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REVIEW 4 major objections 8 minor 70 references

From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go

T0 review · 4 major / 8 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A month-long field study with 14 campus diners found that letting a large language model polish a spoken review—while keeping the human's own words, tone, and control—raised users' willingness to share the review and their self-efficacy…

desk verdict A transparent, small field study; the willingness-to-share effect is defensible, but the self-efficacy claim rests on a retrospective baseline collected after the tool was used, so treat that headline gain as unestablished. read the letter →

arxiv 2412.05445 v2 pith:X4MO4F6Y submitted 2024-12-06 cs.HC cs.AI

Kavindu Ravishan , Dániel Szabó , Niels van Berkel , Aku Visuri , Chi-Lan Yang , Koji Yatani , Simo Hosio This is my paper · ORCID
classification cs.HCcs.AI
keywords onlinereviewsvoiceinputlargelanguagemodelsself-efficacywillingnesstosharemobileapplicationuserstudyspoken
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Typed reviews are effortful, and people who want to leave detailed feedback while moving through the day often give up before hitting submit. This paper asks whether a spoken review, cleaned up by a large language model, can lower that barrier without replacing the human author. In a counterbalanced field study with 14 adults recruited on a university campus, using a mobile app called Vocalizer, participants reported a significantly higher willingness to share LLM-assisted reviews (mean 5.93 vs 4.53 on a 1–7 scale) and higher self-efficacy—confidence in their own ability to write a good review—after using the assisted version (9.00 vs 4.86 on a 0–10 scale). The authors interpret the result as evidence that AI should edit, not create: users ramble, the model trims and clarifies, and users retain control through direct prompts and optional tips. If correct, this points to a practical way for review platforms to encourage richer user contributions without making people feel their words have been taken over by a machine.

What carries the argument

The central object is Vocalizer, a mobile web app that converts a spoken restaurant review into a submission-ready text in two versions: voice-only and LLM-assisted. The LLM-assisted version adds three GPT-4-driven features that carry the argument: (1) an initial automatic cleanup that removes filler words, rambling, and off-topic content while keeping the original tone and English level; (2) a conversational AI agent that obeys user-typed instructions to add, omit, correct, clarify, rephrase, or change sentiment in the review; and (3) a suggestion module that generates review-specific tips grounded in published findings on what makes reviews helpful. The design principle is user autonomy: the LLM edits the user's own words rather than generating a review from scratch, and the user can iterate or restart at any point.

What would settle it

Run the same app with the baseline self-efficacy question asked at onboarding, and track whether participants actually publish their reviews to a live platform instead of just moving a slider. If the onboarding baseline already equals 9/10, or if LAV reviews are not posted more often than VOV reviews, the paper's central claims would not survive.

Watch

Extended reading notes

Core claim

The paper's central claim is that interactive LLM assistance during review writing increases both willingness to share and self-efficacy compared with voice-only transcription, because it converts spontaneous speech into coherent text while preserving the user's content, tone, and control. In the Vocalizer deployment, users recorded up to five minutes of speech, read a polished version, could give free-form instructions to a conversational AI agent, and could request research-grounded improvement tips; all 14 participants preferred the LLM-assisted version. Across 82 LLM-assisted reviews, mean willingness to share was 5.93/7 versus 4.53/7 for 75 voice-only reviews (paired t-test, p < .05), mean satisfaction was 6.15/7, and the AI agent's usefulness averaged 6.09/7. Self-efficacy, defined as confidence in one's ability to give a good review, rose from 4.86/10 unaided to 9.00/10 after the LLM-assisted version, against 8.25 after voice-only, with a significant Friedman test (χ²(3) = 20.15, p < .001) and significant pairwise differences between the unaided baseline and both tool conditions. The authors conclude that the LLM-assisted version increased users' self-efficacy in their review writing abilities.

Load-bearing premise

The load-bearing premise is that the self-report measures are truthful: a 1–7 'willingness to share' answer predicts actual sharing, and a single retrospective self-efficacy score, asked after people had already used both tools, reflects their genuine unaided baseline confidence rather than a glow from the tool.

