This study uses the Self Determination Theory (SDT) to describe and analyse what motivates pilots whilst having limited control over working hours. Pilots work in a challenging environment with variable working hours and high operational demands. This affects the pilots autonomous, intrinsic, extrinsic and controlled motivation. The study uses a qualitative approach interviewing eight Swedish pilots working at different airlines. This study describes and analyses the sense of autonomy, sense of competence and sense of relatedness, and how autonomous motivation, intrinsic motivation, extrinsic motivation, and controlled motivation affects pilots´ motivation. This study finds that pilots value the sense of autonomy that comes from empowerment to take decisions within the strict framework of rules they operate in. The pilots feel a strong sense of competence from training programs and knowledge transfer colleagues, and relatedness to colleagues through a sense of being cared for, all these seems to promote autonomous and intrinsic motivation. Further, intrinsic motivation arises from a passion for flying and executing leadership. Extrinsic motivation arises from salary, and fear of punishment. The pilots lack of ability to control their working hours leads to controlled motivation and stress. The findings of this study could be used to improve pilot welfare and motivation. By understanding and addressing pilot motivation airlines can develop strategies to support its workforce more effectively.

This research aims to improve the design and placement of runway exits at airports through analysis and modeling of the effects that exogenous causal factors have on pilots' landing behavior and exit selections. Incorporating these factors into modeling software will strengthen the software's utility by providing project teams the ability to specify which pilot motivational causal factors apply to a new or existing runway. The main findings suggest pilots' exit selections are deterministic but dependent on the presence (or absence) of six (6) causal factors. A model and two (2) case studies are presented and compared against predictions generated by existing modeling software. The results support a finding that the causal factor model improves motivation-based predictions over current modeling techniques, which are drawn from stochastic distributions. The accuracy of this model enables designers to optimize runway exit placement and geometry to maximize runway capacity. / Master of Science / Airport design engineers currently plan the locations and geometric characteristics of runway exits by balancing the expected fleet mix of aircraft on that runway with the capacity and delay effects that the number and placement of these exits might cause. This technique originated from research beginning in the early 1970s, which found that pilots' exit motivations primarily resulted from the capabilities and limitations of their aircraft. Since pilots tend to "fly by the numbers" (i.e., exhibit predictable approach airspeeds, power levels, wing flaps, touchdown locations, landing speeds, and braking efforts), engineers thus employed design principles in which the numbers, locations and geometries of exits were primarily functions of the physical and performance-based characteristics of the specific types of aircraft expected to utilize the runway. However, in studying more than 4 million landings by a single aircraft type (the Boeing 737-800) at 42 U.S. airports, the evidence in this thesis shows that pilots' exit selections are behaviorally motivated by more than the physics of motion. This thesis aims to refine previous research and engineering methods by showing evidence that pilots' exit selections have as much to do with the presence (or absence) of certain environmental factors within the landing system. These factors (described in detailed within) are unique to each airport's overall physical network of interconnected runways, exits, taxiways, terminals and other features. Within this network, a pilot's landing behavior and exit selection depends on the locational and relational interactions that each exit choice will have on the time and distance to their apron (gate) assignment. These "interactions" are referred to as causal factors – defined as physical features within a landing environment that pilots have little-to-no control over – but which nevertheless influence their specific exit selections. Two (2) runway case studies provided in this thesis evidence a finding that a causal factor model reliably predicts pilots' exit selections better than current modeling techniques, which are drawn from probability-based statistical distributions. The stability and accuracy of the new model enables engineering design and project teams to optimize runway exit placement and geometry to maximize runway capacity, and can be adopted for use in both existing and future runways.