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How Review Quality and Source Credibility Interacts to Affect Review Usefulness: An Expansion of the Elaboration Likelihood Model

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Abstract

This study extends our understanding of what makes an online review useful by examining the effects of review quality (i.e., as a composite variable of review comprehensiveness and review topic consistency) on review usefulness, and the moderating effects of source credibility on the relationship between review quality and review usefulness. The Elaboration Likelihood Model, convergence theory, and cueing effect literature are used to define the variables of review comprehensiveness and review topic consistency. Analyses of 27,517 restaurant reviews from Yelp show that review topic consistency has a positive effect on review usefulness, but, contrary to our hypothesis, review comprehensiveness has a negative effect on review usefulness. We also found source credibility positively moderates the effect of review comprehensiveness on review usefulness, but negatively moderates the effect of review topic consistency on review usefulness. Theoretical and practical implications are discussed.

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Notes

  1. Review usefulness in Yelp and review helpfulness in Amazon essentially serve the same function of expressing online review quality. Therefore, for ease of reading, we use the term “usefulness” henceforth.

  2. We used topic coherence score, which is built upon pointwise mutual information (PMI), because it tends to show the highest correlations with human ratings (Röder et al., 2015)

  3. We manually assign the labels of “food”, “service”, “ambiance”, and “value” to the four topics based on the determinant keywords that appear in each topic with the highest probability.

References

  • Adomavicius, G., Bockstedt, J., Curley, S., & Zhang, J. (2019). Reducing recommender systems biases: An investigation of rating display designs. Forthcoming, MIS Quarterly, 43(4), 1321–1341.

    Google Scholar 

  • Aghakhani, N., Karimi, J., & Salehan, M. (2018). A unified model for the adoption of electronic word of mouth on social network sites: Facebook as the exemplar. International Journal of Electronic Commerce, 22(2), 202–231.

    Article  Google Scholar 

  • Aghakhani, N., Oh, O., Gregg, D. G., & Karimi, J. (2021). Online review consistency matters: An elaboration likelihood model perspective. Information Systems Frontiers, 23, 1287–1301. https://doi.org/10.1007/s10796-020-10030-7

  • Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS Quarterly, 33(2), 339–370.

    Article  Google Scholar 

  • Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers' objectives and review cues. International Journal of Electronic Commerce, 17(2), 99–126.

    Article  Google Scholar 

  • Bailey, A. A. (2005). Consumer awareness and use of product review websites. Journal of Interactive Advertising, 6(1), 68–81.

    Article  Google Scholar 

  • Baker, S. M., & Petty, R. E. (1994). Majority and minority influence: Source-position imbalance as a determinant of message scrutiny. Journal of Personality and Social Psychology, 67(1), 5.

    Article  Google Scholar 

  • Bhattacharyya, S., Banerjee, S., Bose, I., & Kankanhalli, A. (2020). Temporal effects of repeated recognition and lack of recognition on online community contributions. Journal of Management Information Systems, 37(2), 536–562.

    Article  Google Scholar 

  • Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.

    Google Scholar 

  • Cahyani, D. E., & Patasik, I. (2021). Performance comparison of TF-IDF and Word2Vec models for emotion text classification. Bulletin of Electrical Engineering and Informatics, 10(5), 2780–2788.

    Article  Google Scholar 

  • Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511–521.

    Article  Google Scholar 

  • Chang, J., Boyd-Graber, J., Wang, C., Gerrish, S., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. In: Advances in Neural Information Processing Systems- Proceedings of the 2009 conference (pp. 288–296).

  • Cheung, M. Y., Luo, C., Sia, C. L., & Chen, H. (2009). Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 13(4), 9–38.

    Article  Google Scholar 

  • Cheung, C. M.-Y., Sia, C.-L., & Kuan, K. K. (2012). Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective. Journal of the Association for Information Systems, 13(8), 2.

    Article  Google Scholar 

  • Choi, H. S., & Leon, S. (2020). An empirical investigation of online review helpfulness: A big data perspective. Decision Support Systems, 139, 113403.

