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    Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

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    Authors
    Kumar, Anil
    Kabra, Gaurav
    Mussada, Eswara Krishna
    Dash, Manoj Kumar
    Rana, Prashant Singh
    Affiliation
    Xavier Institute of Management, Bhubaneswar, India
    BML Munjal University
    Indian Institute of Information Technology & Management, Gwalior (Madhya Pradesh), India
    Thapar University Patiala, Punjab, India
    Issue Date
    2017-05-30
    
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    Abstract
    A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.
    Citation
    Kumar, A. et al (2017) ‘Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention’, Neural Computing and Applications. Doi: 10.1007/s00521-017-3047-z
    Publisher
    Springer
    Journal
    Neural Computing and Applications
    URI
    http://hdl.handle.net/10545/623312
    DOI
    10.1007/s00521-017-3047-z
    Additional Links
    http://link.springer.com/10.1007/s00521-017-3047-z
    Type
    Article
    Language
    en
    ISSN
    0941-0643
    1433-3058
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00521-017-3047-z
    Scopus Count
    Collections
    Centre for Supply Chain Improvement

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