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The purpose of this paper is to discuss the evolving interpretations of the Covid crisis and its impact on hospitality and tourism.

Scenario planning paper following Framework Foresight about the Covid pandemic and its impact on hospitality and tourism. Research input was gathered from research reports in different disciplines and discussions with an expert panel.

The paper argues that hypothesized recovery scenarios were founded on hope and inaccurate extrapolations, and that hospitality and tourism may head for permanently lower volumes.

The paper contributes to the debate on tourism resilience and hopeful visions of a sustainable restart.

Instead of just focusing on direct pandemic impact and that of governmental measures, a third variable of consumer confidence will be decisive, and more important than expected by many initially, in future scenarios for hospitality and tourism.

The proposed scenarios that were designed with executive level industry input have so far proven more realistic than prevalent views of a swift recovery.

This methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.

Three empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.

A lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.

Computerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.

This research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.

Oral history collections can offer a wealth of detailed information for entrepreneurship researchers. The stories that entrepreneurs tell provide researchers with insight into both perspective and into substantive issues of entrepreneurial behavior. The life stories of entrepreneurs offer students of entrepreneurship insight into both the explicit and the tacit knowledge of working entrepreneurs.