Modeling the Factors Affecting the Image of the Tourist Destination and its Effect on Tourist Satisfaction A case study of Tehran city

Document Type : Research Article

Authors

1 Department of Business Management, Khorramshahr-persian Gulf International Branch, Islamic Azad University, Khorramshahr, Iran

2 Business Management, Roodehen Branch, Islamic Azad University, Roodehen, Iran

Abstract

A B S T R A C T
Tourism as a global activity today has doubled in importance due to the overlap of the three principles of job creation and income generation, tourist satisfaction, and environmental protection. Since different tourist destinations are competitive in attracting tourists, improving and promoting effective indicators in attracting tourists to tourist destinations is of great importance. This study aims to model the factors affecting the image of tourist destinations and their impact on tourist satisfaction in Tehran. The research method is descriptive-analytical, which has been done by the survey method.  The statistical population of this study is tourists visiting Tehran. In order to determine the number of research samples, due to the unknown number of the statistical population, the required number of samples was 354 questionnaires. In order to analyze the data, a factor analysis statistical test, one-sample T in SPSS software, and structural equations in Amos Graphics software environment were used. The results show that using a factor analysis test to measure the effects of image components of tourist destinations on tourist satisfaction, 6 factors whose specific values were greater than 1 were extracted. These six identified factors were able to 63% of the variance of component image effects. Explain tourism on the satisfaction of tourists. Also, the results of the one-sample t-test to calculate the final score of the level of satisfaction with the image of tourist destinations, with a value of (2.39), were in an undesirable and unacceptable situation. Also, the results of second-order structural equation analysis showed that the overall image of the destination factor has the highest factor load on tourists' satisfaction with 60%. The final results of the structural equation model show that the effect of cognitive image on the overall image is equal to (0.21), the effect of the emotional image on the overall image is equal to (0.18), the effect of the persuasive image on the overall image is equal to (0.30), and the effect of the overall image on the satisfaction of tourists according to the route analysis model is equal to (0.27).
Extended Abstract
Introduction
Tourism is one of the most important economic activities around the world, and it is an essential part of the economy of many countries at different stages of development. Spreading tourism on a global scale as a strategic part of socioeconomic and regional development requires planning has highlighted more sustainable tourism; its development limit has yet to be reached, so its relevance has increased in recent decades, and by 2019 it has become a significant factor for several local and national economies. The tourism industry is expected to grow at a rate of 3.3% per year and reach 1.8 billion tourists by 2030. This growth in the number of tourists visiting different destinations in the world increases the competition between destinations, which shows that a destination depends on its ability to maintain a competitive advantage. It should be noted that since March 2020, the situation has worsened, and there has been a significant reduction in expected revenue in the tourism sector due to the COVID-19 pandemic. The World Tourism Organization (UNWTO) announced the negative effects of this pandemic on the tourism industry, with a 74% decrease in international tourism in 2020.
 
Methodology
The present study is of descriptive-analytical type, which has been done by survey method and is based explicitly on structural equation modeling. Structural equations are a comprehensive statistical approach to test hypotheses related to the relationships of observable and latent variables and determine the extent to which data support the theoretical model presented by the researcher. The research has two parts: documentary and quantitative. In the documentary part, the research literature was collected through a library study. In the quantitative part, which includes field survey, the data collection tool is a questionnaire. The research questionnaire was designed based on the indicators, and the Likert scale evaluation method (very low: 1 to very high: 5) was used to score the indicators. The statistical population of this study is tourists visiting Tehran. Since the exact statistics of the number of tourists entering the tourist destinations are unknown, the Cochran sampling method was used for an indefinite and unlimited comprehensive with a 95% confidence level; the estimated number of samples is 354 questionnaires. Internal consistency and validity of the designed questionnaires using Cronbach's alpha coefficient were used. For this purpose, 60 questionnaires were completed and pre-tested, and the obtained alpha coefficient generally equals 0.975. Also, to analyze the data, a factor analysis test was used. A single sample T from the tourist destination image and structural equations in Amos Graphics software were exerted to measure the tourists' satisfaction.
 
