Trust in Online Ride-Sharing Transactions: Impacts of Heterogeneous Order Features

2023 | Journal of Management Information Systems | Citations: 0

Authors: Cheng, Xusen; Fu, Shixuan; Sun, Jianshan; Zuo, Meiyun; Meng, Xiangsong

Abstract: With the development of the sharing economy, online ride-sharing has become a pr ... Expand

Abstract: With the development of the sharing economy, online ride-sharing has become a primary form of commuting. Using secondary transaction data, this study investigates the associations between the heterogeneous features and mutual trust in sharing economy-driven online ride-sharing transactions. Based on an examination of 12,404 ride-sharing orders in Beijing, we propose a set of trust distribution maps using order location data to reveal heterogeneous spatial patterns of the relationship between online ride-sharing transactions and mutual trust. The results show that the historical order completion rate and order distance are positively associated with mutual trust in ride-sharing transactions, whereas order time and departure density negatively and significantly influence mutual trust. Furthermore, we use machine learning algorithms to predict trust. The implications for theory and practice and future research directions are discussed. Collapse

Topics: ride sharing platform sharing platform logistics management geographic information system online review

Methods: machine learning computational algorithm regression analysis method experiment exploratory data analysis

Theories: signaling theory

Fake review detection system for online E-commerce platforms: A supervised general mixed probability approach

2023 | Decision Support Systems | Citations: 0

Authors: Luo, Jiwei; Luo, Jian; Nan, Guofang; Li, Dahui

Abstract: Online consumer reviews play an important role in helping consumers judge the qu ... Expand

Abstract: Online consumer reviews play an important role in helping consumers judge the quality and authenticity of products on e-commerce platforms. However, the constant presence of fake reviews on these platforms has significantly impacted the operation and development of e-commerce platforms. In this study, we develop a novel supervised probabilistic method to detect fake reviews by utilizing the difference in the distribution of nonfraudulent reviews and that of fake reviews. Specifically, we first derive the univariate distributions of several unique features (linguistic, behavioral, and interrelationship features). We then integrate these distributions into two mixed distributions according to their labels to represent the overall difference between non-fraudulent reviews and fake reviews. Next, we randomly generate synthetic review data points with different labels from the above mixed distributions. Finally, we train a Multilayer Perceptron model by using these synthetic review data to obtain a classifier. We conducted several experiments to test the model using several original real-world review datasets. Numerical results indicated that the proposed supervised method outperformed some wellknown sampling models and fake review detection methods, in terms of classification accuracy. Moreover, we extend the proposed method to handle the scenarios with small samples of raw review data. This study con­ tributes to the literature by exploiting the difference in the distribution of non-fraudulent reviews and that of fraudulent reviews, which can improve the accuracy of fake review detection for online platforms. Collapse

Topics: fake review electronic commerce big data decision support system user behavior

Methods: computational algorithm experiment machine learning semi-supervised learning artificial neural network

Improving Imbalanced Machine Learning with Neighborhood-Informed Synthetic Sample Placement

2022 | Journal of Management Information Systems | Citations: 0

Authors: Nasir, Murtaza; Dag, Ali; Simsek, Serhat; Ivanov, Anton; Oztekin, Asil

Abstract: Machine learning is widely used in information systems design. Yet, training alg ... Expand

Abstract: Machine learning is widely used in information systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, fintech, or cybersecurity contexts, certain subclasses are difficult to learn because they are underrepresented in training data. Our study offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm (SANSA), which, in contrast to other solutions, introduces a novel "placement" parameter that can be tuned to adapt to each dataset's unique manifestation of the imbalance. This package can be downloaded for R1. We tested SANSA against seven existing sampling methods used in conjunction with the four most frequently used machine learning models trained on 14 benchmark datasets. Our results provide suggestive evidence that SANSA offers a feasible solution to the imbalance problem for most datasets. Our findings provide practical recommendations for how SANSA can be effectively implemented while reducing the complexity level of an imbalanced learning pipeline. Collapse

Topics: marketing management customer service online review logistics management credit card fraud

Methods: computational algorithm machine learning support vector machine artificial neural network descriptive statistic

Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses

2022 | Electronic Markets | Citations: 2

Authors: Shajalal, Md; Boden, Alexander; Stevens, Gunnar

Abstract: Due to expected positive impacts on business, the application of artificial inte ... Expand

Abstract: Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain. Collapse

Topics: local interpretable model agnostic explanation explainable artificial intelligence shapley additive explanation decision making supply chain management

Methods: machine learning convolutional neural network deep learning experiment literature study

Theories: game theory

Opening the Black-Box of AI: Challenging Pattern Robustness and Improving Theorizing through Explainable AI Methods

2022 | International Conference on Information Systems | Citations: 0

Authors: Stoffels, Dominik; Faltermaier, Stefan; Strunk, Kim; Fiedler, Marina

Abstract: Machine Learning (ML) algorithms, as approach to Artificial Intelligence (AI), ... Expand

Abstract: Machine Learning (ML) algorithms, as approach to Artificial Intelligence (AI), show unprecedented analytical capabilities and tremendous potential for pattern detection in large data sets. Despite researchers showing great interest in these methodologies, ML remains largely underutilized, because the algorithms are a black-box, preventing the interpretation of learned models. Recent research on explainable artificial intelligence (XAI) sheds light on these models by allowing researchers to identify the main determinants of a prediction through post-hoc analyses. Thereby, XAI affords the opportunity to critically reflect on identified patterns, offering the opportunity to enhance decision making and theorizing based on these patterns. Based on two large and publicly available data sets, we show that different variables within the same data set can generate models with similar predictive accuracy. In exploring this issue, we develop guidelines and recommendations for the effective use of XAI in research and particularly for theorizing from identified patterns. Collapse

Topics: explainable artificial intelligence shapley additive explanation sensemaking artificial intelligence data quality

Methods: theory development machine learning computational algorithm support vector machine survey

Theories: information systems theory information theory

Data engineering for fraud detection

2021 | Decision Support Systems | Citations: 1

Authors: Baesens, Bart; Höppner, Sebastiaan; Verdonck, Tim

Abstract: Financial institutions increasingly rely upon data-driven methods for developing ... Expand

Abstract: Financial institutions increasingly rely upon data-driven methods for developing fraud detection systems, which are able to automatically detect and block fraudulent transactions. From a machine learning perspective, the task of detecting suspicious transactions is a binary classification problem and therefore many techniques can be applied. Interpretability is however of utmost importance for the management to have confidence in the model and for designing fraud prevention strategies. Moreover, models that enable the fraud experts to understand the underlying reasons why a case is flagged as suspicious will greatly facilitate their job of investigating the sus­ picious transactions. Therefore, we propose several data engineering techniques to improve the performance of an analytical model while retaining the interpretability property. Our data engineering process is decomposed into several feature and instance engineering steps. We illustrate the improvement in performance of these data engineering steps for popular analytical models on a real payment transactions data set. Collapse

Topics: electronic authentication fraud detection electronic commerce online banking mobile application

Methods: machine learning descriptive statistic experiment feature engineering mathematical model