    Article  Google Scholar 

  • Choi, A. A., Cho, D., Yim, D., Moon, J. Y., & Oh, W. (2019). When seeing helps believing: The interactive effects of previews and reviews on E-book purchases. Information Systems Research, 30(4), 1164–1183.

    Article  Google Scholar 

  • Chou, Y.-C., Chuang, H. H.-C., & Liang, T.-P. (2021). Elaboration likelihood model, endogenous quality indicators, and online review helpfulness. Decision Support Systems, 153, 113683.

    Article  Google Scholar 

  • Chua, A. Y., & Banerjee, S. (2015). Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth. Journal of the Association for Information Science and Technology, 66(2), 354–362.

    Article  Google Scholar 

  • Chunmian, G., Haoyue, S., Jiang, J., & Xiaoying, X. (2021). Investigating the demand for Blockchain talents in the recruitment market: Evidence from topic modeling analysis on job postings. Information & Management, 103513. https://doi.org/10.1016/j.im.2021.103513

  • Costello, F. J., & Lee, K. C. (2021). Exploring investors' expectancies and its impact on project funding success likelihood in crowdfunding by using text analytics and Bayesian networks. Decision Support Systems, 154, 113695.

    Article  Google Scholar 

  • Craciun, G., Zhou, W., & Shan, Z. (2020). Discrete emotions effects on electronic word-of-mouth helpfulness: The moderating role of reviewer gender and contextual emotional tone. Decision Support Systems, 130, 113226.

    Article  Google Scholar 

  • Debortoli, S., Müller, O., Junglas, I., & vom Brocke, J. (2016). Text mining for information systems researchers: An annotated topic modeling tutorial. Communications of the Association for Information Systems, 39(1), 7.

    Google Scholar 

  • Eslami, S. P., Ghasemaghaei, M., & Hassanein, K. (2018). Which online reviews do consumers find most helpful? A multi-method investigation. Decision Support Systems, 113, 32–42.

    Article  Google Scholar 

  • Filieri, R., McLeay, F., Tsui, B., & Lin, Z. (2018). Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services. Information & Management, 55(8), 956–970.

    Article  Google Scholar 

  • Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313.

    Article  Google Scholar 

  • Fullerton, L. (2017). Online reviews impact purchasing decisions for over 93% of consumers, report suggests. Retrieved from https://www.thedrum.com/news/2017/03/27/online-reviews-impact-purchasing-decisions-over-93-consumers-report-suggests. Accessed 20 Mar 2022.

  • Gao, B., Hu, N., & Bose, I. (2017). Follow the herd or be myself? An analysis of consistency in behavior of reviewers and helpfulness of their reviews. Decision Support Systems, 95, 1–11.

    Article  Google Scholar 

  • Ghose, A., & Ipeirotis, P. G. (2010). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498–1512.

    Article  Google Scholar 

  • Ghose, A., Ipeirotis, P. G., & Li, B. (2012). Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3), 493–520.

    Article  Google Scholar 

  • Gjerstad, P., Meyn, P. F., Molnár, P., & Næss, T. D. (2021). Do president Trump's tweets affect financial markets? Decision Support Systems, 147, 113577.

    Article  Google Scholar 

  • Greene, W. H. (1994). Accounting for excess zeros and sample selection in Poisson and negative binomial regression models. Working paper EC-94-10, Department of Economics, Stern School of Business, New York University, New York.

  • Greene, W. (2003). Econometric analysis (4th ed.). Prentice-Hall.

    Google Scholar 

  • Grimes, M. (2012). Global Consumers’ Trust in ‘Earned’Advertising Grows in Importance. Retrieved from (https://www.nielsen.com/us/en/insights/article/2012/consumer-trust-in-online-social-and-mobile-advertising-grows/). Accessed 20 Mar 2022.

  • Guo, B., & Zhou, S. (2017). What makes population perception of review helpfulness: An information processing perspective. Electronic Commerce Research, 17(4), 585–608.