Results and discussion
In order to measure the effects of image components of tourist destinations on tourist satisfaction in the form of several important and limited factors, a factor analysis statistical test has been used. The reason for choosing factor analysis is to evaluate the image of tourist destinations and summarize the research indicators to explain the image of the tourist destination better. In this regard, in order to determine the appropriateness of data related to the set of analyzed variables regarding the image of tourist destinations, the Bartlett test and KMO index were exerted. The significance of the Bartlett test with the number 5646.5466 at the level of 99% confidence and the appropriate value of KMO (0.898) indicates the correlation and appropriateness of the variables for factor analysis. It should be noted that factors have been accepted to determine the number of factors in this study. Whose specific value is greater than 1. Accordingly, 6 factors whose specific values ​​were greater than 1 were extracted. The results show that the six identified factors have been able to explain 63% of the variance of the effects of image components of tourist destinations on tourist satisfaction. The percentage of explanation of each of the identified factors are in order of importance: the first factor is 10.257%, the second factor is 2.661%, the third factor is 2.141%, the fourth factor is 16.776, and the fifth factor is 1.458%, and the sixth factor is 252. / Is allocated to itself.
 
Conclusion
Then, the structural equations in the Amos Graphics software environment were used for first-order factor analysis for all four factors obtained from factor analysis, all of which had a very good fit. After identifying the indicators and image indicators of tourist destinations, first, based on the conceptual model of the research, the destination image indices, and satisfaction, the second-order factor model, a kind of structural equation model, was used. The results of the second-order model test show that the variables of the cognitive image, emotional image, and persuasive image can measure the overall image of tourist destinations, among which cognitive image and emotional image, each with a coefficient of 54%, have the most significant impact on the image. According to the research results of Hernandez-Mogulon et al. (2018), Stylus et al. (2017), and Zhang et al. (2018) showed that the cognitive and emotional image index has the greatest effect on the overall image in attracting and Tourist satisfaction has been aligned.
In order to explain the relationship between tourism destination image and tourist satisfaction, the structural model based on the research's conceptual model shows that the overall image's effect on tourist satisfaction is estimated to be 0.27, which can be said to be effective. The overall picture of tourist satisfaction has been positive and significant and aligns with the results of research by Kiani Feyzabadi et al. (2015) and Shahbazi et al. (2016). It has been found that the results of the study are in line with the results of the research of Hernandez-Mogulon et al. (2018), Stylus et al. (2017), and Zhang et al. (2018). The result is equal to or (0.25), which according to the level of significance can be said that the effect of the persuasive image on the satisfaction of tourists has become significantly positive, which is in line with the results of Papadimitriou et al. (2018), Praiag et al. (2007), Silva Lopez et al. (2021), and Kiani Feyzabadi (2017).
 
Funding
There is no funding support.
 
Authors Contribution
All of the authors approved thecontent of the manuscript and agreed on all aspects of the work.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.

