Smart retailing has become increasingly critical in modern retailing. The paper aims to understand how traditional retailing technology can become smart retailing technology. ANOVA test and Logistic regression modeling were utilized to understand: 1) how to distinguish between non-smart retailing technology and smart retailing technology, 2) the likelihood for non-smart retailing technology to become smart-retailing technology, from the perspective of R&D strategies systematically characterized by a total of 18 patent indicators. It is found that intensified knowledge sourcing, industrial knowledge novelty and firm level knowledge influence increase the probability of becoming smart retailing technology. In addition, R and D results with wide applicability and strong invention scope are necessary for protecting innovation of smart retailing technology.