Configuration-Based Promotion: A New Approach to Destination Image Sustainability
Abstract
:1. Introduction
2. Literature Review
2.1. Dimensions of Tourist Destination Image
2.2. Evaluation Method of Tourist Destination Image
2.3. Sustainable Tourist Destination Image
2.4. Research Review
3. Research Methods
4. Research Process
4.1. Case Selection
4.2. Variable Calibration
4.3. Data Source
4.4. Analysis Process
4.4.1. Setting Anchors
4.4.2. Conditional Analysis
4.4.3. Configuration Analysis
4.4.4. Result Analysis
- (1)
- From the perspective of “segmentation–integration”
- (2)
- From the perspective of “enhancing strengths–supplementing weaknesses”
5. Discussion and Limitations
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Pérez-Tapia, G.; Almeida-García, F.; Mercadé-Melé, P. The “four core elements” as a measuring instrument: From simplicity to complexity in tourist destination. Economies 2021, 9, 53. [Google Scholar] [CrossRef]
- Nazir, M.U.; Yasin, I.; Tat, H.H. Destination image’s mediating role between perceived risks, perceived constraints, and behavioral intention. Heliyon 2021, 7, e07613. [Google Scholar] [CrossRef]
- Qu, Y.; Jia, H.Y. The measurement and analysis of destination image: A comparative study of Nanjing. Hum. Geogr. 2013, 1, 128–134. [Google Scholar]
- Ling, S.J. Image Design of Tourist Destinations; Peking University Press: Beijing, China, 2012. [Google Scholar]
- Gunn, C. Vacationscape: Designing Tourist Regions; Taylor and Francis: Washington, DC, USA, 1972. [Google Scholar]
- Zang, D.X.; Huang, J. A review of overseas studies on destination image: Based on the articles of Tourism Management and Annals of Tourism Research in the Last Decade. Tour. Sci. 2007, 21, 12–19. [Google Scholar]
- Huang, Z.F.; Li, X. On the perception and promotion pattern of tourist destination. Tour. Tribune 2002, 3, 65–70. [Google Scholar]
- Fakeye, P.C.; Crompton, J.L. Image differences between Prospective, First-Time, and Repeat Visitors to the Lower Rio Grande Valley. J. Travel Res. 1991, 30, 10–16. [Google Scholar] [CrossRef]
- Echtner, C.M.; Ritchie, J.B. The measurement of destination image: An empirical assessment. J. Travel Res. 1993, 314, 3–13. [Google Scholar] [CrossRef]
- Wang, L.; Liu, H.T.; Zhao, X.P. Research on the connotation of tourist destination image. J. Xi’an Jiaotong Univ. (Soc. Sci.) 1999, 1, 3–5. [Google Scholar]
- Gartner, W.C. Image formation process. Commun. Channel Syst. Tour. Mark. 1993, 2, 191–216. [Google Scholar] [CrossRef]
- Li, F.; Huang, Y.L.; Zheng, J.Q. Research progress and comments on image measurement methods of tourist destinations. Jiangsu Commer. Forum 2005, 11, 93–95. [Google Scholar]
- Stepchenkova, S.; Morrison, A.M. Russia’s destination image among American pleasure travelers: Revisiting Echtner and Ritchie. Tour. Manag. 2008, 29, 548–560. [Google Scholar] [CrossRef]
- Li, X.; Pan, B.; Zhang, L. The effect of online information search on image development: Insight from a mixed-methods study. J. Travel Res. 2009, 48, 45–47. [Google Scholar] [CrossRef]
- Guo, A.X.; Huang, F.C.; Yang, J. On the impacts of perceived attractiveness of destination images and revisit intentions: A case study of Xiamen. Tour. Sci. 2015, 29, 50–67. [Google Scholar]
- Da Silva, M.A.; Costa, R.A.; Moreira, A.C. The influence of travel agents and tour operators’ perspectives on a tourism destination: The case of Portuguese intermediaries on Brazil’s image. J. Hosp. Tour. Manag. 2018, 34, 93–104. [Google Scholar] [CrossRef]
