Research Article
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The Effect of Mobile Applications of Supermarket Chains on Corporate Reputation: Evaluation with Sentiment Analysis and Text Mining Methods

Year 2025, Volume: 16 Issue: 45, 177 - 193, 28.02.2025
https://doi.org/10.21076/vizyoner.1505641

Abstract

Corporate reputation is a concept that expresses how an organisation is perceived and evaluated by all its stakeholders. A brand with a strong corporate reputation is perceived as reliable, reputable, and successful. Corporate reputation affects the brand's relationships with various stakeholders such as customers, business partners, employees, and society. In the study, the impact of mobile applications of supermarket chains on corporate reputation is analysed and evaluated through the opinions of customers using these applications. The study analyses how factors such as ease of use, functionality, customer satisfaction, and reliability of mobile applications shape the overall reputation of supermarket chains. To make this evaluation, hypotheses are developed to determine whether there is a significant relationship between corporate reputation and customer satisfaction with mobile applications, and hypotheses are tested with appropriate analysis methods. In this process, the data obtained from customer comments are analysed, and the focus is on reaching reliable and scientific results. In line with the feedback of the participants, it is determined that the user-friendliness and smooth operation of mobile applications positively affect corporate reputation by increasing customer satisfaction. In addition, it is determined that technical problems in applications or inadequacies in customer services may damage the reputation of the organisation. The study findings reveal that investments made by supermarket chains in their mobile applications and the performance of these applications have a significant impact on corporate reputation. It is recommended that organizations enhance customer experience and improve application quality to foster customer loyalty and strengthen their corporate reputation