Editorial extensions

If this is right

  • Review platforms that add an LLM polish step to voice input can expect higher stated willingness to publish, with the study's effect size (5.93 vs 4.53 on a 7-point scale) and significant paired t-test.
  • Users will use such agents mostly to add detail and clarify points rather than to fix grammar, so systems should support expansion-prompts well.
  • A single session with an AI-assisted review tool may raise users' confidence in their review-writing ability, potentially motivating more frequent contributions.
  • More experienced reviewers write longer, more specific instructions to the agent, implying the tool's value grows with the user's writing skill.
  • All 14 participants preferred the assisted version, but participant concerns about lost authenticity point to a design tension that any deployment must manage by keeping the author's voice visible.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the willingness-to-share effect is real, the practical payoff is a lower effort barrier for mobile contributions: review platforms could add an 'AI polish' step after voice input and expect both higher output volume and richer content, as users asked for added detail in 30 of the 92 logged prompt modifications.
  • The paper itself flags in Section 5.3 that the unaided self-efficacy baseline was collected in the final questionnaire, after users had already tried both tools; that ordering likely inflates the reported 4.86-to-9.00 jump, so the effect size is an upper bound.
  • A three-arm version—voice-only, voice plus automatic cleanup, and voice plus cleanup and interactive agent—would separate the contribution of the conversational agent from the automatic transcription polish, which the paper acknowledges was confounded by design.
  • The same edit-don't-create pattern could transfer to other user-generated content such as emails, social posts, or accessibility aids for non-native writers, where the relevant question is whether readers perceive the polished text as still authentic; this study measured willingness to share, not reader-side authenticity.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 8 minor

Summary. The paper presents Vocalizer, a mobile web application for leaving restaurant reviews via voice input, with two versions: a voice-only version (VOV) and an LLM-assisted version (LAV) that adds automatic transcript cleaning, an interactive AI agent, and AI-generated improvement tips. Fourteen participants used both versions over a within-subjects field study lasting up to three weeks, with in-app feedback after each review and post-visit and final questionnaires. The paper reports that users made frequent use of the AI agent (42 of 82 LAV reviews), that willingness to share was significantly higher for LAV than VOV reviews (M = 5.93 vs. 4.53, t(14) = -2.39, p < 0.05), and that self-efficacy rose from an 'Unaided' baseline of 4.86 to 9.00 after LAV use, with a significant Friedman test across four conditions. Qualitative analysis of prompts and open-ended responses is used to characterize user editing strategies and perceptions. The central claims are that LLM-assisted editing increases users' willingness to share and their self-efficacy in writing reviews.

Significance. If the results were robust, this would be a useful contribution to the growing HCI literature on AI-assisted content creation, particularly for mobile and time-constrained review writing. The paper's strengths are its longitudinal within-subjects deployment with a working system, the detailed description of the LLM pipeline and prompts (including full prompt listings in appendices), the analysis of real user-AI interactions, and the candid treatment of several limitations. The qualitative analysis of user prompting strategies is also valuable. However, the quantitative evidence rests on a small sample (N = 14) and on self-report measures whose validity is partly undermined by the study design, especially the retrospectively collected self-efficacy baseline. The self-efficacy finding, which is featured in the abstract and conclusion, is not causally established by the current data.

