Introduction
It is stated that effect is a critical component in shaping cognition, attitude, and behavior in the workplace [1]. In light of this information, the effects of the emotional position of employees can be expressed as an important guide for both the success and the vision of the organization in the workplace. The general purpose of this study is to evaluate the psychological structure not only individually but also as an important part of the corporate climate and to analyze its effects. When we examine the psychological states of employees in the context of the concept of happiness, when individuals feel happy in their workplaces, this will increase their productivity for the organization and improve their well-being and life satisfaction levels when they spend time outside the workplace [2]. In addition, employees with higher levels of happiness perform better at work, are more sociable and cooperative, have more self-control, better self-regulation and coping skills, more satisfying relationships, and lower levels of burnout [3]. Happiness has always been a perceivable reality [4]. The fact that the presence of happy employees is directly associated with happy organizations emphasizes the importance of employees feeling happy at work [5].
Organizations need to foster creativity and innovation among employees, which will not only positively impact productive work frameworks but also enhance the competitive position of organizations [6]. Innovative work behavior, which is determined as the dependent variable in the research model, includes a range of activities, including idea generation, promotion, and realization, that contribute to the development and implementation of new ideas, processes, or products within the organization [7, 8]. At this point, as the role of artificial intelligence (AI), whose mediating role is examined in the research, grows in our lives, the relationship between innovative behaviors and the use of AI has become an area of interest that needs to be examined. Furthermore, as organizations integrate AI applications into their operations, it becomes important to understand how human psychology affects the adoption and effectiveness of these technologies. The importance of psychology in AI adoption will be evaluated in this research in parallel with the impact of employee happiness.
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Transformational leadership, another variable whose mediating role is examined in the research model, is a type of leadership that motivates followers and increases their morale and performance [9]. Transformational leaders become effective role models within teams by making decisions and exhibiting behaviors consistent with the proposed goals and vision of the team and motivating members to pursue these goals [10]. Moreover, in an era characterized by remote working and digital transformation, the emotional intelligence and interpersonal skills inherent in transformational leadership are essential for maintaining team cohesion and engagement [11]. As seen in the explanations, this leadership approach can be characterized by the ability of leaders to inspire and motivate their followers and create an environment of innovation and change.
In parallel with this information, the concept of happiness, which societies and individuals accept as the ultimate goal of human existence [12], was determined as the basis of the study in terms of employee happiness in the study. It aimed to determine the mediating roles of transformational leadership and perception of artificial intelligence use in its relationship with innovative business behavior, which is an important requirement of the modern age. When this situation is evaluated in the health sector and nurse population, its relationship with the quality of patient care will appear as an undeniable fact. As a result, the study aims to fill an important gap in the literature by analyzing the serial mediation roles of transformational leadership and artificial intelligence use in the effect of employee happiness on innovative work behavior. In the literature, no studies have been conducted on the relationship of other scales in the research model, especially under the influence of employee happiness. At this point, it is thought that the research can guide new studies.In addition, it is thought that examining the effects of artificial intelligence on both employee happiness and innovative work behavior with the developing language processing technologies in today’s conditions and the effect of transformational leadership on this process can contribute to the literature in detailing the relationship between nursing practices and technology.
Theoretical framework and hypothesis
The mediating role of transformational leadership
Research shows that leaders cannot participate in transformational leadership when they are not happy in their jobs and that happy leaders show more transformational leadership behaviors [13, 14]. It is stated that transformational leaders have a positive effect on increasing organizational innovation [15]. Transformational leadership style is presented as one of the important determinants of innovative work behavior because this leadership approach offers a structure that is more suitable for motivating employees to develop their own skills further. Bass and Avolio (1990) defined transformational leadership with four components: inspirational motivation, idealized influence, intellectual stimulation, and individualized assessment [16]. Bass (1999) emphasized that transformational leaders’ characteristics of inspiring and motivating employees to exhibit positive work behaviors and psychological empowerment, a motivational construct, mediate the effects of transformational leadership on employees’ work outcomes [17].
Many studies support a positive relationship between transformational leadership and innovative work behavior. In this context, there is a significant consensus among scholars that the presence of innovative work behavior is linked to transformational leadership [18‐21]. For example, Lin (2023) [22] stated that transformational leadership increases employees’ innovative work behaviors both directly and through mediating mechanisms such as organizational identity and employee voice. Moreover, this indirect effect was found to be more pronounced in contexts with a strong innovation climate. Groselj et al. (2021) [23] revealed that transformational leadership positively affects innovative work behavior through the mechanism of psychological empowerment, and this effect becomes more pronounced at high levels of psychological empowerment. In the study, it was emphasized that the capacity of transformational leaders to direct employees toward innovative behaviors increases with the employees feel empowered.
