The Conceptual Framework of the Study

Non-AI-implemented hospitals indicate clinical environments where AI technologies have not been integrated or implemented into clinical practice and where traditional approaches are implemented ignoring the evidence of AI benefits in managing complex health demands. Because of the encountered challenges, beneficial TFL impact may facilitate the AI integration process and guide the researcher in addressing main facilitators or barriers to nurses’ intention.

Pictorial design of research attributes of the study:

Attributions studied focus on various aspects which includes.

The current level of TFL among nurses in hospitals.

The current level of intention towards AI utilization amongst nurses in hospitals.

Significance of TFL influence on nurses’ intention toward AI utilization.

The most influencing dimension of TFL on nurses’ intention toward AI utilization.

LITERATURE REVIEW:

Nurses' Perceptions of AI Utilization in Healthcare

Addressing nurses’ perceptions regarding AI integration is important for successful AI implementation. Johnson and White articulates nurse’s perceptions, attitudes, and beliefs, influence AI acceptance and utilization. Several studies revealed the favorable effect of positive perception toward utilizing this technology for improving workflow and patient outcomes ⁶. Another literature study states, nurses have concerns regarding AI utilization, such as loss of autonomy and the loss of the human touch in patient care⁷, Brown and Lee articulates fear of automation-related job displacement, and role changes ⁸. Another literature reveals, nurses are worried about the accuracy, reliability, and transparency of AI, which influence critical decision-making processes ⁹. Leadership support, training adequacy, and access to resources facilitate the smooth integration of AI technologies into clinical practice ^{10, 11}.

Facilitators and barriers to nurses' Intentions toward technology adoption

Conferring evidences from literature, it is understood although nurses perceive AI as a patient care enhancer by reducing errors and positively influencing positive attitudes toward AI integration in clinical settings, nurses addressed' anxieties and misconceptions regarding educational initiatives to raise AI adoption in healthcare ¹². Other evidences speak on some social factors, such as peer approval support technology adoption through social acceptance within the professional community, while managers’ asset and training helps mitigate the challenges and frustrations associated with learning new technologies ^{13, 14}.

Encouraging education, training, and change management initiatives is essential for willingness and confidence in AI utilization stated by Topol, 2019 ¹⁵. Krittanawong et al., additionally, express interdisciplinary collaboration between healthcare organizations and technology vendors is a promising means to accelerate AI implementation ¹⁶. Visionary TFL leaders encourage discovering new care delivery approaches and promote a culture valuing experimentation and collaboration expressed in previous literature ^{17, 18}. Another literature study reveals persuasive leaders and frontline staff collaboration enhances the exchange of ideas, feedback, and insight, and fosters a sense of shared purpose and collective responsibility¹⁹. On the other hand, the study by Cresswell et al, highlights that lack of interoperability and compatibility with existing systems is one of the primary barriers to AI implementation in healthcare settings ²⁰. Fragmented information technology systems may challenge data storage and interoperability as well. Moreover, data privacy, security, and regulatory compliance are impressing encounters that hinder AI utilization spelt by Raghupathi and Raghupathi 2019 in their study ²¹. Besides, the sensitivity of healthcare data demands rigorous privacy regulations, adherence to regulatory requirements, and data safeguarding.

Leadership and Innovation

Understanding the theoretical background guides examining the dynamics that motivate innovation adoption. Numerous theories offer insights into the mechanism of how leadership fosters and aids the new technologies adoption. TFL underlines inspiring and motivating followers to exceed their potential says Vinkenburg in his study²². Lee and Park articulate a convincing vision, develop a culture of creativity and experimentation, encourage status quo challenges, and empower change initiatives ²³. Ali, Khaqan, and Amina (2020) have demonstrated that TFL enhances employees' innovative behavior through trust and work engagement. Innovative leaders demonstrate tolerance for new ideas, risk, ambiguity²⁴, and additional experimentation evidenced in other literature studies by Chen andamp; Huang, 2020; Lee andamp; Kim, 2022^25,
26.

