Article Text

Development and psychometric properties evaluation of nurses’ innovative behaviours inventory in Iran: protocol for a sequential exploratory mixed-method study
  1. Elham Shahidi Delshad1,
  2. Mohsen Soleimani2,
  3. Armin Zareiyan3,
  4. Ali Asghar Ghods2
  1. 1Student Research Committee, Semnan University of Medical Sciences, Semnan, Iran
  2. 2Nursing Care Research Center, School of Nursing and Midwifery, Semnan University of Medical Sciences, Semnan, Iran
  3. 3Research Center for Cancer Screening and Epidemiology & Health in Disaster & Emergencies Department, Aja University of Medical Sciences, Tehran, Iran
  1. Correspondence to Dr Ali Asghar Ghods; aaghods{at}


Introduction Nurses’ innovative behaviours play a crucial role in addressing the challenges including adapting to emerging technologies, resource limitations and social realities such as population ageing that are intricately tied to today’s healthcare landscape. Innovative behaviours improve healthcare quality, patient safety and satisfaction. Organisational factors and individual attributes influence nurses’ inclination to innovate. With the rise of artificial intelligence and novel technology, healthcare institutions are actively engaged in the pursuit of identifying nurses who demonstrate innovative qualities. Developing a comprehensive protocol to elucidate the various dimensions of nurses’ innovative behaviours and constructing a valid measuring instrument, rooted in this protocol represents a significant step in operationalising this concept.

Methods and analysis The study encompasses two phases: a qualitative study combined with a literature review, followed by the design and psychometric evaluation of the instrument. To ensure diversity, a maximum variation purposive sampling method will be used during the qualitative phase to select clinical nurses. In-depth semistructured interviews will be conducted and analysed using conventional content analysis. Additionally, a comprehensive literature review will supplement any missing features not captured in the qualitative phase, ensuring their inclusion in the primary tool. The subsequent quantitative phase will focus on evaluating the questionnaire’s psychometric properties, including face, content and construct validity through exploratory factor analyses (including at least 300 samples) and confirmatory factor analyses (including at least 200 samples). Internal consistency (Cronbach’s alpha), reliability (test–retest), responsiveness, interpretability and scoring will also be assessed.

Ethics and dissemination This study originates from a doctoral dissertation in nursing. Permission and ethical approval from Semnan University of Medical Sciences has been obtained with reference code IR.SEMUMS.1401.226. The study’s findings will ultimately be submitted as a research paper to a peer-reviewed journal.

  • Nursing Care
  • Psychometrics
  • Protocols & guidelines
  • Nurses

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  • The study’s strength lies in its use of a sequential exploratory mixed-method design, which integrates diverse approaches and methods. By collecting both quantitative and qualitative data, a more comprehensive understanding of nurses’ experiences with innovative behaviours is achieved.

  • This protocol facilitates the operationalisation of the concept of innovative behaviours in nursing and contributes to advancing knowledge in this field.

  • To create a more comprehensive item pool, both inductive and comparative methods are employed.

  • One potential limitation of this study is the exclusive recruitment of qualitative phase participants from a single city, which limits the exploration of nurses’ innovative behaviours in rural and underserved clinical settings.


In today’s dynamic healthcare landscape, innovation has become paramount to meet the ever-changing demands and challenges.1 Challenges include adapting to emerging technologies, resource limitations and social realities such as population ageing and the need for integrated care.2 3 Nurses, as front-line care providers, have a unique opportunity to drive innovative solutions that enhance patient outcomes, improve healthcare delivery and advance nursing practice.4 Given their proximity to the point of care, nurses are in an advantageous position to present, promote and implement innovations, including research findings, into practice.5 Specifically, within the context of nursing, innovative behaviour is defined as an individual’s engagement in creating novel approaches and technologies for work and study.6 This behaviour is aimed at enhancing the quality and efficiency of nursing practices, contributing to ongoing improvement in the field.6 It encompasses the ability to overcome potential obstacles in order to develop new procedures, treatment strategies, or policies that contribute to the restoration and enhancement of patients’ or clients’ health.7 Innovations, rooted in the identification of care or organisational problems, offer impactful and effective solutions that can be implemented at multiple levels.8 9 Nurses’ innovative behaviours yield numerous positive outcomes at different levels. For patients, it leads to improved healthcare quality, enhanced patient safety, personalised care and increased patient satisfaction.10 At the individual nurse level, engaging in innovation fosters professional growth, job satisfaction and a sense of empowerment.11 12 Healthcare organisations benefit from nurses’ innovative behaviours (NIB) through improved efficiency, cost-effectiveness, increased competitiveness and enhanced organisational reputation.13