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Similarly, Messmann et al. (2021) [24] showed that transformational leadership positively influences employees’ innovative work behaviors mainly through the satisfaction of the perceived need for competence. In the study, it was concluded that transformational leaders strengthen employees’ sense of efficacy through individual support, intellectual stimulation, and support for innovative solutions, which in turn increases innovative work behaviors. In addition, Sudibjo and Prameswari (2021) [25] found that the effect of transformational leadership on innovative work behavior is not direct, but it creates a positive effect through knowledge-sharing behavior. Bii (2024) [26] examined the mediating role of transformational leadership in the relationship between emotional intelligence and innovative work behavior and argues that transformational leadership has a full mediating role on managers and staff working in the financial sector. Alshahrani et al. (2024) [27] argue that in the Saudi healthcare sector, transformational leadership plays an important mediating role in healthcare organizational performance and that innovation in healthcare contributes to improving performance and sustaining competitive advantages.Based on the literature discussed above, we hypothesize that transformational leadership positively mediates the relationship between employee happiness and innovative work behavior:
H1 = Transformational leadership has a mediating role in the relationship between employee happiness and innovative work behavior.
The mediating role of the use of artificial intelligence
The more positive employees’ emotions and the higher their job happiness, the more innovative behavior can be encouraged [28]. In addition, the mediating role of happiness in the effect of attitude towards digital technology on employees’ performance was determined [29]. It has been observed that research on nurse happiness is mostly investigated in relation to well-known variables such as quality of life, job satisfaction [30], mental health, stress [31], and performance [32].
Artificial intelligence systems, which enable employees to understand, analyze, and respond effectively to complex and dynamic business environments, provide important support in these processes [33]. At this point, it is known that artificial intelligence applications provide technical support for innovative work behavior [34]. However, as an example, Atalla et al. (2024) [35] revealed in their study that nurses’ positive attitudes toward artificial intelligence significantly and positively affect their innovative work behaviors. However, the perception of artificial intelligence alone did not have a significant effect on innovative behaviors.
However, studies on the effect of the use of artificial intelligence on innovative work behavior have been increasing in recent years. Alagele et al. (2025) [36] considered artificial intelligence as an intermediate variable due to its great importance in keeping up with the outside world in updating knowledge and using technology, and argued that there is an important mediating role between organizational climate and innovative work behavior. Verma et al. (2022) [37] stated that AI-supported job characteristics (job autonomy, skill diversity, job complexity, expertise, and information processing) increase employees’ innovative work behaviors. Kong et al. (2024) [38] found that AI-supported job autonomy increases employees’ innovative performance, and this effect is significantly mediated through exploratory behaviors. The study shows that the autonomy provided by AI improves employees’ creativity and innovation capacity, and this relationship is strengthened by trust in AI and proactive personality. Zirar (2023) [39] revealed that the limitations of artificial intelligence (AI) supported technologies can also increase employees’ innovative work behaviors. The study emphasizes that AI’s shortcomings offer ways to encourage creativity and innovation in the workplace; in particular, factors such as AI’s fallibility, algorithmic biases, and job insecurity can stimulate employees’ innovative thinking processes. In parallel with our study, Elkholy et al. (2024) [40] revealed that artificial intelligence applications significantly increased the innovative work behaviors of nurses. The study shows that artificial intelligence improves the working processes of nurses and optimizes business processes by promoting innovation in healthcare. In line with this information, in our study, we suggest that the perception of artificial intelligence use has a mediating role in the relationship between employee happiness and innovative work behavior;
H2 = Perception of artificial intelligence usage has a mediating role in the relationship between employee happiness and innovative work behavior.
Serial mediation effect
When the literature is examined, although there are studies that transformational leadership and the perception of the use of artificial intelligence have a direct effect on innovative work behavior, an indirect relationship or a serial mediating role between the relationship between employee happiness and innovative work behavior has not been identified. In addition, after explaining the mediating roles of transformational leadership and perception of the use of artificial intelligence between employee happiness and innovative work behavior in the previous titles, the following hypothesis was proposed for the whole of these relationships;
H3 = Transformational leadership and perception of using artificial intelligence have a serial mediating role in the relationship between employee happiness and innovative work behavior.
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Fig. 1
Research model
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Method
In this stage of the study, the methodological framework of the research was defined in detail, including the purpose, problem, sample information, and data collection tools.
