PurposeThis study examines the role of continuous trust (i.e., a trust that develops over time as a result of continuous usage interactions) in determining customers' intention to continue using online product recommendations (OPRs).Design/methodology/approachBased on information system (IS), continuance model, and continuous trust, we propose that continuous trust will influence customers’ intention to continue OPRs’ use directly and indirectly via their satisfaction and usefulness of the OPRs. The research model is tested using data collected via an online survey from 626 existing users of OPRs in 15 different countries.FindingsThe empirical results revealed that continuous trust is shown to be a significant predictor of customers’ intention to continue OPRs use for future purchases. Additionally, the customers’ perceived confirmation and continuous trust positively influence their perceived usefulness and satisfaction with the OPRs, which subsequently influence customers’ OPRs continuous usage intention.Research limitations/implicationsThe saliency of continuous trust and usefulness of OPRs present e-retailers with potential fruitful areas to shape future usage of OPRs. In addition, e-retailers must understand that improving the OPR usefulness on its own may not lead to OPRs continuous usage until OPRs trustworthiness is not continually improved. OPRs itself may be convenient and useful, but trustworthy OPRs will pay stronger dividends for customer satisfaction and OPRs’ continuous usage.Originality/valueThe incorporation of continuous trust into the IS continuance model offers a new theoretical lens and an alternative explanation for the OPR continuous usage intention. This study stands in contrast to the large majority of research concerning initial trust and OPRs adoption, in that it focuses on continuous trust (as opposed to initial trust) and on a customers’ OPRs continuous usage intention as opposed to the initial adoption decision.

This study aims to extend expectation-confirmation model (ECM) of IS continuance based on effort-accuracy model (EAM) for predicting and explaining continuous usage of online product recommendation (OPR) that has been ignored in prior literature. The proposed OPR continuance model, incorporating the postadoption beliefs of perceived usefulness, perceived decision quality and perceived decision effort, was empirically validated with data collected from an online survey of 626 existing users of the OPR. Results indicated a good explanatory power of the OPR continuance model (R 2 = 62.1% of OPR continuance intention, R 2 = 53% of satisfaction, R 2 = 50.5% of perceived usefulness, and R 2 = 9% of perceived decision effort, and R 2 = 72.3% of perceived decision quality), with all major paths supported except one. We also analysed the data on the original ECM that reveals lower variances explained compared to the OPR continuance model (D6% in OPR continuance intention, D5.1% in customer satisfaction, and D3.2% in perceived ABOUT THE AUTHORS

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Question classification is one of the essential tasks for automatic question answering implementation in natural language processing (NLP). Recently, there have been several text-mining issues such as text classification, document categorization, web mining, sentiment analysis, and spam filtering that have been successfully achieved by deep learning approaches. In this study, we illustrated and investigated our work on certain deep learning approaches for question classification tasks in an extremely inflected Turkish language. In this study, we trained and tested the deep learning architectures on the questions dataset in Turkish. In addition to this, we used three main deep learning approaches (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)) and we also applied two different deep learning combinations of CNN-GRU and CNN-LSTM architectures. Furthermore, we applied the Word2vec technique with both skip-gram and CBOW methods for word embedding with various vector sizes on a large corpus composed of user questions. By comparing analysis, we conducted an experiment on deep learning architectures based on test and 10-cross fold validation accuracy. Experiment results were obtained to illustrate the effectiveness of various Word2vec techniques that have a considerable impact on the accuracy rate using different deep learning approaches. We attained an accuracy of 93.7% by using these techniques on the question dataset.