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Record W4319998487 · doi:10.3126/jbssr.v7i2.51492

People Perception on Business Opportunities of Banana Restaurants of Tikapur: Evidence from Structural Equation Modelling

2022· article· en· W4319998487 on OpenAlexaff
Bharat Jung Singh, Niranjan Devkota, Krishna Dhakal, Surendra Mahato, Udaya Raj Paudel

Bibliographic record

VenueJournal of Business and Social Sciences Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsMarketingBusinessPerceptionProduct (mathematics)Structural equation modelingDescriptive statisticsAdvertisingPsychologyMathematics

Abstract

fetched live from OpenAlex

Banana restaurant is a new concept in Nepal where the products are made using banana only which gives the first mover advantage in product development and has less explored in Nepal. Therefore, this study aims to analyse consumers’ perception and business opportunity of banana restaurant in Tikapur, Kailali, Nepal. This study adopted explanatory research, and 203 samples were collected from consumers of banana restaurants. In addition, data were collected using structured questionnaire by using KOBO Toolbox. Thus, the obtained data were analysed by using both descriptive and inferential methods. Findings indicate that consumers perceive products of banana restaurant positively and are enjoying the variety of products. Likewise, SEM results indicate that psychological, economic and social characteristics are significant to innovativeness and risk taking, whereas innovativeness and risk taking are significant to managerial performance. Banana restaurant has been contributing with local employment and shows the high level of business opportunity of the banana products in the market of Tikapur (Kailali, Western 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)0.003
metaresearch head score (Gemma)0.000
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 score0.228
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.457
GPT teacher head0.398
Teacher spread0.058 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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