Beyond expectations: mapping satisfaction drivers in online grocery shopping

Abstract Online grocery shopping (OGS) has become a mature research domain, yet most studies still rely on linear and additive frameworks—such as SERVQUAL and loyalty-based models—that assume proportional effects of service attributes on customer satisfaction (CS). This paper advances the literature by adopting a non-linear and asymmetric approach to the evaluation of OGS service features, applying the Kano model to examine how 21 digital and logistical attributes are associated with satisfaction and dissatisfaction. By integrating discrete and continuous Kano analyses with satisfaction–dissatisfaction coefficients, the study highlights patterns that linear models may overlook: trust-related features such as privacy protection and payment options are predominantly classified as must-be requirements, while elements like fast delivery are more often classified as attractive attributes linked to asymmetric satisfaction gains. These classifications reflect respondents’ perceptions as captured by the survey data. A cross-country comparison between Italy and the United States further indicates that these patterns differ across cultural and market contexts. National differences are reported as descriptive comparisons of Kano categories and satisfaction/dissatisfaction coefficients and should not be interpreted as causal effects. The findings extend current theorization of satisfaction formation in hybrid digital–physical services and offer managerial guidance for prioritizing investments in must-be, performance, and delight-generating features. This positioning moves beyond descriptive assessments of attribute performance, providing a more diagnostic and strategically actionable framework for understanding satisfaction dynamics in OGS.