Knowledge has long been recognized as one of the most valuable assets in an organization. Managing and organizing knowledge has become an important corporate strategy for organizations to gain and maintain competitive advantages in the information age. Electronic knowledge repositories (EKRs) have become increasingly popular knowledge sharing tools implemented by organizations to promote knowledge reuse. The goal of this study is to develop and test a research model that explains users' continued usage behavior of EKRs in public accounting firms. Theoretically grounded in the expectation-confirmation model (ECM) and commitment-based model, the research model presented in this study integrates both of these theoretical perspectives to study users' EKR continuance intentions. This study surveyed 230 EKR users from four large public accounting firms. Partial least squares regression was used to test the hypotheses and the explanatory power of the model. Results indicate that perceived usefulness and commitment exhibit a sustained positive influence on continuance intention. Additionally, subjective norms are positively related to calculative commitment and moral commitment. Organizational identification is positively related to affective commitment and moral commitment. Perceived usefulness is positively related to affective commitment and calculative commitment. The model comparisons with the technology acceptance model (TAM) and ECM demonstrated that the integrated model presented in this research explained 1.6% and 0.8% additional variance in continuance intention than both ECM and TAM respectively. Additional multi-group analyses were also conducted to examine the differences between knowledge seekers and contributors and the differences between knowledge novices and experts. This study raises theoretical implications in the area of knowledge management in general and EKRs in particular. It represents one of the first attempts to empirically examine users' continuance intention of knowledge management applications. This study has presented a different perspective on technology acceptance/continued usage by introducing commitment to explain continued IS usage. By integrating commitment and ECM, this study offers a useful framework for future studies on technology use. It demonstrates that both user commitment and perceived usefulness are strong predictors of EKR continuance intention. The results also raise interesting implications for practitioners interested in knowledge management and particularly for public accounting firms how to leverage EKRs to gain a competitive advantage. / Ph. D.

While the education sector keeps embracing and propagating AI-chatbot integration and usage in their pedagogical practices. This study aimed at investigating the impact of AI-chatbots on the knowledge construction process and critical reasoning of university students, using a mixed method approach to sample the University of Nigeria Nsukka (UNN) students’ performances and teachers’ observation. The purpose is to Investigate the extent of the impacts of AI-chatbots usage on the knowledge construction and critical reasoning abilities and to provide some proven approaches to engaging educational AI-chatbot in a manner that does not hamper the natural knowledge construction process according to constructivism theoretical paradigm. Three research questions were poised to harvest the teachers’ observations, which were matched against the principles and assumptions of constructivism learning theory and the result showed that AI-chatbot usage has some positive impact on the students’ knowledge construction and critical reasoning abilities which include learning efficiency enhancement, gendering plethora of perspectives and furnishing the students cognitively. Paradoxically, it also makes the students boycott knowledge construction process, leading to a dearth of experience, irrationality, passive learning, groupthink, academic dishonesty, and a diminished propensity for critical thinking. Recommendations were drawn from the success stories of the teachers which are to orient the students properly on the ethical usage of AI-chatbots, while integrating critical thinking education and praxis approaches in their pedagogical practice.

Event data recorders (EDRs) are an invaluable data source that have begun to, and will increasingly, provide novel insight into motor vehicle crash characteristics. The "black boxes" in automobiles, EDRs directly measure precrash and crash kinematics. This data has the potential to eclipse the many traditional surrogate measures used in vehicle safety that often rely upon assumptions and simplifications of real world crashes. Although EDRs have been equipped in passenger vehicles for over two decades, the recent establishment of regulation has greatly affected the quantity, resolution, duration, and accuracy of the recorded data elements. Thus, there was not only a demand to reestablish confidence in the data, but a need to demonstrate the potential of the data. The objectives of the research presented in this dissertation were to (1) validate EDR data accuracy in full-frontal, side-impact moving deformable barrier, and small overlap crash tests; (2) evaluate EDR survivability beyond regulatory crash tests, (3) determine the seat belt accuracy of current databases, and (4) assess the merits of other vehicle-based crash severity metrics relative to delta-v. This dissertation firstly assessed the capabilities of EDRs. Chapter 2 demonstrated the accuracy of 176 crash tests, corresponding to 29 module types, 5 model years, 9 manufacturers, and 4 testing configurations from 2 regulatory agencies. Beyond accuracy, Chapter 3 established that EDRs are anecdotally capable of surviving extreme events of vehicle fire, vehicle immersion, and high delta; although the frequency of these events are very rare on U.S. highways. The studies in Chapters 4 and 5 evaluated specific applications intended to showcase the potential of EDR data. Even single value data elements from EDRs were shown to be advantageous. In particular, the seat belt use status may become a useful tool to supplement crash investigators, especially in low severity crashes that provide little forensic evidence. Moreover, time-series data from EDRs broadens the number of available vehicle-based crash severity metrics that can be utilized. In particular, EDR data was used to calculate vehicle pulse index (VPI), which was shown to have modestly increased predictive abilities of serious injury compared to the widely used delta-v among belted occupants. Ultimately, this work has strong implications for EDR users, regulatory agencies, and future technologies. / Ph. D.

