Are bidders in auctions anchored by their bidder number?

This article examines whether bidders in a school fundraising auction were anchored by their bidder number. We find no evidence that they were.     

Predicting bid prices by using machine learning methods

It is well-known that empirical analysis suffers from multicollinearity and high dimensionality. In particular, this is much more severe in an empirical study of itemized bids in highway procurement auctions. To overcome this obstacle, this article employs the regularized linear regression for the estimation of a more precise interval for project winning bids. The approach is put to the test using empirical data of highway procurement auctions in Vermont. In our empirical analysis, we first choose a set of crucial tasks that determine a bidder’s bid amounts by using the random forest variable selection method. Given the selected tasks, project bid forecasting is conducted. We compare our proposed methodology with the least square linear model based on the bias and the standard root mean square error of the bid estimates. There is evidence supporting that the suggested approach provides superior forecasts for an interval of winning bids over the competing model. As far as we know, this article is the first attempt to provide reference bids of highway construction contracts.     

Effects of price sorting display on extreme option choice aversion: The role of ease of comparison in multiple option displays

Due to the popularity of online travel agency (OTA) booking platforms, OTAs display diverse information, including features of products, on their websites. Based on choice architecture literature, this study aimed to examine the effect of product quality and price sorting (vs. non-sorting) on extreme option choice aversion and identify the moderating effect of displays that made it difficult to read information. The results of a series of four experimental studies (total n = 2838 online panel members) demonstrated that the tendency to choose the non-extreme or middle-attributed options was stronger when quality and price were displayed sorted (vs. non-sorted). It was theorized that easy comparison of multiple options leads to decision-making. The positive effect of quality and price sorting on extreme option choice aversion was significantly reduced when customers had difficulty reading the displayed information.     

浅析网络广告效果评价方法

随着网络广告应用的不断发展,广告效果也越来越为广告主们所关注,点击率等传统指标已经不能完全说明网络广告的效果。本文认为广告效果由被点击与未被点击两部分构成。文中介绍了网络广告的主要形式,借鉴点击率和转化率,提出利用有效到达率来探讨网络广告真正到达有效受众的比率。     

Measuring Food Product Differentiation by Quality Ratings: A Cross‐Entropy Analysis of Specialty Coffee e‐Auctions

Food quality ratings can be used for product differentiation and strategic marketing. This article develops a differentiation measure based on the cross‐entropy of two distributions for speciality coffees. It examines whether quality ratings achieve their objectives for speciality coffee supply chains. The estimation, using e‐auctions data, indicates that the quality rating is more informative in the Cup of Excellence (CofE) relative to the Q auction. To determine significance, a statistical test for comparing the information measure across different e‐auctions and segments of the quality rating was developed. The significant difference between the two auctions is not a surprise as the auctions trade coffee lots of different volumes and the quality evaluation is more important in the case of the CofE, with its jury of renowned coffee experts. The total cross‐entropy measure of product differentiation shows that the 100‐point quality rating does not carry the same information content and meaning in the two different e‐auctions and their respective supply chains. The cross‐entropy measure of product differentiation is shown to be a good indicator of the information and the value created throughout the speciality food supply chain.     

Role of multidimensional customer brand engagement on customer behavior for online grocery shopping

This study aims to understand the relationship between environmental consciousness and multidimensional (i.e., cognition, affection, and activation aspects) of customer brand engagement (CBE) in relation to online grocery shopping. It also investigates the moderating effect of customer characteristics on the associations among environmental consciousness, CBE, and behavior intention. 358 Korean respondents took part in this research using an online survey to assess the impacts of multidimensional CBE for online grocery. The outcomes reveal that environmental consciousness positively influences cognition and activation aspects of CBE. Additionally, activation, affection, and cognition aspects of CBE positively affect behavioral intention. Multi-group analysis is performed to identify the difference among customers in low and high customer characteristics. In addition, by demonstrating a role for CBE, this study adds to the existing literature on online grocery. Furthermore, the outcomes of this research provide direction for building marketing strategies for online groceries for marketers and practitioners.     

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.     

Customer purchase forecasting for online tourism: A data-driven method with multiplex behavior data

Online tourism has received increasing attention from scholars and practitioners due to its growing contribution to the economy. While related issues have been studied, research on forecasting customer purchases and the influence of forecasting variables, online tourism is still in its infancy. Therefore, this paper aims to develop a data-driven method to achieve two objectives: (1) provide an accurate purchase forecasting model for online tourism and (2) analyze the influence of behavior variables as predictors of online tourism purchases. Based on the real-world multiplex behavior data, the proposed method can predict online tourism purchases accurately by machine learning algorithms. As for the practical implications, the influence of behavior variables is ranked according to the predictive marginal value, and how these important variables affect the final purchase is discussed with the help of partial dependence plots. This research contributes to the purchase forecasting literature and has significant practical implications.     

Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic

Online reviews remain important during the COVID-19 pandemic as they help customers make safe dining decisions. To help restaurants better understand customers’ needs and sustain their business under current circumstance, this study extracts restaurant features that are cared for by customers in current circumstance. This study also introduces deep learning methods to examine customers’ opinions about restaurant features and to detect reviews with mismatched ratings. By analyzing 112,412 restaurant reviews posted during January-June 2020 on Yelp.com, four frequently mentioned restaurant features (e.g., service, food, place, and experience) along with their associated sentiment scores were identified. Findings also show that deep learning algorithms (i.e., Bidirectional LSTM and Simple Embedding + Average Pooling) outperform traditional machine learning algorithms in sentiment classification and review rating prediction. This study strengthens the extant literature by empirically analyzing restaurant reviews posted during the COVID-19 pandemic and discovering suitable deep learning algorithms for different text mining tasks.     

本地偏好、投资者情绪与股票收益率:来自网络论坛的经验证据

本文以投资者在东方财富网股吧针对创业板上市公司发表的90多万条帖子为研究对象,通过IP地址识别,构建本地关注指标;并利用计算机文本挖掘技术,提取网络发帖所体现的情绪倾向,构建投资者情绪指标。实证结果显示,本地关注对股票收益率的影响取决于投资者情绪,当投资者持积极情绪时,本地关注对股票收益率有显著的正向影响;当投资者持消极情绪时,该影响显著为负。在积极情绪和消极情绪下本地关注对股票交易量都有显著正向影响,但积极情绪下的影响程度比消极情绪下更大。此外,本地关注与投资者情绪的交叉效应在上市公司样本数量较多的北京、广东、江苏、上海、浙江这五个区域都存在,并与整体样本的特征基本一致,但在样本数量较少的其他区域并不完全一致。