Johnson et al (2023) study reveals these innovative leaders can drive organizational transformation and promote continuous improvements ²⁷. According to the diffusion of innovation theory by Smith and Brown (2021), innovation’s attributes, communication channels, adopter characteristics, and social environment are adoption influencing factors ²⁸. Recent research by Lee and Martinez (2021) showed a positive correlation between Inspirational motivation and individualized consideration and job satisfaction ²⁹. Similarly, a recent study by Smith and Johnson (2020) demonstrated the positive influence of stimulating intellectual interest and idealized influence on organizational commitment ³⁰. Furthermore, Smith and Jones (2016) proved that TFL behaviors, such as fostering a shared vision and providing innovation support are positively correlated with nurses' attitudes toward change³¹. A supportive experimentation culture that values risk-taking is more favorable to technology adoption is expressed in literature study by Avolio and Bass (1995) ³². In contrast, skepticism of organizational cultures that resist change, fear of failure, and reluctance to adopt new technologies, can obstruct technology adoption stated by Zhang and Wang, (2021) study ³³. Smith and Johnson (2023) express in their study, collaboration and transparency can promote information exchange and support technology adoption³⁴. A flexible and decentralized organization that promotes staff autonomy is more likely to adapt to technological change³⁵articulated by Jones and Smith (2022) in their study, whereas Burgelman, (1983) reveals, a culture that prioritizes individualism, rigid and bureaucratic structures, or secrecy may impede technology adoption³⁶.

METHODOLOGY:

Study Design, Population, and Sampling Method

A quantitative cross-sectional survey is utilized for the collection of data to provide an insight into the relationship between the independent variable TFL and the dependent variable nurses' intentions towards AI utilization in (Zarqa Governmental Hospital, Princess Basma Educational Hospital, and AlBasheer Hospital) in Jordan. The population for this study would be all registered nurses working in the three-targeted hospitals where AI technology has not been implemented. Convenience sampling was used for recruiting participants. After obtaining the institutional review board (IRB), participants were provided with clear information objectives, procedures, risks, and benefits, implied informed consent, confidentiality and anonymity, questionnaire handling, and the right to discontinue or refuse participation without any penalty. Considering the total number of population is 2300 nurses, the required sample size is 331 participants taken from Hair et al., 2014 ³⁷.

Method of Data Collection and Instrument

The researcher used the survey method for collecting data utilizing structured 27-item questionnaire grounded on a Multifactor Leadership Questionnaire (MLQ) for measuring TFL, and Theory of planned behaviors TPB and Technology Acceptance Model TAM for measuring intention is utilized. The analysis process encompasses descriptive statistics, Pearson correlations, and hierarchical regression.

Survey includes validated tools for measuring TFL and nurses' intentions toward AI utilization. As well, demographic information such as age, gender, academic level, years of experience, and job title would be collected to control for potential confounding variables. The questionnaire is disseminated electronically to approach the maximum possible number of targeted staff from different departments and different work schedules.

A 12-item originated from the validated Multifactor Leadership Questionnaire (MLQ) ³⁸ developed by Bass and Avolio to measure the four TFL dimensions, particularly, Perceived Idealized Influence, Perceived Inspirational Motivation, Perceived Intellectual Stimulation, and Perceived Individualized Consideration. The 15 measuring intention items originated from a validated tool that integrated TPB and TAM denoted by Hwang and Kim, 2016 and Hsieh and Wang, 2017 in their study ^39,
40.

The three dimensions of intention according to TPB are Attitude, Subjective Norms, and Perceived Behavioral Control, whereas two intention dimensions according to TAM are Perceived Usefulness and Perceived Ease of Use. Agreement with each statement is rated on a five-point Likert scale to indicate their level of perception regarding TFL behaviors and intentions.

Data Analysis Plan and Statistical Tests:

Quantitative data analysis techniques were employed to examine the relationship between TFL and nurses' intentions toward AI utilization using SPSS v.24. The descriptive statistics summarized the participant characteristics, TFL levels, and intention toward AI utilization. Independent t-tests and one-way ANOVA were employed to explore differences in TFL based on sociodemographic characteristics. Then, the Least Significant Difference (LSD) post hoc tests were used to identify group differences. After that, Pearson correlation coefficients were calculated to explore the relationships between TFL and AI utilization subscales. Finally, a multiple hierarchical regression analysis was conducted to predict nurses' intentions toward AI utilization, controlling for sociodemographic factors. In this two-step model, the sociodemographic variables (age, education, and experience) were entered first, followed by TFL subscales in the second model.

RESULTS:

Characteristics of participants

The majority of nurses were in age between 31 and 40 years (n=146, 43.7%), followed by the generation group 41-50 years (n=88, 26.3%), and 180 (53.9%) of them were female. Regarding educational level, more than half of nurses held bachelor’s degrees (n= 200, 59.9%). Among the participants, 100 (29.9%) had experience between 6 and 10 years. As presented in Table 1.

