<p>Takeover is a business activity which really started in the beginning of the eighties and which still takes a strong part in the business and financial area all over the world. According to our studies as the desire for further acknowledgements and the desire of building a career around financial activities, this study has been naturally conducted in the banking area.</p><p>Regarding the steady use of acquisition like a powerful process with some positive and negative sides, we decided to implement a comparison of different mergers and acquisitions in the banking industry in the United States and Europe. This comparison has been supported and based on the third main topic of our study: performance.</p><p>These large and complex subjects combined together lead to the following hypotheses:</p><p>Hypothesis 1: Performance is not improved after takeover in the banking industry.</p><p>Hypothesis 2: The level of post takeover performance is the same in the U.S. as in the European bank acquisitions.</p><p>Based on the historical data and knowledge, the United States was the pioneer in the development of such gathers in the banking sector. Considering the United States as a reference, a first purpose was to compare them with the bank mergers and acquisitions in Europe. Stating on some possible differences as increasing our own knowledge have been some others purposes which have supported our work.</p><p>A first large part of our work was focused, through a large literature review, on the enhancement of our knowledge as the statements of the basis and support for the analysis.</p><p>To illustrate and to try to answer our research question, we have conducted our study based on a sample of 20 acquisitions which were achieved in the banking industry between March 1998 and May 2004. 10 of these acquisitions had been achieved in the United States as the 10 remaining acquisitions had been executed in Europe.</p><p>The analysis has been achieved by collecting data in Thomson Datastream Advance.</p><p>Based on a quantitative method, we applied two financial models: The Market Model (MM) and the Market-Adjusted Returns Model (MAR) supported by the Cumulative Abnormal Returns Method (CARs).</p><p>The post-takeover study has been delimited on a period of 42 months after the public announcement.</p><p>The study and the comparison between the United States and Europe have shown some differences between the two areas. Nevertheless it seems that negative abnormal returns are usually the case after such takeovers on the whole period studied. Some positive abnormal returns have been recorded at different points in the time into the studying period.</p><p>According to the models we applied, the US banks results seem to be better than the ones of European banks: the differences range from 5,58 to 16,65 points under the MM, and from 1,66 to 18,08 points under the MAR model.</p>

Takeover is a business activity which really started in the beginning of the eighties and which still takes a strong part in the business and financial area all over the world. According to our studies as the desire for further acknowledgements and the desire of building a career around financial activities, this study has been naturally conducted in the banking area. Regarding the steady use of acquisition like a powerful process with some positive and negative sides, we decided to implement a comparison of different mergers and acquisitions in the banking industry in the United States and Europe. This comparison has been supported and based on the third main topic of our study: performance. These large and complex subjects combined together lead to the following hypotheses: Hypothesis 1: Performance is not improved after takeover in the banking industry. Hypothesis 2: The level of post takeover performance is the same in the U.S. as in the European bank acquisitions. Based on the historical data and knowledge, the United States was the pioneer in the development of such gathers in the banking sector. Considering the United States as a reference, a first purpose was to compare them with the bank mergers and acquisitions in Europe. Stating on some possible differences as increasing our own knowledge have been some others purposes which have supported our work. A first large part of our work was focused, through a large literature review, on the enhancement of our knowledge as the statements of the basis and support for the analysis. To illustrate and to try to answer our research question, we have conducted our study based on a sample of 20 acquisitions which were achieved in the banking industry between March 1998 and May 2004. 10 of these acquisitions had been achieved in the United States as the 10 remaining acquisitions had been executed in Europe. The analysis has been achieved by collecting data in Thomson Datastream Advance. Based on a quantitative method, we applied two financial models: The Market Model (MM) and the Market-Adjusted Returns Model (MAR) supported by the Cumulative Abnormal Returns Method (CARs). The post-takeover study has been delimited on a period of 42 months after the public announcement. The study and the comparison between the United States and Europe have shown some differences between the two areas. Nevertheless it seems that negative abnormal returns are usually the case after such takeovers on the whole period studied. Some positive abnormal returns have been recorded at different points in the time into the studying period. According to the models we applied, the US banks results seem to be better than the ones of European banks: the differences range from 5,58 to 16,65 points under the MM, and from 1,66 to 18,08 points under the MAR model.

