Doctor of Philosophy, The Ohio State University, 2010, Food Science and Nutrition

Preference mapping refers to a category of statistical methods used to relate consumer acceptance to a product's characteristics (often measured by sensory descriptive analysis). Several techniques for creating preference maps exist, and they vary in the manner by which these two types of data are related. Techniques are generally classified into three main categories: external, internal, and hybrid. For external preference mapping, the sensory perceptual space is set by descriptive sensory data and consumer preference information is subsequently overlaid onto this sensory space. For internal preference mapping, the consumers' preference for the products (usually overall liking ratings) is used to create a liking space upon which sensory descriptive ratings are subsequently mapped. Hybrid techniques locate products on the map using both consumer and sensory descriptive data simultaneously onto a restricted liking space. Preference mapping studies consume a considerable amount of time and resources. When such investigations are unsuccessful, it is highly desirable to determine the root cause and avoid the same issue in the future. Preference mapping studies are necessarily complex and error can be introduced at any of several steps along the way, such as product selection, descriptive analysis, consumer testing, data analysis and interpretation of the outputs. The purpose of this investigation was to examine these issues and develop best practices for conducting preference mapping studies. One popular technique from each category was selected for investigation using a common dataset. Fifteen commercially available Swiss-type cheeses (10 domestic Swiss cheeses, 4 Baby Swiss cheeses, and 1 imported Swiss Emmenthal) were evaluated by twelve trained panelists using the Spectrum method. Significant differences between the cheeses were exhibited for 15 flavor attributes. The same 15 cheeses were also evaluated for overall liking by 101 untrained consumers (53 female; (open full item for complete abstract)

Committee: Michael E. Mangino PhD (Advisor); Jeannine F. Delwiche PhD (Advisor); W. James Harper PhD (Committee Member); M. Monica Giusti PhD (Committee Member) Subjects: Food Science; Psychology; Statistics

Master of Science (MS), Ohio University, 2001, Industrial and Manufacturing Systems Engineering (Engineering)

Non linear tolerance analysis by response surface methodology

Committee: Richard Gerth (Advisor) Subjects: Engineering, Industrial

Master of Science, University of Toledo, 2021, Mechanical Engineering

Fused deposition modeling (FDM) is the most widely used additive manufacturing technology because of its simplicity and low cost. However, its application is primarily restricted in rapid prototyping due to poor mechanical and surface properties, and limited filament material choices, mostly thermoplastic polymers. In order to address this issue, there has been extensive research on enhancement of mechanical and surface properties of FDM printed polymer parts. Also, few researchers have successfully prepared some superior composite filament materials by mixing different filler particles in polymer matrix. However, the study about the product fabricated from these filaments are only limited to exploring mechanical and thermal properties. Because surface roughness is the most direct means of judging a product's quality, as well as a practical need in most applications, it seems only natural to look into the surface quality of FDM printed composite materials. Therefore, the first objective of this thesis is to investigate the influence of process parameters on surface roughness of FDM printed polymer/metal composite parts using Analysis of Variance Technique. The second objective is to determine the optimum process parameter setting to get the least surface roughness on FDM printed Polymer/Metal composite parts. This is achieved by analyzing the experimental data with response surface methodology (RSM).

Committee: Dr. Ala Qattawi (Committee Chair); Dr. Adam Schroeder (Committee Member); Dr. Brian Trease (Committee Member) Subjects: Mechanical Engineering

PhD, University of Cincinnati, 2018, Arts and Sciences: Psychology

Management of job demands is central to the employee experience. Job demands refer to physical, psychological, social, or organizational aspects of a job that necessitate sustained physical or mental effort and are, as a result, associated with physiological or psychological costs. In regards to well-being, job demands are associated with psychological costs, which in turn produce higher levels of job burnout and reduced employee engagement. Though much is known about the basic relationship between job demands and well-being, job demands are largely viewed as predictable in nature. Due to this assumption, it is unknown whether employees possess accurate assessments of pending demands or whether such accurate knowledge affords psychological benefits. Through the lens of role theory, the current study investigated the impact of congruencies and discrepancies between expected demands and demands actually experienced at work on employee engagement and emotional exhaustion, using day-level role ambiguity as a mediator. Ninety-four full-time employees from various industries and occupations completed two surveys per day for three weeks with an average of 10.7 matched surveys per employee, leading to a final sample of 1,004 observations. Findings suggest that employees do experience differences between expected demands and the level of demands actually experienced, with 46.6% being inaccurate in their estimates. Polynomial regression analyses revealed that incongruent demands did not have a relationship with day-level role ambiguity. However, post-hoc analyses reveal that discrepant relationships between expected and actual demands, or incongruent demands, may result in higher levels of emotional exhaustion. I conclude that though demand incongruence did not impact role ambiguity directly, this new construct shows promise as an understudied construct with potential for directly impacting psychological well-being.

Committee: Stacie Furst-Holloway Ph.D. (Committee Chair); Chung-Yiu Peter Chiu Ph.D. (Committee Member); Justin DeSimone (Committee Member) Subjects: Management

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2014, Psychology/Industrial-Organizational

The concept of Person-Environment (PE) fit has gained strong theoretical and empirical support, demonstrating how the degree to which an individual is congruent with his or her environment is predictive of important individual and organizational outcomes, such as job satisfaction, performance, stress, and turnover (Kristof-Brown, Zimmerman, & Johnson, 2005). The current study expands upon the PE fit literature by examining how similarity between individual safety motivation and organizational safety climate influence safety behaviors. In addition, job attitudes and strain were investigated as outcomes, which have received very little attention in the area of occupational safety research. The current study examined Safety-Specific Person-Environment (SSPE) fit's relation with outcomes using both linear regression and polynomial regression approaches, allowing for a more in depth analysis of the 3-dimenisonal relationships between safety climate, safety motivation, and the outcomes (Edwards & Parr, 1993). Results revealed that SSPE fit was predictive of safety behaviors, job attitudes, and strain. More specifically, when safety climate and safety motivation are congruent, higher levels are associated with more safety behaviors and positive job attitudes and reduced strain, as expected. In addition, when there was discrepancy between the predictors, it was found that safety behaviors and job attitudes were highest and strain was lowest when safety climate exceeded safety motivation. Unexpectedly, results revealed that safety behaviors increased as the amount of discrepancy between safety climate and safety motivation increased. Theoretical and practical implications, as well as suggestions for future research, are discussed.

Committee: Steve Jex (Advisor); McKinney Earl (Committee Member); Chen Yiwei (Committee Chair); Matthews Russell (Committee Member) Subjects: Psychology