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Journals / Alphanumeric Journal / 2025 / Volume: 13 Issue: 2

Evaluation of the effect of mobile applications on corporate reputation with artificial intelligence through user comments: E-Government case

Mehmet Kayakuş(AKDENİZ ÜNİVERSİTESİ)0000-0003-0394-5862
Dilşad Erdoğan(AKDENIZ UNIVERSITY)0000-0002-9117-5994
Pages
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Publication Date
2025-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal the importance of mobile applications for the sustainability of digital public services.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines the impact of e-government mobile applications on corporate reputation through user comments.
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Key Findings & Data:Today, when digitalisation is accelerating, public services offered through mobile applications directly affect user experiences and shape the reputation of institutions. 2000 user comments from the Google Play Store were analysed using artificial intelligence methods, text mining, and sentiment analysis techniques.
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Conclusion & Impact:The findings reveal the importance of mobile applications for the sustainability of digital public services.
🔬Design:Research Article
📍Scope:2000 Verileri
⚙️Variables:Corporate reputation · mobile application · e-government

Abstract

This study examines the impact of e-government mobile applications on corporate reputation through user comments. Today, when digitalisation is accelerating, public services offered through mobile applications directly affect user experiences and shape the reputation of institutions. 2000 user comments from the Google Play Store were analysed using artificial intelligence methods, text mining, and sentiment analysis techniques. It was determined that 45% of the comments were positive, 15% were negative, and 40% were neutral. Positive comments indicate that the application has a positive user perception in general. However, some users were dissatisfied due to technical problems. As a result of text mining, the most frequently mentioned words and phrases of users were analysed, and feedback was categorised through sentiment analysis. In this process, WordNet was used to extract word frequencies, TextBlob was applied to classify user comments into positive, negative, and neutral categories, and Seaborn visualisations such as word clouds were employed to illustrate the findings. The findings reveal the importance of mobile applications for the sustainability of digital public services. It is emphasised that the technical performance of the application should be improved to increase user satisfaction and strengthen institutional reputation.

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Cite This Article

Kayakuş, M., Erdoğan, D. (2025). Evaluation of the effect of mobile applications on corporate reputation with artificial intelligence through user comments: E-Government case. Alphanumeric Journal, Volume: 13 Issue: 2. https://doi.org/10.17093/alphanumeric.1713959

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References (58)