Research purpose and problem
This study was designed to examine the effect of nurses’ happiness on innovative work behaviors and to examine the serial mediating role of transformational leadership and perceptions of artificial intelligence use in this effect. To elaborate briefly, it was tried to determine how the happiness nurses experience at work reflects on their innovative work behaviors and how the perceptions of transformational leadership and the use of artificial intelligence shape this effect in this process.
In particular, the research was conducted to seek answers to three different problems. These problems consist of the question, “Does transformational leadership have a mediating role in the relationship between nurses’ happiness and innovative work behaviors?“, “Does the perception of AI usage have a mediating role in the relationship between nurses’ happiness and innovative work behaviors?” and “Do transformational leadership and perception of AI usage have a serial mediating role in the relationship between nurses’ happiness and innovative work behaviors?“.
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Research design
This study (Fig. 1), which deals with the effect of nurse happiness on innovative work behaviours, is based on a series of research hypotheses based on the theoretical framework. In the study, employee happiness is positioned as an independent variable, innovative work behaviour as a dependent variable, and transformational leadership and perception of artificial intelligence use as serial mediator variables. In this direction, the study was designed cross-sectionally and designed to include serial mediation analysis. The main reasons for the cross-sectional design of the research can be listed as easy and fast collection of data, cost effectiveness and increasing the generalisability of the findings by working on very large samples.
Sample information and research process
The population of the study consists of public employees working as nurses in a training and research hospital located in the Eastern Anatolia region of Turkey. In the study, firstly, in order to determine the population, the human resources departments of the hospitals were consulted, and it was determined that a total of 713 nurses were working in both hospitals. In addition, in line with the information obtained from the human resources unit, it was determined that 37 nurses were on maternity leave, annual leave, or unpaid leave. From this point of view, it was determined that the net sample number was 676. Then, it was determined that collecting data from 246 nurses by convenience sampling method with a 95% confidence interval and 5% error rate was sufficient in terms of sample size and reliability of the study [41, 42]. In this process, 620 questionnaire forms were distributed to nurses working in all hospital units. Finally, due to reasons such as patient density in some units and the unwillingness of some nurses to participate voluntarily, 513 of the distributed questionnaires were returned, and the stage of classifying the suitability of the collected questionnaires for data analysis was started. In this process, it was decided to exclude 23 of the questionnaires from the scope of the analysis due to not answering some scale items or filling only the front side, and 32 of the questionnaires were excluded from the scope of the analysis due to not answering demographic questions or giving the same answers to all items. In the last stage, the remaining 458 questionnaires were decided to be analyzed, and the sample size of the research was determined. Considering the total population of the research, it was observed that the return rate of the questionnaires was 66.75%.
Data collection tools
The study used four scales in addition to the demographic information form. In the demographic information form, nurses’ age, gender, marital status, experience, education level, level of using technological tools, and frequency of using artificial intelligence or other technologies in daily work were examined. All scale items were graded on a 5-point Likert scale ranging from “1: strongly disagree to 5: strongly agree.”
Employee happiness scale
The “Workplace Happiness Scale” developed by Salas-Vallina and Alegre Vidal (2018) [43] and adapted into Turkish by Yozgat and Bilginoğlu (2019) [44] was used to measure the happiness of nurses. The scale consists of nine items and one dimension. Finally, the reliability (cronbach’s alpha) coefficient was found to be 0.89 in the adaptation form of the scale.
Innovative work behavior scale: The “Innovative Behaviour Scale” developed by Scott and Bruce (1994) [45] and adapted into Turkish by Çalışkan et al. (2019) [46] was used to measure the innovative work behaviors of nurses. This scale consists of a single dimension and six items. In addition, the reliability (cronbach’s alpha) coefficient was found to be 0.91 in the adaptation form of the scale.
Transformational leadership scale
“Global Transformational Leadership” scale developed by Carless et al. (2000) [47] was used to measure nurses’ perceptions of transformational leadership. The reliability (cronbach alpha) coefficient of the original form of the scale was found to be 0.93. The scale consists of seven items and one dimension. The adaptation and validation study of this scale into Turkish was carried out by Yavuz (2010) [48].
AI usage perception scale
The scale used by Shinners et al. (2022) [49] in their study was used to measure nurses’ perception of AI use. The scale consists of ten items and two dimensions. These dimensions were expressed as the professional impact of AI and preparation for AI. Finally, the reliability (cronbach’s alpha) coefficient for each dimension of the scale was determined as 0.83 and 0.63, respectively.
