literature review for restaurant management system

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

CHAPTER 2 LITERATURE REVIEW

It is a literature review for restaurant management system

Related Papers

International Journal for Research in Applied Science and Engineering Technology

Adesh Adhao

American Journal of Multidisciplinary Research and Innovation (AJMRI) ISSN: 2158-8155 (Online), 2832-4854 (Print)

Joy Dhon Chakma , Ricky Francis Rozario

The study was conducted to determine the pros and cons of using Restaurant Management System (RMS) based on the views of restaurant staff in the context of Bangladesh. The effectiveness of RMS use in terms of business features was examined and differences were sought. Data were collected using a structured questionnaire. Participants working in restaurants where RMS was used were of the view that RMS simplified operations, increased sales, and improved product / service quality, while those working in restaurants where RMS was not used had higher scores in expressions of difficulty using the system. In addition, RMS has a more positive impact on sales growth and product / service quality delivery according to the chain restaurant staff (p <0.05). Again, restaurant employees with a minimum score of 10 or fewer employees are included in relation to the positive impact of using RMS in terms of operations management and sales growth. Therefore, there is a relationship between business size and RMS usage requirements.

Rasyid Zahari

Advances in Hospitality, Tourism, and the Services Industry

mehmet kocaman

Customers expect a high standard and fast service from enterprises. In addition, competition among enterprises necessitates that enterprises renew themselves, meet customer expectations at maximum level, and raise the standard of products and services. Traditional restaurant management is inadequate to provide all this. This situation led to search, and restaurant management systems (RMS) have been developed. RMS, which emerged in the 1970s, are now much more developed, facilitating both the operation and management process and offering a professional management opportunity. RMS has made it possible for the restaurants to institutionalize and establish chain enterprises. Moreover, income and expense control can be made more effective via RMS. This chapter explains RMS and the operation of RMS via a sample program.

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

IJRASET Publication

The Restaurant Management System provides necessary services for the typical fast food restaurant to manage all the day-today activities. The restaurant management system is there to help communication between all teams within a restaurant by minimizing the probability of human error and getting more efficient and effective information. This system has a built-in POS system with order management solutions and kitchen management to handle all the food production efficiently to get the orders to the customer

Ahasan Bari

eSAT Journals

Existing restaurant systems are partially automated. We have developed a fully automated restaurant management and communication system. This paper covers the partial automation that exists and focuses the details regarding how we have implemented full automation in management and communication for restaurants. It also covers technical aspects of web based application and android application and how they are clubbed together with their advantages and disadvantages. It also lights the future work that can be added on regarding android application to make it more user friendly.

International Journal For Reseacrh In Applied Science And Engineering Technology

Catering Service for food/Online Cafe (OC) is the software designed to order food online for Customers. The prime objective of the project is to provide smart and effective service to the end-users. It is a web-based application that is built using JAVA with a user-friendly interface for customers to browse the catalogue and order the food online. The shop keepers can include as many products as possible and also categorize them as they prioritize, manage orders and process the payments. The clients have to make their own choice and choose their preferred mode of payment and wait for the delivery of their food items. The OC system processes all sorts of information related to the order type options. It facilitates an end to end solution in online food ordering. By adopting this new approach, the information can be accessed with just a single click. The study focuses on a web application in JAVA for online food ordering system and provides an option for the restaurant owners to update their food menu as well. This study also focuses on establishing effective connectivity between the front end (shopkeeper and customer) and back-end (admin) using JAVA database connectivity with MySQL. Keywords: catering, customer, food order, online cafe, shopkeeper I. INTRODUCTION The technological innovations and advancements in information and communication technology have undergone a paradigm change in the last decade. Ever-rapidly evolving strategies that adapt itself to the ever-changing human practices are the need of the hour. Despite innovations happening very rapidly across various industries, Food industry had always been lagging in terms of innovations, novelty and modernization. With online presence being felt across all industries, the food industry has undergone a revolutionary change in practice in the last five years. Food systems these days have undergone a massive shift from being supply-driven to demand-driven [1]. In the past, the food industry traditionally lagged behind other industries in adapting itself to innovations and newer technologies. Recent advances in the field of computer technology and ever-increasing expectations from the end-users (consumers) have made it mandatory for the food industry to bring in a full-fledged automated process that enabled complete transparency in the food sale and distribution process [2]. Another motivation can be considered as the increasing use of smartphones by the customers so that any users of this system get all service of the system. The system will be designed to avoid users doing fatal errors where users can change their profile also where users can track their food items through GPS and where users can provide feedback and recommendations to Restaurants/Mess service providers. There's a need for the system due to lack of a full-fledge application that can fulfil the customer requirements by providing him food from restaurants/mess service. For the students studying in different cities, our system will be very helpful. The flexibility to the Customers/Users to order from either Restaurants or Mess is provided by our system. A recommendation to the customers is also provided from the restaurants/mess owners which are updated daily. There will be no limitation on the amount of order the customer wants by ordering food from our system. As a Startup Business for the developers, the same system application can be used. Real-time customer's feedback ratings are provided by our system with the comments to the restaurants/mess owner. It gives appropriate feedbacks to users, so if there is any error happened, and then there will be a feedback dialogue toward users. To avoid users doing fatal errors and inappropriate action our system application is designed. Input will be taken by the user from the graphical user interface. The major attributes such as name, address, and email-Id, mobile no, other personal related values will give input to the dataset. The User/Customer's Order, Bill, Feedback and Recommendation will provide the output. For the initial implementation of the system, we have considered 2 restaurants and 2 mess services in 5 areas. Khairunnisa proposed a project that presented an in-depth technical operation of the Wireless Ordering System (WOS) that included systems architecture, their function, various limitations and possible recommendations [3]. Our research project aims to design and develop a fully automated wireless food ordering system facility for the customers ordering food from a restaurant.

