Assessing the Impact of Seven Marketing Mix Elements on Restaurant Businesses: Insights from Online Reviews
DOI:
https://doi.org/10.31289/jkbm.v10i2.11499Keywords:
Marketing Mix, Online Reviews, Content Analysis, RestaurantAbstract
This study analyzes the significance of the 7P marketing mix elements on customer perceptions and preferences for restaurants, using Google Maps reviews. Descriptive statistics and content analysis were employed to uncover customer perceptions and preferences. By analyzing 686 reviews from Kebalen Cafe, the analysis was conducted in three stages: exploring relationships among review variables, conducting descriptive statistical analysis and word visualization, and performing quantitative content analysis to identify common phrases related to customer experiences. The results indicate that product and price are the most frequently discussed elements, highlighting the importance of food quality and pricing in in-fluencing customer satisfaction. However, other elements such as place, promotion, process, people, and physical evidence received less attention. The implications for restaurant management suggest the need to increase focus on the less emphasized elements to enhance marketing strategies. Theoretically, this re-search contributes to the marketing literature by utilizing customer-generated online reviews to analyze the marketing mix elements that garner greater customer attention. Practically, these findings can serve as a basis for management to improve restaurant operations, particularly in promotion, service process enhancements, employee training, and improving the restaurant's physical appearance to enhance the overall customer experience.
References
Ali, M. A., Ting, D. H., Ahmad-ur-Rahman, M., Ali, S., Shear, F., & Mazhar, M. (2021). Effect of Online Reviews and Crowd Cues on Restaurant Choice of Customer: Moderating Role of Gender and Perceived Crowding. Frontiers in Psychology, 12. https://www.frontiersin.org/articles/10.3389/fpsyg.2021.780863
Aureliano-Silva, L., Leung, X., & Spers, E. E. (2021). The effect of online reviews on restaurant visit intentions: Applying signaling and involvement theories. Journal of Hospitality and Tourism Technology, 12(4), 672–688. https://doi.org/10.1108/JHTT-06-2020-0143
Baker, M. J., & Hashimoto, B. (2024). Expression of Customer (Dis)satisfaction in Online Restaurant Reviews: The Relationship Between Adversative Connective Constructions and Star Ratings. International Journal of Business Communication, 61(1), 148–180. https://doi.org/10.1177/23294884231200245
Barrera-Barrera, R. (2023). Identifying the attributes of consumer experience in Michelin-starred restaurants: A text-mining analysis of online customer reviews. British Food Journal, 125(13), 579–598. https://doi.org/10.1108/BFJ-05-2023-0408
Berumen Calderón, M. F., Estolano Cristerna, D., Sterling Zozoaga, A. S., & Berumen Calderón, A. R. (2021). Model to assess the selection of the optimal location for a restaurant, a quantitative approach. Case study: Theme restaurants in Cancun, Mexico. Journal of Foodservice Business Research, 24(4), 457–501. https://doi.org/10.1080/15378020.2020.1870786
Bilgihan, A., Seo, S., & Choi, J. (2018). Identifying restaurant satisfiers and dissatisfiers: Suggestions from online reviews. Journal of Hospitality Marketing & Management, 27(5), 601–625. https://doi.org/10.1080/19368623.2018.1396275
Booms, B. H., & Bitner, M. J. (1982). Marketing Services by Managing the Environment. Cornell Hotel and Restaurant Administration Quarterly, 23(1), 35–40. https://doi.org/10.1177/001088048202300107
Bowen, D. E. (2024). An organizational behavior/human resource management perspective on the roles of people in a service organization context: Frameworks and themes. Journal of Service Management, 35(1), 1–21. https://doi.org/10.1108/JOSM-10-2023-0424
Burkov, I., & Gorgadze, A. (2023). From text to insights: Understanding museum consumer behavior through text mining TripAdvisor reviews. International Journal of Tourism Cities, ahead-of-print(ahead-of-print). https://doi.org/10.1108/IJTC-05-2023-0085
Burkov, I., Gorgadze, A., & Trabskaia, I. (2023). Satisfaction dimensions influencing consumers’ behavioral intentions through structural topic modeling analysis of restaurant reviews. Consumer Behavior in Tourism and Hospitality, 18(2), 200–214. https://doi.org/10.1108/CBTH-06-2022-0126
