Search for: All records

Engineering design education frequently focuses on the methods and tools that enable and enhance product creation. These tools range from individual and group ideation techniques to innovation portfolio management for organizations and originate from a diversity of consulting, academic, and industrial sources. The factors that drive the adoption, use, and ongoing success of these tools are not well understood and are likely driven by a complex interaction of human, organizational, and economic factors. This paper investigates innovation method and tool adoption in industry through semi-structured interviews with individuals from a Fortune 500 company. This work explores three resulting themes 1) individual incentives and motivation for adoption, (2) the appropriateness of tool selection for the organizational product domain and compatibility with existing processes, and (3) executive and management support for adoption. The implications for engineering education are also discussed. 

Academics have developed a wide range of tools and methods to support innovation and the product development process. Unfortunately, few of these methods and tools have been widely adopted in industry. The current work seeks to identify what catalyzes and blocks the adoption of R&D innovation tools and methods in large organizations. Semi-structured exploratory interviews were conducted at several U.S.-based Fortune 500 companies. Interviewees include executives, managers, and individual contributors. Future work includes interviews with at least two more organizations with at least eight to ten individuals per organization. Initial interviews were transcribed, and open coding sought themes (commonly called categories) containing the catalysts and barriers. Initial findings indicate six themes that catalyze adoption: Confidence in the Method, Characteristics of the Method, Characteristics of the Practitioner, Practitioner Benefits, Leadership, and Organization. Barriers identified include Organization, Characteristics of the Method, Characteristics of the Practitioner, and Practitioner Drawbacks.This is an example of the abstract style. The abstract should be between 100 and 150 words. 

Makerspaces continue to be a part of many university engineering programs. More work is needed to understand their impacts and how makerspaces should be implemented to maximize their impact for all students. Many of the available approaches to ascertain impact are highly effective but excessively time-intensive, especially for continuous monitoring of a space. This paper presents the use of bipartite network analysis of weighted and unweighted matrices of student tool usage to determine modularity as an easy-to obtain metric to monitor space. To obtain the data needed, an end-of-the-semester survey asks students which tool they used in the space and how frequently. Data was collected in Spring 2021 and Spring 2022 as covid restrictions were being lifted, providing a data set where the modularity values should be changing. Prior work demonstrated unweighted modularity values as an effective tool for identifying changes in the health of a makerspace. Current work explores the inclusion of tool frequency use on the conclusion drawn from modularity analysis. Results show differing patterns of results between the weighted (includes frequency of use) and unweighted (only considers if a tool was used) modularity values. More work needs to explore the use of weighted bipartite network analysis and the benefits it may provide over the much simpler to obtain the unweighted analysis. Additional research is also needed on other methods to monitor the health of a makerspace and the benefits to all of its users. 

Makerspaces continue to be a part of many university engineering programs. More work is needed to understand their impacts and how makerspaces should be implemented to maximize their impact for all students. Many of the available approaches to ascertain impact are highly effective but excessively time-intensive, especially for continuous monitoring of a space. This paper presents the use of bipartite network analysis of weighted and unweighted matrices of student tool usage to determine modularity as an easy-to-obtain metric to monitor space. To obtain the data needed, an end-of-the-semester survey asks students which tool they used in the space and how frequently. Data was collected in Spring 2021 and Spring 2022 as covid restrictions were being lifted, providing a data set where the modularity values should be changing. Prior work demonstrated unweighted modularity values as an effective tool for identifying changes in the health of a makerspace. Current work explores the inclusion of tool frequency use on the conclusion drawn from modularity analysis. Results show differing patterns of results between the weighted (includes frequency of use) and unweighted (only considers if a tool was used) modularity values. More work needs to explore the use of weighted bipartite network analysis and the benefits it may provide over the much simpler to obtain the unweighted analysis. Additional research is also needed on other methods to monitor the health of a makerspace and the benefits to all of its users. 

Prior research emphasizes the benefits of university makerspaces, but overall, quantitative metrics to measure how a makerspace is doing have not been available. Drawing on an analogy to metrics used for the health of industrial ecosystems, this article evaluates changes during and after COVID-19 for two makerspaces. The COVID-19 pandemic disturbed normal life worldwide and campuses were closed. When students returned, campus life looked different, and COVID-19-related restrictions changed frequently. This study uses online surveys distributed to two university makerspaces with different restrictions. Building from the analysis of industrial ecosystems, the data were used to create bipartite network models with students and tools as the two interacting actor groups. Modularity, nestedness, and connectance metrics, which are frequently used in ecology for mutualistic ecosystems, quantified the changing usage patterns. This unique approach provides quantitative benchmarks to measure and compare makerspaces. The two makerspaces were found to have responded very differently to the disruption, though both saw a decline in overall usage and impact on students and the space’s health and had different recoveries. Network analysis is shown to be a valuable method to evaluate the functionality of makerspaces and identify if and how much they change, potentially serving as indicators of unseen issues.