search
Contact Us
HOME

Journal Search Engine

to

Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 1225-7060(Print)
ISSN : 2288-7148(Online)
Journal of The Korean Society of Food Culture Vol.40 No.1 pp.36-45
DOI : https://doi.org/10.7318/KJFC/2025.40.1.36

Exploring Customer Satisfaction and Dissatisfaction Attributes in Korean Restaurants: An Impact-Asymmetry Analysis Approach

Hee Jin Um1, Hee Yoon Kwon1, Sung-Bum Kim2*, Eunhye Park1*
1Department of Food and Nutrition, College of BioNano Technology, Gachon University
2Department of Business Administration, Inha University
* Corresponding authors: Eunhye Park, Graduate School of Education 354, Gachon University, 1342 Seongnam-daero, Seongnam-si, Gyeonggi-do, South Korea Tel: +82-31-750-5974 E-mail: epark@gachon.ac.kr
* Sung-Bum Kim, Department of Business Administration 6-603, Inha University, Inha University, 100 Inha-ro, Michuhol-gu, Incheon,
South Korea Tel: +82-32-860-7739 E-mail: kimsungb@inha.ac.kr
January 23, 2025 February 12, 2025 February 14, 2025

Abstract


This study seeks to explore how key restaurant attributes differently influence customer satisfaction and dissatisfaction across pre-pandemic and post-pandemic periods, as well as across various economic segments. By employing impact asymmetry analysis (IAA), the research identifies the primary drivers of customer satisfaction and dissatisfaction, examining their uneven on customer satisfaction in Korean restaurants. The findings underscore the non-linear and asymmetric nature of customer responses to various service attributes, highlighting the significant influence of economic factors and the pandemic on dining expectations and experiences. This research deepens our understanding of the factors shaping customer satisfaction dynamics, particularly in the context of the evolving post-pandemic restaurant industry.


Customer satisfaction , impact-asymmetry analysis , online reviews , restaurant industry , three-factor theory

초록

    I. Introduction

    The popularity of ethnic restaurants has grown markedlyin recent years, with the U.S. restaurant industry experiencing significant expansion in this segment over the past two decades (Aybek & Özdemir 2022). This trend reflects U.S. consumers’ increasing familiarity with ethnic cuisines, driven by factors such as global travel, greater accessibility to diverse foods, and heightened interest in cultural experiences (Ha 2019). A substantial portion of consumers report having explored a broader range of ethnic cuisines, highlighting the expanding demand for these dining options. As competition within the ethnic restaurant sector intensifies, understanding consumer behavior and developing effective strategies to attract and retain customers have become critical for restaurateurs seeking to a competitive edge (Jang et al. 2011;Ha 2019).

    Understanding customer satisfaction remains a central topic in restaurant research, where service-related factors and key attributes play a significant role in shaping customer perceptions. While traditional research often assumes a linear or symmetric relationship between service attributes and overall satisfaction, emerging evidence suggests the existence of asymmetric relationships (Kano et al. 1984). Prior studies in hospitality and tourism have employed the three-factor theory to examine asymmetric relationships, demonstrating that specific attributes influence satisfaction differently based on their perceived performance (Matzler & Renzl 2007). Previous research has explored the symmetric relationship between attributes and satisfaction; however, it has overlooked the asymmetric influence of attributes on satisfaction, which constraints understanding of which attributes more sensitive to either satisfaction or dissatisfaction (Wang et al. 2022). Impact Asymmetry Analysis (IAA) has been used to assess the significance of these asymmetric relationships in tourism settings (Back 2012).

    Understanding customer satisfaction and dissatisfaction remains challenging due to its multifaceted nature (Park et al., 2019). Contextual factors, such as the COVID-19 pandemic, have further complicated this dynamic by altering consumer perceptions and amplifying or diminishing the effects of specific attributes (Kim et al. 2023). The pandemic has profoundly distrupted the hospitality and tourism industries, prompting shifts in consumer behaviors and expectations. For instance, studies analyzing social media data have identified changes in consumer perceptions of “dining out” before and after COVID-19, revealing variations in the asymmetric effects of attribute performance across different restaurant types (Jung et al. 2021).

