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Cited by 219

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

TANGSERV: A Multiple Item Scale for Measuring Tangible Quality in Foodservice Industry

Raajpoot, Nusser A.·2002·Journal of Foodservice Business Research

Abstract

This paper seeks to explore the domain of tangible quality construct; reviews the existing literature and then proceeds to develop a scale called TANGSERV for measuring tangible quality in food service industry. It captures ambient factors such as music and temperature; design factors such as location and seating arrangement; and product/service factors such as food presentation and food variety. Procedure used in constructing and refining a multiple-item scale is described. Moderately high factor loadings (between 0.725 and 0.799) and an a value of 0.76 suggests that scale is stable and reliable. Similarly, confirmatory factor analysis results show that factor loadings (between 0.660 and 0.830) are significant enough to claim convergent validity while a chi-square difference of 76.830 between fixed and freely estimated models provides an evidence of discriminant validity.

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Metadata

Authors
Raajpoot, N. A.
Venue
Journal of Foodservice Business Research
Published
2002-04
Volume
5
Issue
2
Pages
109-127
ISSN
  • 1537-8020 (ISSN-L)
  • 1537-8039
OpenAlex
W2317923681
Cited by
219
References
4
FWCI
0.73
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: TANGSERV: A Multiple Item Scale for Measuring Tangible Quality in Foodservice Industry. https://app.cochat.ai/papers/10.1300/j369v05n02_08
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Raajpoot, N. A. (2002). TANGSERV: A Multiple Item Scale for Measuring Tangible Quality in Foodservice Industry. Journal of Foodservice Business Research. https://doi.org/10.1300/j369v05n02_08

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.