Browsing by Subject "Human factors"

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Acquisition integration framework for technology enterprise : the human factor
(2010-12) Botes, Daan Jaco; Lewis, Kyle, 1961-; Cowan, Ken
Acquisitions are common in today’s business and people involved in acquisitions face challenges when they become part of this process. This thesis aims to provide an understanding of the human factors that determine the outcome of acquisition integration. Various frameworks exist in the literature that focuses on human and task integration as measures for success. In addition to these, the author explores an additional aspect, customer integration, as an important measure to determine overall integration success. Execution is the key to successful acquisition integration. Employees of a technology company were surveyed to gauge their acquisition experiences over three past acquisitions. The survey was a limited targeted case study that focused on analytical value, rather than statistical value. The survey data is analyzed and aligned with the literature data to identify some possible best practices the technology company could follow in future acquisitions. The survey results are used to establish the implications for the company’s acquisition process and to help the development of a playbook for acquisition integration.
Are icons pictures or logographical words? Statistical, behavioral, and neuroimaging measures of semantic interpretations of four types of visual information
(2012-05) Huang, Sheng-Cheng; Bias, Randolph G.; Dillon, Andrew; Francisco-Revilla, Luis; Schnyer, David; Sussman, Harvey
This dissertation is composed of three studies that use statistical, behavioral, and neuroimaging methods to investigate Chinese and English speakers’ semantic interpretations of four types of visual information including icons, single Chinese characters, single English words, and pictures. The goal is to examine whether people cognitively process icons as logographical words. By collecting survey data from 211 participants, the first study investigated how differently these four types of visual information can express specific meanings without ambiguity on a quantitative scale. In the second study, 78 subjects participated in a behavioral experiment that measured how fast people could correctly interpret the meaning of these four types of visual information in order to estimate the differences in reaction times needed to process these stimuli. The third study employed functional magnetic resonance imaging (fMRI) with 20 participants selected from the second study to identify brain regions that were needed to process these four types of visual information in order to determine if the same or different neural networks were required to process these stimuli. Findings suggest that 1) similar to pictures, icons are statistically more ambiguous than English words and Chinese characters to convey the immediate semantics of objects and concepts; 2) English words and Chinese characters are more effective and efficient than icons and pictures to convey the immediate semantics of objects and concepts in terms of people’s behavioral responses, and 3) according to the neuroimaging data, icons and pictures require more resources of the brain than texts, and the pattern of neural correlates under the condition of reading icons is different from the condition of reading Chinese characters. In conclusion, icons are not cognitively processed as logographical words like Chinese characters although they both stimulate the semantic system in the brain that is needed for language processing. Chinese characters and English words are more evolved and advanced symbols that are less ambiguous, more efficient and easier for a literate brain to understand, whereas graphical representations of objects and concepts such as icons and pictures do not always provide immediate and unambiguous access to meanings and are prone to various interpretations.
Multidisciplinary Accident Investigation
(Council for Advanced Transportation Studies, 1978-10) Valentine, Deborah; Hales, Gary D.; Williams, Martha; Young, Robert K.
This report reviews the literature generated by the Multidisciplinary Accident Investigation (MDAI) studies, sponsored under the Highway Act of 1966. The use of a wide variety of professional disciplines to evaluate accident causation produced detailed information and suggestions relating to human factors, vehicular factors, and environmental factors as causes of accidents. Results of MDAI studies will continue to be useful for generating further research and decisions related to highway safety.
Personality Factors in Accident Causation
(Council for Advanced Transportation Studies, 1977-03) Valentine, Deborah; Williams, Martha; Young, Robert K.
This report reviews the literature on the association of personality traits and accidents. The personality of the accident repeater is reviewed. In general, aggression seems to be a critical link between alcoholism, depression, patterns of reaction to stress, the theory of the accident process, suicide and accidents. However, the research in this area has often been criticized, and countermeasure development to deal with complex psychological forces will be difficult. Rather than view an accident as an isolated event, researchers now propose that accidents are preceded by a number of often recognizable signs which indicate stress, anxiety, and conflict. It may be possible to develop intervention strategies which short circuit the accident process.
Probabilistic modeling with human factors in machine learning
(2020-09-03) Nguyen, An Thanh; Lease, Matthew A.; Wallace, Byron; Durrett, Greg; Liu, Qiang
Although machine learning has been a very popular research area, the human factors have been largely unexplored. In this dissertation, we present our work in three directions: (1) models for better understanding human annotators, (2) systems for interacting with human users, and (3) software tools for human developers. Together, our models, systems, and tools aim at understanding and improving the interactions between humans and machine learning using a probabilistic approach. In the first direction, we present our work in probabilistic models for evaluating annotators, identifying patterns of annotation errors, and predicting subjective annotations. As for the second direction, we study user interaction in the task of fact-checking: predicting the veracity of a claim given reporting articles. We propose a probabilistic model that combines annotator accuracies, article stances, source reputation, and claim veracities. We also present the results of our user studies on how people interact with our system. In the third direction, we introduce our software tools for developing transparent machine learning systems. The tools integrate back-end machine learning models and front-end user interfaces, enabling developers to address the accuracy-transparency trade-off.