Online reviews and high-involvement product sales: Evidence from offline sales in the Chinese automobile industry

Wang, Shutian; Lin, Yan; Zhu, Guoqing

doi:10.1016/j.elerap.2022.101231

Cited by 25 publications

(22 citation statements)

References 48 publications

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“…Further, while the volume of reviews had a significant association with purchase intention of beauty products (Cheung et al. , 2014), the sales rank of DVDs and digital cameras (Ghose and Ipeirotis, 2011) and automobile sales in the Chinese market (S. Wang et al. , 2023), it was found to have no impact on the sales rank of audio-video players (Ghose and Ipeirotis, 2011).…”

Section: Theoretical Background and Hypotheses Developmentmentioning

confidence: 99%

“…While one early study identified the positive effect of the volume of online reviews on future box office revenues in the United States (Duan et al, 2008), another seminal study found no association (Chintagunta et al, 2010). Further, while the volume of reviews had a significant association with purchase intention of beauty products (Cheung et al, 2014), the sales rank of DVDs and digital cameras (Ghose and Ipeirotis, 2011) and automobile sales in the Chinese market (S. Wang et al, 2023), it was found to have no impact on the sales rank of audio-video players (Ghose and Ipeirotis, 2011). In the context of the current study, given that the volume of reviews is a measure of awareness (Duan et al, 2008) and product popularity (Ghose and Ipeirotis, 2011), we expect it will have a positive association with car sales.…”

Section: Features Of Online Reviews and Car Salesmentioning

confidence: 99%

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Understanding the relationship between reviews, search and sales: a study of the Indian car market

Prabhala,

Bose

2023

IMDS

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PurposeWhile there has been extensive research on understanding the effects of online reviews on product sales, there is not enough investigation of the inter-relationships between online reviews, online search and product sales. The study attempts to address this gap in the context of the Indian car market.Design/methodology/approachThe research uses text mining and considers six important review features volume, valence, length, deviation of valence, sentiment and readability within the heuristic and systematic model of information processing. Panel data regression is used along with mediation analysis to study the inter-relationships between features of reviews, online search and sales.FindingsThe study finds that numerical heuristic features significantly affect sales and online search, numerical systematic feature affects sales and the textual heuristic and systematic features do not affect sales or online search in the Indian car market. Further, online search mediates the association between features of reviews and sales of cars.Research limitations/implicationsAlthough only car sales data from India is considered in this research, similar relationships between review features, online search and sales could exist for the car market of other countries as well.Originality/valueThis research uncovers the unique role of online search as a mediator between review features and sales, whereas prior literature has considered review features and online search as independent variables that affect sales.

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Section: Theoretical Background and Hypotheses Developmentmentioning

confidence: 99%

Section: Features Of Online Reviews and Car Salesmentioning

confidence: 99%

Understanding the relationship between reviews, search and sales: a study of the Indian car market

Prabhala,

Bose

2023

IMDS

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“…Word of mouth (WOM) theory (Wang et al, 2023) reveals the transmission of information and opinions among individuals and highlights the impact of ratings and the number of UGC sentiment for recommendation comments. In WOM, positive or negative sentiment in online reviews can affect WOM recommendations and subsequently impact sales.…”

Section: Factors That Influence Consumer Purchasing Decisionsmentioning

confidence: 99%

Is UGC sentiment helpful for recommendation? An application of sentiment-based recommendation model

Gao,

Wang,

Liu

2024

IMDS

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PurposeGiven the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.Design/methodology/approachAfter verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.FindingsThe results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.Practical implicationsThe findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.Originality/valueThis study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

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“…This finding is because sentiment expressions may occasionally be treated by consumers as promotional information which reduces their perceived credibility of such contents (Scholz et al, 2018). On the other hand, several recent studies also noted the positive influence of sentiment expressions posted by sellers on consumer engagement and purchasing intention (Wan & Ren, 2017). Compared with informational expressions, sentiment expressions convey the positive sentiment of product or service providers to consumers to help product or service providers build an emotional relationship with consumers and increase the consumer’s trust (Bai & Yan, 2020; Hsu et al, 2019).…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Power of Sentiment Expressions on Peer-to-Peer Rental Platforms: A Mixed-Method Approach

Zhang

Wei

Zhang

et al. 2023

Journal of Travel Research

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Studies have confirmed the ineffectiveness of sentiment expressions generated by sellers in improving guests’ purchasing intentions. However, how sentiment expressions can influence guest behavior and host performance remains unclear, given the relative importance of seller-generated content in peer-to-peer rental platforms. After collecting data from Airbnb and developing empirical models, this study confirmed that hosts’ sentiment expressions largely benefit from their online performance. This case is especially true when their properties did not receive high-quality negative reviews. To further reveal the mechanism behind this effect, we further conducted two experiments. Results show that trust plays an intermediary role in the relationship between hosts’ sentiment expressions and guests’ purchasing intentions. This work contributes to tourism literature and property owners on peer-to-peer rental platforms in practice.

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Online reviews and high-involvement product sales: Evidence from offline sales in the Chinese automobile industry

Cited by 25 publications

References 48 publications

Understanding the relationship between reviews, search and sales: a study of the Indian car market

Understanding the relationship between reviews, search and sales: a study of the Indian car market

Is UGC sentiment helpful for recommendation? An application of sentiment-based recommendation model

Power of Sentiment Expressions on Peer-to-Peer Rental Platforms: A Mixed-Method Approach

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