References

  • Adnan, K. ve Akbar, R. (2019). Limitations of information extraction methods and techniques for heterogeneous unstructured big data. International Journal of Engineering Business Management, 11, 1-23.
  • Anderson, E. W. ve Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125-143. Birjali, M., Kasri, M. ve Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.
  • Bleier, A. ve Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science, 34(5), 669-688.
  • Caruana, A. ve Ewing, M. T. (2010). How corporate reputation, quality, and value influence online loyalty. Journal of Business Research, 63(9-10), 1103-1110.
  • Chun, R., Da Silva, R., Davies, G. ve Roper, S. (2005). Corporate reputation and competitiveness. Routledge.
  • Chun, R. (2005). Corporate reputation: Meaning and measurement. International journal of management reviews, 7(2), 91-109.
  • Coombs, W.T. (2007). Protecting organization reputations during a crisis: The development and application of situational crisis communication theory. Corporate reputation review, 10, 163-176.
  • Cowie, J. ve Lehnert, W. (1996). Information extraction. Communications of the ACM, 39(1), 80-91.
  • Davenport, T. H. (2013). Analytics 3.0. Harvard business review, 91(12), 64-72.
  • Denecke, K. ve Reichenpfader, D. (2023). Sentiment analysis of clinical narratives: A scoping review. Journal of Biomedical Informatics, 104336.
  • Fombrun, C. ve Van Riel, C. (2003). The reputational landscape. Revealing the Corporation: Perspectives on identity, image, reputation, corporate branding, and corporate-level marketing, Taylor and Francis Group.
  • Gensler, S., Völckner, F., Liu-Thompkins, Y. ve Wiertz, C. (2013). Managing brands in the social media environment. Journal of interactive marketing, 27(4), 242-256.
  • GooglePlay. (2024). Uygulamalar. https://play.google.com/store/apps adresinden 26 Haziran 2024 tarihinde alınmıştır.
  • Hardeniya, T. ve Borikar, D. A. (2016). Dictionary based approach to sentiment analysis-a review. International Journal of Advanced Engineering, Management and Science, 2(5), 239438.
  • Hickman, L., Thapa, S., Tay, L., Cao, M. ve Srinivasan, P. (2022). Text preprocessing for text mining in organizational research: Review and recommendations. Organizational Research Methods, 25(1), 114-146.
  • Islam, T., Islam, R., Pitafi, A. H., Xiaobei, L., Rehmani, M., Irfan, M. ve Mubarak, M. S. (2021). The impact of corporate social responsibility on customer loyalty: The mediating role of corporate reputation, customer satisfaction, and trust. Sustainable Production and Consumption, 25, 123-135.
  • Kathuria, A., Gupta, A. ve Singla, R. (2021). A review of tools and techniques for preprocessing of textual data. Computational Methods and Data Engineering: Proceedings of ICMDE 2020, Volume 1, 407-422.
  • Kayakuş, M. ve Yiğit Açıkgöz, F. (2022). Classification of news texts by categories using machine learning methods. Alphanumeric Journal, 10(2), 155-166.
  • Kayakuş, M. ve Yiğit Açıkgöz, F. (2023). Twitter'da makine öğrenmesi yöntemleriyle sahte haber tespiti. Abant Sosyal Bilimler Dergisi, 23(2), 1017-1027.
  • Kim, A. J. ve Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. Journal of Business Research, 65(10), 1480-1486.
  • Kim, J., Jin, B. ve Swinney, J. L. (2009). The role of etail quality, e-satisfaction and e-trust in online loyalty development process. Journal of retailing and Consumer services, 16(4), 239-247.
  • Kim, J. ve Lee, K. H. (2019). Influence of integration on interactivity in social media luxury brand communities. Journal of Business Research, 99, 422-429.
  • Luo, X. (2007). Consumer negative voice and firm-idiosyncratic stock returns. Journal of Marketing, 71(3), 75-88.
  • Macey, J. (2010). The value of reputation in corporate finance and investment banking (and the related roles of regulation and market efficiency). Journal of Applied Corporate Finance, 22(4), 18-29.
  • Maruthu, P. J., Vimala, D. K. ve Anitha, V. (2016). Efficient feature extraction for text mining. Advances in Natural and Applied Sciences, 10(4), 64-74.
  • Mujtaba, G., Shuib, L., Idris, N., Hoo, W. L., Raj, R. G., Khowaja, K., Shaikh, K. ve Nweke, H. F. (2019). Clinical text classification research trends: systematic literature review and open issues. Expert Systems with Applications, 116, 494-520.
  • Nafea, A. A., Muayad, M. S., Majeed, R. R., Ali, A., Bashaddadh, O. M., Khalaf, M. A., Sami, A. B. N. ve Steiti, A. (2024). A brief review on preprocessing text in arabic language dataset: Techniques and challenges. Babylonian Journal of Artificial Intelligence, 2024, 46-53.
  • Parmar, B. L., Freeman, R. E., Harrison, J.S., Wicks, A. C., Purnell, L. ve De Colle, S. (2010). Stakeholder theory: The state of the art. Academy of Management Annals, 4(1), 403-445.
  • Porter, M. E. ve Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64-88.
  • Qaiser, S. ve Ali, R. (2018). Text mining: use of TF-IDF to examine the relevance of words to documents. International Journal of Computer Applications, 181(1), 25-29.
  • Ray, S., Ow, T. ve Kim, S. S. (2011). Security assurance: How online service providers can influence security control perceptions and gain trust. Decision Sciences, 42(2), 391-412.
  • Roberts, P. W. ve Dowling, G. R. (2002). Corporate reputation and sustained superior financial performance. Strategic Management Journal, 23(12), 1077-1093.
  • Rodríguez-Ibánez, M., Casánez-Ventura, A., Castejón-Mateos, F. ve Cuenca-Jiménez, P.-M. (2023). A review on sentiment analysis from social media platforms. Expert Systems with Applications, 119862. Schonlau, M., Guenther, N. ve Sucholutsky, I. (2017). Text mining with n-gram variables. The Stata Journal, 17(4), 866-881.
  • Shankar, V., Venkatesh, A., Hofacker, C. ve Naik, P. (2010). Mobile marketing in the retailing environment: current insights and future research avenues. Journal of Interactive Marketing, 24(2), 111-120.
  • Stacks, D. W., Dodd, M. D. ve Men, L. R. (2013). Corporate reputation measurement and evaluation. The handbook of communication and corporate reputation, 559-573.
  • Sweeney, J. ve Swait, J. (2008). The effects of brand credibility on customer loyalty. Journal of Retailing and Consumer Services, 15(3), 179-193.
  • Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2, 325-347.
  • Uslu, O. ve Özmen-Akyol, S. (2021). Türkçe haber metinlerinin makine öğrenmesi yöntemleri kullanılarak sınıflandırılması. Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, 2(1), 15-20.
  • Venkatesh, V., Thong, J. Y. ve Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
  • Vijayarani, S., Ilamathi, M. J. ve Nithya, M. (2015). Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication Networks, 5(1), 7-16.
  • Westerman, G., Bonnet, D. ve McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Press.
  • Xu, X., Thong, J. Y. ve Venkatesh, V. (2014). Effects of ICT service innovation and complementary strategies on brand equity and customer loyalty in a consumer technology market. Information Systems Research, 25(4), 710-729.
  • Yue, L., Chen, W., Li, X., Zuo, W. ve Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60, 617-663.
  • Zeithaml, V.A., Parasuraman, A. ve Malhotra, A. (2002). Service quality delivery through web sites: A critical review of extant knowledge. Journal of the Academy of Marketing Science, 30(4), 362-375.
  • Zucco, C., Calabrese, B., Agapito, G., Guzzi, P. H. ve Cannataro, M. (2020). Sentiment analysis for mining texts and social networks data: Methods and tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(1), e1333.

