The Perspectives of Artificial Intelligence Use for Tourist Destination Marketing in Eastern Europe: The Marketing Professionals’ Perspective

Written by Russell W.

1. Introduction

In qualitative studies, thematic analysis represents the most widespread method used for phenomenological research according to Guest et al. (2012). This instrument allows authors to explore the individual perceptions, feelings, and lived experiences of individuals possessing the required knowledge. The thematic analysis process mostly takes the form of identifying codes within raw data that are used as markers to identify repeated meanings within the records (Bryman, 2006). Following the initial processing of interview transcripts, these patterns are used to form overarching themes and subthemes representing qualitative variables reflecting semantic units and ideas within the underlying text (Guest et al., 2012). The sample included fourteen marketing professionals sharing their insights regarding the opinions of Artificial Intelligence (AI) perspectives via semi-structured interviews. This allowed the researchers to follow the pattern guiding the respondents towards the analysed themes for further theme systematisation while also granting them the ability to answer open-ended questions in any way they saw fit to provide rich qualitative data for interpretation (Bryman & Bell, 2015).

2. Data Analysis Steps

The process of data analysis involved the following steps:

1. The composition of preliminary interview questions were based on the previously reviewed theories, frameworks, and empirical evidence. These questions reflected the earlier formulated main research objectives and contributed to their attainment.
2. Pilot tests of the questionnaire with three interviewees were used to identify potential readability issues and improve validity and reliability issues associated with the questions’ design and formulation. The final version of the questionnaire addressing these identified limitations is presented in Appendix A.
3. The main interview phase continued until the moment of data saturation. This process involved audio recordings of live and Zoom meetings to facilitate further analysis and reduce any distractions associated with note-taking during the interviews.
4. Interview transcription and processing. Specific codes were associated with the key themes of this study (e.g. ‘big data analysis’, ‘customer needs prediction’, ‘demand patterns analysis’, ‘automated customer service’, etc.). Interview quotes were added to each theme for further reference.
5. Revising the final list of themes and subthemes. The initial codes were revised to remove potential duplicates and group individual subthemes into larger categories linked with this study’s main research objectives.
6. Calculating the frequency of the themes. This phase provided the capability to identify how often individual themes emerged in interviewee responses and how many interviewees referred to each individual theme. This data was used to reveal key tendencies in participants’ answers.
7. The results of the qualitative analysis were visualised to improve their readability and demonstrate key findings to the readers.
8. The responses of different interviews were compared and contrasted to identify whether some of the identified perspectives were based on individual perceptions of industry-wide trends.

While some qualitative studies utilise software such as NVivo to automate the process of themes’ identification and categorisation, the final sample of this study amounted to 14 respondents. Due to this fact, the author utilised manual coding on Microsoft Word while using Excel visualisation tools to develop the study’s graphical materials. The interviewees were reached by the researcher via their own network of professional contacts in the tourism industry. The selected persons share a number of inclusion criteria such as 5 years of experience in this sphere, awareness of AI marketing tools, and the willingness to sign informed consent forms. The initial sample amounted to 18 persons with 3 participants being used for pilot-testing the interview forms and 2 participants choosing to withdraw from the study before the start of the data analysis. The recordings of 3 live interviews were performed by the researcher using handheld recorded. The remaining ten interviews from other regions of the EU were captured using the embedded Zoom client feature.

3. Themes Identification

As outlined by the Literature Review, the main focus of the analysis was the perspectives of artificial intelligence use for tourist destination marketing in Eastern Europe. The interviewed 14 marketing professionals were recruited from five countries, namely Estonia, Hungary, Poland, Czech Republic, and Croatia. Since the researcher presently resides in Estonia, six out of fourteen respondents came from this area. All of the participants possessed a good knowledge of English, which reduced the need for translation and possible threats to correct data interpretation. The analysis following the procedure described in the previous section revealed six main themes containing fifteen sub-themes. For each of them, the researcher created a table of Interviewee quotes to facilitate further analysis of data. The codes were inserted in brackets within the transcripts (Appendix B) with colour-coding being used for greater convenience of themes’ identification.

While some identified themes overlapped with the earlier analysed studies of Bulchand-Gidumal et al. (2024) and Kim et al. (2024) used for the development of the conceptual framework, newly emerging themes were specifically marked to facilitate further discussion, comparison, and contrasting of the findings. Specific examples of the latter were the use of AI for internal knowledge management that was mostly missing from the analysed literature. While some respondents provided similar information on each sub-theme, multiple quotes were utilised for the analysis to ensure that each of such categories was addressed from multiple standpoints. This approach was deemed necessary to account for differences in individual lived experiences and professional standpoints and obtain a more balanced perspective of the topic (Saunders et al., 2016).

4. Research Ethics

The author of this thesis utilised their network of professional contacts to reach the targeted population members possessing the required experience in the Eastern European tourist industry. Their selection was not influenced by their gender, age, ethnicity, personal beliefs or other characteristics beyond the aforementioned ones. To ensure that the interviewees were fully aware of the project aims and research design, they were asked to sign informed consent forms before entering the study and were reminded of their rights verbally prior to the start of the interviews (Carson, 2001). This procedure allowed them to not proceed with their participation or withdraw their earlier provided consent at any moment preceding the start of the data analysis. The possibility of individual respondents’ identification was further reduced by anonymisation and randomisation of the recordings (Hallebone & Priest, 2008).

References

Bryman, A. (2006). “Integrating quantitative and qualitative research: How is it done?”. Qualitative Research, 6 (1), 97-113. https://doi.org/10.1177/1468794106058877

Bryman, A., & Bell, E. (2015). Business Research Methods. Oxford: Oxford University Press.

Bulchand-Gidumal, J., William Secin, E., O’Connor, P., & Buhalis, D. (2024). Artificial intelligence’s impact on hospitality and tourism marketing: exploring key themes and addressing challenges. Current Issues in Tourism, 27(14), 2345-2362. https://doi.org/10.1080/13683500.2023.2229480

Carson, D. (2001). Qualitative Marketing Research. London: SAGE.

Guest, G, MacQueen, K, & Namey, E. (2012). Applied Thematic Analysis. London: SAGE.

Hallebone, E., & Priest, J. (2008). Business and Management Research: Paradigms and Practices. New York: Macmillan International Higher Education.

Kim, H., So, K., Shin, S., & Li, J. (2024). Artificial intelligence in hospitality and tourism: Insights from industry practices, research literature, and expert opinions. Journal of Hospitality & Tourism Research, 1(1), 1-21. https://doi.org/10.1177/10963480241229235

Saunders, M., Lewis, P., & Thornhill, A. (2016). Research Methods for Business Students. Harlow: Pearson Education Limited.