    Existing studies emphasize customer motivations, factors influencing ethnic dining choices, and authenticity. However, research specifically focused on Korean restaurants and their attributes remains limited (Mak et al. 2012). Despite the growing body of research on customer satisfaction, gaps remain in understanding how asymmetric relationhips between attributes and satisfaction differ across service types and situational context. Although surveys are commonly used in impact asymmetry analysis to assess service attributes (Wong & Lai 2018), online review data offers a unique and underutilized resource for understanding customer satisfaction in ethnic restaurants (Oh & Kim 2020). Moreover, the role of situational factors, such as economic conditions or the pandemic, in shaping the asymmetric relationship between attributes and satisfaction has been insufficiently explored in hospitality research.

    Utilizing the three-factor theory and IAA framework, this research investigates the primary elements influencing customer satisfaction and dissatisfaction through an extensive online review analysis. With data collected from TripAdvisor, a leading tourism platform, this research examines the asymmetric effects of restaurant attributes on satisfaction in Korean restaurants. By analyzing consumer evaluations across pre- and psot- COVDI-19 periods and economic segments, this study provides new insihgts into the dynamics of customer satisfaction.

    II. Materials and Methods

    1. Data collection

    To conduct an Impact Asymmetry Analysis, this study utilized TripAdvisor restaurant reviews. On TripAdvisor, customers can rate key restaurant attributes, including food, service, atmosphere, and value, using a standardized fivepoint scale. Since numeric evaluation of specific attributes is essential information for the Impact Asymmetry Analysis, TripAdvisor reviews were found to be helpful. This study focused on Korean restaurants in the 100 most populated cities in the United States. The selection of these cities was based on population rankings. To compile a comprehensive dataset, reviews of Korean restaurants were collected from TripAdvisor using web scraping techniques with the Beautiful Soup package in Python. Restaurants were classified as Korean if their TripAdvisor listing included keywords related to Korean cuisine. The data collection took place on January 25, 2024, covering reviews posted between December 2007 and January 2024.

    Hence, the initial dataset included 24,028 reviews. As this study was centered around Impact Asymmetry Analysis, it was essential to utilize reviews that offered complete data for all specified attributes. Reviews missing one or more attribute ratings were excluded to ensure consistency in the analysis. As a result of this filtering process, the final sample was reduced to 2,689 reviews, comprising approximately 10% of the original dataset.

    2. Variable preparation

    Based on the TripAdvisor metadata, this study generated two variables relevant to restaurant attribute evaluation and customer satisfaction/dissatisfaction: pandemic phases and food prices. To create a pandemic phase variable, the study categorized the timing of the reviews into two distinct phases relative to the COVID-19 pandemic. Reviews dated before March 2020 were classified under the pre-pandemic phase. Conversely, reviews from March 2020 onwards were considered part of the post-pandemic phase. Of the 2,689 reviews, 2,584 were identified from the pre-pandemic and 105 from the post-pandemic period. While a further division of the postpandemic period into pre- and post-vaccination phases was initially considered, the scarcity of reviews in the postpandemic category necessitated a broader categorization. Despite the limited number of post-pandemic reviews, the dichotomy remains crucial for evaluating any shifts in customer perceptions and behaviors triggered by the pandemic.

    Additionally, the review data were categorized based on the restaurant’s TripAdvisor price classification. This study adhered to TripAdvisor’s three-tier categorization: cheap (‘$’), mid-range (‘$$-$$$’), and fine dining (‘$$$$’). As TripAdvisor does not specify exact price ranges for these categories, our analysis relied on the predefined classification system of the platform. Reviews of cheap restaurants produced 328, mid-range restaurants 1,954, and fine-dining restaurants 334. Such categorization based on price range plays a crucial role, given how economic factors influence customer satisfaction and specific restaurant service attribute evaluations.