Cannabis has been historically difficult to research due to its federal scheduling. However, as legalization of cannabis medically, recreationally, or both in states across the country has increased, so has the need to address the research gaps that persist. The purpose of this study was to explore the patterns of cannabis use and perceptions of college students in Tennessee, which encompass a demographic of mainly young adults who are typically associated with high usage patterns. The study uses quantitative data collected from an online survey sent to a university in East Tennessee to evaluate associations between students’ age, gender, race/ethnicity, class cohort, and political party affiliation. The results demonstrate that most of the students have used cannabis and support cannabis legalization. Policy implications for the campus and state are suggested.

This study incorporates insights from Usage Based Grammar (UBG) into variationist research on morphosyntactic variation in Spanish. Specifically, this dissertation investigates the impact on pronoun use of lexical frequency, or the number of times a finite verb appears in a large data set based on spontaneous speech from 221 speakers in two locales (New York City, NY and Boston, MA), as well as a series of context-based frequency metrics in a Variationist study of Spanish Subject Personal Pronoun (SPP) variation (e.g. Yo creo vs. creo ‘I think’). This investigation elucidates the nature of frequency effects (both lexical and contextual) on pronoun use and on the other linguistic factors that have been shown to impact pronoun use. Through this investigation, this dissertation is able to draw conclusions on the nature of linguistic variation and make inferences surrounding the mental representations underlying sociolinguistic patterns. In the past, frequency has been investigated in subject pronoun production as it pertains to the rate of the finite verb, with researchers counting the instances of each verb's occurrence within a corpus. This approach has produced mixed results. One study has shown that frequency modulates or amplifies the effects of other linguistic predictors, providing evidence that suggests lexical frequency does not directly impact pronoun use in a uniform or monotonic way (Erker & Guy, 2012). A few studies have replicated some version of these modulating effects, though they have not found as consistent amplification effects across linguistic constraints. Other studies have found contradictory frequency effects, showing only a main effect of frequency (high frequency corresponding to high pronoun use in some studies and low pronoun use in others) with no amplification effects, or no frequency effects at all. Further, Usage Based Grammar frameworks, which are often referenced in studies exploring lexical frequency, posit that speakers are not only sensitive to the rate of use of linguistic forms, but also the detailed contexts in which these forms appear. Such “rich memories”, as they are referred to in UBG, are said to constitute the mental representations of these forms. The mixed results in the literature, together with the UBG notion of rich memories, motivate the current study, which investigates the relationship between contextual frequency and pronoun use, since contextual frequency metrics (as opposed to overall frequency) might shed more light on frequency effects in morphosyntactic variation. The contextual frequency metrics analyzed in the current dissertation consist of the frequencies at which finite verbs appear in four combinations of the factor values of two variables, referred to as Switch Reference (i.e. whether the previous verb has a different referent or the same referent as the target site of variation) and Preceding Pronoun (i.e. whether the immediately preceding site of pronominal variation has a pronoun present or absent). The four combinations on which contextual frequency metrics are based are therefore: (1) ‘Different Referent/Preceding Pronoun Present’, (2) ‘Different Referent/Preceding Pronoun Absent’, (3) ‘Same Referent/Preceding Pronoun Present’, or (4) ‘Same Referent/Preceding Pronoun Absent’. Analysis of 88,001 tokens of pronominal presence or absence generally replicate the modulating effects of overall verb frequency observed by Erker & Guy (2012), i.e. the effects of several linguistic factors are amplified for frequent verb forms. Moreover, the analysis of contextual frequency reveals that verb forms must reach a certain overall frequency threshold in order for contextual properties to impact pronoun use. This finding aligns with the UBG prediction that the most frequent context in which a verb appears will dominate the overall pronominal tendencies of the verb, as long as that verb is sufficiently frequent in discourse. Overall, this study concludes that the linguistic variation observed in language use aligns with the usage-based approach that contextual frequency effects accumulate in the mental representations that underlie sociolinguistic patterns.