- Zheng, Q.M.; Chen, Q.Y. On the different projected image and perceived image in the network context: A case study of Tianmen mountain national forest park. J. Neijiang Norm. Univ. 2021, 36, 95–101. [Google Scholar]
- Liang, X.J.; Xue, J.L. Mediating effect of destination image on the relationship between risk perception of smog and revisit intention: A case of Chengdu. Asia Pac. J. Tour. Res. 2021, 26, 1024–1037. [Google Scholar] [CrossRef]
- Chen, Y.; Zheng, Y.Y. Modeling and analysis of tourism image perception of Taizhou ancient city based on sentiment text information. J. Taizhou Univ. 2020, 42, 32–38. [Google Scholar]
- Zhou, D. Research on the image perception of “net red” scenic spot from the perspective of tourism photos: A case study of Badaguan scenic spot in Qingdao. Tour. Overv. 2020, 24, 38–40. [Google Scholar]
- Zeng, R.; Feng, J. Research on the construction of Hunan tourism image from the perspective of multi-modal discourse analysis: Taking the tourism publicity film “so charming in Hunan” as an example. Chinas Collect. Econ. 2021, 10, 131–132. [Google Scholar]
- Walmsley, D.J.; Jenkins, J.M. Tourism cognitive mapping of unfamiliar environments. Ann. Tour. Res. 1992, 19, 268–286. [Google Scholar] [CrossRef]
- Lai, K.; Li, Y. Core-periphery structure of destination image: Concept, Evidence and Implication. Ann. Tour. Res. 2012, 39, 1359–1379. [Google Scholar] [CrossRef]
- Arya, V.; Sharma, S.; Sethi, D.; Shiva, A. Ties that bind tourists: Embedding destination motivators to destination attachment: A study in the context of Kumbh Fair, India. Asia Pac. J. Tour. Res. 2018, 23, 1160–1172. [Google Scholar] [CrossRef]
- De Lima Pereira, M.; Dos Anjos, F.A.; Da Silva Añaña, E.; Weismayer, C. Modelling the overall image of coastal tourism destinations through personal values of tourists: A robust regression approach. J. Outdoor Recreat. Tour. 2021, 35, 100412. [Google Scholar] [CrossRef]
- Gao, S.M.; Zhang, J.X.; Cui, J.F. Tourism image perception of health town based on network text and IPA model: Take aishan SPA town as an example. Hubei Agric. Sci. 2020, 59, 170–175. [Google Scholar]
- Jms, L.; Ling, S.C.; Oh, Y.L.; Khor, S.C. Investigating river destination image by using tri-component model: A case of Malacca river—The Venice of the east. Int. J. Sustain. Soc. 2020, 12, 238–252. [Google Scholar]
- Anastasiei, B.; Dospinescu, N.; Dospinescu, O. Understanding the Adoption of Incentivized Word-of-Mouth in the Online Environment. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 56. [Google Scholar] [CrossRef]
- Ranasinghe, J.P.R.C.; Danthanarayana, C.P.; Ranaweera, R.A.A.K.; Idroos, A.A. Role of destination smartness in shaping tourist satisfaction: A SEM based on technological attributes in Sri Lanka. IOP Conf. Ser. Earth Environ. Sci. 2020, 511, 012001. [Google Scholar] [CrossRef]
- Dospinescu, O.; Dospinescu, N.; Bostan, I. Determinants of e-commerce satisfaction: A comparative study between Romania and Moldova. Kybernetes 2021. ahead-of-print. [Google Scholar] [CrossRef]
- Zhang, L. Tourism destination image construction based on tourism discourse theory. Sci. Technol. Innov. Her. 2020, 19, 236–238. [Google Scholar]
- Ren, Y.M. Research on tourism image perception factors based on grounded theory: A case study of the ancient village Hongcun. J. Luoyang Norm. Univerdity 2021, 40, 34–37. [Google Scholar]
- Wang, S.J. Study on the image building of traditional village tourism based on the theory of cultural memory. Archit. Cult. 2021, 1, 218–219. [Google Scholar]
