Supply networks are composed of large numbers of firms from multiple interrelated industries. Such networks are subject to shifting strategies and objectives within a dynamic environment. In recent years, when faced with a dynamic environment, several disciplines have adopted the Complex Adaptive System (CAS) perspective to gain insights into important issues within their domains of study. Research investigations in the field of supply networks have also begun examining the merits of complexity theory and the CAS perspective. In this article, we bring the applicability of complexity theory and CAS into sharper focus, highlighting its potential for integrating existing supply chain management (SCM) research into a structured body of knowledge while also providing a framework for generating, validating, and refining new theories relevant to real-world supply networks. We suggest several potential research questions to emphasize how a * We sincerely thank Professors Thomas Choi (Arizona State University), David Dilts (Vanderbilt University), and Kevin Dooley (Arizona State University) for their help, guidance, and support. † Corresponding author. 548 Complexity and Adaptivity in Supply NetworksCAS perspective can help in enriching the SCM discipline. We propose that the SCM research community adopt such a dynamic and systems-level orientation that brings to the fore the adaptivity of firms and the complexity of their interrelations that are often inherent in supply networks.

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Background Student evaluations of teaching (SETs) are a widely used metric to evaluate instructor effectiveness and are used to make promotion, tenure, and retention decisions for faculty. There is also growing interest by those outside the university community to use these metrics to evaluate faculty and broader academic performance. Purpose (Hypothesis)This study seeks to understand if and how course and instructor characteristics affect SETs and thereby to improve the usefulness of these metrics. This article aims to statistically examine the relationship between course and instructor characteristics and SETs.Design/Method SETs from a large engineering college at a major public university were evaluated over a seven-semester period that covered 3938 courses taught by 549 unique engineering instructors. Course and instructor demographic data were statistically evaluated for their effects on SETs.Results Course characteristics such as class size, course level, and whether a course was an elective or required had statistically significant effects on SETs. Instructor characteristics of gender and academic rank affected SETs and average course grades, respectively. Average course grades were positively correlated with SETs.Conclusions Data analysis showed that course characteristics, faculty demographics, and average course grades had statistically significant effects on SETs; however, in some cases the effect sizes of these variables were small. Administrators and senior faculty members should be cognizant of these relevant factors and their effects when assigning faculty to certain courses and evaluating their teaching effectiveness using SETs.

In recent years, there have been increasing calls from the government and other organizations to provide easy public access to student evaluations of teaching. Indeed, the increasing ease of displaying and viewing large quantities of information, and competition among universities and majors for students, makes it likely that an era of greater transparency of this type of information is at hand. While students' evaluation of teaching (SET) is one quantitative metric that rates the instructor, it may be influenced by factors that are often beyond the instructor's control. In this study, we analyze a longitudinal data set from both engineering and business schools of a large public university, and identify factors that influence SET. We show which factors have the highest influence on overall SET scores, and contrast these between engineering and business colleges. Colleges within the same university may have differences in the factors affecting SET, and recognition of this is important in effectively and fairly evaluating SET scores. We also provide recommendations regarding information that should be displayed along with the SET, particularly when SET scores are made public, so that instructors are not unduly penalized when their evaluations can be influenced by factors over which they have no control.