Sound-symbolic signaling of online retailer sizes: The moderating effect of shopping goals

Sound symbolism – the ability of certain phonemes to evoke automatic, implicit associations or meanings – has been documented for a variety of consonants and vowels. However, to date, the literature has not documented shopping-related moderators of sound symbolism, and the role of sound symbolism in online retailing remains understudied. Thus, the present investigation extends the sound symbolism literature into the online retailing domain by investigating size-sound symbolism of online retailers' names and introduces a consumer's given shopping goal (searching versus browsing) as a key moderator of size-sound symbolic effects. The findings of four studies indicate that for names of online retailers, back vowels and voiced consonants lead to greater size perceptions of those retailers than front vowels and voiceless consonants. Further, the findings reveal that, compared to browsers, searchers utilize size-sound symbolism as a filtering heuristic, but the effect does not hold for browsers. Additionally, the findings show that size-sound symbolic effects can influence downstream consumer responses by linking size perceptions as a mediator to patronage intentions. Several implications for theory and practice emerge from the findings.     

Exploring the underlying factors of customer value in restaurants: A machine learning approach

This study explores the underlying factors of customer value in a restaurant setting by applying machine learning-based natural language processing techniques to the analysis of a vast amount of online customer reviews. The study identifies 14 factors that reflect a holistic view of previous research on customer value. The findings suggest that the comprehensive approach incorporating cognitive and affective aspects into the experiential perspective of value gain deeper insights into the nature of customer value. Moreover, the study uniquely finds new factors that prior research has rarely investigated. This study proposes a methodological framework that enables researchers to test the quantitative measure of customer perceptions derived from unstructured online reviews. Practitioners will be able to use the findings to understand customers better and enhance service operations.     

Are all five points equal? Scaling heterogeneity in hotel online ratings

Online hotel ratings play a critical role in hotel online reputation building and greatly influence travelers’ decision making. However, self-reported online ratings are vulnerable to scaling heterogeneity due to reviewers’ unique response styles, leading to some incomparability issues. A research framework was developed based on latent state-trait theory and empirically tested using hotel online review data from TripAdvisor. A hierarchical ordered probit (HOPIT) model, which captured threshold differences in hotel online ratings, confirmed the presence of scaling heterogeneity and response styles in the online rating context. Results indicated that younger travelers, women, and travelers with less review expertise used lower thresholds when rating hotels online. Business travelers had the highest rating threshold compared to other types of travelers. Guests staying in high-class hotels tended to have more extreme response styles than those staying in low-class hotels. These findings offer valuable insights for hotel managers and online rating/review sites.     

More than what meets the eye: Understanding the effects of poly-contextual cues in online fashion retailing

While previous studies have examined the presence (vs. absence) of contextual cues or the product-context fit, the effect of multiple cues (poly-cue) remained underexplored. In response, this study investigated the effects of poly- (vs. mono-) cues on the valuation of a product and the underlying mechanism behind these effects. Findings suggest that a poly-cue increases the product valuation via increased consumption vision (Study 1–3), which was pronounced for global processors (Study 2) and attenuated for a product with low contextual versatility (Study 3). This study provides actionable insights for online retailers as well as enrich the relevant literature.     

A purchase decision support model considering consumer personalization about aspirations and risk attitudes

Ranking alternative products to help consumers make better purchase choices is a valuable research topic. Most previous decision support models cannot be well applied to heterogeneous consumers. This paper focuses on establishing a personalized interactive model to assist consumers make better buying decisions with less effort. For the alternative products provided by consumers, we collect online reviews and parameter configurations of alternative products and then obtain the fusing evaluative information. As consumers are dominated by bounded rationality, they only provide partially key attribute weights, based on which, we construct an optimizing model to obtain the optimal attribute weights of customers for products. Then, a satisfaction function is proposed by uniting aspiration levels and risk attitudes of consumers and a compensatory decision rules is established to rank and recommend the brands to consumers. Finally, practicability of this study is illustrated with a real car purchase case. Through the case study, it can be seen that the proposed decision support model generates a personalized list of alternatives based on consumer's own utility function about risk attitudes, aspiration levels, and preferences for product attributes, which further confirms that the proposed model can capture the personalized needs of consumers. Theoretical and managerial implications of this model as well as advantages are further illustrated.     

