinder collective learning.

Routines and Organizational Change: Inertia as a Hidden Source of Adaptation

Routines have been perceived as a source of inertia in the process of organizational change. In this study, we suggest an overlooked, but prevalent, mechanism by which the inertial nature of routin...

Structure, Skill, and Ambition in Organizational Problem-Solving

We extend the classical garbage can model to examine how individual differences in ability and motivation will influence organizational performance. We find that spontaneous coordination provided by an organized anarchy is superior when agents are equally competent. The Weberian bureaucracy of planned coordination is effective when problems require specialist knowledge. However, errors in matching problems to specialized agents are a central challenge for bureaucracies. Actual organizations, therefore, combine elements of organized anarchies and bureaucracies. Heterogeneous motivation compounds coordination problems, but is usually less important than competence. Our findings point to matching and interactive learning as fruitful areas for further study.