The restaurant business plays a crucial role in the food economy and, like many industries, faces numerous challenges due to the rapid pace of change, driven by consumer trends and shifting demographics. In response to the pressure on businesses to adapt to these changing circumstances, new restaurant concepts, organizational structures and technologies have emerged in the hope of finding methods that are more agile during unpredictable times. The development of dynamic capabilities is a critical issue for both academics and practitioners, as it can enhance competitive advantages and improve organizational performance. The purpose of this paper is to examine the impact of dynamic capabilities on innovative potential and organizational performance. Through a literature review and the dynamic capability view, a theoretical model is proposed. This model was validated using the PLS-SEM technique, based on responses from 143 restaurants across eight of the largest Russian cities. The results indicate that dynamic capabilities related to the value proposition serve as a complete mediator in the relationship between a company's innovative potential and its performance in a highly volatile context. The proposed theoretical model is both unique and effective, demonstrating high explanatory power. This study enriches the existing literature on dynamic capabilities by addressing empirical research gaps and elucidating the mechanisms through which dynamic capabilities influence organizational performance.

The COVID-19 pandemic led to changes in and modifications of the role of information and communication technologies in the digitalization of service provision. This paper aims to identify and summarize these changes in business operations, in the context of strategic management in the restaurant industry, triggered by COVID-19. Based on in-depth interviews with 16 key experts in the restaurant industry (CEOs of chain restaurants), this paper clarifies and concretizes the rapidly transforming problem of identifying the main changes in the restaurant market during the pandemic. These depend on a restaurant’s adaptation level to the challenges that arose, using three dimensions (consumer demand, corporate strategy optimization, and the use of the company’s innovative potential). The analysis shows that chain restaurants, which have sufficient resources to solve the problem of staff turnover, develop technological solutions, and build a brand and customer loyalty, were more resistant to problems arising from the spread of the coronavirus infection than other companies in the catering industry. The presence of serious problems associated with consumer demand and the optimization of the format of the offered dishes and service had a significant impact on the relationship between the impact of the spread of the coronavirus infection and the inability to adapt to the new reality of doing business. The findings reveal that companies need to expand their digital capabilities and adapt their management strategy to the post-pandemic conditions to adapt to the post-COVID-19 period. This paper serves as a framework for understanding the role of digital transformation in sustainable business development.

This chapter discusses the sentiment classification of text messages containing customer reviews of an online restaurant service system using machine-learning methods, in particular text mining and multivariate text sentiment analysis. The study determines the structure of value proposition factors based on online restaurant reviews on TripAdvisor, collecting information on consumer preferences and the restaurant services in St. Petersburg (Russia) quality assessment and examines the influence of service format and reviews tonality on ratings restaurants factors. The service format context is proposed as the main attribute influencing the formation of the restaurant business value proposition and of relevance for online reviews. The results showed the key factors in the study of the sentiment were cuisine and dishes, reviews and ratings, and targeted search. MANOVA analysis represented that for special offers and features, reviews and ratings, factors and quantitative star ratings influenced the negative and positive sentiment of online reviews significantly.

The purpose of the chapter is to recognize how customers perceive the value proposition content of network companies providing restaurant services. This is theoretical and empirical research in equal measure. The chapter presents the results of the restaurants value proposition study in Saint Petersburg (Russia) according to industry data and identifies the target audience key needs and industry characteristics in the market, semantic core formation, and clustering in order to identify the main criteria affecting the value proposition formation. The study was conducted among 16 chain restaurant holdings located in St. Petersburg, using statistical data based on the marketing statistical tool Google KeyWord Planner analytic database. The results made it possible to highlight key criteria of the value proposition by which consumers make their choice. This approach will allow standardizing decision-making systems on the possibility of identifying the innovative potential of the company's transition to a qualitatively new level of service and use of production resources.

The main problems and features of combined approach to the complex objects control and management stability analysis are investigated in the paper. Analytical-simulation scenarios and scenarios of intelligent models and systems execution for complex objects control and management stability analysis are given. The paper describes a particular group of models and modelling systems - hybrid intelligent models and systems that allow in conditions of uncertainty, incomplete initial data and complex interdependence between elements of complex objects to evaluate the implications of realization of various scenarios and risk evaluation. The investigations have shown successful possibility of risks evaluation by the combined implementation of the analytical-simulation models and algorithms, and ANFIS method - the method of hybrid neural-fuzzy modelling.