higher-order constructs, which allow modeling a construct on a more abstract higher-level dimension and its more concrete lower-order subdimensions, have become increasingly common in partial least squares structural equation modeling (pls-sem). however, researchers often confuse the specification, estimation, and validation of higher-order constructs, particularly when assessing their reliability and validity. this paper addresses this issue by explaining how to evaluate the results of higher-order constructs in pls-sem using repeated indicators and two-stage approaches, which are widely used in applied social sciences research. focusing on reflective-reflective and reflective-formative types of higher-order constructs, the paper uses the well-known corporate reputation model example to illustrate their specification, estimation, and validation. thereby, it provides guidance for scholars, marketing researchers, and practitioners when using higher-order constructs in their studies. keywords: hierarchical component models; higher-order constructs; partial least squares; path modelling; pls-sem; second-order constructs.higher-order constructs, which allow modeling a construct on a more abstract higher-level dimension and its more concrete lower-order subdimensions, have become increasingly common in partial least squares structural equation modeling (pls-sem). however, researchers often confuse the specification, estimation, and validation of higher-order constructs, particularly when assessing their reliability and validity. this paper addresses this issue by explaining how to evaluate the results of higher-order constructs in pls-sem using repeated indicators and two-stage approaches, which are widely used in applied social sciences research. focusing on reflective-reflective and reflective-formative types of higher-order constructs, the paper uses the well-known corporate reputation model example to illustrate their specification, estimation, and validation. thereby, it provides guidance for scholars, marketing researchers, and practitioners when using higher-order constructs in their studies. keywords: hierarchical component models; higher-order constructs; partial least squares; path modelling; pls-sem; second-order constructs.