Data analysis process
In order to analyze the descriptive, correlative, and multivariate relationships of the data, SPSS 27, AMOS 24, and SPSS PROCESS MACRO analysis programs were used to obtain the results. Firstly, bivariate correlation analysis was performed in the SPSS analysis program to determine the relationships between the research scales. Secondly, the two mediators of the research (transformative leadership and perception of the use of artificial intelligence) were analyzed separately by using the SPSS PROCESS Macro analysis program developed by Hayes (2013) [50]. In order to determine the mediation effect in the analyses, mediation analyses were performed by marking Model 4 and a sample size of 5000 in the SPSS PROCESS Macro program. The results were obtained by testing the direct effect of employee happiness on innovative work behavior and the indirect effect of employee happiness on innovative work behavior (through transformative leadership and perception of artificial intelligence use). After testing two separate mediation models, multiple mediation analyses were performed by marking Model 6 and a sample size of 5000 in the PROCESS Macro program. Finally, the serial multiple mediation model includes three indirect models in the effect of employee happiness on innovative work behavior. These models are: Employee happiness→ transformational leadership→ innovative work behaviour (Model 1); Employee happiness → perception of artificial intelligence use→ innovative work behaviour (Model 2) and Employee happiness→ transformational leadership→ perception of artificial intelligence use→ innovative work behaviour (Model 3).
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Findings
In this part of the study, firstly, demographic information of the participants, scale averages, and validity analyses of the scales were included, and hypothesis tests were performed.
Table 1
Demographic results for the participants
n: 458 | n | % |
|---|---|---|
Gender | ||
Male | 155 | 33,8 |
Female | 303 | 66,2 |
Age | ||
18–25 years old | 101 | 22,1 |
26–33 years old | 73 | 15,9 |
34–41 years old | 100 | 21,8 |
42–49 years old | 96 | 21,0 |
50 years and older | 88 | 19,2 |
Marital status | ||
Single | 191 | 41,7 |
Married | 267 | 58,3 |
Education level | ||
Associate degree | 108 | 23,6 |
Licence | 258 | 56,3 |
Postgraduate | 92 | 20,1 |
Experience status | ||
1–3 years | 98 | 21,3 |
4–6 years | 84 | 18,3 |
7–9 years | 174 | 37,9 |
10 years and over | 102 | 22,3 |
Level of using technological tools | ||
Bad | 152 | 33,1 |
Middle | 197 | 43,0 |
Good | 109 | 23,7 |
How often do you use artificial intelligence or other technologies in your daily work? | ||
I never use it | 39 | 8,5 |
Rarely used | 78 | 17,1 |
I use it sometimes | 184 | 40,1 |
I use it often | 157 | 34,2 |
When the demographic findings of the nurses were examined, it was determined that 66.2% of the participants were female, 58.3% were married, 22.1% were between the ages of 18 and 25, and 56.3% had undergraduate education. It was also found that 37.9% of the nurses had 7–9 years of experience, 43.0% had a medium level of using technological tools, and 40.1% sometimes used artificial intelligence technologies in their daily work.
Scale averages of nurses
Nurses’ employee happiness, innovative work behaviors, transformational leadership perceptions, and AI usage levels were evaluated by descriptive statistical analyses (Fig. 2). When the scale averages were evaluated, 1,00–1,79: very low, 1,80 − 2,59: low, 2,60 − 3,39: medium, 3,40 − 4,19: high, 4,20 − 5,00 indicates very high perception.
Fig. 2
Scale averages
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When the results in Fig. 2 are evaluated, the mean of nurses’ employee happiness (X̄= 3.45), innovative work behaviors (X̄= 3.61), transformational leadership perceptions (X̄= 3.42), and AI usage perception (X̄= 3.46) were determined. When the obtained mean results were evaluated, it was determined that nurses’ employee happiness, innovative work behaviors, transformational leadership perceptions, and AI usage levels were at medium and high levels. According to these results, it can be stated that nurses are happy in their jobs, adopt innovative behaviors, have transformational leadership perceptions, and are warm to the use of artificial intelligence.
Measurement models
In this phase of the research, confirmatory factor analysis (CFA) was applied to confirm the validity of the structure of the variables used in the research model, which has been used in previous studies and has a theoretical basis. The main purpose of confirmatory factor analysis is to verify whether the hypothesized factor structure is supported by the observed variables [51]. Confirmatory factor analysis (CFA) was used to test the construct validity of the measurement model created with the employee happiness, transformational leadership, perception of artificial intelligence use, and innovative business behavior scales used in the research. In this context, the existence of common method variance was also questioned [52]. Common method variance refers to the systematic error sources that arise as a result of the same individual’s evaluation of more than one scale in the same time period in the data collected by the survey method [53]. Harman’s single-factor test method was used to detect and control the common method variance in the CFA analysis. This test evaluates how much of the variance obtained from different measures can be explained by a single factor. If a single factor explains more than 50% of the variance, this is considered an indicator of common method bias and poses a threat to the validity of the measurements. However, if a single factor explains less than 50% of the variance, the risk of method bias is considered low, and this situation indicates the validity of the measurements. As a result of the analysis, it was determined that the single factor variance of the scales in the study was 33%. According to this result, the risk of method bias in the study was low, and the measurements used were valid [54]. Table 2 shows the goodness of fit values for the four-factor measurement model, including all variables in the study and other alternative models (Model 1, Model 2, Model 3, and Model 4).