    Article  Google Scholar 

  • Hong, H., Xu, D., Wang, G. A., & Fan, W. (2017). Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems, 102, 1–11.

    Article  Google Scholar 

  • Hu, N., Liu, L., & Zhang, J. J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology and Management, 9(3), 201–214.

    Article  Google Scholar 

  • Hu, N., Zhang, J., & Pavlou, P. A. (2009). Overcoming the J-shaped distribution of product reviews. Communications of the ACM, 52(10), 144–147.

    Article  Google Scholar 

  • Huang, A. H., Chen, K., Yen, D. C., & Tran, T. P. (2015). A study of factors that contribute to online review helpfulness. Computers in Human Behavior, 48, 17–27.

    Article  Google Scholar 

  • Huang, L., Tan, C.-H., Ke, W., & Wei, K. K. (2018). Helpfulness of online review content: The moderating effects of temporal and social cues. Journal of the Association for Information Systems, 19(6), 3.

    Article  Google Scholar 

  • Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2020). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. Journal of Retailing and Consumer Services, 53, 101736.

    Article  Google Scholar 

  • Jin, Q., Animesh, A., & Pinsonneault, A. (2015). First-mover advantage in online review platform. Proceedings of 36th International Conference on Information Systems. https://aisel.aisnet.org/icis2015/proceedings/eBizeGov/26

  • Jung, Y., & Suh, Y. (2019). Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems, 123, 113074.

    Article  Google Scholar 

  • Kahn, B. K., Strong, D. M., & Wang, R. Y. (2002). Information quality benchmarks: Product and service performance. Communications of the ACM, 45(4), 184–192.

    Article  Google Scholar 

  • Karimi, S., & Wang, F. (2017). Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96, 39–48.

    Article  Google Scholar 

  • Korfiatis, N., García-Bariocanal, E., & Sánchez-Alonso, S. (2012). Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications, 11(3), 205–217.

    Article  Google Scholar 

  • Krumpal, I. (2013). Determinants of social desirability bias in sensitive surveys: A literature review. Quality & Quantity, 47(4), 2025–2047.

    Article  Google Scholar 

  • Kuan, K. K., Hui, K.-L., Prasarnphanich, P., & Lai, H.-Y. (2015). What makes a review voted? An empirical investigation of review voting in online review systems. Journal of the Association for Information Systems, 16(1), 1.

    Article  Google Scholar 

  • Kyriakou, H., Nickerson, J. V., & Sabnis, G. (2017). Knowledge reuse for customization: Metamodels in an open design community for 3D printing. arXiv preprint arXiv:1702.08072.

  • Lee, C. K. H. (2022). How guest-host interactions affect consumer experiences in the sharing economy: New evidence from a configurational analysis based on consumer reviews. Decision Support Systems, 152, 113634.

    Article  Google Scholar 

  • Lee, M., & Youn, S. (2009). Electronic word of mouth (eWOM) how eWOM platforms influence consumer product judgement. International Journal of Advertising, 28(3), 473–499.

    Article  Google Scholar 

  • Li, M., & Huang, P. (2020). Assessing the product review helpfulness: Affective-cognitive evaluation and the moderating effect of feedback mechanism. Information & Management, 57(7), 103359.

    Article  Google Scholar 

  • Li, M., Huang, L., Tan, C.-H., & Wei, K.-K. (2013). Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17(4), 101–136.

    Article  Google Scholar 

  • Liu, F., Lai, K.-H., Wu, J., & Duan, W. (2021). Listening to online reviews: A mixed-methods investigation of customer experience in the sharing economy. Decision Support Systems, 149, 113609.

    Article  Google Scholar 

  • Loria, S. (2020). textblob Documentation. Available at: https://buildmedia.readthedocs.org/media/pdf/textblob/latest/textblob.pdf. Accessed 10 Mar 2022.

  • Luo, C., Luo, X. R., Schatzberg, L., & Sia, C. L. (2013). Impact of informational factors on online recommendation credibility: The moderating role of source credibility. Decision Support Systems, 56, 92–102.