Abhijith Ks

citra wiguna

Indonesia is one of the largest culinary attractions in Southeast Asia. The culinary diversity in Indonesia encourages stakeholders to create restaurant businesses with effective and efficient business processes. One of the efforts to make the restaurant business process effective and efficient is to build a restaurant management information system. By using a system, business actors will be able to control and evaluate their business with appropriate reports. For this reason, the purpose of this study is to build a restaurant management information system using the RAD (Rapid Application Development) method, because building this application requires a systematic, structured, and object-oriented process. The RAD method emphasizes a high quality systems, fast development and delivery and low costs development process [1] so that this process requires a serious role from business actors to build a restaurant management information system. The results of this system design can make ...

RELATED PAPERS

International Journal of Impotence Research

George Christ

Esperanza Velez

American Anthropologist

Jurnal Akuakultur Sungai dan Danau

SYAHRIZAL SYAHRIZAL

Frieda Vandeninden

Proceedings of the 1st International Conference on Education: Current Issues and Digital Technologies (ICECIDT 2021)

Dmitriy Utkin

Manja Abraham

josefa capel martinez

BMC Research Notes

Aklilu Ayele

Ketil Thorstensen

Nazima Ellahi

Jurnal Pascapanen dan Bioteknologi Kelautan dan Perikanan

Dr.Ir.Anies Chamidah, MP

Atik Aprianingsih

xiaoran han

Revista de Psicología

Márcia Stengel

Journal of Oceanography

hendra permana

Metodicki Ogledi Methodical Review

The American Journal of Emergency Medicine

Özlem BİLİR

The Journal of the Gilded Age and Progressive Era

Jørn Brøndal

Journal of Research in Veterinary Medicine

Bahri Yildiz

Acta Crystallographica Section A Foundations and Advances

Karla Ordonez

Brazilian Journal of Otorhinolaryngology

Lesly García

Muxabbat Mirazimova

international food research journal

Marquez-Farías, Fernando

Biological Psychiatry

Ziad Kronfol

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Approaches for restaurant revenue management

• Research Article
• Published: 23 February 2021
• Volume 21 , pages 17–35, ( 2022 )

Cite this article

• Mohit Tyagi 1 &
• Nomesh B. Bolia 1  

1214 Accesses

6 Citations

Explore all metrics

Revenue management (RM) helps predict customer demand to optimize inventory availability and price so that revenue growth can be maximized. The main aim of revenue management is to sell the right product at the right time and for the right price to the right consumer. Over the past two decades, revenue management techniques for restaurant industries have started to appear in the literature. This paper aims to thoroughly review this literature and identify emerging issues. The paper is mainly structured around strategic levers of restaurant revenue management, barriers to the implementation of revenue management strategies in restaurants, and emerging themes in restaurant revenue management. The paper concludes with a summary of key findings and carefully identified directions for future research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Supply chain disruptions and resilience: a major review and future research agenda

K. Katsaliaki, P. Galetsi & S. Kumar

Competitive pricing on online markets: a literature review

Torsten J. Gerpott & Jan Berends

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

Miguel Alves Gomes & Tobias Meisen

Aburto, Luis, and Richard Weber. 2007. A Sequential Hybrid Forecasting System for Demand Prediction. In International Workshop on Machine Learning and Data Mining in Pattern Recognition , 518–32. Berlin, Heidelberg: Springer.

Alexandrov, Alexei, and Martin A. Lariviere. 2011. Are Reservations Recommended?”. Manufacturing & Service Operations Management 14 (2): 218–30. https://doi.org/10.1287/msom.1110.0360 .

Article   Google Scholar  

Alon, Ilan, Min Qi, and Robert J. Sadowski. 2001. Forecasting Aggregate Retail Sales: A Comparison of Artifcial Neural Networks and Traditional Methods. Journal of Retailing and Consumer Services 8 (3): 147–56. https://doi.org/10.1016/S0969-6989(00)00011-4 .

Anderson, Chris K., and Xiaoqing Xie. 2010. Improving Hospitality Industry Sales: Twenty-Five Years of Revenue Management. Cornell Hospitality Quarterly 51 (1): 53–67. https://doi.org/10.1177/1938965509354697 .

Barz, Christiane, and Karl-Heinz Waldmann. 2006. An Application of Markov Decision Processes to the Seat Inventory Control Problem. In Perspectives on Operations Research , 113–28. DUV.

Bayou, Mohamed E., and Lee B. Bennett. 1992. Profitability Analysis for Table-Service Restaurants. The Cornell Hotel and Restaurant Administration Quarterly 33 (2): 49–55. https://doi.org/10.1201/9780203910016.ch13 .

Bertsimas, Dimitris, and Romy Shioda. 2003. Restaurant Revenue Management. Operations Research 51 (3): 472–86. https://doi.org/10.1287/opre.51.3.472.14956 .

Box, George E.P.., Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung. 2015. Time Series Analysis: Forecasting and Control . San Francisco: John Wiley & Sons.

Google Scholar  

Çavuşoğlu, Mehmet. 2012. Electronic Commerce and Turkish Patterns of Online Food Delivery System. Journal of Internet Applications & Management/İnternet Uygulamaları ve Yönetimi Dergisi 3 (1): 45–62.

Cetin, Gurel, Tevfik Demirciftci, and Anil Bilgihan. 2016. Meeting Revenue Management Challenges: Knowledge, Skills and Abilities. International Journal of Hospitality Management 57: 132–42. https://doi.org/10.1016/j.ijhm.2016.06.008 .

Chen, Pin-Chang., Chih-Yao. Lo, Hung-Teng. Chang, and Yu. Locho. 2011. A Study of Applying Artificial Neural Network and Genetic Algorithm in Sales Forecasting Model. Journal of Convergence Information Technology 6 (9): 352–62.

Chiang, Wen Chyuan, Jason C.H.. Chen, and Xu. Xiaojing. 2007. An Overview of Research on Revenue Management: Current Issues and Future Research. International Journal of Revenue Management 1 (1): 97–128. https://doi.org/10.1504/IJRM.2007.011196 .

