Matching Items (5)
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- All Subjects: Productivity
- Genre: Academic theses
- Creators: Sullivan, Kenneth
- Creators: Wiezel, Avi
- Creators: Enders, Stephen G
- Status: Published
Description
Using experience, observations, data, current research, and writings in the field of volunteer management, it was determined there was a need to study the effects of leadership/management practices on the productivity outcomes of a volunteer construction workforce. A simple wood bench that would be tiled and painted was designed to test the areas of Time, Waste, Quality, Safety, and Satisfaction of different volunteer groups. The challenge was bolstered by giving the teams no power tools and limited available resources. A simple design of experiment model was used to test highs and lows in the three management techniques of Instruction, Help, and Encouragement. Each scenario was tested multiple times. Data was collected, normalized and analyzed using statistical analysis software. A few significant findings were discovered. The first; the research showed that there was no significant correlation between the management practices of the leader and the satisfaction of the volunteers. The second; the research also showed when further analyzed into specific realistic scenarios that the organizations would be better to focus on high amounts of Help and Encouragement in order to maximize the productivity of their volunteer construction workforce. This is significant as it allows NPO's and governments to focus their attention where best suited to produce results. The results were shared and the study was further validated as "significant" by conducting interviews with experts in the construction nonprofit sector.
ContributorsPrigge, Diedrich (Author) / Sullivan, Kenneth (Thesis advisor) / Wiezel, Avi (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2013
Description
Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
ContributorsCham, Hei Ning (Author) / Tein, Jenn-Yun (Thesis advisor) / Enders, Stephen G (Thesis advisor) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013
Description
Owner organizations in the architecture, engineering, and construction (AEC) industry are presented with a wide variety of project delivery approaches. Implementation of these approaches, while enticing due to their potential to save money, reduce schedule delays, or improve quality, is extremely difficult to accomplish and requires a concerted change management effort. Research in the field of organizational behavior cautions that perhaps more than half of all organizational change efforts fail to accomplish their intended objectives. This study utilizes an action research approach to analyze change message delivery within owner organizations, model owner project team readiness and adoption of change, and identify the most frequently encountered types of resistance from lead project members. The analysis methodology included Spearman's rank order correlation, variable selection testing via three methods of hierarchical linear regression, relative weight analysis, and one-way ANOVA. Key findings from this study include recommendations for communicating the change message within owner organizations, empirical validation of critical predictors for change readiness and change adoption among project teams, and identification of the most frequently encountered resistive behaviors within change implementation in the AEC industry. A key contribution of this research is the recommendation of change management strategies for use by change practitioners.
ContributorsLines, Brian (Author) / Sullivan, Kenneth (Thesis advisor) / Wiezel, Avi (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2014
Description
The wood-framing trade has not sufficiently been investigated to understand the work task sequencing and coordination among crew members. A new mental framework for a performing crew was developed and tested through four case studies. This framework ensured similar team performance as the one provided by task micro-scheduling in planning software. It also allowed evaluation of the effect of individual coordination within the crew on the crew's productivity. Using design information, a list of micro-activities/tasks and their predecessors was automatically generated for each piece of lumber in the four wood frames. The task precedence was generated by applying elementary geometrical and technological reasoning to each frame. Then, the duration of each task was determined based on observations from videotaped activities. Primavera's (P6) resource leveling rules were used to calculate the sequencing of tasks and the minimum duration of the whole activity for various crew sizes. The results showed quick convergence towards the minimum production time and allowed to use information from Building Information Models (BIM) to automatically establish the optimal crew sizes for frames. Late Start (LS) leveling priority rule gave the shortest duration in every case. However, the logic of LS tasks rule is too complex to be conveyed to the framing crew. Therefore, the new mental framework of a well performing framer was developed and tested to ensure high coordination. This mental framework, based on five simple rules, can be easily taught to the crew and ensures a crew productivity congruent with the one provided by the LS logic. The case studies indicate that once the worst framer in the crew surpasses the limit of 11% deviation from applying the said five rules, every additional percent of deviation reduces the productivity of the whole crew by about 4%.
ContributorsMaghiar, Marcel M (Author) / Wiezel, Avi (Thesis advisor) / Mitropoulos, Panagiotis (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Facilities Management (FM) around the globe at different companies in different industries are often forced to make difficult decisions on whether or not to transition a workplace environment and how to decide what factors of a workplace environment can benefit or hinder a company's productivity. The data and research presented within this paper are targeted at aiding and educating FM in determining what factors to consider in a workplace transition to an open-seating design and validate the importance of recognizing how these factors impact the productivity of the individual and the organization. Data contained in this paper was gathered through two different survey samples: 1) a semiconductor company that transitioned its employees from cubicles and offices to an open-seating environment; and 2) a general study open to professionals and their experiences and opinions on workplace environments. This data was used to validate or disprove the views on open-seating workspace held by the FM industry today. Data on the topic of how employees react to being transitioned to open-seating environments and looking at the breakdown of the results between engineers and non-engineers is examined within this research. Also covered within the research is data on transitions to other seating environments outside of open-seating concepts to evaluate and compare transition types. Lastly, data was gathered and discussed on the amount of time needed to adapt after a transition and what environment types were linked to being the most productive. This research provides insight on workplace environments and transitions and how they have an impact on productivity and can be used in the decision process when considering transitioning environments.
ContributorsThalin, William (Author) / Sullivan, Kenneth (Thesis advisor) / Smithwick, Jake (Committee member) / Stone, Brian (Committee member) / Arizona State University (Publisher)
Created2017