Pilot’s Willingness to Operate in Unmanned Aircraft System Integrated Airspace

The interest in Unmanned Aircraft Systems (UAS) use for private, civil, and commercial purposes such as package delivery, inspection, surveillance, and passenger and cargo transport has gained considerable momentum. As UAS infiltrate the National Airspace System (NAS), there is a need to not only develop viable, safe, and secure solutions for the co-existence of manned and unmanned aircraft, but also determine public acceptance and pilot’s willingness to operate an aircraft in such an integrated environment. Currently there is little or no research on pilot’s perceptions on their willingness to operate an aircraft in UAS integrated airspace and airports. The purpose of this study was to determine what effect the type of UAS integration, the type of UAS operations, and the airspace classification will have on pilot’s perspectives and willingness to operate an aircraft in UAS integrated airspace and airport environment. This study surveyed the eligible pilot population in hypothetical scenarios using convenience sampling to measure their willingness to operate an aircraft in UAS integrated airspace and airports using the Willingness to Pilot an Aircraft Scale, which has been shown to be valid and reliable by Rice, Winter, Capps, Trombley, Robbins, and Milner (2020). A mixed factorial design was used to study the interaction effects between the independent variables and the effects on the dependent variable, i.e., willingness to pilot an aircraft. The results of the mixed analysis of variance (ANOVA) indicated a significant interaction between type of UAS integration and airspace classification. Overall willingness decreased with airspace and differences in willingness to pilot an aircraft were based on segregated and integrated operations. The average pilot’s willingness to pilot an aircraft score differed from the highest score being for Class B, decreasing with decreasing airspace classes, with the lowest being for Class G. Analysis of pilot perspectives collected through open ended questions using text-mining techniques showed agreement with mixed ANOVA analysis that the primary factor in the pilot’s perception was airspace. Key concerns voiced by the pilots were situation awareness, risk and safety of operations, aircraft certification and airworthiness, and operator experience and regulatory conformance. The most positive sentiment was observed among pilots presented with the hypothetical scenario of fully autonomous UAS operations in a segregated environment. Findings from the study could aid regulators in developing better policies, procedures, integration solutions, improved training, and knowledge sharing

Developing a Behavioural Model for Predicting Public Attitudes towards the Use of Unmanned Aerial Systems

This research aimed at developing a conceptual model for predicting public attitudes towards unmanned aircraft and the intention to purchase. Based on technology acceptance theory and risk theory, the influence of perceived benefit, perceived risk and perceived control, has been examined. Research findings, based on PLS-SEM analyses, revealed that perceived benefit has an effect on attitudes and intention. Additionally, perceived risk influenced attitudes, whereas perceived control had no effect on both constructs

Intention to Complain About Unmanned Aircraft System Noise: A Structural Equation Analysis

Aircraft noise has a long and documented history as a source of public annoyance and a driver of noise complaints. The impending large-scale use of unmanned aircraft systems (UAS)s could expose a broader cross-section of the public to a new type of aircraft noise. Recent research notes some reactions to UAS noise, but no rigorous analyses of public intention to complain about UAS noise have been found. Due to the potential proliferation of UASs and their attendant noise, understanding public reaction could advise both government and industry. Governments at all levels could apply the results to inform policies related to providing the public information about UASs, aircraft certification standards (including noise), airspace use, routing, and restrictions to hours of operation. The industry could apply the results to optimize package delivery routes, determine regulation-compliant locations of operational hubs, and influence design of small package delivery aircraft to minimize noise. The purpose of the study was to examine factors, as included in an extended theory of planned behavior, that influence individuals’ intentions to complain about UAS noise. The research questions were: 1) what factors influence individuals’ intentions to complain about UAS noise, and 2) how do these factors affect individuals’ intentions to complain about UAS noise? Data were collected through a cross-sectional survey of a convenience sample of adults in the general public within the United States. Confirmatory factor analysis and structural equation modeling were used to analyze the data. An investigation of moderating interaction effects among select factors was also completed. The study examined the relationships between the measured factors and the general public’s intentions to complain about UAS noise. The results indicated that five factors influence individuals’ intentions to complain about UAS noise. These factors, in order of effect size, are 1) individuals’ attitudes toward complaining about UAS noise, 2) perceived social pressure to complain about UAS noise, 3) perceived usefulness of UASs, 4) perceptions of risks to safety, and 5) familiarity with UASs. Other factors investigated which were not statistically significant include perceived behavioral control, application type/use of UAS, and privacy concerns. The results of the structural model indicated that only one interaction was present at a statistically significant level. Attitude toward complaining about UAS noise and familiarity with UASs showed an interaction effect. As familiarity with UAS increases, the positive relationship between attitude toward complaining about UAS noise and intention to complain about UAS noise was strengthened. The subject research created and validated a theoretical framework which can be used to improve our understanding of and possibly predict individuals’ intentions to complain about UAS noise and help identify significant contributing factors

A Behavioral Research Model for Small Unmanned Aircraft Systems for Data Gathering Operations

According to Hitlin (2017) of the Pew Research Center, only 8% of U.S. citizens own an unmanned aircraft. Additionally, regarding feelings if U.S. citizens saw an unmanned aircraft flying close to where they live, 26% say they would be nervous, 12% feel angry, and 11% are scared. As of March 9, 2018, there were 1,050,328 U.S. small unmanned aircraft system (sUAS) registrations compared to 947,970 November 29, 2017. While sUAS use has increased in the U.S., it has lagged when compared to other items for personal use available to U.S. citizens as 92% own cell phones (Anderson, 2015). This slower acceptance rate identifies a potential need for more research as to why. No studies have specifically focused on individual factors for the behavioral intention of using sUAS for data gathering, encompassing the variables used in this study, nor a Structural Equation Model that shows relevant factors and associated relationships. Also, current ground theories fall short, lacking appropriate variables or modeling ability. Thus, this dissertation study developed a new behavioral research model termed VMUTES to determine the factors that influenced individuals’ intentions to operate small sUASs for data gathering and relationships between those factors. A sUAS system is comprised of integrated hardware, software, processes, or firmware. Data gathering is defined in this study as the transmission or recording of audio, pictures, videos, or collection of other data for modeler, civil, or public use. The new VMUTES model integrates portions of the technology acceptance model (TAM) and theory of planned behavior (TPB) model integrated with new factors: perceived risk and knowledge of regulations. The study used random sampling of Amazon Mechanical Turk® (AMT) members using an AMT Human Intelligence Task (HIT) that included a link to an online cross-sectional large-scale survey to collect data. Data Analysis included descriptive statistics analysis and the SEM process. Besides developing and validating a model and determining influencing factors, attention was also on verifying the relationships between constructs. Study limitations and future research recommendations are also discussed. Results indicated the VMUTES model had a strong predictive power of sUAS use for data gathering with seven of the ten original hypotheses supported while having a good model fit. Four new hypotheses were also identified with three supported. Additionally, all VMUTES model factors except for facilitating conditions were determined to have either a direct or indirect effect on behavioral intention and/or actual behavior with the TAM and TPB related factors having the strongest effects. Practically, this study filled an aviation research knowledge gap for sUAS use for data gathering. It also provided a research model and identified influencing factors of individuals’ behavioral intentions related to sUAS for data gathering. Thus, the newly developed model incorporating new variables can be used for further sUAS research and can provide an adaptable model for aviation and other technology areas to predict and facilitate new technology implementation where current models fall short. Finally, this study explored new and verified previously existing demographic variables for individuals who use sUASs for data gathering