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Turkish Customers’ Perceptions of Dining Experience in Asian Restaurants in Istanbul

Yıl 2022, Cilt: 8 Sayı: 1, 27 - 48, 30.06.2022
https://doi.org/10.26650/jot.2022.8.1.1047512

Öz

This paper analyzes the perceptions of Turkish customers regarding their experiences at Asian restaurants in Istanbul. Within the scope of the study, 1,348 online reviews written in Turkish on TripAdvisor for Asian restaurants operating in Istanbul were analyzed with the latent Dirichlet allocation (LDA) algorithm and sentiment analysis. As a result of the analysis nine dimensions affecting the experiences of Turkish customers at Asian restaurants were determined, four of which were specific to the restaurant (view, staff, place, order) and five of which were related to food (real taste, food, sauce and spice, sushi, flavor). It was found that flavor and food are the main dimensions that positively affect Turkish customers’ Asian restaurant experiences. Order was found to be the most important dimension that negatively affects them. To my knowledge, this is the first study interpreting the perception of Turkish customers’ experiences of Asian restaurants through online reviews in Turkish.

Kaynakça

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  • Arora, R., & Singer, J. (2006). Cognitive and affective service marketing strategies for fine dining resturant managers. Journal of Small Business Strategy, 17(1), 51–62.
  • Arvela, P. (2013). Ethnic food: The Other in Ourselves. In D. Sanderson, & M. Crouch (Eds.), Food: Expressions and impressions (pp. 45–56). Oxford, United Kingdom: Inter-Disciplinary Press.
  • Barrett, L. F. (1996). Hedonic tone, perceived arousal, and item desirability: Three components of self-reported mood. Cognition & Emotion, 10(1), 47–68. https://doi.org/10.1080/026999396380385
  • Bholowalia, P., & Kumar, A. (2014). EBK-means: A clustering technique based on elbow method and k-means in WSN. International Journal of Computer Applications, 105(9), 17–24. https://doi.org/10.5120/18405-9674
  • Blei, D. M., & Lafferty, J. (2009). Topic Models. In A. Srivastava, & M. Sahami (Eds.), Text Mining: Classification, Clustering, and Applications (pp. 71–94). London: Taylor and Francis.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
  • Büschken, J., & Allenby, G. M. (2016). Sentence-based text analysis for customer reviews. Marketing Science 35(6), 953–975. http://dx.doi.org/10.1287/mksc.2016.0993
  • Chicco, D., & Jurman, G. (2020). The advantages of the matthews correlation coefficient (mcc) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 1–13. https://doi.org/10.1186/s12864-019-6413-7
  • Debortoli, S., Müller, O., Junglas, I., & vom Brocke, J. (2016). Text mining for information systems researchers: An annotated topic modeling tutorial. Communications of the Association for Information Systems, 39(1).
  • Ebster, C., & Guist, I. (2004). The role of authenticity in ethnic restaurants. Journal of Foodservice Business Research, 7(2), 41–52. https://doi.org/10.1300/J369v07n02_04
  • Fanelli, R. M., & Di Nocera, A. (2018). Customer perceptions of japanese foods in Italy. Journal of Ethnic Foods, 5(3), 167–176. https://doi.org/10.1016/j.jef.2018.07.001
  • Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
  • Ferdman, R. A. (2015). Asian food: The fastest growing food in the world. The Washington Post, Washington. Retrieved from https://www.washingtonpost.com/news/wonk/wp/2015/02/03/the-fastest-growing-food-in-the-world/ 31.05.2021
