人工智能能力、动态能力和组织创造力:阿拉伯联合酋长国政府组织绩效的促进因素

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-02-01 DOI:10.1108/jm2-11-2022-0272
Hamad Mohamed Almheiri, Syed Zamberi Ahmad, Abdul Rahim Abu Bakar, Khalizani Khalid
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引用次数: 0

摘要

目的本研究旨在利用资源基础理论评估人工智能能力量表的有效性。本研究采用定性和定量分析相结合的方法对人工智能能力量表进行校准。在定性研究中,形成了一套 26 个初始项目。在定量研究中,为了完善和验证量表,使用了从 344 名公共管理人员那里获得的自我报告数据。研究结果提供了实证证据,表明人工智能能力的存在对动态能力、组织创造力和绩效产生了积极而显著的影响。研究还发现,动态能力部分调节了人工智能能力与组织创造力和绩效之间的关系,而组织创造力则部分调节了动态能力与组织创造力之间的关系。原创性/价值在政府部门开展的有关人工智能能力的研究数量有限,而且这些研究结果往往相互矛盾,没有定论。此外,这些研究表明,文献并未充分探讨组织层面的互补性资源在促进政府组织内独特能力发展方面的意义。本文借鉴基于资源的理论,提出了一个可供政府组织用来评估其人工智能能力与组织绩效关系的框架。
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Artificial intelligence capabilities, dynamic capabilities and organizational creativity: contributing factors to the United Arab Emirates Government’s organizational performance

Purpose

This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these capabilities on the organizational-level resources of dynamic capabilities and organizational creativity, ultimately influencing the overall performance of government organizations.

Design/methodology/approach

The calibration of artificial intelligence capabilities scale was conducted using a combination of qualitative and quantitative analysis tools. A set of 26 initial items was formed in the qualitative study. In the quantitative study, self-reported data obtained from 344 public managers was used for the purposes of refining and validating the scale. Hypothesis testing is carried out to examine the relationship between theoretical constructs for the purpose of nomological testing.

Findings

Results provide empirical evidence that the presence of artificial intelligence capabilities positively and significantly impacts dynamic capabilities, organizational creativity and performance. Dynamic capabilities also found to partially mediate artificial intelligence capabilities relationship with organizational creativity and performance, and organizational creativity partially mediates dynamic capabilities – organizational creativity link.

Practical implications

The application of artificial intelligence holds promise for improving decision-making and problem-solving processes, thereby increasing the perceived value of public service. This can be achieved through the implementation of regulatory frameworks that serve as a blueprint for enhancing value and performance.

Originality/value

There are a limited number of studies on artificial intelligence capabilities conducted in the government sector, and these studies often present conflicting and inconclusive findings. Moreover, these studies indicate literature has not adequately explored the significance of organizational-level complementarity resources in facilitating the development of unique capabilities within government organizations. This paper presents a framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory.

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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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