    Article  Google Scholar 

  • Mannes, A. E. (2009). Are we wise about the wisdom of crowds? The use of group judgments in belief revision. Management Science, 55(8), 1267–1279.

    Article  Google Scholar 

  • Mariani, M. M., & Borghi, M. (2020). Online review helpfulness and firms’ financial performance: An empirical study in a service industry. International Journal of Electronic Commerce, 24(4), 421–449.

    Article  Google Scholar 

  • Moscovici, S. (1980). Toward a theory of conversion behavior. Advances in Experimental Social Psychology, 13, 209–239.

    Article  Google Scholar 

  • Mousavizadeh, M., Koohikamali, M., Salehan, M., & Kim, D. J. (2020). An investigation of peripheral and central cues of online customer review voting and helpfulness through the Lens of elaboration likelihood model. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-10069-6

  • Mudambi, S. M., Schuff, D., & Zhang, Z. (2014). Why aren't the stars aligned? An analysis of online review content and star ratings. 47th Hawaii International Conference on System Sciences.

  • Nemeth, C. J. (1986). Differential contributions of majority and minority influence. Psychological Review, 93(1), 23.

    Article  Google Scholar 

  • Pettijohn, L. S., Pettijohn, C. E., & Luke, R. H. (1997). An evaluation of fast food restaurant satisfaction: Determinants, competitive comparisons and impact on future patronage. Journal of Restaurant & Foodservice Marketing, 2(3), 3–20.

    Article  Google Scholar 

  • Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19(1), 123–205.

  • Qahri-Saremi, H., & Montazemi, A. R. (2019). Factors affecting the adoption of an electronic word of mouth message: A Meta-analysis. Journal of Management Information Systems, 36(3), 969–1001.

    Article  Google Scholar 

  • Qiu, L., Pang, J., & Lim, K. H. (2012). Effects of conflicting aggregated rating on eWOM review credibility and diagnosticity: The moderating role of review valence. Decision Support Systems, 54(1), 631–643.

    Article  Google Scholar 

  • Racherla, P., & Friske, W. (2012). Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548–559.

    Article  Google Scholar 

  • Ren, J., & Nickerson, J. V. (2019). Arousal, valence, and volume: How the influence of online review characteristics differs with respect to utilitarian and hedonic products. European Journal of Information Systems, 28(3), 272–290.

    Article  Google Scholar 

  • Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. Proceedings of the eighth ACM international conference on Web search and data mining,

  • Sahni, T., Chandak, C., Chedeti, N. R., & Singh, M. (2017). Efficient twitter sentiment classification using subjective distant supervision. Proceedings of 9th International Conference on Communication Systems and Networks (COMSNETS),

  • Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40.

    Article  Google Scholar 

  • Schindler, R. M., & Bickart, B. (2012). Perceived helpfulness of online consumer reviews: The role of message content and style. Journal of Consumer Behaviour, 11(3), 234–243.

    Article  Google Scholar 

  • Siering, M., Muntermann, J., & Rajagopalan, B. (2018). Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems, 108, 1–12.

    Article  Google Scholar 

  • Slof, D., Frasincar, F., & Matsiiako, V. (2021). A competing risks model based on latent Dirichlet allocation for predicting churn reasons. Decision Support Systems, 146, 113541.

    Article  Google Scholar 

  • Sniezek, J. A., & Buckley, T. (1995). Cueing and cognitive conflict in judge-advisor decision making. Organizational Behavior and Human Decision Processes, 62(2), 159–174.

    Article  Google Scholar 

  • Spool, J. M. (2009). The magic behind amazon’s 2.7 billion dollar question. User Interface Engineering http://www.uie.com/articles/magicbehindamazon/. Accessed 10 Mar 2022.

  • Sun, X., Han, M., & Feng, J. (2019). Helpfulness of online reviews: Examining review informativeness and classification thresholds by search products and experience products. Decision Support Systems, 124, 113099.

    Article  Google Scholar 

  • Sussman, S. W., & Siegal, W. S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65.

