Nurses’ Innovative Behaviours Inventory

1. Abstract

The Nurses’ Innovative Behaviours Inventory (NIBI) is a specialized psychometric instrument designed to evaluate, quantify, and track how registered nurses initiate, champion, and execute novel clinical, operational, and technological solutions in dynamic healthcare environments. With modern healthcare systems characterized by rapid technological breakthroughs—including artificial intelligence in healthcare, electronic health records, and complex biomedical devices—nurses increasingly serve as frontline innovators rather than passive implementers of care protocols. Developed by Elham Shahidi Delshad, Mohsen Soleimani, Armin Zareiyan, and Ali Asghar Ghods, the NIBI addresses a critical diagnostic void in nursing management and healthcare organizational psychology.

The instrument was engineered through a rigorous sequential exploratory mixed-methods research design. The qualitative foundation utilized empirical inductive content analysis from in-depth semi-structured interviews with practicing bedside nurses and clinical administrators, supplemented by a comprehensive scoping literature review to guarantee content saturation. The resulting conceptual structure reflects the unique realities of contemporary healthcare, balancing individual cognitive creativity with institutional risk mitigation and patient safety mandates. Quantitatively, the scale undergoes a split-sample cross-validation framework: an initial exploratory factor analysis (EFA) with a sample cohort (minimum N = 300) to unveil the latent dimensional structure, followed by confirmatory factor analysis (CFA) in an independent cohort (minimum N = 200) to confirm structural integrity, fit indices, and measurement invariance.

Psychometrically, the NIBI protocol incorporates classical test theory and contemporary measurement standards, assessing content validity through quantitative indices (Lawshe’s Content Validity Ratio and the Content Validity Index), internal consistency using Cronbach’s alpha and McDonald’s omega, and temporal stability via two-week test-retest intraclass correlation coefficients (ICC). By providing a standardized metric of innovation, the NIBI enables hospital executives, nurse managers, and academic researchers to establish empirical baselines, assess institutional innovation climates, and evaluate targeted workplace interventions designed to elevate patient outcomes and organizational agility.

2. Keywords

Nurses’ Innovative Behaviours Inventory, innovative work behavior, psychometrics, scale development, nursing management, healthcare innovation, sequential exploratory design, content validity, factor analysis, organizational psychology

3. Authors

The Nurses’ Innovative Behaviours Inventory was conceptualized, designed, and psychometrically evaluated by a multidisciplinary team of academic nurse researchers and clinical methodologists based in Iran:

• Elham Shahidi Delshad — Student Research Committee, Semnan University of Medical Sciences, Semnan, Iran. Clinical investigator specializing in nursing administration and behavioral measurement.
• Mohsen Soleimani — Nursing Care Research Center, School of Nursing and Midwifery, Semnan University of Medical Sciences, Semnan, Iran. Expert in clinical nursing interventions and healthcare organizational behavior.
• Armin Zareiyan — Research Center for Cancer Screening and Epidemiology & Health in Disaster & Emergencies Department, Aja University of Medical Sciences, Tehran, Iran. Methodologist specializing in advanced biostatistics, instrument development, and epidemiological study design.
• Ali Asghar Ghods (Corresponding Author) — Nursing Care Research Center, School of Nursing and Midwifery, Semnan University of Medical Sciences, Semnan, Iran. Email contact: [email protected]. Research lead in nursing professional development, quality improvement systems, and healthcare psychometrics.

4. Purpose

The central aim of the Nurses’ Innovative Behaviours Inventory is to operationalize and measure the specific spectrum of innovative actions carried out by professional nurses across acute, ambulatory, and institutional healthcare contexts. While innovation has long been recognized as a core driver of organizational efficiency, clinical excellence, and patient safety, health administration literature historically lacked psychometric tools developed exclusively for the nursing profession. Generic workplace innovation scales borrowed from industrial-organizational psychology typically evaluate corporate behaviors—such as product commercialization, profit optimization, and competitive disruption—that fail to capture the ethical, regulatory, and patient-centered realities of bedside clinical care.

Nurses operate under strict professional licensure boundaries, evidence-based guidelines, and zero-tolerance mandates for medical errors. Consequently, innovation in nursing is distinct: it involves modifying care protocols to alleviate patient discomfort, designing intuitive bedside adaptations, overcoming logistical bottlenecks during health crises, and integrating novel clinical software under severe temporal and cognitive pressure. When healthcare administrators utilize generic scales to evaluate nursing personnel, these critical, often invisible frontline improvisations are overlooked or incorrectly classified as non-compliant procedural deviations.

