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This study aims to uncover how heuristic information cues (HIC) and systematic information cues (SIC) of online reviews influence review helpfulness and examine a moderating role of social influence in the process of assessing review helpfulness. In particular, this study conceptualizes a theoretical framework based on dual-process and social influence theory (SIT) and empirically tests the proposed hypotheses by analyzing a broad set of actual customer review data.

For 4,177,377 online reviews posted on Yelp.com from 2004 to 2018, this study used data mining and text analysis to extract independent variables. Zero-inflated negative binomial regression analysis was conducted to test the hypothesized model.

The present study demonstrates that both HIC and SIC have a significant relationship with review helpfulness. Normative social influence cue (NSIC) strengthened the relationship between HIC and review helpfulness. However, the moderating effect of NSIC was not valid in the relationship between SIC and review helpfulness.

This study contributes to the extant research on review helpfulness by providing a conceptual framework underpinned by dual-process theory and SIT. The study not only identifies determinants of review helpfulness but also reveals how social influences can impact individuals’ judgment on review helpfulness. By offering a state-of-the-art analysis with a vast amount of online reviews, this study contributes to the methodological improvement of further empirical research.

本论文旨在揭示网络评论的启发性信息源和系统性信息源对于评论有用性的影响, 以及检验社会影响在评论有用性的调节作用。其中, 本论文基于双重历程理论和社会影响理论来构建理论模型, 并且利用实际数据来验证假设, 通过分析一系列实际客户评论数据。

本论文样本数据为2004年至2018年Yelp.com上面的4,177,377网络评论。本论文采用数据挖掘和文本分析的方法来提取自变量。本论文采用零膨胀负二项回归模型来验证假设。

研究结果表明, 启发性和系统性信息源都对网络评论有用性有着显著作用。规范性社会影响加强了启发性信息源对评论有用性的作用。然而, 规范性社会影响对系统性信息源与评论有用性的关系并未起到有效的调节作用。

本论文对现有评论有用性的文献有着补充贡献, 其采用双重历程理论和社会影响理论来构建理论模型。本论文不仅指出评论有用性的影响因素, 而且展示了社会影响如何影响个人对评论有用性的判断。本论文的样本数据庞大, 数据分析夯实, 这对于进一步的实际测量研究有着方法改进方面的贡献。

This study aims to propose a model to explain online review helpfulness grounded on both previously identified constructs (e.g. review length) and new ones, which have been analyzed in other online reviews’ contexts but not to explain helpfulness.

A total of 112,856 reviews published in TripAdvisor about 21 Las Vegas hotels were collected and a random forest model was trained to assess if a review has received a helpful vote or not.

After confirming the validity of the proposed model, each of the constructs was evaluated to assess its contribution to explaining helpfulness. Specifically, a newly proposed construct, the response lag of the manager’s replies to reviews, was among the most relevant constructs.

The achieved results suggest that hoteliers should invest not only in responding to the most interesting reviews from the hotel’s perspective but also that they should do it quickly to increase the likeliness of the review being considered helpful to others.

论：项解释酒店业中在线评论有用性的模型

研究目的：本论文提出一项模型, 用于解释在线网络的有用性, 这项模型基于之前文献定义的结构（如评论长短）以及在其他在线评论题材下分析的新结构。而这些结构之前未用来解释有用性。

研究设计/方法/途径：样本数据为112,856条TripAdvisor关于21家拉斯维加斯酒店的评论。本论文创建了一种随机森林模型, 用于检测是否一条评论是有用的。

研究结果：本论文肯定了提出模型的有效性, 此外, 本论文评估了每个模型结构, 检测其是否解释了有用性。具体地说, 本论文提出了一种新的结构, 经理回复评论的时间长短, 是最相关的结构之一。

研究原创性/价值：研究结果表明, 酒店经理应该不仅从酒店角度出发回复最有意思的评论, 而且要快速回复, 以增加其他用户评价这条评论是否对他人决策的有用性。