undefined

How could Generative AI support and add value to non-technology companies : A qualitative study

Publiceringsår

2025

Upphovspersoner

Modgil, Sachin; Gupta, Shivam; Kar, Arpan Kumar; Tuunanen, Tuure

Abstrakt

With the spread of generative AI, non-technology companies are also adopting it at a faster rate. Therefore, this study aims to study the appropriation of Generative AI to create value to non-technology businesses through a knowledge based view of the firm. To achieve this objective, we followed a semi-structured interview schedule, where 98 qualitative data points were collected and analysed. We follow open, axial and selective coding along with Gioia methodology for analysis. Findings indicate that companies employ Generative AI for risk management, where potential threats, impact of possible hazards and degree of uncertainty in the business environment are considered in decision-making. Generative AI also helps in knowledge integration, where assimilation, adaptation, application and implementation are achieved. Findings also suggest that an improved business outlook can be achieved regarding accurate demand forecasting, real-time insights, contextual understanding and alignment to the vision through Generative AI. It is also observed that companies are investing in Generative AI to achieve competitive advantage and greater significance. The contribution of this study lies in the development of four propositions and a framework for generative AI-driven value for non-technology companies. The framework also uncovers the internal flow among key elements from risk identification to integration to developing the outlook and driving utility.
Visa mer

Organisationer och upphovspersoner

Jyväskylä universitet

Tuunanen Tuure Orcid -palvelun logo

Publikationstyp

Publikationsform

Artikel

Moderpublikationens typ

Tidning

Artikelstyp

En originalartikel

Målgrupp

Vetenskaplig

Kollegialt utvärderad

Kollegialt utvärderad

UKM:s publikationstyp

A1 Originalartikel i en vetenskaplig tidskrift

Publikationskanalens uppgifter

Förläggare

Elsevier

Volym

139

Artikelnummer

103124

Publikationsforum

68123

Publikationsforumsnivå

3

Öppen tillgång

Öppen tillgänglighet i förläggarens tjänst

Nej

Parallellsparad

Ja

Övriga uppgifter

Vetenskapsområden

Data- och informationsvetenskap

Nyckelord

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Publiceringsland

Nederländerna

Förlagets internationalitet

Internationell

Språk

engelska

Internationell sampublikation

Ja

Sampublikation med ett företag

Nej

DOI

10.1016/j.technovation.2024.103124

Publikationen ingår i undervisnings- och kulturministeriets datainsamling

Ja