Determinants of the helpfulness of specialty coffee reviews on Amazon
DOI:
https://doi.org/10.25186/.v20i.2273Abstract
With the increasing volume of online reviews on platforms like Amazon, customers must put considerable effort into isolating useful information from irrelevant or ambiguous content. One solution implemented by e-commerce sites to address this challenge is product review systems, where customers can vote on whether they find a review helpful, thereby reducing uncertainty in purchase decisions. Therefore, it is crucial to understand what makes a review helpful and how to enhance customer access to these valuable reviews. This study aims to analyze the content characteristics of online reviews that influence their perceived helpfulness in the context of specialty coffee products sold on Amazon. A content analysis was conducted on 2369 reviews to identify elements that contribute to their informational value. Subsequently, a zero-inflated negative binomial regression model was applied to test the hypotheses, addressing issues of overdispersion and an excess of zeros in the response variable. The findings suggest that aspects such as multimedia format, review depth, and the inclusion of extrinsic product attributes (price and quality) are key factors that enhance review helpfulness. On one hand, the use of images and videos helps consumers visualize the product and understand its features, while detailed and extensive reviews provide more comprehensive information. Moreover, factors like price and quality allow consumers to assess whether the product meets their needs and preferences. These findings are crucial for developing more effective marketing strategies in the coffee industry by providing a more precise understanding of the attributes most valued by consumers.
Key words: eWOM; e-commerce; negative bonimal regression; online reviews; specialty coffee.
References
ACEBRÓN, L.; DOPICO, D. The importance of intrinsic and extrinsic cues to expected and experienced quality: an empirical application for beef. Food Quality and Preference, 11(3):229-238, 2000.
AMRULLAH, A. et al. Consumer decision to buy brown rice in traditional markets. IOP Conference Series: Earth and Environmental Science, 681(1):168121, 2021.
ARIADNA, B. et al. Modelos de innovación en la producción de café en la Sierra Norte de Puebla-México. Revista de Ciencias Sociales, 27(3):443-458, 2021.
BAKHSHIAN, S.; LEE, Y. Influence of extrinsic and intrinsic attributes on consumers’ attitude and intention of using wearable technology. International Journal of Human-Computer Interaction, 39(3):562-574, 2022.
BARAHONA, I. et al. Sensory attributes of coffee beverages and their relation to price and package information: A case study of Colombian customers’ preferences. Food Science and Nutrition, 8(2):1173-1186, 2020.
BEKALO, D. B.; KEBEDE, D. T. Zero-inflated models for count data: An application to number of antenatal care service visits. Annals of Data Science, 8(4):683-708, 2021.
BISWAS, B.; SENGUPTA, P.; GANGULY, B. Your reviews or mine? Exploring the determinants of “perceived helpfulness” of online reviews: A cross-cultural study. Electronic Markets, 32(3):1083-1102, 2021.
CHOI, H. S.; LEON, S. When trust cues help helpfulness: Investigating the effect of trust cues on online review helpfulness using big data survey based on the Amazon platform. Electronic Commerce Research, 2023.
ESLAMI, S. P.; GHASEMAGHAEI, M.; HASSANEIN, K. Which online reviews do consumers find most helpful? A multi-method investigation. Decision Support Systems, 113:32-42, 2018.
FAN, L.; ZHANG, X. The combination signaling effect of text and image on mobile phone review helpfulness - the moderating effect of signaling environment. IEEE Access, 8:122736-122746, 2020.
FEUCHT, Y.; ZANDER, K. Consumers' willingness to pay for climate-friendly food in European countries. Proceedings in System Dynamics and Innovation in Food Networks, 2017.
GOH, K. Y.; HENG, C. S.; LIN, Z. Social media brand community and consumer behavior: Quantifying the relative impact of user- and marketer-generated content. Information Systems Research, 24(1):88-107, 2013.
GRUNERT, K. Sustainability in the food sector: A consumer behaviour perspective. International Journal on Food System Dynamics, 2:207-218, 2011.
HAIR, J.; ANDERSON, R. Multivariate data analysis. Prentice Hall, London, 2009. 672p.
