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Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

To examine the impact of manufacturer reputation, retailer reputation, and product price on consumers’ perceived quality and purchasing behavior regarding organic milk.

Employing a 2 × 2 experiment, data were collected from 1,259 consumers in 32 provinces in China.

When a low-reputation manufacturer sells products through a high-reputation retailer, it improves consumers’ perception of quality and positively influences their purchasing behavior. Interestingly, setting higher prices for products manufactured by low-reputation companies and selling them through high-reputation retailers did not significantly enhance consumers’ perceived quality and deter their purchasing behavior.

The analysis expands the framework for cue diagnosis. While the existing framework primarily focuses on the influence of cue-type combinations on perceived quality, it does not integrate purchasing behavior into the conceptual framework. This limitation hinders people understanding of the theoretical mechanisms underlying the use of cues in purchasing decisions. This paper address this by gradually introducing variables, such as retailer reputation and product price, into the baseline model, thereby extending this theory. In addition, this paper advances the marketing research literature within the business-to-business-to-consumer context by examining the additive effects of manufacturer reputation, retailer reputation, and product price on consumers’ perception of quality and purchasing behavior.