Artificial Intelligence

Kasemsap, Kijpokin

doi:10.4018/978-1-5225-2440-3.ch022

Cited by 8 publications

(4 citation statements)

References 98 publications

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“…Since 2000, particularly after 2015, the rapid development of intelligent hardware (sensors and chips), the evolution of algorithms, and the support of big data have constantly driven the development of AI. AI technologies, such as natural language processing, machine learning, and deep learning, bring sophisticated data analysis capabilities to existing applications across a wide range of industries and greatly facilitate firms' management, planning, and operation (Kasemsap, 2017).…”

Section: Introductionmentioning

confidence: 99%

Explore success factors that impact artificial intelligence adoption on telecom industry in China

Chen

2020

Journal of Management Analytics

136

142

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As the core driving force of the new round of informatization development and industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, this paper proposes a framework to explore the impacts of success factors on AI adoption in telecom industry by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. Particularly, this framework consists of factors regarding external environment, organizational capabilities, and innovation attributes of AI. The framework is empirically tested with data collected by surveying telecom companies in China. Structural equation modeling is applied to analyze the data. The study provides support for firms' decision-making and resource allocation regarding AI adoption.

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Section: Introductionmentioning

confidence: 99%

Explore success factors that impact artificial intelligence adoption on telecom industry in China

Chen

2020

Journal of Management Analytics

136

142

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“…AI encompasses the method that gives machines intelligent complexity. Kasemsap (2017) indicates AI assists machines in discovering the optimum solution for complicated issues in a humanlike manner. AI offers advantages over natural intelligence, according to Pannu (2015), since it is more consistent, is less expensive, lasts longer, is easier to copy and disseminate, and can execute and record specific activities quicker and better than people.…”

Section: Artificial Intelligence Evolutionmentioning

confidence: 99%

“…AI currently has a wide range of capabilities, including the ability to write complicated algorithms, forecast options, communicate with humans in actual time, provide answers and mine trillions of information (Perifanis, & Kitsios, 2023). Deep learning, machine learning and natural language processing are examples of AI technologies that add advanced data analysis abilities to current applications along with a comprehensive variety of sectors, making planning, management, and operation much easier (Kasemsap, 2017;Chen, 2019).…”

Section: Artificial Intelligence Evolutionmentioning

confidence: 99%

Exploring the Impact of Managerial Capabilities on the Innovation Potential of Artificial Intelligence and Organizational Capabilities: A Literature Review

Odeibat

2023

REASER

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Cognitive business functions based on artificial intelligence (AI) have been utilized bybusinesses to enhance competitive advantage, performance, profitability, and economic growth. Given AI's innovation in organizational capability and managerial concerns, there is a limitation in the existing literature about managerial matters that clarify the indirect and direct effects of AI and the adopted technology and its impacts on the organization and the environment. As a necessary consequence, this article explores the impact of managerial capability on AI and organizational capability. This article addresses the subsequent research queries: What impact do the technical capabilities of AI have on managerial capabilities? Will managerial support affect managerial capability? What is the impact of the complexities of AI on managerial capabilities? To what extent does managerial capability have an impact on the compatibility of AI? In order to achieve the research objectives, a set of previous literature was reviewed and analyzed. As shown in the discussion section, by investigating the organizational capability and innovation attributes of AI, the findings of this research clarify the relationship between managerial capabilities and AI adoption.

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“…Furthermore, as part of the endeavor to ensure the success of digital transformation strategies, the establishment of AI-based business models is expanding (Verhoef et al 2021). AI technology, which includes natural language processing, machine learning, and deep learning, offers extensive and diverse data analysis capabilities across multiple industries, providing convenience in business management, planning, and operations (Kasemsap 2017).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Competitiveness of AI Technology-Based Startups in the Digital Era

Lee,

Kim,

Ivan

2023

Administrative Sciences

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Artificial Intelligence (AI) startups possess four key attributes; being small enterprises, adopting AI technology, undergoing digital transformation, and using big data systems to enhance their competitiveness. This study aims to identify the key influencing factors needed to enhance the competitiveness of AI technology-based startups and to suggest a decision-making model to improve the technology and business competitiveness of AI startups in the digital era. To achieve this, the hierarchy concept framework was built with four evaluation areas based on the mechanism-based view theory, and the 16 evaluation factors that can influence were identified through existing literature, combining factors related to the digital transformation, technological application, and business competitiveness of the startups. These factors were analyzed using the Analytic Hierarchy Process (AHP) by the survey, targeting experts in South Korea. The analysis results indicate that the subject area was the most crucial for the business competitiveness of AI startups. It was also revealed that the subject’s strategic mind is the most significant factor to AI startups’ success. In the case of two control groups, categorized as ‘AI experts’ and ‘startup experts’, AI experts chose the subject as the most important area, whereas startup experts selected the environment, and significant differences were observed in all other factors. The results of this study will provide implications for strengthening the business competitiveness of AI startups and factors important for the growth of AI startups in this era.

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Artificial Intelligence

Cited by 8 publications

References 98 publications

Explore success factors that impact artificial intelligence adoption on telecom industry in China

Explore success factors that impact artificial intelligence adoption on telecom industry in China

Exploring the Impact of Managerial Capabilities on the Innovation Potential of Artificial Intelligence and Organizational Capabilities: A Literature Review

Enhancing the Competitiveness of AI Technology-Based Startups in the Digital Era

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