Kolbe ATM is an index developed by Kathy Kolbe to measure the conative traits on an individual. The index assigns each individual a value in four categories, or Action Modes, that indicates their level of insistence on a scale of 1 to 10 in that Action Mode (Kolbe, 2004). The four Action Modes are:
• Fact Finder – handling of information or facts
• Follow Thru – need to pattern or organize
• Quick Start – management of risk or uncertainty
• Implementor – interaction with space or tangibles
The Kolbe A (TM) index assigns each individual a value that indicates their level of insistence with 1-3 representing resistant, preventing problems in a particular Action Mode; 4-6 indicating accommodation, flexibility in a particular Action Mode; and 7-10 indicating insistence in an Action Mode, initiating solutions in that Action Mode (Kolbe, 2004).
To promote retention of conative diversity, this study examines conative diversity in two engineering student populations, a predominately freshmen population at Chandler Gilbert Community College and a predominately junior population at Arizona State University. Students in both population took a survey that asked them to self-report their GPA, satisfaction with required courses in their major, Kolbe ATM conative index, and how much their conative traits help them in each of the classes on the survey. The classes in the survey included two junior level classes at ASU, Engineering Business Practices and Structural Analysis; as well as four freshmen engineering classes, Physics Lecture, Physics Lab, English Composition, and Calculus I.
This study finds that student satisfaction has no meaningful correlation with student GPA.
The study also finds that engineering programs have a dearth of resistant Fact Finders from the freshmen level on and losses resistant Follow Thrus and insistent Quick Starts as time progresses. Students whose conative indices align well with the structure of the engineering program tend to consider their conative traits helpful to them in their engineering studies. Students whose conative indices misalign with the structure of the program report that they consider their strengths less helpful to them in their engineering studies.
This study recommends further research into the relationship between satisfaction with major and conation and into perceived helpfulness of conative traits by students. Educators should continue to use Kolbe A (TM) in the classroom and perform further research on the impacts of conation on diversity in engineering programs.
This dissertation argues that established thinking harbors misconceptions about infrastructure systems that diminish attempts to improve their resilience. Widespread efforts based on the current canon focus on improving data analytics, establishing resilience goals, reducing failure probabilities, and measuring cascading losses. Unfortunately, none of these pursuits change the resilience of an infrastructure system, because none of them result in knowledge about how data is used, goals are set, or failures occur. Through the examination of each misconception, this dissertation results in practical, new approaches for infrastructure systems to respond to unforeseen failures via sensing, adapting, and anticipating processes. Specifically, infrastructure resilience is improved by sensing when data analytics include the modeler-in-the-loop, adapting to stress contexts by switching between multiple resilience strategies, and anticipating crisis coordination activities prior to experiencing a failure.
Overall, results demonstrate that current resilience thinking needs to change because it does not differentiate resilience from risk. The majority of research thinks resilience is a property that a system has, like a noun, when resilience is really an action a system does, like a verb. Treating resilience as a noun only strengthens commitment to risk-based practices that do not protect infrastructure from unknown events. Instead, switching to thinking about resilience as a verb overcomes prevalent misconceptions about data, goals, systems, and failures, and may bring a necessary, radical change to the way infrastructure is protected in the future.
Resilient infrastructure research has produced a myriad of conflicting definitions and analytic frameworks, highlighting the difficulty of creating a foundational theory that informs disciplines as diverse as business, engineering, ecology, and disaster risk reduction. Nevertheless, there is growing agreement that resilience is a desirable property for infrastructure systems – i.e., that more resilience is always better. Unfortunately, this view ignore that the fact that a single concept of resilience is insufficient to ensure effective performance under diverse and volatile stresses. Scholarship in resilience engineering has identified at least four irreducible resilience concepts, including: rebound, robustness, graceful extensibility, and sustained adaptability.
In this paper, we clarify the meaning of the word resilience and its use, explain the advantages of the pluralistic approach to advancing resilience theory, and clarify two of the four conceptual understandings: robustness and graceful extensibility. Furthermore, we draw upon examples in electric power, transportation, and water systems that illustrate positive and negative cases of resilience in infrastructure management and crisis response. The following conclusions result:
1. Robustness and graceful extensibility are different strategies for resilience that draw upon different system characteristics.
2. Neither robustness nor extensibility can prevent all hazards.
3. While systems can perform both strategies simultaneously, their drawbacks are different.
Robust infrastructure systems fail when policies and procedures become stale, or when faced with overwhelming surprise. Extensible systems fail when a lack of coordination or exhaustion of resources results from decompensation. Consequently, resilience is found neither only in robustness, nor only in extensibility, but in the capacity apply both and switch between them at will.