Digital business model innovation among small and medium‐sized enterprises (SMEs)

In light of the rise of generative AI and recent debates about the socio‐political implications of large‐language models, chatbots, and the like, this paper analyzes the E.U.’s Artificial Intelligence Act (AIA), the world's first comprehensive attempt by a government body to address and mitigate the potentially negative impacts of AI technologies. The paper critically analyzes the AIA from a business and computer ethics point of view—a perspective currently lacking in the academic (e.g., GBOE‐related) literature. It evaluates, in particular, the AIA's strengths and weaknesses and proposes reform measures that could help to strengthen the AIA. Among the AIA's strengths are its legally binding character, extra‐territoriality, ability to address data quality and discrimination risks, and institutional innovations such as the AI Board and publicly accessible logs and database for AI systems. Among its main weaknesses are its lack of effective enforcement, oversight, and control, absence of procedural rights and remedy mechanisms, inadequate worker protection, institutional ambiguities, insufficient funding and staffing, and inadequate consideration of sustainability issues. Reform suggestions include establishing independent conformity assessment procedures, strengthening democratic accountability and judicial oversight, introducing redress and complaint mechanisms, ensuring the participation and inclusion of workers, guaranteeing political independence of the AI Board, providing enhanced funding and staffing of market surveillance authorities, and mandating “green AI.”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mitigating the adverse effects of AI with the European Union's artificial intelligence act: Hype or hope?

Wörsdörfer

2023

“…and micro-businesses, especially considering their unique challenges in adopting new technologies and sensing market shifts, as highlighted by (Nyamrunda & Freeman, 2021;Sukrat & Leeraphong, 2023;Teoh et al, 2023).…”

Section: Limitationsmentioning

confidence: 99%

Cultivating strategic agility – An empirical investigation into best practice

Christofi,

Chourides,

Papageorgiou

2023

Amidst the fluidity of today's post‐COVID business environment, this paper aims to fortify the discourse on strategic agility, by presenting a comprehensive best practice framework for its development and sustenance. This is achieved by carrying out an empirical investigation on four best practice multinational corporations. Despite abundant scholarly attention, a critical gap persists in the empirical examination of specific functional practices and their role for fostering strategic agility. This research fills this void, leveraging in‐depth interviews with strategic‐level managers and analyzing archival data, revealing that strategic agility necessitates a systemic approach and congruent efforts across organizational functions. Key findings underscore the role knowledge management as the linchpin, with corresponding emphasis on dynamic talent management, strategic partnerships, open innovation, digitalization, and sustainability. As a result, this paper contributes to the strategic management literature by offering practical insights for managers of global businesses in navigating the complexities of a rapidly evolving competitive landscape towards organizational excellence

“…However, many other technologies struggle to keep pace with the application of possible and public concerns (Florkowski & Olivas‐Luján, 2006; Pan et al., 2022). This is because cutting‐edge technologies ensure better transparency, support decentralization, increase traceability, and enhance immutability in organizations (Long et al., 2022; Teoh et al., 2023). The adoption of AI refers to the use of algorithm‐based systems with learning functions and cognitive capabilities that can perform tasks that normally require human intelligence (Guenole & Feinzig, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Tamble et al (2019) argued that many companies are not ready and capable of adopting AI in HRM functions. However, these arguments lack empirical support due to a lack of research in different industry and country contexts, as AI has gained tremendous popularity due to the global COVID-19 pandemic, which relied heavily on technology (Lim & To, 2022;Teoh et al, 2023). Therefore, it is crucial to conduct more studies on the application and adoption of AI in various contexts.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence adoption among human resource professionals: Does market turbulence play a role?

Islam,

Aldaihani,

Saatchi

2023

Drawing from the Technology Acceptance Model (TAM) and Technology‐Organization‐Environment (TOE) framework, this study investigates the factors that influence the adoption of artificial intelligence (AI) among human resource (HR) professionals in the tourism organizations of Malaysia. Responses were collected from 389 HR employees working at tourism organizations. Based on a partial least squares‐structural equation modeling (PLS‐SEM) analysis, results revealed that perceived ease of use, perceived usefulness, technology competency, top management support, and competitive pressure have significantly positive impacts on AI adoption in the tourism industry in Malaysia. The study's contribution to the TAM and TOE models is the inclusion of market turbulence as a moderating factor, which has been found to be a significant moderator as it enhances the understanding of HR practitioners and others regarding the factors that are most influential in AI adoption among HR employees in the tourism industry. The model developed in this study highlights how market turbulence can foster the relationship between AI adoption and organizations. In addition to the managerial and theoretical implications, the study also discusses its limitations and outlines directions for future research.