Do attractions attract tourists?
Bibliographic record
Abstract
Many studies revealed travelers’ motives to travel a destination but do not answer explicitly what specific features of a destination influence travelers’ decisions. Dann’s (1976) “push and pull” factors best capture the relationship between the consumer and the destination. But, most work examines only one aspect of the equation. Pearce believed regardless of nationality or experience, all travelers do have the same core motives on traveling to a destination, which is seeking relaxation, novelty and relationship enhancement (appendix 1). As the layer move outward, the importance of motive is getting less mainly due to experience. McKercher developed tourism product taxonomy to standardize the tourism terminology (appendix 2). He further argued that as the tourist’s need becomes more specific, middle and outer, he or she will be attracted to items that appear at the lower end of the taxonomic tree (appendix 3). Alternately, if needs are general (core) then attractions, as defined by a higher tier in the taxonomy, will satisfy these needs. This paper explores the gap by comparing Pearce’s Travel Career pattern model with McKercher’s tourism product taxonomy to look at the relationship between motives and destination attributes. This paper attempted to answer if the destinations position themselves differently for different markets? And if the same markets targeted differently by different destinations? To examine the relationship between tourists’ motivation and attractions, and how attractions attract tourists. The study asks ‘do attractions attract tourists?’ focusses on Chinese, Australian, and Japanese who travel to two identified Asian (Singapore and Hong Kong) and two identified non-Asian (Canada and New Zealand) destinations. An in-depth understanding of the source market (Chinese, Australian and Japanese) is conducted by analysis of the travelling patterns, behaviors and interest activities towards four destinations (Singapore, Canada and New Zealand). Chinese are known to seek shopping and visiting iconic sites, and Australian are more adventure in selecting destinations. Japanese are looking for value when traveling, yet safety is their concern. Findings reveal that Chinese mainly fall under Pearce’s core motives. Australian weighted more towards the outer layer motives compared to Japanese and to avoid over-crowded destinations. Linking with McKercher’s idea of ‘nature of attraction’ in his ‘role of individual attractions in drawing tourists to a destination’, the top 10 attractions for all destinations among all three markets are more or less about the same. One of the reasons is the successful destination promotions and marketing images imprinted in the travelers’ minds (push-pull factors). In conclusion, the answer is ‘it depends’, on what the destination provides in terms of the number of attractions, distance concern, ease of access, safety and etc. It also relates to the complexity of travelers’ experience, expectation, motivation, disposable income and available time. Practical contribution helps the tourism boards and destination markers to set promotion plans according to the potential and target markets’ need and the attractions availability (existing or potential). In terms of theoretical implication, a further assessment of how attractions attract tourists is conducted by comparing the source market’s travelling pattern and experience towards various destinations are placed in Pearce’s three layers of motives, and a further test on McKercher’s framework on attractions and needs relationship. Noting that some data from the tourism boards are not up-to-date, some are dated back in 2013, which market may have changed due to different life stages, hence when comparing the same market’s destination, the consistency of data interpretation is a challenge.This paper focuses on a general overview of each source market and attraction taxonomy. As discussed, large country may have more opportunity to satisfy more motives due to size and available activities. In other words, different parts or regions will have different attributes (including atmosphere, cultures, climates/ seasons) which cater to different tourists’ needs. Further in-depth research is suggested to understand how attractions attract tourist in a specific part of countries, such as provinces or even cities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".