5,918 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    Comparison of Various Machine Learning Models for Estimating Construction Projects Sales Valuation Using Economic Variables and Indices

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    The capability of various machine learning techniques in predicting construction project profit in residential buildings using a combination of economic variables and indices (EV&Is) and physical and financial variables (P&F) as input variables remain uncertain. Although recent studies have primarily focused on identifying the factors influencing the sales of construction projects due to their significant short-term impact on a country's economy, the prediction of these parameters is crucial for ensuring project sustainability. While techniques such as regression and artificial neural networks have been utilized to estimate construction project sales, limited research has been conducted in this area. The application of machine learning techniques presents several advantages over conventional methods, including reductions in cost, time, and effort. Therefore, this study aims to predict the sales valuation of construction projects using various machine learning approaches, incorporating different EV&Is and P&F as input features for these models and subsequently generating the sales valuation as the output. This research will undertake a comparative analysis to investigate the efficiency of the different machine learning models, identifying the most effective approach for estimating the sales valuation of construction projects. By leveraging machine learning techniques, it is anticipated that the accuracy of sales valuation predictions will be enhanced, ultimately resulting in more sustainable and successful construction projects. In general, the findings of this research reveal that the extremely randomized trees model delivers the best performance, while the decision tree model exhibits the least satisfactory performance in predicting the sales valuation of construction projects

    The development of an international model for technology adoption: the case of Hong Kong

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    The purpose of this study is to examine the causal relationships between the internal beliefs formation of a decision-maker on technology adoption and the extent of the development of a technology adoptive behaviour. In particular, this study aims to develop an International Model For Technology Adoption (IMTA), which builds upon the Theory of Planned Behaviour (Ajzen 1992) and improves on the framework of the Technology Acceptance Model (Davis 1986). The development of such a model requires an understanding of the environmental factors which shape the cognitive processes of the decision maker. Hence, this is a behavioural model which investigates the constructs influencing the adoption behaviour and how the interaction between these constructs and the external variables can impact on the decision making process at the level of the firm. Previous research on technology transfer and innovation diffusion has classified factors affecting the diffusion process into two dimensions: 1) external-influence and 2) internal-influence. Hence, in this research, the International Model For Technology Adoption looks at how the endogenous and exogenous factors enter into the cognitive process of a technology adoption decision through which attitudes and behavioural intentions are shaped. Under the IMTA, the behavioural intention to adopt is a function of two exogenous variables, 1) Strategic Choice, and 2) Environmental Control. The Environmental Control factor is further categorised by two exogenous factors, namely, 1) Government Influence, and 2) Competitive Influence. In addition, the Competitive Influence factor is, in turn, classified into five forces: namely, 1) Industry Structure, 2) Price Intensity, 3) Demand Uncertainty, 4) Information Exposure, 5) Domestic Availability. Regarding the cognitive process which forms the attitude to adopt, it is hypothesised to be affected by six other endogenous beliefs: 1) Compatibility; 2) Enhanced Value; 3) Perceived Benefits; 4)Adaptative Experiences, 5) Perceived Difficulty; and 6) Suppliersā€™ Commitment. A survey research method was utilised in this study and the research instrument was developed after a comprehensive review of the relevant literature and an expert interview. A total of 298 completed questionnaires were returned; giving a response rate of 13.56%. Of the 298 questionnaires, 39 of the responses were unusable with missing date. This gives a total of 259 usable questionnaires and an effective response rate of 11.78%. The results of the analysis suggested that the fitness of the International Model For Technology Adoption was good and the data of this study supported the overall structure of the IMTA. When compared with the null model, which was used by the EQS as a baseline model to judge to overall fitness for the IMTA, the IMTA yielded a value of 0.914 in the Comparative Fit index; hence, indication of a good fit model. In addition, the results of the principal component analysis also illustrated that the 16-factor International Model For Technology Adoption was an adequate model to capture the information collected during the survey. The results shown that this 16-factor structure represented nearly 77% of the total variance of all items. A further analysis into the factor structure, again, revealed that there existed a perfect match between the conceptual dimensionality of the International Model For Technology Adoption and the empirical data collected in the survey. However, the results of the hypotheses testing on the individual constructs were mixed. While not all the magnitude of these ten hypotheses was statistically significant, almost all pointed to the direction conceptualised by the IMTA. From these results, it can be interpreted that while the results of the structural equation modelling analysis provided overall support to the International Model For Technology Adoption, the results of individual constructs of the Model revealed that some constructs were forming a larger impact than others in the decision making process to adopt foreign technology. In particular, the intention to adopt was greatly affected by the attitude of the prospective adopters, the influence of the government and the degree of industry rivalry. However, the impact of the overall competitive influence factor on the intention to adopt was not supported by the results. Again, the existence of investment alternative was also not a serious concern for the prospective adopters

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Bayesian Forecasting in Economics and Finance: A Modern Review

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    The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified explicitly, and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large, or complex. The current state of play in Bayesian forecasting in economics and finance is the subject of this review. The aim is to provide the reader with an overview of modern approaches to the field, set in some historical context; and with sufficient computational detail given to assist the reader with implementation.Comment: The paper is now published online at: https://doi.org/10.1016/j.ijforecast.2023.05.00

    Predictive Demand Response Modeling for Logistic Systems Innovation and Optimization

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    In the ever-increasing dynamics of global business markets, logistic systems must optimize the usage of all possible sources to continually innovate. Scenario-based demand prediction plays an important role in the effective economic operations and planning of logistics. However, many uncertainties and demand variability, which are associated with innovative changes, complicate demand forecasting and expose system operators to the risk of failing to meet demand. This dissertation presents new approaches to predictively explore how customer preferences will change and consequently demand would respond to the new setup of services caused by an innovative transformation of the logistic layout. The critical challenge is that the responses from customers in particular and demand in general to the innovative changes and corresponding adjustments are uncertain and unknown in practice, and there is no historical data to learn from and directly support the predictive model. In this dissertation, we are dealing with three different predictive demand response modeling approaches, jointly shaping a new methodological pathway. Chapter 1 provides a novel approach for predictive modeling probabilistic customer behavior over new service offers which are much faster than ever done before, based on the case of a large Chinese parcel-delivery service provider. Chapter 2 introduces an approach for predicting scenario-based erection-site demand schedules under uncertainty of disruptive events in construction projects whose logistics transformed from traditional to modular style, based on the case of a USA-based innovative leader in modular building production. For such a leader to advance in its logistics design innovations and associated capacity adjustments, and also to enhance its capability for taking more market share, it is crucial to estimate potential future demand for modular construction and corresponding probable projects in terms of their potential location, size, and characteristics. For this purpose, Chapter 3 introduces a methodological approach for estimating scenario-based future demand for modular construction projects to be implemented over the US metropolitan statistical areas.Ph.D

    Innovation in Energy Security and Long-Term Energy Efficiency ā…”

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    The sustainable development of our planet depends on the use of energy. The increasing world population inevitably causes an increase in the demand for energy, which, on the one hand, threatens us with the potential to encounter a shortage of energy supply, and, on the other hand, causes the deterioration of the environment. Therefore, our task is to reduce this demand through different innovative solutions (i.e., both technological and social). Social marketing and economic policies can also play their role by affecting the behavior of households and companies and by causing behavioral change oriented to energy stewardship, with an overall switch to renewable energy resources. This reprint provides a platform for the exchange of a wide range of ideas, which, ultimately, would facilitate driving societies toward long-term energy efficiency
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