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  • Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(1), 5228–5235. https://doi.org/10.1073pnas.0307752101
  • Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent Dirichlet allocation. Tourism Management, 59, 467–483. https://doi.org/10.1016/j.tourman.2016.09.009
  • Gustafsson, I.-B. (2004). Culinary arts and meal science–a new scientific research discipline. Food Service Technology, 4(1), 9–20. https://doi.org/10.1111/j.1471-5740.2003.00083.x
  • Gustafsson , I.-B., Öström, Å., Johansson, J., & Mossberg, L. (2006). The five aspects meal model: A tool for developing meal services in restaurants. Journal of Foodservice, 17, 84–93. https://doi.org/10.1111/j.1745-4506.2006.00023.x
  • Ha, J., & Jang, S.C.S. (2010). Effects of service quality and food quality: The moderating role of atmospherics in an ethnic restaurant segment. International Journal of Hospitality Management, 29(3), 520–529. https://doi.org/10.1016/j.ijhm.2009.12.005
  • Hallowell, I. (1955). Culture and Experience. Philadelphia: University of Pennsylvania Press.
  • Hindle, A., Ernst, N. A., Godfrey, M. W., & Mylopoulos, J. (2013). Automated topic naming. Empirical Software Engineering, 18(6), 1125–1155. https://doi.org/10.1007/s10664-012-9209-9
  • Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and Organizations, Software of the Mind, Intercultural Cooperation and its Importance for Survival. New York: Mcgraw-Hill.
  • Hua, T., Lu, C.-T., Choo, J., & Reddy, C. K. (2020). Probabilistic topic modeling for comparative analysis of document collections. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(2), 1–27. https://doi.org/10.1145/3369873
  • Huang, J., Rogers, S., & Joo, E. (2014). Improving restaurants by extracting subtopics from Yelp reviews. Social Media Expo 2014.
  • Jang, S. C. S., Ha, A., & Silkes, C. A. (2009). Perceived attributes of asian foods: From the perspective of the American customers. International Journal of Hospitality Management, 28(1), 63–70. https://doi.org/10.1016/j.ijhm.2008.03.007
  • Jang, S. C. S., & Ha, A. (2009). Asian foods in the U.S: Developments, customer profiles, and experiences. Journal of Foodservice Business Research, 12(4), 403–412. https://doi.org/10.1080/15378020903344372
  • Jia, S. S. (2020). Motivation and satisfaction of Chinese and US tourists in restaurants: A cross-cultural text mining of online reviews. Tourism Management, 78, 104071. https://doi.org/10.1016/j.tourman.2019.104071
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  • Josiam, B., Sohail, M. S., & Monteiro, P. (2007). Curry cuisine: Perceptions of Indian restaurants in Malaysia. Tourismos: An International Journal of Tourism, 2(2), 25–38.
  • Kirilenko, A. P., Stepchenkova, S. O., Kim, H., & Li, X. R. (2018). Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research, 57(8), 1012–1025. https://doi.org/10.1177/0047287517729757
  • La Pastina, A. C., & Straubhaar, J. D. (2005). Multiple proximities between television genres and audiences: The Schism between telenovelas’ global distribution and local consumption. International Communication Gazette, 67(3), 271–288. https://doi.org/10.1177/0016549205052231
  • Le, T. H., Arcodia, C., Novais, M. A., Kralj, A., & Phan, T. C. (2021). Exploring the multi-dimensionality of authenticity in dining experiences using online reviews. Tourism Management, 85, 104292. https://doi.org/10.1016/j.tourman.2021.104292
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  • 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
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Yıl 2022, Cilt: 8 Sayı: 1, 27 - 48, 30.06.2022
https://doi.org/10.26650/jot.2022.8.1.1047512