    3. Impact Asymmetry Analysis

    1) Penalty Indices (PI) and Reward Indices (RI)

    This study adopted the Impact Asymmetry Analysis approach as outlined in the prior research by Back (2012). The penalty-reward contrast analysis method was implemented to comprehend the asymmetrical impacts of Korean restaurant attributes on customer satisfaction and dissatisfaction. This analysis aims to generate reward indices (RI) and penalty indices (PI) to understand the distinct effects of particular restaurant attribute performance (i.e., food, service, atmosphere, and value) on both customer satisfaction and dissatisfaction.

    In conducting the penalty-reward contrast analysis, dummy variables were assigned to restaurant attribute ratings, where the lowest rating (1 out of 5) was represented as 0 and the highest rating (5 out of 5) was coded as 1. Then, the linear regression models were constructed with the encoded dummy variables, with encoded restaurant attributes as the independent variables and the overall customer satisfaction score as a dependent variable. As a result of the linear regression analysis, the unstandardized coefficients for the highest attribute rating functioned as RI, whereas those for the lowest attribute rating functioned as PI. These indices illustrate the effects of specific restaurant attribute performance on CS and DS. PI and RI were assessed before and after the pandemic phases, and meal price ranges were divided into cheap, mid-range, and fine-dining.

    2) RICS and IA evaluation of restaurant service attributes

    The Range of Impact on Customer Satisfaction (RICS) index was generated using the PI and RI for each restaurant attribute. Specifically, the absolute values of the PI and RI for each attribute were added up to obtain RICS. RICS scores measure each restaurant attribute's impact on overall customer satisfaction. Furthermore, three more indices of Satisfaction-Generating Potential (SGP), Dissatisfaction- Generating Potential (DGP), and Impact Asymmetry (IA) were generated with the RICS scores and both the RI and PI scores. For each restaurant attribute i, the SGP was calculated by dividing the RI by the RICS score as indicated in Equation (1). Also, the DGP score was calculated by dividing the absolute value of the PI by the RICS score as detailed in Equation (2). Finally, the IA was calculated with the difference between SGP and DGP as shown in Equation (3). The IA index can demonstrate the role of each restaurant attribute in determining customer satisfaction or dissatisfaction.

    SGP i = RI i RICS i
    (1)

    DGP i = | PI i | RICS i
    (2)

    CapCapIA i = SGP i − DGP i
    (3)

    3) Categorizing restaurant attributes by Impact Asymmetry Analysis

    To categorize restaurant attributes based on the Impact Asymmetry Analysis (IAA), attributes are visualized on a grid, with the RICS on the x-axis and IA on the y-axis. The analysis also considers two variables: the pandemic phases (before and after) and different food price ranges (cheap, mid-range, and fine dining). Based on quartiles, the Range of Impact on Customer Satisfaction is divided into three groups: low, mid, and high impacts. RICS scores under the first quartile value (0.65) are categorized as low impact.

    RICS scores are classified into three groups: low, mid, and high, determined by quartiles. Scores below the first quartile (0.65) fall into the low-impact category, scores between the first and third quartiles (0.65 to 1.95) are classified as midimpact, and scores above the third quartile (1.95) are considered high-impact. In the previous research(Mikulic & Prebežac 2008;Back 2012), IA was divided into five categories as follows:

    • “Frustrator” (IA ≤ -0.7)

    • “Dissatisfier” (-0.7 < IA ≤ -0.2)

    • “Hybrid” (-0.2 < IA < 0.2)

    • “Satisfier” (0.2 ≤ IA < 0.7), and

    • “Delighter” (IA ≤ 0.7)

    Negative IA values for “Frustrators” and “Dissatisfiers” denote that DGP is noticeably more significant than the SGP. This implies that if the performance of specific restaurant attributes is poor, it is highly likely to cause customer dissatisfaction. However, even if these attributes are favorable, they will unlikely lead to customer satisfaction. In the case of “Satisfier” and “Delighter,” where the IA values are positive, SGP exceeds DGP. This indicates even when the performance of the detailed attributes of the restaurant is poor, there is a low possibility of leading to customer dissatisfaction. Still, when the performance of the corresponding qualities is good, there is a high possibility of causing customer satisfaction. In the case of “Hybrid,” the difference between the SGP and DGP values is insignificant. This means that when the performance of the detailed attributes of the restaurant is poor, this leads to customer dissatisfaction. Still, when the performance of the detailed qualities is good, this leads to customer satisfaction.

