Review Reports - Artificial Intelligence Chatbots as Assistants for Media Users: The Cases of El País and El Espectador

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The core design (two-case qualitative study using semi-structured interviews + observation) is workable but key elements that journals typically treat as must-fix are currently missing or underdeveloped especially transparent reporting of sampling/participants and a reproducible analysis workflow (codebook detail, coding process, and systematic observation protocol), plus completed ethics and data availability statements the template text still appears in the manuscript These are substantial methodological/analytic revisions rather than minor edits.

The manuscript notes interviews with key figures but doesn’t clearly report how many interviews, which roles (editorial/product/engineering/management) which organization each belongs to. The Results cite Interview 1–4 repeatedly implying a small N, add a short participant table for example the role, outlet, responsibility area, and describe the sampling approach. Interview protocol transparency is missing. You justify semi-structured interviews conceptually

but don’t provide the actual guide structure

Observation is discussed, but the way it’s currently implemented feels too loose to carry real methodological weight. You note that the chatbot was observed and analyzed while also acknowledging that observation is “not the best way” because outputs vary depending on the user, the prompt, and prior interaction history that’s a fair point but it makes the case for more structure, not less. If chatbot behavior is variable, then the observation needs a clear protocol: suggest a fixed set of prompts, repeated runs over multiple days, consistent account settings a record of the system-version at the time of testing, and an explicit rubric for what you’re evaluating. The design claims two parts (interviews + observation) but the results are driven mainly by interview quotations. If observation is intended as triangulation, show it analytically: example -Interviewees claim X; our standardized prompt tests show Y. Right now, observation seems illustrative rather than evidentiary. The observation section reads more like a set of informal demonstrations than a dataset that another researcher could reproduce or audit. Table 1 lists broad categories and usefulness but the analysis needs operational definitions: what counts as evidence for “motivation impact-user reception-- inclusion/exclusion rules; and at least a couple of example coded excerpts per category without that, it’s hard to assess analytic rigor (and easy for the analysis to drift into narrative summary. Key details are missing who coded the transcripts whether coding was iterative; how disagreements (if multiple coders) were resolved whether you used any qualitative software and what steps you took to reduce confirmation bias. Even if there was only one coder, you can strengthen trustworthiness via reflexive memos, an audit trail, and peer debriefing.

Author Response

Review 1: The English could be improved to more clearly express the research.

Dear Editor,

We would like to express our sincere gratitude for your detailed, precise, and highly constructive review. We truly appreciate your insightful comments and wish every editorial process were as supportive as yours.

We believe we have incorporated all the requested changes to the best of our ability. The revisions are as follows:

1 - A table has been added identifying the key individuals interviewed in each media outlet. We have also clarified the rationale for the sample selection and its size.

2 - The interview protocol has been included, together with an explanation of how the interview guide was developed. This guide is attached as an appendix. To minimize confirmation bias, we have added a section describing the guide’s development process, including its theoretical foundations and references to previous studies.

3 - The chatbot observation process has been repeated. Given that several months had passed since the initial observation, we considered it advisable to update the data. The new observation produced similar results; however, in response to your suggestion, we have made this information more visible and included an example of the rubric used for evaluation.

4 - In the content analysis section, we have clarified how the analytical categories were defined. A table now details this explanation and the supporting evidence for each category. We have also added information on conflict resolution and inter-coder reliability.

5 - Finally, the English language throughout the manuscript has been improved to meet the journal’s quality standards.

We hope these revisions will be sufficient to demonstrate the article’s suitability for publication. Once again, we sincerely thank you for your valuable feedback and kind assistance.

Sincerely,

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript addresses a timely and relevant topic and is generally well-organized and clearly written. The research question, methodology, and the positioning of the findings in relation to the existing literature are presented in a clear and comprehensible manner. However, the study is significantly weakened by the absence of a clearly articulated theoretical framework, which limits its ability to provide a deeper analytical understanding of the issue under investigation, or a significant theoretical contribution.

To be suitable for publication, the research must be grounded in an appropriate theoretical lens. This theoretical perspective should be embedded into the very backbone of the manuscript, ideally informing the research structure, and the findings must be interpreted rigorously through this framework. I strongly encourage the authors to explore and integrate such theoretical pathways to deepen the discussion and elevate the manuscript's scholarly contribution.

