CoChat
Sign Up
Cited by 180Impact of AI and Big Data on Business and SocietySMEs Development and Digital MarketingCOVID-19 Pandemic Impacts

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

Open access
Drydakis, Nick·2022·Information Systems Frontiers
DOI 10.1007/s10796-022-10249-6PMC PMC8893980
Open this paper in CoChat — free accountOpen PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s10796-022-10249-6

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • For SMEs in developing economies, cultivating dynamic capabilities is essential for effectively integrating AI into business processes, upskilling employees, and aligning AI adoption with strategic goals.

    “For SMEs in developing economies, cultivating dynamic capabilities is essential for effectively integrating AI into business processes, upskilling employees, and aligning AI adoption with their strategic goals (Drydakis, 2022; Grimaldi et al., 2013; Soluk et al., 2023).”

    From Artificial Intelligence Adoption for Sustainable Development in SMEs: Challenges, Strategic Pathways, and Lessons for Developing Economies (2026) · cites this paper as Drydakis, 2022

    The Theoretical framework (§ AI and dynamic capabilities; page unavailable) links AI in SMEs to dynamic capabilities and changes in operational processes. The Discussion (§ Limitations and future research; page unavailable) advises firms to assess readiness before integrating AI and to invest in staff who can use data. But these passages do not establish that cultivating dynamic capabilities is essential for AI integration, employee upskilling, or alignment with strategic goals specifically in developing economies. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 138 · Cited by 180

Research works printed but not linked: 117

  1. Akpan, I. J. Udoh, E. A. P., & Adebisi, B. (2020). Small Business Awareness and Adoption of State-Of-The-Art Technologies in Emerging and Developing Markets, and Lessons from the COVID-19 Pandemic. Journal of Small Business and Entrepreneurship. 10.1080/08276331.2020.1820185 · DOI
  2. Al Suwaidi F., Alshurideh M., Al Kurdi B., & Salloum S. A. (2021). The Impact of Innovation Management in SMEs Performance: A Systematic Review. In A. E. Hassanien, A. Slowik, V. Snášel, H. El-Deeb & F. M. Tolba (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing (Vol. 1261). Cham: Springer. 10.1007/978-3-030-58669-0_64 · DOI
  3. Andreß HJ Golsch K Schmidt AW Applied Panel Data Analysis for Economic and Social Surveys 2013 Berlin/Heidelberg Springer
  4. Anshari M Almunawar MN Lim SA Al-Mudimigh A Customer Relationship Management and Big Data Enabled: Personalization and Customization of Services Applied Computing and Informatics 2019 15 2 94 101 10.1016/j.aci.2018.05.004 · DOI
  5. Archibugi D Filippetti A Frenz M Economic Crisis and Innovation: Is Destruction Prevailing Over Accumulation? Research Policy 2013 42 2 303 314 10.1016/j.respol.2012.07.002 · DOI
  6. Armour J Sako M AI-Enabled Business Models in Legal Services: From Traditional Law Firms to Next-Generation Law Companies? Journal of Professions and Organization 2020 7 1 27 46 10.1093/jpo/joaa001 · DOI
  7. Basri W Examining the Impact of Artificial Intelligence (AI)-Assisted Social Media Marketing on the Performance of Small and Medium Enterprises: Toward Effective Business Management in the Saudi Arabian Context International Journal of Computational Intelligence Systems 2021 13 1 142 152 10.2991/ijcis.d.200127.002 · DOI
  8. Bartika AW Bertrand M Cullenc Z Glaeserd EL Lucac M Stanton C The Impact of COVID-19 on Small Business Outcomes and Expectations PNAS 2020 117 30 17656 17666 10.1073/pnas.2006991117 32651281 PMC7395529 · DOI
