<p>Internet banking has an impact on banking performance as well as customer’s daily life. Customers are getting more used to use Internet banking services because the Internet is so popular and common available, it is more convenient and comfortable for customers to use banking services on the Internet. Hereby Internet banking also helps to improve banking service and increasing banks profitability by reducing costs. It also increases the overall value of the banks services by providing convenience, reliable, faster, cheaper services. The purpose of study is to find out which services customer prefers to execute the Internet bank and which services customers prefer to execute through branch office and based on this, what combination of services are desirable for banks customers in Visby. Finding shows that young and educated people are more frequently users of Internet banking for balance inquiry and for viewing the transaction history of his/her account. For some services customers prefer to visit branch office when opining a new account, deposit money, insurances service and loan activities. Banks encourage their customers to use online services but there is need that there are some activities that customer must go to the branch office.</p>

Internet banking has an impact on banking performance as well as customer’s daily life. Customers are getting more used to use Internet banking services because the Internet is so popular and common available, it is more convenient and comfortable for customers to use banking services on the Internet. Hereby Internet banking also helps to improve banking service and increasing banks profitability by reducing costs. It also increases the overall value of the banks services by providing convenience, reliable, faster, cheaper services. The purpose of study is to find out which services customer prefers to execute the Internet bank and which services customers prefer to execute through branch office and based on this, what combination of services are desirable for banks customers in Visby. Finding shows that young and educated people are more frequently users of Internet banking for balance inquiry and for viewing the transaction history of his/her account. For some services customers prefer to visit branch office when opining a new account, deposit money, insurances service and loan activities. Banks encourage their customers to use online services but there is need that there are some activities that customer must go to the branch office.

In the field of vehicle durability, many individuals are focusing on methods for better replicating the durability a user will experience throughout the typical design lifespan of a vehicle (e.g., 100,000 miles). To estimate user durability a means of understand the types of damaging events and driving styles of uses must be understood. The difficulty with accurately estimating customer usage is, firstly, there is a large pool of possible roads for a user to drive along, for example, there are over 4 million miles of public roads in the United States, alone [1]. In addition, while measurements of these surfaces could be collected it would be impractical for two reasons, the first is the financial and extreme time burden this would take. Second, when collecting measurements of a road surface only the current state of a road surface can be measured, thus as a road deteriorates or is repaved the measurements collected would no longer be an accurate representation of the road. It should be mentioned that even, if all of the road surfaces were measured performing simulation and analysis of all of these road surfaces would be computationally intensive. Instead, it would be beneficial if select events that account for a significant portion of the damage a vehicle experiences can be identified. These damaging events could then be used in more complex vehicle simulation models and as input and validation of proving ground and laboratory durability testing. The objective of this research is to provide a means for improved estimation of vehicle durability, specifically a means for identifying, characterizing, and grouping unique separable damaging events from a road profile measurement. In order to achieve this objective a measure that can be used to identify separate damaging events from a road profile is developed. This measure is defined as Localized Pseudo Damage (LPD), which identifies the amount of damage each individual road excitation makes to the total accumulated damage for a single load path in a vehicle system. LPD is defined as a damage density to minimize the effect of measurement spacing on the resulting metric. The developed LPD measure is causal in that the value of LPD at a location is not affected by any future locations. In addition, for a singular event (e.g., impulse or step) in the absences of other excitations, the LPD value at the singular event location is equivalent to the total pseudo damage divided by the step size at the location. Once a measure of pseudo damage density is known at multiple locations along a road profile for multiple load paths of interest, then separable damaging events can be identified. To identify separable damaging events the activity of the vehicle system must be considered because separate damaging events can only occur when a region of inactivity is present across all load paths. Subsequently, an optimization problem is formed to determine the optimal active regions to maintain. The cost function associated with the optimization problem is defined to minimize the cost (number of locations maintained in damaging events) and maximize the benefit (the amount of pseudo damage maintained). Lastly, a statistical test is developed to assess if separate damaging events can be considered to be from the same general class of events based on their damage characteristics. The developed assessment methods establish the similarity between two more separable damaging events based on application specific user defined inputs. In the development, two example similarity metrics are defined. The first similarity metric is in terms of distance and the second is in terms of likelihood (probability). The developed statistical analysis uses the current state-of-the-art in clustering algorithms to allow for multiple damaging events to be identified and grouped together. / Doctor of Philosophy / In the automotive field determining the level of damage a typical production vehicle experiences over its lifetime has always been a desirable criterion to identify. This criterion is commonly referred to as customer usage. By understanding the typical customer usage of a vehicle over the lifetime of a vehicle, automotive engineers are able to improve the design of vehicle components. The issue with defining customer usage is that there are millions of miles of roads that a customer can travel on and millions of customers that all have unique driving characteristics. While it is possible to collect measurements of these road surfaces to use in further vehicle simulations, it is not feasible both from a financial and time perspective. In addition, the simulation and analysis of all road surfaces would be computationally intensive. However, if select damaging events (regions of the road surface that excessively contribute to accumulated damage) are identified, then they can be used in more complex vehicle durability analyses with lower computational efforts. In conventional damage analysis a total amount of accumulated damage is established for a known road surface. The issue with defining damage this way is that unique events which likely contributed a large amount of the accumulated damage cannot be identified. The first objective of this research is to define damage as a function of the vehicle's location along a road surface. Then, unique and separable damaging events can be identified and separated from sections of the road that do not significantly contribute to the accumulated damage. After defining this measure, an optimization problem is developed to identify damaging events based on maximizing the benefit (amount of damage accounted for in damaging events) and minimizing the cost (amount of road surface retained). Unique and separable damaging events are identified by solving this optimization problem. While the optimization problem identifies unique, separable damaging events, it is likely that some damaging events contain similar characteristics to each other. When performing additional durability analysis, it would be beneficial to form connections between similar damaging events to allow for analysis to be performed based on groups of events. To identify damaging events with similar characteristics, a statistical analysis is developed as the last contribution of this work. By combining this analysis with current state-of-the-art clustering algorithms and user provided definitions based on applications, similar damaging events are able to be grouped together.