The era of artificial intelligence (AI) and novel technology calls for the identification of innovative nurses and the cultivation of a culture of innovation among all nurses more than ever.14 15 With the rapid advancements in healthcare technology, nurses’ expertise and innovative thinking are pivotal in harnessing the full potential of these advancements.16 Innovative nurses may drive the integration of technology into healthcare practices and ensure that patient care evolves with the times.17 Therefore, it is crucial to recognise and nurture innovative nurses, providing them with the necessary support, resources and opportunities to thrive in an ever-evolving healthcare landscape.18 Recent studies have shed light on effective strategies to promote NIBs. Innovative approaches such as innovation incubators,19 interdisciplinary collaboration,20 investigating innovation challenges and dedicated time for creativity21 have shown promising results in cultivating and sustaining nurses’ innovation.

The existing body of research currently lacks a comprehensive framework that can effectively identify the specific dimensions of innovative behaviours within the context of modern nursing, which is increasingly intertwined with technological advancements such as AI. It is crucial to acknowledge that the nursing context possesses unique characteristics that blend care and innovation.22 These traits encompass a holistic care philosophy, patient-centred focus, interdisciplinary collaboration, critical thinking and ethical considerations.22 The intersection of components of the nursing setting may allow nurses to embrace advancements in the field of healthcare.23 However, healthcare organisations face challenges in systematically recognising and identifying nurses who demonstrate innovative qualities due to the absence of a standardised measuring tool. This limitation hampers the fostering and support of a culture of innovation within the nursing profession.22 Consequently, there is a pressing need to operationalise this concept with precise and scientifically sound instructions. Motivated by the potential benefits of NIBs mentioned above, we have undertaken the task of designing and developing a robust study protocol that precisely outlines the dimensions of innovative behaviours exhibited by nurses. This study aims to explain the key aspects of designing and conducting a development and psychometric properties evaluation of NIBs inventory (NIBI). This would contribute to the generation of new knowledge by identifying the dimensions of NIBs, facilitating the operationalisation of this concept, and enabling measurement through a validated and reliable tool.


This will be a sequential exploratory mixed-methods approach, incorporating a design that combines qualitative and quantitative studies.24 The ontology and epistemology of mixed-method studies encompass the acknowledgement of multiple realities and the significance of comprehending phenomena from diverse perspectives.25 By incorporating both qualitative and quantitative methods, researchers aim to capture a more comprehensive understanding of the studied phenomenon. qualitative methods delve into the subjective experiences, meanings and contexts, providing depth and richness to the analysis. Quantitative methods, on the other hand, contribute statistical data and objective measurements, offering breadth and generalisability.26 Due to the intricate and multifaceted nature of innovative behaviours, characterised by creative problem-solving and idea generation,27 we have chosen a sequential mixed methods design for the study protocol. This design will integrate both qualitative and quantitative data, providing a comprehensive exploration of the dimensions and intricacies of innovative behaviours in nursing. Embracing this approach will enable us to gather diverse insights, thereby enhancing the validity and reliability of the intended measuring instrument’s development. The qualitative phase aims to investigate the concept of NIB by gathering insights from nurses’ experiences and conducting a thorough review of existing literature. Subsequently, the primary components of NIB would be formulated. Moving on to the second phase, the quantitative aspect of the study focuses on evaluating the psychometric properties of the developed instrument (figure 1). This research study received ethical approval in November 2022. It is currently underway and is expected to last until December 2024.

Figure 1

Study stages. The preliminary instrument crafted at the conclusion of phase 1 of the study transforms into the final instrument following the conclusion of the second phase. CFA, confirmatory factor analysis; EFA, exploratory factor analysis.

The qualitative phase


The study population for this study comprises nurses who possess a minimum of 1 year of experience in clinical practice. The selection of participants will be limited to nurses working within healthcare organisations such as hospitals and clinics in Tehran, Iran. At this stage, the researcher will formally invite eligible nurses to participate by communicating with clinical centres. Interested individuals will then reach out to the researcher for additional details about the study. Participants are encouraged to communicate their preferred time and location if they decide to partake. Involvement in the study will be entirely voluntary, and participants will provide written informed consent after receiving a thorough overview of the study’s objectives.