Choi, Sunmee, and Anna S. Mattila. 2005. Impact of Information on Customer Fairness Perceptions of Hotel Revenue Management. Cornell Hotel and Restaurant Administration Quarterly 46 (4): 444–51. https://doi.org/10.1177/0010880404270032 .

Choi, Sunmee, and Anna S. Mattila. 2004. Hotel Revenue Management and Its Impact on Customers’ Perceptions of Fairness. Journal of Revenue and Pricing Management 2 (4): 303–14. https://doi.org/10.1057/palgrave.rpm.5170079 .

Cohen, E., R. Mesika, and Z. Schwartz. 1998. Multidimensional Approach to Menu Sales Mix Analysis. Praxis 2 (1): 130–44.

Coxe, Stefany, Stephen G. West, and Leona S. Aiken. 2009. The Analysis of Count Data: A Gentle Introduction to Poisson Regression and Its Alternatives. Journal of Personality Assessment 91 (2): 121–36. https://doi.org/10.1080/00223890802634175 .

Cranage, David A., and William P. Andrew. 1992. A Comparison of Time Series and Econometric Models for Forecasting Restaurant Sales. International Journal of Hospitality Management 11 (2): 129–42. https://doi.org/10.1016/0278-4319(92)90006-H .

Cravy. 2018. The Restaurant Industry—A Global Perspective. 2018. https://medium.com/@CravyHQ/the-restaurant-industry-a-global-perspective-26cea1b91701 .

Cross, Robert G. 1997. Launching the Revenue Rocket: How Revenue Management Can Work for Your Business. Cornell Hotel and Restaurant Administration Quarterly 38 (2): 32–43.

Cui, Yao, Izak Duenyas, and Ozge Sahin. 2018. Unbundling of Ancillary Service: How Does Price Discrimination of Main Service Matter?”. Manufacturing and Service Operations Management 20 (3): 455–66. https://doi.org/10.1287/msom.2017.0646 .

De Vries, Jelle, Debjit Roy, and René De Koster. 2018. Worth the Wait? How Restaurant Waiting Time Influences Customer Behavior and Revenue. Journal of Operations Management 63: 59–78. https://doi.org/10.1016/j.jom.2018.05.001 .

Desiraju, Ramarao, and Steven M. Shugan. 1999. Strategic Yield Service And. Journal of Marketing 63 (1): 44–56.

Dickson, Duncan, Robert C. Ford, and Bruce Laval. 2005. Managing Real and Virtual Waits in Hospitality and Service Organizations. Cornell Hotel and Restaurant Administration Quarterly 46 (1): 52–68. https://doi.org/10.1177/0010880404271560 .

Doan Ngoc, Ha. 2013. Demand Creation of Online Services for B2B and Consumer Market-Food Delivery in Vietnam .

Dodds, William B., Kent B. Monroe, and Dhruv Grewal. 1991. Effect of Price, Brand, and Store Information Buyers ’ Evaluations. Journal of Marketing Research 28 (3): 307–19.

Escobari, Diego, Nicholas G. Rupp, and Joseph Meskey. 2018. Dynamic Price Discrimination in Airlines. 88078.

Etemad-Sajadi, Reza. 2018. Are Customers Ready to Accept Revenue Management Practices in the Restaurant Industry?”. International Journal of Quality & Reliability Management 35 (4): 846–56.

Fard, Farzad Alavi, Malick Sy, and Dmitry Ivanov. 2019. Optimal Overbooking Strategies in the Airlines Using Dynamic Programming Approach in Continuous Time. Transportation Research Part E: Logistics and Transportation Review 128: 384–99. https://doi.org/10.1016/j.tre.2019.07.001 .

Fischer, John W. 2005. At Your Service: A Hands-On Guide to the Professional Dining Room . New York: John Wiley & Sons Incorporated.

Forst, Frank G. 1992. Forecasting Restaurant Sales Using Multiple Regression And Box-Jenkins Analysis. Journal of Applied Business Research (JABR) 8 (2): 15. https://doi.org/10.19030/jabr.v8i2.6157 .

Fukui, Hideki, and Koki Nagata. 2020. How Do Passengers React to Airlines’ Overbooking Strategies? Evidence from the US Airlines. Transportation Research Part A: Policy and Practice 132: 242–55.

Gregorash, Bill J. 2016. Restaurant Revenue Management: Apply Reservation Management?”. Information Technology and Tourism 16 (4): 331–46. https://doi.org/10.1007/s40558-016-0065-0 .

Grewal, Dhruv, Kent B. Monroe, and R. Krishnan. 1998. The Effects of Price-Comparison Advertising on Buyers’ Perceptions of Acquisition Value, Transaction Value, and Behavioral Intentions. Journal of Marketing 62 (2): 46–59. https://doi.org/10.2307/1252160 .

Guerriero, Francesca, Giovanna Miglionico, and Filomena Olivito. 2014. Strategic and Operational Decisions in Restaurant Revenue Management. European Journal of Operational Research 237 (3): 1119–32.

Guillet, Basak Denizci. 2020. An Evolutionary Analysis of Revenue Management Research in Hospitality and Tourism: Is There a Paradigm Shift?”. International Journal of Contemporary Hospitality Management 32 (2): 560–87. https://doi.org/10.1108/IJCHM-06-2019-0515 .

Guillet, Basak Denizci, Yuanyuan Guo, and Rob Law. 2015. Segmenting Hotel Customers Based on Rate Fences through Conjoint and Cluster Analysis. Journal of Travel & Tourism Marketing 32 (7): 835–51. https://doi.org/10.1080/10548408.2015.1063825 .

Guillet, Basak Denizci, Rob Law, and Deniz Kucukusta. 2018. How Do Restaurant Customers Make Trade-Offs among Rate Fences? Journal of Foodservice Business Research 21 (4): 359–76. https://doi.org/10.1080/15378020.2017.1401896 .

Guillet, Basak Denizci, and Ibrahim Mohammed. 2015. Revenue Management Research in Hospitality and Tourism. International Journal of Contemporary Hospitality Management 27 (4): 526–60. https://doi.org/10.1108/IJCHM-06-2014-0295 .