From a clinical governance perspective, the NIBI serves three interrelated purposes:

1. Diagnostic Baselining: The instrument allows healthcare organizations to conduct comprehensive audits of their nursing workforce’s current innovative capacity. This enables executive leaders to benchmark individual clinical units (e.g., intensive care, emergency medicine, oncology, pediatric units) against institutional norms to identify cultural or leadership impediments to continuous improvement.
2. Evaluation of Training and Structural Interventions: As hospitals invest heavily in innovation incubators, clinical nurse specialist leadership programs, and multidisciplinary hackathons, the NIBI provides a sensitive, standardized dependent measure to evaluate pre- and post-intervention shifts in behavioral innovation among clinical staff.
3. Workforce Retention and Well-Being Research: Contemporary research in organizational psychology links professional autonomy and innovation to heightened job satisfaction, decreased structural cynicism, and reduced psychological burnout. Deploying the NIBI within longitudinal occupational health studies clarifies how empowering nurses to implement creative solutions buffers against chronic moral injury and staffing turnover.

5. Psychological Construct

The psychological construct underlying the NIBI is Innovative Work Behavior (IWB) within Clinical Nursing. In psychometrics and organizational psychology, innovative behavior is conceptualized not merely as a personality trait (e.g., openness to experience or creative ideation) but as an active, multi-phase behavioral process through which an employee recognizes a problem, develops a novel solution, builds administrative coalitions to support the idea, and operationalizes it within everyday practice.

Within the nursing discipline, this construct operates through a dynamic synthesis of clinical intuition, systemic problem-solving, and professional resilience. The construct comprises four primary functional dimensions:

1. Clinical Problem Recognition and Opportunity Exploration

This initial stage involves cognitive vigilance and environmental scanning. A nurse exhibiting high levels of this dimension notices subtle workflow inefficiencies, recurring medical errors, patient physical discomfort, or gaps in hygiene protocols that standard administrative procedures fail to resolve. For example, rather than repeatedly working around a defective supplies-access layout in an intensive care unit, the nurse identifies the layout itself as an addressable systemic failure.

2. Contextual Idea Generation (Creative Synthesis)

Idea generation in nursing requires generating solutions that are both clinically novel and strictly compatible with patient safety ethics. Unlike unconstrained corporate innovation, clinical creativity requires synthesis of pharmacology, anatomy, human factors engineering, and infection control. An illustrative manifestation involves conceptualizing a novel securement technique for pediatric peripheral intravenous catheters that diminishes mechanical phlebitis while preserving skin integrity.

3. Idea Championing and Interprofessional Coalition Building

Healthcare institutions are notoriously hierarchical, characterized by distinct boundaries between medical staff, nursing personnel, pharmacy teams, and hospital administration. Therefore, an essential dimension of innovative behavior is the socio-political labor of building alliances, articulating the clinical and fiscal value of the proposed change, and convincing gatekeepers (e.g., nurse supervisors, medical directors, risk management committees) to endorse trial implementation. This dimension assesses communication efficacy, professional assertiveness, and peer mobilization.

4. Implementation, Realization, and Institutionalization

The terminal phase of the construct encompasses translating theoretical or prototype concepts into standard operating procedures. This involves piloting the workflow change within a target unit, drafting updated clinical checklists, training rotating shifts of nursing colleagues, troubleshooting unforeseen clinical friction, and sustaining the change over time until it becomes embedded institutional knowledge.

6. Theoretical Framework

The Nurses’ Innovative Behaviours Inventory is grounded in an integrative synthesis of three established theoretical models from organizational behavior, human motivation, and systemic change: Scott and Bruce’s Model of Individual Innovation, Amabile’s Componential Theory of Creativity, and Kanter’s Theory of Structural Empowerment.

Scott and Bruce’s Multi-Stage Model of Innovation (1994)

The primary architectural scaffolding of the NIBI stems from Scott and Bruce’s (1994) seminal conceptualization of workplace innovation. Scott and Bruce posited that innovative behavior does not follow a neat, lock-step linear trajectory, but rather an iterative, non-linear progression consisting of three interrelated phases: idea generation, idea promotion, and idea realization. Their model underscores that individual attributes (such as cognitive style and self-efficacy) interact continuously with workplace social exchange dynamics—specifically Leader-Member Exchange (LMX) and the psychological climate for innovation. In the context of nursing, this theoretical premise emphasizes that a nurse’s propensity to generate and actualize novel care solutions depends directly on supportive supervisory relationships and an institutional climate that tolerates calculated, safe trial-and-error.