<p dir="ltr">Automated Driving Systems (ADS) have evolved significantly over the past decade. With conditionally automated driving systems still requiring constant driver supervision and human intervention upon system request, a driver’s readiness to take over from an ADS has significant safety implications. Research suggests that drivers using ADS are more likely to engage in non-driving-related tasks (NDRTs), and this engagement can deteriorate takeover performance. However, different NDRTs can involve engagement of physical, visual and/or cognitive resources, which all can affect the takeover process in different ways. The potential interaction effects among these factors may be the cause of mixed empirical findings regarding the influence of NDRT engagement on takeover readiness and performance. Additionally, with more advanced ADS, takeover scenarios are likely to be less urgent. Yet, the ways in which drivers behave in response to a takeover request to intervene during such less urgent scenarios while engaged in NDRTs is still not well understood.</p><p dir="ltr">The purpose of this dissertation is to provide a better understanding of drivers’ response behavior during a conditionally automated vehicle takeover process by analyzing drivers’ motor, visual, and cognitive readiness in response to a takeover request (TOR). The work was completed in two phases. The first phase focused on the effects of pre-takeover visual engagement on takeover readiness in urgent situations. Two experiments were conducted as part of this first phase. Particularly, Study 1 investigated drivers’ post-TOR visual attention allocation and cognitive readiness after continuous visual NDRT engagement before a TOR. Study 2 examined drivers’ pre-TOR visual attention allocation and takeover performance both during and after voluntary engagement with visual NDRTs. The second phase used a non-urgent takeover scenario to investigate drivers’ takeover behavior and visual attention allocation when prioritizing the engagement of visual-manual NDRTs that differed in terms of cognitive engagement levels.</p><p dir="ltr">Study 1 required continuous visual attention in NDRTs and manipulated only the location of visual attention before an auditory TOR. Dependent measures included duration, location, and directness eye-tracking measures after the TOR, as well as freeze-probe cognitive readiness scores. Overall, delayed visual attention re-allocation in the driving scene, less dispersed gaze patterns, and worse perception and comprehension of road hazards were associated with off-road visual NDRT engagement. In addition, no significant benefit of enforcing on-road visual attention before the TOR, compared to the baseline condition without NDRT requirements, were found. These findings highlight the need to investigate the effects of more naturalistic NDRT engagement on takeover attention reallocation and takeover performance.</p><p dir="ltr">Study 2 complemented Study 1 by allowing voluntary switching of visual attention between the NDRT and the driving scene prior to the TOR, with the driving task being a priority. In addition, Study 2 investigated drivers’ takeover quality and understanding of the takeover scene using the appropriateness of their takeover decisions. Dependent measures were pre- and post-takeover eye-tracking measures, aligning to those used in Study 1, in addition to motor response measures, longitudinal and lateral vehicle control measures, and decisions made in response to a road obstacle. Overall, the driver’s post-TOR behaviors were not significantly affected by NDRT conditions, but visual NDRT-induced differences in gaze distribution were associated with the appropriateness of takeover decisions.</p><p dir="ltr">Finally, Study 3 used knowledge from prior studies to isolate the effects of different levels of cognitive engagement in real-world visual-manual NDRTs. The purpose was to investigate the effects of cognitive engagement on drivers’ visual attention allocation before and during the takeover, as well as on takeover performance in non-urgent takeover scenarios, where NDRT engagement was a priority. Dependent measures included eye-tracking measures, takeover response time, and vehicle control measures, used in prior studies. In summary, engagement in NDRTs with higher levels of cognitive engagement resulted in significant differences in pre-TOR visual attention allocation and less stable takeover maneuvers.</p><p dir="ltr">The findings from this work contribute to a better understanding of the effects of different components of NDRT engagement on takeover performance in conditionally automated driving systems. Ultimately, this work can contribute to improving the design of next-generation human-machine interfaces in surface transportation, including driver monitoring systems and in-vehicle displays, that promote safer human-automation integration in future ADS.</p>

<p>Emerging technologies such as real-time travel information systems and automated vehicles (AVs) have profound impacts on driver decision-making behavior. While they generally have positive impacts by enabling drivers to make more informed decisions or by reducing their driving effort, there are several concerns related to inadequate consideration of cognitive and psychological aspects in their design. In this context, this dissertation analyzes different aspects of driver cognition and psychology that arise from drivers’ interactions with these technologies using physiological data collected in two sets of driving simulator experiments.</p> <p>This research analyzes the latent cognitive and psychological effects of real-time travel information using electroencephalogram (EEG) data measured in the first set of driving simulator experiments. Using insights from the previous analysis, a hybrid route choice modeling framework is proposed that incorporates the impacts of the latent information-induced cognitive and psychological effects along with other explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors) on drivers’ route choice decisions. EEG data is analyzed to extract two latent cognitive variables that capture the driver’s cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. </p> <p>Several safety concerns emerge for the transition of control from the automated driving system to a human driver after the vehicle issues a takeover warning under conditional vehicle automation (SAE Level 3). In this context, this study investigates the impacts of driver’s pre-warning cognitive state on takeover performance (i.e., driving performance while resuming manual control) using EEG data measured in the second set of driving simulator experiments. However, there is no comprehensive metric available in the literature that could be used to benchmark the role of driver’s pre-warning cognitive state on takeover performance, as most existing studies ignore the interdependencies between the associated driving performance indicators by analyzing them independently. This study proposes a novel comprehensive takeover performance metric, Takeover Performance Index (TOPI), that combines multiple driving performance indicators representing different aspects of takeover performance. </p> <p>Acknowledging the practical limitations of EEG data to have real-world applications, this dissertation evaluates the driver’s situational awareness (SA) and mental stress using eye-tracking and heart rate measures, respectively, that can be obtained from in-vehicle driver monitoring systems in real-time. The differences in SA and mental stress over time, their correlations, and their impacts on the TOPI are analyzed to evaluate the efficacy of using eye-tracking and heart rate measures for estimating the overall takeover performance in conditionally AVs.</p> The study findings can assist information service providers and auto manufacturers to incorporate driver cognition and psychology in designing safer real-time information and their delivery systems. They can also aid traffic operators to incorporate cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers’ route choices. Further, the study findings provide valuable insights to design operating and licensing strategies, and regulations for conditionally automated vehicles. They can also assist auto manufacturers in designing integrated in-vehicle driver monitoring and warning systems that enhance road safety and user experience.