Süpermarket Zincirlerinin Mobil Uygulamalarının Kurumsal İtibarına Etkisi: Duygu Analizi ve Metin Madenciliği Yöntemleriyle Değerlendirme

Year 2025, Volume: 16 Issue: 45, 177 - 193, 28.02.2025
https://doi.org/10.21076/vizyoner.1505641

Abstract

Kurumsal itibar, bir kurumun tüm paydaşları tarafından nasıl algılandığını ve değerlendirildiğini ifade eden bir kavramdır. Güçlü bir kurumsal itibara sahip bir marka güvenilir, saygın ve başarılı olarak algılanır. Kurumsal itibar, markanın müşteriler, iş ortakları, çalışanlar ve toplum gibi çeşitli paydaşlarla ilişkilerini etkiler. Bu çalışmada, süpermarket zincirlerinin mobil uygulamalarının kurumsal itibara olan etkisi, bu uygulamaları kullanan müşterilerin görüşleri üzerinden analiz edilerek değerlendirilmiştir. Araştırmada, mobil uygulamaların kullanım kolaylığı, işlevselliği, müşteri memnuniyeti ve güvenilirlik gibi faktörlerin, süpermarket zincirlerinin genel itibarını nasıl şekillendirdiği incelenmiştir. Bu değerlendirmeyi yapabilmek için, kurumsal itibar ile mobil uygulamaya ilişkin müşteri memnuniyeti arasında anlamlı ilişki olup olmadığını belirlemek amacıyla hipotezler geliştirilmiş ve hipotezler uygun analiz yöntemleriyle test edilmiştir. Bu süreçte, müşteri yorumlarından elde edilen veriler analiz edilerek güvenilir ve bilimsel sonuçlara ulaşılmasına odaklanılmıştır. Katılımcıların geri bildirimleri doğrultusunda, mobil uygulamaların kullanıcı dostu olması ve sorunsuz çalışması, müşteri memnuniyetini artırarak kurumsal itibarı olumlu yönde etkilediği tespit edilmiştir. Bunun yanında, uygulamalarda yaşanan teknik aksaklıklar veya müşteri hizmetlerindeki yetersizliklerin, kurum itibarını zedeleyebileceği belirlenmiştir. Çalışma sonucunda, süpermarket zincirlerinin mobil uygulamalarına yaptıkları yatırımların ve bu uygulamaların performansının, kurumsal itibar üzerinde önemli etkisi olduğu ortaya konmuştur. Kurumlara, müşteri deneyimini iyileştirerek ve uygulama kalitesini artırarak hem müşteri sadakatini sağlayabilecekleri hem de kurumsal itibarlarını güçlendirebilecekleri önerisinde bulunulmaktadır.

