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

Financial risk prediction in supply chain finance based on buyer transaction behavior

2023 | Decision Support Systems | Citations: 0

Authors: Yi, Zelong; Liang, Zhuomin; Xie, Tongtong; Li, Fan

Abstract: Financial risk in supply chain finance (SCF) is defined as the possibility that ... Expand

Abstract: Financial risk in supply chain finance (SCF) is defined as the possibility that suppliers fall into liquidity crisis due to delayed payment. Predicting financial risk is important for supply chain stability. In this paper, a financial risk prediction model is developed using XGBoost and then evaluated by applying buyer transaction behavior data. We further construct single and hybrid models, respectively, and compare their performance using receiver operating characteristic curve (ROC), area under the ROC curve (AUC), and F1-Score. Last, feature importance and partial dependence plots (PDPs) are employed for model interpretation. The results show that XGBoost model can effectively predict potential financial risks, and shed lights on managers’ payment practice. This paper is one of the few studies that develop new models to examine financial risks in SCF empirically. Collapse

Topics: artificial intelligence supply chain management big data decision making partial dependence plot

Methods: machine learning XGBoost LightGBM computational algorithm random forest classification

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

A prescriptive analytics framework for efficient E-commerce order delivery

2021 | Decision Support Systems | Citations: 0

Authors: Kandula, Shanthan; Krishnamoorthy, Srikumar; Roy, Debjit

Abstract: Achieving timely last-mile order delivery is often the most challenging part of ... Expand

Abstract: Achieving timely last-mile order delivery is often the most challenging part of an e-commerce order fulfillment. Effective management of last-mile operations can result in significant cost savings and lead to increased customer satisfaction. Currently, due to the lack of customer availability information, the schedules followed by delivery agents are optimized for the shortest tour distance. Therefore, orders are not delivered in customer-preferred time periods resulting in missed deliveries. Missed deliveries are undesirable since they incur additional costs. In this paper, we propose a decision support framework that is intended to improve delivery success rates while reducing delivery costs. Our framework generates delivery schedules by predicting the appropriate delivery time periods for order delivery. In particular, the proposed framework works in two stages. In the first stage, order delivery success for every order throughout the delivery shift is predicted using machine learning models. The predictions are used as an input for the optimization scheme, which generates delivery schedules in the second stage. The proposed framework is evaluated on two real-world datasets collected from a large e-commerce platform. The results indicate the effectiveness of the decision support framework in enabling savings of up to 10.2% in delivery costs when compared to the current industry practice. Collapse

Topics: decision support electronic commerce geographic information system logistics management decision support system

Methods: machine learning XGBoost simulation random forest classification computational algorithm

Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection

2021 | Decision Support Systems | Citations: 0

Authors: Kou, Gang; Xu, Yong; Peng, Yi; Shen, Feng; Chen, Yang; Chang, Kun; Kou, Shaomin

Abstract: Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) ... Expand

Abstract: Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) are built using accountingbased financial ratios. This study proposes a bankruptcy prediction model for SMEs that uses transactional data and payment network–based variables under a scenario where no financial (accounting) data are required. Offline and online test results both confirmed the predictive capability and economic benefit of transactional data–based variables. However, incorporating those features in predictive models produces high dimensional problems, which deteriorates model interpretability and increases feature acquisition costs. Thus, we propose a two-stage multiobjective feature-selection method that optimizes the number of features as well as model classification performance. The results showed that the proposed model achieved similar classification perfor­ mance while greatly reducing the cardinality of the feature subset. Finally, the feature importance evaluation for features in the optimal subset confirmed the importance of transactional data and payment network-based variables for bankruptcy prediction. Collapse

Topics: permutation feature importance accounting information privacy law legal context decision making

Methods: machine learning computational algorithm experiment support vector machine decision tree classification

Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning

2021 | Information Systems Frontiers | Citations: 0

Authors: Rathore, Hemant; Sahay, Sanjay K.; Nikam, Piyush; Sewak, Mohit

Abstract: Since the inception of Andoroid OS, smartphones sales have been growing exponent ... Expand