Guo, Xiaolong, and Xiabing Zheng. 2017. Examination of Restaurants Online Pricing Strategies: A Game Analytical Approach. Journal of Hospitality Marketing and Management 26 (6): 659–73. https://doi.org/10.1080/19368623.2017.1272085 .

Hayes, David K., and Lynn Huffman. 1985. Menu Analysis: A Better Way. Cornell Hotel and Restaurant Administration Quarterly 25 (4): 64–70.

Heide, Morten, Camilla White, Kjell Grønhaug, and Terje M. Østrem. 2008. Pricing Strategies in the Restaurant Industry. Scandinavian Journal of Hospitality and Tourism ISSN 8 (3): 251–69. https://doi.org/10.1080/15022250802451065 .

Heo, Cindy Y. 2013. Restaurant Revenue Management. Revenue Management for Hospitality and Tourism , 118–29.

Heo, Cindy Yoonjoung. 2016. Exploring Group-Buying Platforms for Restaurant Revenue Management. International Journal of Hospitality Management 52: 154–59. https://doi.org/10.1016/j.ijhm.2015.07.009 .

Heo, Cindy Yoonjoung. 2017. International Journal of Hospitality Management New Performance Indicators for Restaurant Revenue Management: ProPASH and ProPASM. International Journal of Hospitality Management 61: 1–3. https://doi.org/10.1016/j.ijhm.2016.10.005 .

Heo, Cindy Yoonjoung, and Seoki Lee. 2011. Influences of Consumer Characteristics on Fairness Perceptions of Revenue Management Pricing in the Hotel Industry. International Journal of Hospitality Management 30 (2): 243–51. https://doi.org/10.1016/j.ijhm.2010.07.002 .

Heo, Cindy Yoonjoung, Seoki Lee, Anna Mattila, and Hu. Clark. 2013. Restaurant Revenue Management: Do Perceived Capacity Scarcity and Price Differences Matter?”. International Journal of Hospitality Management 35: 316–26.

Hoang, Phi. 2007. The Future of Revenue Management and Pricing Science. Journal of Revenue and Pricing Management 6 (2): 151–53. https://doi.org/10.1057/palgrave.rpm.5160069 .

Horton, Brett W. 2001. Labor and Menu Category: Effects on Analysis. Hospitality Review 19 (2): 35–46.

Hossain, Ferdaus, and Adesoji O. Adelaja. 2000. Consumers ’ Interest in Alternative Food Delivery Systems: Results from a Consumer Survey in New Jersey. Journal of Food Distribution Research 31: 49–67.

Hu, Clark, Ming Chen, and Shiang-Lih Chen. McCain. 2004. Forecasting in Short-Term Planning and Management for a Casino Buffet Restaurant Clark. Journal of Travel & Tourism Marketing 16 (2–3): 79–98. https://doi.org/10.1300/J073v16n02 .

Ishigaki, Tsukasa, Takeshi Takenaka, and Yoichi Motomura. 2010. Customer-Item Category Based Knowledge Discovery Support System and Its Application to Department Store Service. In 2010 IEEE Asia-Pacific Services Computing Conference , 371–77. IEEE. https://doi.org/10.1109/APSCC.2010.69 .

Kansara, Darshini, and Mradul Mishra. 2019. Indian Restaurant & Food Service Industry—Structure & Prospects .

Kasavana, Michael L. 2001. EMarketing: Restaurant Websites That Click. Journal of Hospitality & Leisure Marketing 9 (3–4): 161–78. https://doi.org/10.1300/J150v09n03 .

Kasavana, Michael L., and Donald I. Smith. 1982. Menu Engineering: A Practical Guide to Menu Analysis . Hospitality Publications.

Khan, Mahmood A. 2020. Technological Disruptions in Restaurant Services: Impact of Innovations and Delivery Services. Journal of Hospitality and Tourism Research 20 (10): 1–18. https://doi.org/10.1177/1096348020908636 .

Kim, Kwangji, Mi.-jung Kim, and Jae-kyoon Jun. 2020. Small Queuing Restaurant Sustainable Revenue Management. Sustainability 12 (8): 3477.

Kim, Woo Gon, Jun Justin Li, and Robert A. Brymer. 2016. The Impact of Social Media Reviews on Restaurant Performance: The Moderating Role of Excellence Certificate. International Journal of Hospitality Management 55: 41–51. https://doi.org/10.1016/j.ijhm.2016.03.001 .

Kimes, Sheryl E. 1989. The Basics of Yield Management. Cornell Hotel and Restaurant Administration Quarterly 30 (3): 14–19.

Kimes, Sheryl E. 1994. Perceived Fairness of Yield Management: Applying Yield-Management Principles to Rate Structures Is Complicated by What Consumers Perceive as Unfair Practices. Cornell Hotel and Restaurant Administration Quarterly 35 (1): 22–29.

Kimes, Sheryl E. 1999. Implementing Restaurant Revenue Management: A Five-Step Approach. Cornell Hotel and Restaurant Administration Quarterly 40 (3): 16–21.

Kimes, Sheryl E. 2002. Perceived Fairness of Yield Management. Cornell Hotel and Restaurant Administration Quarterly 43 (1): 21–30. https://doi.org/10.1016/S0010-8804(02)80005-2 .

Kimes, Sheryl E. 2004. Restaurant Revenue Management: Implementation at Chevys Arrowhead. Cornell Hotel and Restaurant Administration Quarterly 45 (1): 52–67. https://doi.org/10.1177/0010880403260107 .

Kimes, Sheryl E. 2005. Restaurant Revenue Management: Could It Work?”. Journal of Revenue and Pricing Management 4 (1): 95–97. https://doi.org/10.1057/palgrave.rpm.5170132 .

Kimes, Sheryl E. 2008. The Role of Technology in Restaurant Revenue Management. Cornell Hospitality Quarterly 49 (3): 297–309. https://doi.org/10.1177/1938965508322768 .

