Introduction
Health sector development is one of the most critical concerns of any country, which is only possible with the development of human resources and their belief in their ability to perform all their duties and creativity and innovation in their work [1]. Innovation plays a vital role in the competitiveness and success of healthcare organizations. In today’s competitive environment, it is necessary to create unique ideas that can be implemented to achieve organizational success. Encouraging employees to produce and implement innovative ideas can help these organizations succeed [2]. The creative behavior of the staff forms the basis of intra-organizational innovation and entrepreneurship [3]. Creativity and innovation for organizations are critical to constantly changing environments [4]. Innovative behavior refers to creating, developing, and implementing new ideas in a workgroup or organization [3]. Innovation is critical to promoting health care, which means implementing new ideas to prevent disease and improve patient care. Nursing innovation is about replacing old and new methods and strengthening current methods [5].
Health and nursing care research towards sick, safe, and quality care is changing. Economic policies have increased focus on cost control, productivity, and efficiency [6]. Eligible nurses are the most reliable specialists in the world, and peer -to -part -to -peer teams are among the most trustworthy experts and peers-in-law [7]. Innovation in nursing and medicine is significant but is often overlooked. Recognizing and rewarding innovative behaviors can encourage healthcare professionals to address existing challenges and improve patient safety and satisfaction [8]. Nurses at the forefront of health care are in an excellent position to identify areas that need to improve patient care. With their knowledge and expertise, they can offer solutions, test new ideas and technologies, and provide feedback on their effectiveness. By doing so, they play a vital role in guiding innovation in the healthcare sector and ultimately increase the quality of patient care [9]. Nursing innovation includes motivation and cognition and has three main components: creating knowledge, innovative behavior, and publishing. Encouraging innovative behaviors among nurses can lead to better medical treatment, improved care quality, and increased work efficiency [5].
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Nurses who possess a positive attitude towards novel ideas have the potential to make significant contributions to the healthcare system. Their ability to generate innovative ideas is essential for progress [8]. Innovation begins with exploring and producing ideas by employees who identify and develop solutions [10]. Then, employees must convince colleagues and managers to practice and implement their ideas. This process is known as innovative behaviors (IB), a specific context and task that creates value [11]. Nurses’ innovative behaviors include introducing new skills, developing new ideas, and fulfilling them. Creative behaviors are often similar concepts in producing creative and valuable ideas. Innovative behavior is not only creating new and original ideas but also implementing them and managing the entire implementation of these ideas [12]. Nurses can help promote health justice through innovative behaviors. They can challenge the status quo, encourage creative thinking, and facilitate the creation of new approaches. The initiatives require a systematic and data-based process of evaluation, innovation, implementation, evaluation, and publication [11, 12].
Lucas et al. [3] developed a survey to assess employees’ innovative behavior as a multifaceted concept rather than merely measuring “innovative actions.” Their model highlights the significance of employees in driving micro-entrepreneurship within organizations, emphasizing the impact of managerial, organizational, and cultural support for innovation. The Innovative Behavior Inventory (IBI) encompasses idea generation, concept sharing, project initiation, collaboration, and overcoming challenges. This framework aids researchers in understanding internal behaviors and allows employees to recognize their strengths and weaknesses in fostering entrepreneurship [3].
In today’s world, innovation is crucial in driving progress across various fields, especially in healthcare. The Lucas Innovative Behavior inventory is a comprehensive tool that assesses an individual’s potential for innovation by evaluating several factors, such as generating ideas, initiating activities, involving others, and overcoming obstacles. As the healthcare industry continues to evolve, it is essential to identify and nurture innovative talents that can contribute to developing new practices and technologies. This study aims to evaluate the innovative behavior inventory psychometric properties among Iranian nurses and identify its strengths and weaknesses in local settings. The inventory ability to measure innovative behavior can help healthcare organizations identify individuals with innovation potential and foster a culture of creativity. This study’s findings can help Iranian healthcare organizations develop innovative practices to improve healthcare services’ quality and efficiency. Organizations can use the questionnaire to identify and nurture innovative talents by providing them with the necessary resources and support.
Furthermore, by promoting a culture of innovation, organizations can create an environment encouraging experimentation and risk-taking and developing new and improved healthcare practices. In conclusion, the Lucas Innovative Behavior inventory is a reliable and credible tool to help healthcare organizations identify individuals with innovation potential and foster a culture of creativity. This study’s findings can provide insights into the questionnaire’s psychometric properties in Iranian healthcare settings, leading to innovative practices that can improve the quality of healthcare services.