References

  • Adnan, K. ve Akbar, R. (2019). Limitations of information extraction methods and techniques for heterogeneous unstructured big data. International Journal of Engineering Business Management, 11, 1-23.
  • Anderson, E. W. ve Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125-143. Birjali, M., Kasri, M. ve Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.
  • Bleier, A. ve Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science, 34(5), 669-688.
  • Caruana, A. ve Ewing, M. T. (2010). How corporate reputation, quality, and value influence online loyalty. Journal of Business Research, 63(9-10), 1103-1110.
  • Chun, R., Da Silva, R., Davies, G. ve Roper, S. (2005). Corporate reputation and competitiveness. Routledge.
  • Chun, R. (2005). Corporate reputation: Meaning and measurement. International journal of management reviews, 7(2), 91-109.
  • Coombs, W.T. (2007). Protecting organization reputations during a crisis: The development and application of situational crisis communication theory. Corporate reputation review, 10, 163-176.
  • Cowie, J. ve Lehnert, W. (1996). Information extraction. Communications of the ACM, 39(1), 80-91.
  • Davenport, T. H. (2013). Analytics 3.0. Harvard business review, 91(12), 64-72.
  • Denecke, K. ve Reichenpfader, D. (2023). Sentiment analysis of clinical narratives: A scoping review. Journal of Biomedical Informatics, 104336.
  • Fombrun, C. ve Van Riel, C. (2003). The reputational landscape. Revealing the Corporation: Perspectives on identity, image, reputation, corporate branding, and corporate-level marketing, Taylor and Francis Group.
  • Gensler, S., Völckner, F., Liu-Thompkins, Y. ve Wiertz, C. (2013). Managing brands in the social media environment. Journal of interactive marketing, 27(4), 242-256.
  • GooglePlay. (2024). Uygulamalar. https://play.google.com/store/apps adresinden 26 Haziran 2024 tarihinde alınmıştır.
  • Hardeniya, T. ve Borikar, D. A. (2016). Dictionary based approach to sentiment analysis-a review. International Journal of Advanced Engineering, Management and Science, 2(5), 239438.
  • Hickman, L., Thapa, S., Tay, L., Cao, M. ve Srinivasan, P. (2022). Text preprocessing for text mining in organizational research: Review and recommendations. Organizational Research Methods, 25(1), 114-146.
  • Islam, T., Islam, R., Pitafi, A. H., Xiaobei, L., Rehmani, M., Irfan, M. ve Mubarak, M. S. (2021). The impact of corporate social responsibility on customer loyalty: The mediating role of corporate reputation, customer satisfaction, and trust. Sustainable Production and Consumption, 25, 123-135.
  • Kathuria, A., Gupta, A. ve Singla, R. (2021). A review of tools and techniques for preprocessing of textual data. Computational Methods and Data Engineering: Proceedings of ICMDE 2020, Volume 1, 407-422.
  • Kayakuş, M. ve Yiğit Açıkgöz, F. (2022). Classification of news texts by categories using machine learning methods. Alphanumeric Journal, 10(2), 155-166.
  • Kayakuş, M. ve Yiğit Açıkgöz, F. (2023). Twitter'da makine öğrenmesi yöntemleriyle sahte haber tespiti. Abant Sosyal Bilimler Dergisi, 23(2), 1017-1027.
  • Kim, A. J. ve Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. Journal of Business Research, 65(10), 1480-1486.
  • Kim, J., Jin, B. ve Swinney, J. L. (2009). The role of etail quality, e-satisfaction and e-trust in online loyalty development process. Journal of retailing and Consumer services, 16(4), 239-247.
  • Kim, J. ve Lee, K. H. (2019). Influence of integration on interactivity in social media luxury brand communities. Journal of Business Research, 99, 422-429.
  • Luo, X. (2007). Consumer negative voice and firm-idiosyncratic stock returns. Journal of Marketing, 71(3), 75-88.
  • Macey, J. (2010). The value of reputation in corporate finance and investment banking (and the related roles of regulation and market efficiency). Journal of Applied Corporate Finance, 22(4), 18-29.
  • Maruthu, P. J., Vimala, D. K. ve Anitha, V. (2016). Efficient feature extraction for text mining. Advances in Natural and Applied Sciences, 10(4), 64-74.