major comments (4)
  1. [Section 4.6 and Section 5.3] The self-efficacy baseline is retrospective and is load-bearing for the headline claim. The 'Unaided' score of 4.86 was collected in the final questionnaire, after participants had already used both VOV and LAV; Section 5.3 explicitly states that the 'self-efficacy measurement about the users' prior belief in being able to leave a good review was captured in the final questionnaire.' The key comparison, LAV (9.00) versus Unaided (4.86), therefore contrasts a post-intervention state with a recalled prior state, which is vulnerable to recall bias and effort justification. The paper nevertheless concludes that 'the LAV increased users' self-efficacy in their review writing abilities.' This causal statement is not supported by the data as presented. The authors should either reframe the result as a perceived, retrospectively assessed difference, or provide a genuine pre-intervention baseline (e.g., from the background questionnaire) and re-run the analysis with that baseline.
  2. [Section 4.2.1] The willingness-to-share t-test is reported as 't(14) = -2.39' for what is described as a paired-sample test with 14 participants. For a paired t-test, the degrees of freedom should be 13, not 14. The authors should correct the reported statistic and provide the exact p-value (with df = 13, p is approximately 0.032, so the conclusion remains significant, but the reporting is inaccurate). They should also clarify whether the analysis used per-participant means across the repeated reviews, and consider whether a non-parametric alternative (e.g., Wilcoxon signed-rank) is more appropriate given the small sample and skew evident in the VOV distribution (SD = 1.85). Reporting an effect size and confidence interval would strengthen the paper.
  3. [Sections 3.1-3.3 and 5.3] The study design confounds the automatic LLM-based cleansing of the transcript with the interactive AI agent. The LAV condition includes both the automatic initial improvement (removal of filler words, rephrasing) and the user-facing AI agent, while the VOV condition includes neither. The abstract and conclusion attribute the observed benefits to 'interactive AI features,' but the experiment cannot isolate the contribution of the interactive agent from the automatic cleansing. This is acknowledged in Section 5.3 as a trade-off, but the causal language in the rest of the paper does not temper the claim. The authors should either restrict their conclusions to the overall LAV system or explicitly discuss what a three-condition design (VOV, VOV with automatic cleansing only, and full LAV) would be needed to establish the specific effect of the interactive AI features.
  4. [Section 4.6] The Friedman test across four 'conditions' includes 'Overall,' which is not an experimental condition. 'Overall' is a final-questionnaire global rating of the concept of voice-input review creation, not a condition the participants actually experienced in the same way as VOV or LAV. Including this hypothetical rating as a repeated-measures condition inflates the test and makes the reported chi-square (χ2(3) = 20.15) difficult to interpret. The post-hoc comparisons are also only reported for LAV versus Unaided and Overall versus Unaided, omitting the comparisons involving the actual experimental conditions (VOV versus LAV, VOV versus Unaided). The authors should either restrict the statistical test to the two experimental conditions or clearly justify why 'Overall' is a valid within-subjects condition.
minor comments (8)
  1. [Additional Key Words] The key words contain a typo: 'applicaitons' should be 'applications.'
  2. [Section 3.3.3] There are several typos: 'administed' should be 'administered,' and 'participantsoverall' is missing a space and should be 'participants' overall.'
  3. [Section 4.4] The Spearman correlation of 0.94 is reported without the sample size; since the study has only 14 participants, it would be helpful to state n and show the scatterplot with individual data points (Figure 7). Given the small n, this correlation may be driven by a few participants, so the interpretation should be cautious.
  4. [Section 4.2.3] The statement 'All of the 20 improvement suggestions were considered helpful' would be clearer if the denominator and the response format are specified; the paper mentions a 'simple thumbs-up mechanism' but does not report how many of the 20 suggestions received thumbs-up versus thumbs-down at the item level.
  5. [Section 3.3.3] The self-efficacy question uses a 0-10 scale while the other Likert items use 1-7; this should be stated explicitly in the text and in Figure 9, since otherwise readers may assume a common scale.
  6. [Appendix B and Appendix C] The prompt listings contain formatting artifacts, including spaces in the middle of variable names (e.g., 'r e f in e I ns t r uc t i on s') and a typo in Appendix C ('genarated'). These should be cleaned up for readability.
  7. [Section 4.6] The post-hoc p-values are reported as 'p < 0.001' without the test statistic or the Bonferroni-adjusted significance threshold; providing the actual adjusted p-values and Wilcoxon test statistics would improve reproducibility.
  8. [Appendix E] In Table 4, the user instruction 'not bricks or balls but maybe crotchets' appears to contain a typo; if the intended word is 'croquettes,' the table entry should be corrected to avoid confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the empirical claims rest on direct self-report measurements, not on fitted parameters, defining equations, or load-bearing self-citations.

full rationale

The paper's central claims are empirical: users were more willing to share LLM-assisted (LAV) reviews than voice-only (VOV) reviews, and self-efficacy ratings were higher after using Vocalizer. These are direct, in-app or questionnaire self-reports, not quantities derived from an equation whose inputs already contain the conclusion. The willingness-to-share result (Section 4.2.1) is a paired comparison of 1-7 slider responses collected immediately before submission in each condition; nothing is fit or predicted from those responses. The self-efficacy analysis (Section 4.6) compares questionnaire ratings across conditions, and the paper explicitly acknowledges in Section 5.3 that the 'Unaided' baseline was captured retrospectively in the final questionnaire rather than in an onboarding survey. That is a methodological validity limitation, not a circular step: the 'Unaided' rating is a self-report measure, not a parameter constructed from the outcome, and the paper does not claim to derive it from the LAV result. The only self-citations in the reference list, [41] and [53], are used for background statements about overreliance on AI and personality effects on LLM advice perception; they are not the basis of the reported effect and are not load-bearing for the central claims. No ansatz is smuggled in via citation, no uniqueness theorem is imported, and no known result is renamed. The evaluation is self-contained against the study's own measured data, so the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No fitted parameters, no new theoretical entities. The analysis is a user study, so the ledger records four domain assumptions that the quantitative conclusions depend on.