Kimes, Sheryl E. 2011. Customer Attitudes towards Restaurant Reservations Policies. Journal of Revenue and Pricing Management 10 (3): 244–60. https://doi.org/10.1057/rpm.2009.24 .

Kimes, Sheryl E. 2011b. Customer Perceptions of Electronic Food Ordering. Cornell Hospitality Report 11 (10): 6–15. https://scholarship.sha.cornell.edu/chrpubs .

Kimes, Sheryl E. 2011. The Future of Distribution Management in the Restaurant Industry. Journal of Revenue and Pricing Management 10 (2): 189–94. https://doi.org/10.1057/rpm.2011.1 .

Kimes, Sheryl E., Deborah I. Barrash, and John E. Alexander. 1999. Developing a Restaurant Revenue-Management Strategy. Cornell Hotel and Restaurant Administration Quarterly 40 (5): 18–29. https://doi.org/10.1177/001088049904000505 .

Kimes, Sheryl E., and Jonathan Beard. 2013. The Future of Restaurant Revenue Management. Journal of Revenue and Pricing Management 12 (5): 464–69. https://doi.org/10.1057/rpm.2013.22 .

Kimes, Sheryl E., Richard B. Chase, Sunmee Choi, Philip Y. Lee, and Elizabeth N. Ngonzi. 1998. Restaurant Revenue Management: Applying Yield Management to the Restaurant Industry. Cornell Hotel and Restaurant Administration Quarterly 39 (3): 32–39.

Kimes, Sheryl E., and Jeannette Ho. 2019. Implementing Revenue Management in Your Restaurants: A Case Study with Fairmont Raffles Hotels International. Cornell Hospitality Report . Vol. 19.

Kimes, Sheryl E., and Leo M. Renaghan. 2011. The Role of Space in Revenue Management. In Revenue Management , 17–28. London: Palgrave Macmillan.

Chapter   Google Scholar  

Kimes, Sheryl E., and Stephani K A. Robson. 2004. The Impact of Restaurant Table Characteristics on Meal Duration and Spending. Cornell Hotel and Restaurant Administration Quarterly 45 (4): 333–46. https://doi.org/10.1177/0010880404270063 .

Kimes, Sheryl E., and Gary M. Thompson. 2004. Restaurant Revenue Management at Chevys: Determining the Best Table Mix. Decision Sciences 35 (3): 371–92. https://doi.org/10.1111/j.0011-7315.2004.02531.x .

Kimes, Sheryl E., and Gary M. Thompson. 2005. An Evaluation of Heuristic Methods for Determining the Best Table Mix in Full-Service Restaurants. Journal of Operations Management 23 (6): 599–617. https://doi.org/10.1016/j.jom.2004.07.010 .

Kimes, Sheryl E., and Jochen Wirtz. 2002. Perceived Fairness of Demand-Based Pricing for Restaurants. Cornell Hotel and Restaurant Administration Quarterly 43 (1): 31–37. https://doi.org/10.1016/S0010-8804(02)80006-4 .

Kimes, Sheryl E., and Jochen Wirtz. 2003. Has Revenue Management Become Acceptable?: Findings From an International Study on the Perceived Fairness of Rate Fences. Journal of Service Research 6 (2): 125–35. https://doi.org/10.1177/1094670503257038 .

Kimes, Sheryl E., and Jochen Wirtz. 2007. Customer Satisfaction with Seating Policies in Casual-Dining Restaurants. Cornell Hospitality Report . Vol. 7. https://scholarship.sha.cornell.edu/chrpubs/63 .

Kimes, Sheryl E., and Jochen Wirtz. 2016. Revenue Management in Restaurants: Unbundling Pricing for Reservations from the Core Service. Cornell Hospitality Report . Vol. 16.

Kisilevich, Slava, Daniel Keim, and Lior Rokach. 2013. A GIS-Based Decision Support System for Hotel Room Rate Estimation and Temporal Price Prediction: The Hotel Brokers’ Context. Decision Support Systems 54 (2): 1119–33.

Kivela, Jaksa, Robert Inbakaran, and John Reece. 1999. Consumer Research in the Restaurant Environment, Part 1: A Conceptual Model of Dining Satisfaction and Return Patronage. International Journal of Contemporary Hospitality Management 11 (5): 205–22.

Koshiba, Hitoshi, Takeshi Takenaka, and Yoichi Motomura. 2012. Service Management System Based on Computational Customer Models Using Large-Scale Log Data of Chain Stores. In Th International Conference on Applied Human Factors and Ergonomics , 57–64.

Koshiba, Hitoshi, Takeshi Takenaka, and Yoichi Motomura. 2013. A Service Demand Forecasting Method Using a Customer Classification Model. In The Philosopher’s Stone for Sustainability , 281–85. Berlin, Heidelberg: Springer.

Kuo, R.J. 2001. A Sales Forecasting System Based on Fuzzy Neural Network with Initial Weights Generated by Genetic Algorithm. European Journal of Operational Research 129 (3): 496–517. https://doi.org/10.1016/S0377-2217(99)00463-4 .

Kwon, SoYeon, and SooCheong Shawn. Jang. 2011. Price Bundling Presentation and Consumer’s Bundle Choice: The Role of Quality Certainty. International Journal of Hospitality Management 30 (2): 337–44.

Lai, Hao Bin, Shahrim Karim Jack, Steven E. Krauss, and Farah A. C. Ishak. 2019. Can Restaurant Revenue Management Work with Menu Analysis ?”. Journal of Revenue and Pricing Management 18 (3): 204–12. https://doi.org/10.1057/s41272-019-00194-6 .

Lasek, Agnieszka, Nick Cercone, and Jim Saunders. 2016. Restaurant Sales and Customer Demand Forecasting: Literature Survey and Categorization of Methods. In Smart City 360° , 479–91. Cham: Springer. https://doi.org/10.1007/978-3-319-33681-7 .

