Interpersonal conflict in organizations plays an important role in performance, but the exact nature of that role remains unclear. Among conflict researchers a shift has occurred from believing that all conflict is debilitating for organizational performance to the realization that there are both positive and negative aspects of conflict. Contemporary research adopts a distinction between task conflict and relationship conflict. Conceptually, a positive relationship has been proposed between task conflict and performance, while a negative relationship has been proposed between relationship conflict and performance. Empirically, however, there has been wide variation in the findings linking either type of conflict with performance. A recent meta-analysis by De Wit and Greer (2008) found that across studies linking task conflict and performance, findings reflected positive, negative and no relationships. Similarly, for relationship conflict, though a predominantly negative relationship was found across studies, there was wide variation in relationship magnitudes across studies. These meta-analytic results show that the effects sizes across both types of conflict studies are mainly negative. However, given the large standard deviation estimates in both cases, there were also positive effect sizes in some of the studies. The wide variation across studies leads to the conclusion that in most studies conflict is detrimental, but in some it truly can be useful. Also, it suggests that the relationship between both task and relationship conflict and performance needs to be clarified. Consequently, this research aims to examine the source of the inconsistencies within the conflict literature by introducing a behavioral taxonomy to help explain the relationship between performance and the two types of conflict. Using a quasi-experimental design to study conflict, I will be able to induce conflict and observe the team behavioral dynamics as they unfold. Revised file, GMc 5/28/2014 per Dean DePauw / Ph. D.

<p dir="ltr">Empathic design involves two social practices – one is collaboration with users to elicit and make a meaning of user experiences, and another is intrateam collaboration to develop a mutually understood, agreed-upon interpretation of user experiences among team members. This study is focused on the latter phenomenon, conceptualizing this social practice as team learning for shared empathic understandings. Through this conceptualization, this study aimed to characterize a social mechanism underlying intrateam collaboration in empathic design in terms of how professionals interact with each other to develop and apply shared empathic understandings to design ideas within a team over a design process. For this objective, I conducted a conversation analysis to examine one professional design team’s conversations over a design journey from need-finding to initial ideation to prototyping and testing, exploring team members’ conversational behaviors revealed in conversational exchanges. More specifically, I investigated their conversational behaviors at both the team and individual levels: team learning behaviors (i.e., construction, co-construction, and constructive conflict) for team-level collective behaviors and interpersonal reactions (e.g., move, question, block, etc.) and empathy perspectives (i.e., the first, second, and third-person perspectives) for individual-level behaviors. Through this investigation, I found that a team’s design journey can be characterized by their travel among the team learning behaviors during design conversations and that each type of team learning behavior can be featured by frequently used interpersonal reactions and empathy perspective transitions at the individual level. Through this behavior-oriented, holistic view of team learning for shared empathic understandings, this study provides fresh insights into what conversational behaviors can be more used at the team and individual levels and how these behaviors can facilitate a team to arrive at team-level empathic understandings and design ideas. I discuss the research and educational implications of this study and future research ideas based on this study.</p>

<p>he use of student-centered instructional strategies is a common practice in engineering classes. However, understanding which instructional strategies have a more profound effect on students’ performance and motivation is fundamental in course design. Such comparisons would allow instructors to design and plan their courses with better learning activities, which could lead to better student engagement and learning. In this three-paper dissertation, I explored the relative effectiveness of two instructional strategies 1) reflective thinking, and 2) teamwork participation by primarily using quantitative methods. Self-regulated learning theory and the Interactive-Constructive-Active-Passive (ICAP) framework guided the selection of these two strategies.</p><p>The first study investigated the relationship of an instructional strategy and a motivational construct through the following research questions: 1) Do students with high academic self-efficacy generate high-quality reflections? 2) To what degree do students’ self-efficacy beliefs and reflection quality scores predict their learning outcomes? Bivariate Pearson product-moment correlation and multiple linear regression were used to analyze the relationships.</p><p>In the second study, I focused on studying the relative effectiveness of two instructional strategies on a motivational construct in a larger engineering class. More specifically, the second study focused on understanding change in students’ participation in two instructional strategies (i.e., reflective thinking and teamwork) and students’ achievement goals. Further, the study investigated the unique contribution of instructional strategies on students’ academic performance and changes in achievement goals. I used stepwise hierarchical regression, simultaneous regression, and repeated measures ANOVA to analyze the data.</p><p></p><p>The third study focused on investigating the role of the same two instructional strategies on students’ academic performance and multiple motivational constructs (i.e., self-efficacy, task value, and engagement). I used structural equation modeling, and repeated measures ANOVA to analyze the data.</p>