Usage-based approaches assume that children’s early utterances are item-based. This has been demonstrated in a number of studies using the tracebackmethod. In this approach, a small amount of “target utterances” from a child language corpus is “traced back” to earlier utterances. Drawing on a case study of German, this paper provides a critical evaluation of the method from a usage-based perspective. In particular, we check how factors inherent to corpus data as well as methodological choices influence the results of traceback studies. To this end, we present four case studies in which we change thresholds and the composition of the main corpus, use a cross-corpus approach tracing one child’s utterances back to another child’s corpus, and reverse and randomize the target utterances. Overall, the results show that the method can provide interesting insights—particularly regarding different pathways of language acquisition—but they also show the limitations of the method.

In today's highly competitive and deregulated electricity market, companies in the generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers' load profiles. This strategic information is vital in load forecasting, demand-side management planning and long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: 1) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. 2) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for load profile characterization and classification. 3) The frequency domain load profile statistics and forecasting models. Two different models were introduced in this dissertation: the first one is the wavelet load forecast model and the other one is a stochastic model that incorporates local weather condition and frequency domain load profile statistics to perform medium term load profile forecast. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately. / Ph. D. / In today’s highly competitive and deregulated electricity market, companies in the electricity power generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers’ electricity consumption behavior. This strategic information is vital in forecasting and managing the future electricity demand. This information is also very important in utility company’s long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of electric load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: I) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. II) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for categorizing the load profile data. III) The frequency domain load profile statistics and forecasting models. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately.

No / Prior research has shown that individual differences in users' cognitive style and gender can have a significant effect on their usage and perceived usefulness of management information systems. We argue that these differences may also extend to computer-mediated knowledge management systems (KMS), although previous research has not tested this empirically. Where employees are expected to use KMS for acquiring and sharing knowledge, we posit that some will gain more benefit than others, due to their innate personal characteristics, specifically gender and cognitive style. Based on a sample of 212 software developers in one large IS organization, we re-open these dormant debates about the effects of cognitive style and gender on technology usage. The paper contains four main findings. First, we present support for the proposition that cognitive style has an impact on KMS usage, although not for all components of the system. Second, that gender significantly affects KMS usage, with males being more likely to use such systems than females. Third, we find a small interaction effect between cognitive style and gender, but only for the use of data mining. Finally, the data suggest that there is a strong association between KMS usage levels and perceived usefulness. We conclude that if organizations do not recognize the inherent diversity of the workforce, and accommodate gender and cognitive style differences into their knowledge management strategies, they may be likely to propagate an intrinsic disadvantage, to the detriment of females and intuitive thinkers.

Yes / Based on a critical review of the Unified Theory of Acceptance and Use of Technology (UTAUT), this study first formalized an alternative theoretical model for explaining the acceptance and use of information system (IS) and information technology (IT) innovations. The revised theoretical model was then empirically examined using a combination of meta-analysis and structural equation modelling (MASEM) techniques. The meta-analysis was based on 1600 observations on 21 relationships coded from 162 prior studies on IS/IT acceptance and use. The SEM analysis showed that attitude: was central to behavioural intentions and usage behaviours, partially mediated the effects of exogenous constructs on behavioural intentions, and had a direct influence on usage behaviours. A number of implications for theory and practice are derived based on the findings.

Within the last 30 years, Manufacturing Resource Planning (MRP-type) computer systems have quickly evolved from basic materials requirement planning software to today's enterprise resource planning (ERP) integrated software packages that reside on client/server computer architecture. However, given the magnitude of influence these computer systems encompass, very little research has been conducted to monitor and improve how companies are actually using these MRP, advanced MRP and ERP computer systems. In practical terms, where is the typical manufacturing organization today in terms of MRP systems development? To what extent is software being applied for use in enterprise integration? A survey study of manufacturing companies in Virginia and Tennessee was conducted to evaluate the current use and performance of computer systems to support manufacturing applications. This study explores the reasons why organizations chose to use these systems, the problems and benefits derived from the MRP/ERP systems, and the characteristics of the types of companies that have benefited from the use of MRP-type systems. The survey participants evaluated the performance of their manufacturing or enterprise planning systems on the basis of data accuracy, customer satisfaction, user satisfaction, systems effectiveness, convenience, information relevance, and software reliability. Furthermore, success factors associated with organizational performance were tested and evaluated. The correlation between perceived performance was tested against the influence of upper management support, the level of emphasis on training, sources of technical expertise, and organizational experience with MRP-type systems. / Master of Science