    Article  Google Scholar 

  • Topaloglu, O., & Dass, M. (2019). The impact of online review content and linguistic style matching on new product sales: The moderating role of review helpfulness. Decision Sciences. https://doi.org/10.1111/deci.12378

  • Vakulenko, S., Müller, O., & Brocke, J. V. (2014). Enriching iTunes App Store categories via topic modeling. Proceedings of 35th International Conference on Information Systems (pp. 1–11).

  • Yin, D., Bond, S. D., & Zhang, H. (2014). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38(2), 539–560.

    Article  Google Scholar 

  • Yin, J., Ngiam, K. Y., & Teo, H. H. (2020). Work design in healthcare artificial intelligence applications: The role of advice provision timing. Proceedings of 41st International Conference on Information Systems. https://aisel.aisnet.org/icis2020/is_health/is_health/10

  • Yüksel, A., & Yüksel, F. (2003). Measurement of tourist satisfaction with restaurant services: A segment-based approach. Journal of Vacation Marketing, 9(1), 52–68.

    Article  Google Scholar 

  • Zhang, L., Yan, Q., & Zhang, L. (2020). A text analytics framework for understanding the relationships among host self-description, trust perception and purchase behavior on Airbnb. Decision Support Systems, 133, 113288.

    Article  Google Scholar 

  • Zhao, K., Stylianou, A. C., & Zheng, Y. (2018). Sources and impacts of social influence from online anonymous user reviews. Information & Management, 55(1), 16–30.

    Article  Google Scholar 

  • Zhou, L., Wang, W., Xu, J. D., Liu, T., & Gu, J. (2018). Perceived information transparency in B2C e-commerce: An empirical investigation. Information & Management, 55(7), 912–927.

    Article  Google Scholar 

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Correspondence to Onook Oh.

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Appendices

Appendix 1 Figures

Fig. 1
figure 1

Theoretical model

Fig. 2
figure 2

Topic coherence and perplexity scores

Appendix 2 Tables

Table 1 Summary of the effects of central and peripheral cues on review usefulness
Table 2 Descriptive statistics (N = 27,517)
Table 3 The correlation matrix
Table 4 Data analysis
Table 5 Robustness check

Appendix 3: Qualitative Analyses of Sample Review Texts

A topic cut-off value of 25% assumes equal distributions of the four different topics (i.e., the topics of “value,” “food,” “ambiance,” and “service”) across a review text. This assumption ignores the possibility that some topics might be more importantly described than other topics. As a result, we generated a random sample of 300 reviews using four different topic cut-off values of 25%, 20%, 15%, and 10%. After reading and qualitatively examining the 300 sample reviews, we determined that a topic cut-off value of 10% best represents the number of topics contained in a review text. Review comprehensiveness was accordingly re-estimated with the new 10% topic cut-off value. The four tables below show exemplary review texts with the topic of “value” (first table), “food” (second table), “ambiance” (third table), and “service” (fourth table) having different topic cut-off values of 25%, 20%, 15%, and 10%

Review

The score for the Topic of “Value”

Phenomenal value. The team that runs this place really has a winner in their hands and I hope they hold the course with excellent food at a reasonable price. I’ve tried most the dishes and all have been well executed. The staff treats everyone very well and even offers up an occasional beer or wine tasting, which is a nice touch. Go, eat, enjoy

0.25

Breakfast review. The breakfast here is a little pricy but you should know that before coming in. There are cheaper options like potato pancakes for 5$ or new York egg sandwich for $8. I tried the eggs Benedict and the NY egg sandwich. Both delicious NY egg sandwich was a big portion. Came with two fried eggs and two big slices of bacon. Eggs Benedict was cooked to perfection. The spinach made it different and the holidase sauce made well. Coffee was strong for those coffee lovers. Yum.

