Customer Experience in Online Shopping: A Structural Modeling Approach

Online retail's rapid growth in India has triggered both untapped growth opportunities and challenges to maintain consumer “stickiness” to retailer websites. In this context, online customer experience (OCE) has emerged as a strategic differentiator for sustainable competitive edge. However, there is a paucity of empirical research in this field. Therefore, drawing on extant literature and qualitative research of online shoppers, this study proposes an integrated model of OCE with antecedents, components, and outcome variables. Data for this study were collected from Indian online shoppers; the data are empirically tested along with the moderating effect of time availability on OCE. Results of the study largely support the model and contribute to knowledge creation on OCE in the Indian context and to strategy development for online retailers.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Information and Knowledge as Antecedents of Consumer Attitudes and Intentions to Buy and Recommend Fair-Trade Products

The authors propose and empirically test a causal model to understand how the availability of fair-trade information and consumer knowledge about this issue affect consumers’ attitudes and intentions toward fair-trade products. The model is built upon the attitude-behavior paradigm and the premises of agency theory. It is tested through structural equation modeling with a sample of 292 Spanish consumers. The findings are that consumers do not have good knowledge about fair trade and that this is significantly determined by the lack of information about this in the market. It is also observed that consumers’ perceptions about the availability of fair-trade information have negative effects on their concern about this issue and that such information as is available is not effective in reducing consumer skepticism. The research represents an extension of previous fair-trade literature because the role of information and communication in improving consumer attitudes and buying intentions has rarely been explored in the case of ethical products.     

Contrasting user generated videos versus brand generated videos in ecommerce

This empirical research (n = 177) compared the impact of user generated videos and brand generated videos on online shopper perceptions, attitudes and purchase intentions. The study was guided by the technology acceptance model. Source of videos was a between-subjects factor. The findings indicated that online shoppers did not differentiate between the two video sources when evaluating the perceived usefulness of the videos. Conversely, user generated videos had a significantly greater impact on attitude toward the ad and attitude toward the brand than brand generated videos. Further, there was no significant main effect of video source on shopper purchase intentions. Theoretical and practical implications are discussed.     

Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China

It is difficult to predict the financial distress of unlisted public firms due to their longer disclosure cycle of accounting information and more inadequate continuity of market trading information compared to listed firms. In this paper, we propose a framework to predict the financial distress of unlisted public firms using current reports. Specifically, to better represent the meaning of current report texts, we propose a semantic feature extraction method based on a word embedding technology. Empirical results show that current reports contain more effective information for predicting the financial distress of unlisted public firms compared with periodic reports. In addition, semantic features extracted using our proposed method significantly improve the predictive performance, and their enhancing effect is superior to that of topic features and sentiment features. Our study also provides implications for stakeholders such as investors and creditors.     

一种基于孪生神经网络的深度异构网络嵌入算法

针对现有异构网络嵌入方法导致的捕获关系冗余和模糊的问题，提出了一种基于孪生神经网络的深度异构网络嵌入模型。首先，基于面向关系的深度嵌入(Relation-Oriented Deep Embedding，RODE)框架构建了异构网络嵌入模型，以区分同型节点和异型节点之间的关系；其次，将同型节点与异类节点之间的相似性近似到低维空间，通过构建多任务的孪生神经网络来实现节点之间结构和语义关系的深度嵌入；最后，选取四个数据集执行典型网络挖掘任务，并与其他六种算法进行实验对比分析。实验结果表明，保持相同类型节点之间的相似性有助于提高节点分类效率，且损失函数在提高异构网络嵌入质量方面具有良好的优越性；RODE模型能够有效提高稀疏网络的嵌入质量，且具有良好的稳定性和鲁棒性。     

“市民社会”及其在《资本论》中的逻辑地位(一)——兼与沈越教授商榷马恩著作的翻译问题

市民社会在马克思著作中具有两类四种含义,应基于马克思独特的思维方法把握不同含义之间的内在关系。作为资产阶级意识形态核心理念的"市民社会",与所谓普世价值密切相关,它是马克思理论集中批判的对象。如果说马克思具有某种市民社会理论的话,也只能看成基于唯物史观把握资本主义生产内在机理的理论。应从原则高度明确反对将两种性质的市民社会理论有意无意地混淆起来的做法。沈越教授将市民社会臆想为没有阶级属性的永恒的"中产阶级"社会。认为西方经济学、西方哲学等只是这种没有阶级属性的市民的意识形态,不具有资产阶级性质,应该不加批判地原原本本地"拿来"。他还试图通过个别德文词的辨析,一方面将苏俄马克思主义和中国既有的马克思主义理论贴上"伪马克思主义"的标签,另一方面把马克思理论整体性地改造成没有阶级属性的意识形态意义上的市民社会理论。实质上是有意识地将马克思理论与西方意识形态混为一谈。     

Reflexive Organizing for Knowledge Sharing: An Ethnomethodological Study of Service Technicians

This study examines how organizing is done reflexively through practice in the context of knowledge sharing. Organizing concerns reduction of equivocality and sensemaking so that actions can be interpreted and coordinated. Reflexivity refers to the fact that this organizing is done through talk, and that talk is an action that requires organizing. To examine how this reflexive organizing is accomplished, detailed analysis of video‐recorded interactions among photocopier service technicians revealed various interactional methods to make actions of requesting and offering assistance understandable and relevant. To explain these methods, Goffman's concept of embedding is applied. By embedding other social situations in the current talk, one can project a certain sense of one's talk. This reflexive organizing clarifies that organizing is part of, not separate from, any practice and that knowledge sharing is accomplished not through a retrospective narrative but through reflexive construction of the situation in which talk is made possible.     

对固定化微生物技术处理含油废水的研究

固定化微生物技术是一种新兴的生物处理技术,具有较高的生物活性、固液易于分离、处理效率高等优点,在废水处理中具有独特的优势和巨大的潜力,已经在各种有机废水领域得到广泛应用,近年来其在处理石油石化废水的研究也不断深入。本文简单介绍了微生物固定化载体和方法及其在含油废水处理中的研究成果,并对其发展趋势进行了展望。     

大体积混凝土主墩承台一次性浇筑施工工艺

大体积整体浇筑混凝土工艺,可明显缩短工期。工艺控制重点是通过混凝土配合比设计、外加剂的选择、冷却管布设、温差控制和混凝土浇筑养护等方面,消除混凝土温差应力、收缩等原因形成的裂缝,使得混凝土结构整体性好,安全可靠。