Tagged / dimitrios buhalis

Tourism, marketing and health in 2022

BRIAN, international, Publishing, writing

In his overview of 2022 on LinkedIn Professor Dimitrios Buhalis reminded us that: “The Encyclopedia of Tourism Management and Marketing Marketing was finally published with 1250 entries contributed by 1500 academics from all over the world to produce 4 volumes and 3528 pages. This will work brought together the best thinking process and brains in tourism management to contribute to the rebuilding of the tourism industry, globally, and contribution to communities around the world.”

We are happy to have made a small contribution to this book.  Professor Padam Simkhada (BU Visiting Faculty and Professor at the University of Huddersfield) and I contributed the chapter on trekking guides in Nepal and sexual health [1].

 

 

Have a happy and healthy 2023!

Prof. Edwin van Teijlingen

Centre for Midwifery, Maternal & Perinatal Health (CMMPH)

 

Reference:

  1. Simkhada, P., van Teijlingen E. (2022) Sexual relationships and trekking guides. In: Encyclopedia of Tourism Management and Marketing, Buhalis, D. (ed.), Cheltenham, Edward Elgar Publishing, pages: 77-79.

Professor Dimitrios Buhalis recognised as Highly Cited Researcher by Clarivate for the third year

Publishing

Congratulations to Professor Dimitrios Buhalis, who has been recognised by Clarivate™ as one of the world’s most influential researchers who have been most frequently cited by their peers over the last decade.

Dimitrios Buhalis

Clarivate provides information, data and insights to universities, nonprofits, funding organisations, publishers, corporations, government organisations and law firms across the world to help accelerate and advance innovation.

Fewer than 0.1% (1 in 1,000) of the world’s population of scientists and social scientists received the Highly Cited Researchers™ distinction in 2022.

Highly Cited Researchers have demonstrated significant and broad influence reflected in their publication of multiple highly cited papers over the last decade. These highly cited papers rank in the top 1% by citations for a field in the Web of Science™.

Professor Buhalis has been named as a Highly Cited Researcher in the field of Social Science for the past three years.

He said: “It is extremely rewarding to know that the research I’ve been doing in the last 30 years has been useful to many other researchers to build their research and develop this concept. It is also very rewarding to know that the research has an impact on society, bringing value to different stakeholders and communities around the world.

“Of course, the research has been happening with many collaborators, including students and researchers and colleagues from all over the world, and most have been co-authored with several of my 200 collaborators.”

Professor Buhalis is a strategic management and marketing expert with specialisms around information communication technology applications in the tourism, travel, hospitality and leisure industries.

“All my research is about relevance and impact on business practice and global policy and it is cutting edge,” he said.

‘It is forecasting the future and identifies enabling technologies that bring value to different stakeholders and, by doing so, designing a better future.”

He added: “Being able to forecast the future and identifying technologies that can support progress is a critical element of the research, and that is why it is published early, before other researchers engage in inquiry, and that’s why it’s widely cited.”

“My advice would be to follow your heart, make relevant and useful cutting edge research that contributes to society globally, and citation will follow.”

New Publication: de Souza, J., Mendes, LF., Buhalis, D., 2020, Evaluating the effectiveness of tourist promotions to improve the competitiveness of destinations, Tourism Economics, Vol. 26(6), pp, 1001–1020,

New Publication: de Souza, J., Mendes, LF., Buhalis, D., 2020, Evaluating the effectiveness of tourist promotions to improve the competitiveness of destinations, Tourism Economics, Vol. 26(6), pp, 1001–1020, https://doi.org/10.1177/1354816619846748
 
This study focuses on the evaluation of the tourist destination advertising effectiveness. The destination advertising response DAR model was used to analyze data on the effectiveness of destination promotional campaigns on visitor expenditure, in six trip facets: destination, accommodations, attractions, restaurants, events, and shopping. Independent sample t-tests were conducted to identify any differences in total destination spending among the groups of those visitors influenced for each trip facet. A multiple regression analysis was performed to discriminate the performance of the travel facets expenditures in the estimation of total expenditures. Significant results indicate that the “destination,” “accommodations,” and “restaurants” facets directly influence the total expenditures. Self-planners had the highest variance, explaining in total visitor expenditure compared to the regression analysis results of the other two groups (i.e. travel agencies and online travel agencies). The study also explores how destinations can improve their competitiveness on tourist advertising by using technologies.
 
Keywords tourism, destination, marketing, advertising, competitiveness, DAR model, destinations, technologies

new paper published  Volchek, K., Liu, A., Song, H., & Buhalis, D. (2018) Forecasting tourist arrivals at attractions: Search engine empowered methodologies. 

new paper published  Volchek, K., Liu, A., Song, H., & Buhalis, D. (2018) Forecasting tourist arrivals at attractions: Search engine empowered methodologies. Tourism Economics. https://doi.org/10.1177/1354816618811558

Abstract

Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro level. The number of visits to five London museums is forecast and the predictive powers of Naïve I, seasonal Naïve, seasonal autoregressive moving average, seasonal autoregressive moving average with explanatory variables, SARMAX-mixed frequency data sampling and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances the forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher frequency search query data allows for the generation of weekly predictions, which are essential for attraction- and destination-level planning.

Keywords: artificial intelligence, attractions, forecasting, Google Trends, search engine, tourist demand

It’s official – FP7 has competetive success rates

FP7 has unfairly gained a reputation as being extremely difficult to obtain. The latest figures released from the EC show that FP7 actually had a success rate of 21% last year (a massive €3.9 billion of research funding was distributed through 63 calls for proposals). Our recent blogpost on Research Councils show that the EC is actually higher than many of our home funders.

The statistically greater chance of success, coupled with the added benefits of gaining EU funding as testified by BU academics such as Sherry Jeary and Dimitrios Buhalis shows that we should all be looking to the EU for funding. If you are a BU member of staff and have an idea for EU funding you want to discuss, drop me an email.