0.2

My cousin decided to treat me to dinner, courtesy of EY, and I decided to choose the one place that I’ve always wanted to come, Bottega Louie! The ambiance is definitely extremely beautiful with high ceilings and low light candles. The open kitchen is also a nice touch. They don’t take reservations but we were seating immediately (not sure how that happened but I’m not complaining!). We were put in a corner seat, which was a terrible place to sit because you can’t see anything but that was perfectly fine with me. Also, the menu is extensive BUT IS SO SMALL. I could barely read it! They start you off with a leaf shaped bread and butter, which was a bit hard but had great flavor. We started with the portobello fries, which are ACTUALLY MUSHROOMS. For some reason I thought it would just be oil, like truffle fries. The flavor wasn’t bad but I actually dislike mushrooms so I wouldn’t order that again... We ordered the Louie Salad, which was SO delicious. It was tangy and fresh and the jumbo shrimp was extremely flavorful. Definitely my favorite salad and I don’t even like salad. We also ordered the Hanger steak as well as the Trenne Pasta? I’m not sure if if that’s what the pasta was called but it’s the pasta that they’re known for that’s crunchy. The steak was extremely flavorful and I wasn’t too big of a fan of the pasta. In the end we got a DELICIOUS creme brulee and some macarons from the front. The service was great but they were a bit on the slow side. Overall, I want to come back and try brunch. I loved the ambiance and the fact that the meal was free (because it’s expensive!).. But I wasn’t WOWed and it didn’t meet my expectations.

0.15

There’s nothing really wrong with Quartino, but I haven’t found any particular reason to pick it. The food is modestly priced and modestly good. Each trip, one dish usually ends up being terrible though, and there’s nothing excellent to offset it. Add the cramped space, the poor quality of the bread (that bread “Q” is such a tease), and the indifferent service, and I’m just not into the experience. I am docking a star for shared plates, since enough is enough of this trend. I’d like to have dinner when I go out for dinner. I’ve been here at least a half dozen times, and one trip stands out as excellent. This was for a large group -- for which Quartino’s is perfect. But if you have a smaller party you might as well go elsewhere. My girlfriend loves this place, and she’s going to hate this

0.1

Review

The score for the Topic of “Food”

I hear the sandwiches here are the bomb. I wouldn’t know. What I do know is that the Mac N′ Cheese will knock your socks off with it’s creamy cheesiness.

0.25

Nice restaurant. Busy on the weekends and they do not take reservations, Walk in Only. Clam pasta dish was wonderful and of course the portobello Mushroom fries. DO NOT order the lobster soup. It is the worst, It tasted like fishy water and was salty and VERY FISHY. I was very disappointed by the dish. I wish I could have sent it back or said something about it because it was terrible.

0.2

Been there at least 4 times now, my friends and I love it. Amazing food, cozy space, great service. They have a great oyster happy hour special too. Seafood is delicious- what’s offered changes over time- and the rotating assortment of beer on tap keeps the experience fresh and exciting. Out of what I had the most recent time, I’d most recommend the marlin crudo. But everything else was tasty too.

0.15

Wasn’t terribly impressed. Sangria was delicious. Unfortunately, my brother and I both thought the food was particularly bland. The short rib was nice and tender, just needs more flavor. The tortellini with prosciutto and peas was alright, also bland tasting. Shrimp risotto was watery and very bland. Kind of disappointed. The atmosphere is busy and loud, which was okay for us, would be a good place for a first date where there’s action happening. Great place to people watch. I doubt I will return though. Much better Italian food in the city. Our server was friendly and outgoing, which is always appreciated.

0.1

Review

The score for the Topic of “Ambience”

complimentary drink, which wasn’t necessary, but we accepted:) Once we were seated, our waitress was great. She was attentive, friendly, and knowledgeable. When we were about to order dessert, the same manager came to our table and asked us to move tables to accommodate a handicapped guest as our table was wheelchair accessible. We gladly moved, no problem. For our “trouble” we were given more free drinks and the manager brought 4 complimentary desserts to our table. He was so attentive and we truly felt valued and appreciated. Overall, food was delicious, service was outstanding, and atmosphere was cozy and authentic. Will absolutely be back!