    III. Result and Discussion

    1. IAA – Pandemic phases

    <Figure 1> demonstrates the differences between RICS and IA values across pandemic phases (before the pandemic [prior to March 202] and after the pandemic [after March 2020]). The top panel of Figure 1 illustrates the RICS scores for different restaurant attributes, highlighting the highest impact of service on customer satisfaction before the pandemic, followed by food, value, and atmosphere. However, during the pandemic, the order shifted to service, atmosphere, food, and value, indicating a change in the relative importance of these attributes in shaping customer satisfaction. This shift suggests that the role of each attribute in influencing customer satisfaction evolved before and after the pandemic.

    The bottom panel of <Figure 1> presents the IA scores, demonstrating a positive IA value for food during the pandemic, signifying its strong potential to generate customer satisfaction. In contrast, the remaining attributes (service, value, and atmosphere) exhibited negative IA values before and after the pandemic, indicating their persistent potential to generate customer dissatisfaction. This finding underscores the need for improvements in these areas, particularly in service and value, to enhance the overall dining experience.

    The results of the Impact Asymmetry Analysis (IAA) for restaurant attributes before and after the pandemic are presented in <Table 1> and <Figure 2>. Before the pandemic, food and service had high RICS scores of 2.067 and 2.084, respectively. This shows that food and service had a high impact on customer satisfaction. Value and atmosphere had moderate RICS scores of 1.549 and 0.730, respectively, with a mild effect on customer satisfaction. Regarding the IA result, food was categorized as Hybrid. Value and atmosphere served as Dissatisfiers, while service served as Frustrator.

    During/After the pandemic, service maintained the highest RICS score of 2.076, with the highest RICS score on customer satisfaction, consistent with the pre-pandemic phase. Atmosphere (RICSAtmosphere=1.854) had a high impact on customer satisfaction, while this attribute moderately affected customer satisfaction before the pandemic. Food (RICSFood=1.506) and value (RICSValue=1.428) moderately impacted customer satisfaction. Regarding the IA outcome, food was categorized as Delighter, while the other attributes (i.e., service, value, and atmosphere) were Dissatisfiers.

    The findings revealed a shift in the importance of food attributes, which were highly valued pre-pandemic but saw moderate importance post-pandemic. This shift suggests a consumer focus transitioning from food quality to other factors, likely influenced by pandemic-related concerns. Post-pandemic, food was the only attribute to achieve ‘Delighter’ status, reflecting high satisfaction with food quality. Conversely, the service attribute consistently registered as a ‘Dissatisfier’ across both periods, emphasizing the need for significant improvements in service quality. Similarly, the value attribute, while moderately impactful, remained a source of dissatisfaction after the pandemic. This may be attributed to rising ingredient costs and increased labor expenses, which led to higher menu prices in restaurants (Żurek & Rudy 2024). Additionally, the shift toward kiosks and digital menus as a response to labor shortages may have contributed to dissatisfaction with service quality (Morosan & Bowen 2022). As a result, customers became more sensitive to the perceived value of Korean dining experiences, highlighting the need for strategies to enhance both service quality and value perception.

    2. IAA – Price range

    <Figure 3> illustrates the differences between RICS and IA values across food prices (i.e., cheap, mid-range, and fine-dining). <Figure 3> visualizes the RICS and IA values across different pricing categories (low-cost, mid-range, and high-cost restaurants) before and after the pandemic. The top panel of <Figure 3> presents the RICS scores, showing that food had the highest impact in low-cost restaurants, whereas service had the highest impact in mid-range and fine-dining restaurants. This indicates that customer satisfaction in affordable dining is primarily driven by food quality, while service plays a more critical role in mid-range and high-end restaurants. The bottom panel of <Figure 3> illustrates the IA scores, revealing that food had a positive IA score in midrange restaurants, while atmosphere had a positive IA score in fine-dining restaurants. This suggests that food quality in mid-range restaurants and atmosphere in fine-dining establishments had the highest potential to generate customer satisfaction. In contrast, all other attributes exhibited strong negative IA scores, indicating that service, value, and atmosphere in lower-priced segments remained key sources of dissatisfaction across all pricing categories.

