Matching Items (4)
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- All Subjects: Interpersonal communication
- Creators: Li, Baoxin
- Creators: Romero, Mary
Description
The division of domestic labor has far-reaching implications for "private" life (e.g. relational satisfaction and conflict) and for "public" paid labor (e.g. time and dedication in the workplace and career advancement). Although several theories have been developed and tested, they do not sufficiently explain the consistent findings that women in mixed sex households perform a majority of the domestic labor. Without understanding the causes for differences in task performance, past research encouraging communicative solutions to ameliorate conflict was ineffective in changing task allocation and performance. Therefore, it is necessary to understand theoretical explanations that drive domestic labor behavior to develop effective solutions. The recent integrative theory of the division of domestic labor attempts to explain how individuals interact with household partners to allocate domestic tasks. Recognizing the complexity of the division of domestic labor, the integrative theory considers individual, dyadic, and societal factors that influence task allocation. Because clear differences in task performance have been found in mixed sex households, this study separates sex and gender as distinct variables by considering same-sex roommate relationships, essentially removing sex differences from the living arrangement. Furthermore, this study considers individual threshold levels as described by the integrative theory in order to test the theoretical underpinnings. Specifically, this study is designed to investigate the relationships between individual cleanliness threshold levels and gender, sex, perceptions of satisfaction, equity, and frequency of conflict in same-sex roommate relationships. Results indicate support of the integrative theory of the division of domestic labor. Regarding gender differences, partial support for the theory appeared in that feminine individuals have lower threshold levels than masculine individuals. Regarding sex differences, women possess lower individual threshold levels (i.e. more bothered when a task is undone) compared to men, which likely accounts for why existing research indicates that women spend more time performing domestic tasks. What is more, individuals with higher threshold levels report greater relational satisfaction. Further, individuals whose threshold levels differ from their living partner report lower relational satisfaction and greater conflict frequency. Finally, in terms of equity, both overbenefited and underbenefited individuals experience more conflict than those who feel their relationship is equitable. These results provide theoretical support for the integrative theory of the division of labor. Furthermore, the development and testing of a threshold measure scale can be used practically for future research and for better roommate pairings by universities. In addition, communication scholars, family practitioners and counselors, and universities can apply these theoretically grounded research findings to develop and test strategies to reduce conflict and increase relational satisfaction among roommates and couples.
ContributorsRiforgiate, Sarah (Author) / Alberts, Jess K. (Thesis advisor) / Mongeau, Paul (Thesis advisor) / Roberto, Anthony (Committee member) / Romero, Mary (Committee member) / Arizona State University (Publisher)
Created2011
Description
The goals of this dissertation were to develop a measurement called the
Empathetic Expressions Scale (EES) for Negative and Positive Events, to evaluate expressions of empathy from the receiver perspective, and to provide initial evidence for empathetic expressions as a separate construct from the empathy experience. A series of studies were conducted using three separately collected sets of data. Through the use of Exploratory Factor Analysis (EFA), the EES for Negative Events and the EES for Positive Events were created from the emerged factors. A five-factor structure emerged for the EES for Negative Events, which include Verbal Affirmation, Experience Sharing, Empathetic Voice, Emotional Reactivity, and Empathetic Touch. This scale was found to have good convergent and discriminant validity through the process of construct validation and good local and model fit through Confirmatory Factor Analysis (CFA). A four-factor structure and two-factor structure emerged for the EES for Positive Events. The four factors include Verbal Affirmation, Experience Sharing, Empathetic Voice, and Emotional Reactivity. The two factors in the second structure include Celebratory Touch and Hugs.The final study focused on evaluating different empathetic expressions from the receiver perspective. From the receiver perspective, the participants rated five types of empathetic expressions in response to negative or positive events disclosure. According to the findings, Emotional Reactivity was rated as the most effective empathetic expression in negative events on both levels of supportiveness and message quality scales whereas Verbal Affirmation received the lowest ratings on both criteria. In positive events, Experience Sharing was evaluated as the most supportive and highest quality message whereas Verbal Affirmation was evaluated the lowest on both criteria. Taken together, the series of studies presented in this dissertation provided evidence for the development and validity of the EES for Negative and Positive Events.
