The results were organized to reflect the validity and reliability of the research instrument, demographic characteristics of participants, and perceptions regarding several dimensions of AI in administrative processes.
Findings related to the validity of the instrumentFace validity
The validity of the developed questionnaire was initially evaluated using face validity. A panel of experts meticulously assessed the questionnaire items for their clarity, relevance, and coherence. Their feedback led to several minor revisions that not only refined the language but also improved the instrument’s overall robustness by ensuring that the items effectively captured the intended constructs. These revisions contributed to a more comprehensive approach to measuring academic leaders’ attitudes toward AI integration, enabling the instrument to yield meaningful data that reflected participants’ genuine perspectives.
Internal consistency
The internal consistency of the questionnaire was assessed using Pearson’s correlation coefficient, which allowed for a comprehensive measurement of how well the individual items correlated with their respective constructs. The findings presented in Table 1 reveal strong positive correlations across various dimensions of the questionnaire, indicating that the items consistently reflect the underlying constructs they aim to measure. For example, the correlation between trust in AI and perceived benefits yielded a notable correlation coefficient of 0.690, suggesting a significant relationship in which greater trust is associated with a higher acknowledgment of perceived benefits provided by AI systems. This correlation emphasizes the importance of fostering trust among academic leaders to enhance the potential acceptance and utilization of AI in administrative contexts.
Table 1 Internal correlation between phrases and axis.Findings related to the reliability of the instrument
The reliability of the questionnaire was measured using Cronbach’s alpha coefficient, as shown in Table 2. Each dimension of the questionnaire displayed high reliability, with the overall coefficients reflecting Cronbach’s alpha scores exceeding the acceptable threshold of 0.700. The total reliability coefficient of 0.897 affirms that the instrument is stable across multiple administrations and can be used to produce consistent results regarding academic leaders’ attitudes toward AI integration. This high level of reliability reassures stakeholders that the findings of this study are grounded in a solid methodological framework, thus bolstering the credibility of the data collected.
Table 2 Cronbach’s Alpha coefficients.Findings related to the participants’ demographic data
Table 3 summarizes the demographic characteristics of the participants, showing a diverse representation in terms of gender, academic rank, and professional experience. Specifically, 54% of the participants were male and 41% were female, suggesting a relatively balanced gender representation in leadership roles within educational institutions. Regarding academic rank, 41% held titles as Associate Professors, while both Assistant Professors and Professors accounted for 29.5% each, indicating diversity in leadership perspectives across different levels of the academic hierarchy. Notably, a substantial portion of leaders (52.4%) had over 10 years of academic experience, suggesting a wealth of experience that informs their attitudes toward AI integration. This demographic variety enhances the generalizability of the research, allowing for a nuanced understanding of perceptions of AI across various academic contexts.
Table 3 The demographic data of the participants.Findings related to the perceptions of AI in administrative processes
The exploration of perceptions surrounding AI integration in administrative processes highlights five key areas: Trust in AI, Perceived Benefits of AI, and Readiness for AI Adoption, Concerns and Ethical Implications, and Challenges in Using AI.
The trust of AI
As shown in Table 4, the average score reflecting academic leaders’ trust in AI was 3.78, indicating a generally positive perception. The highest-ranked item was the belief that AI improves managerial efficiency (mean = 4.18), reinforcing the notion that academic leaders recognize AI’s potential to enhance institutional performance. However, reliance on AI systems for administrative tasks was comparatively lower (mean = 3.47), suggesting some hesitation among leaders regarding their complete dependence on technology. This dichotomy underscores a pivotal area for future research and development to build mechanisms that enhance leaders’ trust and reliance on AI.
Perceived benefits of AI
The findings in Table 5 illustrate that academic leaders perceived AI as highly beneficial for enhancing administrative operations, with an average score of 4.18. The highest-ranked benefit was identified as the ability of AI applications to streamline administrative work processes (mean = 4.28), highlighting the consensus on role of AI in improving operational efficiency and productivity. This widespread recognition of benefits serves as a strong foundation for advocating further integration of AI within institutional frameworks.
