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The IoT-embedded smart homes have a high level of home automation and could change many aspects of the residents' daily lives, such as control, convenience, comfort, and energy-saving. The rise of voice-based virtual assistants like Amazon's Alexa, Google assistants in the past five years has brought new potentials to provide occupants with a convenient and intuitive interface to interact with smart homes through conversations. However, the one-way communications in the form of user commands to control building systems does not result in the optimal course of actions. As such, in this thesis, we proposed the concept of proactive smart home assistants and explored the occupants' perception towards smart home assistants proactively providing suggestions to adapt them into energy-saving behaviors. We also investigated the impact of occupants' personal features on their intention in taking energy-saving behaviors. A comprehensive data collection was conducted through online surveys, in which 307 valid responses with participant's personal profile information, their perceptions of smart home assistants, and their feedback to our designed messages were collected. The first manuscript compared participants' responses to traditional plain-text energy-saving suggestions and suggestions provided by smart home assistants. The nudging effect of smart home assistants was justified to be significant in affecting occupant's energy-saving behaviors. Occupant's thermal comfort range, smart home device previous experience, values and beliefs were then proved to have significant impact on their intention in taking the smart home assistant's suggestions. The second manuscript fitted 21 personal characteristics features in machine learning models (SVM, Random Forest, Logistic Regression) to predict occupant's intention and attitude towards energy-saving suggestions. The results indicated that occupant's beliefs about interests in taking actions and beliefs about energy expenses, occupant's education level, residence occupancy type, thermal comfort ranges, and smart home device experiences are important features in occupants' energy-saving behavior intention prediction. This research demonstrates the effect of proactive smart home assistants in human-building interaction as well as the impact of personal characteristic features on occupant's energy-saving behaviors, paving a path to the future development of bi-directional human-building communication. / Master of Science / With the technology development in the fields of the Internet of Things (IoT), smart homes have made it possible to help occupants conserve energy in an efficient way without sacrificing the occupants' comfort. The rise of voice-based virtual assistants like Amazon's Alexa, Google assistants accompany the proliferation of smart speaker products in the past five years has brought new potentials to provide occupants with a convenient and intuitive interface to interact with smart homes through conversations. Based on IoT, the virtual assistants are able to control a broad range of Wi-Fi connected home devices like thermostats, lighting systems, and security systems. As such, through the simple wake words (e.g., "Alexa", "Hey, Google"), occupants can easily control the home environment with their voice commands. Despite the potentials brought by these voice-based virtual assistants, it has been shown that users might not know about all the supported features and limit their interaction with smart home assistants to simple daily tasks. The one-way communications in the form of user commands to control building systems do not result in the optimal course of actions. Therefore, in this study, we have envisioned that these virtual assistants, coupled with their corresponding smart home ecosystems could act proactively as a bridge to facilitate human-building interaction and achieve goals like nudging occupants to adopt sustainable and healthy behaviors. A comprehensive data collection was conducted through online surveys, in which 307 valid responses with participant's personal profile information, their perceptions of smart home assistants, and their feedback to our designed messages were collected. The first manuscript compared participants' responses to traditional plain-text energy-saving suggestions and suggestions provided by smart home assistants. The nudging effect of smart home assistants was justified to be significant in affecting occupant's energy-saving behaviors. Occupant's thermal comfort range, smart home device previous experience, values and beliefs were then proved to have significant impact on their intention in taking the smart home assistant's suggestions. The second manuscript fitted 21 personal characteristics features in machine learning models (SVM, Random Forest, Logistic Regression) to predict occupant's intention and attitude towards energy-saving suggestions. The results indicated that occupant's beliefs about interests in taking actions and beliefs about energy expenses, occupant's education level, residence occupancy type, thermal comfort ranges, and smart home device experiences are important features in occupants' energy-saving behavior intention prediction. This research demonstrates the effect of proactive smart home assistants in human-building interaction as well as the impact of personal characteristic features on occupant's energy-saving behaviors, paving a path to the future development of bi-directional human-building communication.

This thesis consists of an introductory part and four self-contained papers related to individual financial behavior and risk-taking in financial markets. In Paper [I] we estimate within-family and community social interaction effects upon an individual’s stock market entry, participation, and exit decision. Interestingly, community sentiment towards the stock market (based on portfolio outcomes in the community) does not influence individuals’ likelihood to enter, while a positive sentiment increases (decreases) the likelihood of participation (exit). Overall, the results stress the importance of accounting for family social influence and highlight potentially important differences between family and community effects in individuals’ stock market participation. In Paper [II] novel evidence is provided indicating that the influence from family (parents and partners) and peer social interaction on individuals’ stock market participation vary over different types of individuals. Results imply that individuals’ exposure to, and valuation of, stock market related social signals are of importance and thus, contribute to the understanding of the heterogeneous influence of social interaction. Overall, the results are interesting and enhance the understanding of the underlying mechanisms of social interaction on individuals’ financial decision making. In Paper [III] the impact of divorce ­­­on individual financial behavior is empirically examined in a dynamic setting. Evidence that divorcing individuals increase their saving rates before the divorce is presented. This may be seen as a response to the increase in background risk that divorce produces. After the divorce, a negative divorce effect on individual saving rates and risky asset shares are established, which may lead to disparities in wealth accumulation possibilities between married and divorced. Women are, on average, shown to not adjust their precautionary savings to the same extent as men before the divorce. I also provide tentative evidence that women reduce their financial risk-taking more than men after a divorce, which could be a result of inequalities in financial positions or an adjustment towards individual preferences.   Paper [IV] provides novel empirical evidence that gender identity is of importance for individuals’ financial risk-taking. Specifically, by use of matching and by dividing male and females into those with “traditional” versus “nontraditional” gender identities, comparison of average risk-taking between groupings indicate that over a third (about 35-40%) of the identified total gender risk differential is explained by differences in gender identities. Results further indicate that risky financial market participation is 19 percentage points higher in groups of women with nontraditional, compared with traditional, gender identities. The results, obtained while conditioning upon a vast number of controls, are robust towards a large number of alternative explanations and indicate that some individuals (mainly women) partly are fostered by society, through identity formation and socially constructed norms, to a relatively lower financial risk-taking.