Doctor of Business Administration (D.B.A.), Franklin University, 2023, Business Administration

The COVID-19 pandemic spurred a significant retail shift, with consumers turning to online shopping due to safety concerns and lockdowns. Retailers quickly adopted omnichannel strategies, merging online and offline channels to stay relevant and enhance the shopping experience. This research, grounded in innovation diffusion theory, examined the pandemic's influence on customer behavioral intentions regarding omnichannel capabilities. Using a quantitative research approach with a survey in Northwest Arkansas, the study explored the relationship between innovation diffusion attributes and customer omnichannel Buy-Online-Pickup-at-the-Store (BOPS) behavioral intention. A ten-point Likert scale survey was adapted from Kapoor to gather data from 190 respondents online. The respondent's Intention to Use BOPS increased from 36.8% pre-pandemic to 84% post-pandemic. Data was analyzed using Pearson correlation for each characteristic and regression for the combined attribute and customer intention to use BOPS. Notably, relative advantage, compatibility, and observability attributes significantly impacted the model, whereas trialability and complexity lacked significance within the combined model. The findings suggested that customers prioritize buy-Online-Pickup-at-the-Store's relative advantage, compatibility, and observability when making adoption decisions. While complexity and trialability are essential, their significance diminishes when considered with other attributes. This study contributes valuable insights into consumer behavior during crises and the evolving retail landscape post-crisis. These findings can guide strategies for optimizing omnichannel capabilities and enhancing customer adoption.

Committee: Sherry Abernathy (Committee Chair); Tim Reymann (Committee Member); Charles Fenner (Committee Member) Subjects: Behavioral Sciences; Business Administration; Management; Marketing; Technology

Master of Science (MS), Ohio University, 2020, Industrial and Systems Engineering (Engineering and Technology)

A nontraditional decision tree approach was used to predict worker intent to use passive occupational exoskeletons in various manufacturing tasks. A dataset adapted from a previous study containing 33 records of participant, exoskeleton, and task combinations was used to create multiple decision tree models. Worker intent to use the exoskeleton was used as the target variable for all decision tree models. Data were collected during two separate sessions with fifteen participants at five manufacturing facilities in Ohio. Participants wore exoskeletons for under 30 minutes in each session and answered questions pertaining to personal characteristics, task characteristics, and personal preferences. Response data were used to create practitioner and research decision tree models. The practitioner models classified worker intent to use exoskeletons using only task characteristics and personal characteristics. Research models used personal characteristics, task characteristics, and personal preferences features to predict intent to use with all collected data. Both practitioner and research models may be useful for practitioners and exoskeleton developers for better understanding factors related to intent to use exoskeletons. All models created in the study yielded findings consistent with previous exoskeleton literature. This study demonstrated the ability of classification trees to identify nonlinear relationships in datasets relating to intent to use assistive technologies.

Committee: Diana Schwerha Ph.D (Advisor); Gary Weckman Ph.D (Committee Member); Dean Bruckner Ph.D (Committee Member); Timothy Ryan Ph.D (Committee Member) Subjects: Industrial Engineering