- Ren, C.Y.; Shi, H.; Liao, H.; Zhou, W.R. Research on image perception of world heritage sites based on UGC: Taking Emei mountain scenic area as an example. Bord. Econ. Cult. 2021, 1, 63–71. [Google Scholar]
- Tavitiyaman, P.; Qu, H.L.; Tsang, W.L.; Lam, C.W.R. Smart tourism application and destination image: Mediating role of theory of mind. Asia Pac. J. Tour. Res. 2021, 26, 905–920. [Google Scholar] [CrossRef]
- Zhu, Z.H.; Zhu, M.R.; Zhu, M.X.; Shi, R.; Yuan, Q.J. Research on homogenization of tourism destination terrain image based on network big data: Take Jiangnan ancient town as an example. China Tour. Rev. 2021, 2, 116–130. [Google Scholar]
- Li, L.; Zhang, J.; Li, S.S. The schema of destination image perception based on deep learning: A case study of Jiuzhaigou scenic spot. J. Anhui Norm. Univ. (Nat. Sci.) 2021, 3, 276–284. [Google Scholar]
- Fernando, I.; Rajapaksha, R.M.P.D.K.; Kumari, K.W.S.N. Tea tourism as a marketing tool: A strategy to develop the image of Sri Lanka as an attractive tourism destination. PIN Fernando, RMPDK Rajapaksha and KWSN Kumari. Kelaniya J. Manag. 2016, 5, 64–79. [Google Scholar] [CrossRef] [Green Version]
- Sheresheva, M.Y.; Polukhina, A.N.; Oborin, M.S. Marketing issues of sustainable tourism development in Russian regions. J. Tour. Herit. Serv. Mark. 2020, 6, 33–38. [Google Scholar]
- Hua, H.; Wondirad, A. Tourism network in urban agglomerated destinations: Implications for sustainable tourism destination development through a critical literature review. Sustainability 2020, 13, 285. [Google Scholar] [CrossRef]
- Leković, K.; Tomić, S.; Marić, D.; Ćurčić, N.V. Cognitive component of the image of a rural tourism destination as a sustainable development potential. Sustainability 2020, 12, 9413. [Google Scholar] [CrossRef]
- Lee, S.W.; Xue, K. A model of destination loyalty: Integrating destination image and sustainable tourism. Asia Pac. J. Tour. Res. 2020, 25, 393–408. [Google Scholar] [CrossRef]
- Dospinescu, N. The public relations events in promoting brand identity of the city. Econ. Appl. Inform. 2014, 1, 39–46. [Google Scholar]
- Marchi, V.; Raschi, A.; Martelli, F. Evaluating perception of sustainability initiatives invested in the coastal area of Versilia, Italy. Sustainability 2020, 13, 332. [Google Scholar] [CrossRef]
- Ragin, C.C. Redesigning Social Inquiry—Fuzzy Sets and Beyond; China Machine Press: Beijing, China, 2019. [Google Scholar]
- Zhang, M.; Du, Y.Z. Qualitative comparative analysis (QCA) in management and organization research: Position, tactics, and directions. Chin. J. Manag. 2019, 16, 1312–1323. [Google Scholar]
- Zhang, L.; Yuan, X.H. The international comparison of maternity protection policy based on QCA method. Soc. Secur. Stud. 2019, 4, 87–94. [Google Scholar]
- Rihoux, B.; Ragin, C.C. Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques; China Machine Press: Beijing, China, 2019. [Google Scholar]
- Hu, M.J.; Liang, Y.L. A review of domestic studies on tourism destination image measurement. Hubei Agric. Sci. 2019, 58, 11–15. [Google Scholar]
- Baloglu, S.; Henthorne, T.L.; Sahin, S. Destination image and brand personality of Jamaica: A model of tourist behavior. J. Travel Tour. Mark. 2014, 31, 1057–1070. [Google Scholar] [CrossRef]
- Lee, C.K.; Lee, Y.K.; Lee, B.K. Korea’s destination image formed by the 2002 world cup. Ann. Tour. Res. 2005, 32, 839–858. [Google Scholar] [CrossRef]
- Shandong Provincial Bureau of Culture and Tourism. List of Scenic Spot in Shandong Province. April 2020. Available online: http://whhly.shandong.gov.cn/art/2020/4/24/art_100526_9032316.html (accessed on 20 July 2020).