Purposeful sampling will be employed to optimise the range of participants, thereby promoting diversity within the study group, as evident through demographic factors such as age, gender, education and marital status, as well as professional background.28 The recruitment process will persist until data saturation is achieved, signifying that no novel concepts emerge during the interviews.29

Data collection

The data collection process will use both the inductive and deductive methods. Inductively, codes will be derived from semistructured personal interviews conducted with nurses. The individual interviews will be conducted at selected times and locations, as preferred by the participants. These interviews will enable the extraction of firsthand insights and perspectives from the participants. Additionally, the deductive approach will involve extracting codes from the literature review, allowing for a comprehensive exploration of existing knowledge and theories related to the topic of NIBs. During the data collection phase, the interviews will be carefully analysed using the conventional qualitative content analysis method.30 Given the unpredictable nature of sample size in qualitative studies, data collection will continue until data saturation is achieved.31 The interviews will be guided by an interview guide32 that will be developed in consultation with experts in qualitative research and nursing researcher (online supplemental material) to ensure methodological rigour and relevance.33 Qualitative research experts will contribute to the robustness of the study’s design, aligning questions with established qualitative principles.34 Researchers will provide domain-specific insights, ensuring the questions are meaningful within the context of discipline.32

Prior to the interviews, the participants will be provided with a clear explanation of the study’s objectives and the purpose of the interviews presented in both written and verbal formats. Each interview will commence with an open-ended and factual question concerning the challenges they encounter in their work and the approaches they employ to address these challenges. Subsequent questions will be tailored based on their responses to the initial question and in accordance with the interview guide. Probing questions such as ‘What do you mean by this?’ or ‘Can you provide more detailed explanations?’ will be used when necessary to delve deeper into specific topics. Furthermore, participants will be given an opportunity at the end of each interview to address any points they feel may have been overlooked. Participants’ non-verbal cues, including tone, silence, emphasis and body gestures, will be documented during the interviews to capture a comprehensive understanding of their communication. All interviews will be recorded and transcribed verbatim immediately following the interview.

Data analysis

To analyse the collected data, a content analysis approach using the conventional method will be employed. The Zhang and Wildemuth framework will be used for this purpose.35 Certainly, other methods of qualitative content analysis would also be done, however, considering that Zhang and Wildemuth have clearly stated the steps of qualitative content analysis, their proposed framework will be used. It is worth noting that the inherent nature of this method of inductive qualitative content analysis is not significantly different from other frameworks such as ‘Graneheim and Lundman’36 or ‘Hsieh and Shannon’.37 Before conducting each subsequent interview, the transcription, analysis and coding of the previous interview will be completed. During the analysis process, codes, subcategories, categories and themes will be derived from the transcribed data. Initial codes that are related will be combined and labelled to form subcategories and categories. Through consensus among researchers, the underlying meaning of the text and the main themes will be extracted. This process will persist throughout the entirety of the qualitative data analysis phase until its completion that ultimately leading to a comprehensive understanding of the concept of NIBs. The extracted themes, main categories and relevant findings from the existing literature and instruments will be used to generate the primary item pool for the NIBI.

Accuracy and rigour

In the current study, efforts will be made to ensure data rigour. Lincoln and Guba38 proposed the criteria of credibility, dependability, transferability and confirmability as practical strategies for achieving rigour in research.38A series of meticulous activities will be conducted throughout the research process: Beginning with the formulation of precise research questions, the study will involve repeated readings for a profound understanding of the data.39 A coding system with clear definitions will be established, and multiple coders will collaborate to enhance reliability.40 Regular team meetings, peer reviews and an audit trail will ensure transparency and consistency. Reflexivity, consensus building, member checking and continuous saturation checks will be integral components, fostering accuracy, dependability and a thorough exploration of the content.39

Item generation

Each code will be meticulously examined to create an initial item pool for the development of the scale. Within each specific subcategory, the items will be derived from the identified themes and subthemes, incorporating the participants’ actual expressions as much as possible. Subsequently, instructions and response options will be incorporated for each item. It will be essential to maintain simplicity and adhere to the published guidelines41 while constructing the items.