HELLALBI, M. A.; BOUABDALAH, A. Elaboration of a generalized mixed model for the wind speed distribution and an assessment of wind energy in algerian coastal regions and at the capes. Energy Conversion and Management, 305(1):118265, 2024.
HENG, Y. et al. Exploring hidden factors behind online food shopping from amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services, 42:161-168, 2018.
HONG, S.; PITTMAN, M. eWOM anatomy of online product reviews: Interaction effects of review number, valence, and star ratings on perceived credibility. International Journal of Advertising, 39:892-920, 2020.
HU, X.; YANG, Y. What makes online reviews helpful in tourism and hospitality? A bare-bones meta-analysis. Journal of Hospitality Marketing & Management, 30(2):139-158, 2021.
HUSSEIN, M.; SILVA, A.; FRASER, I. Linking intrinsic quality attributes of agricultural produce to revealed consumer preferences. Food Quality and Preference, 41:180-188, 2015.
ISMAGILOVA, E. et al. The effect of electronic word of mouth communications on intention to buy: A meta-analysis. Information Systems Frontiers, 22(5):1203-1226, 2020.
JIA, H.; SHIN, S.; JIAO, J. Does the length of a review matter in perceived helpfulness? The moderating role of product experience. Journal of Research in Interactive Marketing, 16(2):221-236, 2022.
KARIMI, S.; WANG, F. Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96:39-48, 2017.
KOBAYASHI, M. L.; BENASSI, M. de T. Impact of packaging characteristics on consumer purchase intention: Instant coffee in refill packs and glass jars. Journal of Sensory Studies, 30(3):169-180, 2015.
KRIPPENDORFF, K. Content analysis: An introduction to its methodology. (4th ed.). Sage Press, 2018. 456p.
KUAN, K. et al. What makes a review voted? An empirical investigation of review voting in online review systems. Journal of the Association for Information Systems, 16(1):48-71, 2015.
KUNDU, S.; CHAKRABORTI, S. A comparative study of online consumer reviews of Apple iPhone across Amazon, Twitter and MouthShut platforms. Electronic Commerce Research, 22(3):925-950, 2022.
LAI, G. et al. “Fulfilled by Amazon”: A strategic perspective of competition at the e-commerce platform. Manufacturing and Service Operations Management, 24(3):1406-1420, 2022.
LEE, J.; PARK, C. The effects of corporate, review and reviewer characteristics on the helpfulness of online reviews: the moderating role of culture. Internet Research, 32(5):1562-1594, 2022.
LEUNG, D. Unraveling the interplay of review depth, review breadth, and review language style on review usefulness and review adoption. International Journal of Hospitality Management, 97:102989, 2021.
LI, L.; FU, L.; ZHANG, W. Impact of text diversity on review helpfulness: A topic modeling approach. Interdisciplinary Journal of Information, Knowledge, and Management, 17:87-100, 2022.
LI, Y.; XIE, Y. Is a picture worth a thousand words? An empirical study of image content and social media engagement. Journal of Marketing Research, 51(1):002224371988111, 2019.
LI, Z. G.; MURRAY, L. W.; SCOTT, D. Global sourcing, multiple country-of-origin facets, and consumer reactions. Journal of Business Research, 47(2):121-133, 2000.
LIANG, S.; SCHUCKERT, M.; LAW, R. How to improve the stated helpfulness of hotel reviews? A multilevel approach. International Journal of Contemporary Hospitality Management, 31(2):953-977, 2019.
LIS, B. In eWOM we trust. Business & Information Systems Engineering, 5:129-140, 2013.
MALEKPOUR, M.; YAZDANI, M.; REZVANI, H. Investigating the relationship between intrinsic and extrinsic product attributes with customer satisfaction: Implications for food products. British Food Journal, 124(13):578-598, 2022.
MITRA, S.; JENAMANI, M. Helpfulness of online consumer reviews: A multi-perspective approach. Information Processing and Management, 58(3):102538, 2021.