Table 1. Characteristics of Participants (n= 334)

Variables	Frequency (n)	Percentage (%)
Age
20-30	68	20.4
31-40	146	43.7
41-50	88	26.3
> 50	32	9.6
Gender
Male	154	46.1
Female	180	53.9
Educational Level
Diploma degree	78	23.4
Bachelor’s degree	200	59.9
Master’s degree	40	12.0
Doctorate degree	16	4.8
Length of experience
1-5	64	19.2
6-10	100	29.9
11-15	84	25.1
> 15	86	25.7

Transformational Leadership Level

Table 2 shows that the mean TFL score was 38.5 (SD=6.4), indicating a high TFL level. In terms of subscales, the mean reveals a high level of subscales, and the majority of nurses (85.3%, M= 10.1, SD= 1.97) indicate a high level of idealized influence. Regarding inspiring motivation, most nurses (80.2%, M= 9.7, SD= 1.98) successfully inspire and motivate their colleagues toward common goals. Furthermore, the considerable majority (74.3%, M= 9.4, SD= 2.11) promote innovation and creativity, challenging the status quo and cultivating a critical-thinking culture. Finally, 68.3% of nurses are highly concerned about their colleagues' specific needs (M= 9.2, SD= 2.11).

Nurses' Intentions towards AI Utilization

The participants who responded to the nurses' intentions towards AI utilization tool showed a high level of intentions towards AI utilization (M= 4.2, SD= .58). The mean of attitudes towards AI was 4.31 (SD= .62), denoting that nurses exhibit a positive and enthusiastic stance towards AI utilization. Regarding subjective norms, the mean shows a high level (M= 3.9, SD= .82) where nurses are under societal pressure when adopting particular AI utilization. Also, the high mean of perceived behavioral control (M= 4.2, SD= .73) indicates that nurses present a high level of control over engaging in AI utilization. The nurses had a high perception that using AI leads to enhanced job performance (M= 4.3, SD= .64). Furthermore, the mean of perceived ease of use was 4.2 (SD= .70), expressing that nurses demonstrate a high perception that using AI will be effortless.

Table 2. Level of TFL Scales and Subscales

Scale and Subscales	Frequency (n)	Percentage(%)
Total Transformational Leadership; M=38.46, SD=6.37
Perceived Idealized Influence; M=10.10, SD=1.97
Low	8	2.4
Moderate	41	12.3
High	285	85.3
Perceived Inspirational Motivation; M=9.73, SD=1.98
Low	8	2.4
Moderate	58	17.4
High	268	80.2
Perceived Intellectual Stimulation; M=9.41, SD=2.07
Low	8	2.4
Moderate	78	23.4
High	248	74.3
Perceived Individualized Consideration; M=9.21, SD=2.08
Low	12	3.6
Moderate	94	28.1
High	228	68.3
Note. SD: Standard Deviation

Differences between TFL and intention Towards AI Utilization according to sociodemographic and professional characteristics

As presented in Table 3. The independent t-test exhibits a non-statistically significant difference in gender with TFL and nurses' intentions toward AI utilization (P> 0.05). One-way ANOVA exhibits statistically significant differences in age, educational level, and experience in TFL (perceived idealized influence, perceived inspirational motivation, perceived intellectual stimulation, and perceived individualized consideration) and nurses' intentions towards AI utilization (attitude, subjective norms, perceived behavioral control, perceived usefulness, and perceived ease of use).

Table 3.

Differences of Participants Characteristics with Transformational Leadership

Variables	TFL			PII
Variables	Mean (SD)	t or F	P value	Mean (SD)	t or F	P value
Age^b		6.364	<0.001**		8.854	<.001**
20-30	35.53 (7.10)			9.06(2.66)
31-40	39.04 (5.61)			10.32 (1.76)
41-50	39.39 (6.61)			10.52 (1.55)
> 50	39.44 (5.77)			10.19 (1.40)
Gender^a		0.171	0.865		-1.320	0.188
Male	38.52 (6.33)			9.95 (2.19)
Female	38.40 (6.43)			10.23 (1.76)
Educational Level^b		1.077	0.359		1.282	0.281
Diploma degree	37.90 (7.58)			9.97 (2.56)
Bachelor’s degree	38.31 (5.95)			10.06 (1.80)
Master’s degree	40.00 (6.14)			10.20 (1.74)
Doctorate degree	39.13 (5.48)			11.00 (.89)
Length of experience^b		8.870	<0.001**		10.184	<0.001**
1-5	35.09 (6.93)			9.00 (2.76)
6-10	38.74 (6.47)			10.10 (1.90)
11-15	40.21 (4.16)			10.64 (1.28)
> 15	38.91 (6.78)			10.40 (1.58)