  9. Becheikh N Landry R Amara N Lessons from Innovation Empirical Studies in the Manufacturing Sector: A Systematic Review of the Literature from 1993–2003 Technovation 2006 26 5/6 644 664 10.1016/j.technovation.2005.06.016 · DOI
  10. Bengio Y Courville A Vincent P Representation Learning: A Review and New Perspectives IEEE Transactions on Pattern Analysis and Machine Intelligence 2013 35 8 1798 1828 10.1109/TPAMI.2013.50 23787338 · DOI · Paper page
  11. Courville BY A. and Vincent P. Representation Learning: A Review and New Perspectives IEEE Transactions on Pattern Analysis and Machine Intelligence 2013 35 8 1798 1828 10.1109/TPAMI.2013.50 23787338 · DOI · Paper page
  12. Bernroider EWN Wong CWY Lai KH From Dynamic Capabilities to ERP Enabled Business Improvements: The Mediating Effect of the Implementation Project International Journal of Project Management 2014 32 2 350 362 10.1016/j.ijproman.2013.05.006 · DOI
  13. Borch OJ Madsen EL Dynamic Capabilities Facilitating Innovative Strategies in SMEs International Journal of Technoentrepreneurship 2007 1 1 109 125 10.1504/IJTE.2007.014731 · DOI
  14. Brem A Viardot E Nylund PA Implications of the Coronavirus (COVID-19) Outbreak for Innovation: Which Technologies Will Improve Our Lives? Technological Forecasting and Social Change 2021 163 120451 10.1016/j.techfore.2020.120451 33191956 PMC7648540 · DOI
  15. Breznik L Hisrich RD Dynamic Capabilities vs. Innovation Capability: Are They Related? Journal of Small Business and Enterprise Development 2014 21 3 368 384 10.1108/JSBED-02-2014-0018 · DOI
  16. Camacho-Miñano MDM Segovia-Vargas MJ Pascual-Ezama D Which Characteristics Predict the Survival of Insolvent Firms? An SME Reorganization Prediction Model Journal of Small Business Management 2015 53 2 340 354 10.1111/jsbm.12076 · DOI
  17. Campbell C Sands S Ferraro C Tsao H-Y Mavrommatis A From Data to Action: How Marketers can Leverage AI Business Horizons 2020 63 227 243 10.1016/j.bushor.2019.12.002 · DOI
  18. Canhoto A Clear F Artificial Intelligence and Machine Learning as Business Tools: A Framework for Diagnosing Value Destruction Potential Business Horizons 2020 63 2 183 193 10.1016/j.bushor.2019.11.003 · DOI
  19. Chan CML Teoh SY Yeow A Pan G Agility in Responding to Disruptive Digital Innovation: Case Study of an SME Information Systems Journal 2018 29 2 436 455 10.1111/isj.12215 · DOI
  20. Chan L Morgan I Simon H Alshabanat F Ober D Gentry J Min D Cao R Survey of AI in Cybersecurity for Information Technology Management. IEEE Technology and Engineering Management Conference 2018 Atlanta TEMSCON
  21. Chan YE Krishnamurthy R Desjardins C Technology-Driven Innovation in Small Firms MIS Quarterly Executive 2020 19 1 39 55 10.17705/2msqe.00024 · DOI
  22. Chung M Ko E Joung H Kim SJ Chatbot E-Service and Customer Satisfaction Regarding Luxury Brands Journal of Business Research 2018 117 1 9
  23. Dadteev K Shchukin B Nemeshaev S Using Artificial Intelligence Technologies to Predict Cash Flow Procedia Computer Science 2020 169 264 268 10.1016/j.procs.2020.02.163 · DOI
  24. Davenport TH Guha A Grewal D Bressgott T How Artificial Intelligence Will Change the Future of Marketing Journal of the Academy of Marketing Science 2020 48 1 24 42 10.1007/s11747-019-00696-0 · DOI · Paper page
  25. Davidsson P Entrepreneurial Opportunities and the Entrepreneurship Nexus: A Re-Conceptualization Journal of Business Venturing 2015 30 5 674 695 10.1016/j.jbusvent.2015.01.002 · DOI
  26. Davidsson, P., Recker, J., & von Briel, F. (2018). External Enablement of New Venture Creation: A Framework. Academy of Management Perspectives, 34(3), 311–332.
