Examining how AI capabilities can foster organizational performance in public organizations

Mikalef, Patrick; Lemmer, Kristina; Schaefer, Cindy; Ylinen, Maija; Fjørtoft, Siw Olsen; Torvatn, Hans; Gupta, Michèle; Niehaves, Bjoern

doi:10.1016/j.giq.2022.101797

Cited by 39 publications

(41 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the UAE, AI adoption is prevalent in government entities, benefiting various sectors such as healthcare, oil and gas, security, energy and transportation (Al Badi et al , 2022; Ghenai et al , 2022; Hujran et al , 2021). While previous studies identified factors affecting AI adoption, little research explores strategies for effectively using AI to enhance overall OP (Dwivedi et al , 2021; Mikalef et al , 2023; Valle-Cruz and García-Contreras, 2023). Several studies have demonstrated that the use of AI in public administration has resulted in enhancements in the perceived value of public services (Robles and Mallinson, 2023), the decision-making processes (Gaozhao et al , 2023) and the allocation of resources (Mikalef et al , 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

“…While previous studies identified factors affecting AI adoption, little research explores strategies for effectively using AI to enhance overall OP (Dwivedi et al , 2021; Mikalef et al , 2023; Valle-Cruz and García-Contreras, 2023). Several studies have demonstrated that the use of AI in public administration has resulted in enhancements in the perceived value of public services (Robles and Mallinson, 2023), the decision-making processes (Gaozhao et al , 2023) and the allocation of resources (Mikalef et al , 2023). Consequently, these improvements have led to increased effectiveness, efficiency and convenience for citizens (Yigitcanlar et al , 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, there has been a growing emphasis on artificial intelligence’s (AI) crucial role in government organizations (Medaglia et al , 2023). This is driven by the need to adapt operations to the digital landscape for better services to residents and stakeholders (Dubey et al , 2023; Mikalef et al , 2023). AI’s potential to leverage large data sets and advanced methods can enhance government organizations’ capacity to adapt to the changing operational context and improve service delivery (Acciarini et al , 2023; Manis and Madhavaram, 2023; Sahoo et al , 2023).…”

Section: Introductionmentioning

confidence: 99%

“…However, many organizations face challenges in effectively using AI solutions (Neumann et al , 2022; Robles and Mallinson, 2023). Consequently, it remains uncertain how government organizations can optimize their investment in AI by leveraging complementary resources and enhancing organizational performance (OP) (Mikalef et al , 2023). To optimize AI investment, they must leverage resources and enhance OP.…”

Section: Introductionmentioning

confidence: 99%

“…This inquiry pertains to the use of AI applications to enhance OP, particularly in government organizations. Recent research emphasizes the need for a suitable combination of resources to harness AI’s potential effectively (Chowdhury et al , 2023; Mikalef et al , 2023). However, numerous government organizations are hesitant to implement AI due to factors like insufficient resources and organizational obstacles (Neumann et al , 2022; Wirtz et al , 2022).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Artificial intelligence capabilities, dynamic capabilities and organizational creativity: contributing factors to the United Arab Emirates Government’s organizational performance

Almheiri,

Ahmad,

Abu Bakar

et al. 2024

JM2

View full text Add to dashboard Cite

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.

show abstract