The NIBI will use a Likert scale with five response options, ranging from ‘always’ to ‘never’. The wording of these options aims to capture a spectrum of sentiments, ensuring a comprehensive and nuanced understanding of participants’ perspectives.42

Literature review

Following the establishment of a clear definition and dimensions of NIBs, a literature review will be undertaken. This review aims to identify any features that may not have been identified in the qualitative study or extracted in the qualitative section. If such features or statements are missing, they will be added to the item pool for further consideration. In this stage, the primary guide for the study will be York University’s five-step guide.43 This guide offers a comprehensive set of structured and detailed instructions for designing, implementing and reporting results in systematic review studies.44 The steps outlined in this guide include: determining the review question, establishing selection criteria for studies, identifying relevant studies, data extraction, data synthesis and plan for dissemination.45 Electronic databases including MEDLINE (via PubMed), Scopus, PsycINFO (via EBSCO), CINAHL (via EBSCO) and ProQuest will be searched without any time limitations. A combination of keywords such as “Nurses’ Innovative behaviors,” “Innovative Work Behavior” and “Tool (Scale, Inventory, Instrument, Questionnaire) Development” will be used to retrieve relevant literature.

The quantitative phase

In this phase, a quantitative study with a cross-sectional design employing an approach of instrument design and psychometrics will be conducted to evaluate the psychometric properties of the NIBI. The assessment will encompass various properties, including face validity, content validity, construct validity and reliability. These evaluations are crucial for ensuring the robustness and accuracy of the inventory.

Face validity assessment

Face validity refers to the extent to which the statements within a measurement tool appear to be relevant and appropriate for measuring the specific construct or subject they were designed to assess.46 The assessment of the face validity of this study will be conducted using both quantitative and qualitative approaches. In the quantitative approach, a 5-point Likert scale will be used to assess each item of the questionnaire. The scale consists of the following options: completely understandable (5 points), understandable to some extent (4 points), moderately understandable (3 points), slightly understandable (2 points) and not understandable at all (1 point). To evaluate face validity, a sample of 10 nurses will be selected using a convenience sampling method. The inclusion criteria for this stage include holding at least a bachelor’s degree in nursing, having more than 1 year of work experience, and expressing willingness to participate in the study. Nurses who were part of the qualitative phase will not be recruited for this step. They will be asked to review each item and select one option from the Likert scale. Subsequently, the impact score for each item will be calculated using the formula:

Impact score=frequency (%)×comprehensiveness.

The frequency refers to the percentage of respondents who assigned a score of 4 or 5 to each item, indicating the average level of understanding based on the Likert scale. If the impact score surpasses 1.5, the item will be considered suitable for further analysis and retained. Conversely, items scoring below 1.5 will not be eliminated but will undergo review and modification.47

To assess face validity qualitatively, face-to-face interviews will be conducted with the same group of 10 participating nurses involved in the quantitative stage. Items with a face validity score below 1.5 will be specifically targeted for examination. The interviews will aim to explore the following aspects:

Difficulty: This refers to the comprehension of phrases or words that proved challenging for the respondents to understand.

Relevancy: This entails evaluating the accuracy and appropriateness of the items in relation to the considered structure and its dimensions, as perceived by the respondents.

Ambiguity: This involves investigating whether the expressions of the items were misunderstood or if the words lacked sufficient clarity of meaning.47

Content validity assessment

Content validity offers insights into how well elements of an assessment instrument align with and accurately represent the intended construct for a specific assessment objective.48 In this stage, content validity will be assessed through both qualitative and quantitative approaches.

For the qualitative assessment of content validity, a panel of 10 university professors from various Iranian universities, each possessing significant research expertise in the field of innovation, will be invited via email and in person to assess the questionnaire’s grammar, wording, item allocation and scaling. Their feedback will be used to make necessary modifications to the items.

Content validity ratio

To assess the necessity of the questionnaire items, the study will employ the content validity ratio (CVR). This ratio will be calculated by inviting experts to evaluate each item on a three-point scale, encompassing the categories of ‘not essential’, ‘useful but not essential’ and ‘essential’. The scale will range from 1 to 3, allowing experts to assign scores accordingly. The CVR will be computed using the following formula:

Embedded Image

where Ne represents the number of experts indicating an item as ‘essential’, and N represents the total number of experts participating. The determination of item acceptance will be based on the Lawshe Table (1975), which takes into account the number of experts involved.49 In this study, with 10 participating experts, the minimum acceptable CVR score, based on Lawshe’s recommendation, is set at 0.62. Items with a score lower than 0.62 will be removed.