Abstract: Since the inception of Andoroid OS, smartphones sales have been growing exponentially, and today it enjoys the monopoly in the smartphone marketplace. The widespread adoption of Android smartphones has drawn the attention of malware designers, which threatens the Android ecosystem. The current state-of-the-art Android malware detection systems are based on machine learning and deep learning models. Despite having superior performance, these models are susceptible to adversarial attack. Therefore in this paper, we developed eight Android malware detection models based on machine learning and deep neural network and investigated their robustness against the adversarial attacks. For the purpose, we created new variants of malware using Reinforcement Learning, which will be misclassified as benign by the existing Android malware detection models. We propose two novel attack strategies, namely single policy attack and multiple policy attack using reinforcement learning for white-box and grey-box scenario respectively. Putting ourselves in adversary’ shoes, we designed adversarial attacks on the detection models with the goal of maximising fooling rate, while making minimum modifications to the Android application and ensuring that the app’s functionality and behaviour does not change. We achieved an average fooling rate of 44.21% and 53.20% across all the eight detection models with maximum five modifications using a single policy attack and multiple policy attack, respectively. The highest fooling rate of 86.09% with five changes was attained against the decision tree based model using the multiple policy approach. Finally, we propose an adversarial defence strategy which reduces the average fooling rate by threefold to 15.22% against a single policy attack, thereby increasing the robustness of the detection models i.e. the proposed model can effectively detect variants (metamorphic) of malware. The experimental analysis shows that our proposed Android malware detection system using reinforcement learning is more robust against adversarial attacks. Collapse

Topics: malware Android mobile application computer virus protection database system

Methods: classification method computational algorithm experiment reinforcement learning support vector machine

Predicting donation behavior: Acquisition modeling in the nonprofit sector using Facebook data

2021 | Decision Support Systems | Citations: 0

Authors: Schetgen, Lisa; Bogaert, Matthias; Van den Poel, Dirk

Abstract: The purpose of this study is to demonstrate the value of Facebook data in predic ... Expand

Abstract: The purpose of this study is to demonstrate the value of Facebook data in predicting first-time donation behavior. More specifically, we provide evidence that Facebook data can be used as a valuable data source for nonprofit organizations in acquiring new donors. To do so, we evaluate three different dimensionality reduction techniques (i.e., singular value decomposition, non-negative matrix factorization, and latent Dirichlet allocation) over seven classification techniques (i.e., logistic regression, k-nearest neighbors, bagged trees, random forest, adaboost, extreme gradient boosting, and artificial neural networks) using five times twofold cross-validation. Next, we assess what type of Facebook data and which predictors are most important. The results indicate that we can predict first-time donation behavior based on Facebook data with high predictive performance. Our benchmark indicates that the combination of singular value decomposition and logistic regression outperforms all other analytical methodologies with an area under the receiver operating characteristic of 0.72 and a top decile lift of 3.33. The results show that Facebook pages and categories of Facebook pages are the most important data types. The most important predictors are dimensions related to age, education, residence, materialism, responsible consumption, and interest in nonprofits. The presented acquisition models can be used by nonprofit organiza­ tions to implement a one-to-one targeted marketing campaign towards Facebook fans. To the best of our knowledge, our study is the first to determine the predictive value of Facebook data for nonprofits in a real-life acquisition context. Collapse

Topics: Facebook social media decision support system web page source code

Methods: machine learning data transformation computational algorithm dimensionality reduction F-test

An Industry-Agnostic Approach for the Prediction of Return Shipments

2020 | Americas Conference on Information Systems | Citations: 0

Authors: Hofmann, Adrian; Gwinner, Fabian; Fuchs, Kevin; Winkelmann, Axel

Abstract: Return shipments are a major problem in E-commerce not only economically for th ... Expand

Abstract: Return shipments are a major problem in E-commerce not only economically for the companies selling the products, but also for the environment. We introduce a universally applicable Decision Support System (DSS) for the prediction of product returns. Most of the prediction algorithms used in the research were developed on datasets with high return rates and are product feature centric. However, these algorithms work best for fast-moving products with many features that can be found in the fashion industry. We tackle these challenges by using Design Science Research (DSR) to develop a prediction mechanism based on the current shopping cart of a customer. Using a dataset from a German technical wholesale company, we validate and demonstrate our approach. Thus, we identify consumption patterns within fulfillment datasets containing a low product return rate at sufficient accuracy. This allows supplying a broader industry with prediction algorithms that have low return rates. Collapse

Topics: electronic commerce decision support system enterprise resource planning computer hardware software developer

Methods: computational algorithm machine learning literature study design science Cross-Industry Standard Process for Data Mining

Feature Generation Using Machine Learning from Large Sparse Financial Data

2019 | Americas Conference on Information Systems | Citations: 0

Authors: Choi, Minjung

Abstract: The modeling of customer features has become a core component in modern financi ... Expand

Abstract: The modeling of customer features has become a core component in modern financial analytics. There are several difficulties in adopting conventional machine learning (ML) methodologies to finance domain: distributional asymmetry in the observations, class imbalance in the training labels, and data sparsity resulting from infrequent occurrence. In this study, we try to address the statistical challenges of financial data. Then, we test feature processing using multiple machine learning approaches in combination with established methods. We evaluate separate feature selection results as part of a prediction pipeline, and show how they differ across models. The empirical implications of the feature transformation and selection on the prediction outcomes are discussed. Collapse

Methods: data transformation experiment computational algorithm random forest classification principal component analysis