  27. Day GS Schoemaker PJH Adapting to Fast-Changing Markets and Technologies California Management Review 2016 58 4 59 78 10.1525/cmr.2016.58.4.59 · DOI
  28. DeVellis RF Scale Development: Theory and Applications. Applied Social Research Methods Series 2003 Thousand Oaks CA Sage Publications
  29. Donthu N Gustafsson A Effects of COVID-19 on Business and Research Journal of Business Research 2020 117 284 289 10.1016/j.jbusres.2020.06.008 32536736 PMC7280091 · DOI · Paper page
  30. Drydakis N Mobile Applications Aiming to Facilitate Immigrants’ Societal Integration and Overall Level of Integration, Health and Mental Health Does Artificial Intelligence Enhance Outcomes? Computers in Human Behavior 2021 117 April 106661 10.1016/j.chb.2020.106661 · DOI
  31. Eisenhardt KM Martin JA Dynamic Capabilities: What Are They? Strategic Management Journal 2000 21 10/11 1105 1121 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E · DOI
  32. Ezrachi A Stucke M Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy 2016 Harvard University Press
  33. Fan X Ning N Deng N The Impact of the Quality of Intelligent Experience on Smart Retail Engagement Marketing Intelligence and Planning 2020 38 7 877 891 10.1108/MIP-09-2019-0439 · DOI
  34. Felsberger, A. Qaiser, F. H., Choudhary, A., & Reiner, G. (2022). The Impact of Industry 4.0 on the Reconciliation of Dynamic Capabilities: Evidence from the European Manufacturing Industries. Production Planning and Control, 33(2–3), 277–300.
  35. Garbellano S Da Veiga MDR Dynamic Capabilities in Italian Leading SMEs Adopting Industry 4.0. Measuring Business Excellence 2019 23 4 472 483 10.1108/MBE-06-2019-0058 · DOI
  36. Gaspar R Pedro C Panagiotopoulos P Seibt B Beyond Positive or Negative: Qualitative Sentiment Analysis of Social Media Reactions to Unexpected Stressful Events Computers in Human Behavior 2016 56 179 191 10.1016/j.chb.2015.11.040 · DOI
  37. Gautier, A., Ittoo, A., & Van Cleynenbreugel, P. (2020). AI Algorithms, Price Discrimination and Collusion: A Technological, Economic and Legal Perspective. European Journal of Law and Economics, 50, 405–435.
  38. Ghobakhloo M Ching NT Adoption of Digital Technologies of Smart Manufacturing in SMEs Journal of Industrial Information Integration 2019 16 100107 10.1016/j.jii.2019.100107 · DOI
  39. Greenleaf G Mowbray A Chung P Building Sustainable Free Legal Advisory Systems: Experiences from the History of AI & Law Computer Law and Security Review 2018 34 314 326 10.1016/j.clsr.2018.02.007 · DOI
  40. Guo, H., & Polak, P. (2021). Artificial Intelligence and Financial Technology FinTech: How AI Is Being Used Under the Pandemic in 2020. In A. Hamdan, A. E. Hassanien, A. Razzaque & B. Alareeni (Eds.), The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success. Studies in Computational Intelligence (Vol. 935, pp. 169–186). Cham: Springer. 10.1007/978-3-030-62796-6_9 · DOI
  41. Hamal S Senvar O Effectiveness of Machine Learning Classifiers in Detecting Financial Accounting Fraud for Turkish SMEs International Journal of Computational Intelligence Systems 2021 14 1 769 782 10.2991/ijcis.d.210203.007 · DOI
  42. Hamilton RH Davison HK The Search for Skills: Knowledge Stars and Innovation in the Hiring Process Business Horizons 2018 61 3 409e419 10.1016/j.bushor.2018.01.006 · DOI
  43. Hanadi AL Aruna M Technology Innovation for SME Growth: A Perception for the Emerging Economies Journal of Economics and Sustainable Development 2013 4 3 156 162
  44. Hansen EB Bøgh S Artificial Intelligence and Internet of Things in Small and Medium-Sized Enterprises: A Survey Journal of Manufacturing Systems 2021 58 B 362 372 10.1016/j.jmsy.2020.08.009 · DOI
  45. Hatfield JM Social Engineering in Cybersecurity: The Evolution of a Concept Computer and Security 2018 73 102 113 10.1016/j.cose.2017.10.008 · DOI