Variables	PIM			PIS			PIC
Variables	Mean (SD)	t or F	P value	Mean (SD)	t or F	P value	Mean (SD)	t or F	P value
Age^b		3.841	0.010*		1.742	0.158		3.409	0.01 8*
20-30	9.03 (2.07)			8.91 (2.30)			8.53 (2.33)
31-40	9.84 (1.99)			9.59 (1.58)			9.30 (1.89)
41-50	9.98 (1.99)			9.48 (2.44)			9.41 (2.14)
> 50	10.06 (1.32)			9.50 (2.33)			9.69 (1.99)
Gender^a		-0.805	0.422		1.295	0.196		1.250	0.212
Male	9.64 (1.99)			9.57 (1.98)			9.36 (1.95)
Female	9.81 (1.97)			9.28 (2.14)			9.08 (2.19)
Educational Level^b		1.040	0.375		2.001	0.114		.574	0.633
Diploma degree	9.44 (2.18)			9.28 (2.58)			9.21 (2.15)
Bachelor’s degree	9.76 (2.00)			9.34 (1.84)			9.15 (1.96)
Master’s degree	10.05 (1.71)			10.15(1.73)			9.60 (2.27)
Doctorate degree	10.00 (1.03)			9.13 (2.55)			9.00 (2.83)
Length of experience^b		5.692	<0.001**		3.670	0.013*		4.250	0.006*
1-5	8.88 (2.06)			8.75 (2.09)			8.47 (2.23)
6-10	9.78 (2.17)			9.66 (1.87)			9.20 (2.12)
11-15	10.14 (1.55)			9.76 (1.78)			9.67 (1.71)
> 15	9.91 (1.90)			9.28 (2.41)			9.33 (2.16)

Note. a: Independent t-test and, b: One-way ANOVA, TFL: Transformational Leadership, PII: Perceived Idealized Influence, PIM: Perceived Inspirational Motivation, PIS: Perceived Intellectual Stimulation, PIC: Perceived Individualized Consideration, *Significant at p < 0.05, **Significant at p < 0.001

Differences in TFL

The presented TFL differences according to sociodemographic and professional characteristics, such as age and experience illustrate significant variations. Younger nurses (20–30 years) recorded significantly lower TFL levels compared to other age groups indicating development and leadership strengthening with age and experience articulated by Bass and Avolio, 1994 in their study ⁵⁰.

Further, the higher TFL exhibited by older staff is attributed to greater professional maturity and leadership experience with age expressed in Northouse, 2021 study ⁴². In terms of experience, nurses with 1–5 years of experience exhibit lower TFL and AI utilization intentions compared to more experienced nurses. This may justified by the development of leadership skills and confidence in utilizing technology over time. The higher TFL and intention exhibited by 6-10 years of experience staff is a strong indicator that mid-career professionals could be properly positioned for leading technological change communicated by Gore et al., 2020 study ⁵¹.

Generally, the data analysis suggests that TFL plays a key role in adjusting nurses' intentions as nurses with high TFL traits possess a higher probability of embracing AI technologies. Moreover, age, educational level, and experience are strongly influencing TFL and AI utilization intentions. Although TFL acts as an enhancer of nurses’ readiness and willingness to technologies adoption, the analysis illustrates a relatively decorous strength relationship between TFL and nurses' intentions towards AI utilization (r = 0.295, p < 0.001). In parallel, several researchers argue that emphasizing vision, motivation, and individualized support are essential capabilities for promoting positive attitudes toward technological change also articulated in Bass and Avolio, 1994; Northouse, 2021studies ^50,42. However, the modest correlation directs the existence of other organization key players such as culture, and infrastructure availability which may direct the shaping of nurses' intentions for AI utilization divulged by Venkatesh et al., 2003 ⁵².