  1. Aggarwal, C. C. (2012). Mining Text Streams. In Mining Text Data (pp. 297–321). Springer US. https://doi.org/10.1007/978-1-4614-3223-4_9
  2. Ahuja, S., & Dubey, G. (2017). Clustering and sentiment analysis on Twitter data. 2017 2nd International Conference on Telecommunication and Networks (TEL-NET), 1–5. https://doi.org/10.1109/tel-net.2017.8343568
  3. Al Ali, F., Stephens, M., & Pereira, V. (2023). Government e-Services and Reputation. In Doing Business in the Middle East (pp. 235–252). Routledge. https://doi.org/10.4324/9781003005766-19
  4. Al-Besher, A., & Kumar, K. (2022). Use of artificial intelligence to enhance e-government services. Measurement: Sensors, 24, 100484. https://doi.org/10.1016/j.measen.2022.100484
  5. Alkan, Ö., & Ünver, Ş. (2020). Türkiye'de E-Devlet Hizmetlerinin Kullanımını Etkileyen Faktörlerin Analizi. Atatürk Üniversitesi İktisadi Ve İdari Bilimler Dergisi. https://doi.org/10.16951/atauniiibd.757571
  6. Ateş, H., & Yavuz, Ö. (2019). Elektronik Devletten Mobil Devlete Geçiş: Fırsatlar, Tehditler ve Uygulama Örnekleri. Bilgi Ekonomisi Ve Yönetimi Dergisi, 14(2), 155–177.
  7. Bertot, J. C., Jaeger, P. T., & Grimes, J. M. (2010). Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies. Government Information Quarterly, 27(3), 264–271. https://doi.org/10.1016/j.giq.2010.03.001
  8. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
  9. Çankaya Kurnaz, S. (2024). Türkiye'nin e-devlet olgunluk düzeyi: BM e-devlet gelişmişlik endeksi üzerinden bir değerlendirme. Yönetim Bilimleri Dergisi. https://doi.org/10.35408/comuybd.1513698
  10. Carter, L., & Bélanger, F. (2005). The utilization of e-government services: citizen trust, innovation and acceptance factors⁎. Information Systems Journal, 15(1), 5–25. https://doi.org/10.1111/j.1365-2575.2005.00183.x
  11. Castilla, R., Pacheco, A., & Franco, J. (2023). Digital government: Mobile applications and their impact on access to public information. Softwarex, 22, 101382. https://doi.org/10.1016/j.softx.2023.101382
  12. Çelen, F. K., Çelik, A., & Seferoğlu, S. S. (2011). Türkiye'deki e-Devlet Uygulamalarının Değerlendirilmesi. XIII. Akademik Bilişim Konferansı Bildirileri, 59–67.
  13. Chun, R. (2005). Corporate reputation: Meaning and measurement. International Journal of Management Reviews, 7(2), 91–109. https://doi.org/10.1111/j.1468-2370.2005.00109.x
  14. Dede, A. (2024). Dijital vatandaşlığın dokuz boyutu ve e-devlet uygulamaları. EKEV Akademi Dergisi, 97, 15–27. https://doi.org/10.17753/sosekev.1365162
  15. Dijkmans, C., Kerkhof, P., & Beukeboom, C. J. (2015). A stage to engage: Social media use and corporate reputation. Tourism Management, 47, 58–67. https://doi.org/10.1016/j.tourman.2014.09.005
  16. Dowling, G. (2006). Reputation risk: it is the board's ultimate responsibility. Journal of Business Strategy, 27(2), 59–68. https://doi.org/10.1108/02756660610650055
  17. Feldman, R., & Sanger, J. (2006). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press. https://doi.org/10.1017/cbo9780511546914
  18. Fombrun, C. J. (1996). Reputation: Realizing Value from the Corporate Image. Harvard Business School Press.
  19. Fombrun, C. J., & Riel, C. B. M. v. (2004). Fame & fortune: how successful companies build winning reputations. Pearson Education.
  20. Gebauer, J., Tang, Y., & Baimai, C. (2007). User requirements of mobile technology: results from a content analysis of user reviews. Information Systems and E-Business Management, 6(4), 361–384. https://doi.org/10.1007/s10257-007-0074-9
  21. GooglePlay. (2024). E-Devlet Kapısı. https://play.google.com/store/apps/details?id=tr.gov.turkiye.edevlet.kapisi&hl=tr
  22. Gupta, K. P., Singh, S., & Bhaskar, P. (2016). Citizen adoption of e-government: a literature review and conceptual framework. Electronic Government, An International Journal, 12(2), 160. https://doi.org/10.1504/eg.2016.076134
  23. Gupta, S., & Mathur, N. (2024). User-centred exploration of m-governance adoption: identifying and analysing determinants. 18. https://doi.org/10.1108/TG-01-2024-0027
  24. Hakimi, M., Salem, M. H., Miskinyar, M. S., & Sazish, B. (2023). Integrating Artificial Intelligence into E-Government: Navigating Challenges, Opportunities, and Policy Implications. International Journal of Academic and Practical Research, 2(2), 11–21.
  25. Heeks, R. (2006). Implementing and Managing eGovernment: An International Text. SAGE Publications Ltd. https://doi.org/10.4135/9781446220191
  26. Heimerl, F., Lohmann, S., Lange, S., & Ertl, T. (2014). Word Cloud Explorer: Text Analytics Based on Word Clouds. 2014 47th Hawaii International Conference on System Sciences, 1833–1842. https://doi.org/10.1109/hicss.2014.231
  27. Kavut, S. (2024). Toplumların Dijital Dönüşüm Aracı Olarak Yapay Zekâ Çalışmaları: Türkiye'nin ve Türk Devletleri Teşkilatının Yapay Zekâ Kullanımı Üzerine Bir Analiz. Erciyes İletişim Dergisi, 11(1), 325–344. https://doi.org/10.17680/erciyesiletisim.1346576
  28. Kayakuş, M., & Yiğit Açıkgöz, F. (2023). Twitter'da Makine Öğrenmesi Yöntemleriyle Sahte Haber Tespiti. Abant Sosyal Bilimler Dergisi, 23(2), 1017–1027. https://doi.org/10.11616/asbi.1266179