Öz

Kaynakça

  • Aktas-Polat, S., & Polat, S. (2021). Discovery of factors affecting tourists’ fine dining experiences at five-star hotel restaurants in Istanbul. British Food Journal. https://doi.org/10.1108/BFJ-02-2021-0138
  • Alba, J., & Chattopadhyay, A. (1986). Salience effects in brand recall. Journal of Marketing Research, 23(4), 363–369.
  • Alghamdi, R., & Alfalqi, K. (2015). A survey of topic modeling in text mining. International Journal of Advanced Computer Science and Applications, 6(1), 147–153.
  • Arora, R., & Singer, J. (2006). Cognitive and affective service marketing strategies for fine dining resturant managers. Journal of Small Business Strategy, 17(1), 51–62.
  • Arvela, P. (2013). Ethnic food: The Other in Ourselves. In D. Sanderson, & M. Crouch (Eds.), Food: Expressions and impressions (pp. 45–56). Oxford, United Kingdom: Inter-Disciplinary Press.
  • Barrett, L. F. (1996). Hedonic tone, perceived arousal, and item desirability: Three components of self-reported mood. Cognition & Emotion, 10(1), 47–68. https://doi.org/10.1080/026999396380385
  • Bholowalia, P., & Kumar, A. (2014). EBK-means: A clustering technique based on elbow method and k-means in WSN. International Journal of Computer Applications, 105(9), 17–24. https://doi.org/10.5120/18405-9674
  • Blei, D. M., & Lafferty, J. (2009). Topic Models. In A. Srivastava, & M. Sahami (Eds.), Text Mining: Classification, Clustering, and Applications (pp. 71–94). London: Taylor and Francis.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
  • Büschken, J., & Allenby, G. M. (2016). Sentence-based text analysis for customer reviews. Marketing Science 35(6), 953–975. http://dx.doi.org/10.1287/mksc.2016.0993
  • Chicco, D., & Jurman, G. (2020). The advantages of the matthews correlation coefficient (mcc) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 1–13. https://doi.org/10.1186/s12864-019-6413-7
  • Debortoli, S., Müller, O., Junglas, I., & vom Brocke, J. (2016). Text mining for information systems researchers: An annotated topic modeling tutorial. Communications of the Association for Information Systems, 39(1).
  • Ebster, C., & Guist, I. (2004). The role of authenticity in ethnic restaurants. Journal of Foodservice Business Research, 7(2), 41–52. https://doi.org/10.1300/J369v07n02_04
  • Fanelli, R. M., & Di Nocera, A. (2018). Customer perceptions of japanese foods in Italy. Journal of Ethnic Foods, 5(3), 167–176. https://doi.org/10.1016/j.jef.2018.07.001
  • Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
  • Ferdman, R. A. (2015). Asian food: The fastest growing food in the world. The Washington Post, Washington. Retrieved from https://www.washingtonpost.com/news/wonk/wp/2015/02/03/the-fastest-growing-food-in-the-world/ 31.05.2021
  • Germann Molz, J. (20079. Eating difference: The cosmopolitan mobilities of culinary tourism. Space and Culture, 10(1), 77–93. https://doi.org/10.1177/1206331206296383
  • Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(1), 5228–5235. https://doi.org/10.1073pnas.0307752101
  • Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent Dirichlet allocation. Tourism Management, 59, 467–483. https://doi.org/10.1016/j.tourman.2016.09.009
  • Gustafsson, I.-B. (2004). Culinary arts and meal science–a new scientific research discipline. Food Service Technology, 4(1), 9–20. https://doi.org/10.1111/j.1471-5740.2003.00083.x
  • Gustafsson , I.-B., Öström, Å., Johansson, J., & Mossberg, L. (2006). The five aspects meal model: A tool for developing meal services in restaurants. Journal of Foodservice, 17, 84–93. https://doi.org/10.1111/j.1745-4506.2006.00023.x
  • Ha, J., & Jang, S.C.S. (2010). Effects of service quality and food quality: The moderating role of atmospherics in an ethnic restaurant segment. International Journal of Hospitality Management, 29(3), 520–529. https://doi.org/10.1016/j.ijhm.2009.12.005
  • Hallowell, I. (1955). Culture and Experience. Philadelphia: University of Pennsylvania Press.
  • Hindle, A., Ernst, N. A., Godfrey, M. W., & Mylopoulos, J. (2013). Automated topic naming. Empirical Software Engineering, 18(6), 1125–1155. https://doi.org/10.1007/s10664-012-9209-9
  • Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and Organizations, Software of the Mind, Intercultural Cooperation and its Importance for Survival. New York: Mcgraw-Hill.
  • Hua, T., Lu, C.-T., Choo, J., & Reddy, C. K. (2020). Probabilistic topic modeling for comparative analysis of document collections. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(2), 1–27. https://doi.org/10.1145/3369873
  • Huang, J., Rogers, S., & Joo, E. (2014). Improving restaurants by extracting subtopics from Yelp reviews. Social Media Expo 2014.
