Table 3. Barriers to artificial... | Emerald Publishing

Table 3.

Barriers to artificial intelligence adoption in the public sector

The TOE category	First-order concepts	Second-order themes	Aggregate dimensions
Technology-related barriers	Lack of basic understanding regarding the potential AI applications Old thinking models or skills	Technological competences	Technological competence gap
Organization-related barriers	Lack of basic understanding regarding the opportunities of AI value propositions and their use cases Service design skills for AI development Competence and continuous learning Lack of time Lack of funding Lack of key resources Seeing the AI project difficult, slow down starting new AI adoption initiatives Different perspectives Negative attitude toward AI adoption Negative thinking models Poor prior experiences slow down starting new AI adoption initiatives The prudence of officials and issues of responsibility Silo-like thinking Crossing the organizational boundaries Managers’ understanding of how to apply AI Change of operating culture	Design competences	Insufficient AI capabilities
		Lack of resources	Management challenges for AI adoption
		Conflicting expectations
		Attitude
		Working culture
		Leadership
Environment-related barriers	Challenges regarding understanding different stakeholders hinder cross-functional cooperation The interest of different actors in the ecosystem Transparency rules, trustworthiness of AI, and data protection slow AI adoption Legal obstacles to trying Security issues Humans need to make final decisions according to the law	Cross-functional cooperation	Networked collaboration
Environment-related barriers		Putting regulations and security into practice	Application of regulation

The TOE category	First-order concepts	Second-order themes	Aggregate dimensions
Technology-related barriers	Lack of basic understanding regarding the potential AI applications Old thinking models or skills	Technological competences	Technological competence gap
Organization-related barriers	Lack of basic understanding regarding the opportunities of AI value propositions and their use cases Service design skills for AI development Competence and continuous learning Lack of time Lack of funding Lack of key resources Seeing the AI project difficult, slow down starting new AI adoption initiatives Different perspectives Negative attitude toward AI adoption Negative thinking models Poor prior experiences slow down starting new AI adoption initiatives The prudence of officials and issues of responsibility Silo-like thinking Crossing the organizational boundaries Managers’ understanding of how to apply AI Change of operating culture	Design competences	Insufficient AI capabilities
		Lack of resources	Management challenges for AI adoption
		Conflicting expectations
		Attitude
		Working culture
		Leadership
Environment-related barriers	Challenges regarding understanding different stakeholders hinder cross-functional cooperation The interest of different actors in the ecosystem Transparency rules, trustworthiness of AI, and data protection slow AI adoption Legal obstacles to trying Security issues Humans need to make final decisions according to the law	Cross-functional cooperation	Networked collaboration
Environment-related barriers		Putting regulations and security into practice	Application of regulation

Source(s): Created by the author; main TOE categories adapted from DePietro et al. (1990)