Moreover, an in-depth examination reflects weak significant positive relationships of TFL subscales and overall intentions towards AI utilization (p < 0.05) as these traits are the cornerstone for AI augmenting environment stated by Avolio and Bass, 2004⁵³. For instance, idealized influence is significantly correlated with subjective norms (r = 0.229, p < 0.001) because role modeling perception enables a positive social context that views technology adoption favorably as described by the TPB Ajzen, I. (1991) ⁵⁴, Correspondingly, articulating a vision and inspiring others is weakly and positively associated with all intentions dimensions (p < 0.01). Motivational leaders form optimistic perceptions about AI's easiness and utility could strengthen confidence in AI integration. Remarkably, the effect of inspirational motivation on perceived behavioral control (r = 0.244, p < 0.001) mirrors the leadership-generated empowerment that innervates engagement competency discloses in a literature study by Venkatesh et al., 2003 ⁵². Intellectual stimulation behavior such as encouraging creative thinking and problem solving is positively correlated with intentions dimensions (p < 0.001). As a result, creating a challenging environment may foster AI accepting workplace, and leaders who challenge their teams to think critically may foster openness for AI adoption. The relationship between intellectual stimulation and attitudes (r = 0.200, p < 0.001) ensures its solid impact on valuable and favorable AI perception which may speed up AI adoption confirmed by Holden and Karsh, 2010 ⁵⁵. Recognition of individual’s needs, the core of individualized consideration, has a weak positive relationship with AI proposed that tailoring leaders' support to individual nurses may intensify confidence in adopting AI and shrink change resistance evidences Avolio and Bass, 2004 ⁵⁶ in their study. Its positive relationship with attitudes (r = 0.213, p < 0.001) emphasizes the significance of individualized support in the formation of a promising affinity towards technology.

In conclusion, the significant positive relationships between TFL and the five intention subscales indicate that unaided TFL may be insufficient for initiating a high AI adoption level. Various environmental factors such as readiness, training accessibility, and perceived AI practicality are credible determinants of nurses' intentions stated in Topol, 2019 literature study ¹⁵. Therefore, synergism between TFL behaviors, practical experience, and familiarization with AI technology is a prospective effective approach for shaping nurses' behavior. While inspiring confidence and motivation are TFL-enhanced behaviors, optimizing intention behaviors requires a broader and more comprehensive approach. Targeted training, education workshops, and organizational support are necessary to comprehend the AI potential and motivate adoption indicated in previous literature by Krittanawong et al., 2019 ⁵⁷. Further research should elaborate and investigate a practical mechanism to unite TFL and other influential factors for successful AI implementation.

The insights view about TFL and AI relation from the multiple regression analysis guide understanding of intention predictors. The two-step model analysis examined the sociodemographic and professional characteristics impact and incorporated the four subscales of TFL as predictors. In the first Model, 11.3% of the variance in intention is explained by sociodemographic and professional characteristics (R² = 0.113, p < 0.001). Furthermore, level of education and work experience are significant predictors (B = 0.211, p < 0.01 for educational level, and B = -0.307, p < 0.01 for work experience). Hence, nurses with higher education possess a stronger intention, while those with longer experience exhibit a lower intention to AI adoption. Analogous to these findings, Venkatesh et al ⁵². Found that openness and adoption of new technology increased with advanced education and this may attributed to greater technology exposure and higher innovation Acceptance, similar views expressed in study by Topol, 2019,¹⁵. On the other hand, nurses with longer experience may be reluctant due to their preferences for comfort zones with traditional work routines or possibly, because of skepticism toward any routine disrupting change indicated by Holden and Karsh, 2010⁵⁵.

Introducing the TFL subscales in Model 2 increases the explanatory power significantly to reach 24.2% of intention variance (R² = 0.242, F(7,326) = 14.848, p < 0.001). Then, the finding suggests the substantial TFL role in formulating readiness for AI engagement. Outstandingly, the perceived intellectual stimulation emerged as a significant predictor (B = 0.271, p < 0.01) which reflects the importance of encouraging critical thinking, and creativity to push intention for adoption steps forward. Furthermore, intellectual stimulation (IS) reflects perception beyond traditional systems and augments involvement in credible innovations, which finally eases the introduction of disruptive technologies like AI stated in a study by Avolio and Bass, 2004⁵³. IS subscale is the sole significant predictor among TFL dimensions that reinforce curiosity environment and innovation-welcoming culture and ultimately facilitate adoption. Questioning the status quo and developing new ideas ease AI utilization and ability to stand with the evolving healthcare demands stated by Northouse, 2021⁴². In past literature, consolidation of the learning environment can expedite the transition process to novel technologies expressed in Davis, 1989⁵.