QIN, J.; ZHENG, P.; WANG, X. Comprehensive helpfulness of online reviews: A dynamic strategy for ranking reviews by intrinsic and extrinsic helpfulness. Decision Support Systems, 163:113859, 2022.
ROY, G.; DATTA, B.; MUKHERJEE, S. Role of electronic word-of-mouth content and valence in influencing online purchase behavior. Journal of Marketing Communications, 25:661-684, 2018.
SAMOGGIA, A.; RIEDEL, B. Coffee consumption and purchasing behavior review: Insights for further research. Appetite, 129:70-81, 2018.
SEO, H. et al. Effect of coffee bean aroma on the rat brain assessed by sleep deprivation: A selected transcript and 2D gel-based proteome analysis. Journal of Agricultural and Food Chemistry, 56, 4665-4673, 2008.
SIERING, M.; MUNTERMANN, J.; RAJAGOPALAN, B. Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems, 108:1-12, 2018.
SON, Y. et al. The determinants of helpful hotel reviews: A social influence perspective. Sustainability, 14(22):14881, 2022.
SRIVASTAVA, V.; KALRO, A.D. Enhancing the helpfulness of online consumer reviews: The role of latent (content) factors. Journal of Interactive Marketing, 48:33-50, 2019.
SUN, X.; HAN, M.; FENG, J. Helpfulness of online reviews: Examining review informativeness and classification thresholds by search products and experience products. Decision Support Systems, 124:113099, 2019.
TEUBER, R.; HERRMANN, R. Towards a differentiated modeling of origin effects in hedonic analysis: An application to auction prices of specialty coffee. Food Policy, 37(6):732-740, 2012.
THOTA, P.; RAMEZ, E. Web scraping of COVID-19 news stories to create datasets for sentiment and emotion analysis. The 14th PErvasive Technologies Related to Assistive Environments Conference, 2021.
TOROK, A. et al. The competitiveness of global coffee trade. International Journal of Economics and Financial, 8:1-6, 2018.
TRAORE, T.; WILSON, N.; FIELDS, D. What explains specialty coffee quality scores and prices: A case study from the cup of excellence program. Journal of Agricultural and Applied Economics, 50(3):349-368, 2018.
VIDANAGAMA, D.; SILVA, T.; KARUNANANDA, A. Deceptive consumer review detection: A survey. Artificial Intelligence Review, 53:1323-1352, 2019.
XIAO, L.; LUO, L.; KE, T. The influence of eWOM information structures on consumers’ purchase intentions. Electronic Commerce Research, 24:1713-1735, 2022.
YA, O.; ALTON, C. An investigation of factors that contribute to movie review helpfulness in China. Lecture Notes in Engineering and Computer Science, 2243:150-155, 2021.
YANG, S.; ZHOU, C.; CHEN, Y. Do topic consistency and linguistic style similarity affect online review helpfulness? An elaboration likelihood model perspective. Information Processing & Management, 58(3):102521, 2021.
YANG, Y. Impacts of temporal contiguity on creation and consumption of e-WOM in hotel context: A combined perspective of review rating and text. International Journal of Hospitality Management, 105:103256, 2022.
ZHANG, J.; YANG, Y.; DING, J. Information criteria for model selection. Wiley Interdisciplinary Reviews: Computational Statistics, 15(5):e1607, 2023.
ZHANG, Z. et al. Predicting review helpfulness in the omnichannel retailing context: An elaboration likelihood model perspective. Frontiers in Psychology, 13:958386, 2022.
ZHANG, Z.; WANG, X.; WU, R. Is the devil in the details? Construal-level effects on perceived usefulness of online reviews for experience services. Electronic Commerce Research and Applications, 46:101033, 2021.
ZHOU, S.; TU, L. The effect of social dynamics in online review voting behavior. Journal of Retailing and Consumer Services, 69:103120, 2022.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Coffee Science - ISSN 1984-3909The copyright of the articles published in this journal belongs to the authors, with the journal holding the first publication rights. As the articles are published in this journal under open access, they may be freely used, with proper attribution, for educational and non-commercial purposes