Content Validity Index

Two Content Validity Indices (CVIs) will be calculated in this study: the Individual Item-CVI and the Scale-CVI. To determine the Individual Item-CVI, the questionnaire will be distributed to a panel of 10 experts,50 who will be asked to score each item on a four-point scale (1) : irrelevant, (2) moderately relevant, (3) relevant and (4) absolutely relevant. The Individual Item-CVI will then be calculated by dividing the number of experts who scored an item as either 3 or 4 by the total number of experts. Subsequently, modified kappa statistics will be employed to account for the chance agreement. To calculate modified kappa, the probability of chance agreement must first be determined using the following formula, where N represents the number of panellists and A represents the number of panellists who agree that the item is relevant:

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Next, the modified kappa will be computed using the following formula:

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Based on the criterion established by Fleiss (1981), kappa values exceeding 0.75 will regarded as ‘excellent’. Furthermore, Polit and Yang highlighted that an Individual Item-CVI value surpassing 0.78 is equivalent to a modified kappa value greater than 0.75. Consequently, an I-CVI value above 0.78 will serve as evidence of the item’s sufficient relevance.51 Items with a score lower than 0.78 will be removed.

To compute the Scale-CVI, the sum of the total CVI for each Individual Item-CVIitem will be divided by the total number of items in the instrument.52 In this approach, the desired value for S-CVI/Ave is 0.90, while the minimum acceptable value should be 0.80.50

Item analysis

Before proceeding with the assessment of construct validity, a pilot study will be conducted to evaluate the internal consistency of the NIBI. At this stage, the primary tool, having undergone face validity and content validity stages, will be distributed to 40 clinical nurses for their responses by convenience sampling method. The inclusion criteria for this stage include holding at least a bachelor’s degree in nursing, having more than 1 year of work experience and expressing willingness to participate in the study. Nurses who were part of the qualitative phase will not be recruited for this step. This step aims to identify any potential issues by estimating Cronbach’s alpha and inter-item correlation. Items that demonstrate a corrected item-total correlation score below 0.3 will be excluded from the next stages of psychometrics evaluations.53

Construct validity

Construct validity refers to the extent to which an instrument is suitable for measuring the intended construct.54 It is a measure of how well the instrument aligns with the concept it is designed to measure.50

Sampling and sample size

The study population for this stage of the study will consist of nurses involved in clinical practice within healthcare organisations across Iran. Convenience sampling will be employed to select participants during this phase. Data collection will be conducted online. To facilitate this, a Google Forms online questionnaire will be developed, and the corresponding URL link will be distributed to nurses via email or popular social networking applications such as Telegram or WhatsApp. The data collected through Google Forms will be exported to an Excel file for further analysis.

For exploratory factor analysis (EFA), the minimum sample size will be set at 300 individuals,55 or an alternative guideline of having 5–10 participants per item.56 In this research study, two separate independent samples will be collected. The first sample will consist of at least 300 participants and will be used for EFA. The second sample, comprising at least 200 distinct samples from the first one, will be gathered to conduct confirmatory factor analysis (CFA).57

Statistical data analysis

To assess the construct validity and uncover the latent constructs of the NIBI, EFA will be employed. In this process, the adequacy of the sampling and the appropriateness of the data will be evaluated using the Kaiser-Meyer-Olkin (KMO) method and Bartlett’s sphericity test.58 A KMO statistic exceeding 0.9 will be considered excellent.58 Factors will be primarily extracted using Varimax rotation, based on eigenvalues greater than 1 and a scree plot. The presence of an item within a latent factor will be determined based on a factor loading close to or exceeding 0.3, estimated using the formula: CV=5.152/√ (n−2), where CV represents the number of extractable factors, and ‘n’ represents the sample size.59 The number of factors will be estimated using Horn’s parallel analysis.60 Furthermore, items with commonalities below 0.2 will be excluded from the EFA.61

The CFA aims to assess the proposed factor structure from the EFA and confirm its validity with a different sample of participants. To evaluate the structural factors, CFA will be employed, using the maximum-likelihood method and the widely used goodness-of-fit indices. Model fitness will be assessed using several indices, including the root mean square error of approximation, Parsimonious Fit Index, Parsimonious Comparative Fit Index (CFI), Tucker-Lewis’s Index, CFI, Incremental Fit Index, and CMIN/DF. These indices will be used to determine the adequacy of the model fit. In this study, all statistical analyses will be conducted by using the SPSS (V.26)62 and AMOS63 software. A maximum error margin of 5% will be deemed acceptable for all tests.