    <Table 2> and <Figure 4> demonstrate the IAA result for restaurant attributes across varying food prices. For cheap restaurants, food had the highest RICS score of 2.475, indicating that food had the highest impact on customer satisfaction. Service and value had moderate RICS scores of 1.862 and 1.573, respectively, with a moderate impact on customer satisfaction. Atmosphere had the lowest RICS score of 0.636, demonstrating a low impact on customer satisfaction. Regarding the IA result, food and value attributes were categorized as Dissatisfier, while service and atmosphere attributes were categorized as Frustrator.

    For mid-range price restaurants, service had the highest RICS score of 2.073 with the highest impact on customer satisfaction. Food and value had moderate RICS scores of 1.638 and 1.585, respectively, with a mild effect on customer satisfaction. Atmosphere had the lowest RICS score of 0.777 among restaurant attributes, but this attribute was categorized into moderate impact. Regarding IA outcomes, all restaurant attributes (i.e., service, value, atmosphere), except for food categorized as Hybrid, were classified as Dissatisfier.

    For fine-dining restaurants, service had the highest RICS score of 2.178, with the highest RICS score on customer satisfaction, similar to the mid-range price restaurant. Food (RICSFood=1.935), Value (RICSValue=1.890), and Atmosphere (RICSAtmosphere=0.845) had a moderate impact on customer satisfaction. For the IA result of fine-dining restaurants, the atmosphere is satisfactory, with the highest IA score. Food was categorized into Hybrid. Service and value were categorized into Frustrator.

    The study found that customer responses varied significantly across price categories, highlighting how economic factors shape dining expectations. In low-cost Korean restaurants, food had the highest RICS score, indicating its strong influence on satisfaction. Service, while moderate in lowcost venues, had a significant impact in mid-range and highcost venues but failed to meet expectations, particularly in high-cost settings. This finding suggests a gap between price and perceived service quality. Post-pandemic, atmosphere became more important, particularly in high-cost restaurants, where it emerged as a ‘Satisfier’, reflecting heightened sensitivity to hygiene and safety. Omar et al. (2021) found that unclean restaurant environments can intensify customers’ fear of infectious diseases, emphasizing the need for strict hygiene standards to mitigate such concerns in the postpandemic era. Similarly, the recent study of Kumar et al. (in press) found that restaurant safety measures significantly impact customer revisit intentions after the pandemic.

    IV. Summary and Conclusion

    Similar to a previous study (Back 2012), which applied IAA to explore customer satisfaction in the restaurant industry, this study confirmed the non-linear and asymmetric nature of customer reactions to service attributes, emphasizing the significance of food, service, atmosphere, and value. While prior research has established the asymmetric relationship between service attributes and overall satisfaction (Lee & Choi 2020), the roles of restaurant attributes as satisfiers or dissatisfiers varied considerably.

    Existing studies have often neglected sample diversity, relying on narrow contexts using survey data (Back 2012;Ju et al. 2019). This study, however, extended IAA across a borader dataset, analyzing multiple Korean restaurants in the top 100 U.S. cities. This approach allowed for a more comprehensive examination of customer satisfaction drivers across different regions and competitive landscapes. Previous research has shown that satisfied customers do not always exhibit loyal, as satisfaction can fluctuate basen on situational factors (Jung & Yoon 2012). This study explored how external factors, such as the COVID-19 pandemic and restaurant price categories, influenced consumer perceptions.

    This research makes theoretical contributions to the literature on ethnic restaurants, particularly Korean restaurants outside of Korea. While most studies have focused on n consumer behavior in hotels or satisfaction with different ethnic cuisines (Min & Lee 2014), this study fills a gap by exploring the relationship between restaurant attributrs and satisfaction in the Korean restaurants. Existing research has often relied on surveys, but this study emphasized the growing importance of online reviews in shaping consumer perceptions (Hennig-Thurau et al. 2010). By using “Big data”, this study offeres robust evidence of asymmetric effects between attributes and customer satisfaction, unlike previous research that relied on “small data” from limited market segments.

