How does the concept of quantity influence the measurement and analysis of electronic word of mouth (eWOM)?
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The quantity of electronic word-of-mouth (eWOM) plays a significant role in influencing its measurement and analysis. Research suggests that the influence of review quantity in eWOM outweighs that of review quality, indicating that the volume of reviews can have a more substantial impact on consumer behavior. Additionally, information overload, which is related to the quantity of eWOM, can increase eWOM negativity and polarity, affecting consumer opinions. Furthermore, the effectiveness of eWOM can be measured to determine purchase intention, highlighting the importance of considering the quantity of eWOM in analyzing its impact on consumer behavior. Overall, the quantity of eWOM is a crucial factor that marketers and researchers need to consider when evaluating and understanding its effects.
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2 Citations | Not addressed in the paper. |
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1 Citations | The quantity of electronic word-of-mouth (eWOM) communication can be measured through factors like source, message, medium, and receiver to determine its impact on purchase intention effectively. |
| The quantity of information in electronic word-of-mouth (eWOM) can lead to information overload, increasing negativity and polarity in consumer opinions, impacting measurement and analysis. | |
| Quantity of reviews in e-WOM has a greater impact than review quality, influencing consumers' adoption. Review consistency negatively affects informational influence, while perceived persuasiveness and social influence remain unaffected by confirmation bias. |
Related Questions
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What indicators can be used to measure wordload?5 answersIndicators that can be used to measure wordload include quality indicators based on text characteristics such as average sentence length, spelling mistakes, and abbreviations. These indicators can be automatically measured using natural language processing libraries like Stanford CoreNLP and NLTK. By implementing these indicators, the quality of text mining results can be predicted and evaluated.
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