Cognitive analytics enabled responsible artificial intelligence for business model innovation: A multilayer perceptron neural networks estimation

IF 10.5 1区管理学 Q1 BUSINESS

Journal of Business Research Pub Date : 2024-06-18 DOI:10.1016/j.jbusres.2024.114788

Rama Prasad Kanungo , Rui Liu , Suraksha Gupta

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Abstract

Cognitive analytics employs and analyses complex and heterogeneous data sources generating deeper insights that mimic the natural intelligence of the human brain. Cognitive analytics-enabled Artificial Intelligence (AI) that promotes Business Model Innovation (BMI) for the efficiency of the healthcare system is a nascent and undertheorized domain. Within the healthcare management systems, stakeholders’ engagement with AI, particularly with responsible AI, to optimize BMI and improve business performance is bounded by several caveats. Using the Technology Acceptance Model (TAM) and Social Network Theory (SNT) as our conceptual foci, we empirically examine through the Multilayer Perceptron Neural Network the extent to which responsible AI leads to Business Model Innovation (BMI) through the stakeholders’ engagement. Our contributions are novel which demonstrate that cognitive analytics-enabled responsible AI is central to innovation, and healthcare stakeholders exhibit a robust propensity to reorientate and innovate their existing BMI to achieve improved business performance. It has significant implications for innovation, AI and cognitive analytics literature.

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用于商业模式创新的人工智能认知分析：多层感知器神经网络估算

认知分析利用和分析复杂的异构数据源，模仿人脑的自然智能，产生更深刻的见解。以认知分析为基础的人工智能（AI）可促进商业模式创新（BMI），从而提高医疗保健系统的效率，但这是一个新兴且理论化不足的领域。在医疗保健管理系统中，利益相关者参与人工智能，尤其是负责任的人工智能，以优化业务模式创新（BMI）和提高业务绩效，受到一些注意事项的限制。我们以技术接受模型（TAM）和社会网络理论（SNT）为概念焦点，通过多层感知器神经网络实证研究了负责任的人工智能在多大程度上通过利益相关者的参与实现了商业模式创新（BMI）。我们的贡献是新颖的，它证明了认知分析支持的负责任人工智能是创新的核心，医疗保健利益相关者表现出了调整和创新其现有商业模式的强烈倾向，以实现业务绩效的提高。这对创新、人工智能和认知分析文献具有重要意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Business Research BUSINESS-

CiteScore

20.30

自引率

10.60%

发文量

956

期刊介绍： The Journal of Business Research aims to publish research that is rigorous, relevant, and potentially impactful. It examines a wide variety of business decision contexts, processes, and activities, developing insights that are meaningful for theory, practice, and/or society at large. The research is intended to generate meaningful debates in academia and practice, that are thought provoking and have the potential to make a difference to conceptual thinking and/or practice. The Journal is published for a broad range of stakeholders, including scholars, researchers, executives, and policy makers. It aids the application of its research to practical situations and theoretical findings to the reality of the business world as well as to society. The Journal is abstracted and indexed in several databases, including Social Sciences Citation Index, ANBAR, Current Contents, Management Contents, Management Literature in Brief, PsycINFO, Information Service, RePEc, Academic Journal Guide, ABI/Inform, INSPEC, etc.

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