Conformity feedback in an online review helpfulness evaluation task leads to less negative feedback-related negativity amplitudes and more positive P300 amplitudes.

Guo, Daomeng; Zhao, Yang; Zhang, Liyi; Wang, Xuan; Yin, Cong

doi:10.1037/npe0000102

Cited by 17 publications

(18 citation statements)

References 44 publications

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“…Also, the impacts of online reviews on sales is lower, comparing with experience products with samples than without. Furthermore, research suggests that people rated positive reviews as more helpful than negative reviews, whilst it took longer to evaluate negative reviews (Guo, Zhao, Zhang, & Wen, 2019). In addition, the uncertainty that is induced by online reviews can be softened by providing samples.…”

Section: Online Consumer Datamentioning

confidence: 99%

Cloud-based big data analytics for customer insight-driven design innovation in SMEs

Ying

Soroka

Han

et al. 2020

International Journal of Information Management

121

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Fast development of IT and ICT facilitate customers to post a large volume of their concerns and expectation online, which are widely accepted to be a valuable resource for product designers. However, it is found that only a small number of small and medium-sized enterprises (SMEs) have capabilities to leverage customer online insights for design innovation, which often demonstrate a significant share in national economies growth. To discover the beneath reasons regarding the barrier that prevent them to make effective utilization, in this study, as a concrete example, manufacturing SMEs in the South Wales and Greater Manchester industrial areas of the UK are focused and their potential motivations for using and knowledge of big data-based customer analytics are investigated. An exploratory survey was conducted in terms of the type of customer data they have, the storage approaches, the volume of customer data, etc. Next, a carefully devised exploratory study was undertaken to understand how SMEs perceive the relations between customer data and product design, how about their expectations from big customer data analytics and what really challenges SMEs to exploit the value of big customer data. Besides, a demonstration platform is developed to present SMEs an automatic process of analysing customer online reviews and the capacity on customer insights acquisition and strategic decision making. Finally, findings from two focus groups indicate the different managerial and technical considerations required for SMEs considering implementing big data and customer analytics. This study encourages SMEs to welcome big customer data and suggests that a cloud-based approach may be the most appropriate way of giving access to big data analytics techniques.

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Section: Online Consumer Datamentioning

confidence: 99%

Cloud-based big data analytics for customer insight-driven design innovation in SMEs

Ying

Soroka

Han

et al. 2020

International Journal of Information Management

121

View full text Add to dashboard Cite

show abstract

“…In addition, when the reviewers expressed perceptions of mutual behavior among readers, the likelihood of helpfulness vote decreased. It has also been revealed that readers take more time to assess the helpfulness of negative reviews compared to positive reviews [46]. Recently, a study reported the negative relationship between review polarity and review helpfulness [47].…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

et al. 2020

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The increasing volume of online reviews and the use of review platforms leave tracks that can be used to explore interesting patterns. It is in the primary interest of businesses to retain and improve their reputation. Reviewers, on the other hand, tend to write reviews that can influence and attract people's attention, which often leads to deliberate deviations from past rating behavior. Until now, very limited studies have attempted to explore the impact of user rating behavior on review helpfulness. However, there are more perspectives of user behavior in selecting and rating businesses that still need to be investigated. Moreover, previous studies gave more attention to the review features and reported inconsistent findings on the importance of the features. To fill this gap, we introduce new and modify existing business and reviewer features and propose a user-focused mechanism for review selection. This study aims to investigate and report changes in business reputation, user choice, and rating behavior through descriptive and comparative analysis. Furthermore, the relevance of various features for review helpfulness is identified by correlation, linear regression, and negative binomial regression. The analysis performed on the Yelp dataset shows that the reputation of the businesses has changed slightly over time. Moreover, 46% of the users chose a business with a minimum of 4 stars. The majority of users give 4-star ratings, and 60% of reviewers adopt irregular rating behavior. Our results show a slight improvement by using user rating behavior and choice features. Whereas, the significant increase in R 2 indicates the importance of reviewer popularity and experience features. The overall results show that the most significant features of review helpfulness are average user helpfulness, number of user reviews, average business helpfulness, and review length. The outcomes of this study provide important theoretical and practical implications for researchers, businesses, and reviewers. INDEX TERMS Business reputation, rating behavior, review helpfulness, user profiling, crowd-sourced reviews.

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“…Table 2 presents the definitions of the dependent variable, independent variables, and control variables in this study. The choice and operationalization of these variables are in accordance with prior studies in the online review literature [49,53,[65][66][67]. The final dataset for analysis contained 39,114 course reviews from 5216 sessions of 3276 MOOCs.…”

Section: Variablesmentioning

confidence: 99%

Reviewer Experience vs. Expertise: Which Matters More for Good Course Reviews in Online Learning?

Fang

Wang

2021

Sustainability

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With a surging number of online courses on MOOC (Massive Open Online Course) platforms, online learners face increasing difficulties in choosing which courses to take. Online course reviews posted by previous learners provide valuable information for prospective learners to make informed course selections. This research investigates the effects of reviewer experience and expertise on reviewer competence in contributing high-quality and helpful reviews for online courses. The empirical study of 39,114 online reviews from 3276 online courses on a leading MOOC platform in China reveals that both reviewer experience and expertise positively affect reviewer competence in contributing helpful reviews. In particular, the effect of reviewer expertise on reviewer competence in contributing helpful reviews is much more prominent than that of reviewer experience. Reviewer experience and expertise do not interact in enhancing reviewer competence. The analysis also reveals distinct groups of reviewers. Specifically, reviewers with low expertise and low experience contribute the majority of the reviews; reviewers with high expertise and high experience are rare, accounting for a small portion of the reviews; the rest of the reviews are from reviewers with high expertise, but low experience, or those with low expertise, but high experience. Our work offers a new analytical approach to online learning and online review literature by considering reviewer experience and expertise as reviewer competence dimensions. The results suggest the necessity of focusing on reviewer expertise, instead of reviewer experience, in choosing and recommending reviewers for online courses.

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Conformity feedback in an online review helpfulness evaluation task leads to less negative feedback-related negativity amplitudes and more positive P300 amplitudes.

Cited by 17 publications

References 44 publications

Cloud-based big data analytics for customer insight-driven design innovation in SMEs

Cloud-based big data analytics for customer insight-driven design innovation in SMEs

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

Reviewer Experience vs. Expertise: Which Matters More for Good Course Reviews in Online Learning?

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