The purpose of this study was to test the comparability of several psychological instruments commonly used in Information Systems (IS) cognitive style research. The objective was to determine the limitations of: existing instruments in IS cognitive style research. The motivation for this research was the inconclusive findings reported in IS human information processing research. The study used a repeated measures design. Each individual completed the following cognitive style and personality instruments; the Myers-Briggs Type Indicator, the Group Embedded-figures Test, the Learning Style Inventory, the Human Information Processing Survey, and the Torrance Tests of Creative Thinking (Figural Form A). The individual also completed a dichotic listening and tachistoscope task. Data was collected from business administration, BCIS, liberal arts majors, and IS professionals. The results of this study indicate IS researchers need to consider several factors in the use of these instruments. A direct relationship exists between cognitive ability and results on the GEFT. Cognitive ability should be considered a moderating variable in interpreting the results of the GEFT. Also, the hypothesis that the GEFT is a surrogate for analytical and low-analytical abilities is not supported by this study. Other reported results include the inappropriateness of using the TTCT with adult populations. Also, the MBTI appears to be an appropriate instrument for measuring cognitive styles in IS research. This study reported that gender is a moderating factor on the classification of MBTI types in that gender is not evenly divided among feeling and thinking types. This study reported no relationships between the cognitive style and personality instruments with the physiological measurements. The validity of the physiological measurements could be the underlying factor for not reporting any significant relationships. IS researchers will benefit from this study through an improved understanding of the appropriateness and applicability of these instruments. This benefits research through the strengthening of instrument selection.

As computers pervade aspects of daily life, users demand software that is easy to use. It has been suggested that adding human factors engineers (HFEs) to software development teams would help software development companies meet these user demands. However, there are qualitative data which suggest that software developers (80s) and HFEs do not communicate well with each other. It is believed that this lack of communication has inhibited the use of HFEs on software development teams. It is further believed that this lack of communication is due in part to the differences in the frames of reference of HFEs and 50s. Thus, the objectives of this thesis are: 1. To develop an instrument which can be used to determine the differences in the frames of reference of HFEs and 80s. 2. To test the instrument. Three questionnaires were developed to probe the differences in the frames of reference of HFEs and 80s. The first, a background questionnaire, probed for information concerning software development experience and knowledge of specific software industry terms. The second was a software development activities questionnaire which was used to ascertain the importance of participation of certain professionals in software development activities. Finally, the usability information questionnaire was used to determine what type of supporting information would be necessary for a design change at certain points in the development of the product. Participants (30 HFEs and 30 80s) completed the questionnaires. It was found that HFEs and 80s do differ in their frames of reference. It was also found that some of these differences could cause a lack of communication between HFEs and 50s. It is suggested that software companies provide interdisciplinary training for their employees to help reduce these differences and to improve communication. / Master of Science

Indiana University-Purdue University Indianapolis (IUPUI) / The reliability of information in health social network sites (HSNS) is an imperative concern since false information can cause tremendous damage to health consumers. In this thesis, we introduce a trust framework which captures both human trust level and its uncertainty, and also present advantages of using the trust framework to intensify the dependability of HSNS, namely filtering information, increasing the efficiency of pharmacy marketing, and modeling how to monitor reliability of health information. Several experiments which were conducted on real health social networks validate the applicability of the trust framework in the real scenarios.

<p>Surface electromyography (sEMG) has been used to monitor muscle activity and predict fatigue in the workplaces. However, objectively measuring fatigue is challenging in complex work with unpredictable work cycles, where sEMG may be influenced by the dynamically changing posture demands. The sEMG is affected by various variables and substantial change in mean power frequencies (MPF), and a decline over 8-9% is primarily considered musculoskeletal fatigue. These MPF thresholds have been frequently used, and there were limited efforts to test their appropriateness in determining musculoskeletal fatigue in live workplaces (which predominantly consist of complex tasks). In addition, the techniques that consider both muscular and postural measurements that incorporate dynamic posture changes observed in complex work have not yet been explored. The overall objective of this work is to leverage both postural and muscular cues to identify musculoskeletal fatigue in complex tasks/jobs (i.e., tasks involving different levels of exertions, durations, and postures). The work was completed in two studies.</p> The first study aimed to (1) predict subjective fatigue using objective measurements in non-repetitive tasks, (2) determine whether the musculoskeletal fatigue thresholds in non-repetitive tasks differed from the previously reported threshold, and (3) utilize the empirically calculated thresholds to test their appropriateness in determining musculoskeletal fatigue in live surgical workplaces. The findings showed that the multi-modal measurements indicate better sensitivity than single-modality (sEMG) measurements in detecting decreases in MPF, a predictor of fatigue. In addition, the results showed that the thresholds in dynamic non-repetitive tasks, like surgery, are different than the previously reported 8% threshold. Additionally, implementing muscle-specific thresholds increased the likelihood of more accurately reporting subjective fatigue. The second study aimed to develop a multi-modal fatigue index to detect musculoskeletal fatigue. A controlled laboratory study was performed to simulate the non-repetitive physical demands at different postures. A series of experiments were conducted to test the effectiveness of various metrics/models to identify subjective fatigue in complex tasks. Next, the composite fatigue index (CFI) function was developed using the time-synced integration of both muscular signals (measured with sEMG sensors) and postural signals (measured with Inertial Measurement Unit (IMU) sensors). The variables from sEMG (amplitude, frequency, and the number of muscles showing signs of fatigue) and IMU (the prevalence of static and demanding postures and the number of shoulders in static/demanding posture) sensors were integrated to generate the CFI function. The prevalence of static/demanding postures was developed using the cumulative exposures to static/demanding postures based on the material fatigue failure theory. The single value fatigue index was obtained using the resultant CFI function, which incorporates both muscular and postural variables, to quantify the muscular fatigue in dynamic non-repetitive tasks. The findings suggested that the propagation of musculoskeletal fatigue can be detected using the multi-modal composite fatigue index in complex tasks. The resultant CFI function was then applied to surgery tasks to differentiate the fatigued and non-fatigued groups. The findings showed that the multi-modal fatigue assessment techniques could be utilized to incorporate the muscular and postural measurements to identify fatigue in complex tasks beyond single-modality assessment approaches.