LeBruto, Stephen M., William J. Quain, and Robert A. Ashley. 1995. Menu Engineering: A Model Including Labor. Hospitality Review 13 (1): 41–49. http://digitalcommons.fiu.edu/hospitalityreview/vol13/iss1/5/?utm_source=digitalcommons.fiu.edu/hospitalityreview/vol13/iss1/5&utm_medium=PDF&utm_campaign=PDFCoverPages .

Liu, Lon-Mu., Siddhartha Bhattacharyya, Stanley L. Sclove, Rong Chen, and William J. Lattyak. 2001. Data Mining on Time Series: An Illustration Using Fast-Food Restaurant Franchise Data. Computational Statistics and Data Analysis 37 (4): 455–76. https://doi.org/10.1016/S0167-9473(01)00014-7 .

Luca, Michael. 2016. Reviews, Reputation, and Revenue: The Case of Yelp.Com. Harvard Business School NOM Unit Working Paper .

Martin-Consuegea, David, Angel Millan, Estrella Diaz, and Eunju Ko. 2010. The Effects of Price Salience on Consumer Perception and Purchase Intentions. Journal of Global Academy of Marketing Science 20 (2): 149–63. https://doi.org/10.1080/12297119.2010.9730187 .

Mathe-Soulek, Kimberly, Matt Krawczyk, Robert J. Harrington, and Michael Ottenbacher. 2016. The Impact of Price-Based and New Product Promotions on Fast Food Restaurant Sales and Stock Prices. Journal of Food Products Marketing 22 (1): 100–117. https://doi.org/10.1080/10454446.2014.949996 .

Mathies, Christine, and Siegfried Gudergan. 2007. Revenue Management and Customer Centric Marketing—How Do They Influence Travellers’ Choices?”. Journal of Revenue and Pricing Management 6 (4): 331–46. https://doi.org/10.1057/palgrave.rpm.5160109 .

Mcguire, Kelly A., and Sheryl E. Kimes. 2006. The Perceived Fairness of Waitlist-Management Techniques for Restaurants. Cornell Hotel and Restaurant Administration Quarterly 47 (2): 121–34. https://doi.org/10.1177/0010880405284212 .

Miao, Qing, Yihua Li, and Xiubin B. Wang. 2018. Restaurant Reservation Management Considering Table Combination. Pesquisa Operacional 38 (1): 73–86. https://doi.org/10.1590/0101-7438.2018.038.01.0073 .

Miller, Jack E. 1992. Menu Pricing and Strategy . Van Nostrand Reinhold.

Miller, James J., Cynthia S. McCahon, and Judy L. Miller. 1991. Foodservice Forecasting Using Simple Mathematical Models. Hospitality Research Journal 15 (1): 43–58. https://doi.org/10.1177/109634809101500105 .

Miller, James J., Cynthia S. McCahon, and Judy L. Miller. 1993. Foodservice Forecasting: Differences in Selection of Simple Mathematical Models Based on Short-Term and Long-Term Data Sets. Hospitality Research Journal 16 (2): 93–102.

Milliman, Ronald E. 1986. The Influence of Background Music on the Behavior of Restaurant Patrons. Journal of Consumer Research 13 (2): 286–89. https://doi.org/10.1086/209068 .

Morgan, Michael S., and Pradeep K. Chintagunta. 1997. Forecasting Restaurant Sales Using Self-Selectivity Models. Journal of Retailing and Consumer Services 4 (2): 117–28. https://doi.org/10.1016/s0969-6989(96)00035-5 .

Mudie, P., and A. Pirrie. 2006. Services Marketing Management . New York: Elsevier.

Muller, Christopher. 2018. Restaurant Delivery: Are the ‘ODP’ the Industry’s ‘OTA’? Part I & II. Boston Hospitality Review .

Nazlan, Nadia Hanin, Sarah Tanford, Carola Raab, and Choongbeom (CB) Choi. . 2018. The Influence of Scarcity Cues and Price Bundling on Menu Item Selection. Journal of Foodservice Business Research 21 (4): 420–39. https://doi.org/10.1080/15378020.2018.1440129 .

Nemeschansky, Ben. 2020. Listen to Your Customer - How to Manage Your Restaurant More Effectively. Journal of Foodservice Business Research 23 (1): 17–45. https://doi.org/10.1080/15378020.2019.1671119 .

Noone, Breffni M., Sheryl E. Kimes, Anna S. Mattila, and Jochen Wirtz. 2007. The Effect of Meal Pace on Customer Satisfaction. Cornell Hotel and Restaurant Administration Quarterly 48 (3): 231–45. https://doi.org/10.1177/0010880407304020 .

Noone, Breffni M., Sheryl E. Kimes, Anna S. Mattila, and Jochen Wirtz. 2009. Perceived Service Encounter Pace and Customer Satisfaction An Empirical Study of Restaurant Experiences. Journal of Service Management 20 (4): 380–403. https://doi.org/10.1108/09564230910978494 .

Noone, Breffni M., and Chung Hun Lee. 2011. Hotel Overbooking: The Effect of Overcompensation on Customers’ Reactions to Denied Service. Journal of Hospitality and Tourism Research 35 (3): 334–57. https://doi.org/10.1177/1096348010382238 .

Noone, Breffni M., and Kelly A. McGuire. 2014. Effects of Price and User-Generated Content on Consumers’ Prepurchase Evaluations of Variably Priced Services. Journal of Hospitality and Tourism Research 38 (4): 562–81. https://doi.org/10.1177/1096348012461551 .

Norvell, Tim, and Alisha Horky. 2017. A Framework and Model to Evaluate Promotions: A Restaurant Cross-Promotion in-Market Study. Journal of Revenue and Pricing Management 16 (4): 345–56. https://doi.org/10.1057/rpm.2016.14 .

NRAI. 2019. I ndian Restaurant Industry Employed 7.3 Million People in 2018-19: NRAI . SNS Web. 2019. https://www.thestatesman.com/business/indian-restaurant-industry-employed-7-3-million-people-2018-19-nrai-1502753854.html .