assumptions (4)
  • domain assumption Self-reported willingness to share predicts actual sharing behavior.
    Q1 is a single slider item (Section 3.3.3); no behavioral follow-up verifies that participants would publish the review.
  • domain assumption The retrospective "unaided" self-efficacy rating is an unbiased baseline.
    Measured in the final questionnaire after using both versions; acknowledged as a limitation in Section 5.3.
  • domain assumption GPT-4's automatic cleansing and agent edits preserve the original facts and sentiment.
    Prompt instructions in Appendix A and B demand this, but there is no verification against ground truth or human raters.
  • domain assumption The 14 university participants represent the broader population of online reviewers.
    Recruitment through a university participation system and campus posters (Section 3.3.1); no external validation.

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Cite this review

Pith. "Pith review of From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go." pith.science (2026). https://pith.science/paper/X4MO4F6Y

@misc{pith2026241205445,
  author       = {Pith},
  title        = {Pith review of: From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X4MO4F6Y}},
  note         = {Machine review of arXiv:2412.05445}
}
read the original abstract

Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challenge, we developed Vocalizer, a mobile application that enables users to provide reviews through voice input, with enhancements from a large language model (LLM). In a longitudinal study, we analysed user interactions with the app, focusing on AI-driven features that help refine and improve reviews. Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews. We also show how interactive AI features can improve users self-efficacy and willingness to share reviews online. Finally, we discuss the opportunities and challenges of integrating AI assistance into review-writing systems.

Figures

Figures reproduced from arXiv: 2412.05445 by the authors.

Figure 1
Figure 1. Usage of the voice-only (VOV) and the LLM-assisted versions (LAV) of Vocalizer. On the left: Vocalizer transcribes the spoken [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Flowcharts showing the operation of both versions of Vocalizer. On the left, the voice-only version, and on the right, the LLM [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Comparison of an example original review and its AI-enhanced revision. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Core functionalities of the LLM-Assisted Version of Vocalizer presented chronologically from left to right. 1 - Selecting [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Study design diagram showing the order of tasks and questionnaires for each participant. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Feedback response distribution for evaluating the VOV and LAV. The figure illustrates user perceptions regarding the usefulness, [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Scatter plot illustrating the relationship between participants’ self-reported experience levels in reviewing restaurants and the [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Results of the User Experience Questionnaire Scores for VOV and LAV. The heatmap displays the average ratings across [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Self-efficacy scores for writing engaging and informative reviews before, during, and after utilizing the AI tool. Ratings for [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]

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Works this paper leans on

70 extracted references · 58 canonical work pages

  1. [1]

    Saddam Abdullah, Philippe Van Cauwenberge, Heidi Vander Bauwhede, and Peter O’Connor. 2024. Review Ratings, Sentiment in Review Comments, and Restaurant Profitability: Firm-Level Evidence. Cornell Hospitality Quarterly 65, 3 (2024), 378–392. https://doi.org/10.1177/19389655231214758

    work page doi:10.1177/19389655231214758 2024

  2. [2]

    Yunjeong Ahn and Jieun Lee. 2024. The Impact of Online Reviews on Consumers’ Purchase Intentions: Examining the Social Influence of Online Reviews, Group Similarity, and Self-Construal. Journal of Theoretical and Applied Electronic Commerce Research 19, 2 (2024), 1060–1078

    work page 2024

  3. [3]

    Hana Alfira, Pamuji Gesang Raharjo, and Al Agus Kristiadi. 2024. Enhancing Consumer Trust and Purchase Decisions Through Online Reviews and Hedonic Factors: A Study of Jakarta Coffee Shops. Valley International Journal Digital Library (2024), 6804–6814. Manuscript submitted to ACM 18 Ravishan et al

    work page 2024

  4. [4]

    Clinton Amos and Lixuan Zhang. 2024. Consumer reactions to perceived undisclosed ChatGPT usage in an online review context. Telematics and Informatics 93 (2024), 102163. https://doi.org/10.1016/j.tele.2024.102163

    arXiv 2024

  5. [5]

    Albert Bandura et al. 2006. Guide for constructing self-efficacy scales. Self-efficacy beliefs of adolescents 5, 1 (2006), 307–337

    work page 2006

  6. [6]

    Best dinner ever!!!

