Consumer confusion, caused by product similarity, choice and/or information overload, and the presence of ambiguous information, can negatively affect consumersʼ decision making, and thereby also companiesʼ profitability. The purpose of this quantitative study was to investigate how the three variables (i.e. similarity, overload, ambiguity) of Walsh et al.ʼs (2007) consumer confusion proneness model affect consumersʼ decision postponement and brand loyalty, concerning low involvement products. A conceptual framework based on consumer behavior- and consumer confusion literature, was utilized to form six hypotheses predicting the causality between the different variables. After validating and adapting the scale to data gathered through a survey, regarding Swedish studentsʼ purchasing habits of laundry detergent, two standard multiple regressions revealed that one hypothesis was supported; overload confusion proneness decreases brand loyalty in a low involvement product category. All implications were then discussed from practitionersʼ and researchersʼ points of view, concluding with possible limitations and further research.

The ability to effectively match supply and demand can lead to significant revenue benefits in the airline industry. Airline supply management deals with assigning the right resources (i.e., aircraft and crew) to the right routes in the flight network. Due to certain crew regulations, operating characteristics, and constraints of the airline companies, these supply management decisions need to be made well in advance of departures, at a time when demand is highly uncertain. However, demand forecasts improve markedly over time, as more information on demand patterns is gathered. Thus, exploiting the flexibilities in the system that allows the partial postponement of supply decisions to a later time, when more accurate demand information is obtained, can significantly improve the airline's revenue. In this thesis, we propose and analyze the Demand Driven Swapping (DDS) approach that aims at improving the airline's revenue by reducing the supply-demand mismatches through dynamically swapping aircraft as departures approach. This research has been done in collaboration with our industrial partner, the United Airlines Research and Development Division. Due to the proximity to departures, the DDS problem is restricted by two main constraints: 1) the initial crew schedule needs to be kept intact (due to certain union contracts); and 2) airport services and operations need to be preserved to the greatest extent possible. As a result, only a limited number of simple swaps can be performed between aircraft types of the same family (i.e. crew-compatible aircraft types). However, the swaps can be potentially performed on a daily basis given the initial fleet assignments. Clearly, the swapping criteria, frequency, and timing will highly impact the revenue benefits of the DDS approach. When the swapping decisions are made several weeks prior to departures (i.e., 4-6 weeks before departures), they will not cause much disturbance to the operations, but will be performed under highly uncertain demand information. On the other hand, swapping decisions that are delayed to a time later (i.e., 1-3 weeks before departures) will decrease the possibility of bad swaps, but will result in larger costs due to the higher disruptions to airport services and operations. Thus our research objective is to provide guidelines and principles on how the flexible capacity should be managed in the system. For this purpose, we study the effectiveness of different swapping strategies, characterized in terms of their frequency and timing, for hedging against the demand uncertainty. We first study stylized analytical models to gain insights into the critical parameters that affect these benefits. Simulation models are then conducted to test the validity of our analytical findings as well as to analyze more complex strategies and assess the dynamic performance of these strategies. The analytical results indicate that strategies that make the swapping decision early in time (in order to minimize disturbances to the operations) perform very well on routes, where the demand uncertainty is low and the expected demands on the legs are well-balanced. Otherwise, a swapping strategy, which revises the swapping decision over time, should be implemented. Our simulation results, based on real data obtained from United Airlines, confirm the analytical findings. / Master of Science

Online travel agencies (OTAs) play an important role in the online travel market. OTAs offer various features, rich information, and numerous hotel choices for customers. All these hotel choices, accompanied by detailed information and supported by various website features, are generally beneficial and advantageous for hotel customers. They allow hotel customers to evaluate numerous hotels based on different characteristics, including price, location, amenities, and ratings. However, the reasons why OTA users postpone their booking decisions and switch to other hotel booking channels remain unclear. Therefore, this dissertation aimed to explore the determinants of OTA users' intention to postpone their hotel booking decisions and their intention to switch to other hotel booking channels by developing and testing a conceptual framework based on the integration of stressor-strain-outcome (SSO) framework and information processing theory (IPT). A sequential exploratory mixed-methods approach, comprising two phases (i.e., qualitative and quantitative) was employed as the research design for this dissertation. In the first phase, themes that hold significance in influencing OTA users' intention to postpone their hotel booking decisions and intention to switch to other hotel booking channels were explored by conducting thematic analysis on qualitative data collected from OTA users through three online focus groups. In the second phase, the themes that emerged from the qualitative data analysis were utilized to formulate a quantitative survey instrument for a more in-depth investigation of the research problem. A total of 700 online surveys were collected, and structural equation modeling (SEM) was conducted to analyze the quantitative data and test the study hypotheses. The study results revealed that information overload, choice overload, and system feature overload, as stressors, positively influenced strain factors including similarity confusion, ambiguity confusion, time risk, psychological risk, performance risk, privacy risk, emotional exhaustion, decision task difficulty, and untrustworthiness toward OTAs. The results also indicated that similarity confusion, performance risk, emotional exhaustion, decision task difficulty, and untrustworthiness toward OTA, as strain factors, had a positive impact on OTA users' intention to postpone their hotel booking decisions. The results further indicated that ambiguity confusion, time risk, performance risk, decision task difficulty, as strain factors, had a positive impact on OTA users' intention to switch to other hotel booking channels. This dissertation provides valuable theoretical contributions to the existing body of knowledge in the realms of travel, tourism and hospitality industries and offers significant practical implications for OTA operators to generate effective and efficient strategies to be more competitive in the travel market and avoid unwanted consequences.