    The study also has practical implications for managing ethnic Korean restaurants across various economic contexts. The IAA findings highlight the need for restaurant operator to maintain or enhance food quality, particularly in light of the pandemic’s operational challenges. Additionally, while service and atmosphere gained importance post-pandemic, these did not correspond with higher satisfaction levels, suggesting a need for strategic imporvements in these areas. For low-cost Korean restaurants, even modest enhancements in dining atmosphere—such as improved cleanliness, seating arrangements, or décor—could positively influence customer satisfaction without significantly increasing costs. In midrange Korean restaurants, balancing service efficiency with personalized customer interactions may be key to improving perceived service quality. Given that service has remained a ‘Dissatisfier,’ implementing better staff training, optimizing wait times, or incorporating a hybrid approach of digital ordering with human assistance could help enhance customer experiences.

    For high-cost Korean restaurants, aligning pricing strategies with customer expectations for food and service quality is essential. As customers in this segment are more sensitive to the overall dining experience, investing in premium service, high-quality ingredients, and exclusive dining atmospheres may enhance perceived value and satisfaction.

    However, this study has limitations. It relied solely on data from TripAdvisor, which may limit the findings’ generalizability. Future studies should compare data from multiple platforms. Additionally, while numerical ratings were used for IAA, text analysis could prvide deeper insights into the reasons behing customer satisfaction or dissatisfaction. The study also focused on U.S. data, so the results may not be generalizable to ethnic restaurants in other regions. While this study aims to explore the potential impact of key variables under the changing conditions of the COVID-19 pandemic, the imbalance in the number of reviews before and after the pandemic remains a limitation. To improve generalizability, future research should seek to collect a more balanced dataset. Future studies could also compare customer perception across different periods: before, during, and after COVID-19 pandemic. Finally, this study is limited by its reliance on TripAdvisor reviews, which may primarily reflect tourist preferences rather than local dining behaviors, potentially affecting the generalizability of the findings. Additionally, it does not account for demographic backgrounds of customers or explore other types of ethnic restaurants, leaving room for future research to incorporate local review platforms, consumer surveys, and diverse restaurant types for a more comprehensive understanding.

    Acknowledgments

    This work was supported by Jungseok Logistics Foundation.

    Author biography

    Hee Jin Um (Department of Food Nutrition, Gachon University, Undergraduate Student, 0009-0005-3704-1361)

    Hee Yoon Kwon (Department of Food Nutrition, Gachon University, Undergraduate Student, 0009-0007-1790-6422)

    Sung-Bum Kim (Department of Business Administration, Inha University, Associate Professor, 0000-0003-2817-6457)

    Eunhye Park (Department of Food Nutrition, Gachon University, Assistant Professor, 0000-0002-5470-3065)

    Conflict of Interest

    No potential conflict of interest relevant to this article was reported.

    Figure

    KJFC-40-1-36_F1.gif

    The difference in RICS and IA values by price (Upper: RICS values, Lower: IA values).

    KJFC-40-1-36_F2.gif

    Impact Asymmetric Analysis of restaurant attributes across food prices.

    KJFC-40-1-36_F3.gif

    The difference in RICS and IA values by price (Upper: RICS values, Lower: IA values).

    KJFC-40-1-36_F4.gif

    Impact Asymmetric Analysis of restaurant attributes across food prices.

    Table

    Impact Asymmetric Analysis results across pandemic phases

    Impact Asymmetric Analysis results across food prices.