    Alberto Bartoli, Andrea De Lorenzo, Eric Medvet, Dennis Morello, and Fabiano Tarlao. 2016. " Best dinner ever!!!": automatic generation of restaurant reviews with LSTM-RNN. In 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) . IEEE, 721–724

    work page 2016

  7. [7]

    Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2 (2006), 77–101. https: //doi.org/10.1191/1478088706qp063oa

    doi:10.1191/1478088706qp063oa 2006

  8. [8]

    BrightLocal. 2023. Local Consumer Review Survey. https://www.brightlocal.com/research/local-consumer-review-survey/ [Accessed: 11-Aug-2024]

    work page 2023

Show all 70 references
  1. [9]

    Chua and Snehasish Banerjee

    Alton Y.K. Chua and Snehasish Banerjee. 2016. Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Computers in Human Behavior 54 (2016), 547–554. https://doi.org/10.1016/j.chb.2015.08.057

    2016 doi

  2. [10]

    Silvia Nanda Putri Erito. 2023. Exploring ESP Students’ Perception toward the Potential of Artificial Intelligence to Promote Students’ Self-Efficacy in English Writing Skill. Journal of English Language Learning 7, 2 (2023), 457–464

    2023

  3. [11]

    Bin Fang, Qiang Ye, Deniz Kucukusta, and Rob Law. 2016. Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management 52 (2016), 498–506. https://doi.org/10.1016/j.tourman.2015.07.018

    2016 doi

  4. [12]

    Xiaoxuan Fang, Davy Tsz Kit Ng, Jac Ka Lok Leung, and Samuel Kai Wah Chu. 2023. A systematic review of artificial intelligence technologies used for story writing. Education and Information Technologies 28, 11 (2023), 14361–14397

    2023

  5. [13]

    Michael Fernandes, Logan Walls, Sean Munson, Jessica Hullman, and Matthew Kay. 2018. Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI...

    2018

  6. [14]

    Dongpu Fu, Yili Hong, Kanliang Wang, and Weiguo Fan. 2018. Effects of membership tier on user content generation behaviors: Evidence from online reviews. Electronic Commerce Research 18 (2018), 457–483

    2018

  7. [15]

    Christopher Furner, Tom Yoon, Robert Zinko, and Samuel Goh. 2021. The Influence of Reviewer and Consumer Congruence in Online Word-of-Mouth Transactions. Journal of Electronic Commerce in Organizations 19 (07 2021), 1–15. https://doi.org/10.4018/JECO.2021070101

    2021 doi

  8. [16]

    Alessandro Gambetti and Qiwei Han. 2023. Combat ai with ai: Counteract machine-generated fake restaurant reviews on social media. arXiv preprint arXiv:2302.07731 (2023)

    2023 arXiv

  9. [17]

    Alessandro Gambetti and Qiwei Han. 2024. AiGen-FoodReview: A Multimodal Dataset of Machine-Generated Restaurant Reviews and Images on Social Media. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 18. 1935–1945

    2024

  10. [18]

    Giovanny Haro-Sosa, Beatriz Moliner-Velázquez, Irene Gil-Saura, and María Fuentes-Blasco. 2024. Influence of electronic word-of-mouth on restaurant choice decisions: does it depend on gender in the millennial generation? Journal of Theoretical and Applied Electronic Commerce R...

    2024

  11. [19]

    Hui-Wen Huang, Zehui Li, and Linda Taylor. 2020. The effectiveness of using grammarly to improve students’ writing skills. InProceedings of the 5th International Conference on Distance Education and Learning . 122–127

    2020

  12. [20]

    Yunhui Huang, Changxin Li, Jiang Wu, and Zhijie Lin. 2018. Online customer reviews and consumer evaluation: The role of review font.Information & Management 55, 4 (2018), 430–440. https://doi.org/10.1016/j.im.2017.10.003

    2018 doi

  13. [21]

    Sourabh Jain. 2024. Influence of Online Consumer Reviews on Brand Reputation. International Journal For Science Technology And Engineering 12, 4 (2024), 4785–4790. https://doi.org/10.22214/ijraset.2024.61013

    2024

  14. [22]

    Kalpana Jayavalan, Abu Bakar Razali, and B Abu. 2018. Effectiveness of online grammar checker to improve secondary students’ English narrative essay writing. International Research Journal of Education and Sciences 2, 1 (2018), 1–6

    2018

  15. [23]

    del Pobil

    Jina Kim, Soyeon Shin, Kunwoo Bae, Soyoung Oh, Eunil Park, and Angel P. del Pobil. 2020. Can AI be a content generator? Effects of content generators and information delivery methods on the psychology of content consumers. Telematics and Informatics 55 (2020), 101452. https: /...