Amabile’s Componential Theory of Organizational Creativity (1988, 1997)

To capture why individual nurses engage in the cognitive effort required to devise new clinical practices, the developers incorporated Teresa Amabile’s Componential Theory of Creativity. Amabile argues that creative execution requires the confluence of three internal components: domain-relevant skills (clinical expertise, biomedical education), creativity-relevant processes (flexible thinking, cognitive persistence), and intrinsic task motivation. The NIBI assumes that intrinsic motivation—namely, the profound humanistic desire to minimize patient pain, protect vulnerable lives, and streamline distressing shifts—serves as the psychological spark propelling nurses to pursue innovation despite systemic resource constraints and emotional exhaustion.

Kanter’s Structural Empowerment and Complex Adaptive Systems

Because healthcare institutions function as high-reliability, complex adaptive systems, individual psychological motivation alone is insufficient to manifest innovative behaviors. Rosabeth Moss Kanter’s Theory of Structural Empowerment provides the institutional underpinning for the scale. Kanter established that employees must have systemic access to four organizational structures to execute innovation: access to information, access to resources, access to support, and opportunity to learn and grow. When clinical hospital environments provide these structural lines of power, nurses transition from routine rule-followers into active change agents who proactively reshape healthcare delivery.

7. Validity

The validation architecture of the Nurses’ Innovative Behaviours Inventory adheres to modern psychometric standards established by the American Educational Research Association (AERA), the American Psychological Association (APA), and the COSMIN (Consensus-based Standards for the selection of health Measurement Instruments) initiative. The validation pipeline is executed across distinct qualitative and quantitative phases:

Content and Face Validity

Content validity was established through a sequential, mixed-methods qualitative phase. First, qualitative content analysis was executed via in-depth, semi-structured interviews with clinical bedside nurses, head nurses, and nursing supervisors across tertiary university teaching hospitals. The interview protocol focused on lived experiences of clinical problem-solving, overcoming administrative friction, and introducing novel care techniques. This was paired with a formal scoping review of clinical innovation literature.

Initial item pools were reviewed by an expert panel comprising nursing administrators, scale development methodologists, and hospital quality assurance specialists. Quantitative content appraisal utilized two classic psychometric parameters:

• Content Validity Ratio (CVR): Evaluated using Lawshe’s formula:
  $$\text{CVR} = \frac{n_e – (N/2)}{N/2}$$
  where $n_e$ represents the number of panelists deeming an item “essential” and $N$ is the total panel size. Items failing to reach Lawshe’s statistical significance threshold (based on panel sample size, typically $ge 0.62$ for 10-15 experts) were removed.
• Content Validity Index (CVI): Evaluated at both the individual item level (I-CVI) and scale level (S-CVI/Ave). Items with an I-CVI below 0.78 were revised or excised, targeting an aggregate S-CVI/Ave exceeding 0.90, confirming that the tool comprehensively operationalizes the target construct.
• Face Validity: Evaluated qualitatively through cognitive debriefing interviews with 10–15 practicing staff nurses to identify and rectify ambiguities in item wording, reading level, and syntactic construction. Quantitatively, an Item Impact Score was calculated (Frequency $\times$ Importance); items with an impact score below 1.5 were eliminated.

Construct Validity (Convergent and Discriminant Protocols)

To verify that the NIBI genuinely measures innovative behaviors rather than adjacent psychological phenomena, the quantitative protocol incorporates formal construct validation testing. Convergent validity is examined by correlating NIBI total and subscale scores with validated scales of Structural Empowerment (e.g., the Conditions for Work Effectiveness Questionnaire-II) and psychological safety. Strong positive bivariate correlations ($r ge 0.50$, $p < 0.001$) corroborate theoretical expectations.

Discriminant validity is assessed using the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations within structural equation modeling. The average variance extracted (AVE) of each latent factor must exceed the squared inter-construct correlations, and HTMT ratios should remain safely below the 0.85 conservative threshold, establishing that innovative behavior is empirically distinct from routine task performance, standardized clinical competence, and general job compliance.