0.25

I lived down the street from this joint which was a blessing and a curse in so many ways... Loved the pizza, hate the next morning workout, loved the market dessert choices, hate the guilt I feel the next day... I like taking out of town visitors to this place for the ambiance, but the wait can be insane on weekends. Overall, the menu offers a good selection, and pricing is very reasonable!

0.2

Went here for a late dinner and was quite impressed. There are two locations in the Chicago area, and it’s a great place to go if you’re looking for some quality American food. From fried chicken to kale salad, there’s something here for everyone. The restaurant itself has a casual feel, and it set up like an upscale diner. It’s spacious and it a good option for groups. I made a reservation for three and we were seated right away upon arriving. The beer selection on tap was interesting, and I hadn’t heard of many of them. We ended up getting a round of the Yumyum, which was a decent IPA. I was in the mood for something light and healthy, so I ordered the quinoa kitchen salad, which was made from fresh, unique ingredients {romain lettuce, quinoa, almonds, radish, mint, feta, leeks, topped with crispy onion flakes} that complemented each other well! I was surprised at how filling it ended up being - I was only able to make it through half of it. The rest of the table decided on burgers (turkey and backyard) which they enjoyed. I tried the sweet potato fries, which were on the thicker side so they tasted healthier and ended up being a generous amount of potato. The prices here are decent, and are comparable to others restaurants in the area {$14 burgers and salads}. I would definitely be back for another visit next time I’m in the area.

0.15

I’m in Atlanta for the week and had to get some good southern food that I can’t get in AZ and I can definitely say that Poor Calvin’s was the right choice. We came on an early Wednesday night so luckily there was no wait to be seated. We got the special appetizer for the day, salmon hush puppies. This was AMAZING (my mouth is watering just thinking about it). Our group also got the fried chicken with lobster mac & cheese + collard greens. This was a good choice. The serving was also large so I had some to-go. The atmosphere is classy yet cozy here. I enjoyed my first time and I’ll keep this place in mind when I’m back in Atlanta.

0.1

Review

The score for the Topic of “Service”

I’ve been here several times and have enjoyed every meal. Bottega Louie is well priced, and the food is consistently good. Waiters are friendly and deliver excellent service. I like ordering several small plates and sharing them with the table.

0.25

Believe the hype! We chose Upstate based on location then read the reviews. You guys...so right! Very organized wait list with text notification system. The hour wait, spent at a friendly dive across the street, was worth it! The smell of the food at the next table had us drooling in anticipation as soon as we sat down. Excellent service -- casual and friendly, great menu and the food was spectacular. Prices are very reasonable -- $11 small plate/$15 main -- and portions are generous. I couldn’t finish the house favorite clams with fettuccine and lord knows, I tried. I would marry that fettuccine if NY ever passed human/food equality laws. Thinly sliced buttery whisky cake was the perfect finish. I will be back.

0.2

We decided to try this place due to good reviews and were a bit disappointed. The food was presented to a good standard and the taste was great. It has been just a little expensive for what we get and the portions are on the smaller side. The server was very helpful and extremely knowledgeable on the menu choices so we were able to enjoy both the food and drinks selected. Highly recommend this to anyone wanting a wonderful dining experience.

0.15

Sooo expensive, mediocre desert.. on top of that, terrible customer service! Oh theres No free parking.. 38 for 5 macaroons and 2 pastries.. We got there and there’s no parking on the streets so we had to park in a nearby structure for $5.. Once we walked in it was a market place.. Super noisy and packed.. We stood infront of their counter for 15 mins waiting for help.. Oh there’s no line so whoever is the rudest gets the service.. Sorry other reviewers.. But this place is terrible.. I like the decorations though.. Macaroons everywhere.. But the macaroon themselves were not good for that price..

0.1

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Aghakhani, N., Oh, O., Gregg, D. et al. How Review Quality and Source Credibility Interacts to Affect Review Usefulness: An Expansion of the Elaboration Likelihood Model. Inf Syst Front 25, 1513–1531 (2023). https://doi.org/10.1007/s10796-022-10299-w

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