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Methods
Design and setting
Our study employed a cross-sectional research design with convenience sampling to collect data from clinical nurses working in public hospitals in Ardabil, Iran. Convenience sampling was chosen for its practicality and efficiency in accessing a readily available population of clinical nurses within the specified timeframe. However, it is essential to acknowledge that this method may introduce potential biases, such as selection bias, which could affect the generalizability of the findings. Despite these limitations, the sample offers valuable insights into the innovative behaviors of nurses in this specific context. Data collection occurred between September 2022 and July 2023 through a structured online questionnaire.
Study sample
The study surveyed 321 clinical nurses in public hospitals affiliated with Ardabil University of Medical Sciences. Participants were selected based on the following criteria: their consent to participate, full-time employment in public hospitals in Ardabil, Iran, experience in various wards (including Emergency, ICU, medical, etc.), and a minimum of six months of work experience in the hospital setting. Only those who provided incomplete responses to the questionnaire were excluded from the analysis.
In cultural adaptation and validation studies, it is recommended that the sample size should be ten times the number of items in the scale [13]. The minimum accepted sample size was 200 participants. However, 321 clinical nurses participated in the current study during the data collection. This is because a larger sample size provides more statistical power, which can increase the accuracy and reliability of the results. This sample size was deemed appropriate and sufficient for conducting confirmatory factor analysis (CFA).
Instruments
Nurse information form
Data was collected using a structured online questionnaire between September 2022 and July 2023. The demographic information section included eight closed-ended questions that gathered data on participants’ age, work experience, gender, marital status, and education level. This information helps in understanding the characteristics of the sample, allowing for better contextualization of the results and their applicability to similar healthcare settings. Providing a detailed demographic profile enhances the study’s relevance and assists in assessing the generalizability of the findings to broader populations of clinical nurses.
Innovative behavior inventory
Lukas and Stephan developed the Innovative Behavior Inventory (IBI) by thoroughly reviewing other innovative behavior scales [3]. They employed a rigorous approach to ensure the accuracy and reliability of the IBI, which has since become a widely used tool for assessing innovative behavior in various settings. While reviewing other scales, they either removed or modified some of the items in those scales. The IBI includes six dimensions and 20 items rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). The IBI’s six dimensions are as follows: idea generation (3 items), idea search (3 items), idea communication (4 items), implementation starting activities (3 items), involving others (3 items), and overcoming obstacles (4 items). The Cronbach’s ɑ values of the dimensions of the IBI ranged from 0.60 to 0.88.
Psychometric testing
Translation procedure
The guidelines suggested by the World Health Organization (WHO) were utilized to translate the IBI. Considering the defined standard in tool localization, permission was first obtained from Dr. Martin Lukes, the tool developer. Afterward, the tool was translated into Persian by two proficient translators individually. A group of experts in the field discussed the two translations, and both translators’ viewpoints were considered and compared. After the Persian version was approved, it was handed over to an expert in Persian Literature who was responsible for revising it in terms of grammar and phrasing. Afterwards, the Persian version was back-translated into English by a proficient translator. After completing the translation process, the developer received the translated version for further review. The items were meticulously scrutinized and verified to meet the requirements.
Face validity
Ten nurses with different clinical experiences and job tenures were interviewed in person to assess the tool’s face validity. They were asked to evaluate items related to ambiguity, difficulty, and suitability and suggest alternative terms if necessary. Subsequently, the suggested changes were incorporated. After performing forward-backward translation and evaluating its face validity, the tool’s understandable version was selected and adapted to Iranian culture.
Content validity
We assessed the item-level content validity index (I-CVI) and the scale-level content validity index (S-CVI) for the Innovative Behavior Inventory (IBI). The I-CVI for each item was calculated by determining the proportion of experts rated the item as relevant (scores of 3 or 4) out of the total number of experts who participated in the content validity index assessment [14]. To compute the S-CVI/Ave, we averaged the I-CVI values across all items in the inventory. Acceptable thresholds for the I-CVI and S-CVI/Ave were more significant than 0.78 and 0.90, respectively [14, 15].