  46. Helfat CE Peteraf MA Understanding Dynamic Capabilities: Progress Along A Developmental Path Strategic Organization 2009 7 1 91 102 10.1177/1476127008100133 · DOI · Paper page
  47. Helfat CE Peteraf MA Managerial Cognitive Capabilities and the Microfoundations of Dynamic Capabilities Strategic Management Journal 2015 36 6 831 850 10.1002/smj.2247 · DOI · Paper page
  48. Hercheui M Ranjith R Improving Organization Dynamic Capabilities Using Artificial Intelligence Global Journal of Business Research 2020 14 1 87 96
  49. Hsu L-C Wang C-H Clarifying the Effect of Intellectual Capital on Performance: The Mediating Role of Dynamic Capability British Journal of Management 2012 23 2 179 205 10.1111/j.1467-8551.2010.00718.x · DOI
  50. Jablonska RM Polkowski Z Artificial Intelligence Based Processes in SMEs Studies and Proceedings of Polish Association for Knowledge Management 2017 86 13 23
  51. Jiang M Cui P Faloutsos C Suspicious Behavior Detection: Current Trends and Future Directions IEEE Intelligent Systems 2016 31 1 31 39 10.1109/MIS.2016.5 · DOI
  52. Juergensen J Guimón J Narula R European SMEs Amidst the COVID-19 Crisis: Assessing Impact and Policy Responses Journal of Industrial and Business Economics 2020 47 499 510 10.1007/s40812-020-00169-4 · DOI
  53. Katz R Jung J Callorda F Can Digitization Mitigate the Economic Damage of a Pandemic? Evidence from SARS. Telecommunications Policy 2020 44 10 102044 10.1016/j.telpol.2020.102044 33012958 PMC7521900 · DOI
  54. Keshavarz F Waheed AA Rachdi B Alhajj R Review Spam Detection by Highlighting Potential Spammers and Diminishing their Effect International Journal E-Business Research 2018 14 1 1 23
  55. Kietzmann J Paschen J Rae Treen E Artificial Intelligence in Advertising: How Marketers Can Leverage Artificial Intelligence along the Consumer Journey Journal of Advertising Research 2018 58 3 263 267 10.2501/JAR-2018-035 · DOI · Paper page
  56. Kim HK So WH Je SM A Big Data Framework for Network Security of Small and Medium Enterprises for Future Computing Journal of Supercomputing 2019 75 3334 3367 10.1007/s11227-019-02815-8 · DOI
  57. Klein VB Todesco JL COVID-19 Crisis and SMEs Responses: The Role of Digital Transformation Knowledge and Progress Management 2021 28 2 117 133 10.1002/kpm.1660 · DOI · Paper page
  58. Knowles C Hansen E Dibrell C Measuring Firm Innovativeness: Development and Refinement of a New Scale Journal of Forest Products Business Research 2008 5 5 1 24
  59. Korinek A Stiglitz J Ajay A Gans J Goldfarb A Artificial Intelligence and Its Implications for Income Distribution and Unemployment The Economics of Artificial Intelligence: An Agenda 2018 University of Chicago Press 349 390
  60. Köse U Improving Content Marketing Processes with the Approaches by Artificial Intelligence Ecoforum Journal 2017 6 1 1 8
  61. Kreiser PM Marino LD Kuratko DF Weaver KM Disaggregating Entrepreneurial Orientation: The Non-Linear Impact of Innovativeness, Proactiveness and Risk-Taking on SME Performance Small Business Economics 2013 40 2 273 291 10.1007/s11187-012-9460-x · DOI
  62. Kumar A Ayedee N Technology Adoption: A Solution for SMEs to Overcome Problems During COVID-19 Academy of Marketing Studies Journal 2021 25 1 1 16
  63. Kumar, A., & Kalse, A. (2021). Usage and Adoption of Artificial Intelligence in SMEs. Materials Today: Proceedings. 10.1016/j.matpr.2021.01.595 · DOI
  64. Kumaravel T Bizu B Convolutional Neural Network for Customer's Opinion on Amazon Products International Journal of Recent Technology and Engineering 2019 8 3 6634 6643
  65. Laaksonen O Peltoniemi M The Essence of Dynamic Capabilities and their Measurement International Journal of Management Review 2016 20 2 184 205 10.1111/ijmr.12122 · DOI
  66. Laudon KC Laudon JP Management Information Systems: Managing the Digital Firm 2019 Harlow Pearson Education Limited
