AI-based quality risk management in omnichannel operations: O2O food dissimilarity

Online-to-offline (O2O) omnichannel retail has grown very fast, but in the case of food, differences between product illustrations posted online and the actual goods that consumers receive have been a persistent problem. Hence, this pioneering study proposes a two-stage AI deep-learning method for mitigation of the quality risk posed to O2O omnichannel operations by O2O food dissimilarity, and investigates what information should be disclosed online to minimize the gap between O2O consumer expectations and perceptions, based on information theory and expectation-confirmation theory. Its empirical results reveal, first, that the proposed method can successfully assess the similarity between offline versions of products and the online images utilized for marketing them; and second, that restaurants can use food-similarity records to determine which online food marketing images are most likely to achieve positive expectation confirmation. Lastly, it recommends that online food platforms include multiple, varied images of most heterogeneous foods, as a further means of avoiding O2O dissimilarity problems.