Keywords


  1. Kayani Faizabadi, Z. (2017). Effective factors on loyalty to the tourist destination with the mediating role of tourists' satisfaction (case study: Shahr Kashan). Journal of Urban Tourism, 5(4), 105-120. https://doi.org/10.22059/jut.2018.239030.366‌[In Persian].
  2. Ghafari, M., & Mam Ramezani, K. (2018). Presenting a model for investigating the effect of online recommendation communication on the intention to travel to a tourism destination. Social Studies of Tourism, 7(13), 101-124. ‌[In Persian].
  3. Karroubi, M., Ebrahimi, M., & Qasimpour, F. (2013) Investigating the relationship between customer satisfaction and loyalty in the hotel industry, a case study: 4 and 5 star hotels in Shiraz. Journal of Urban Tourism, 1(1), 97-112. ‌ https://doi.org/10.22059/jut.2014.53158 [In Persian].
  4. Ana ˜ na, ˜ E., Rodrigues, R., & Flores, L. (2018). Competitive performance as a substitute for competiveness measurement in tourism destinations: an integrative study. International Journal of Tourism Cities, 4(2), 207–219.
  5. Artiga, S., Stephens, J., & Damico, A. (2015). The impact of the coverage gap in states not expanding Medicaid by race and ethnicity. Disparities policy. The Henry J. Kaiser Family Foundation. https://doi.org/10.1016/j.seps.2016.10.006
  6. Baloglu, S., & Mccleary, K.W. (1999). “A model of destination image formation”. Annals of Tourism Research, 26 (4), 868-897. https://doi.org/10.1016/S0160-7383(99)00030-4
  7. Beerli, A., & Martin, J. D. (2004) Factors influencing destination images. Annals of Tourism Research, 31 (3), 657-681. https://doi.org/10.1016/j.annals.2004.01.010
  8. Borsekova, K., Vanova, A., & Vitalisova, K. (2017). Smart specialization for smart spatial development: innovative strategies for building competitive advantages in tourism in Slovakia. Socio economic Planning Sciences, 58, 39-50. https://doi.org/10.1016/j.seps.2016.10.004
  9. Bu, N. (2018). The 22nd session of the UNWTO general. Assembly–Special Session on Smart Tourism: Chengdu, China, Anatolia, 29(1), 143–145. https://doi.org/10.1080/13032917.2017.1393720
  10. Buhalis, D. (2000). Marketing the competitive destination of the future. Tourism Management 21(1), 97–116. https://doi.org/10.1016/S0261-5177(99)00095-3
  11. Buhalis, D., & Inversini, A. (2016). Smarttourism destinations: Ecosystems for tourism destination com-petitiveness. International Journal of Tourism Cities, 2(2), 108–124 https://doi.org/10.1108/IJTC-12-2015-0032
  12. Chen, C. F., & Chen, F. S. (2010). Experience quality, perceived value, satisfaction and behavioral intentions for heritage tourists. Tourism management, 31(1), 29-35. https://doi.org/10.1016/j.tourman.2009.02.008
  13. Chen, C. F., & Phou, S. (2013). A closer look at destination: Image, personality, relationship and loyalty. Tourism management, 36, 269-278. https://doi.org/10.1016/j.tourman.2012.11.015
  14. Chi, C. G. Q. (2011). Destination loyalty formation and travelers’ demographic characteristics: A multiple group analysis approach. Journal of Hospitality & Tourism Research, 35(2), 191-212. https://doi.org/10.1177/1096348010382233
  15. Chin, W. L., & Hampton, M. P. (2020). The relationship between destination competitiveness and residents’ quality of life: lessons from Bali. Tourism and hospitality management26(2), 311-336. https://doi.org/10.20867/thm.26.2.3
  16. Cronjé, D. F., & du Plessis, E. (2020). A review on tourism destination competitiveness. Journal of Hospitality and Tourism Management, 45, 256-265. https://doi.org/10.1016/j.jhtm.2020.06.012
  17. Djeri, L., Stamenković, P., Blešić, I., Milićević, S., & Ivkov, M. (2018). An importance-performance analysis of destination competitiveness factors: case of Jablanica district in Serbia. Economic Research-Ekonomska Istraživanja, 31(1), 811-826. https://doi.org/10.1080/1331677X.2018.1456351