Table 5 Perceived benefits of AI.Concerns and ethical implications related to AI
The average score for the ethical implications of AI is 3.66, as shown in Table 6. Leaders expressed significant concerns about transparency in AI algorithms (mean = 3.75) and the potential biases embedded within AI systems (mean = 3.49). This reflects critical awareness of the ethical issues associated with AI use. The need for transparent and fair AI systems is paramount, as leaders balance the benefits of technology with ethical considerations regarding decision-making processes.
Table 6 Concerns and ethical implications related to AI.Readiness for adopting and embracing AI
Table 7 shows an average readiness score of 3.90 among academic leaders to integrate AI. The item indicating a willingness to explore AI applications ranked highest (mean = 4.10), suggesting openness to innovation. Conversely, hesitance was evident when the participants expressed the least readiness to integrate AI into administrative procedures. This suggests a cautious approach towards widespread adoption and indicates the need for targeted professional development and training initiatives to better prepare leaders and staff for integration.
Table 7 Readiness for adopting and embracing AI.Findings related to challenges at using AI
Table 8 reveals that the identified challenges to implementing AI yielded an average score of 4.12, indicating the recognition of significant obstacles. The most pronounced hurdle was the lack of awareness and training regarding AI among academic staff (mean = 4.32), underscoring a critical need for investment in educational efforts to support AI integration. Other challenges included financial constraints (mean = 3.99) and resistance to change (mean = 4.12), highlighting the systemic barriers that need to be addressed to facilitate effective AI adoption. This insight emphasizes the importance of developing comprehensive training and resource allocation strategies to alleviate perceived challenges.
Table 8 Challenges at using AI.Hypotheses testing
To test our hypotheses (H1, H2, and H3), a simple linear regression was conducted, while we used multiple regression to combine the effects of these factors. To determine the relationship between AI trust and academic leadership readiness for adopting and embracing AI, in the simple linear regression, trust in AI was considered as an independent variable, and academic leadership readiness for adopting and embracing AI as a dependent variable.
Table 9 presents the results of the analysis. As shown in Table 9, an R-value of 0.184 indicates a weak positive correlation between AI trust and academic leadership readiness for adopting and embracing AI. An R2 value of 0.034 indicates that only about 3.4% of the variance in academic leadership readiness for adopting and embracing AI can be attributed to trust in AI, meaning that the influence of trust in AI is minimal. The F value is 3.608, and the p value is 0.060, which is slightly above the typical significance level of 0.05. This indicates that the model is not statistically significant at the 0.05 level, although it is close to this level. Therefore, the model was not highly significant. Several values were considered for the regression coefficient of trust in AI. The unstandardized coefficient (B = 0.171) indicates a positive relationship between trust in AI and academic leadership readiness to adopt and embrace AI. The higher the trust in AI, the higher is the leadership readiness. There were also t and p values of 1.899 and 0.060, respectively. Since the p value is slightly above the typical significance level, the relationship is not statistically significant at the level of 0.05. This suggests a trend toward significance, but does not provide strong enough evidence to confidently confirm the positive effect of trust in AI. A constant B value of 3.012 represents the baseline level of leadership readiness when trust is zero. While the results indicate a positive relationship between trust in AI and academic leadership’s readiness to adopt and embrace AI, the small statistical significance suggests that further research with larger samples is needed to confirm this relationship.
Table 9 Results for simple regression analysis for the impact of trust of AI on the adopting and embracing AI.
To test the relationship between the perceived benefits of AI and academic leadership readiness for adopting and embracing AI, a simple linear regression was used, in which the perceived benefits of AI were considered as an independent variable and academic leadership readiness for adopting and embracing AI as a dependent variable. Table 10 presents the results of the analysis.
Table 10 Results for simple regression analysis for the impact of the Perceived benefits of AI on adopting and embracing AI.