  67. Li N Du S Zheng H Xue M Zhu H Fake Reviews Tell No Tales? Dissecting Click Farming in Content-Generated Social Networks China Communications 2018 15 4 98 109 10.1109/CC.2018.8357744 · DOI
  68. Li L Su F Zhang W Mao JY Digital Transformation by SME Entrepreneurs: A Capability Perspective Information Systems Journal 2018 28 6 1129 1157 10.1111/isj.12153 · DOI
  69. Li H Special Section Introduction: Artificial Intelligence and Advertising Journal of Advertising 2019 48 4 333 337 10.1080/00913367.2019.1654947 · DOI
  70. Lichtenthaler U An Intelligence-Based View of Firm Performance: Profiting from Artificial Intelligence Journal of Innovation Management 2019 7 1 7 20 10.24840/2183-0606_007.001_0002 · DOI
  71. Lokman AS Zain JM Extension and Prerequisite: An Algorithm to Enable Relations Between Responses in Chatbot Technology Journal of Computer Science 2010 6 10 1212 1218 10.3844/jcssp.2010.1212.1218 · DOI
  72. McKelvie A Davidsson P From Resource Base to Dynamic Capabilities: An Investigation of New Firms British Journal of Management 2009 20 S 63 80 10.1111/j.1467-8551.2008.00613.x · DOI
  73. Menard SW Handbook of Longitudinal Research: Design, Measurement, and Analysis 2008 Elsevier
  74. Mendonça C Andrade A Dynamic Capabilities and Their Relations with Elements of Digital Transformation in Portugal Journal of Information Systems Engineering and Management 2018 3 3 23
  75. Morgan SL Handbook of Causal Analysis for Social Research 2013 Springer
  76. Mrabet ZE El Mrabet Z Kaabouch NE Ghazi H El Ghazi H Cyber-Security in Smart Grid: Survey and Challenges Computers and Electric Engineering 2018 67 469 482 10.1016/j.compeleceng.2018.01.015 · DOI · Paper page
  77. Netermeyer RG Bearden WO Sharma S Scaling Procedures: Issues and Applications 2003 Sage Publications, Thousand Oaks
  78. Newell S Marabelli M Strategic Opportunities (and Challenges) of Algorithmic Decision-Making: A Call for Action on the Long-Term Societal Effects of ‘Datification’ The Journal of Strategic Information Systems 2015 24 1 3e14 10.1016/j.jsis.2015.02.001 · DOI · Paper page
  79. Niemimaa M Interdisciplinary Review of Business Continuity from An Information Systems Perspective: Toward an Integrative Framework Communications of the Association for Information Systems 2015 37 4 69 105
  80. Papadopoulos T Baltas KN Baltac ME The Use of Digital Technologies by Small and Medium Enterprises During COVID-19: Implications for Theory and Practice International Journal of Information Management 2020 55 102192 10.1016/j.ijinfomgt.2020.102192 32836646 PMC7327446 · DOI
  81. Pardhan S Drydakis N Associating the Change in New COVID-19 Cases to GDP per Capita in 38 European Countries in the First Wave of the Pandemic Frontiers in Public Health: Health Economics 2021 8 582140 10.3389/fpubh.2020.582140 PMC7854531 33553086 · DOI
  82. Pedauga, L., Sáez, F., & Delgado-Márquez, B. L. (2022). Macroeconomic Lockdown and SMEs: The impact of the COVID-19 Pandemic in Spain. Small Business Economics, 58, 665–688. PMC7934814 38624620
  83. Pérez, P., De-La-Cruz, F., Guerrón, X., Conrado, G., Quiroz-Palma, P., & Molina, W. (2019). “ChatPy: Conversational Agent for SMEs.” In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1–6). Coimbra: CISTI. 10.23919/CISTI.2019.8760624 · DOI
  84. Piccialli, F., di Cola, V., Giampaolo, F., & Cuomo, S. (2021). The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic. Information Systems Frontiers, 23, 1467–1497. PMC8072097 33935585
  85. Pisano G Teece D How to Capture Value from Innovation: Shaping Intellectual Property and Industry Architecture California Management Review 2007 50 1 278 296 10.2307/41166428 · DOI
