Exploring the inhibitors for competitive AI software development through cloud driven transformation

Hasteer, Nitasha; Sindhwani, Rahul; Behl, Abhishek; Varshney, Akul; Sharma, Adityansh

Abstract

COVID-19 has compelled every sector to shift towards virtualizing, and to curb the spread of this deadly virus, technologies like cloud computing, artificial intelligence, and IoT have played a vital role. Cloud-based crowdsourcing is a sourcing method through which organizations may post application development tasks as a contest, and the crowd may participate to win prizes or earn monetary support through online means. Many IT firms hesitate to adopt this methodology for competitive software development despite its advantages. This study's primary purpose is to discover the inhibitors and criteria restricting IT firms from incorporating cloud-based crowdsourcing in their workflow. An extensive literature review has been conducted to identify the inhibitors and criteria. The authors applied a novel three-phased methodology to the identified inhibitors to analyze them with mixed method approach. Authors have used the m-TISM method to develop a hierarchal model and find the interrelation between the identified inhibitors, which were further analyzed and segmented with the help of MICMAC analysis. The PF-AHP method was used to find the weightage of the identified criteria, and the PF-CoCoSo method was applied to find the ranking of the inhibitors considering the weights of the criteria. The study reveals that Difficulty in Price Fixing, Labor Exploitation, and Risk of Dependency are the critical challenges to adoption. The study reflects how the Contingency Management Theory and the Resource Dependency Theory acts as frameworks for navigating complexity and reducing risks in the ever-changing world of software development. The study concludes with key inputs that may help IT firms adopt and promote cloud-based crowdsourcing methodology for software development. These inputs may help companies to attract experienced developers who prefer working from home and prove worthy even during disruptions caused due to unprecedented situations.

Citation

Hasteer, N., Sindhwani, R., Behl, A., Varshney, A., & Sharma, A. (2023). Exploring the inhibitors for competitive AI software development through cloud driven transformation. Annals of Operations Research, https://doi.org/10.1007/s10479-023-05619-5

• Journal Article Type: Article
• Acceptance Date: Sep 18, 2023
• Online Publication Date: Oct 18, 2023
• Publication Date: Oct 18, 2023
• Deposit Date: Jul 2, 2024
• Journal: Annals of Operations Research
• Print ISSN: 0254-5330
• Electronic ISSN: 1572-9338
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s10479-023-05619-5
• Keywords: Crowdsourcing; Cloud computing; Inhibitors; Artificial intelligence; Virtualization; Mixed method approach
• Public URL: https://keele-repository.worktribe.com/output/855899