Pavesic, David V. 1983. Cost/Margin Analysis: A Third Approach to Menu Pricing and Design. International Journal of Hospitality Management 2 (3): 127–34. https://doi.org/10.1016/0278-4319(83)90033-6 .

Pimentel, Victor, Aishajiang Aizezikali, and Tim Baker. 2019. Hotel Revenue Management: Benefits of Simultaneous Overbooking and Allocation Problem Formulation in Price Optimization. Computers and Industrial Engineering 137: 106073. https://doi.org/10.1016/j.cie.2019.106073 .

Raman, Arun, and Debjit Roy. 2015. An Analytical Modeling Framework for Determining the Optimal Table Mix in Restaurants. IFAC-PapersOnLine 48 (3): 225–30.

Rao, Akshay R., and Kent B. Monroe. 1989. The Effect of Price, Brand Name, and Store Name on Buyers’ Perceptions of Product Quality: An Integrative Review. Journal of Marketing Research 26 (3): 351–57. https://doi.org/10.2307/3172907 .

Raul, Nataasha, Yash Shah, and Mehul Devganiya. 2016. Restaurant Revenue Prediction Using Machine Learning. International Journal of Engineering And Science 6 (4): 91–94.

Reynolds, Dennis, Imran Rahman, and William Balinbin. 2013. Econometric Modeling of the U.S. Restaurant Industry. International Journal of Hospitality Management 34: 317–23. https://doi.org/10.1016/j.ijhm.2013.04.003 .

Riasi, Arash, Zvi Schwartz, and Srikanth Beldona. 2019. Hotel Overbooking Taxonomy: Who and How?”. International Journal of Hospitality Management 82: 1–4.

Rowson, Bill, Wouter van Poppel, and Sjoerd Gehrels. 2016. Wasted Millions: Revenue Management in Dutch Culinary Restaurants. Research in Hospitality Management 6 (2): 127–33. https://doi.org/10.1080/22243534.2016.1253278 .

Ryu, Kisang, and Alfonso Sanchez. 2003. The Evaluation of Forecasting Methods at an Institutional Foodservice Dining Facility. The Journal of Hospitality Financial Management 11 (1): 27–45. https://doi.org/10.1080/10913211.2003.10653769 .

Saito, Taiga, Akihiko Takahashi, and Noriaki Koide. 2017. Optimal Overbooking Strategy in Online Hotel Booking Systems . CIRJE, Faculty of Economics. 2017. http://www.cirje.e.u-tokyo.ac.jp/research/03research02dp.html .

See-Kwong, Goh, N.G. Soo-Ryue, Wong Shiun-Yi, and Chong Lily. 2017. Outsourcing to Online Food Delivery Services: Perspective of F&B Business Owners. The Journal of Internet Banking and Commerce 22 (2): 1–18.

Sellers, Kimberly F., and Galit Shmueli. 2010. A Flexible Regression Model for Count Data. The Annals of Applied Statistics 4 (2): 943–61. https://doi.org/10.1214/09-AOAS306 .

Shaw, Mary. 2014. Loyalty Program Attributed to Starbucks Sales Success . 2014. https://www.customerinsightgroup.com/loyaltyblog/loyalty-program-attributed-to-starbucks-sales-success/ .

Sill, Brian, and Robert Decker. 1999. Applying Capacity-Management Science: The Case of Browns Restaurants. Cornell Hotel and Restaurant Administration Quarterly 40 (3): 22–30.

Song, Boqian, and Michael Z.F.. Li. 2018. Dynamic Pricing with Service Unbundling. Production and Operations Management 27 (7): 1334–54. https://doi.org/10.1111/poms.12871 .

Susskind, Alex M. 2010. Guest Service Management and Processes in Restaurants: What We Have Learned in Fifty Years. Cornell Hospitality Quarterly 51 (4): 479–82. https://doi.org/10.1177/1938965510375028 .

Susskind, Alex M., Dennis Reynolds, and Eriko Tsuchiya. 2004. An Evaluation of Guests’ Preferred Incentives to Shift Time-Variable Demand in Restaurant. Cornell Hotel and Restaurant Administration Quarterly 45 (1): 68–84. https://doi.org/10.1177/0010880403260108 .

Tan, Tom Fangyun, and Serguei Netessine. 2019. At Your Service on the Table: Impact of Tabletop Technology on Restaurant Performance. Management Science 66: 4496–4515.

Tang, Jason, Toni Repetti, and Carola Raab. 2019. Perceived Fairness of Revenue Management Practices in Casual and Fine-Dining Restaurants. Journal of Hospitality and Tourism Insights 2 (1): 92–108. https://doi.org/10.1108/JHTI-10-2018-0063 .

Thompson, Gary. 2015. Restaurant Reservations Optimization Tool. Cornell Hospitality Tool 6 (4): 3–10. http://scholarship.sha.cornell.edu/chrtools .

Thompson, Gary M. 2002. Optimizing a Restaurant’s Seating Capacity. Cornell Hotel and Restaurant Administration Quarterly 43 (4): 48–57. https://doi.org/10.1177/0010880402434005 .

Thompson, Gary M. 2007. Restaurant Capacity Effectiveness: Leaving Money on the Tables . https://scholarship.sha.cornell.edu/chrpubs/143 .

Thompson, Gary M. 2009. (Mythical) Revenue Benefits of Reducing Dining Duration in Restaurants. Cornell Hospitality Quarterly 50 (1): 96–112. https://doi.org/10.1177/1938965508328422 .

Thompson, Gary M. 2010. Restaurant Profitability Management: The Evolution of Restaurant Revenue Management. Cornell Hospitality Quarterly 51 (3): 308–22. https://doi.org/10.1177/1938965510368653 .

Thompson, Gary M. 2011. Cherry-Picking Customers by Party Size in Restaurants. Journal of Service Research 14 (2): 201–13. https://doi.org/10.1177/1094670511401367 .