    2020

  16. [24]

    Balázs Kovács. 2024. The Turing test of online reviews: Can we tell the difference between human-written and GPT-4-written online reviews? Marketing Letters (2024), 1–16

    2024

  17. [25]

    Yukyung Lee, Soonwon Ka, Bokyung Son, Pilsung Kang, and Jaewook Kang. 2024. Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models. arXiv:2404.13919 [cs.CL] https://arxiv.org/abs/2404.13919

    2024 arXiv

  18. [26]

    Yen-Fen Lee, Gwo-Jen Hwang, and Pei-Ying Chen. 2022. Impacts of an AI-based cha bot on college students’ after-class review, academic performance, self-efficacy, learning attitude, and motivation. Educational technology research and development 70, 5 (2022), 1843–1865

    2022

  19. [27]

    Hengyun Li, Chuhan (Renee) Wang, Fang Meng, and Zili Zhang. 2019. Making restaurant reviews useful and/or enjoyable? The impacts of temporal, explanatory, and sensory cues. International Journal of Hospitality Management 83 (2019), 257–265. https://doi.org/10.1016/j.ijhm.2018.11.002

    2019 doi

  20. [28]

    Yu, Gang Li, and Huicai Gao

    Hengyun Li, Bruce X.B. Yu, Gang Li, and Huicai Gao. 2023. Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management 96 (2023), 104707. https://doi.org/10.1016/j.tourman.2022.104707

    2023

  21. [29]

    Zhuoyan Li, Chen Liang, Jing Peng, and Ming Yin. 2024. The Value, Benefits, and Concerns of Generative AI-Powered Assistance in Writing. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–25

    2024

  22. [30]

    Sai Liang, Markus Schuckert, and Rob Law. 2019. How to improve the stated helpfulness of hotel reviews? A multilevel approach. International Journal of Contemporary Hospitality Management 31, 2 (2019), 953–977. https://doi.org/10.1108/IJCHM-02-2018-0134 Manuscript submitted to...

    2019 doi

  23. [31]

    Zamfirescu-Pereira, Matthew G Lee, Sauhard Jain, Shanqing Cai, Piyawat Lertvittayakumjorn, Michael Xuelin Huang, Shumin Zhai, Bjoern Hartmann, and Can Liu

    Susan Lin, Jeremy Warner, J.D. Zamfirescu-Pereira, Matthew G Lee, Sauhard Jain, Shanqing Cai, Piyawat Lertvittayakumjorn, Michael Xuelin Huang, Shumin Zhai, Bjoern Hartmann, and Can Liu. 2024. Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation. In Proce...

    2024

  24. [32]

    Lijuan Luo, Siqi Duan, Shanshan Shang, and Yu Pan. 2021. What makes a helpful online review? Empirical evidence on the effects of review and reviewer characteristics. Online Information Review 45, 3 (2021), 614–632

    2021

  25. [33]

    Guillermo Marco, Julio Gonzalo, Ramón del Castillo, and María Teresa Mateo Girona. 2024. Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing? arXiv:2407.01119 [cs.CL] https://arxiv.org/abs/2407.01119

    2024 arXiv

  26. [34]

    Kelsi Matwick and Keri Matwick. 2018. Restaurant reviews and college writing: A framework for teaching.Teaching Journalism & Mass Communication 8, 2 (2018), 25–37

    2018

  27. [35]

    Vinney Miriam van Mersbergen and Alexis E

    Lisa A. Vinney Miriam van Mersbergen and Alexis E. Payne. 2020. Cognitive influences on perceived phonatory exertion using the Borg CR10. Logo- pedics Phoniatrics Vocology 45, 3 (2020), 123–133. https://doi.org/10.1080/14015439.2019.1617895 arXiv:https://doi.org/10.1080/140154...