  86. Priyono A Moin A Putri VNAO Identifying Digital Transformation Paths in the Business Model of SMEs during the COVID-19 Pandemic Journal of Open Innovation: Technology, Market and Complexity 2020 6 104 10.3390/joitmc6040104 · DOI · Paper page
  87. Qin X Jiang Z The Impact of AI on the Advertising Process: The Chinese Experience Journal of Advertising 2019 48 4 338 346 10.1080/00913367.2019.1652122 · DOI
  88. Rebón F Castander I Argandoña J Gerrikagoitia JK Alzua-Sorzabal A An Antifraud System for Tourism SMEs in the Context of Electronic Operations with Credit Cards American Journal of Intelligent Systems 2015 5 1 27 33
  89. Rekha AG Abdulla MS Asharaf S Artificial Intelligence Marketing: An Application of a novel Lightly Trained Support Vector Data Description Journal of Information and Optimization Sciences 2016 37 5 681 691 10.1080/02522667.2016.1191186 · DOI
  90. Rostain T Robots Versus Lawyers: A User-Centered Approach Georgetown Journal of Legal Ethics 2017 30 559
  91. Salehi-Esfahani S Ozturk AB Negative Reviews: Formation, Spread, and Halt of Opportunistic Behavior International Journal of Hospitality Management 2018 74 138 146 10.1016/j.ijhm.2018.06.022 · DOI
  92. Shabbir J Anwer T Artificial Intelligence and its Role in Near Future Journal of Latex Class Files 2015 14 8 1 11
  93. Shepherd DA Williams TA Spontaneous Venturing: An Entrepreneurial Approach to Alleviating Suffering in the Aftermath of a Disaster 2018 MIT Press
  94. Sood N The Power of AI to Transform the Global SME Credit Landscape, in. S. Chishti, I. Bartoletti, A. Leslie, and. S. M. Millie, The AI Book: The Artificial Intelligence Handbook for Investors, Entrepreneurs and FinTech Visionaries 2020 New Jersey John Wiley and Sons 52 53
  95. Surendiran R Rajan KP Sathish KM Study on the Customer Targeting Using Association Rule Mining International Journal on Computer Science and Engineering 2010 2 7 2483 2484
  96. Sunday CE Vera CCE Examining Information and Communication Technology (ICT) Adoption in SMEs: A Dynamic Capabilities Approach Journal of Enterprise Information Management 2018 31 2 338 356 10.1108/JEIM-12-2014-0125 · DOI
  97. Teece D Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance Strategy Management Journal 2007 28 13 1319 1350 10.1002/smj.640 · DOI · Paper page
  98. Teece D Peteraf M Leih S Dynamic Capabilities and Organizational Agility: Risk, Uncertainty, and Strategy in the Innovation Economy California Management Review 2016 58 4 13 35 10.1525/cmr.2016.58.4.13 · DOI · Paper page
  99. Teece D Business Models and Dynamic Capabilities Long Range Planning 2018 51 1 40 49 10.1016/j.lrp.2017.06.007 · DOI · Paper page
  100. Teodorescu, H. N. L. (2014). Survey of IC&T in Disaster Mitigation and Disaster Situation Management. In H. N. Teodorescu, A. Kirschenbaum, S. Cojocaru & C. Bruderlein (Eds.), Improving Disaster Resilience and Mitigation - IT Means and Tools. NATO Science for Peace and Security Series C: Environmental Security (pp. 3–21). Dordrecht: Springer. 10.1007/978-94-017-9136-6_1 · DOI
  101. Ulas D Digital Transformation Process and SMEs Procedia Computer Science 2019 158 662 671 10.1016/j.procs.2019.09.101 · DOI
  102. Ulrich, P. Frank, V., & Kratt, M. (2021). Adoption of Artificial Intelligence Technologies in German SMEs – Results from an Empirical Study. In S. Hundal, A. Kostyuk & D. Govorun (Eds.), Corporate governance: A search for emerging trends in the pandemic times (Vol. 189, pp. 76–84). Dubai: PACIS. 10.22495/cgsetpt13 · DOI
  103. Um T Kim T Chung N How Does an Intelligence Chatbot Affect Customers Compared with Self-Service Technology for Sustainable Services? Sustainability 2020 12 5119 10.3390/su12125119 · DOI
  104. Yang Y Kolesnikova A Lessmann S Ma TS M-T. and Johnson, J. E. V. Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting European Journal of Operational Research 2018 283 1 217 234 10.1016/j.ejor.2014.01.042 · DOI