Table 2
Goodness of fit values for the measurement model and alternative models
Models | CMIN(χ2) | DF | χ2/df | RMSEA | CFI | TLI | SRMR |
|---|---|---|---|---|---|---|---|
Research model | 843.114 | 450 | 1.84 | 0.04 | 0.95 | 0.94 | 0.05 |
Model 1 | 910.586 | 453 | 2.01 | 0.05 | 0.93 | 0.92 | 0.08 |
Model 2 | 1055.555 | 455 | 2.31 | 0.10 | 0.90 | 0.89 | 0.10 |
Model 3 | 1099.685 | 456 | 2.41 | 0.12 | 0.89 | 0.88 | 0.11 |
Model 4 | 2173.694 | 457 | 4.76 | 0.13 | 0.72 | 0.69 | 0.12 |
The confirmatory factor analysis results in Table 2 show the fit indices of the research model and alternative models. When Table 2 is examined, it is tested that the four-factor measurement model of the research (employee happiness, transformational leadership, perception of artificial intelligence use, and innovative work behavior) has better goodness of fit values than four different alternative models (Model 1, Model 2, Model 3, Model 4). The obtained goodness of fit values (χ2/df = 1.84; RMSEA = 0.04; CFI = 0.95; TLI = 0.94; SRMR = 0.05) are within the accepted ranges [35]. According to the results of the table, it was observed that the research model had the best-fit index values compared to other models.
Correlation and reliability analyses
Table 3 shows the reliability (Cronbach’s Alpha) and correlation analysis results of the four main variables of the study (Employee Happiness (EH), Transformational Leadership (TL), Perceptions of Artificial Intelligence (PAI), and Innovative Work Behaviour (IWB)).
Table 3
Correlation and reliability findings of the scales
EH | TL | PAI | IWB | α | |
|---|---|---|---|---|---|
Employee happiness (EH) | - | 0.84 | |||
Transformational leadership (TL) | 0.73** | - | 0.87 | ||
Perceptions of artificial intelligence (PAI) | 0.78** | 0.76** | - | 0.83 | |
Innovative work behaviour (IWB) | 0.40** | 0.36** | 0.43** | - | 0.82 |
According to the correlation analysis in Table 3, there are strong positive correlations between employee happiness and transformational leadership r = 0.73 (p < 0.01), between employee happiness and AI perceptions r = 0.78 (p < 0.01), between transformational leadership and AI perceptions r = 0.76 (p < 0.01). The innovative work behavior variable has lower but significant correlations with other variables. For example, the correlation between employee happiness and innovative work behavior is r = 0.40 (p < 0.01). These results show that employee happiness and transformational leadership are positively related to perceptions of artificial intelligence and innovative work behaviors. In particular, the high correlation between employee happiness and transformational leadership suggests that employees’ interactions with their leaders in the work environment may positively affect their happiness levels [10, 55]. In addition, Cronbach’s Alpha (α) coefficients in Table 3 were calculated as 0.84 (Employee Happiness), 0.87 (Transformational Leadership), 0.83 (Artificial Intelligence Perceptions) and 0.82 (Innovative Work Behaviour) for the variables respectively. These values indicate that the measurement tools of all variables have high internal consistency and can be considered reliable. A Cronbach’s Alpha coefficient above 0.70 generally indicates that the measurements are reliable [56].
Hypothesis testing
In order to test the first model and the first hypothesis of the study, “the mediating role of transformational leadership in the effect of employee happiness on innovative work behavior,” regression analysis was applied using the Bootstrap method. It is known that the analyses conducted using the Bootstrap method yield more reliable results than the traditional method of Baron and Kenny (1986) [57] and the Sobel test [41]. In the analyses of the research, Process Macro, developed by Hayes (2018) [58], was used, and Model 4 was preferred for resampling 5000 with the Bootstrap technique.