- World Heritage Convention of United Nations Educational, Scientific and Cultural Organization. The World Heritage List in China. March 2020. Available online: https://whc.unesco.org/en/list (accessed on 5 August 2020).
- Weihai Municipal Bureau of Culture and Tourism. List of Star-Rated Hotel in Weihai City. March 2021. Available online: http://wlj.weihai.gov.cn/art/2021/3/3/art_58217_2538017.html (accessed on 5 March 2021).
- Yantai Municipal Bureau of Culture and Tourism. List of Travel Agency in Yantai City. August 2021. Available online: http://whlyj.yantai.gov.cn/art/2021/8/23/art_42223_2904426.html (accessed on 23 August 2021).
- Shandong Provincial Bureau of Statistics. Shandong Statistical Yearbook. March 2020. Available online: http://tjj.shandong.gov.cn/tjnj/nj2020/indexch.htm (accessed on 7 August 2020).
- Echtner, C.M.; Ritchie, J.R.B. The meaning and measurement of destination image. J. Tour. Stud. 2003, 14, 38–48. [Google Scholar]
- Codurasa, A.; Clemente, J.A.; Ruiz, J. A novel application of fuzzy-set qualitative comparative analysis to GEM data. J. Bus. Res. 2016, 69, 1265–1270. [Google Scholar] [CrossRef]
- Zhang, M.; Chen, W.H.; Lan, H.L. Why do Chinese enterprises completely acquire foreign high-tech enterprises—A fuzzy set qualitative comparative analysis (fsQCA) based on 94 cases. China Ind. Econ. 2019, 4, 117–135. [Google Scholar]
Cities | GV (Score) | CUV (Score) | SH (Score) | AI (Number) | TP (Millions) | TA (Number) | AV (Score) | COV (Score) |
---|---|---|---|---|---|---|---|---|
Qingdao | 272 | 491 | 320 | 368 | 47.6 | 505 | 142 | 16 |
Ji’nan | 143 | 187 | 255 | 314 | 31.9 | 323 | 158 | 20 |
Zibo | 148.5 | 233 | 80 | 141 | 6.1 | 202 | 138 | 16 |
Zaozhuang | 127 | 142 | 56 | 101 | 25.7 | 179 | 159 | 18 |
Yantai | 275.5 | 302.5 | 244 | 273 | 59.1 | 252 | 148 | 20 |
Weifang | 254.5 | 286 | 161 | 185 | 60.8 | 246 | 161 | 19 |
Ji’ning | 220 | 499 | 133 | 344 | 40.7 | 243 | 151 | 16 |
Linyi | 223 | 313 | 87 | 135 | 51.6 | 155 | 157 | 16 |
Tai’an | 187.5 | 230 | 76 | 123 | 29.8 | 107 | 157 | 20 |
Liaocheng | 92 | 160.5 | 24 | 80 | 20.1 | 117 | 143 | 16 |
Heze | 39.5 | 62 | 21 | 201 | 49.7 | 121 | 137 | 16 |
Dezhou | 55.5 | 230 | 57 | 95 | 19.1 | 101 | 151 | 16 |
Binzhou | 140 | 296.5 | 21 | 59 | 10.6 | 182 | 152 | 16 |
Dongying | 72.5 | 275 | 80 | 45 | 6.7 | 290 | 147 | 19 |
Weihai | 175 | 163.5 | 168 | 152 | 33.8 | 234 | 163 | 20 |
Rizhao | 188 | 165 | 46 | 50 | 25.3 | 112 | 158 | 17 |
Laiwu | 54.5 | 62 | 26 | 34 | 1.9 | 33 | 155 | 18 |