Author Response

Dear Editor,

We would like to express our sincere gratitude for your detailed and highly constructive review.

You are absolutely right in noting the need for a stronger and more clearly articulated theoretical framework. We acknowledge that overlooking this aspect in the initial version was a mistake on our part, as we underestimated its importance. In the revised manuscript, we have now provided a detailed explanation of the theoretical approach, which is grounded in figurational sociology — a perspective developed by scholars such as Norbert Elias.

Moreover, we have substantially strengthened the theoretical framework by incorporating the contributions of several additional authors whose work enriches the analysis.

We hope that these revisions have enhanced the article’s theoretical depth and will satisfactorily address any remaining concerns.

Once again, we sincerely thank you for your valuable guidance and kind understanding.

With best regards.

Reviewer 3 Report

Comments and Suggestions for Authors

The article follows a standard structure. It addresses a current topic, the current state of research is adequate—although it needs improvement—the methodology is employed qualitatively (in the study of two cases), and the conclusions are coherent. The work is well-designed, but addressing the objectives requires a robust methodology. It relies heavily on semi-structured interviews, which may provide a partial or insufficient view of the complexity of chatbots' impact on journalism—specifically, the users have interaction with newspaper content through them.

The work is interesting and publishable, but it needs to strengthen its current state of research and incorporate a focus group of users and experts to contribute their perspectives and yield more robust results.

In any case, I understand that this methodological reinforcement could be optional if the author(s) adequately argue the validity of their observations and effectively integrate the interview findings. What needs improvement is the state of the art, comparing it with current benchmark work in the field. As an example:Perreault, G., & Ohme, J. (2025). ChatBots as Artificial Intermediaries? Adaptation to Artificial Intelligence in Newsrooms. Journalism Studies, 26(15), 1914–1935. https://doi.org/10.1080/1461670X.2025.2567894

Wu, S. (2024). Journalists as individual users of artificial intelligence: Examining journalists’ “value-motivated use” of ChatGPT and other AI tools within and without the newsroom.Journalism, 0(0).https://doi.org/10.1177/14648849241303047

Junzhe Gao, Abdullah Promise Opute, Caroline Jawad, Meng Zhan,
The influence of artificial intelligence chatbot problem solving on customers’ continued usage intention in e-commerce platforms: an expectation-confirmation model approach,
Journal of Business Research,Volume 200,2025,115661,ISSN 0148-2963,
https://doi.org/10.1016/j.jbusres.2025.115661.
The article, in any case, is publishable with changes.

Author Response

Dear Editor,

Please allow us to express our sincere appreciation for the very positive reception of our article. Your recommendation for its acceptance is truly encouraging and serves as strong motivation for us to continue improving our work.

We are deeply grateful for your thoughtful suggestions, all of which we have fully implemented. Thanks to your feedback and that of the other reviewers, our theoretical framework has been enriched with all the references listed below.

Once again, we extend our heartfelt thanks. It has been a genuine pleasure to receive your valuable feedback.

Sincerely,

Adams, J. (20249 Examining Ethical and Social Implications of Digital Mental Health Technologies Through Expert Interviews and Sociotechnical Systems Theory. DISO 3, 24. https://doi.org/10.1007/s44206-024-00110-5

Ali, M., Wood‐Harper, T., & Wood, B. (2024). Understanding the technical and social paradoxes of learning management systems usage in higher education: A sociotechnical perspective. Systems Research and Behavioral Science, 41(1), 134–152.
https://doi.org/10.1002/sres.2945

Asadi A. R. and Hemadi R., Design and implementation of a chatbot for e-commerce, Information Communication Technology and Doing Business. (2018) 1–10.