  105. Yang X Mao S Gao H Duan Y Zou Q Novel Financial Capital Flow Forecast Framework Using Time Series Theory and Deep Learning: A Case Study Analysis of Yu'e Bao Transaction Data IEEE Access 2019 7 70662 70672 10.1109/ACCESS.2019.2919189 · DOI
  106. van Esch P Overton LRA Van Esch LJ Mass Media Social Marketing Campaigns: A Review International Business Research 2014 7 6 1-17
  107. Vishnoi SK Bagga T Sharma A Wani SN Artificial Intelligence Enabled Marketing Solutions: A Review Indian Journal of Economics and Business 2018 17 4 167 177
  108. Wagner, D. N. (2021). Economic AI Literacy: A Source of Competitive Advantage. In B. Christiansen & T. Škrinjarić (Eds.), Handbook of Research on Applied AI for International Business and Marketing Applications (pp. 135–152). Hershey: IGI Global. 10.4018/978-1-7998-5077-9.ch008 · DOI
  109. Wang Y Shi X Thrive, Not Just Survive: Enhance Dynamic Capabilities of SMEs Through IS Competence Journal of Systems and Information Technology 2011 13 2 200 222 10.1108/13287261111136016 · DOI
  110. Wang Z Hou T Song D Li Z Kong T Detecting Review Spammer Groups Via Bipartite Graph Projection Computer Journal 2016 59 6 861 874 10.1093/comjnl/bxv068 · DOI
  111. Wang W Deng ST C-W. and Pan, Y. Smart Generation System of Personalized Advertising Copy and Its Application to Advertising Practice and Research Journal of Advertising 2019 48 4 356 365 10.1080/00913367.2019.1652121 · DOI
  112. Warner KSR Wäger M Building Dynamic Capabilities for Digital Transformation: An Ongoing Process of Strategic Renewal Long Range Planning 2019 52 326 349 10.1016/j.lrp.2018.12.001 · DOI
  113. Woodcock R Personalized Pricing as Monopolization Connecticut Law Review 2019 51 2 1 63
  114. Wooldridge JM Econometric Analysis of Cross Section and Panel Data 2010 2 The MIT Press
  115. Wu Υ Ngai ΕWTWu P. and Wu, C. Fake Online Reviews: Literature Review, Synthesis, and Directions for Future Research Decision Support Systems 2020 132 113280 10.1016/j.dss.2020.113280 · DOI
  116. Xinyue, C., Zhaoyu, X., & Yue, Z. (2020). Using Machine Learning to Forecast Future Earnings. Atlantic Economic Journal, 48, 543–545.
  117. Zhuang M Cui G Peng L Manufactured Opinions: The Effect of Manipulating Online Product Reviews Journal of Business Research 2018 87 24 35 10.1016/j.jbusres.2018.02.016 · DOI

Authors
Drydakis, N.
Affiliations
IZA - Institute of Labor Economics (DE)
Corresponding author
Drydakis, Nick
ORCID
Drydakis, Nick
Venue
Information Systems Frontiers
Publisher
Springer Science and Business Media LLC
Published
2022-03-04
Volume
24
Issue
4
Pages
1223-1247
ISSN
  • 1387-3326 (ISSN-L)
  • 1572-9419
DOI
10.1007/s10796-022-10249-6
PMC
PMC8893980
OpenAlex
W4214942876
Cited by
180
References
138
Citations by year
2026: 57 · 2025: 46 · 2024: 39 · 2023: 29 · 2022: 9
FWCI
18.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Information Systems Frontiers (publishedVersion)
  • SSRN Electronic Journal (acceptedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Apollo (University of Cambridge) (submittedVersion)
  • Anglia Ruskin Research Online (Anglia Ruskin University) (submittedVersion)
  • Econstor (Econstor) (submittedVersion)
  • Figshare (submittedVersion, cc-by-nc-nd)
  • Apollo
Funders
  • Anglia Ruskin University
  • University of Cambridge
  • University of Toronto
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Social Sciences › Demography › SMEs Development and Digital Marketing
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › COVID-19 Pandemic Impacts
Keywords
artificial intelligence adoption, small and medium-sized enterprises, business risk, COVID-19 pandemic, dynamic capabilities, cash flow forecasting, digital marketing, human resource management, business pivoting

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review