摘要 : Purpose The main purpose of this study is to create a reliable and valid scale to assess the destination food image perceived by the tourists regarding the food in Turkey within the cognitive and affective image component framewor... 展开 Purpose The main purpose of this study is to create a reliable and valid scale to assess the destination food image perceived by the tourists regarding the food in Turkey within the cognitive and affective image component framework. In line with this purpose, both scale development and scale adaptation studies are conducted, and measurement invariance of the scale for gender is analyzed. Design/methodology/approach This study uses the survey model among quantitative research methods. Scale development processes are used to assess the cognitive image; the construct validity is analyzed with exploratory factor analysis (n = 328), confirmatory factor analysis (n = 425) and convergent and discriminant validity. Scale adaptation processes are followed to assess the affective image, and construct validity is tested with confirmatory factor analysis (n = 425). The reliability of both scales is investigated with Cronbach's alpha. Cochran-Mantel-Haenszel (CMH) analysis is conducted for measurement invariance for gender. Findings Construct validity and reliability provided the desired values in all processes. Measurement invariance results proved that the scale does not change according to genders. Research limitations/implications The data obtained in this study have geographical limitations, and the data represent tourists visiting Antalya, an important tourism destination in Turkey. Practical implications The scale will provide concrete information about the destination food image and help practitioners to test the model and develop future strategies for the destination. Originality/value This study presents an integrated approach to understanding the destination food image and expands theoretical and empirical evidence by creating a scale that measures both cognitive and affective image component. Scale-invariant shows that there is no item bias for analyzed gender and contributes to generalizability. 收起