Market and nonmarket strategy in the US hospitality industry

Purpose We surveyed managers and other professionals in 142 US hospitality firms to investigate the links among relative firm size, market strategies (e.g. broad cost leadership and global growth) and nonmarket strategies. It helps explain how business size influences these dynamics within the hospitality industry. Previous research has primarily focused on generic market strategies without adequately considering the nuanced impact of firm size on performance outcomes. Design/methodology/approach The data are analyzed using PLS-SEM and machine learning (ML) methods, specifically the glmnet algorithm for Lasso regression, to explore the relationships between firm size, market strategies and performance. ML’s ability to manage high-dimensional data and nonlinear relationships provides a nuanced analysis that surpasses traditional statistical methods, enhancing the accuracy and depth of our findings. Findings The results depict a negative link between broad cost leadership and firm performance and a positive link between a global growth emphasis and firm performance. Firm size did not influence either market strategy or performance, but nonmarket orientation fully mediated the relationship between firm size and performance. Research limitations/implications We assessed nonfinancial and financial performance with self-typing scales; objective measures can help evaluate strategy-performance linkages through a different lens and potentially reduce the influence of common method variance. Also, our assessment was limited to a cross-section of US hospitality firms. Additional work in the hospitality industry is required to identify and corroborate nonmarket strategies at the firm, strategic group and industry levels. Originality/value The current study employs both PLS-SEM and machine learning. It reinforces configuration theory by combining market and nonmarket methods as business performance indicators in hospitality firms.