The large potential of big data and how it has brought value into various industries have been established in research. Since big data has such large potential if handled and analyzed in the right way, revealing information to support decision making in an organization, this thesis is conducted as a case study at an automotive manufacturer with access to large amounts of customer usage data of their vehicles. The reason for performing an analysis of this kind of data is based on the cornerstones of Total Quality Management with the end objective of increasing customer satisfaction of the concerned products or services. The case study includes a data analysis exploring how and if patterns about what affects fuel consumption can be revealed from aggregated customer usage data of trucks linked to truck applications. Based on the case study, conclusions are drawn about how a company can use this type of analysis as well as how to handle the data in order to turn it into business value. The data analysis reveals properties describing truck usage using Factor Analysis and Principal Component Analysis. Especially one property is concluded to be important as it appears in the result of both techniques. Based on these properties the trucks are clustered using k-means and Hierarchical Clustering which shows groups of trucks where the importance of the properties varies. Due to the homogeneity and complexity of the chosen data, the clusters of trucks cannot be linked to truck applications. This would require data that is more easily interpretable. Finally, the importance for fuel consumption in the clusters is explored using model estimation. A comparison of Principal Component Regression (PCR) and the two regularization techniques Lasso and Elastic Net is made. PCR results in poor models difficult to evaluate. The two regularization techniques however outperform PCR, both giving a higher and very similar explained variance. The three techniques do not show obvious similarities in the models and no conclusions can therefore be drawn concerning what is important for fuel consumption. During the data analysis many problems with the data are discovered, which are linked to managerial and technical issues of big data. This leads to for example that some of the parameters interesting for the analysis cannot be used and this is likely to have an impact on the inability to get unanimous results in the model estimations. It is also concluded that the data was not originally intended for this type of analysis of large populations, but rather for testing and engineering purposes. Nevertheless, this type of data still contains valuable information and can be used if managed in the right way. From the case study it can be concluded that in order to use the data for more advanced analysis a big-data plan is needed at a strategic level in the organization. The plan summarizes the suggested solution for the managerial issues of the big data for the organization. This plan describes how to handle the data, how the analytic models revealing the information should be designed and the tools and organizational capabilities needed to support the people using the information.