Classification of spammer and nonspammer content in online social network using genetic algorithm-based feature selection

ABSTRACT

The emergence of online social network invokes social actors to share their personal information digitally. Moreover, it provides the facility to maintain their links with people of same interest globally. Take advantage of these services; it has become a fascinating testbed to invite various threats like a spammer. Detection of spammer in OSN is one of the most critical tasks. Spammer not only spreads unwanted or bad advertisement but does certain malicious activity in others' profiles. By clearly understanding the activities of different threats, some incremental and accurate approaches are needed for detecting spammer content and profiles involved in these activities by using social network services. Therefore, the focus of this article is to detect spammer content and account, specifically on the leading microblogging platform called Twitter. We propose a hybrid approach which leverages the capabilities of various machine learning algorithms to separate spammer and nonspammer contents and account. Initially, the optimisation algorithm called genetic algorithm analyses the various features and selects the best suitable features that influence the behaviour of user account, and these features are then used to train classifiers. Our framework achieved to severalise spammer and nonspammer content in an effective way. Finally, to prove the efficiency of our proposed framework, a comparative analysis is conducted with some existing state-of-art techniques. The experimental analysis shows that our approach achieves a high detection rate of 99.6%, which is better than other state-of-art techniques.     

网上技术交易趋势预测模型及应用研究

网上技术交易平台开展交易趋势预测和交易潜力识别，可提高平台技术信息管理水平和网上技术市场运作效率。从网上技术市场和专利情报分析理论出发，提出网上技术交易趋势预测分析指标，设计构建预测模型和相关流程方法，并以广州知识产权交易中心的网上挂牌专利数据为例，进行定量测度和评价分析，从而提出发展网上交易服务能力的建议。结果表明，提出的技术交易趋势预测模型与流程具有适用性和可操作性，可针对网上平台技术资源确定差异化预测类型及其关键影响指标，建立基于技术领域分类的平台分析体系。研究结论可为网上平台设计基于自身特点的技术信息管理策略，提高信息服务专业化能力提供参考。     

To be similar or to be different? The effect of hotel managers’ rote response on subsequent reviews

Hotel managers increasingly compose rote responses (e.g., similar responses repeated from prior responses) to reviews when dealing with customers' opinions while improving efficiency and saving cost. However, such responses may be ineffective because they are unspecific and add little information to reviewers' comments. To address this dilemma, we examine the degree of managers' rote responses to identify the similarity of managers' response texts. Basing on panel data of 334,671 reviews and 169,794 manager responses from 868 hotels in TripAdvisor, we find that customers leave fewer reviews and lower review valence during the subsequent month, when the level of roteness of managers’ responses is higher. However, rote responses only decrease the volume of subsequent positive reviews but have no influence on the volume of negative reviews. Moreover, such effects are weaker for chain hotels than for independent ones. Therefore, managers should differentiate their responses to online reviews, particularly for independent hotels.     

A cross-country comparison of online deal popularity effect

The paper examines the effect of online deal popularity for service deals in a cross-country context. A 2 (deal popularity: low vs high) X 2 (culture: collectivist vs individualist) quasi experimental design was used to collect data in Australia and Taiwan to examine the consumer reactions toward deal popularity information of service deals on e-retailing websites. Results show consumers' perceived performance and psychological risk mediate the effect of deal popularity on purchase intention both in the Australian and Taiwanese samples. In addition, consumers' reactions towards online popularity information for online service deals do differ across cultures. While high deal popularity increases the Australian consumers' performance and psychological risk perceptions, which in turn lowers their purchase intention, high deal popularity decreases the Taiwanese consumers’ risk perception and enhances their purchase intention. This paper contributes to the broader fields of retailing and services studies by providing insights for online retailers concerning how deal popularity can be leveraged as a means to reduce perceptions of risk for Taiwanese consumers but with caveats for their Australian counterparts. As a first study that examines the effect of online deal popularity for service deals in a cross-country context, it extends the body of knowledge in a fast-changing domain of consumer behavior e-commerce settings.     

Guest satisfaction & dissatisfaction in luxury hotels: An application of big data

In order to understand the pivotal attributes of luxury hotel service in Malaysia, this study analyses big data in the form of online reviews, as available in TripAdvisor. The content analysis, which was performed using the word frequency analysis has revealed that the main themes of luxury hotel service quality include hotel-related attributes, room-related attributes, staff-related attributes, travel-related attributes, and possible outcomes. The critical incident technique has also been performed to examine the antecedents and outcomes of hotel guests’ satisfaction and dissatisfaction. In this study, quality of rooms and interaction with employees have been determined as major drivers of customers’ word of mouth and revisit intentions. This study contributes with an empirical analysis of particular features of textual context and discussion of the concept of luxury service in the developing countries has been largely neglected so far.