Customer Retention: Behaviour Perspective Model of Ghanaian Telecommunication Industry Using Multinomial Regression Analysis

To stay competitive, the mobile telecommunication companies spend millions of Ghana cedi each year on building long-term relationships with their customers. Marketing managers are constantly challenged with the problem of where to channel the limited resources in order to retain existing customers. This study approaches the customer retention problem in the mobile phone sector from a behavioural perspective, applying the Behavioural Perspective Model as the main analytical framework and further exploits some other factors that influence customer retention. The model includes a set of pre-behaviour and post-behaviour factors to study consumer choice, and explains its relevant drivers in a viable and comprehensive way, grounded in radical behaviourism. Data for the analysis were collected from tertiary students from Accra and Takoradi. Data collected were analysed using the multinomial regression technique. Analysis of the data revealed that the Behaviour setting factor is the only significant element in Behaviour Perspective Model. Further exploitation of behaviour situation revealed that the number of networks a customer uses, previous experience of a customer and customer’s intention are significant factors in determining customer retention in Ghana’s mobile telecommunication industry.

A socioeconomic approach to the profile of microcredit holders from the Hispanic minority in the USA

The subject of this study is the microcredit market in the USA,more specifically in Florida.The justification for choosing this specific state is the massive presence of the Hispanic population.This will facilitate a generalization of the obtained results to the microcredit market in Latin American countries.Thus,the objective of this study is to analyze the profile of microcredit holders and their companies from socioeconomic and financial points of view.As our data also consider the degree of repayment of the microloans included in the sample,the clients’profile is related to the punctuality or default of their corresponding loan repayments using the methodology of multi-nomial logit regression.The variables used in this study refer to personal information concerning borrowers(gender,age,education level,and marital status),the economic situation of their respective companies(closeness to the lender,number of workers,and revenues),and the characteristics of granted loans(principal,term,and purpose).However,the results of the regression show that only two variables are significant at the 5%significance level:the borrower’s age,which has a positive effect on repay-ment punctuality,and the loan term,which exhibits a negative effect.The findings of this study have clear implications,as they can help lenders design suitable microloans adjusted to customer profiles.Finally,future research should include other demograph-ics and characteristics of affected companies.

Comparison of multinomial logistic regression and logistic regression： which is more efficient in allocating land use？

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/ allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

Optimal Poisson Subsampling for Softmax Regression

Softmax regression,which is also called multinomial logistic regression,is widely used in various fields for modeling the relationship between covariates and categorical responses with multiple levels.The increasing volumes of data bring new challenges for parameter estimation in softmax regression,and the optimal subsampling method is an effective way to solve them.However,optimal subsampling with replacement requires to access all the sampling probabilities simultaneously to draw a subsample,and the resultant subsample could contain duplicate observations.In this paper,the authors consider Poisson subsampling for its higher estimation accuracy and applicability in the scenario that the data exceed the memory limit.The authors derive the asymptotic properties of the general Poisson subsampling estimator and obtain optimal subsampling probabilities by minimizing the asymptotic variance-covariance matrix under both A-and L-optimality criteria.The optimal subsampling probabilities contain unknown quantities from the full dataset,so the authors suggest an approximately optimal Poisson subsampling algorithm which contains two sampling steps,with the first step as a pilot phase.The authors demonstrate the performance of our optimal Poisson subsampling algorithm through numerical simulations and real data examples.

Contraceptive Method Choice Among Newly Married Couples and Influential Factors in Shanghai Municipality

A follow-up study with 7,826 representative newly married couples for fifteen months after their weddings in Shanghai Municipality showed that among the 3, 412 couples who actually adopted contraceptive method, rhythm was the main choice; the proportion for couples taking the contraceptive pill was much higher among sexually active couples before their weddings. The proportions of adopting rhythm or condom or the both, however, increased afterwards.About 86% of couples who had ever planned adopting the rhythm at registration actually used it. In fact, 16% of those who had ever planned to take pills eventually made this choice, because of their worry about any adverse side effects on mother's and fetus' health. Their knowledge about contraception,especially the pills, was incomprehensiue. APProximately 62% of condom users had not been given any instruction regarding its use when they got this contracoptive device one year later. Half of the pill and spermicide users learnt these respective methods from their friends or relatives. The proportion of delivering contraceptiues alter marriage by;F.P.P. was rather low. By fitting the multinomial logistic regression model, it is indicated that couple's evaluation on contraceptiue methods and contraceptiue goal were the main factors determining newlyweds' method of choice. Wife's knowledge on contraception and the accessibility of contraceptives and devices also influenced the method choice to some extent.

Spatial-Aware Supervised Learning for Hyper-Spectral Image Classification Comprehensive Assessment

A comprehensive assessment of the spatial-aware supervised learning algorithms for hyper-spectral image(HSI)classification was presented.For this purpose,standard support vector machines(SVMs),multinomial logistic regression(MLR)and sparse representation(SR) based supervised learning algorithm were compared both theoretically and experimentally.Performance of the discussed techniques was evaluated in terms of overall accuracy,average accuracy,kappa statistic coefficients,and sparsity of the solutions.Execution time,the computational burden,and the capability of the methods were investigated by using probabilistic analysis.For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used.Experiments show that integrating spectral-spatial context can further improve the accuracy,reduce the misclassification error although the cost of computational time will be increased.

Farmer's Perception on Supply-Demand Matching of New Variety and Its Influence Factors

Using disordered multinomial logistic regression and multiple linear regression method,385 copies of questionnaires on farmer are analyzed to explore the relationship between peasant's psychological traits,peasant's cognition on seed technology and perception on supplydemand matching of new variety.Research results show that the vast majority of farmers think that current new variety is at high-level supplydemand balance and the oversupply status,and updating speed of new variety on the market is faster;the farmers preferring risk,seeking innovation and having strong learning and cognition ability may select high-level supply-demand matching state,and the farmers understanding the importance and difference of seed technology tend to choose high-level supply-demand matching situation;the farmers with strong learning and cognition ability can acknowledge the importance and difference of seed technology,while the farmers preferring risk can perceive the difference of seed technology;psychology seeking the innovation and learning and cognition ability affect the farmer's perception on supplydemand matching status of new variety via affecting the farmer's cognition on technical difference.

The Perception of Flood Risks: A Case Study of Babessi in Rural Cameroon

Although risk perception of natural hazards has been identified as an important determinant for sound policy design,there is limited empirical research on it in developing countries.This article narrows the empirical literature gap.It draws from Babessi,a rural town in the Northwest Region of Cameroon.Babessi was hit by a severe flash flood in 2012.The cross-disciplinary lens applied here deciphers the complexity arising from flood hazards,often embedded in contexts characterized by poverty,a state that is constrained in disaster relief,and market-based solutions being absent.Primary data were collected via snowball sampling.Multinomial logistic regression analysis suggests that individuals with leadership functions,for example,heads of households,perceive flood risk higher,probably due to their role as household providers.We found that risk perception is linked to location,which in turn is associated with religious affiliation.Christians perceive floods riskier than Muslims because the former traditionally reside at the foot of hills and the latter uphill;rendering Muslims less exposed and eventually less affected by floods.Finally,public disaster relief appears to have built up trust and subsequently reduced risk perception,even if some victims remained skeptical of state disaster relief.This indicates strong potential benefits of public transfers for flood risk management in developing countries.