When push comes to shove: How coach versus student athlete misconduct affects event attendance intentions

Though onand off-the-field misconduct is common among U.S. college athletic programs, little is known regarding the ramifications that may result. Drawing on social learning theory, the current research suggests consumers intentions (e.g., likelihood of attending a game) differ depending on violator's team role. Across one qualitative and five experimental studies, we demonstrate that consumers' intentions are influenced by violator's team role, such that likelihood of attending a game is lower when a coach (vs. student athlete) misbehaves, an effect driven by evaluation of the academic institution. This effect is robust across both winning and losing records and moderated by perceived fairness of the university's actions toward the violator.     

Fear appeals and coping appeals for health product promotion: Impulsive purchasing or psychological distancing?

The prevalence of online health communities (OHCs) motivates health product companies to manipulate persuasion-based marketing strategies, including fear appeals and coping appeals, to promote health products. However, whether such a practice is effective remains unclear. Respondents may either impulsively purchase the recommended health products or simply distance themselves psychologically from the information. Building on protection motivation theory (PMT), this study uncovers the mechanisms behind these two coping responses to persuasive messages, namely, the impulsive purchasing and psychological distancing of OHC users. Correspondingly, we evaluated 366 valid questionnaires, and the results reveal that (1) when encountering fear appeals, perceived health threat can evoke cyberchondria, leading to psychological distancing; and (2) when encountering coping appeals, perceived response efficacy leads to product affection, fostering impulsive purchasing. This study provides new insights into the effects of persuasive messages for health product promotion in social commerce. Marketers should design appropriate persuasive messages to foster impulsive purchasing by generating individuals’ product affection and avoiding cyberchondria.     

A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior

Developments in battery electric vehicles (BEVs) have received more and more attentions in the last decades due to alleviating carbon emissions and energy crisis. Consequently, how to rank alternative BEVs to assist consumers make better purchasing decisions is a worthy research study. However, there are still some defects in the existing studies for ranking of BEVs: 1) the evaluation index system of BEVs is not comprehensive; 2) the determination of criteria weights cannot be well applied to the actual purchase scenarios; and 3) the psychological behavior of consumers is ignored. To address those shortcomings, this paper proposes a decision support model to assist with consumers to buy BEVs. First, a systematic evaluation criteria system of BEVs including quantitative and qualitative indicators from parameter configurations and online reviews is constructed. Then, a weight algorithm considering consumer learning is proposed to determine the criteria weights. Furthermore, a decision support process considering consumers' regret avoidance behavior is proposed. Finally, an actual BEV purchase case is given to illustrate the practicability of the decision support model. This can be seen in case studies the proposed support model can be well applied to consumers with different regret avoidance behaviours.     

Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities

Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Post-script—Retail forecasting: Research and practice

This note updates the 2019 review article “Retail forecasting: Research and practice” in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Organizational attention and learning under regulatory intervention: Governmental investigation into auto engine quality

We examine the efficacy of government regulation on a firm's product. We draw on the behavioural approach of organization research in order to understand the micromechanisms whereby the regulatory intervention process affects corporate operation. We suggest that while government investigations may limit the improvements in product quality by distracting a firm's attention, this unintended outcome depends on the extent to which the firm engages in a substantive problem‐solving process with the regulator during an investigation process. A longitudinal analysis of the US government's investigation into motor vehicle engine production offers overall support for our argument. The paper concludes with a discussion of the implications that our findings present to learning theory and institutional literature. Copyright © 2017 ASAC. Published by John Wiley & Sons, Ltd.     

Career mentoring in context: A multilevel study on differentiated career mentoring and career mentoring climate

This study explores how supervisor career mentoring contributes to contemporary organizational career development, which strives to foster employees' promotability while strengthening their intention to stay. Specifically, we focus on the implications of career mentoring in team contexts. Applying a multilevel framework, we distinguish between individual‐level differentiated mentoring (i.e., an employee's mentoring perceptions as compared to those of other team members) and group‐level career mentoring climate (i.e., the average perception across all group members). In a workplace setting, we collected data from vocational job starters (N ranged from 230 to 290) and their company supervisors (N ranged from 56 to 68). We find that career mentoring climate positively relates to promotability, more so than differentiated career mentoring. Both career mentoring climate and differentiated career mentoring are positively related to the intention to stay. At the individual level, this relationship is mediated by job satisfaction. We discuss theoretical and practical implications of differentiated and group‐level mentoring.