Organizational Learning and Business Model Innovation:the Moderating Role of Network

Many start-ups end up with the failure because they are unable to establish the effective business model. We use the organizational learning theory and network theory to study the mechanism of business model innovation for start-ups. Based on the 256 data of start-ups, the results of PLS structural equation model analysis show that both acquisitive learning and experimental learning can significantly promote the business model innovation. The formal network and informal network play different moderating roles on the relation of organizational learning and business model innovation

Empirical Research on the Impacts of Geographic Boundary-spanning Search on Innovation Performance

Boundary-spanning search has been argued to be important for the success of innovation. There are various kinds of dimensions about organizational search activities. Past studies on boundary-spanning search have focused mainly on technological dimension. We characterize the boundary-spanning search on geographic dimension. We propose a conceptual model and 6 hypotheses. Data from 156 firms were collected to test above hypotheses. The results show that both local search and nonlocal search have positive effect on incremental innovation. What’s more, local search is more positive than nonlocal search on incremental innovation. Meanwhile, both local search and nonlocal search have positive effect on breakthrough innovation. However, it is not supported by data that nonlocal is more positive than local search on breakthrough innovation

Testing Darwin’s transoceanic dispersal hypothesis for the inland nettle family (Urticaceae)

Dispersal is a fundamental ecological process, yet demonstrating the occurrence and importance of long-distance dispersal (LDD) remains difficult, having rarely been examined for widespread, non-coastal plants. To address this issue, we integrated phylogenetic, molecular dating, biogeographical, ecological, seed biology and oceanographic data for the inland Urticaceae. We found that Urticaceae originated in Eurasia c. 69 Ma, followed by >= 92 LDD events between landmasses. Under experimental conditions, seeds of many Urticaceae floated for > 220 days, and remained viable after 10 months in seawater, long enough for most detected LDD events, according to oceanographic current modelling. Ecological traits analyses indicated that preferences for disturbed habitats might facilitate LDD. Nearly half of all LDD events involved dioecious taxa, so population establishment in dioecious Urticaceae requires multiple seeds, or occasional selfing. Our work shows that seawater LDD played an important role in shaping the geographical distributions of Urticaceae, providing empirical evidence for Darwin's transoceanic dispersal hypothesis

Six new species of Elatostema (Urticaceae) from Yunnan

Six new species of the genus Elatostema (Urticaceae), E. dentatocaudatum, E. baoshanense, E. cuipingfengense, E. viridicostatum, E. flexuosicaule and E. globosostigmatum, from Yunnan Province, China are described and illustrated. The diagnostic differences between the six new species and their respective allies are given

Prediction of Customers’ Subscription to Time Deposits Based on SMOTEENN-XGBoost Model

In the fierce competitive banking industry, accurate prediction of customers’ subscription to time deposits is vital for banks. This can reduce unnecessary time and energy spent on targeted customer service to improve bank efficiency. Traditional prediction methods do not handle the imbalanced data problem very well. In this paper, in order to minimize the impacts from imbalanced data, we combine Synthetic Minority Oversampling Technique (SMOTE)and Edited Nearest Neighbor Technique (ENN) to make the data as balanced as possible. Then, Extreme Gradient Boosting algorithm (XGBoost) is adopted as classification algorithm to improve the accuracy of data classification results. For clarity, this model is called SMOTEENN-XGBoost model. A bank customers dataset published on the Kaggle platform is used to demonstrate its effect by numerical experiments. We compare the performance of the SMOTEENN-XGBoost in this paper with Decision Tree (DT), Adaptive Boosting (AdaBoost), XGBoost, SMOTE-XGBoost in terms of Accuracy (ACC), Area Under ROC Curve (AUC), and Geometric-mean (G-mean). The results show that the mean ACC, AUC and G-mean of SMOTEENN-XGBoost model are 0.92, 0.97, and 0.92, which are better than the other models. It indicates that this model has good classification performance and can effectively dig out potential customers

An AdaBoost-DT Model for Credit Scoring

Credit scoring for loan applicants is an essential measure to reduce the risk of personal credit loan. Due to low percentage of non-performing loans, credit scoring is typically considered as an imbalanced classification problem. It is difficult to adress this kind problem using a single classifier. In order to settle the problem of imbalanced samples in credit scoring system, an ensemble learning classification model named AdaBoost-DT is proposed. In this model, we employ adaptive boosting (AdaBoost) to cascade multiple decision trees (DT). The weights of the base classifier can be adjusted automatically by enhancing the learning of misclassified samples. In order to verify the effectiveness empirically, we use data from Kaggle platform. Ten-fold cross-validation is carried out to evaluate and compare the performance among AdaBoost-DT model, DT, and Random Forest. The empirical results show that AdaBoost-DT model has higher accuracy. This model is valuable for banks and other financial institutions to evaluate customers’ credit efficiently

Prediction of Credit Card Defaulters Based on SMOTE-XGBoost Model

Credit card defaulters are on the rise year by year, which would lead commercial banks into a serious business crisis. It is important for commercial banks to control the default rate of credit cards. According to the low percentage of defaulters, it is challenging to predict them using a traditional machine learning algorithm. To address this problem, an improved ensemble learning model is proposed, where the Synthetic Minority Oversampling Technique (SMOTE) is used to oversample the data set, and the Extreme Gradient Boosting algorithm (XGBoost) is introduced to construct the predicting model. For clarity, this model is called a SMOTE-XGBoost model. Customer default data from the UCI machine learning dataset is used to empirically test the effectiveness. In terms of Recall, ACC, and AUC values, ten-fold cross-validation is carried out to evaluate and compare the performance between the SMOTE-XGBoost model and other models, including the general XGBoost model and Random Forest. The empirical results show that the SMOTE-XGBoost model performs well and outperforms other models

Single image De-haze based on a new dark channel estimation method

Conference Name:2012 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2012. Conference Address: Zhangjiajie, China. Time:May 25, 2012 - May 27, 2012.IEEE Beijing Section; Hunan University of Humanities, Science and Technology; Tongji University; Xiamen University; Central South UniversityThe image quality is effected dramatically by the weather conditions such as haze, fog and smoke, in surveillance or vehicle systems. In this paper, we propose a novel algorithm to remove haze from a single image. A new dark channel estimation method is employed by subtracting three times variance from mean to approximate the minimum value, which is defined as the dark channel in a local area. To achieve anisotropic local mean and variance, a fast bilateral filter is introduced. Its complexity is a linear function of the size of the image, which makes real-time visibility restoration possible. Compared with existing algorithm based on dark channel prior, the proposed algorithm have better performance by reducing the halos introduced by large patches very well with less computational cost. 漏 2012 IEEE