  29. Kim, J., & Lennon, S. J. (2013). Effects of reputation and website quality on online consumers' emotion, perceived risk and purchase intention: Based on the stimulus-organism-response model. Journal of Research in Interactive Marketing, 7(1), 33–56. https://doi.org/10.1108/17505931311316734
  30. Liu, B. (2012). Sentiment Analysis and Opinion Mining (1st ed. 2012). Springer International Publishing.
  31. Loria, S., Keen, P., Honnibal, M., Yankovsky, R., Karesh, D., & Dempsey, E. (2014, ). Textblob: Simplified Text Processing. https://textblob.readthedocs.io/en/dev/
  32. Melin, U., Axelsson, K., & Söderström, F. (2016). Managing the development of e-ID in a public e-service context: Challenges and path dependencies from a life-cycle perspective. Transforming Government: People, Process and Policy, 10(1), 72–98. https://doi.org/10.1108/tg-11-2013-0046
  33. Miller, G. A. (1995). WordNet: a lexical database for English. Communications of the ACM, 38(11), 39–41. https://doi.org/10.1145/219717.219748
  34. Moon, M. J. (2002). The Evolution of E-Government among Municipalities: Rhetoric or Reality?. Public Administration Review, 62(4), 424–433. https://doi.org/10.1111/0033-3352.00196
  35. Naghizade, K., Karakethüdaoğlu, M., & Akbiyik, A. (2018, ). Mobil Uygulama Geliştirmede Geri Bildirimin Önemi: Türkiye E-Devlet Örneği. International Congress on Economics and Business 2018.
  36. Nam, W., Lee, J., & Jang, B.-C. (2022). Text summarization of dialogue based on BERT. Journal of the Korea Society of Computer and Information, 27(8), 41–47. https://doi.org/10.9708/jksci.2022.27.08.041
  37. Naralan, A. (2010). E-Devlet'e Etki Eden Faktörler. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 12(2), 457–468.
  38. Ojo, A., Janowski, T., & Awotwi, J. (2013). Enabling development through governance and mobile technology. Government Information Quarterly, 30, S32–S45. https://doi.org/10.1016/j.giq.2012.10.004
  39. Özdemir, E., & Türkoğlu, İ. (2022). Yazılım Güvenlik Açıklarının Evrişimsel Sinir Ağları (CNN) ile Sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34(2), 517–529. https://doi.org/10.35234/fumbd.1076870
  40. Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000011
  41. Ramos, J. E. (2003). Using TF-IDF to Determine Word Relevance in Document Queries. Proceedings of the First Instructional Conference on Machine Learning, 133–142. https://api.semanticscholar.org/CorpusID:14638345
  42. Rana, N. P., Dwivedi, Y. K., & Williams, M. D. (2013). Analysing challenges, barriers and CSF of egov adoption. Transforming Government: People, Process and Policy, 7(2), 177–198. https://doi.org/10.1108/17506161311325350
  43. Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89, 14–46. https://doi.org/10.1016/j.knosys.2015.06.015
  44. Schonlau, M., Guenther, N., & Sucholutsky, I. (2017). Text Mining with n-gram Variables. The Stata Journal: Promoting Communications on Statistics and Stata, 17(4), 866–881. https://doi.org/10.1177/1536867x1801700406
  45. Shareef, M. A., Kumar, V., Kumar, U., & Dwivedi, Y. K. (2011). e-Government Adoption Model (GAM): Differing service maturity levels. Government Information Quarterly, 28(1), 17–35. https://doi.org/10.1016/j.giq.2010.05.006
  46. Sial, A. H., Rashdi, S. Y. S., & Khan, A. H. (2021). International Journal of Advanced Trends in Computer Science and Engineering, 10(1), 277–281. https://doi.org/10.30534/ijatcse/2021/391012021
  47. Sri, V. R., Niharika, C., Maneesh, K., & Ismail, D. M. (2019). Sentiment Analysis of Patients' Opinions in Healthcare using Lexicon-based Method. International Journal of Engineering and Advanced Technology, 9(1), 6977–6981. https://doi.org/10.35940/ijeat.a2141.109119
  48. Sucu, İ. (2022). Kurumsal Sosyal Sorumluluk Kampanyalarının Marka İmajına Etkisi: Turkcell Markası Örneği. Yalova Sosyal Bilimler Dergisi, 12(2), 140–147.
  49. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics, 37(2), 267–307. https://doi.org/10.1162/coli_a_00049
  50. Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163–173. https://doi.org/10.1002/asi.21662
  51. Tosun, A. (2024). Kamu Yönetiminde Hizmet Sunumunda Bir Paradigma Değişimi: E-Devlet. International Journal of Social Sciences, 8(33), 137–154. https://doi.org/10.52096/usbd8.33.10
  52. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology1. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
  53. Viegas, F., Wattenberg, M., & Feinberg, J. (2009). Participatory Visualization with Wordle. IEEE Transactions on Visualization and Computer Graphics, 15(6), 1137–1144. https://doi.org/10.1109/tvcg.2009.171
  54. Vrabie, C. (2023). E-Government 3.0: An AI Model to Use for Enhanced Local Democracies. Sustainability, 15(12), 9572. https://doi.org/10.3390/su15129572
  55. Waskom, M. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021. https://doi.org/10.21105/joss.03021
  56. Yıldırım, S., & Yıldız, T. (2018). Türkçe için karşılaştırmalı metin sınıflandırma analizi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(5), 879–886.
  57. Yılmaz, H., & Yumuşak, S. (2021). Açık Kaynak Doğal Dil İşleme Kütüphaneleri. İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 3(1), 81–85. https://doi.org/10.47769/izufbed.879217
  58. Yürük, E., & Öztaş, N. (2017). E-Devlet Ana Kapıları Üzerine İnceleme: Türkiye ve Seçilmiş Ülkeler. Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 22(Kayfor 15 Özel Sayısı), 2133–2157.