For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
3
views
165
references
 Top references
cited by
6
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
1 collections
    0
    shares
      617
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: not found

      Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic

      research-article
      Author(s): 1 , 2 , 3 , 4 , 5 ,
      Publication date (Electronic):
      Journal: Information Systems Frontiers
      Publisher: Springer US
      Keywords: SMEs, Business Risks, COVID-19 pandemic, Artificial Intelligence, Dynamic Capabilities

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The study utilises the International Labor Organization’s SMEs COVID-19 pandemic business risks scale to determine whether Artificial Intelligence (AI) applications are associated with reduced business risks for SMEs. A new 10-item scale was developed to capture the use of AI applications in core services such as marketing and sales, pricing and cash flow. Data were collected from 317 SMEs between April and June 2020, with follow-up data gathered between October and December 2020 in London, England. AI applications to target consumers online, offer cash flow forecasting and facilitate HR activities are associated with reduced business risks caused by the COVID-19 pandemic for both small and medium enterprises. The study indicates that AI enables SMEs to boost their dynamic capabilities by leveraging technology to meet new types of demand, move at speed to pivot business operations, boost efficiency and thus, reduce their business risks.

          Related collections

          Most cited references165

          • Record: found
          • Abstract: not found
          • Article: not found

          Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance

          David J. Teece (2007)
          Article 0 comments Cited 1081 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Dynamic capabilities: what are they?

            Kathleen M. Eisenhardt, Jeffrey A Martin (2000)
            Article 0 comments Cited 943 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Representation learning: a review and new perspectives.

              Y Bengio, A. Courville, P. Vincent (2013)
              The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
              Article 0 comments Cited 884 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Nick Drydakis:
                ORCID: http://orcid.org/0000-0001-9827-6678
                nick.drydakis@aru.ac.uk
                Bio :
                Nick Drydakis

                is a Professor in Economics in the Faculty of Business and Law at Anglia Ruskin University, where he directs the Centre for Pluralist Economics. He is the Course Convenor for the Economics of Growth and Development and the Economics of Inequality and Poverty at the University of Cambridge and an Academic Associate at Pembroke College at the University of Cambridge. He collaborates with the Centre for Science and Policy at the University of Cambridge as an Academic Expert. Professor Drydakis is a Research Fellow in the Institute of Labor Economics (Bonn) and Fellow in the Global Labor Organization (Essen). He is an Academic Editor at PLOS Global Public Health. Professor Drydakis has published on mobile applications, Artificial Intelligence and social and health outcomes. He has published single author articles in widely renowned journals, including: the Industrial Relations: A Journal of Economy and Society; Computers in Human Behavior; Human Relations; Social Science and Medicine; and Journal of Vocational Behavior. Professor Drydakis has worked on EU and World Bank research programmes, such as the Progress Programme, Horizon 2020, European Territorial Cooperation Programme and Knowledge Platform Programme.

                Journal
                Journal ID (nlm-ta): Inf Syst Front
                Journal ID (iso-abbrev): Inf Syst Front
                Title: Information Systems Frontiers
                Publisher: Springer US (New York )
                ISSN (Print): 1387-3326
                ISSN (Electronic): 1572-9419
                Publication date (Electronic): 4 March 2022
                Pages: 1-25
                Affiliations
                [1 ]GRID grid.5115.0, ISNI 0000 0001 2299 5510, School of Economics, International Business and Law, Centre for Pluralist Economics, Faculty of Business and Law, , Anglia Ruskin University, ; East Road, Cambridge, CB 1 1PT UK
                [2 ]GRID grid.5335.0, ISNI 0000000121885934, Pembroke College, University of Cambridge, ; Cambridge, UK
                [3 ]GRID grid.5335.0, ISNI 0000000121885934, Centre for Science and Policy, , University of Cambridge, ; Cambridge, UK
                [4 ]Global Labor Organization, Essen, Germany
                [5 ]GRID grid.424879.4, ISNI 0000 0001 1010 4418, Institute for Labor Economics, ; Bonn, Germany
                Author information
                http://orcid.org/0000-0001-9827-6678
                Article
                Publisher ID: 10249
                DOI: 10.1007/s10796-022-10249-6
                PMC ID: 8893980
                SO-VID: a9db132f-7ba1-4318-89bb-d3fb2e39b2ce
                Copyright © © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date accepted : 19 January 2022
                Categories
                Subject: Article

                Keywords: smes,business risks,covid-19 pandemic,artificial intelligence,dynamic capabilities
                Data availability:
                Keywords: smes, business risks, covid-19 pandemic, artificial intelligence, dynamic capabilities

                Comments

                Comment on this article

                scite_

                Similar content617

                See all similar

                Cited by6

                See all cited by

                Most referenced authors823

                See all reference authors