    2020

  28. [36]

    Sriram Mudambi and David A. Schuff. 2010. What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.comCOM. Journal of Marketing Research 47, 1 (2010), 133–147

    2010

  29. [37]

    Young Namkung and SooCheong Jang. 2008. Are highly satisfied restaurant customers really different? A quality perception perspective.International Journal of Contemporary Hospitality Management 20, 2 (2008), 142–155

    2008

  30. [38]

    Nabi Nazari, Muhammad Salman Shabbir, and Roy Setiawan. 2021. Application of Artificial Intelligence powered digital writing assistant in higher education: randomized controlled trial. Heliyon 7, 5 (2021)

    2021

  31. [39]

    Ngo-Ye and Atish P

    Thomas L. Ngo-Ye and Atish P. Sinha. 2014. The influence of reviewer engagement characteristics on online review helpfulness: A text regression model. Decision Support Systems 61 (2014), 47–58. https://doi.org/10.1016/j.dss.2014.01.011

    2014 doi

  32. [40]

    Birna Van Riemsdijk Ni Kang, Ding Ding

    M. Birna Van Riemsdijk Ni Kang, Ding Ding. 2021. Self-identification with a Virtual Experience and Its Moderating Effect on Self-efficacy and Presence. International Journal of Human–Computer Interaction 37, 2 (2021), 181–196. https://doi.org/10.1080/10447318.2020.1812909

    2021

  33. [41]

    Jonas Oppenlaender, Aku Visuri, Ville Paananen, Rhema Linder, and Johanna Silvennoinen. 2023. Text-to-image generation: Perceptions and realities. arXiv preprint arXiv:2303.13530 (2023)

    2023 arXiv

  34. [42]

    Sangwon Park and Juan L. Nicolau. 2015. Asymmetric effects of online consumer reviews. Annals of Tourism Research 50 (2015), 67–83. https: //doi.org/10.1016/j.annals.2014.10.007

    2015 doi

  35. [43]

    Nikolaos Pellas. 2023. The Effects of Generative AI Platforms on Undergraduates’ Narrative Intelligence and Writing Self-Efficacy.Education Sciences 13, 11 (2023), 1155

    2023

  36. [44]

    Ilona Pezenka and Christian Weismayer. 2020. Which factors influence locals’ and visitors’ overall restaurant evaluations? International Journal of Contemporary Hospitality Management 32, 9 (2020), 2793–2812

    2020

  37. [45]

    Pradeep Racherla and Wesley Friske. 2012. Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications 11, 6 (2012), 548–559. https://doi.org/10.1016/j.elerap.2012.06.003 Informa...

    2012 doi

  38. [46]

    Mary Rice. 2007. Using customer reviews to build critical reading skills. English Journal 97, 1 (2007), 89–93

    2007

  39. [47]

    Martin Schrepp, Andreas Hinderks, and Jörg Thomaschewski. 2017. Design and evaluation of a short version of the user experience questionnaire (UEQ-S). International Journal of Interactive Multimedia and Artificial Intelligence, 4 (6), 103-108. (2017)

    2017

  40. [48]

    Grammarly

    Arafat Shahriar and Fatema Begum Laboni. 2023. Potential Success in English Writing Skills Using Artificial Intelligence “Grammarly”. DIU Journal of Humanities and Social Science 10, 1 (2023), 57–75

    2023

  41. [49]

    Meghna Singh, Anam Fatma, Vimal Bhatt, and Binod Sinha. 2023. E-Word of Mouth and Restaurant-goers: an Empirical Study on the Influence of e-WOM on the Selection of Restaurants. In 2023 International Conference On Cyber Management And Engineering (CyMaEn) . IEEE, 474–478

    2023

  42. [50]

    Rinji Suzuki, Kazuhiro Akiyama, Tadahiko Kumamoto, and Akiyo Nadamoto. 2020. Analysis of High-value Reviews based on Sentiment. In Proceedings of the 21st International Conference on Information Integration and Web-Based Applications & Services (Munich, Germany) (iiW AS2019). ...