8. Reliability

The reliability evaluation protocol for the Nurses’ Innovative Behaviours Inventory investigates both internal consistency across items and temporal stability across repeated administrations under stationary baseline conditions:

Internal Consistency

To ensure items within each hypothesized latent dimension measure the same underlying construct, internal consistency is evaluated using both traditional Cronbach’s alpha ($\alpha$) and composite reliability (McDonald’s $\omega$). While Cronbach’s alpha is reported for historical comparability, it assumes tau-equivalence (equal factor loadings across items), which is rarely satisfied in behavioral scales. Consequently, McDonald’s omega is calculated to provide an unbiased estimate of reliability under congeneric modeling assumptions. The developmental benchmark mandates that both $\alpha$ and $\omega$ exceed 0.80 for all subscales and exceed 0.90 for the aggregate inventory, indicating high internal consistency without excessive item redundancy.

Temporal Stability (Test-Retest Reliability)

Because innovative work behavior functions as a relatively stable behavioral pattern supported by personal attributes and institutional climate, the instrument must exhibit reliable test-retest reproducibility over short intervals where no organizational reorganization has taken place. The study protocol specifies administering the inventory twice to a dedicated cohort of clinical nurses ($n ge 30-50$) across a two-week interval.

Stability is assessed via the Intraclass Correlation Coefficient (ICC) using a two-way mixed-effects model with absolute agreement (ICC 2,1). In accordance with COSMIN guidelines, an ICC $ge 0.75$ represents acceptable reproducibility, while values exceeding 0.85 denote excellent stability. Furthermore, absolute measurement error is quantified via the Standard Error of Measurement (SEM):

$$\text{SEM} = \text{SD} \times \sqrt{1 – \text{ICC}}$$

alongside the Minimal Detectable Change (MDC = $1.96 \times \sqrt{2} \times \text{SEM}$), ensuring clinical administrators can differentiate true behavioral shifts following training interventions from random measurement noise.

9. Factor Analysis

The latent structural evaluation of the NIBI uses a rigorous split-sample cross-validation framework to prevent statistical model overfitting and ensure structural reproducibility across independent cohorts.

Sample Size and Sampling Adequacy

Following modern recommendations for scale validation, the quantitative protocol recruits an aggregate nationwide sample of more than 500 clinical nurses across healthcare systems in Iran. The dataset is randomly split into two independent cohorts:

• Cohort 1 (Exploratory Factor Analysis, minimum N = 300): Satisfies the classic rule-of-thumb of at least 10 participants per item, providing adequate statistical power to evaluate factor extraction stability.
• Cohort 2 (Confirmatory Factor Analysis, minimum N = 200–250): Serves as the independent hold-out sample to test whether the empirically derived exploratory model replicates without post-hoc modification.

Sampling adequacy is assessed using the Kaiser-Meyer-Olkin (KMO) metric, with a target benchmark $ge 0.80$ (categorized as “meritorious” to “marvelous”), and Bartlett’s Test of Sphericity ($p < 0.001$), rejecting the null hypothesis that the correlation matrix is an identity matrix.

Exploratory Factor Analysis (EFA)

Within Cohort 1, factor extraction is conducted using Principal Axis Factoring (PAF) or Maximum Likelihood (ML) estimation. Because latent dimensions of innovative behavior are theoretically correlated (e.g., idea generation correlates with idea championing), an oblique rotation method (Promax or Direct Oblimin) is applied rather than orthogonal Varimax rotation.

To avoid over-factorization, the number of factors to retain is determined using a convergence of three criteria:

1. Kaiser’s Criterion: Eigenvalues greater than 1.0.
2. Cattell’s Scree Plot: Visual inspection of the scree inflection point.
3. Horn’s Parallel Analysis: The gold standard in psychometrics, where empirical eigenvalues are compared against 95th percentile eigenvalues derived from randomly generated simulated datasets of identical dimensions.

Items with factor loadings below 0.40, cross-loadings greater than 0.32 on secondary factors, or communalities below 0.30 are marked for elimination.

Confirmatory Factor Analysis (CFA)

Using Cohort 2, Confirmatory Factor Analysis is modeled using AMOS or lavaan in R to evaluate how well the observed covariance structure aligns with the hypothesized multidimensional model. Model fit is appraised using standard multivariate goodness-of-fit indices:

• Chi-square to degrees of freedom ratio ($\chi^2/df$): Values between 1.0 and 3.0 denote acceptable model fit.
• Comparative Fit Index (CFI): Benchmark $ge 0.95$ indicates superior fit ($ge 0.90$ acceptable).
• Tucker-Lewis Index (TLI): Benchmark $ge 0.95$ indicates superior fit.
• Root Mean Square Error of Approximation (RMSEA): Values $le 0.06$ with a 90% confidence interval upper bound $le 0.08$.
• Standardized Root Mean Square Residual (SRMR): Benchmark $le 0.08$.

