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Artificial Intelligence-Based Digital Transformation for Sustainable Societies: The Prevailing Effect of COVID-19 Crises

Abbas Tarhini et al.

Pacific Asia Journal of the Association for Information Systems2022https://doi.org/10.17705/1pais.14201article
AJG 2
Weight
0.67

What the paper says

Digital transformation (DT) is becoming a necessity for enterprises in different industries all over the world. The necessity for DT turned out to be inevitable with the coronavirus (COVID-19) outbreak. On March 2020, the World Health Organization (WHO) announced the COIVD-19 as global pandemic that caused thousands of deaths and brought the world to a standstill with huge economic burden (World Health Organization, 2020). This pandemic accelerated the need for organizations to transform using extensive digitization in response to the unprecedented change (Morgan, 2020). However, significant obstacles still encounter these organizations when it comes to recommending the necessary changes to make the digital leap. In today’s artificial intelligence age, digital transformation has enormous impact across societies (Nambisan, 2019; George & Paul, 2020; Majchrzak, Markus, Wareham, 2016); organizations need to realize that both digital transformation and artificial intelligence have to go hand in hand to help achieve prosperous digital transformation (Wuest et al., 2020). Therefore, becoming a digital enterprise is much more than investing in latest digital technologies. It is about adopting artificial intelligence to support the business strategy (Joda et al. 2020), searching for new business models (Kotarba, 2018), and considering afresh how to embed the social impact in the measure of the success of the business.

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https://doi.org/https://doi.org/10.17705/1pais.14201

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@article{abbas2022,
  title        = {{Artificial Intelligence-Based Digital Transformation for Sustainable Societies: The Prevailing Effect of COVID-19 Crises}},
  author       = {Abbas Tarhini et al.},
  journal      = {Pacific Asia Journal of the Association for Information Systems},
  year         = {2022},
  doi          = {https://doi.org/https://doi.org/10.17705/1pais.14201},
}

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Evidence weight

0.67

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.80 × 0.4 = 0.32
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.