The results of the analysis in Table 4, which evaluates the effect of employee happiness on innovative work behaviour through transformational leadership, show that there is a strong and positive relationship between transformational leadership and employee happiness (β = 0.875, p < 0.001). Confidence interval values (LLCI = 0.801, ULCI = 0.945) confirm that this relationship is statistically significant and positive. In the analyses regarding the effect of transformational leadership and employee happiness on innovative work behaviour, the direct effect of leadership is significant but relatively low (β = 0.116, p < 0.001). On the other hand, employee happiness has a more significant effect on innovative work behaviour (β = 0.293, p < 0.001) and the total effect is at the level of (β = 0.394). As a result, transformational leadership perception plays a mediating role in the relationship between employee happiness and innovative work behaviour (mediating effect = 0.101, LLCI = 0.022, ULCI = 0.252). These results suggest that employee happiness indirectly contributes to innovative work behaviours through transformational leadership. Finally, the findings indicate that employee happiness creates an environment that encourages innovation and that leadership influence leads to innovative behaviours [59, 60]. These results support the hypothesis H1.
Table 4
Regression analysis results for model 1 mediation test
Transformational leadership (TL) | ||||||
Variables | β | SE | T | P | LLCI | ULCI |
Employee happiness (EH) | 0.875 | 0.038 | 23.103 | 0.000 | 0.801 | 0.945 |
Innovative work behaviour (IWB) | ||||||
Variables | β | SH | T | P | LLCI | ULCI |
Transformational leadership (TL) | 0.116 | 0.052 | 2.232 | 0.000 | 0.014 | 0.218 |
Employee happiness (EH) (Direct impact effect) | 0.293 | 0.062 | 4.737 | 0.000 | 0.171 | 0.415 |
Employee happiness (EH) (Total impact) | 0.394 | 0.062 | 9.353 | 0.000 | 0.312 | 0.477 |
β | SE | LLCI | ULCI | |||
Mediating effect | 0.101 | 0.070 | 0.022 | 0.252 | ||
The results of the analyses in Table 5 show that perceptions of artificial intelligence have a very strong and positive effect on employee happiness (β = 0.727, p < 0.001). Confidence interval values (LLCI = 0.674, ULCI = 0.781) confirm that this relationship is statistically significant. When the results of the analysis evaluating the effect of AI perception and employee happiness on innovative work behaviour are examined, it is revealed that AI perceptions have a significant effect on innovative work behaviour (β = 0.301, p < 0.001). This shows that employees’ positive views of artificial intelligence support their innovative work behaviours. Considering the direct effect of employee happiness on innovative work behaviour (β = 0.176, p < 0.001) and its total effect (β = 0.394), it is understood that happiness is a factor that encourages innovation. Thanks to these significant effects, the mediating role of nurses’ perception of artificial intelligence use could be questioned. According to the last finding in Table 5, it is seen that nurses’ perceptions of artificial intelligence use play a mediating role in the relationship between employee happiness and innovative work behaviour (β = 0.218, LLCI = 0.104, ULCI = 0.346). The results show that employee happiness has an indirect effect on innovative behaviours through the perception of the use of artificial intelligence. According to the results obtained, hypothesis H2 is also accepted.
Table 5
Regression analysis results for model 2 mediation test
Perceptions of artificial intelligence (PAI) | ||||||
Variables | β | SE | T | P | LLCI | ULCI |
Employee happiness (EH) | 0.727 | 0.027 | 26.619 | 0.000 | 0.674 | 0.781 |
Innovative work behaviour (IWB) | ||||||
Variables | β | SH | T | P | LLCI | ULCI |
Perceptions of artificial intelligence (PAI) | 0.301 | 0.071 | 4.236 | 0.000 | 0.161 | 0.440 |
Employee happiness (EH) (Direct impact effect) | 0.176 | 0.066 | 2.656 | 0.000 | 0.046 | 0.306 |
Employee happiness (EH) (Total impact) | 0.394 | 0.042 | 9.353 | 0.000 | 0.312 | 0.477 |
β | SE | LLCI | ULCI | |||
Mediating effect | 0.218 | 0.061 | 0.104 | 0.346 | ||
Model 3 results
[Employee Happiness→ Transformational Leadership→ Perceptions of Artificial Intelligence→ Innovative Work Behaviour]
Fig. 3
Serial mediation analysis results
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As seen in Fig. 3, Process Macro Model 6 model was used for serial multiple mediation analysis. In the model, there are two mediating variables, three indirect effects and one direct effect. These effects are as follows as shown in Fig. 3: (a1) the direct effect of employee happiness (ES) on transformational leadership (TL) was significant (β = 0.875, 95% CI [0.801, 0.945]); (a2) the direct effect of employee happiness (ES) on perceived use of artificial intelligence (PAI) was significant (β =. 443, 95% CI [0.372, 0.514]); (a3) the direct effect of transformational leadership (TL) on perception of AI use (PAI) was significant (β = 0.325, 95% CI [0.266,. 385]); (c1) the direct effect of employee happiness (EH) on innovative work behaviour (IWB) was significant (β = 0.166, 95% CI [0.028, 0.305]); (b1) the direct effect of transformational leadership (TL) on innovative work behaviour (IWB) was significant (β =. 023, 95% CI [0.090, 0.155]); and (b2) the direct effect of perception of artificial intelligence usage (PAI) on innovative work behaviour (IWB) was significant (β = 0.286, 95% CI [0.130, 0.443]). Based on these significant effects, the results of the direct and indirect effect analyses regarding the serial mediation role of the research are examined in Table 6.
