The Recording Industry Association of America claims to lose millions of dollars each year from music piracy (RIAA, 2007). However, instead of causing loss, digital music piracy may activate norms of reciprocity in music pirates. When pirating music, people may feel some obligation to reciprocate by purchasing music or related merchandise. The theory of planned behavior was used to investigate such a possibility and to provide a framework for scale development. Reliable scales were developed for all measured constructs. Regarding piracy, the RIAA’s claim may have some merit. Specifically, previous piracy was associated with decreased reported likelihood to purchase music. However, previous piracy was associated with increased intent to make future music-related purchases. Reciprocity partially mediated this relationship.

No description available.

No description available.

No description available.

No description available.

This dissertation is focused on the design and evaluation of machine learning algorithms for modeling information precursors for use in event modeling and forecasting. Given an online stream of information (e.g., news articles, social media postings), how can we model and understand how events unfold, how they influence each other, and how they can act as determinants of future events? First, we study information reciprocity in joint news and social media streams to capture how events evolve. We present an online story chaining algorithm which links related news articles together in a low complexity manner and a mechanism to classify the interaction between a news article and social media (Twitter) activity into four categories. This is followed by identification of major information sources for a given story chain based on the interaction states of news and Twitter. We demonstrate through this study that Twitter as a social network platform serves as a fast way to draw attention from the public to many social events such as sports, whereas news media is quicker to report events regarding political, economical, and business issues. In the second problem we focus on forecasting and understanding large-scale societal events from open source datasets. Our goal here is to develop algorithms that can automatically reconstruct precursors to societal events. We develop a nested framework involving multi-instance learning for mining precursors by harnessing temporal constraints. We evaluate the proposed model for various event categories in multiple geo-locations with comprehensive experiments. Next, to reinforce the fact that events are typically inter-connected and influenced by events in other locations, we develop an approach that creates personalized models for exploring spatio-temporal event correlations; this approach also helps tackle data/label sparsity problems across geolocations. Finally, this dissertation demonstrates how our algorithms can be used to study key characteristics of mass events such as protests. Some mass gatherings run the risk of turning violent, causing damage to both property and people. We propose a tailored solution for uncovering triggers from both news media and social media for violent event analysis. This work was partially supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC000337, the Office of Naval Research under contract N00014-16-C-1054, and the U.S. Department of Homeland Security under Grant Award Number 2017-ST-061-CINA01. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the author and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of NSF, IARPA, DoI/NBC, or the US Government. / Ph. D. / Today, massive open source information is widely available through news and social media, but analyzing this information is a complex task. It is imperative to develop algorithms that can automatically reconstruct the clues to societal events that are reported in news or social media. The focus of this dissertation is on simultaneously uncovering precursors to societal events and using such precursors to forecast upcoming events. We develop various machine learning algorithms that can model event-related data and determine the key happenings prior to an event that have the greatest predictability to such events in the future. We use our algorithms to understand the nature of precursors to civil unrest events (protests, strikes, and ‘occupy’ events) and why some of these events turn violent.

No / People working in the helping professions have been found to be vulnerable to the development of burnout and research has suggested a relationship between dementia care and burnout. Literature suggests that the development of burnout may be linked to a number of factors, including lack of reciprocity, low self-efficacy and organizational factors. The study explored burnout in staff for older people with dementia and examined the roles of reciprocity, self-efficacy and organizational factors and aimed to identify which of these variables was the greatest predictor of burnout. Sixty—one members of staff in continuing care homes for people with dementia completed self-report questionnaires. Self-efficacy was found to be the greatest predictor of burnout. Findings from the study also emphasized the connections of reciprocity, occupational commitment, demographic factors and self-efficacy with burnout. The clinical implications of the study, methodological considerations and recommendations for future research are discussed.

Research on organisational learning (OL) was mainly positioned within the psychological and sociological domains. Past and extant research on OL focused on the behavioural, cognitive and intuitive perspectives in addition to a growing track of research grounded on social theory. So far, a countless number of research studies attempted to address inter-organisational learning (IOL) from various perspectives. However, the lack of understanding of how IOL occurs in networks can be observed due to the social tensions that are created at the inter-organisational level such as free-riding and knowledge leakage. This thesis, therefore, aims to draw theoretical explanations of IOL and how it occurs in learning networks, taking into consideration similarities and contradictions amongst a network’s participating organisations. Towards this end, the thesis employs two theoretical lenses, namely structure-agency and social exchange theories to draw conclusions that provide fresh explanations of how networks are helpful in fostering or hindering learning activities in addition to how reciprocity as an efficacy device mediates IOL dynamics. Positioned within a qualitative vein, the thesis employs an interpretive perspective to collect and analyse empirical evidence. The qualitative data were developed through a mixture of participant observations, semi-structured interviews and casual conversations with network administrators and participants. The data were analysed using thematic analysis which generated codes, following which conclusions were drawn. The main contributions of this article are (1) unfolding the network as agency which provides a fresh understanding of how the agential role of networks mediates IOL and (2) drawing a framework of dimensions of reciprocal exchanges that explains how IOL occurs in networks. The first conclusion of this thesis explained how the agential role is socially constructed and how the interpretive device facilitated this construction. The second conclusion of this thesis explained how reciprocal exchanges mediate IOL and provide a framework that suggested IOL can be better understood through temporal, spatial, directional and symmetrical perspectives.

No description available.

LD2668 .T4 1941 W71 / Master of Science