Empathetic Expressions Scale (EES) for Negative and Positive Events, to evaluate expressions of empathy from the receiver perspective, and to provide initial evidence for empathetic expressions as a separate construct from the empathy experience. A series of studies were conducted using three separately collected sets of data. Through the use of Exploratory Factor Analysis (EFA), the EES for Negative Events and the EES for Positive Events were created from the emerged factors. A five-factor structure emerged for the EES for Negative Events, which include Verbal Affirmation, Experience Sharing, Empathetic Voice, Emotional Reactivity, and Empathetic Touch. This scale was found to have good convergent and discriminant validity through the process of construct validation and good local and model fit through Confirmatory Factor Analysis (CFA). A four-factor structure and two-factor structure emerged for the EES for Positive Events. The four factors include Verbal Affirmation, Experience Sharing, Empathetic Voice, and Emotional Reactivity. The two factors in the second structure include Celebratory Touch and Hugs.The final study focused on evaluating different empathetic expressions from the receiver perspective. From the receiver perspective, the participants rated five types of empathetic expressions in response to negative or positive events disclosure. According to the findings, Emotional Reactivity was rated as the most effective empathetic expression in negative events on both levels of supportiveness and message quality scales whereas Verbal Affirmation received the lowest ratings on both criteria. In positive events, Experience Sharing was evaluated as the most supportive and highest quality message whereas Verbal Affirmation was evaluated the lowest on both criteria. Taken together, the series of studies presented in this dissertation provided evidence for the development and validity of the EES for Negative and Positive Events.
ContributorsSuwinyattichaiporn, Tara (Author) / Guerrero, Laura K. (Thesis advisor) / Broome, Benjamin (Committee member) / Romero, Mary (Committee member) / Arizona State University (Publisher)
Created2016
Description
Social situational awareness, or the attentiveness to one's social surroundings, including the people, their interactions and their behaviors is a complex sensory-cognitive-motor task that requires one to be engaged thoroughly in understanding their social interactions. These interactions are formed out of the elements of human interpersonal communication including both verbal and non-verbal cues. While the verbal cues are instructive and delivered through speech, the non-verbal cues are mostly interpretive and requires the full attention of the participants to understand, comprehend and respond to them appropriately. Unfortunately certain situations are not conducive for a person to have complete access to their social surroundings, especially the non-verbal cues. For example, a person is who is blind or visually impaired may find that the non-verbal cues like smiling, head nod, eye contact, body gestures and facial expressions of their interaction partners are not accessible due to their sensory deprivation. The same could be said of people who are remotely engaged in a conversation and physically separated to have a visual access to one's body and facial mannerisms. This dissertation describes novel multimedia technologies to aid situations where it is necessary to mediate social situational information between interacting participants. As an example of the proposed system, an evidence-based model for understanding the accessibility problem faced by people who are blind or visually impaired is described in detail. From the derived model, a sleuth of sensing and delivery technologies that use state-of-the-art computer vision algorithms in combination with novel haptic interfaces are developed towards a) A Dyadic Interaction Assistant, capable of helping individuals who are blind to access important head and face based non-verbal communicative cues during one-on-one dyadic interactions, and b) A Group Interaction Assistant, capable of provide situational awareness about the interaction partners and their dynamics to a user who is blind, while also providing important social feedback about their own body mannerisms. The goal is to increase the effective social situational information that one has access to, with the conjuncture that a good awareness of one's social surroundings gives them the ability to understand and empathize with their interaction partners better. Extending the work from an important social interaction assistive technology, the need for enriched social situational awareness is everyday professional situations are also discussed, including, a) enriched remote interactions between physically separated interaction partners, and b) enriched communication between medical professionals during critical care procedures, towards enhanced patient safety. In the concluding remarks, this dissertation engages the readers into a science and technology policy discussion on the potential effect of a new technology like the social interaction assistant on the society. Discussing along the policy lines, social disability is highlighted as an important area that requires special attention from researchers and policy makers. Given that the proposed technology relies on wearable inconspicuous cameras, the discussion of privacy policies is extended to encompass newly evolving interpersonal interaction recorders, like the one presented in this dissertation.
ContributorsKrishna, Sreekar (Author) / Panchanathan, Sethuraman (Thesis advisor) / Black, John A. (Committee member) / Qian, Gang (Committee member) / Li, Baoxin (Committee member) / Shiota, Michelle (Committee member) / Arizona State University (Publisher)
Created2011
Description
The rapid advancements of technology have greatly extended the ubiquitous nature of smartphones acting as a gateway to numerous social media applications. This brings an immense convenience to the users of these applications wishing to stay connected to other individuals through sharing their statuses, posting their opinions, experiences, suggestions, etc on online social networks (OSNs). Exploring and analyzing this data has a great potential to enable deep and fine-grained insights into the behavior, emotions, and language of individuals in a society. This proposed dissertation focuses on utilizing these online social footprints to research two main threads – 1) Analysis: to study the behavior of individuals online (content analysis) and 2) Synthesis: to build models that influence the behavior of individuals offline (incomplete action models for decision-making).
A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data.
The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.
A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data.
The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.
ContributorsManikonda, Lydia (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / De Choudhury, Munmun (Committee member) / Kamar, Ece (Committee member) / Arizona State University (Publisher)
Created2019