Capitalizing on Content: Information Adoption in Two Online communities

Zhang, Wei; Watts, Stephanie

doi:10.17705/1jais.00149

Cited by 244 publications

(84 citation statements)

References 51 publications

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“…Receivers who are seeking out WOM to guide their choices can vary in their motivation levels, which affects their attention to different components of WOM. When motivation is low, receivers focus on product popularity and pay more attention to summary statistics such as average star ratings; when motivation is high, receivers focus on specific product information and pay more attention to review text (e.g., Doh & Hwang, ; Lee, Park, & Han, ; Martin & Lueg, ; Park & Lee, ; Park, Lee, & Han, ; Watts & Zhang, ; Wu, Che, Chan, & Lu, ; Wu & Wang, ). Because these specific WOM components can each affect sales (Babić Rosario et al, ; Chevalier & Mayzlin, ; Forman, Ghose, & Wiesenfeld, ), differential attention to these components should alter how WOM impacts receivers.…”

Section: Receivermentioning

confidence: 99%

“…For example, reviews for material (vs. experiential) purchases are considered more helpful and more strongly influence receivers’ choices, because they are perceived as more reflective of the objective quality of the purchase (Dai et al, ). Further, motivation to seek WOM is higher (e.g., information search is prolonged) when receivers are exposed to negative information (Varga & Albuquerque, ), and motivation to process increases when receivers’ prior knowledge or beliefs are disconfirmed (Karmarkar & Tormala, ; Watts & Zhang, ). Increased processing can increase persuasion, which may increase or decrease product evaluations and purchase likelihood, depending on the valence of the WOM (Karmarkar & Tormala, ; Varga & Albuquerque, ).…”

Section: Receivermentioning

confidence: 99%

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How online word‐of‐mouth impacts receivers

Moore

Lafreniere

2019

Consumer Psychology Review

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Online word‐of‐mouth (WOM) can impact consumers’ product evaluations, purchase intentions, and choices—but when does it do so? How do those receiving WOM know whether to rely on a particular message? This article suggests that the multiple players involved in online WOM (receivers, senders, sellers, platforms, and other consumers) each have their own interests, which are often in conflict. Thus, receivers of WOM are faced with a judgment task in deciding what information to rely on: They must make inferences about the product in question and about the players who provide or present WOM. To do so, they use signals embedded in various components of WOM, such as average star ratings, message content, or sender characteristics. The product and player information provided by these signals shapes the impact of WOM by allowing receivers to make inferences about (a) their likelihood of product satisfaction, and (b) the trustworthiness of WOM players, and therefore the trustworthiness of their content. This article summarizes how each player changes the impact of online WOM, providing a lens for understanding the current literature in online WOM, offering insights for theory in this context, and opening up pathways for future research.

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Section: Receivermentioning

confidence: 99%

Section: Receivermentioning

confidence: 99%

How online word‐of‐mouth impacts receivers

Moore

Lafreniere

2019

Consumer Psychology Review

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“…The ELM explains how individuals adopt information and then change their will and behavior. Moreover, recent literature has also demonstrated that this model can be applied in the context of online information acceptance, argument quality, and source credibility; these are taken as the direct objects of information adoption, and their influence has been repeatedly verified [ 23 , 24 ]. Simultaneously, individuals’ perceived information usefulness based on information quality and source characteristics plays a crucial intermediary role in information adoption.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Public Adoption of COVID-19 Prevention and Treatment Information During an Infodemic: Cross-sectional Survey Study

Han¹,

Jiang²,

Guo³

2021

J Med Internet Res

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Background With the spread of COVID-19, an infodemic is also emerging. In public health emergencies, the use of information to enable disease prevention and treatment is incredibly important. Although both the information adoption model (IAM) and health belief model (HBM) have their own merits, they only focus on information or public influence factors, respectively, to explain the public’s intention to adopt online prevention and treatment information. Objective The aim of this study was to fill this gap by using a combination of the IAM and the HBM as the framework for exploring the influencing factors and paths in public health events that affect the public’s adoption of online health information and health behaviors, focusing on both objective and subjective factors. Methods We carried out an online survey to collect responses from participants in China (N=501). Structural equation modeling was used to evaluate items, and confirmatory factor analysis was used to calculate construct reliability and validity. The goodness of fit of the model and mediation effects were analyzed. Results The overall fitness indices for the model developed in this study indicated an acceptable fit. Adoption intention was predicted by information characteristics (β=.266, P<.001) and perceived usefulness (β=.565, P<.001), which jointly explained nearly 67% of the adoption intention variance. Information characteristics (β=.244, P<.001), perceived drawbacks (β=–.097, P=.002), perceived benefits (β=.512, P<.001), and self-efficacy (β=.141, P<.001) jointly determined perceived usefulness and explained about 81% of the variance of perceived usefulness. However, social influence did not have a statistically significant impact on perceived usefulness, and self-efficacy did not significantly influence adoption intention directly. Conclusions By integrating IAM and HBM, this study provided the insight and understanding that perceived usefulness and adoption intention of online health information could be influenced by information characteristics, people’s perceptions of information drawbacks and benefits, and self-efficacy. Moreover, people also exhibited proactive behavior rather than reactive behavior to adopt information. Thus, we should consider these factors when helping the informed public obtain useful information via two approaches: one is to improve the quality of government-based and other official information, and the other is to improve the public’s capacity to obtain information, in order to promote truth and fight rumors. This will, in turn, contribute to saving lives as the pandemic continues to unfold and run its course.

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“…OCRs are particularly relevant for travel businesses as they influence consumers' alternatives evaluation and purchase intentions (Filieri and McLeay 2014;Filieri 2015;D.-H. Park, Lee, and Han 2007;Zhang and Watts 2008), product consideration and choice (Gupta and Harris 2010;J.-H. Huang and Chen 2006;Senecal and Nantel 2004;Vermeulen and Seegers 2009), and they impact on hotels' sales (Ye et al 2011), preferences (Viglia, Furlan, and Ladrón-de-Guevara 2014) and financial performance (Phillips et al 2017, Raguseo andVitari 2017).…”

Section: Introductionmentioning

confidence: 99%

What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics

Filieri

Raguseo

Vitari

2019

International Journal of Hospitality Management

119

105

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Online customer reviews (OCRs) are increasingly used by travelers to inform their purchase decisions. However, the vast amount of reviews available nowadays may increase travellers' effort in information processing. In order to facilitate traveller's decisions, social commerce organizations must help travellers rapidly identify the most helpful reviews to reduce their cognitive effort. Academic literature has often documented that negative reviews are judged as helpful by consumers. However, extremely negative reviews are not always perceived as such. This study is the first that unveils what factors moderate the influence of extremely negative reviews on review helpfulness. The study has adopted a sample of 7,455 online customer reviews of hotels to test hypotheses. Findings show that reviews with extremely negative ratings are more likely to be helpful when the review is long and easy to read and when the reviewer is an expert or discloses his identity (geographical origin).

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Capitalizing on Content: Information Adoption in Two Online communities

Cited by 244 publications

References 51 publications

How online word‐of‐mouth impacts receivers

How online word‐of‐mouth impacts receivers

Factors Affecting Public Adoption of COVID-19 Prevention and Treatment Information During an Infodemic: Cross-sectional Survey Study

What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics

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