DiPietro, R. B., & Levitt, J. (2019). Restaurant Authenticity: Factors That Influence Perception, Satisfaction and Return Intentions at Regional American-Style Restaurants. International Journal of Hospitality & Tourism Administration, 20(1), 101–127. https://doi.org/10.1080/15256480.2017.1359734
Gan, Q., Ferns, B. H., Yu, Y., & Jin, L. (2017). A Text Mining and Multidimensional Sentiment Analysis of Online Restaurant Reviews. Journal of Quality Assurance in Hospitality & Tourism, 18(4), 465–492. https://doi.org/10.1080/1528008X.2016.1250243
Ghasemaghaei, M., Eslami, S. P., Deal, K., & Hassanein, K. (2018). Reviews’ length and sentiment as correlates of online reviews’ ratings. Internet Research, 28(3), 544–563. https://doi.org/10.1108/IntR-12-2016-0394
Ihwandi, I., & Sukmana, F. H. (2022). Hotel Tua dan Ulasan Online Negatif: Apa Yang Dikatakan Pelanggan? Jurnal Master Pariwisata (JUMPA), 354–381. https://doi.org/10.24843/JUMPA.2022.v09.i01.p16
Jeaheng, Y., Al-Ansi, A., Chua, B.-L., Ngah, A. H., Ryu, H. B., Ariza-Montes, A., & Han, H. (2023). Influence of Thai Street Food Quality, Price, and Involvement on Traveler Behavioral Intention: Exploring Cultural Difference (Eastern versus Western). Psychology Research and Behavior Management, 16, 223–240. https://doi.org/10.2147/PRBM.S371806
Jin, W., Chen, Y., Yang, S., Zhou, S., Jiang, H., & Wei, J. (2023). Personalized managerial response and negative inconsistent review helpfulness: The mediating effect of perceived response helpfulness. Journal of Retailing and Consumer Services, 74, 103398. https://doi.org/10.1016/j.jretconser.2023.103398
Kim, B., & Velthuis, O. (2021). From reactivity to reputation management: Online consumer review systems in the restaurant industry. Journal of Cultural Economy, 14(6), 675–693. https://doi.org/10.1080/17530350.2021.1895280
Kim, J., Lee, M., Kwon, W., Park, H., & Back, K.-J. (2022). Why am I satisfied? See my reviews – Price and location matter in the restaurant industry. International Journal of Hospitality Management, 101, 103111. https://doi.org/10.1016/j.ijhm.2021.103111
Kim, M.-S., & Kim, J. (2018). Linking marketing mix elements to passion-driven behavior toward a brand: Evidence from the foodservice industry. International Journal of Contemporary Hospitality Management, 30(10), 3040–3058. https://doi.org/10.1108/IJCHM-10-2017-0630
Lee, J., & Kim, Y.-K. (2020). Online Reviews of Restaurants: Expectation-Confirmation Theory. Journal of Quality Assurance in Hospitality & Tourism, 21(5), 582–599. https://doi.org/10.1080/1528008X.2020.1712308
Li, H., Yu, B. X. B., Li, G., & Gao, H. (2023). Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 104707. https://doi.org/10.1016/j.tourman.2022.104707
Li, Z., Chan, C., Chen, Y.-F., Chan, W. W. H., & Im, U. L. (2023). Millennials’ Hotel Restaurant Visit Intention: An Analysis of Key Online Opinion Leaders’ Digital Marketing Content. Journal of Quality Assurance in Hospitality & Tourism, 0(0), 1–30. https://doi.org/10.1080/1528008X.2023.2219467
Liu, H., Feng, S., & Hu, X. (Simon). (2022). Process vs. outcome: Effects of food photo types in online restaurant reviews on consumers’ purchase intention. International Journal of Hospitality Management, 102, 103179. https://doi.org/10.1016/j.ijhm.2022.103179
Liu, J., Yu, Y., Mehraliyev, F., Hu, S., & Chen, J. (2022). What affects the online ratings of restaurant consumers: A research perspective on text-mining big data analysis. International Journal of Contemporary Hospitality Management, 34(10), 3607–3633. https://doi.org/10.1108/IJCHM-06-2021-0749
Mahmood, A., & Khan, H. U. (2019). Identification of critical factors for assessing the quality of restaurants using data mining approaches. The Electronic Library, 37(6), 952–969. https://doi.org/10.1108/EL-12-2018-0241
Mathayomchan, B., & Taecharungroj, V. (2020). “How was your meal?” Examining customer experience using Google maps reviews. International Journal of Hospitality Management, 90, 102641. https://doi.org/10.1016/j.ijhm.2020.102641
Nazlan, N. H., Zhang, H., Sun, J., & Chang, W. (2023). Navigating the online reputation maze: Impact of review availability and heuristic cues on restaurant influencer marketing effectiveness. Journal of Hospitality Marketing & Management, 0(0), 1–20. https://doi.org/10.1080/19368623.2023.2246471
Oliveira, B., & Casais, B. (2019). The importance of user-generated photos in restaurant selection. Journal of Hospitality and Tourism Technology, 10(1), 2–14. https://doi.org/10.1108/JHTT-11-2017-0130