  18. Dmitrović, T., Knežević Cvelbar, L., Kolar, T., Makovec Brenčič, M., Ograjenšek, I., & Žabkar, V. (2009). Conceptualizing tourist satisfaction at the destination level. International Journal of Culture. Tourism and Hospitality Research, 3(2), 116-126. https://doi.org/10.1108/17506180910962122
  19. Gallarza, M.G., Saura, I.G., & Calderon Garcı ´a, H. (2001), “Measuring destination image an approach by an attribute-based analysis”, Tourism Review, 56 (1/2), 13-22. https://doi.org/10.1108/eb058352
  20. Gartner, W. C. (1993). Image formation process. Journal of Travel & Tourism Marketing, 2, 191-215. https://doi.org/10.1300/J073v02n02_12
  21. Gretzel, Ulrikh, Scarpino-Johns. (2018). Destination resilience and smart tourism destinations. Tourism Review International, 22, 263-276. https://doi.org/10.3727/154427218X15369305779065
  22. Homburg, C., Koschate, N., & Hoyer, W. D. (2006). The role of cognition and affect in the formation of customer satisfaction: a dynamic perspective. Journal of Marketing, 70(3), 21-31 https://doi.org/10.1509/jmkg.70.3.021
  23. Hossam, M. (2016). Evaluation of tourists' satisfaction with rural tourism destinations (case study: villages of Foman city), Quarterly of Human Settlements Planning Studies, 41, 819-803. [In Persian] 
  24. Khuong, M. N. & Luan, P. D. (2015). Factors Affecting Tourists' Satisfaction towards Nam Cat Tien National Park, Vietnam-A Mediation Analysis of Perceived Value. International Journal of Innovation. Management and Technology, 6(4), 238. DOI: 10.7763/IJIMT.2015.V6.609
  25. Lai, K., & Li, Y. (2012). “Core-periphery structure of destination image: concept, evidence and implication”. Annals of Tourism Research, 39 (3), 1359-1379. https://doi.org/10.1016/j.annals.2012.02.008
  26. Liao, C. S., & Chuang, H. K. (2020). Tourist preferences for package tour attributes in tourism destination design and development. Journal of Vacation Marketing, 26(2), 230-246. https://doi.org/10.1177/1356766719880250
  27. Lopes, H. S., Remoaldo, P. C., Ribeiro, V., & Martín-Vide, J. (2021). Perceptions of human thermal comfort in an urban tourism destination–A case study of Porto (Portugal). Building and Environment205, 108246. https://doi.org/10.1016/j.buildenv.2021.108246
  28. Luštický, M., & Štumpf, P. (2021). Leverage points of tourism destination competitiveness dynamics. Tourism Management Perspectives38, 100792. https://doi.org/10.1016/j.tmp.2021.100792
  29. Elnabawi, M.H., & Hamza, N. (2020). Behavioural perspectives of outdoor thermal comfort in urban areas: a critical review. Atmosphere, 11 (51). https://doi.org/10.3390/atmos11010051
  30. Baruti, M.M., Johansson, E., & Yahia, M.W. (2020). Urbanites’ outdoor thermal comfort in the informal urban fabric of warm-humid Dar es Salaam, Tanzania, Sustain. Cities Soc, 62, 102380. https://doi.org/10.1016/j.scs.2020.102380
  31. Matiei Langroudi, S. H., Ferdowsi, S., & Shahmohammadi, H. R. (2016) Explaining the effects of tourist satisfaction in marine tourism marketing (case study: coastal areas of Golestan province). Regional Planning Quarterly, 26, 54- 21. ‌ dor/20.1001.1.22516735.1396.7.26.4.9 [In Persian].
  32. Mogollón, J. M., Duarte, P. A., & Folgado-Fernández, J. A. (2018). The contribution of cultural events to the formation of the cognitive and affective images of a tourist destination. Journal of Destination Marketing & Management, 8, 170-178. https://doi.org/10.1016/j.jdmm.2017.03.004
  33. Murphy, L. (1999). “Australia’s image as a holiday destination-perceptions of backpacker visitors”. Journal of Travel & Tourism Marketing, 8 (3), 21-45. https://doi.org/10.1300/J073v08n03_02
  34. Neal, J. D. & Gursoy, D. (2008). A multifaceted analysis of tourism satisfaction. Journal of Travel Research, 47(1), 53-62. https://doi.org/10.1177/0047287507312434
  35. Osman, Z. & Sentosa, I. (2013). Mediating Effect of Customer Satisfaction on Service Quality and Customer Loyalty Relationship in Malaysian Rural Tourism. International Journal of Economics and Management Studies, 2(1), 25-37. https://ssrn.com/abstract=2196815