  • Jang, S. C. S., Ha, A., & Silkes, C. A. (2009). Perceived attributes of asian foods: From the perspective of the American customers. International Journal of Hospitality Management, 28(1), 63–70. https://doi.org/10.1016/j.ijhm.2008.03.007
  • Jang, S. C. S., & Ha, A. (2009). Asian foods in the U.S: Developments, customer profiles, and experiences. Journal of Foodservice Business Research, 12(4), 403–412. https://doi.org/10.1080/15378020903344372
  • Jia, S. S. (2020). Motivation and satisfaction of Chinese and US tourists in restaurants: A cross-cultural text mining of online reviews. Tourism Management, 78, 104071. https://doi.org/10.1016/j.tourman.2019.104071
  • Jiao, Y., & Du, P. (2016). Performance measures in evaluating machine learning based bioinformatics predictors for classifications. Quantitative Biology, 4(4), 320–330. https://doi.org/10.1007/s40484-016-0081-2
  • Jo, Y., & Oh, A. H. (2011). Aspect and sentiment unification model for online review analysis. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, 815–824.
  • Johns, N., & Pine, R. (2002). Consumer behaviour in the food service industry: A review. International Journal of Hospitality Management, 21(2), 119–134. https://doi.org/10.1016/S0278-4319(02)00008-7
  • Josiam, B., Sohail, M. S., & Monteiro, P. (2007). Curry cuisine: Perceptions of Indian restaurants in Malaysia. Tourismos: An International Journal of Tourism, 2(2), 25–38.
  • Kirilenko, A. P., Stepchenkova, S. O., Kim, H., & Li, X. R. (2018). Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research, 57(8), 1012–1025. https://doi.org/10.1177/0047287517729757
  • La Pastina, A. C., & Straubhaar, J. D. (2005). Multiple proximities between television genres and audiences: The Schism between telenovelas’ global distribution and local consumption. International Communication Gazette, 67(3), 271–288. https://doi.org/10.1177/0016549205052231
  • Le, T. H., Arcodia, C., Novais, M. A., Kralj, A., & Phan, T. C. (2021). Exploring the multi-dimensionality of authenticity in dining experiences using online reviews. Tourism Management, 85, 104292. https://doi.org/10.1016/j.tourman.2021.104292
  • Lee, L. E., Niode, O., Simonne, A. H., & Bruhn, C. M. (2012). Consumer perceptions on food safety in Asian and Mexican restaurants. Food Control, 26(2), 531–538. https://doi.org/10.1016/j.foodcont.2012.02.010
  • Lin, C., & He, Y. (2009). Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, 375–384. CIKM’09, November 2–6, 2009, Hong Kong, China
  • Liu, Y., & Jang, S. C. S. (2009). Perceptions of Chinese restaurants in the US: What affects customer satisfaction and behavioral intentions? International Journal of Hospitality Management, 28(3), 338–348. https://doi.org/10.1016/j.ijhm.2008.10.008
  • Lu, S., & Fine, G. A. (1995). The presentation of ethnic authenticity: Chinese food as a social accomplishment. The Sociological Quarterly, 36(3), 535–553.
  • Lupton, D. (1994). Food, memory and meaning: The symbolic and social nature of food events. Sociological Review, 42(4), 664–687. https://doi.org/10.1111/j.1467-954X.1994.tb00105.x.
  • Ma, J. E., Qu, H., Njite, D., & Chen, S. (2011). Western and Asian customers’ perception towards Chinese restaurants in the United States. Journal of Quality Assurance in Hospitality & Tourism, 12(2), 121–139. https://doi.org/10.1080/1528008X.2011.541818
  • 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
  • Min, K.-H., & Han, S. (2017). Local consumers’ perceptions and preferences for Asian ethnic foods. International Journal of Tourism Sciences, 17(3), 165–179. https://doi.org/10.1080/15980634.2017.1349628
  • Nakayama, M., & Wan, Y. (2019). Same sushi, different impressions: A cross-cultural analysis of Yelp reviews. Information Technology & Tourism, 21(2), 181–207. https://doi.org/10.1007/s40558-018-0136-5
  • Oh, M. M., & Kim, S. S. (2020). Dimensionality of ethnic food fine dining experience: An application of semantic network analysis. Tourism Management Perspectives, 35, 100719. https://doi.org/10.1016/j.tmp.2020.100719
  • Onorati, M. G., & Giardullo, P. (2020). Social media as taste re-mediators: Emerging patterns of food taste on TripAdvisor. Food, Culture & Society, 23(3), 347–365. https://doi.org/10.1080/15528014.2020.1715074
  • Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000001
  • Park, S. B., Jang, J., & Ok, C. M. (2016). Analyzing Twitter to explore perceptions of Asian restaurants. Journal of Hospitality and Tourism Technology, 7(4), 405–422. https://doi.org/10.1108/JHTT-08-2016-0042
  • Pezenka, I., & Weismayer, C. (2020). Which factors influence locals’ and visitors’ overall restaurant evaluations? International Journal of Contemporary Hospitality Management, 32(9), 2793–2812. https://doi.org/10.1108/IJCHM-09-2019-0796
