IMPORTANCE-PERFORMANCE ANALYSIS OF A RESTAURANT MOBILE BOOKING AND RESERVATION APPLICATION

This study examined issues with performance and ongoing user dissatisfaction in the OpenTable application, a well-known restaurant booking and reservation platform in the Philippines. Researchers use the Importance-Performance Analysis (IPA) model to assess OpenTable's importance and performance. Researchers also use Gummesson's 4Q-Quality Model to improve the analysis structure. Researchers gathered comprehensive data by distributing a structured survey questionnaire to 120 individuals who use OpenTable. Ethical considerations guide researchers' investigation at every step. The researchers showed their commitment to methodological and ethical standards by thoroughly outlining the research's design, sampling approach, data gathering methods, and interpretation approaches. A Likert scale was used in the survey questionnaire for performance analysis and importance assessment, and it has been validated through a methodical approach, the findings revealed that: The most significant discrepancy between performance and importance can be observed in the product and service quality (0.70), followed by Designed Quality, Relational Quality (0.59), and Outcome Quality (0.56). For the greatest influence on consumer/users’ satisfaction, this proposes putting efforts to make improvements in product and service quality, relational quality, and designed quality. OpenTable is recommended to focus on enhancing communication channels, brand identity, and user experience. Management should place a high priority on resource allocation and transparent communication, and feedback is encouraged. It's recommended that features like responsive reservation updates and live chat assistance be included.

Publication Details

Journal
Knowledge Commons (Lakehead University)
Published
2024-08-04
DOI
https://doi.org/10.17613/m928-kn85
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
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article

IMPORTANCE-PERFORMANCE ANALYSIS OF A RESTAURANT MOBILE BOOKING AND RESERVATION APPLICATION

Knowledge Commons (Lakehead University)
Technology Adoption and User Behaviour
article

IMPORTANCE-PERFORMANCE ANALYSIS OF A RESTAURANT MOBILE BOOKING AND RESERVATION APPLICATION

article en

Abstract

This study examined issues with performance and ongoing user dissatisfaction in the OpenTable application, a well-known restaurant booking and reservation platform in the Philippines. Researchers use the Importance-Performance Analysis (IPA) model to assess OpenTable's importance and performance. Researchers also use Gummesson's 4Q-Quality Model to improve the analysis structure. Researchers gathered comprehensive data by distributing a structured survey questionnaire to 120 individuals who use OpenTable. Ethical considerations guide researchers' investigation at every step. The researchers showed their commitment to methodological and ethical standards by thoroughly outlining the research's design, sampling approach, data gathering methods, and interpretation approaches. A Likert scale was used in the survey questionnaire for performance analysis and importance assessment, and it has been validated through a methodical approach, the findings revealed that: The most significant discrepancy between performance and importance can be observed in the product and service quality (0.70), followed by Designed Quality, Relational Quality (0.59), and Outcome Quality (0.56). For the greatest influence on consumer/users’ satisfaction, this proposes putting efforts to make improvements in product and service quality, relational quality, and designed quality. OpenTable is recommended to focus on enhancing communication channels, brand identity, and user experience. Management should place a high priority on resource allocation and transparent communication, and feedback is encouraged. It's recommended that features like responsive reservation updates and live chat assistance be included.

Knowledge Commons (Lakehead University)
Openalex Percentile: Top 30%
Technology Adoption and User Behaviour
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