Exploring Employee Engagement Among Three Non-Salaried Employees: A Phenomenological Study

This exploratory empirical phenomological study looks at employee engagement using Kahn (1990) and Maslow’s (1970) motivational theories to understand the experience of non-salaried employees. This study finds four themes that seem to affect employee engagement: work environment, employee’s supervisor, individual characteristics of the employee, and opportunity for learning.

Placing immigrant incorporation: Identity, trust, and civic engagement in Little Havana

Immigrant incorporation in the United States has been a topic of concern and debate since the founding of the nation. Scholars have studied many aspects of the phenomenon, including economic, political, social, and spatial. The most influential paradigm of immigrant incorporation in the US has been, and continues to be, assimilation, and the most important place in and scale at which incorporation occurs is the neighborhood. This dissertation captures both of these integral aspects of immigrant incorporation through its consideration of three dimensions of assimilation – identity, trust, and civic engagement – among Latin American immigrants and American-born Latinos in Little Havana, a predominantly immigrant neighborhood in Miami, Florida. Data discussed in the dissertation were gathered through surveys and interviews as part of a National Science Foundation-funded study carried out in 2005-2006. The combination of quantitative and qualitative data allows for a nuanced understanding of how immigrant incorporation is occurring locally during the first decade of the twentieth century. Findings reveal that overall Latin American immigrants and their American-born offspring appear to be becoming American with regard to their ethnic and racial identities quickly, evidenced through the salience and active employment of panethnic labels, while at the same time they are actively reshaping the identificational structure. The Latino population, however, is not monolithic and is cleaved by diversity within the group, including country of origin and socioeconomic status. These same factors impede group cohesion in terms of trust and its correlate, community. Nevertheless, the historically dominant ancestry group in Little Havana – Cubans – has been able to reach notable levels of trust and build and conserve a more solid sense of community than non-Cuban residents. With respect to civic engagement, neighborhood residents generally participate at rates lower than the overall US population and ethnic subpopulations. This is not the case for political engagement, however, where self-reported voting registration and turnout in Little Havana surpasses that of most benchmarked populations. The empirical evidence presented in this dissertation on the case of Latinos in Little Havana challenges the ways that identity, trust, and civic engagement are conceptualized and theorized, especially among immigrants to the US.

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^