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Analysing Factors Affecting the Adoption of Ride-Hailing Services (RHS) in India? A Step-Wise LCCA-MCDM Modeling Approach

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Eeshan Bhaduri, Shagufta Pal, Arkopal Kishore Goswami

Applications · Submitted 16 Aug 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Simultaneously, prioritization of ride-hailing specific attributes was carried out to assess how RHS will operate in a sustainable way.

how it is stated · asserted 0.96 read the record →

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arXiv ID2208.07804 (version 1)
Pith IDDALEXRLR, Pith's permanent record of this paper and its review
CitationArkopal Kishore Goswami, Eeshan Bhaduri, Shagufta Pal. "Analysing Factors Affecting the Adoption of Ride-Hailing Services (RHS) in India? A Step-Wise LCCA-MCDM Modeling Approach." arXiv:2208.07804 (2022). https://pith.science/paper/DALEXRLR
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,307 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.05 · human participants 0.69 · controlled comparison 0.03 · randomized assignment 0.02 · states quantitative result 0.03 · compares with prior results 0.07 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

The study examines heterogeneity in travel behaviour among ride-hailing services (RHS) users by including attitudes, in order to reinforce conventional user-segmentation approaches. Simultaneously, prioritization of ride-hailing specific attributes was carried out to assess how RHS will operate in a sustainable way.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The study examines heterogeneity in travel behaviour among ride-hailing services (RHS) users by including attitudes, in order to reinforce conventional user-segmentation approaches. contribution 0.95 0.83 0.07 asserted 0.97
s1 Simultaneously, prioritization of ride-hailing specific attributes was carried out to assess how RHS will operate in a sustainable way. contribution 0.53 0.89 0.09 asserted 0.96

What kind of paper the classifier reports this is: empirical study 0.57 · evidence from analysis of existing data 0.45.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The study examines heterogeneity in travel behaviour among ride-hailing services (RHS) users by including attitudes, in order to reinforce conventional user-segmentation approaches. Simultaneously, prioritization of ride-hailing specific attributes was carried out to assess how RHS will operate in a sustainable way.

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