Thompson, Gary M. 2011. Inaccuracy of the ‘Naïve Table Mix’ Calculations. Cornell Hospitality Quarterly 52 (3): 241–52. https://doi.org/10.1177/1938965511398261 .

Thompson, Gary M., and Gary M. Thompson. 2015. An Evaluation of Integer Programming Models for Restaurant Reservations. Journal of Revenue and Pricing Management 14 (5): 305–20. https://doi.org/10.1057/rpm.2015.17 .

Thompson, Gary M. 2019. The Value of Timing Flexibility in Restaurant Reservations. Cornell Hospitality Quarterly 60 (4): 378–88. https://doi.org/10.1177/1938965518805685 .

Thompson, Gary M., and Robert J. Kwortnik. 2008. Pooling Restaurant Reservations to Increase Service Efficiency. Journal of Service Research 10 (4): 335–46. https://doi.org/10.1177/1094670508314267 .

Thompson, Gary M. 2003. Dedicated or Combinable? A Simulation to Determine Optimal Dedicated or Combinable? https://scholarship.sha.cornell.edu/chrpubs .

Tse, Tony S.M.., and Yiu-Tung. Poon. 2017. Modeling No-Shows, Cancellations, Overbooking, and Walk-Ins in Restaurant Revenue Management. Journal of Foodservice Business Research 20 (2): 127–45. https://doi.org/10.1080/15378020.2016.1198626 .

Upadhyay, Amit, and Nomesh B. Bolia. 2014. An Optimization Based Decision Support System for Integrated Planning and Scheduling on Dedicated Freight Corridors. International Journal of Production Research 52 (24): 7416–35. https://doi.org/10.1080/00207543.2014.932463 .

Vinod, B. 2019. Hotel Retailing with Attribute-Based Room Pricing and Inventory Control. Journal of Revenue and Pricing Management 18 (6): 429–33. https://doi.org/10.1057/s41272-019-00207-4 .

Wang, Jiana-Fu., Yi-Chiun. Lin, Chen-Feng. Kuo, and Shao-Jen. Weng. 2017. Cherry-Picking Restaurant Reservation Customers. Asia Pacific Management Review 22 (3): 113–21.

Whelan-Ryan, Fiona. 2000. Yield Management and the Restaurant Industry. In Yield Management: Strategies for the Service Industries , 271–88.

Wulu, J.T., Jr., K.P. Singh, F. Famoye, T.N. Thomas, and G. McGwin. 2002. Regression Analysis of Count Data. Journal of the Indian Society of Agricultural Statistics 55 (2): 220–31. https://doi.org/10.2307/2670019 .

Xu, Jingmei, Hu. Li, Xiaolong Guo, and Xia Yan. 2020. Online Cooperation Mechanism: Game Analysis between a Restaurant and a Third-Party Website. Journal of Revenue and Pricing Management 19 (1): 61–73. https://doi.org/10.1057/s41272-018-00181-3 .

Zhang, Peter G. 2003. Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing 50: 159–75. https://doi.org/10.1016/S0925-2312(01)00702-0 .

Download references

Author information

Authors and affiliations.

Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, India

Mohit Tyagi & Nomesh B. Bolia

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Mohit Tyagi .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Tyagi, M., Bolia, N.B. Approaches for restaurant revenue management. J Revenue Pricing Manag 21 , 17–35 (2022). https://doi.org/10.1057/s41272-021-00288-0

Download citation

Received : 20 November 2020

Accepted : 19 January 2021

Published : 23 February 2021

Issue Date : February 2022

DOI : https://doi.org/10.1057/s41272-021-00288-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Restaurant revenue management (RRM)
• Arrival management
• Price management
• Find a journal
• Publish with us
• Track your research

To read this content please select one of the options below:

Please note you do not have access to teaching notes, information and communication technologies in food services and restaurants: a systematic review.

International Journal of Contemporary Hospitality Management

ISSN : 0959-6119

Article publication date: 18 January 2022

Issue publication date: 11 March 2022

Information and communication technologies (ICTs) are a key player in the food services and restaurants sector; thus, the aim of this work consists in studying the previous research on ICTs in food services and restaurants in the context of tourism and hospitality through a systematic literature review.

Design/methodology/approach

The systematic literature review is performed on full papers published in journals included in the Journal Citation Report of the WoS in the category of Hospitality, Leisure, Sport and Tourism. A total of 165 articles from 28 journals are analyzed, following different criteria, such as the research methods, perspectives, statistical techniques, geographical focus, topics, technologies, authors and universities.

The restaurant sector is more and more based on the creation of experiences and ICTs, through their multiple possibilities, can undoubtedly contribute to adding value to the simple meal and create and recreate experiences to attract and retain customers who are increasingly sophisticated and hooked on ICTs. ICTs are basic for managers taking decision at the highest level in food services and restaurants, so ICTs should not be seen as a technical tool but as an essential element for top management.

Research limitations/implications

This paper examined articles from very well-known tourism and hospitality journals, leaving aside others as well as different publication formats such as books or papers presented at conferences.

Originality/value

A significant contribution made with this paper is the availability of a list of topics in the context of ICTs in food services and restaurants. These topics are classified into three areas (Consumers, Suppliers and Environment and Tendencies) that can serve as a future research framework. The paper also provides useful information to restaurant managers about ICTs, to researchers for their future projects and to academics for their courses.

• Literature review
• Food services
• Restaurants
• Tourism and hospitality

Gonzalez, R. , Gasco, J. and Llopis, J. (2022), "Information and communication technologies in food services and restaurants: a systematic review", International Journal of Contemporary Hospitality Management , Vol. 34 No. 4, pp. 1423-1447. https://doi.org/10.1108/IJCHM-05-2021-0624

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

• Human Resource Management

Chapter 2: Review of Related Literature

Related documents

Add this document to collection(s)

You can add this document to your study collection(s)

Add this document to saved

You can add this document to your saved list

Suggest us how to improve StudyLib

(For complaints, use another form )

Input it if you want to receive answer