Innovative replenishment management for perishable items using logistic regression and grey analysis

Huang, Jia Yen

doi:10.1504/ijbpm.2014.060151

Cited by 3 publications

(3 citation statements)

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“…From the above analysis, the appropriate model for P = 0% with which to fit the data is the following:

Y = - 0.393 - 4.240 x_{1} + 3.55 x_{2} - 9.072 x_{3} + 3.984 x_{4} + 8.021 x_{5} - 4.356 x_{6} - 3.418 x_{7} + 68.204 x_{8} - 67.816 x_{9} + 5.274 x_{10}

where x 1 is SPC, x 2 is CTIV, x 3 is MLB, x 4 is MLS, x 5 is SLB, x 6 is DOMPSL, x 7 is NDT, x 8 is FISH, x 9 is FISHR and x 10 is PTB. A positive regression coefficient means that the explanatory variable increases the probability of the outcome whereas a negative regression coefficient means that the variable decreases the probability of that outcome (J. Y. Huang, ).…”

Section: Data Analysis and Discussionmentioning

confidence: 99%

“…A positive regression coefficient means that the explanatory variable increases the probability of the outcome whereas a negative regression coefficient means that the variable decreases the probability of that outcome (J. Y. Huang, 2014). This study uses a confusion matrix to summarize the performance of logistic regression prediction for binary classification tasks.…”

Section: Screening Of Chip Indicatorsmentioning

confidence: 99%

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Using social media mining technology to improve stock price forecast accuracy

Huang

Liu

2019

Journal of Forecasting

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Many stock investors make investment decisions based on stock-price-related chip indicators. However, in addition to quantified data, financial news often has a nonnegligible impact on stock price. Nowadays, as new reviews are posted daily on social media, there may be value in using web opinions to improve the performance of stock price prediction. To this end, we use logistic regression to screen the chip indicators and establish a basic stock price prediction model. Then, we employ text mining technology to quantify the unstructured data of social media opinions on stock-related news into sentiment scores, which are found to correlate significantly with the change extent of the stock price. Based on the findings that the higher the sentiment scores, the lower the prediction accuracy of the logistic regression model, we propose an improved prediction approach that integrates sentiment scores into the logistic regression model. Our results show that the proposed model can improve the prediction accuracy for stock prices, and can thus provide a new reference for investment strategies for stock investors.

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“…From the above analysis, the appropriate model for P = 0% with which to fit the data is the following:

Y = - 0.393 - 4.240 x_{1} + 3.55 x_{2} - 9.072 x_{3} + 3.984 x_{4} + 8.021 x_{5} - 4.356 x_{6} - 3.418 x_{7} + 68.204 x_{8} - 67.816 x_{9} + 5.274 x_{10}

Section: Data Analysis and Discussionmentioning

confidence: 99%

Section: Screening Of Chip Indicatorsmentioning

confidence: 99%

Using social media mining technology to improve stock price forecast accuracy

Huang

Liu

2019

Journal of Forecasting

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“…Logistic regression is part of a category of generalized linear models (Huang, 2014), and it can be used to classify objects into two classes based on a series of observations. Logistic regression allows one to predict a discrete outcome from a set of variables that are dichotomous, such as presence/absence.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

Analysis on the influential factors of transformation resistance of small and medium enterprise managers

Huang¹,

Lin

2020

Journal of General Management

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The majority of small and medium-sized enterprises (SMEs) in Taiwan are original equipment manufacturers (OEMs) or original design manufacturers (ODMs). Given the growing intensity of global competition, transformation from OEM or ODM to a branded enterprise is a key concern for SMEs. SMEs’ branding processes are subject to numerous problems and among them, resistance by organization members is considered the most critical. Previous studies have largely focused on factors contributing to branding success or resistance by an organization’s employees, and few have addressed the implications of SMEs managers’ resistance to branding. To address the research gap, this study constructs a cognitive model for managers’ resistant behavior while drawing on the theory of planned behavior. Then, applying structural equation modeling, this study confirms that four exogenous variables, perceived belief, changes in job characteristics, organizational inertia, and social factors, directly influence two endogenous variables, managers’ attitude toward resistant behavior and the subjective norms of resistant behavior. Using demographic variables as independent variables, this study conducts a logistic regression analysis to predict companies resistant to branding on the basis of their background characteristics. The results highlight that the three factors with the highest predictive powers are organizational inertia, job position, and capitalization.

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