Recognizing that the content validity index may be inflated due to random agreement among experts, we corrected the probability of chance agreement (pc). This was calculated using the formula \ (pc = \left[\frac{N!} {A! (N-A)!} \Right] \cdot 0.5^N \), where \ (N \) represents the total number of experts and \ (A \) denotes the number who rated an item as relevant. Subsequently, we computed the modified Kappa (k*) for each item using the formula \ (k* = \frac {(I-CVI - pc)} {(1-pc)} \). This approach provided a statistically valid measure of each item’s performance, with k* values interpreted as follows: ≤ 0.39 (poor), 0.40–0.59 (fair), 0.60–0.74 (good), and > 0.74 (excellent) [14].
Additionally, to enhance the assessment’s accuracy, we analyzed the distribution of scores among participants. If more than 15% of respondents achieved the highest or lowest possible score, it indicated a potential ceiling or floor effect. Feedback from experts during the assessment process was invaluable; they provided insights on item clarity, relevance, and cultural appropriateness, which informed necessary revisions and strengthened the overall validity of the inventory. This iterative process of expert review and item adjustment ensured that the IBI was contextually relevant and methodologically sound [16].
Construct validity
A confirmatory factor analysis (CFA) was conducted on the Persian version of the inventory to assess the construct validity of the IBI. The objective was to determine whether the Persian version aligns with the six-factor construct of the original IBI. We opted not to use exploratory factor analysis (EFA) in our study as IBI was originally developed using EFA [17]. Using CFA allows for thoroughly examining a predetermined factor structure or hypothetical theory through robust statistical analysis. This technique assesses each item’s effectiveness in evaluating the scale’s dimensionality. In numerous research studies, CFA has often been regarded as a significant measure for culturally adapted scales [18]. The parameters are estimated using maximum likelihood estimation (MLE). To evaluate the suitability of the model fit indices, specific criteria are used. The Chi-square (χ2) goodness of fit should have a p-value greater than 0.05, the Chi-square/degree of freedom (χ2/df) should be less than 3, the Root Mean Square Error of Approximation (RMSEA) should be less than 0.08, the Goodness of Fit Index (GFI) should be greater than 0.90, the Comparative Fit Index (CFI) should be greater than 0.90, the Tucker-Lewis index (TLI) should be greater than 0.90, the Relative Fit Index (RFI) should be greater than 0.90, and the Parsimony Normal Fit Index (PNFI) should be greater than 0.50 [19, 20]. In this study, we considered factor loadings above 0.3 [21] and T-values greater than 1.96 acceptable and statistically significant in our analysis. These thresholds were chosen based on established guidelines in the field and serve as indicators of the strength and importance of the variables in our model [13].
Convergent and discriminant validity
The IBI was evaluated for its convergent validity in this research, using different indices such as Composite Reliability (CR) and Average Variance Extracted (AVE). It is advisable to ensure the CR values are above 0.7 and the AVE values of the constructs are above 0.5 to confirm convergent validity (CR > AVE). In this study, we assessed the discriminant validity using the Fornell and Larcker criterion and the Heterotrait-Monotrait (HTMT) correlation ratio developed by Henseler et al. The Fornell and Larcker criterion mandates that the Average Shared Squared Variance (ASV) and the Maximum Shared Squared Variance (MSV) should be lower than the Average Variance Extracted (AVE). To confirm discriminant validity, it is recommended that the values of the HTMT matrix should be below 0.9 [13, 22‐25].
Reliability
To assess the internal consistency of the Persian version of IBI, we used four indices: Cronbach’s alpha coefficient (α), McDonald’s omega (ω), Coefficient H, and mean inter-item correlation (ρ). Values above 0.7 for the parameters α, ω, and Coefficient H were considered within an acceptable range [26‐31]. A mean inter-item correlation ranging from 0.15 to 0.5 is generally considered acceptable [32].
To assess the Persian version of the IBI’s stability, we employed the test-retest reliability method by two-way random model to calculate the Intraclass Correlation Coefficient (ICC). Data were gathered from 40 clinical nurses at two-week intervals. The researchers considered an ICC value of 0.75 or above acceptable [33].