  • Mujtaba, G., Shuib, L., Idris, N., Hoo, W. L., Raj, R. G., Khowaja, K., Shaikh, K. ve Nweke, H. F. (2019). Clinical text classification research trends: systematic literature review and open issues. Expert Systems with Applications, 116, 494-520.
  • Nafea, A. A., Muayad, M. S., Majeed, R. R., Ali, A., Bashaddadh, O. M., Khalaf, M. A., Sami, A. B. N. ve Steiti, A. (2024). A brief review on preprocessing text in arabic language dataset: Techniques and challenges. Babylonian Journal of Artificial Intelligence, 2024, 46-53.
  • Parmar, B. L., Freeman, R. E., Harrison, J.S., Wicks, A. C., Purnell, L. ve De Colle, S. (2010). Stakeholder theory: The state of the art. Academy of Management Annals, 4(1), 403-445.
  • Porter, M. E. ve Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64-88.
  • Qaiser, S. ve Ali, R. (2018). Text mining: use of TF-IDF to examine the relevance of words to documents. International Journal of Computer Applications, 181(1), 25-29.
  • Ray, S., Ow, T. ve Kim, S. S. (2011). Security assurance: How online service providers can influence security control perceptions and gain trust. Decision Sciences, 42(2), 391-412.
  • Roberts, P. W. ve Dowling, G. R. (2002). Corporate reputation and sustained superior financial performance. Strategic Management Journal, 23(12), 1077-1093.
  • Rodríguez-Ibánez, M., Casánez-Ventura, A., Castejón-Mateos, F. ve Cuenca-Jiménez, P.-M. (2023). A review on sentiment analysis from social media platforms. Expert Systems with Applications, 119862. Schonlau, M., Guenther, N. ve Sucholutsky, I. (2017). Text mining with n-gram variables. The Stata Journal, 17(4), 866-881.
  • Shankar, V., Venkatesh, A., Hofacker, C. ve Naik, P. (2010). Mobile marketing in the retailing environment: current insights and future research avenues. Journal of Interactive Marketing, 24(2), 111-120.
  • Stacks, D. W., Dodd, M. D. ve Men, L. R. (2013). Corporate reputation measurement and evaluation. The handbook of communication and corporate reputation, 559-573.
  • Sweeney, J. ve Swait, J. (2008). The effects of brand credibility on customer loyalty. Journal of Retailing and Consumer Services, 15(3), 179-193.
  • Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2, 325-347.
  • Uslu, O. ve Özmen-Akyol, S. (2021). Türkçe haber metinlerinin makine öğrenmesi yöntemleri kullanılarak sınıflandırılması. Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, 2(1), 15-20.
  • Venkatesh, V., Thong, J. Y. ve Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
  • Vijayarani, S., Ilamathi, M. J. ve Nithya, M. (2015). Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication Networks, 5(1), 7-16.
  • Westerman, G., Bonnet, D. ve McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Press.
  • Xu, X., Thong, J. Y. ve Venkatesh, V. (2014). Effects of ICT service innovation and complementary strategies on brand equity and customer loyalty in a consumer technology market. Information Systems Research, 25(4), 710-729.
  • Yue, L., Chen, W., Li, X., Zuo, W. ve Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60, 617-663.
  • Zeithaml, V.A., Parasuraman, A. ve Malhotra, A. (2002). Service quality delivery through web sites: A critical review of extant knowledge. Journal of the Academy of Marketing Science, 30(4), 362-375.
  • Zucco, C., Calabrese, B., Agapito, G., Guzzi, P. H. ve Cannataro, M. (2020). Sentiment analysis for mining texts and social networks data: Methods and tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(1), e1333.
There are 45 citations in total.

Details

Primary Language Turkish
Subjects Public Relations, Reputation Management
Journal Section Research Articles
Authors

Fatma Yiğit Açıkgöz 0000-0003-3748-1496

Mehmet Kayakuş 0000-0003-0394-5862

Publication Date February 28, 2025
Submission Date June 27, 2024
Acceptance Date January 26, 2025
Published in Issue Year 2025 Volume: 16 Issue: 45

Cite

APA Yiğit Açıkgöz, F., & Kayakuş, M. (2025). Süpermarket Zincirlerinin Mobil Uygulamalarının Kurumsal İtibarına Etkisi: Duygu Analizi ve Metin Madenciliği Yöntemleriyle Değerlendirme. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 16(45), 177-193. https://doi.org/10.21076/vizyoner.1505641

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