  • Polat, S., & Aktas-Polat, S. (2020). Transformation of local culinary through gastronomy tourism. Sosyoekonomi, 28(43), 243–256. https://doi.org/10.17233/sosyoekonomi.2020.01.14
  • Ponnam, A., & Balaji, M. (2014). Matching visitation-motives and restaurant attributes in casual dining restaurants. International Journal of Hospitality Management, 37, 47–57. https://doi.org/10.1016/j.ijhm.2013.10.004
  • Raudenbush, B., & Capiola, A. (2012). Physiological responses of food neophobics and food neophilics to food and non-food stimuli. Appetite, 58(3), 1106–1108. https://doi.org/10.1016/j.appet.2012.02.042
  • Rhee, H. T., Yang, S. B., & Kim, K. (2016). Exploring the comparative salience of restaurant attributes: A conjoint analysis approach. International Journal of Information Management, 36(6), 1360–1370. https://doi.org/10.1016/j.ijinfomgt.2016.03.001
  • Rozin, P., & Vollmecke, T. A. (1986). Food likes and dislikes. Annual Review of Nutrition, 6(1), 433–456. https://doi.org/10.1146/annurev.nu.06.070186.002245
  • Sangkaew, N., & Zhu, H. (2020). Understanding tourists’ experiences at local markets in Phuket: An analysis of TripAdvisor reviews. Journal of Quality Assurance in Hospitality & Tourism. https://doi.org/10.1080/1528008X.2020.1848747
  • Situmeang, F., de Boer, N., & Zhang, A. (2020). Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews. International Journal of Market Research, 62(2), 195–215. https://doi.org/10.1177/1470785319863619
  • Sloan, E. A. (2001). Ethnic foods in the decade ahead. Food Technology, 55(10), 18–26.
  • Stickel, C., Ebner, M., Steinbach-Nordmann, S., Searle, G., & Holzinger, A. (2009). Emotion detection: Application of the valence arousal space for rapid biological usability testing to enhance universal access. International Conference on Universal Access in Human-Computer Interaction (pp. 615–624). Berlin, Heidelberg: Springer.
  • Sukalakamala, P., & Boyce, J. B. (2007). Customer perceptions for expectations and acceptance of an authentic dining experience in Thai restaurants. Journal of Foodservice, 18(2), 69–75. https://doi.org/10.1111/j.1745-4506.2007.00048.x
  • Sutherland, I., & Kiatkawsin, K. (2020). Determinants of guest experience in airbnb: A Topic modeling approach using LDA. Sustainability, 12(8), 3402. https://doi.org/10.3390/su12083402
  • Sutherland, I., Sim, Y., Lee, S. K., Byun, J., & Kiatkawsin, K. (2020). Topic modeling of online accommodation reviews via latent Dirichlet allocation. Sustainability, 12(5), 1821. https://doi.org/10.3390/su12051821
  • Taecharungroj, V., & Mathayomchan, B. (2019). Analysing TripAdvisor reviews of tourist attractions in Phuket, Thailand. Tourism Management, 75: 550–568. https://doi.org/10.1016/j.tourman.2019.06.020
  • Taecharungroj, V., Warnaby, G., & Parker, C. (2021). Responding to the voice of the markets: An analysis of Tripadvisor reviews of UK retail markets. Journal of Place Management and Development, 14(2), 180–200. https://doi.org/10.1108/JPMD-02-2020-0016
  • Tey, Y. S., Arsil, P., Brindal, M., Liew, S. Y., Teoh, C. T., & Terano, R. (2018). Personal values underlying ethnic food choice: Means-end evidence for Japanese food. Journal of Ethnic Foods, 5(1), 33–39. https://doi.org/10.1016/j.jef.2017.12.003
  • Turgeon, L., & Pastinelli, M. (2002). “Eat the world”: Postcolonial encounters in Quebec city’s ethnic restaurants. Journal of American Folklore, 115(456), 247–268. https://www.jstor.org/stable/4129222
  • Van den Berghe, P. L. (1984). Ethnic cuisine: Culture in nature. Ethnic and Racial Studies, 7(3), 387–397. https://doi.org/10.1080/01419870.1984.9993452
  • van Trijp, Hans, C. M., & van Kleef, E. (2008). Newness, value and new product performance. Trends in Food Science & Technology, 19(11), 562–573. https://doi.org/10.1016/j.tifs.2008.03.004
  • Vermeulen, I. E., & Seegers, D. (2009). Tried and tested: The impact of online hotel reviews on consumer consideration. Tourism Management, 30(1), 123–127. https://doi.org/10.1016/j.tourman.2008.04.008
  • Wang, W., Feng, Y., & Dai, W. (2018). Topic analysis of online reviews for two competitive products using latent Dirichlet allocation. Electronic Commerce Research and Applications, 29, 142–156. https://doi.org/10.1016/j.elerap.2018.04.003
  • Warde, A. (2000). Cultural flows and spread of ethnic restaurants. In, D. Kalb, M. Van der Land, R. Staring, B. Van Steenbergen, & N. Wilterdink (Eds.), The Ends of Globalization: Bringing Society Back In (pp. 299–316). USA: Rowman & Littlefield Inc.
  • Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9), 2839–2846. https://doi.org/10.1016/j.patcog.2015.03.009
Toplam 73 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Semra Aktas Polat 0000-0002-2324-2200