Chand, S. P. (2025). Methods of Data Collection in Qualitative Research: Interviews, Focus Groups, Observations, and Document Analysis. Advances in Educational Research and Evaluation, 6(1), 303-317.
https://doi.org/10.25082/AERE.2025.01.001

Dahlman, S., Gulbrandsen, I. T., & Just, S. N. (2021). Algorithms as organizational figuration: The sociotechnical arrangements of a fintech start-up. Big Data & Society, 8(1). https://doi.org/10.1177/20539517211026702

Dengel, A.; Gehrlein, R.; Fernes, D.; Görlich, S.; Maurer, J.; Pham, H.H.; Großmann, G.; Eisermann, N.D. (2023) Qualitative Research Methods for Large Language Models: Conducting Semi-Structured Interviews with ChatGPT and BARD. Computer Science Education. Informatics 10, 78. https://doi.org/10.3390/informatics10040078

Denny, E., & Weckesser, A. (2022). How to do qualitative research? Qualitative research methods. BJOG: An International Journal of Obstetrics and Gynaecology, 129(7), 1166–1171. https://doi.org/10.1111/1471-0528.17150

Elias, N. (1995). Technization and civilization. Theory, Culture & Society, 12(3), 7–42. https://doi.org/10.1177/026327695012003002

Gao, J., Opute, A. P., Jawad, C., & Zhan, M. (2025). The influence of artificial intelligence chatbot problem solving on customers’ continued usage intention in e-commerce platforms: an expectation-confirmation model approach. Journal of Business Research, 200, 115661.
https://doi.org/10.1016/j.jbusres.2025.115661

Hepp, A., Loosen, W., Dreyer, S., Jarke, J., Kannengießer, S., Katzenbach, C., Malaka, R., Pfadenhauer, M., Puschmann, C., & Schulz, W. (2023). ChatGPT, LaMDA, and the hype around communicative AI: The automation of communication as a field of research in media and communication studies. Human-Machine Communication, 6, 41-63. https://doi.org/10.30658/hmc.6.4

Kallio, H., Pietilä, A., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: developing a framework for a qualitative semi-structured interview guide. Journal of advanced nursing, 72 (12), 2954-2965.
https://doi.org/10.1111/jan.13031

Knott, E., Rao, A., Summers, K., & Teeger, C. (2022). Interviews in the social sciences. Nature Reviews Methods Primers, 2.
https://doi.org/10.1038/s43586-022-00150-6

Joshi H., Agarwal V., Ghodke A., Gupta D., and Gaikwad S., Proposal of chat based automated system for online shopping, American Journal of Neural Networks and Applications. (2017) 3, no. 1, 1–4, https://doi.org/10.11648/j.ajnna.20170301.11

Mayring, Phillip (2014). Qualitative Content Analysis. Theoretical Foundation, Basic Procedures and Software Solution. Klagenfurt, Austria: Autoedition.
https://qualitative-content-analysis.org/wp-content/uploads/Mayring2014QualitativeContentAnalysis.pdf

Møller, L. A. (2024). Designing Algorithmic Editors: How Newspapers Embed and Encode Journalistic Values into News Recommender Systems. Digital Journalism, 12(7), 926–944. https://doi-org.bucm.idm.oclc.org/10.1080/21670811.2023.2215832

Oguntosin, V., & Olomo, A. (2021). Development of an E‐Commerce Chatbot for a University Shopping Mall. Applied Computational Intelligence and Soft Computing, 2021(1), 6630326.
https://onlinelibrary.wiley.com/doi/10.1155/2021/6630326

Perreault, G., & Ohme, J. (2025). ChatBots as Artificial Intermediaries? Adaptation to Artificial Intelligence in Newsrooms. Journalism Studies, 26(15), 1914–1935. https://doi.org/10.1080/1461670X.2025.2567894

Webster, J. Sociotechnical Imaginaries in a Postdigital World: Teachers’ Perceptions of Digital Citizenship Education. Postdigit Sci Educ 7, 770–787 (2025).
https://doi.org/10.1007/s42438-025-00557-w

Wu, S. (2024). Journalists as individual users of artificial intelligence: Examining journalists’ “value-motivated use” of ChatGPT and other AI tools within and without the newsroom. Journalism, 27(2), 388-406. https://doi-org.bucm.idm.oclc.org/10.1177/14648849241303047

Zanzotto, F. (2019). Human-in-the-Loop Artificial Intelligence. Journal of Artificial Intelligence Research, 64 (1): 243–252.
https://doi.org/10.48550/arXiv.1710.08191

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have addressed all reviewer comments satisfactorily, and the revised manuscript reflects the requested changes. Although the revisions are noted, the manuscript would still benefit from a thorough English-language edit to improve academic tone and precision. The results section should be presented in a clearer, and more readable format