Statistical analysis
Data were analyzed using IBM SPSS Statistics (version 26.0) and IBM SPSS AMOS (version 24.0). Descriptive statistics, including mean, standard deviation, frequency, and percentages, were calculated. Reliability was assessed using Cronbach’s alpha (α), McDonald’s omega (ω), Coefficient H, and mean inter-item correlation (ρ), with acceptable values set at > 0.7 for α, ω, and H, and 0.15–0.5 for inter-item correlation. Construct validity was evaluated through confirmatory factor analysis (CFA) to verify alignment with the original IBI’s six-factor structure, using maximum likelihood estimation. Fit indices included χ² p-value > 0.05, χ²/df < 3, RMSEA < 0.08, and CFI > 0.90. Convergent validity was confirmed with Composite Reliability (CR > 0.7) and Average Variance Extracted (AVE > 0.5), while discriminant validity was assessed using Fornell and Larcker criteria. Statistical significance was set at p < 0.05.
Results
Characteristics of the participants
Three hundred twenty-one clinical nurses participated in the study, with an average age of 34.28 years (standard deviation: 7.05 years) and an average work experience of 10.48 years (standard deviation: 7.03 years). Among the participants, 78.2% were female. Additionally, more than two-thirds of the nurses were married (n = 219, 68.2%), and a significant majority held a bachelor’s degree (n = 285, 88.8%) (Table 1).
Table 1
Socio-demographic characteristics of the participants (N = 321)
Variables | Categories | Mean ± SD | |
|---|---|---|---|
Age (year) | 34.28 ± 7.05 | ||
Work experience | 10.48 ± 7.03 | ||
Percentage | No. | ||
Gender | Male | 21.8 | 70 |
Female | 78.2 | 251 | |
Marital status | Single | 31.8 | 102 |
Married | 68.2 | 219 | |
Educational level | Associate Degree | 11.2 | 36 |
Bachelor’s degree | 88.8 | 285 | |
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Face validity
Feedback from ten clinical nurses indicated that the translated IBI required minor revisions to improve clarity and relevance. All items were retained for further analysis, as each was deemed essential for the target population.
Content validity
The Persian version of the IBI demonstrated strong content validity, with a scale-level content validity index (S-CVI/Ave) of 0.93. Individual items achieved an item-level content validity index (I-CVI) greater than 0.78, with modified Kappa (k*) values exceeding 0.74, indicating excellent content validity. No floor or ceiling effects were observed in the subscales (Table 2).
Table 2
The results for the content validity of the IBI (n = 321)
Item | A | I-CVI | pc | k* |
|---|---|---|---|---|
1 | 10 | 1 | 0.001 | 1 |
2 | 10 | 1 | 0.001 | 1 |
3 | 9 | 0.9 | 0.01 | 0.89 |
4 | 8 | 0.8 | 0.044 | 0.79 |
5 | 10 | 1 | 0.001 | 1 |
6 | 10 | 1 | 0.001 | 1 |
7 | 10 | 1 | 0.001 | 1 |
8 | 8 | 0.8 | 0.044 | 0.79 |
9 | 9 | 0.9 | 0.01 | 0.89 |
10 | 8 | 0.8 | 0.044 | 0.79 |
11 | 10 | 1 | 0.001 | 1 |
12 | 10 | 1 | 0.001 | 1 |
13 | 10 | 1 | 0.001 | 1 |
14 | 8 | 0.8 | 0.044 | 0.79 |
15 | 8 | 0.8 | 0.044 | 0.79 |
16 | 10 | 1 | 0.001 | 1 |
17 | 9 | 0.9 | 0.01 | 0.89 |
18 | 9 | 0.9 | 0.01 | 0.89 |
19 | 10 | 1 | 0.001 | 1 |
20 | 10 | 1 | 0.001 | 1 |
Descriptive statistics of the 35-item OLI-DS
The mean score for the 20-item IBI was 3.51 (SD = 0.62), with subscale mean scores ranging from 3.27 to 3.71. The negative skewness of the scores suggests a generally positive perception among participants (Table 3).