    Reference

    1. Aybek G, Özdemir B. 2022. Effects of ethnic restaurant experience on prospective tourist intentions: Mediating role of food image. Tour. Manag. Perspect., 44:101034
    2. Back KJ. 2012. Impact-range performance analysis and asymmetry analysis for improving quality of Korean food attributes. Int. J. Hosp. Manag., 31:535-543
    3. Ha, J. 2019. “Why do you dine at Korean restaurants?” An ethnic food consumption model approach. J. Foodserv. Bus. Res., 22(2):132-149
    4. Hennig-Thurau T, Malthouse EC, Friege C, Gensler S, Lobschat L, Rangaswamy A, Skiera B. 2010. The impact of new media on customer relationships. J. Serv. Res., 13(3): 311-330
    5. Jang S, Liu Y, Namkung Y. 2011. Effects of authentic atmospherics in ethnic restaurants: Investigating Chinese restaurants. Int. J. Contemp. Hosp. Manag., 23(5):662-680
    6. Ju Y, Back KJ, Choi Y, Lee JS. 2019. Exploring Airbnb service quality attributes and their asymmetric effects on customer satisfaction. Int. J. Hosp. Manag., 77:342-352
    7. Jung HS, Yoon HH. 2012. Why do satisfied customers switch? Focus on the restaurant patron variety-seeking orientation and purchase decision involvement. Int. J. Hosp. Manag., 31:875-884
    8. Jung HS, Yoon HH, Song MKA. 2021. Study on dining-out trends using big data: Focusing on changes since COVID- 19. Sustain., 13:11480
    9. Kano T, Seraku N, Takahashi F, Tsuji S. 1984. Attractive quality and must-be quality. J. Jpn. Soc. Qual. Control, 14:39-48
    10. Kim JM, Liu J, Park KK-C. 2023. The dynamics in asymmetric effects of multi-attributes on customer satisfaction: evidence from COVID-19. Int. J. Contemp. Hosp. Manag., 35(10): 3497-3517
    11. Kumar J, Islam MT, Mehmood W, Sangaran G. in press. The impact of restaurant safety measures on customer revisit intentions in the post-COVID era. J. Foodserv. Bus. Res.
    12. Lee J-S, Choi M. 2020. Examiing the asymmetric effect of multishopping tourism attributes on overall shopping destination satisfaction. J. Travel Res., 59(3):004728751983237
    13. Mak AH, Lumbers M., Eves A. 2012. Globalisation and food consumption in tourism. Ann. Tour. Res., 39(1):171-196
    14. Matzler K, Renzl B. 2007. Assessing asymmetric effects in the formation of employee satisfaction. Tour. Manag., 28(4): 1093-1103
    15. Mikulic J, Prebežac D. 2008. Prioritizing improvement of service attributes using impact range-performance analysis and impact-asymmetry analysis. Manag. Serv. Qual., 18(6): 559-576
    16. Min K-H, Lee TJ. 2014. Customer satisfaction with Korean restaurants in Australia and their role as ambassadors for tourism marketing. J. Travel Tour. Mark., 31-34:493-506
    17. Morosan C, Bowen JT. 2022. Labor shortage solution: redefining hospitality through digitization. Int. J. Contemp. Hosp. Manag., 34(12):4674-4685.
    18. Oh M, Kim S. 2020. Dimensionality of ethnic food fine dining experience: An application of semantic network analysis. Tour. Manag. Perspect., 35:100719
    19. Omar NA, Jusoh A, Alwi SFS. Ramsaran R. 2021. Restaurant hygiene attributes and consumers' fear of COVID-19: The mediating role of perceived infection and the moderating role of trust in management. J. Foodserv. Bus. Res., 24(4):368-389.
    20. Park S, Yang Y, Wang M-S. 2019. Travel distance and hotel service satisfaction: an inverted U-shaped relationship. Int. J. Hosp. Manag., 76:261-270
    21. Wang P, Han L, Mei R. 2022. An impact asymmetry analysis of small urban green space attributes to enhance visitor satisfaction. Int. J. Environ. Res. Public Health, 19:2922.
    22. Wong JWC, Lai IKW. 2018. Evaluating value co-creation activities in exhibitions: An impact asymmetry analysis. Int. J. Hosp. Manag., 72:118-131
    23. Żurek J, Rudy M. 2024. Impact of the COVID-19 pandemic on changes in consumer purchasing behavior in the food market with a focus on meat and meat products—A comprehensive literature review. Foods, 13(6):933.
    Copyright © Korean Society of Food Culture. All right reserved.