Table 6
Regression results for mediation effect
Direct Effect of X on Y | Effect | SE | T | LLCI | ULCI |
Model 1 EH→ IWB | 0.293 | 0.062 | 4.737 | 0.171 | 0.415 |
Model 2 EH→ IWB | 0.176 | 0.066 | 2.656 | 0.046 | 0.306 |
Model 3 EH→ IWB | 0.166 | 0.071 | 2.358 | 0.028 | 0.305 |
Indirect Effect of X on Y | Effect | BootSE | BootLLCI | BootULCI | |
Model 1 EH→ TL→ IWB | 0.101 | 0.068 | 0.022 | 0.246 | |
Model 2 EH→ PAI→ IWB | 0.218 | 0.061 | 0.104 | 0.346 | |
Model 3 EH→ TL→ PAI→ IWB | 0.228 | 0.074 | 0.091 | 0.380 | |
When the results of the analysis of the data in Table 6 are examined, it is seen that the serial mediation effect of employee happiness on innovative work behavior through transformational leadership and perception of using artificial intelligence is statistically significant (B = 0.228, 95% CI [0.091, 0.380]). According to this result, hypothesis H3 is also accepted.
Discussion
This study revealed that nurse happiness positively affects innovative work behaviors both directly and indirectly through transformational leadership and perception of artificial intelligence use. The findings are in line with similar studies in the literature and expand the theoretical framework in this field.
The literature explaining the relationship between transformational leadership and innovative work behaviors is quite rich. For example, Sharifirad (2013) [61] found that transformational leadership increases employee well-being and innovative work behaviors through empathic listening and psychological trust. Weng et al. (2013) [62] emphasized that transformational leadership has an indirect effect on innovation through organizational factors such as patient safety climate and innovation climate. Similarly, Afsar and Masood (2017) [63] found that transformational leadership increases innovative work behaviors depending on the level of trust and uncertainty avoidance. These findings explain the positive effect of transformational leadership on nurse happiness and its support for innovative work behaviors in our study.
The perception of using artificial intelligence stands out as another important factor that supports nurses’ innovative behaviors. Li et al. (2024) [64] showed that artificial intelligence encourages innovative behaviors by increasing the sense of self-efficacy and reducing anxiety. At the same time, Elkholy et al. (2024) [40] stated that the use of artificial intelligence supports nurses’ innovative behaviors by strengthening job control. Atalla et al. (2024) [35] stated that positive attitudes toward the use of artificial intelligence combined with ethical awareness increased innovative work behaviors. Elsawy et al. (2024) [65] emphasized that there is a strong positive relationship between nurse welfare level and perception of artificial intelligence and that increasing awareness of artificial intelligence positively affects welfare. This study revealed that nurses’ lack of knowledge about artificial intelligence affects not only their perceptions but also their overall well-being. In our study, the findings that nurses’ well-being positively interacts with the perception of the use of artificial intelligence and that this situation supports innovative work behaviors are in direct agreement with the literature.
Nurse happiness shows its effect on innovative work behaviors not only at the individual level but also in interaction with organizational factors. For example, Al-shami et al. (2023) [66] emphasized that employee happiness increases innovative work behaviors through organizational citizenship behavior. Hashemian et al. (2024) [67] and Jing et al. (2021) [68] stated that an enjoyable environment in the workplace improves nurses’ innovative capacity by increasing emotional commitment. These findings overlap with the findings of our study, which showed that nurse happiness combined with transformational leadership and perception of artificial intelligence support innovative work behaviors.
