Purpose: Using data from 104 countries over a six-year period (2009-2014), this study proposes a value-added predictor in service industries based on the eight indicators of the prosperity index, namely economy, entrepreneurship and opportunity, governance, education, health, safety and security, personal freedom, and social capital.
Design/methodology/approach: The fuzzy-set qualitative comparative analysis (fsQCA) and complexity theory, a relatively novel approach for developing and testing the conceptual model, are used for asymmetric modelling of value added in service industries, and the predictive validity of the proposed configural model is tested.
Findings: Apart from advancing method and theory, this study simulates causal conditions (i.e., recipes) leading to both high and low scores of the value added of services. The configural conditions indicating a high/low level of value added in service industries can be used as a guiding strategy for marketers, investors and policy makers.
Originality/value: An analysis of worldwide data provides complex models demonstrating both how to regulate country conditions to achieve a high value-added score and select a foreign country for investment that offers a high level of value added service.
Olya, Hossein GT
De Vita, Glauco
Faculty of Business\Oxford School of Hospitality and Management
Year of publication: 2018Date of RADAR deposit: 2017-12-19