  36. Osti, L. Disegna, M. & Brida, J. G. (2012). Repeat visits and intentions to revisit a sporting event and its nearby destinations. Journal of Vacation Marketing, 18(1), 31-42. https://doi.org/10.1177/1356766711428803
  37. Papadimitriou, D., Apostolopoulou, A., & Kaplanidou, K. (2013). Destination personality, affective image, and behavioral intentions in domestic urban tourism. Journal of Travel Research, 54, 302-315. doi:10.1177/ 0047287513516389
  38. Papadimitriou, D., Kaplanidou, K., & Apostolopoulou, A. (2018). Destination image components and word-of-mouth intentions in urban tourism: A multigroup approach. Journal of Hospitality & Tourism Research, 42(4), 503-527. https://doi.org/10.1177/1096348015584443
  39. Pechlaner, H., Kozak, M., & Volgger, M. (2014). Destination leadership: A new paradigm for tourist destinations?. Tourism Review, 69(1), 1–9. https://doi.org/10.1108/TR-09-2013-0053
  40. Pereira, H. c., salgueiro, M. De F. & Rito, P. (2016) Online purchase determinants of loyalty: The mediating effect of satisfaction in tourism. Journal of Retailing and Consumer Services, 30, 279-291. https://doi.org/10.1016/j.jretconser.2016.01.003
  41. Qu, H., Kim, L. H., & Im, H. H. (2011). A model of destination branding: Integrating the concepts of the branding and destination image. Tourism Management, 32, 465-476. https://doi.org/10.1016/j.tourman.2010.03.014
  42. Reynolds, W.H. (1965). “The role of the consumer in image building”. California Management Review, 7 (3), 69-76. https://doi.org/10.2307/41165634
  43. Sanai Moghadam, S., Rahmani, B., Murid Sadat, P., & Taheri, Nabiullah. (2019). Modeling the effects of the environmental quality of tourist destinations on the satisfaction of tourists in rural areas, a case study: the central part of Dana city. Tourism Planning and Development, 9(33), 47-70. https://doi.org/10.22080/jtpd.2020.18286.3239‌[In Persian]. 
  44. Seyfi, S., & Hall, C. M. (2020). Political transitions and transition events in a tourism destination. International Journal of Tourism Research, 22(4), 493-506. https://doi.org/10.1002/jtr.2351
  45. Shahbazi Shiran. H., & Esmi, R. (2019). The impact of destination image and satisfaction on the loyalty of heritage tourists: presenting a conceptual model (case study: Sheikh Safiuddin Ardabili World Collection). Journal of Social Studies of Tourism, 8 (16), 266-243. https://doi.org/10.22059/jut.2018.239030.366 ‌[In Persian].
  46. Smith SLJ and Xiao H (2008) Culinary tourism supply chains: a preliminary examination. Journal of Travel Research 46(3), 289–299. https://doi.org/10.1177/0047287506303981
  47. Stylos, N., Bellou, V., Andronikidis, A., & Vassiliadis, C. A. (2017). Linking the dots among destination images, place attachment, and revisit intentions: A study among British and Russian tourists. Tourism Management, 60, 15-29. https://doi.org/10.1016/j.tourman.2016.11.006
  48. Tasci, A. D. A., Gartner, W. C., & Cavusgil, S. T. (2007). Conceptualization and operationalization of destination image. Journal of Hospitality & Tourism Research, 31, 194-223. https://doi.org/10.1177/1096348006297290
  49. Volgger, M., & Pechlaner, H. (2014). Requirements for destination management organizations in destination governance: Understanding DMO success. Tourism Management, 41, 64-75. https://doi.org/10.1016/j.tourman.2013.09.001
  50. World Tourism Organization. (2019). International tourism highlights 2019 Edition. Retrieved from https://www.e-unwto.org/doi/pdf/10.18111/9789284421152
  51. Zhang, C., & Xiao, H. (2014). Destination development in China: towards an effective model of explanation. Journal of Sustainable Tourism 22(2), 214–233. https://doi.org/10.1080/09669582.2013.839692
  52. Zhang, H., Wu, Y., & Buhalis, D. (2018). A model of perceived image, memorable tourism experiences and revisit intention. Journal of destination marketing & management, 8, 326-336. https://doi.org/10.1016/j.jdmm.2017.06.004