Studies addressing how negative work environments affect innovative work behaviors are also noteworthy. Zhou et al. (2020) [69] stated that workplace violence (mobbing, sabotage, harassment) negatively affects innovative work behaviors through employee well-being. In contrast, positive leadership approaches and technological support mechanisms are important tools that encourage innovation. For example, Singh et al. (2023) [70] found that transformational leadership positively influences innovative work behaviors through employee engagement and psychological capital. Moreover, Odugbesan et al. (2023) [71] showed that transformational leadership and the use of artificial intelligence reinforce innovative behaviors within the framework of green talent management.
Finally, the impact of artificial intelligence technology on nursing leadership is also noteworthy. Sullivan et al. (2024) [72] stated that artificial intelligence has the potential for nurse leaders to produce innovative solutions and strengthen their teams. In this context, our study, in which transformational leadership and artificial intelligence perception are discussed together, makes an important contribution to the literature.
Overall, this study reveals that transformational leadership and perception of the use of artificial intelligence are important mediators in the relationship between nurse happiness and innovative work behaviors. These findings pave the way for the development of leadership and technological strategies that promote innovation in healthcare.
Conclusion
When the research findings are evaluated, it is revealed that nurse happiness has a positive effect on innovative work behaviors both directly and indirectly through perceptions of transformational leadership and artificial intelligence (AI) use. In particular, it has been determined that nurse happiness positively affects transformational leadership and perceptions of AI use and increases innovative work behaviors. According to the mediation analyses, it was observed that transformational leadership and perception of AI use are important mediators in the current relationship.
The final conclusion of the study is that when nurses are happier at work, they are more likely to perceive leadership as transformational and adopt AI technologies, which increases their tendency to engage in innovative work behaviours in general. Moreover, the strong relationships between nurse happiness, transformational leadership and AI perceptions are emphasised as important factors to promote innovation in healthcare settings. These findings suggest that increasing nurse happiness and utilizing transformational leadership can facilitate the adoption of AI and encourage nurses’ innovative behaviors.
Based on the research findings, it is seen that curriculum updates with artificial intelligence content, trainings that can be created within the framework of ethical rules for the use of AI, data security and patient privacy standards can have a positive impact in the context of the nursing profession. In addition, the establishment of mentoring programs for nurses with leadership potential can have significant effects in line with the findings.
Limitations and future research
Although the research has the important results mentioned above, it also has some limitations. The most important of these limitations is that our research is based on a sample in a region and a health institution. This situation limits the generalisability of the results. In addition, employee happiness may not be experienced to the same extent and depth in every organization and sector. Existing variables of the research can be examined on employees of different sectors to contribute to the generalization of the results. Again, the second important constraint of this research is that the study was designed cross-sectionally. Longitudinal studies are needed to determine causal relationships and to examine the relationships between existing variables in more detail. Conducting future studies over different time periods or longer time periods may contribute to a more rational generalization of the relationships between variables. Additionally, the concept of happiness can be examined qualitatively to provide a more comprehensive understanding for future research. In addition, considering the potential of organisational policies, workload and team dynamics, which were not taken into account in the study, to affect the results, the generalisability of the findings may be limited. Moreover, perceptions regarding the use of artificial intelligence may vary according to the participants’ level of access to technology. This change may also be effective on the severity and direction of the results. Finally, the fact that some nurses did not want to participate in the study voluntarily at the point of collecting the research data, and some nurses could not be included in the study due to being on leave, report, or shift change is also seen as a limitation.
While writing the literature review and theoretical framework of the research, some deficiencies in the field were identified, and some suggestions for future research could be developed. Recently, the effects of technological pressures, digital transformations, techno-stress, telehealth, telemedicine technologies, and artificial intelligence-supported tools that affect nurse happiness, such as work stress, fear of not being able to keep up or inability to use technology efficiently, and the negative reflections of these effects on innovative behaviors are increasing, and this situation has emerged as an important problem in nursing practices. In order to overcome this problem, the possible effects of digital leadership, as well as transformational leadership, can be investigated. In addition, the effects of issues such as organizational culture, change management, organizational support, organizational support, peer support, change fatigue, or work-life balance can also be examined in order to develop strategies that can support the integration of digital technologies that can increase the innovative behavior patterns of nurses in this digital transformation era and their adaptation to these technologies.
Acknowledgements
We would like to thank all the nurses who voluntarily participated in the study and gave their time.
Declarations
Ethical approval
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Erzincan Binali Yıldırım University Ethics Committee (E-88012460-050.04-399968-09/05). Informed consent was obtained from all individual participants included in the study. We noted in the introductory statement that the study was to be completed anonymously and that completing the questionnaire in its entirety and submitting it constituted voluntary participation in our survey. The study data are strictly confidential and are for research use only.
Informed consent
Written informed consent was obtained from the patients participating in the study.
Competing interests
The authors declare no competing interests.
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