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.     

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.     

Website quality and users' intention to use digital libraries: Examining users’ attitudes,online co-creation experiences,and eWOM

This research aims to examine how the website quality affects the intention of digital library users to use the website by considering factors of user's attitudes, online co-creation experiences, and electronic word-of-mouth. The statistical population of the research is composed of the users of Astan Qods Razavi digital library, which is one of the oldest digital libraries in Iran. Data was collected from 402 participants who use the library and analyzed in SPSS and PLS softwares. Construct validity was assessed and confirmed using convergent validity and divergent validity. Data reliability was assessed and confirmed using Cronbach's alpha and composite reliability. The research confirms that website quality affects the attitudes of users towards the website and their intention to participate in online co-creation and eWOM. The effects of user's attitudes to the online co-creation experience, eWOM, and intention to use the library, as well as the effects of the online co-creation experience and eWOM on the intention to use the digital library were also confirmed. Considering the results of the research, it is recommended to digital libraries to provide an efficient user-friendly website designed to increase user participation and establish close contact with them and in this way increase their intention to use digital library services.     

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.     

游客感知视角下乡村旅游质效提升维度研究——以西双版纳傣族园为例

发展乡村旅游是实施乡村振兴战略的重要途径，对促进乡村经济发展具有重要作用。以西双版纳傣族园为案例研究地，通过SPSS和AMOS实证探究游客感知视角下乡村旅游质效提升的维度。结果表明：游客感知视角下的乡村旅游质效提升包含旅游地感知、地方认同感、主观幸福感、环境责任行为4个维度；地方认同感和旅游地感知对乡村旅游质效提升的影响力较强，其次是主观幸福感和环境责任行为。     

土地市场扭曲如何影响制造业出口产品质量升级*——基于土地市场交易数据和制造业企业数据的实证分析

地方政府“以地谋发展”的策略在促进各地区制造业大规模集聚和出口贸易快速增长的同时，也势必会给企业出口产品质量带来深刻影响。本文综合利用中国土地市场网城市土地交易数据、中国工业企业数据、中国海关进出口产品数据和中国城市面板数据，实证检验了土地市场扭曲对企业出口产品质量升级的影响，并对其内在机制进行了探讨。研究发现：中国城市建设用地配置存在明显的工业偏向性，进而导致工业用地价格被低估，产生工业用地应得收益大于实际价格的反向扭曲问题。这种反向扭曲可通过抑制技术进步、阻碍产业结构高级化、弱化集聚经济效应等机制显著降低制造业企业出口产品质量。土地市场扭曲对企业出口产品质量升级的影响具有明显的异质性特征。具体而言，土地市场扭曲不利于一般贸易企业与混合贸易企业出口产品质量提升，但对加工贸易企业出口产品质量提升具有促进作用。土地市场扭曲对企业出口产品质量升级的抑制作用由东到西依次递增。土地市场扭曲不利于外资企业和国有企业出口产品质量提升，对集体企业及民营企业的影响不显著。     

The impact of reverse logistics process coordination on third party relationship quality: A moderated mediation model for multichannel retailers in the fashion industry

Multichannel retailing is a widely adopted strategy in the fashion industry. Companies in this industry find it a source of competitive advantage to invest in reverse logistics infrastructure. However, limited empirical studies investigate the enablers of the relationship quality between the retailers and their reverse logistics service providers. This study quantifies the impact of reverse logistics process coordination between retailers and logistics service providers on relationship quality. Moreover, it tests the mediating role of reverse logistics service quality and the moderating role of conflict frequency in this relationship. Data were collected through a survey using a purposive sample of 241 retail store managers from the fashion retail industry of Pakistan. For this purpose, a self-administered questionnaire was developed using a five-point Likert Scale to gauge the responses. Conditional process analysis was used to evaluate the moderated mediation model. The findings showed a significant positive impact of reverse logistics process coordination on the relationship quality with a logistics service provider, a significant positive mediation effect of reverse logistics service quality, and a significant moderation effect of conflict frequency on the indirect relationship. However, conflict frequency, contrary to the hypothesis in the study, strengthened the indirect relationship. Furthermore, the moderation effect of conflict frequency on the direct relationship was insignificant. This study will help managers better understand the best practices leading to effective management of reverse logistics processes, particularly product returns.     

Exploring the factors that drive consumers to use contactless delivery services in the context of the continued COVID-19 pandemic

The spread of the COVID-19 pandemic has resulted in the launch of contactless delivery services. This research integrates resource matching, service quality evaluation, and perceived value theories to explore the factors that promote the use contactless delivery services. The data was obtained through questionnaire surveys, and research hypotheses were verified through the structural equation modelling approach. With the exception of convenience, the results show that privacy, reliability, security, and flexibility have a significantly positive effect on consumers' intention to use “contactless” delivery services through consumers' perceived value. This study contributes to the literature by introducing theoretical frameworks from various paradigms and enriches the academic research on existing theoretical structure models. It also helps optimize resource allocation and realize the social environment related to coexisting with the COVID-19 pandemic.     

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.