The results in Table 10 demonstrate the relationship between the perceived benefits of AI and academic leadership readiness to adopt and embrace AI. The significance of the model can be inferred from the F-value of 4.320 and p value of 0.040. As the p value is less than the typical significance level of 0.05, this indicates that the regression model is statistically significant, meaning that a significant portion of the variance in academic leadership readiness for adopting and embracing AI is due to the perceived benefits of AI. The strength of the relationship can be inferred from the R2 value of 0.040, meaning that ~4% of the variance in academic leadership readiness to adopt and embrace AI can be attributed to the perceived benefits of AI. While this indicates a statistically significant relationship between the perceived benefits of AI and academic leadership readiness for adopting and embracing AI, the effect size was relatively small. Regarding the regression coefficients, the B coefficient and t value were 0.200 and 2.078, respectively. Therefore, the perceived benefits of AI were statistically significant. For every unit increase in perceived benefits, academic leadership readiness for adopting and embracing AI increased by ~0.200 units, holding other factors constant. A constant coefficient B of 2.823 represents the baseline level of leadership readiness when perceived benefits are zero. Thus, the results indicate a statistically significant positive relationship between the perceived benefits of AI and the academic leadership readiness for adopting and embracing AI Therefore, the greater the perceived benefits of AI, the greater the readiness of academic leadership for adopting and embracing AI.
To determine the relationship between concerns and ethical implications related to AI and academic leadership readiness for adopting and embracing AI, in the simple linear regression, the concerns and ethical implications related to AI were considered as independent variable and academic leadership readiness to adopt and embrace AI as a dependent variable. Table 11 shows the simple regression analysis for the impact of concerns and ethical implications related to AI on academic leadership readiness for adopting and embracing AI.
Table 11 Results for simple regression for the impact of concerns and ethical implications related to AI on adopting and embracing AI.
It can be noted from Table 11 the relationship between the concerns and ethical implications related to AI and the academic leadership readiness for adopting and embracing AI. Where Sig. value of 0.000 indicates a statistically significant positive relationship between ethical considerations related to AI and academic leadership readiness for adopting and embracing AI. An R2 value of 0.116 indicates that ~11.6% of the variance in academic leadership readiness to adopt and embrace AI can be attributed to ethical considerations related to AI. Regarding the effect size, the unstandardized coefficient B of 0.362 indicates that, for every unit increase in ethical considerations, there is an increase in leadership readiness of ~0.362 units, holding other factors constant. A standardized beta coefficient of 0.341 confirmed a moderate positive effect. A constant B of 2.243 represents the baseline level of leadership readiness when the ethical considerations related to AI are zero. An F-value of 13.565 and p value of 0.000 indicate that the model is statistically significant, confirming the importance of concerns and ethical implications related to AI in predicting leadership readiness.
In this analysis, we employed multiple regression to report the combined effect of all factors on academic leadership readiness to adopt and embrace AI. In this repression, the three factors were considered as independent variables, and academic leadership readiness to adopt and embrace AI as a dependent variable. Table 12 shows the results of the multiple regression analysis. The multiple regression model indicated a statistically significant effect, with F and p values of 4.739 and 0.004, respectively, indicating that a significant portion of the variance in leadership readiness was attributable to the combined influence of these variables (R2 = 0.123).
Table 12 Results for the multiple regression analysis for the impact of the combined effect of all factors on the adopting and embracing AI.
Regarding the results for concerns and ethical implications related to AI, B, p, and beta values of 0.470, 0.003, and 0.442, respectively, indicate that ethical considerations have a significant positive effect on leadership readiness, highlighting the importance of ethical considerations in shaping leadership attitudes toward AI adoption. Regarding both trust in AI and perceived benefits, B and p values of −0.040 and 0.755 for trust in AI and −0.098 and 0.519 for perceived benefits indicate that these two variables do not exhibit any statistically significant individual effects on leadership readiness in this model, as p values are above the typical significance level of 0.05. Thus, the results indicate that the combined effect of trust, perceived benefits, concerns, and ethical implications related to AI is statistically significant. Concerns and ethical implications related to AI are considered more important; therefore, addressing ethical considerations related to AI is crucial for enhancing leadership readiness for adopting AI. The results also indicate a limited effect of trust and perceived benefits due to the negative, non-statistically significant coefficients. Therefore, these variables did not independently affect leadership readiness to a statistically significant degree. Thus, in this model, concerns and ethical implications related to AI are considered more important than trust or perceived benefits.