Surprisingly, traits of idealized influence, inspirational motivation, or individualized consideration dimensions are not significant predictors of nurses’ intentions despite their central position in TFL. This denotes that adoption necessitates the actualizing of other determinants of AI adoption. A comprehensive understanding, insightful thinking, and problem-solving deployment have become urgent over traditional leadership qualities. This distinction underscores the importance of context-specific leadership traits when guiding teams through technological transitions, revealed in study by Venkatesh et al., 2003 ⁵².

Another worthy finding is the negative experience impact in lowering willingness to AI adoption could potentially ascribed to uncertainty and fear of displacement caused by technologies. The previous research introduced technology resistance as a consequence to worries about role changes and the perceived undesirability of new skills learning articulated by Holden and Karsh, 2010⁵⁵. Consequently, leaders need to treat experienced nurses with particular attention and provide them with intellectual stimulation to minimize expected resistance. In summary, the study emphasizes the essential TFL role, particularly intellectual stimulation in modeling nurses’ intentions toward AI utilization. In addition, intellectual engagement is more impressive than education and experience to encourage openness to novel technologies. Expansion of new technologies integration demands specialized approaches for creating an adoption-accelerating culture. At last, future studies require exploring the best strategies to augment intellectual stimulation and develop targeted interventions for groups with potentially higher resistance or lower intentions.

LIMITATIONS:

The utilized cross-sectional design limits the ability to establish causal relationships between TFL and nurses' intentions. Furthermore, self-reporting is due to bias due to social desirability or possible inaccurate self-assessment. Conducting the study in non-AI-implemented governmental hospitals limits the generalizability of the findings. Additionally, sole measurement of TFL style without accounting the other available styles or some organizational factors on staff intention. Finally, localizing staff recruitment into three secondary hospitals might inhibit study generalizability to different healthcare settings. Consequently, further research should address these limitations and consider directing a broader range of populations, involving various types of hospitals, and other different influencing factors.

CONCLUSION:

In conclusion, this study highlights the TFL, especially the intellectual stimulation trait key role in influencing nurses' intentions toward applying AI in non-AI-implemented hospitals. Robust critical thinking and creativity, encouraging taking risks have a higher prone to embracing AI technologies. However, the TFL contributions in modeling positive attitudes are inadequate for widespread AI adoption. Training, infrastructure suitability, and work environment characteristics are similarly impressive. Remarkably, middle-experience length staff with higher education demonstrated greater intention indicating that this cluster may be the pioneers in leading the actual implementation and change process. Conversely, younger and less experienced nurses recorded lesser intention values requisite for targeted intervention. Further studies should elaborate on operational strengthening to augment intellectual stimulation and address causes for resistance among other studied groups to raise the probability of successful AI adoption in healthcare settings.

CONFLICT OF INTEREST:

There is no conflict interest between authors.

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Received on 05.06.2024 Modified on 19.09.2024

Research J. Pharm. and Tech 2024; 17(11):5469-5479.

DOI: 10.52711/0974-360X.2024.00837

Variables	TL	PII	PIM	PIS	PIC
IAIU	0.295**	0.112*	0.235**	0.343**	0.232**
AAIU	0.200**	0.091	0.105	0.267**	0.162**
SN	0.360**	0.229**	0.305**	0.374**	0.221**
PBC	0.244**	0.080	0.187**	0.288**	0.208**
PU	0.221**	0.103	0.174**	0.244**	0.171**
PEU	0.213**	-0.001	0.205**	0.261**	0.199**

Model	Predictors	b	B	t	p-value
1	Age	0.095	0.146	1.785	0.075
	Education	0.164	0.211	3.869	<0.001**
	Length of experience	-0.165	-0.307	-3.750	<0.001**
	R= .337; R² = .113; adjusted R² = .105, F = 14.056, p < 0.001
2	Age	0.085	0.131	1.715	0.087
	Gender	0.144	0.185	3.617	<0.001**
	Length of experience	-0.179	-.333	-4.310	<0.001**
	Perceived Idealized Influence	-0.014	-0.049	-0.777	0.438
	Perceived Inspirational Motivation	0.038	0.129	1.929	0.055
	Perceived Intellectual Stimulation	0.075	0.271	4.241	<0.001**
	Perceived Individualized Consideration	0.013	0.046	0.724	0.470
	R= .492; R² = .242; adjusted R² = .225, F = 14.848, p < 0.001