    2020

  43. [51]

    Donna Talarico. 2023. Engineering answers: Honing your AI prompts for more effective results. Recruiting & Retaining Adult Learners 25, 12 (2023), 1–3. https://doi.org/10.1002/nsr.31064

    2023 doi

  44. [52]

    Abhishek Tandon, Aakash Aakash, Anu G Aggarwal, and PK Kapur. 2021. Analyzing the impact of review recency on helpfulness through econometric modeling. International Journal of System Assurance Engineering and Management 12 (2021), 104–111

    2021

  45. [53]

    Joel Wester, Sander de Jong, Henning Pohl, and Niels van Berkel. 2024. Exploring people’s perceptions of LLM-generated advice. Computers in Human Behavior: Artificial Humans 2, 2 (2024), 100072. https://doi.org/10.1016/j.chbah.2024.100072

    2024

  46. [54]

    Kyung Hyan Yoo and Ulrike Gretzel. 2008. What motivates consumers to write online travel reviews? Information Technology & Tourism 10, 4 (2008), 283–295

    2008

  47. [55]

    Mengxia Zhang and Lan Luo. 2023. Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp. Management Science 69, 1 (2023), 25–50. https://doi.org/10.1287/mnsc.2022.4359

    2023

  48. [56]

    Yilun Zhao, Haowei Zhang, Shengyun Si, Linyong Nan, Xiangru Tang, and Arman Cohan. 2023. Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios. arXiv preprint arXiv:2305.14987 (2023). Manuscript submitted to ACM 20 Ravishan et ...

    2023 arXiv

  49. [57]

    The minced beef in the dish was soft, finely chopped, and seemed to be cooked with minimal spices

    Be More Specific: You could provide more details about the minced meat dish such as its texture, the type of meat used, and how it’s prepared. For example, "The minced beef in the dish was soft, finely chopped, and seemed to be cooked with minimal spices. "

  50. [58]

    The flavor of the meat was milder than I prefer, lacking in spices which left it tasting a bit bland

    Cause-effect Relationships: Specify what about the flavor was mild and how it affected your experience. For instance, you might say, "The flavor of the meat was milder than I prefer, lacking in spices which left it tasting a bit bland. "

  51. [59]

    You might describe the ingredients in the salad, whether it was fresh, and how well it complemented the meat dish

    Details about salad: Adding more details about the salad can also improve your review. You might describe the ingredients in the salad, whether it was fresh, and how well it complemented the meat dish

  52. [60]

    I was really looking forward to a burst of flavors but was slightly disappointed by the milder than expected taste

    Reaction Words: Use more expressive language to convey your feelings towards the dish. This gives a more personal touch to your review and makes it more enjoyable for readers. You could say something like "I was really looking forward to a burst of flavors but was slightly dis...

  53. [61]

    Readability: Try to use shorter sentences and simpler words to ensure your review is easy to understand for all readers

  54. [62]

    While the meat dish was decent, I couldn’t help but wish for a more robust flavor to satiate my taste buds

    Extreme Sentiment: Conveying a stronger sentiment can make your review more valuable. For example, you could say, "While the meat dish was decent, I couldn’t help but wish for a more robust flavor to satiate my taste buds. " Remember, these are just tips to guide you in writin...

  55. [63]

    extensive variety of dishes

    Include more specifics: Your review mentions an "extensive variety of dishes" and flexibility in the serving options. Could you mention a few specific dishes that you enjoyed or unique serving options that stand out? This would bolster your review’s credibility

  56. [64]

    Add more details about the food: Considering food plays a pivotal role in a restaurant’s evaluation, it would add value if you could provide more information about the food quality, taste or presentation

  57. [65]

    Did it exceed your expectations or give you a memorable moment?

    Reaction words and experiences: It can be quite helpful to emphasize how the restaurant’s dishes made you feel or enhanced your dining experience. Did it exceed your expectations or give you a memorable moment?

  58. [66]

    Cause-and-effect explanations: You mentioned dining at the restaurant when you’re at the university. Could you explain why? For example, is it conveniently located? Or is it a perfect spot for a quick lunch between classes? Remember, the more precise and easy to understand you...

  59. [67]

    Provide specific examples: What exactly about the ambiance pleasantly surprised you? Was it the decor, lighting, music, or seating arrangement? As for the food, share which dishes you particularly enjoyed and why

  60. [68]

    Was the staff friendly, courteous, and prompt in serving you?

    Include the service aspect: Mention the quality of service received. Was the staff friendly, courteous, and prompt in serving you?

  61. [69]

    Maybe you could share your reasons for visiting this place

    Write about your personal experience: Adding personal anecdotes or stories will make your review more relatable and interesting. Maybe you could share your reasons for visiting this place

  62. [70]

    selection of

    Communicate your emotions: Express more clearly how the restaurant made you feel. Happy, satisfied, delighted? This will add emotional depth to your review. 5. Improve readability: Verify that your review is easy-to-read and follows a coherent order. Ensure your sentences are ...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.