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Record W4415119744 · doi:10.1108/sajm-12-2024-0036

Motivation and behavioural intention of religious tourists at Buddhist sites: a study of Lumbini – the birth place of Gautam Buddha

2025· article· en· W4415119744 on OpenAlexaff
Amit Kumar Mishra

Bibliographic record

VenueSouth Asian Journal of Marketing · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsCampion College
Fundersnot available
KeywordsStructural equation modelingTourismConfirmatory factor analysisReligious tourismExploratory factor analysisBuddhismConceptual modelService (business)Politics

Abstract

fetched live from OpenAlex

Purpose Tourism has emerged as an important driving force for the economic development of nations worldwide. It contributes to society from economic, social and political perspectives, as well as sustainable tourism development helps increase environmental benefits and reduce negative consequences on local communities. Thus, the study aims to identify the motivational factors of religious tourists visiting Lumbini, Nepal and their impact on Behavioural Intention (BI). Design/methodology/approach To achieve the study's objective, 21 items related to religious motivation and four items related to BI were identified from previous research studies. The data were collected from 390 tourists visiting Lumbini. Furthermore, the study employed exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modelling (SEM) to analyse and confirm the conceptual model proposed for the study. Findings The EFA identified a four-factor model for religious motivation, such as Cultural and Historical attributes, Service Quality, Religious Belief and Socialisation. The CFA analysis confirmed that the proposed model is a good fit. The study also found that service quality is the primary motivational factor influencing BI, followed by Religious Belief and Socialisation. However, Cultural and Historical attributes have an insignificant impact on the BI of religious tourists visiting Lumbini. Research limitations/implications To investigate the relationship between religious tourists' motivation and BI, the required data were collected from a single destination Lumbini. Further study can be done including a greater number of religious sites in Nepal, such as Swambhu and Namobuddha, to understand the relations better. Another limitation is that the study has identified only four factors of motivation which influence tourists' BIs. Further study may include more factors of motivation such as personal enrichment, religious learning, relaxation, pleasure-seeking, novelty-seeking, enjoyment, family togetherness, social status, etc. and also further study should investigate the mediating effect of tourists' satisfaction and the moderating role of demographic variables such as age, gender on the relationship. Practical implications This study provides actionable insights for enhancing religious tourism in Lumbini. It highlights the need for improved service quality, including better accommodations, accessibility and customer interactions, as key to influencing tourists' BIs. Emphasising religious belief, it suggests creating informative materials, guided tours and collaborations with religious organizations to deepen visitors' experiences. Socialisation opportunities, such as festivals and community events, should be prioritised to foster engagement. Although cultural and historical factors showed limited impact, preserving and promoting these attributes can attract culturally inclined tourists. These strategies collectively support sustainable tourism development and Lumbini's positioning as a global religious hub. Originality/value This study contributes to the literature by applying SEM to examine the structural relationship between motivational factors and BIs among tourists visiting Lumbini, addressing a gap in research on religious tourism in Nepal.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.269
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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