10. Instrument / Measurement Tool

• Test Name: Nurses’ Innovative Behaviours Inventory (NIBI)
• Test Type: Psychometric self-report rating scale
• Target Population: Registered nurses, clinical nurse specialists, head nurses, and nursing managers in healthcare facilities
• Age Group: Adults (employed nursing professionals, typically 20–65 years)
• Language of Development: Persian (Farsi); cross-cultural adaptations and translations follow WHO and ISPOR methodological standards
• Administration Format: Standardized paper-and-pencil questionnaire or secure digital/online survey administration
• Estimated Completion Time: Approximately 8 to 12 minutes
• Response Scale: 5-point Likert-type scale ranging from 1 (“Strongly Disagree” or “Never / Rarely”) to 5 (“Strongly Agree” or “Always / Very Frequently”)
• Scoring Architecture:
  • Subscale scores are derived by summing or averaging item ratings within each designated factor (e.g., Problem Identification, Idea Generation, Idea Promotion, Idea Implementation).
  • An overall Global Innovative Behavior Composite Score is calculated by computing the mean or sum across all scale items.
  • Higher aggregate and dimensional scores correspond to higher levels of clinical innovation engagement in nursing care.
• Intended Applications: Clinical governance benchmarking, evaluation of hospital-based innovation incubators, baseline assessment for nursing leadership interventions, and academic research in health services management.

11. Permissions & Fee and Test Year

• Publication Year: The study protocol establishing the instrument was formally published in 2024.
• Primary Reference Article: Delshad, E. S., Soleimani, M., Zareiyan, A., & Ghods, A. A. (2024). Nurses’ Innovative Behaviours Inventory: A study protocol for instrument development. BMJ Open, 14, e077056.
• Accessibility and Fees: The study protocol is an open-access publication distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) license. The specific instrument items, scoring rubrics, and permission for clinical or non-commercial research use are managed directly by the principal investigators and corresponding author.
• Permission Contact: Inquiries regarding access to the complete Persian instrument, permissions for linguistic translation, or academic collaborative validation studies should be addressed to Dr. Ali Asghar Ghods via email at [email protected].

12. References

Afsar, B., Badir, Y., & Khan, M. M. (2018). Do nurses display innovative work behavior when their values match with hospitals’ values? European Journal of Innovation Management, 21(1), 157–171. https://doi.org/10.1108/EJIM-01-2017-0007

Albert, M. A. (2018). Operationalizing a nursing innovation center within a health care system. Nursing Administration Quarterly, 42(1), 43–53. https://doi.org/10.1097/NAQ.0000000000000266

Amabile, T. M. (1988). A model of creativity and innovation in organizations. Research in Organizational Behavior, 10(1), 123–167.

Asurakkody, T. A., & Kim, S. C. (2018). Innovative behavior in nursing context: A concept analysis. Asian Nursing Research, 12(4), 237–244. https://doi.org/10.1016/j.anr.2018.11.003

Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health, 6, 149. https://doi.org/10.3389/fpubh.2018.00149

Delshad, E. S., Soleimani, M., Zareiyan, A., & Ghods, A. A. (2024). Nurses’ Innovative Behaviours Inventory: A study protocol for instrument development. BMJ Open, 14(3), e077056. https://doi.org/10.1136/bmjopen-2023-077056

Kanter, R. M. (1993). Men and women of the corporation (2nd ed.). Basic Books.

Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x

Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J., Stratford, P. W., Knol, D. L., Bouter, L. M., & de Vet, H. C. (2010). The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: An international Delphi study. Quality of Life Research, 19(4), 539–549. https://doi.org/10.1007/s11136-010-9606-8

Polit, D. F., & Beck, C. T. (2020). Essentials of nursing research: Appraising evidence for nursing practice (10th ed.). Wolters Kluwer.

Scott, S. G., & Bruce, R. A. (1994). Determinants of innovative behavior: A path model of individual innovation in the workplace. Academy of Management Journal, 37(3), 580–607. https://doi.org/10.2307/256701

Wang, L., Tao, H., Bowers, B. J., Brown, R., & Zhang, Y. (2019). The mediating role of inclusive leadership: Work engagement and innovative behaviour among Chinese head nurses. Journal of Nursing Management, 27(4), 688–696. https://doi.org/10.1111/jonm.12754

13. Items of the Scale