This master thesis aims to investigate the possibilities of predicting purchase intentions of customers during their sales processes in the real estate sector. Also, the web activity of customers on a real estate company’s web site is used as the basis for the forecasting. A machine learning framework has been developed, where its compliance with the GDPR is also assessed.  Five supervised machine learning algorithms – logistic regression, k-nearest neighbors, decision tree, random forest, multilayer perceptron – have been utilized for predicting the classes of the customers: buyers and non-buyers. Three data sets were generated, which represented the total number of active customers at different points in time: at the same day as a sales process starts (day 0) and 10 and 20 days after it. The algorithms were applied and evaluated on these data sets to identify when it is suitable to predict the purchase intentions of customers. To increase the generalization capability of the algorithms, hyperparameter optimization along with data resampling by combining undersampling and synthetic minority over-sampling techniques, k-fold cross validation and mutual information, as feature selection, were applied. The results show that the number of visited web pages, sessions, searched projects (concerning accommodations) and searched locations were relevant for all three data sets. The average price (in total and per square meter) of the most frequently visited web page regarding projects were also included in all the data sets. In addition, the total number of registration of interests sent, and the total amount of time spent on the company’s web site were considered in the second (day 10) and third data set (day 20). Further, a multilayer perceptron – applied 10 days after the start of a sales process – was considered as the optimal model for classifying the purchase intentions of customers. Moreover, the developed machine learning framework is argued to be compliant with the GDPR. Further evaluation regarding the compliance needs to be conducted if the methodology of this machine learning framework would be implemented in practice.

京都大学 / 0048 / 新制・課程博士 / 博士(人間・環境学) / 甲第19903号 / 人博第789号 / 新制||人||190(附属図書館) / 28||人博||789(吉田南総合図書館) / 32980 / 京都大学大学院人間・環境学研究科相関環境学専攻 / (主査)准教授 三浦 智行, 教授 川本 卓男, 教授 宮下 英明 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19979号 / 工博第4223号 / 新制||工||1653(附属図書館) / 33075 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 藤井 聡, 准教授 SCHMOECKER Jan-Dirk, 准教授 宇野 伸宏 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Thesis (M.Com. (Information Systems))--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic and Business Sciences, 2015. / This research study investigated the key factors that contribute to both current usage and future usage intention of online banking by South African users. The research was focused on online banking from all banks and regions in South Africa, hence making the results generalisable to the South African context. The variables (Performance Expectancy, Effort Expectancy, Facilitating Conditions and Social Influence) were derived from UTAUT model. Trust, Switching Costs and Availability of Alternatives were also added. The conceptual model was moderated by age, gender and experience. The variables used within the study were derived from literature review based on previous studies. From a survey of 300 online banking consumers, results were drawn from 105 responses. In South Africa, Trust, Effort expectancy and facilitating conditions, presented significant and positive impact on future usage intention of using online banking. Age and Experience were also significant to Future Usage intentions. However Social Influence, Switching Costs and Availability of Alternatives do not play a considerable role in influencing the user’s future usage in South Africa. While current usage did not directly impact future usage intentions of using online banking, the findings on frequency of usage and functionality (applications) added value to understanding the concept of current usage. The model had an R2 = 0.25 which suggests that within the South African context, there are more factors to be considered for academia and practitioners to understand online banking behaviour amongst consumers. We cannot completely apply theories and models from other countries to the South African context. There is a need to look at factors unique to South Africa, including how customers interpret questionnaires. The research also contributes to the current body of work and interest by banks in South Africa to move from “bricks to clicks”, (moving customers from the branches onto electronic platforms) that are more accessible from any location, and require less staff to operate. To do that, practitioners need add “trust” factors to online banking as well as increase frequency of users logging on to transact. It is vital for banks to also focus on functionality that is simple, reliable and dependable, as well as adds value to customers to ensure their frequent and continued usage thereof.

The purpose of this paper is to enhance our understanding of how salespeople in B2B organizations perceive the usefulness of digital solutions in their work. The use of digital solutions continues to increase in business-to-business (B2B)-selling. Yet limited research has so far focused on how salespeople in B2B organizations perceive the usage of digital solutions. Previous studies have found that perceived usefulness is effective for understanding user attitudes and intentions, and at large their acceptance and usage of digital solutions. Concurrently, previous studies have found that organizations struggle with ensuring that digital solutions are used according to management’s intentions. Perceived usefulness therefore becomes a door into understanding salespeoples’ perceptions of digital solutions, and ultimately their usage of them. In order to achieve the purpose of this paper, a single case study is used, conducted through semi-structured interviews with a sales unit of a chemical company. Four main conclusions were made based on the findings: there is a need for the salespeople to understand why the digital solutions are relevant for their work, digital solutions can contribute to a higher workload for the salespeople, comprehensive training is important for how digital solutions gets used, and there can exist support for managing problems in the form of expert colleagues. These findings contribute to existing literature by highlighting conditions that influence salespeople’s acceptance and usage of digital solutions, and provide nuanced insights into the way salespeople end up using digital solutions in a certain way.

This research analyzed themes relating to the concepts of risk, risk taking, and pleasure by completing a secondary analysis of participant transcripts collected from the PrEP-RN study conducted by Dr. O'Byrne at the University of Ottawa in collaboration with Ottawa Public Health. Analysis was completed to relate risk, risk taking and pleasure in the context of PrEP-RN, but also identify areas for educational improvement in relation to the selected concepts. The research question sought explanation on the relationship that risk has with risk reduction and risk taking, including analysis of condom usage, risk conversation with partners and general sexual health practices while on PrEP. Additionally, the research sought to answer if PrEP-RN participation had any impact on sexual pleasure, analyzing subthemes of stereotyping, security and desire relating to pleasure. Transcript analysis identified that a reduction in condom usage, improved frequency of sexually transmitted infection testing, promotion of conversations with partners surrounding PrEP and sexual health, and a reduction in sexual fear and anxiety with partners were major themes stemming from PrEP-RN participation. A need to increase education surrounding continued condom usage while on PrEP and identify ways to reduce stigmatization of PrEP were identified as ways to improve the PrEP-RN program.

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Background. Environmental sustainability and reducing energy consumption are important and relevant topics today. Energy consumption by data centres is constantly increasing. One factor that could be affecting this is what web frameworks are being used. Objectives. We wanted to investigate whether there is a difference in energy consumption depending on the selected web framework for an API web server. An improvement should not come with overhanging negative side effects. Therefore, energy usage was to be contrasted with response time. In addition, we wanted to see how the choice of response format affects these metrics. In the case of finding any considerable impact on energy usage, without compromising the response time, the goal was to communicate this in order to increase the awareness among software developers. Methods. A literature review was done in order to gather existing information on how to conduct an experiment measuring software energy consumption. We evaluated available tools for measuring consumed application energy. An experiment then compared four popular web frameworks in regard to both energy usage, and response time. Django, Express, Laravel, and Spring Boot were selected for the experiment. Metrics measured were energy usage and response time. The experiment was executed with three different amounts of concurrent requests vusers = {10, 100, 250}. Results. The literature study resulted in a selection of software tools for measuring software energy consumption to choose from. The tool perf was chosen for the experiment. In the experiment, the response format was shown to affect the response time, but not the energy consumption. Increasing the amount of concurrent users made for larger differences between frameworks, both regarding energy usage and response time. Express and Spring Boot show the best performance in both regards for all amounts of concurrent requests. Conclusions. Express and Spring Boot are the clear winners out of the four compared frameworks. Both in terms of energy usage and response time, they had the best results. Django is not a web framework to recommend if response time is of importance.