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

IF 11.1 1区 管理学 Q1 ENGINEERING, INDUSTRIAL
Sachin Modgil , Shivam Gupta , Arpan Kumar Kar , Tuure Tuunanen
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引用次数: 0

Abstract

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.
生成式人工智能如何支持非技术公司并为其增值--一项定性研究
随着生成式人工智能的普及,非技术企业也在以更快的速度采用它。因此,本研究旨在通过基于知识的企业视角,研究如何利用生成式人工智能为非技术企业创造价值。为实现这一目标,我们采用了半结构式访谈方法,收集并分析了 98 个定性数据点。我们采用开放式、轴向和选择性编码以及 Gioia 方法进行分析。研究结果表明,企业采用生成式人工智能进行风险管理,在决策中考虑潜在威胁、可能发生的危险的影响以及商业环境的不确定性程度。生成式人工智能还有助于知识整合,实现同化、适应、应用和实施。研究结果还表明,通过生成式人工智能,可以在准确的需求预测、实时洞察、背景理解和与愿景保持一致等方面改善业务前景。研究还发现,企业正在投资生成式人工智能,以实现竞争优势和更大的意义。本研究的贡献在于为非技术公司开发了四个命题和一个生成式人工智能驱动价值的框架。该框架还揭示了从风险识别到整合,再到发展前景和驱动效用等关键要素之间的内部流程。
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来源期刊
Technovation
Technovation 管理科学-工程:工业
CiteScore
15.10
自引率
11.20%
发文量
208
审稿时长
91 days
期刊介绍: The interdisciplinary journal Technovation covers various aspects of technological innovation, exploring processes, products, and social impacts. It examines innovation in both process and product realms, including social innovations like regulatory frameworks and non-economic benefits. Topics range from emerging trends and capital for development to managing technology-intensive ventures and innovation in organizations of different sizes. It also discusses organizational structures, investment strategies for science and technology enterprises, and the roles of technological innovators. Additionally, it addresses technology transfer between developing countries and innovation across enterprise, political, and economic systems.
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