IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i22p9681-d1515479.html
   My bibliography  Save this article

Brand Reputation and Trust: The Impact on Customer Satisfaction and Loyalty for the Hewlett-Packard Brand

Author

Listed:
  • Fatma Yiğit Açikgöz

    (Department of Marketing and Advertising, Social Sciences Vocational School, Akdeniz University, Antalya 07058, Türkiye)

  • Mehmet Kayakuş

    (Department of Management Information Systems, Faculty of Social and Human Sciences, Akdeniz Unversity, Antalya 07800, Türkiye)

  • Bianca-Ștefania Zăbavă

    (Department of Biotechnical Systems, Faculty of Biotechnical Systems Engineering, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania)

  • Onder Kabas

    (Department of Machine, Technical Science Vocational School, Akdeniz University, Antalya 07070, Türkiye)

Abstract

Reputation is shaped depending on factors such as the quality of products and services offered by a brand to its stakeholders, its reliability, and its innovative aspect in the eyes of stakeholders. The sustainability of a brand reputation depends on the brand creating a positive perception by fulfilling its social responsibilities and maintaining this perception in the long term. In this study, the brand reputation of Hewlett-Packard (HP) computers is evaluated through customer reviews. The data set in the study consists of 2012 customer reviews obtained from Hepsiburada, one of the most widely used e-commerce platforms in Turkey. Sentiment analysis and text mining artificial intelligence methods were used in the study. For sentiment analysis, the Naive Bayes method, which is one of the machine learning methods, was used, and the comments were divided into three groups as positive, negative, and neutral. In the study, 82% of the customer comments were positive, 11% were negative, and 7% were neutral. The fact that most of the comments consist of positive sentiments shows that HP Computer has a positive reputation in the eyes of stakeholders consisting of customers. Comments consisting of negative and neutral emotions show the aspects that the brand needs to improve. In the study, the text mining method emphasises the prominent features of the brand in the comments. This study makes an important contribution to the reputation assessment of brands and to ensuring sustainable brand reputation.

