Purpose: Internet-famous food (also known as “online celebrity” food) is very popular in the digital age. This study aims to investigate consumer attitudes and understand consumer behavior towards Internet-famous food. Design/methodology/approach: The authors collected 136,835 online comments regarding “Internet-famous food” from Dianping platform between 2016 and 2019 using a web scraper. A sentiment lexicon for Internet-famous food was constructed, and sentiment analysis is further conducted to understand consumer attitudes. Additionally, the authors use topic analysis and time series analysis to study consumer behavior. Findings: Sentiment analysis showed that the number of consumers' comments decreased over time with the attitudes being overall positive, and the Internet-famous food industry has a positive prospect; time series analysis showed that the consumption of Internet-famous food was not affected by the season; topic analysis showed that consumers' comments on Internet-famous food were rich with a large variety, covering food categories, brand, quality, service, environment and price. Originality/value: To the authors’ knowledge, limited research has focused on public opinions regarding “Internet-famous food”. This is the first study on consumer behavior towards Internet-famous food. This article provides a unique insight into the purchasing behavior and attitude of Chinese Internet-famous food consumers through text mining.

This paper proposes a transformative framework where process improvement (standardisation) and staffing optimisation (integer programming) are integrated to enhance the performance of managed care organisations (MCOs) considering future surging demand. Four models (baseline and optimised for current and future states) are developed and compared using simulation. We find significant differences for all performance metrics between three alternatives and baseline model for current state: (1) optimised model for current state achieves cost reduction (31.71%) and higher staff utilisation (63.14%), without significant capacity increase; and (2) baseline model for future state results in significant capacity gain (2.05%), with less significant cost reduction and utilisation. Furthermore, optimised model for future state obtains largest capacity gain (2.31%), greatest cost reduction (44.43%), and highest utilisation (93.43%). All models result in acceptable average queue length. Additionally, process improvement affects all performance metrics, especially capacity, whereas staffing optimisation influences all other performance metrics than capacity.