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Table 3
Descriptive statistics, floor and ceiling effects of the 20-item IBI (n = 321)
Dimensions | No. of item | Possible range | Mean ± SD | Skewness | Kurtosis | Floor effect (%) | Ceiling effect (%) |
|---|---|---|---|---|---|---|---|
Idea generation | 3 | 1–5 | 3.52 ± 0.73 | -0.720 | 0.620 | 2 (0.6%) | 9 (2.8%) |
Idea search | 3 | 1–5 | 3.71 ± 0.77 | -0.960 | 1.697 | 4 (1.2%) | 27 (8.4%) |
Idea communication | 4 | 1–5 | 3.59 ± 0.76 | -0.731 | 1.057 | 4 (1.2%) | 18 (5.6%) |
Implementation starting activities | 3 | 1–5 | 3.27 ± 0.82 | -0.239 | 0.267 | 6 (1.9%) | 16 (5.0%) |
Involving others | 3 | 1–5 | 3.51 ± 0.76 | -0.774 | 0.691 | 3 (0.9%) | 11 (3.4%) |
Overcoming obstacles | 4 | 1–5 | 3.46 ± 0.76 | -0.360 | 0.073 | 1 (0.3%) | 10 (3.1%) |
Total (20-item IBI) | 20 | 1–5 | 3.51 ± 0.62 | -0.569 | 1.215 | 0 (0.0%) | 5 (1.6%) |
Construct validity
Confirmatory factor analysis (CFA) confirmed the six-factor structure of the IBI, with all factor loadings above 0.3 (p < 0.001) (Fig. 1). Goodness-of-fit indices indicated an acceptable model fit: χ² = 333.67, df = 154, p < 0.001, RMSEA = 0.060, CFI = 0.958, and TLI = 0.948 (Table 4).
Fig. 1
The confirmatory factor analysis model of the Persian version of the IBI (n = 321)
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Table 4
Goodness-of-fit statistics for CFA models of the IBI (n = 321)
Indices | Acceptable values | Fit index in the CFA model |
|---|---|---|
χ2, df, p-value | p > 0.05 | χ2 = 333.67, df = 154, p < 0.001 |
χ2/df | < 3 | 2.167 |
RMSEA | < 0.08 | 0.060 |
GFI | > 0.90 | 0.907 |
CFI | > 0.90 | 0.958 |
TLI | > 0.90 | 0.948 |
RFI | > 0.90 | 0.908 |
RNFI | > 0.50 | 0.750 |
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Convergent and discriminant validity
Convergent validity was supported by Composite Reliability (CR) and Average Variance Extracted (AVE) values exceeding the recommended thresholds. Discriminant validity was confirmed with MSV and ASV values lower than AVE, and all HTMT values were below 0.9 (Table 5).
Table 5
Indices of the convergent and discriminant validity of the IBI (n = 321)
Dimensions | CR | AVE | MSV | ASV | HTMT | |||||
|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |||||
1. Idea generation | 0.801 | 0.593 | 0.572 | 0.487 | - | |||||
2. Idea search | 0.898 | 0.747 | 0.672 | 0.479 | 0.401 | - | ||||
3. Idea communication | 0.825 | 0.679 | 0.672 | 0.508 | 0.183 | 0.257 | - | |||
4. Implementation starting activities | 0.872 | 0.695 | 0.624 | 0.463 | 0.467 | 0.328 | 0.394 | - | ||
5. Involving others | 0.866 | 0.683 | 0.518 | 0.413 | 0.336 | 0.423 | 0.267 | 0.229 | - | |
6. Overcoming obstacles | 0.8364 | 0.5630 | 0.5184 | 0.3987 | 0.388 | 0.478 | 0.369 | 0.315 | 0.237 | - |
Reliability
The IBI’s internal consistency was excellent, with Cronbach’s alpha (α = 0.946), McDonald’s omega (ω = 0.945), and Coefficient H (0.977). The test-retest reliability showed high stability, with an Intraclass Correlation Coefficient (ICC) of 0.942, indicating strong reliability across all dimensions (Table 6).
Table 6
Internal consistency and stability of the IBI (n = 321)
Dimensions | α | ω | H | ρ | ICC (95% CI) |
|---|---|---|---|---|---|
Idea generation | 0.798 | 0.807 | 0.798 | 0.467 | 0.777 (0.711–0.826) |
Idea search | 0.874 | 0.881 | 0.915 | 0.498 | 0.873 (0.847–0.896) |
Idea communication | 0.890 | 0.893 | 0.903 | 0.473 | 0.886 (0.863–0.906) |
Implementation starting activities | 0.872 | 0.875 | 0.876 | 0.496 | 0.865 (0.832–0.891) |
Involving others | 0.862 | 0.921 | 0.867 | 0.476 | 0.860 (0.832–0.885) |
Overcoming obstacles | 0.833 | 0.839 | 0.848 | 0.453 | 0.830 (0.798–0.859) |
Total (20-item IBI) | 0.946 | 0.945 | 0.977 | 0.468 | 0.942 (0.933–0.951) |
The results section presents visual aids such as graphs and tables to underscore significant outcomes related to factor loadings and reliability coefficients. For example, Fig. 1 illustrates the findings from the confirmatory factor analysis, highlighting the six-factor structure of the Innovative Behavior Inventory (IBI). Furthermore, Tables 4 and 6 offer a concise summary of the model fit indices and reliability metrics. Comparisons with existing literature suggest that the IBI exhibits comparable or superior content validity and reliability.
