When Karma strikes back: A model of seller manipulation of consumer reviews in an online marketplace

Wu, Ruhai; Qiu, Chun

doi:10.1016/j.jbusres.2022.113316

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Despite possible bias, reviews exert significant influence and often surpass personal experience when it comes to making consumer decisions (Chen et al, 2022;Zhao et al, 2012). Whilst these reviews may lack reporting and purchasing bias and may even be fake, they still influence purchasing decisions (Chakraborty, 2019;Wu and Qiu, 2023), making them valuable decisionmaking tools.…”

Section: User-generated Content Decision Science and Corporate Perfor...mentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

“…, 2012). Whilst these reviews may lack reporting and purchasing bias and may even be fake, they still influence purchasing decisions (Chakraborty, 2019; Wu and Qiu, 2023), making them valuable decision-making tools. Strategic pricing and price sensitivity: User-generated content can also help companies develop effective pricing strategies, especially important for products where consumers are increasingly price-sensitive or have a lot of choice (Deloitte, 2015). Reviews influence consumers' perceptions of value and often overshadow brand value, influencing pricing decisions (Chakraborty, 2019; Chen et al.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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From data to decision: distilling decision intelligence from user-generated content

Redek,

Godnov

2024

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PurposeThe Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.Design/methodology/approachUsing Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.FindingsThe paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.Research limitations/implicationsThe paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.Originality/valueThe study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

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Section: User-generated Content Decision Science and Corporate Perfor...mentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

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From data to decision: distilling decision intelligence from user-generated content

Redek,

Godnov

2024

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“…In marketing research, review valence has been found to have a significant impact on perceived usefulness and behaviorial intentions [13,14], however, the research results are inconsistent as to whether positive or negative reviews have a greater impact on consumer responses [15]. According to negativity bias [16,17], many studies indicated that negative reviews are more useful than positive reviews and have a greater impact on consumers' purchase decisions [3,13,14]. Nonetheless, some other studies have found that regarding hedonic consumption (e.g., tourism, music), consumers perceive negative reviews to be less useful than positive reviews [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

How Review Valence Shapes Visit Intention: Affective Commitment and Destination Reputation

Zhao,

Tang,

Yang

et al. 2024

Sustainability

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In the era of social media, online reviews have become a crucial factor influencing the exposure of tourist destinations and the decision-making of potential tourists, exerting a profound impact on the sustainable development of these destinations. However, the influence of review valence on visit intention, especially the role of affective commitment and reputation (ability vs. responsibility), remains unclear. Drawing on emotion as a social information theory, this paper aims to elucidate the direct impact of different review valences on tourists’ visit intentions, as well as mediating mechanisms and boundary conditions. Three experiments indicate that positive (vs. negative) reviews can activate stronger affective commitment and visit intention, with affective commitment also playing a mediating role. Additionally, destination reputation significantly moderates the after-effects of review valences. More specifically, a responsibility reputation (compared with an ability reputation) weakens the effect of negative valence on affective commitment and visit intention. This study provides valuable theoretical insights into how emotional elements in online reviews influence the emotions and attitudes of potential tourists. Particularly for tourism managers, review valence and responsibility reputation hold practical significance in destination marketing.

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“…However, it is important to note that positive e-WOM is not always genuine. Companies may engage in astroturfing, a practice where they create fake positive reviews to manipulate consumer perception [21]. This can ultimately backfire and harm the brand's reputation.…”

mentioning

confidence: 99%

Word-of-Mouth Engagement in Online Social Networks: Influence of Network Centrality and Density

Anastasiei

Dospinescu

2023

Electronics

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This paper investigates the effect of network centrality and network density on the propensity to engage in positive and negative eWOM, using social networks usage as a moderating variable. The research method was Structural Equation Modeling, and the data were collected through a survey conducted on 436 respondents from Romania. Findings showed that centrality and density only affect negative eWOM intent, the relationship being stronger at higher levels of network usage. In consequence, influential network members are more readily inclined to produce unfavorable eWOM. Subsequently, companies should make continuous efforts to spot and turn around bad publicity online.

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When Karma strikes back: A model of seller manipulation of consumer reviews in an online marketplace

Cited by 10 publications

References 31 publications

From data to decision: distilling decision intelligence from user-generated content

From data to decision: distilling decision intelligence from user-generated content

How Review Valence Shapes Visit Intention: Affective Commitment and Destination Reputation

Word-of-Mouth Engagement in Online Social Networks: Influence of Network Centrality and Density

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