Dora Sinaga (A Tourism & Hospitality trainer)

Authors: Yanshuo Song, Xiaodong Qiu and Jiatong Liu

Aim of this research is to examine how AI technology acceptance driven by top management support, perceived usefulness and perceived ease of use, affect the decision making efficiency, organisational performance and the degree of AI involvement.

https://doi.org/10.3390/systems13080683

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Theory used. The main construct of this research is Technology Acceptance Models (TAM). Authors choose this theory to explain user technology adoption behaviour. There are 3 variables used; 1. The external variables 2. Independent variables, 3. Dependent variables, 4. Controller.

• External variables: Perceived usefulness and perceived ease. Perceive usefulness; User believe that technology can make their work easier. Perceive ease; user believe technology is easy to use.
• Independent variable: Senior leadership (Top Management).
• Dependent variable: 1. Organisational performance, 2. Degree of AI involvement, 3. Enterprise scale, industry type, 4. Organisational culture, 5. Employee technical ability.

Research is using quantitavie, using SEM with the sample size 420 respondents. The analysis technique of this research is CFA and VIF

The main finding:

Top management support enhances AI acceptance through perceived and AI acceptance further promotes the comprehensive involvement of AI technology by improving decision efficiency and organizational. This indicates that the role of leadership in technology. Adoption is not limited to resource configuration and strategic planning but also involve shaping employees’ perceptions of technological value and convenience.

Future research: The study adopts a rational-structural approach to decision-making, it underrepresents the emotional and value-laden dimensions of organizational judgment. Cognitive and affective biases, ethical intuitions, and identity-based reasoning are known to shape managerial decisions, often in ways that transcend logic-based AI predictions. for future research that incorporates ethical governance, labor impacts, cross-cultural sensitivity, and emotion value dynamics to better account for the complex, multidimensional nature of AI driven, decision making, in organisation.

Future research should adopt a comparative framework to explore the cultural adaptability of AI adoption models and the differential impact of organizational norms on acceptance dynamics.