Olson, E. D., & Ro, H. (2020). Company Response to Negative Online Reviews: The Effects of Procedural Justice, Interactional Justice, and Social Presence. Cornell Hospitality Quarterly, 61(3), 312–331. https://doi.org/10.1177/1938965519892902
Parsa, H. G., Kreeger, J. C., van der Rest, J.-P., Xie, L. “Karen,” & Lamb, J. (2021). Why Restaurants Fail? Part V: Role of Economic Factors, Risk, Density, Location, Cuisine, Health Code Violations and GIS Factors. International Journal of Hospitality & Tourism Administration, 22(2), 142–167. https://doi.org/10.1080/15256480.2019.1598908
Polas, M. R. H., Raju, V., Hossen, S. M., Karim, A. M., & Tabash, M. I. (2022). Customer’s revisit intention: Empirical evidence on Gen-Z from Bangladesh towards halal restaurants. Journal of Public Affairs, 22(3), e2572. https://doi.org/10.1002/pa.2572
Praesri, S., Meekun, K., Lee, T. J., & Hyun, S. S. (2022). Marketing mix factors and a business development model for street food tourism. Journal of Hospitality and Tourism Management, 52, 123–127. https://doi.org/10.1016/j.jhtm.2022.06.007
Puspa, R. D. A., Abriandi, & Tambun, S. (2023). Peran Lokasi dan Persepsi Harga terhadap Minat Beli Ulang Konsumen Mixue: Gaya Hidup sebagai Pemoderasi. JKBM (JURNAL KONSEP BISNIS DAN MANAJEMEN), 10(1), Article 1. https://doi.org/10.31289/jkbm.v10i1.10011
Sahir, S. H., & Rosmawati, R. (2020). Improve Marketing Mix for Marketing Plan Strategic in Coffeeshop Business. Management Analysis Journal, 9(4), Article 4. https://doi.org/10.15294/maj.v9i4.42613
Salehi-Esfahani, S., Ravichandran, S., Israeli, A., & Bolden III, E. (2016). Investigating Information Adoption Tendencies Based on Restaurants’ User-Generated Content Utilizing a Modified Information Adoption Model. Journal of Hospitality Marketing & Management, 25(8), 925–953. https://doi.org/10.1080/19368623.2016.1171190
Saydam, M. B., & Altun, Ö. (2023). An analysis of British Michelin-starred restaurants: Guests’ online reviews. British Food Journal, 125(11), 4214–4228. https://doi.org/10.1108/BFJ-03-2023-0236
Setiawan, A., & Sukmana, F. H. (2023). Mengurai Pengalaman Positif Tamu Saat Menginap di Sheraton Senggigi Beach Resort: Bukti Dari Ulasan TripAdvisor. Jurnal Kepariwisataan Indonesia: Jurnal Penelitian Dan Pengembangan Kepariwisataan Indonesia, 17(1), Article 1. https://doi.org/10.47608/jki.v17i12023.64-84
Shin, S., Shin, H. H., & Gim, J. (2023). How positive do testimonials on a restaurant website need to be? Impact of positivity of testimonial reviews on customers’ decision-making. International Journal of Hospitality Management, 108, 103382. https://doi.org/10.1016/j.ijhm.2022.103382
Stein, A., & Ramaseshan, B. (2016). Towards the identification of customer experience touch point elements. Journal of Retailing and Consumer Services, 30, 8–19. https://doi.org/10.1016/j.jretconser.2015.12.001
Sukmana, F. H. (2018). Pengaruh Persepsi Risiko, Variasi, Kualitas Dan Harga Produk Terhadap Sikap Konsumen. Valid: Jurnal Ilmiah, 15(1), Article 1. http://journal.stieamm.ac.id/index.php/valid/article/view/46
Sukmana, F. H., Mayani, E., & Fadah, I. (2023). Analyzing Consumer Online Reviews for Enhancing Restaurant Marketing Strategy: Applying the 7Ps Marketing Mix Framework. Proceedings of the ICON 2023 International Conference, 2, : 907-918. https://doi.org/10.32528/issh.v2i3.369
Vu, H. Q., Li, G., Law, R., & Zhang, Y. (2019). Exploring Tourist Dining Preferences Based on Restaurant Reviews. Journal of Travel Research, 58(1), 149–167. https://doi.org/10.1177/0047287517744672
Yalcinkaya, B., & Just, D. R. (2023). Comparison of Customer Reviews for Local and Chain Restaurants: Multilevel Approach to Google Reviews Data. Cornell Hospitality Quarterly, 64(1), 63–73. https://doi.org/10.1177/19389655221102388
Yang, S.-B., Hlee, S., Lee, J., & Koo, C. (2017). An empirical examination of online restaurant reviews on Yelp.com: A dual coding theory perspective. International Journal of Contemporary Hospitality Management, 29(2), 817–839. https://doi.org/10.1108/IJCHM-11-2015-0643
Zavira, C. M., Ismoyowati, D., & Yuliando, H. (2023). Korean Restaurants’ Consumer Needs Based on Marketing Mix Through the Kano Model. AGRARIS: Journal of Agribusiness and Rural Development Research, 9(1), Article 1. https://doi.org/10.18196/agraris.v9i1.184
Zhao, F., & Liu, H. (2023). Modeling customer satisfaction and revisit intention from online restaurant reviews: An attribute-level analysis. Industrial Management & Data Systems, 123(5), 1548–1568. https://doi.org/10.1108/IMDS-09-2022-0570
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