How smart is retailing?

Ang Lu Lin, Hsin Ning Su, Yun Wei Hung, Hsin Lun Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 2019 the 3rd International Conference on E-Society, E-Education and E-Technology, ICSET 2019
PublisherAssociation for Computing Machinery
Pages64-69
Number of pages6
ISBN (Electronic)9781450372305
DOIs
StatePublished - 15 Aug 2019
Event3rd International Conference on E-Society, E-Education and E-Technology, ICSET 2019 - Taipei, Taiwan
Duration: 15 Aug 201917 Aug 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on E-Society, E-Education and E-Technology, ICSET 2019
CountryTaiwan
CityTaipei
Period15/08/1917/08/19

Keywords

  • Innovation
  • Patent
  • Smart Retailing

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  • Cite this

    Lin, A. L., Su, H. N., Hung, Y. W., & Chiang, H. L. (2019). How smart is retailing? In Proceedings of 2019 the 3rd International Conference on E-Society, E-Education and E-Technology, ICSET 2019 (pp. 64-69). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3355966.3355981