Yayımlanma Tarihi 30 Haziran 2022
Gönderilme Tarihi 26 Aralık 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 8 Sayı: 1

Kaynak Göster

APA Aktas Polat, S. (2022). Turkish Customers’ Perceptions of Dining Experience in Asian Restaurants in Istanbul. Journal of Tourismology, 8(1), 27-48. https://doi.org/10.26650/jot.2022.8.1.1047512
AMA Aktas Polat S. Turkish Customers’ Perceptions of Dining Experience in Asian Restaurants in Istanbul. Journal of Tourismology. Haziran 2022;8(1):27-48. doi:10.26650/jot.2022.8.1.1047512
Chicago Aktas Polat, Semra. “Turkish Customers’ Perceptions of Dining Experience in Asian Restaurants in Istanbul”. Journal of Tourismology 8, sy. 1 (Haziran 2022): 27-48. https://doi.org/10.26650/jot.2022.8.1.1047512.
EndNote Aktas Polat S (01 Haziran 2022) Turkish Customers’ Perceptions of Dining Experience in Asian Restaurants in Istanbul. Journal of Tourismology 8 1 27–48.
IEEE S. Aktas Polat, “Turkish Customers’ Perceptions of Dining Experience in Asian Restaurants in Istanbul”, Journal of Tourismology, c. 8, sy. 1, ss. 27–48, 2022, doi: 10.26650/jot.2022.8.1.1047512.
ISNAD Aktas Polat, Semra. “Turkish Customers’ Perceptions of Dining Experience in Asian Restaurants in Istanbul”. Journal of Tourismology 8/1 (Haziran 2022), 27-48. https://doi.org/10.26650/jot.2022.8.1.1047512.
JAMA Aktas Polat S. Turkish Customers’ Perceptions of Dining Experience in Asian Restaurants in Istanbul. Journal of Tourismology. 2022;8:27–48.
MLA Aktas Polat, Semra. “Turkish Customers’ Perceptions of Dining Experience in Asian Restaurants in Istanbul”. Journal of Tourismology, c. 8, sy. 1, 2022, ss. 27-48, doi:10.26650/jot.2022.8.1.1047512.
Vancouver Aktas Polat S. Turkish Customers’ Perceptions of Dining Experience in Asian Restaurants in Istanbul. Journal of Tourismology. 2022;8(1):27-48.