Discussion
This study aimed to translate and evaluate the psychometric properties of the Persian version of the Innovative Behavior Inventory-20 (IBI-20) among clinical nurses in northwest Iran. A total of 321 clinical nurses participated in the study. The IBI-20 scale can be a valuable tool to assess the innovative behaviors of nurses, which can play a crucial role in addressing challenges such as adapting to emerging technologies, resource limitations, and social realities like population aging, which are intricately tied to the healthcare landscape today [34]. Innovative behaviors can improve healthcare quality, patient safety, and satisfaction [35]. Organizational factors and individual attributes influence the inclination of nurses to innovate. With the advent of artificial intelligence and novel technology, healthcare institutions aim to identify nurses who demonstrate innovative qualities [36, 37].
The innovative behavior inventory comprises 20 items classified into six subscales. These six dimensions are Idea Generation, Idea Search, Idea Communication, Implementation Starting Activities, Involving Others, and Overcoming Obstacles [3]. Sönmez et al. asserted that innovation is crucial to guarantee sustainable success and to provide more suitable, effective, and affordable treatment and care in health services [5]. In an organization, innovation champions play a vital role in implementing ideas by creating plans, acquiring necessary resources, involving people, communicating a vision, and overcoming obstacles. Finally, innovation outputs refer to the reports of the changes achieved by implementing novel ideas in an organization [3]. Nurses are crucial to healthcare organizations’ success. Their innovative behavior and productivity improve patient outcomes and drive competitiveness and sustainability [38]. Prioritizing nurses’ professional development and empowerment benefits patients and the organization’s growth and success [39]. We conducted a study on Iranian clinical nurses to validate the innovative behavior inventory developed by Lucas et al. [3]. Our research shows that IBI is a reliable and valid tool to measure employees’ innovative behavior. This is the first study to be carried out in Iran.
Validity in testing and measurement refers to how well an assessment assesses the specific construct or trait it is intended to measure. A test with high validity effectively measures the targeted construct or trait, while a test with low validity may not produce accurate results [40]. The IBI translation underwent two stages of validation to ensure its accuracy. First, clinical nurses provided feedback on the translated version in the face validation process, which was evaluated using face-to-face interviews and quantitative methods. Second, its content validity was assessed using the content validity index by 12 experts, and a score of S-CVI = 0.93 was reported. In a study conducted by Sönmez et al., the validity of the content of the questionnaire items was determined to be relatively high, with a score of 0.94 for IBI, based on experts’ opinions [5]. According to the literature, the content validity score of a scale should be greater than or equal to 0.80 [5, 41]. This score shows that the linguistic and content validity of the items is high. Therefore, the Persian version of IBI was found to have excellent content validity, with none of its six subscales showing a floor or ceiling effect.
Confirmatory factor analysis (CFA) was conducted to assess the psychometric properties and validity of the innovative behavior inventory among Iranian nurses. The main objective of CFA is to ensure that the instrument used is reliable and valid and measures the variables correctly [42]. The hypothesized factor structure was evaluated, item-factor relationships were confirmed, and all model fit indices were satisfactory, with results consistent with the original instrument [3]. This comprehensive analysis provided strong evidence for the scale’s construct validity, indicating that it accurately reflects the intended theoretical concepts and can be reliably used to measure innovative behavior among Iranian nurses. These findings offer robust empirical support and validate its use for research and practical applications to comprehend and promote innovative practices among nurses in the Iranian healthcare system.
Reliability analysis is a measure of the consistency and accuracy of a measurement [42]. The 20-item IBI structure showed excellent internal consistency, with Cronbach’s alpha (α = 0.946), McDonald’s omega (ω = 0.945), Coefficient H (0.977), and mean inter-item correlation (ρ = 0.468) all surpassing the recommended thresholds. In addition, Cronbach’s alpha, McDonald’s omega, and Coefficient H values for all six latent factors exceeded 0.7, indicating strong internal consistency across the dimensions. The mean inter-item correlation values ranged from 0.15 to 0.5, further supporting the excellent internal consistency of the six dimensions. The stability of the IBI was established through a test-retest procedure, which revealed a high Intraclass Correlation Coefficient (ICC = 0.942, 95% CI: 0.933–0.951), demonstrating the scale’s high temporal stability. Notably, all six dimensions of the IBI had ICC values greater than 0.75, further confirming the reliable and consistent measurement of the underlying constructs. The IBI is a dependable and valid tool for evaluating innovative behaviors due to its robust psychometric properties, excellent internal consistency, and high test-retest reliability.