Suggested Citation

  • Fatma Yiğit Açikgöz & Mehmet Kayakuş & Bianca-Ștefania Zăbavă & Onder Kabas, 2024. "Brand Reputation and Trust: The Impact on Customer Satisfaction and Loyalty for the Hewlett-Packard Brand," Sustainability, MDPI, vol. 16(22), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9681-:d:1515479
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/22/9681/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/22/9681/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hidayatus Sibyan & Jozef Svajlenka & Hermawan Hermawan & Nasyiin Faqih & Annisa Nabila Arrizqi, 2022. "Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    2. Rui Song & Wen Shi & Wanyi Qin & Xingjian Xue & Hongzhou Jin, 2024. "Exploring Passengers’ Emotions and Satisfaction: A Comparative Analysis of Airport and Railway Station through Online Reviews," Sustainability, MDPI, vol. 16(5), pages 1-23, March.
    3. Mehmet Kayakuş & Fatma Yiğit Açikgöz & Mirela Nicoleta Dinca & Onder Kabas, 2024. "Sustainable Brand Reputation: Evaluation of iPhone Customer Reviews with Machine Learning and Sentiment Analysis," Sustainability, MDPI, vol. 16(14), pages 1-17, July.
    4. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    5. Ugur Bilgin & Selin Soner Kara, 2024. "Identification of Perceived Challenges in the Green Energy Transition by Turkish Society through Sentiment Analysis," Sustainability, MDPI, vol. 16(8), pages 1-22, April.
    6. Mehmet Kayakuş & Fatma Yiğit Açıkgöz, 2022. "Classification of News Texts by Categories Using Machine Learning Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 10(2), pages 155-166, December.
    7. Shangfeng Jiang & Fujun Xiao, 2024. "How Teachers Contribute to the Sustainability of the University Brand: Evidence from China," Sustainability, MDPI, vol. 16(9), pages 1-16, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fahrettin Kayan & Yasemin Bilişli & Mehmet Kayakuş & Fatma Yiğit Açıkgöz & Agah Başdeğirmen & Meltem Güler, 2025. "Analysing Sustainability and Green Energy with Artificial Intelligence: A Turkish English Social Media Perspective," Sustainability, MDPI, vol. 17(5), pages 1-23, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diwanji, Vaibhav S. & Cortese, Juliann, 2020. "Contrasting user generated videos versus brand generated videos in ecommerce," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    2. Shijie Lu & Xin (Shane) Wang & Neil Bendle, 2020. "Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry," Management Science, INFORMS, vol. 66(5), pages 2140-2162, May.
    3. Goyal, Sanjeev & Heidari, Hoda & Kearns, Michael, 2019. "Competitive contagion in networks," Games and Economic Behavior, Elsevier, vol. 113(C), pages 58-79.
    4. Inyoung Chae & Andrew T. Stephen & Yakov Bart & Dai Yao, 2017. "Spillover Effects in Seeded Word-of-Mouth Marketing Campaigns," Marketing Science, INFORMS, vol. 36(1), pages 89-104, January.
    5. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    6. Duan, Yongrui & Liu, Tonghui & Mao, Zhixin, 2022. "How online reviews and coupons affect sales and pricing: An empirical study based on e-commerce platform," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    7. Steven M. Shugan, 2005. "Brand Loyalty Programs: Are They Shams?," Marketing Science, INFORMS, vol. 24(2), pages 185-193.
    8. Marc Bourreau & Michel Gensollen & François Moreau & Patrick Waelbroeck, 2013. "“Selling less of more?” The impact of digitization on record companies," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 37(3), pages 327-346, August.
    9. K. Sudhir, 2018. "Editorial—Introducing A New Section— Marketing Science: Frontiers," Marketing Science, INFORMS, vol. 37(1), pages 1-4, January.
    10. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    11. Liu, Yizao & Rui, Huaxia, 2014. "Social Media and Consumer Engagement in the Carbonated Soft Drinks Market," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169815, Agricultural and Applied Economics Association.
    12. Esther Calderon-Monge & Domingo Ribeiro-Soriano, 2024. "The role of digitalization in business and management: a systematic literature review," Review of Managerial Science, Springer, vol. 18(2), pages 449-491, February.
    13. Yufei Zhang & Clay M. Voorhees & G. Tomas M. Hult, 2024. "Dynamic interplays between online reviews and marketing promotions," Journal of the Academy of Marketing Science, Springer, vol. 52(6), pages 1820-1841, November.
    14. Atabek Atayev & Maarten Janssen, 2024. "Information Acquisition And Diffusion In Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(2), pages 729-753, May.
    15. Rohit Aggarwal & Ram Gopal & Alok Gupta & Harpreet Singh, 2012. "Putting Money Where the Mouths Are: The Relation Between Venture Financing and Electronic Word-of-Mouth," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 976-992, September.
    16. Sheng Tun Li & Thuong Thi Pham & Hui Chi Chuang & Zhi-Wei Wang, 2016. "Does reliable information matter? Towards a trustworthy co-created recommendation model by mining unboxing reviews," Information Systems and e-Business Management, Springer, vol. 14(1), pages 71-99, February.
    17. André Luis Araujo da Fonseca & Paula Castro Pires de Souza Chimenti & Maribel Carvalho Suarez, 2023. "Using deep learning language models as scaffolding tools in interpretive research," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 27(Vol. 27 N), pages 230021-2300.
    18. Inman, J. Jeffrey & Nikolova, Hristina, 2017. "Shopper-Facing Retail Technology: A Retailer Adoption Decision Framework Incorporating Shopper Attitudes and Privacy Concerns," Journal of Retailing, Elsevier, vol. 93(1), pages 7-28.
    19. Pauwels, Koen & Aksehirli, Zeynep & Lackman, Andrew, 2016. "Like the ad or the brand? Marketing stimulates different electronic word-of-mouth content to drive online and offline performance," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 639-655.
    20. Pooja Katyal & Reetika Sehgal & Anand Kumar Gupta, 2025. "The impact of online consumer reviews on purchase intentions for electronic products," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(9), pages 3178-3197, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9681-:d:1515479. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.