Evidence map›Paper›PMID 33551922›Full record

ArticleFrontiers in psychology2020

A Meaning-Aware Cultural Tourism Intelligent Navigation System Based on Anticipatory Calculation.

Lei Meng, Yuan Liu

Abstract read
In one paragraph

Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lei MengSchool of Design, Jiangnan University, Wuxi, China.
Yuan LiuSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To improve the personalized service of cultural tourism, anticipatory calculation has become an essential technology in the content design of intelligence navigation system. Culture tourism, as a form of leisure activity, is being favored by an increasing number of people, which calls for further improvements in the cultural consumption experience. An important component of cultural tourism is for tourists to experience intangible cultural heritage projects with local characteristics. However, from the perspective of user needs and the content adaptive system, there are few suitable intelligent navigation and user demand anticipatory systems for intangible cultural heritage content. Purple clay culture is one of the first batches of national intangible cultural heritage protection projects in China. Therefore, taking purple clay culture exhibition as an example, this paper attempts to analyze the personalized information demand of tourism consumption experience in intangible cultural heritage communication activities with affective computing and meaning-driven innovative design method, by taking the content design in the navigation system as the research object. This paper uses the theory of planned behavior to calculate the relationship between tourists' attitude, experience behavior, and display information demand. The findings indicate two issues. First, tourists' demand for the entertainment and leisure attributes of intangible cultural heritage is greater than the demand for educational function attributes. Second, the meaning elements of information can change tourists' beliefs in intangible cultural heritage and affect their attitude and behavior toward such heritage. According to the research results, strengthening the meaning elements of specific group information can improve people's cultural identity and tourism satisfaction. The research results provide the basis for the content design direction of future museum intelligent navigation systems.

Indexed as

affective computingartificial intelligencecontent designintangible cultural heritage exhibitionmeaning innovationnavigation systemtheory of planned behavior

Identifiers

PMID33551922
PMCPMC7862136

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.