BMF CP83: Information seeking, recommendation mechanism, and space tourism intention

AISDL Team

September 25, 2024

“Her delicate figure embodies the phrase “a swan’s grace,” yet she possesses incredible strength, easily lifting a three-ounce fish up to her branch. But most importantly, her ethereal attire earns her the title of “Dreamy Wings.” Her entire being is like an ethereal painting, a playful creation of nature using colors, lines, dots, strokes, and patches. One can only exclaim, “Absolutely enchanting. Is this real or an illusion?””

—In “The Philosophy of Awakening”; Wild Wise Weird (2024)

[COLLABORATIVE PROJECT]

1. Project description

1.1. Main objectives

The current study is conducted to examine the following research questions:

• What information sources on social media are associated with the general intention to try space tourism?
• Does the automatically recommended information moderate the associations between multiple sources of information and the intention to try space tourism?

1.2. Materials

The granular interaction thinking of mindsponge theory will be used for the conceptual development of this study, while Bayesian Mindsponge Framework (BMF) analytics will be used for statistical analysis [1-4]. The dataset comprises 361 respondents through snowball and convenient sampling targeted to samples interested in space tourism [5]. Statistical analyses will be conducted using the Bayesvl R package, which utilizes the Markov chain Monte Carlo (MCMC) algorithm for estimation [6]. For the sake of research transparency and reducing research and reproducibility costs, we have stored all data and computer code on Zenodo: https://zenodo.org/records/13834867.

1.3. Main findings

The preliminary analysis shows that almost all information sources (e.g., public figures, official enterprises’ accounts, friends and family, and automatically recommended information) have ambiguous associations with the intention to try space tourism. Meanwhile, proactive searching of space tourism information is positively associated with trying space tourism, and the automatically recommended information further reinforces the positive association (see Figure 1).



Figure 1: Estimated coefficients

2. Collaboration procedure

Prospective participants should follow these steps for registering to participate in this research project:

1. Create an account on the website (preferably using an institution email).
2. Comment your name, affiliation, and your desired role in the project below this post.
3. Patiently wait for the formal agreement on the project from the AISDL mentor.

If you have further inquiries, please contact us at aisdl_team@mindsponge.info

If you have been invited to join the project by an AISDL member, you are still encouraged to follow the above formal steps.

All the resources for conducting and writing the research manuscript will be distributed upon project participation.

AISDL mentor for this project: Minh-Hoang Nguyen

AISDL members who have joined this project: Quan-Hoang Vuong, Viet-Phuong La.

The research project strictly adheres to scientific integrity standards, including authorship rights and obligations, without incurring an economic burden at participants’ expenses.

References

[1] Vuong QH. (2023). Mindsponge theory. Walter de Gruyter GmbH.

[2] Vuong QH, Nguyen MH, La VP. (2022). The mindsponge and BMF analytics for innovative thinking in social sciences and humanities. Walter de Gruyter GmbH. https://www.amazon.com/dp/8367405102/

[3] Vuong QH, Nguyen MH. (2024). Better economics for the Earth: A lesson from quantum and information theories. https://www.amazon.com/dp/B0D98L5K44

[4] Vuong QH, Nguyen MH. (2024). Further on informational quanta, interactions, and entropy under the granular view of value formation. https://dx.doi.org/10.2139/ssrn.4922461

[5] Peng KL, et al. (2023). Space tourism flow generated from social media data. Data in Brief, 48, 109061. https://doi.org/10.1016/j.dib.2023.109061

[6] La VP, Vuong QH. (2019). bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with ‘Stan’. The Comprehensive R Archive Network. https://cran.r-project.org/web/packages/bayesvl/index.html

[7] Vuong QH. (2024). Wild Wise Weird. https://www.amazon.com/dp/B0BG2NNHY6