Our study revealed that the Innovative Behavior Inventory (IBI) is an effective tool for measuring the innovative behavior of nursing staff. The IBI is based on six key factors essential for evaluating innovative behavior in nurses. These factors include idea generation, idea search, idea communication, implementation starting activities, involving others, and overcoming obstacles. The IBI comprises 20 items rated on a 5-point Likert scale ranging from strongly disagree = 1 to strongly agree = 5, with higher scores indicating more excellent innovative behavior. Further studies are necessary to determine whether the IBI can accurately assess innovative behavior in nurses in a clinical setting. The IBI is a valuable tool for evaluating innovative behavior and can be used to identify areas of strength and weakness in nurse innovation. The results of this study have significant implications for nursing practice and research, as they provide a framework for understanding the innovative behavior of nurses and the factors that contribute to it. Ultimately, the IBI has the potential to improve patient outcomes by encouraging and promoting innovation among nursing staff.
Limitation
This study undertook a comprehensive translation and validation process for the Persian version of the Innovative Behavior Inventory (IBI-20), involving a substantial sample of 321 participants. The IBI-20 demonstrated exceptional internal consistency and test-retest reliability. However, several limitations warrant consideration. The cross-sectional design restricts the ability to establish causal relationships among variables. Furthermore, since the study was conducted in a single geographical area (Ardabil, Iran), the generalizability of the findings to other contexts may be limited. There is also a potential for response bias, as participants may have provided socially desirable responses instead of accurately reflecting their perceptions. Unique cultural factors in the region could further influence responses, highlighting the need for caution in interpreting the results.
For future research, it is recommended to utilize longitudinal designs to capture the dynamics of innovative behaviors over time more effectively. Expanding the study to include diverse healthcare settings across various regions of Iran would enhance the generalizability of the findings. Furthermore, specific areas for further exploration could involve examining the impact of organizational culture on innovative behaviors, the role of different leadership styles in fostering innovation, and assessing innovative behaviors across various healthcare professions beyond nursing. This broader perspective would offer a more comprehensive understanding of the factors influencing innovative behaviors in healthcare settings.
Conclusion
The study aimed to translate and validate the Persian version of the Innovative Behavior Inventory-20 (IBI-20) among clinical nurses in Iran. The IBI-20 is a valuable tool for assessing the innovative behaviors of nurses, which are crucial in addressing challenges in the healthcare landscape, such as adapting to new technologies, resource limitations, and population aging. The study found that the Persian version of the IBI-20 is a reliable and valid instrument to measure employees’ innovative behavior. The translation underwent a rigorous validation process, including face validity and content validity assessments and confirmatory factor analysis to establish the scale’s construct validity. The IBI-20 demonstrated excellent internal consistency, temporal stability, and psychometric properties, making it a dependable tool for evaluating innovative behaviors among Iranian nurses. The findings of this study have significant implications for nursing practice and research, as the IBI-20 can be used to identify areas of strength and weakness in nurse innovation, ultimately contributing to improved patient outcomes through the promotion of innovation among nursing staff.
Acknowledgements
We would like to thank the nurses who made this study possible. The authors are also grateful to the research deputy of Ardabil University of Medical Sciences, Iran (arums- 182) for their financial support.
Declarations
Ethical approval
The study was approved by the Ethics Committee of Ardabil University of Medical Sciences (number: IR.ARUMS.REC.1401.153).
Human ethics and consent to participate declarations
The study received approval from the Ardabil University of Medical Sciences Ethics Committee (IR.ARUMS.REC.1401.153). It was carried out by the principles outlined in the Declaration of Helsinki (1975) for medical research. Before participation, permission was obtained from the tool designer and the relevant healthcare center authorities. Participants were provided a comprehensive explanation of the study’s nature and objectives. They were informed that participation was voluntary and that they could participate or withdraw at any time without any consequences. Informed written consent was obtained from all participants, and the results were published anonymously to ensure confidentiality and protect the participants’ data.
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
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