Category of Variable | Set’s Goal | Anchors | |||
---|---|---|---|---|---|
Non-Affiliated Point | Cross-Point | Fully Affiliated Point | |||
Outcome Variables | TTL | High | 62.00 | 71.53 | 83.00 |
Antecedent Variables | GV | High | 62.00 | 79.47 | 99.00 |
CUV | High | 67.00 | 76.06 | 99.00 | |
SH | High | 63.00 | 71.24 | 91.00 | |
AI | Much | 61.00 | 74.59 | 97.00 | |
TP | Big | 62.00 | 79.00 | 98.00 | |
TA | Much | 65.00 | 73.76 | 84.00 | |
AV | Strong | 61.00 | 81.94 | 96.00 | |
COV | Strong | 70.00 | 75.88 | 90.00 |
Antecedent Variables | High Image | Non-High Image | |||
---|---|---|---|---|---|
Consistency | Coverage | Consistency | Coverage | ||
Native Variable | GV/~GV | 0.388 | 0.436 | 0.823 | 0.897 |
CUV/~CUV | 0.359 | 0.493 | 0.809 | 0.750 | |
Induced Variable | SH/~SH | 0.335 | 0.443 | 0.874 | 0.830 |
AI/~AI | 0.401 | 0.457 | 0.778 | 0.837 | |
TP/~TP | 0.394 | 0.483 | 0.811 | 0.816 | |
TA/~TA | 0.229 | 0.356 | 0.952 | 0.816 | |
Composite Variable | AV/~AV | 0.588 | 0.582 | 0.546 | 0.684 |
COV/~COV | 0.364 | 0.466 | 0.665 | 0.647 |
Combination of Variable | High Image Configuration | Non-high Image Configuration | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | ||
Native Variable | GV | ● | ● | ● | ||||||
CUV | ● | ● | ||||||||
Induced Variable | SH | ● | ● | |||||||
AI | • | • | ||||||||
TP | • | • | • | |||||||
TA | • | • | ||||||||
Composite Variable | AV | • | • | • | ||||||
COV | ● | ● | ||||||||
Consistency | 0.853 | 0.976 | 1.000 | 0.991 | 0.960 | 0.982 | 0.982 | 0.982 | 0.965 | |
Raw Coverage | 0.380 | 0.426 | 0.472 | 0.283 | 0.413 | 0.398 | 0.410 | 0.406 | 0.522 | |
Unique Coverage | 0.068 | 0.074 | 0.167 | 0.018 | 0.066 | 0.039 | 0.039 | 0.101 | 0.134 | |
Consistency of the overall Plan | 0.911 | 0.964 | ||||||||
Coverage of the Overall Plan | 0.763 | 0.780 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, Y.; Xiao, H.; Bu, N.; Luo, J.; Xia, H.; Kong, L.; Yu, H. Configuration-Based Promotion: A New Approach to Destination Image Sustainability. Sustainability 2021, 13, 12174. https://doi.org/10.3390/su132112174
Li Y, Xiao H, Bu N, Luo J, Xia H, Kong L, Yu H. Configuration-Based Promotion: A New Approach to Destination Image Sustainability. Sustainability. 2021; 13(21):12174. https://doi.org/10.3390/su132112174
Chicago/Turabian StyleLi, Yanan, Honggen Xiao, Naipeng Bu, Jianji Luo, Hui Xia, Liyuan Kong, and Haoyue Yu. 2021. "Configuration-Based Promotion: A New Approach to Destination Image Sustainability" Sustainability 13, no. 21: 12174. https://doi.org/10.3390/su132112174