Reliability pertains to the degree to which the observed variances in test scores can be attributed to genuine discrepancies in the characteristics being studied, as opposed to variations caused by random errors.47 To assess the reliability of the NIBI and measurement stability, two methods will be employed: internal consistency and test–retest reliability analysis.

Internal consistency

To evaluate the internal consistency of the measures, various statistical indicators will be used, namely Cronbach’s alpha (α), McDonald’s omega (Ω) and the average inter-item correlation (AIC). The thresholds for determining acceptable internal consistency will be based on previous research, where coefficient values of α and Ω exceeding 0.7 were considered satisfactory,64 while an AIC range of 0.2–0.4 was deemed acceptable.65

Test–retest reliability

To assess the stability of the NIBI, the intraclass correlation coefficients (ICC) will be employed.66 The ICC will be calculated using a two-way random effect model with a 2-week interval66 among a sample of 30 clinical nurses.


Responsiveness refers to the capability of an instrument to detect changes as they occur.67 In order to evaluate the responsiveness of the NIBI, the SE of measurement (SEM) and the minimum detectable changes (MDC) will be used.

The SEM will be calculated to quantify the errors in the scale scores. The formula for SEM is as follows:

SEM=SD Pooled × √ (1−ICC)

where SD Pooled represents the pooled SD.

The calculation of the MDC will be performed using the following formula:


This formula accounts for the SEM and applies a multiplier of 1.96 to achieve a 95% confidence level. The resulting MDC value represents the minimum amount of change that can be reliably detected by the instrument.68 In line with established criteria, an acceptable level of MDC is defined as being below 30%. Furthermore, an MDC value below 10% will considered to be excellent.69


Interpretability refers to the minimum important change in the score of the specific instrument, or the extent to which a change in the instrument’s score holds meaningful significance.70 To assess the interpretability of the NIBI, various factors will be examined, including the distribution of total scores across the entire sample, as well as the presence of floor and ceiling effects. In our study, the presence of floor and ceiling effects will be determined by calculating the percentage of participants who achieve the lowest and highest scores, respectively, on the NIBI. If more than 15% of respondents obtain the lowest or highest score, it will be considered indicative of the presence of floor or ceiling effects.47

Normal distributions, outliers

To examine the normal distributions of the data, both univariate and multivariate analyses will be conducted using measures of skewness and kurtosis. The multivariate distributions will be assessed to determine their normality and identify any potential multivariate outliers. The evaluation of multivariate normality will involve calculating Mardia’s coefficient of multivariate kurtosis, aiming for a value below 20.58 The presence of multivariate outliers will be determined by evaluating the Mahalanobis distance. Items with a Mahalanobis distance below p<0.001 will be classified as multivariate outliers.71


The instrument responses will be scored using a Likert scale. To facilitate interpretation and comparison, the scores will be transformed into a 0–100 scale using the following formula.72

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By converting the scores to standard values, a higher average score, closer to 100, will indicate a higher level of innovative behaviours among clinical nurses.

Patient and public involvement

Not applicable.


This study aims to elucidate the essential elements involved in the development and psychometric properties evaluation of NIBI. This would contribute to the advancement of knowledge by pinpointing the dimensions of NIBs, streamlining the operationalisation of this concept and facilitating measurement through a validated and reliable measurement tool.

To explain a concept, various approaches have been proposed.51 Using existing instruments, literature review or researchers’ experiences are among them.73 74 Conducting a qualitative study, included in our research, is the most effective method for explaining a concept, compiling and selecting suitable items to design a measurement tool related to that concept.51 In the psychometric phase of this study, almost all the indicators of validity and reliability will be measured. Construct validity is the main phase in instrument psychometric evaluations.75 EFA and CFA with two separate samples will be used for construct validity to achieve a suitable result and discover meaningful factors.51

Like any research, this study faces challenges, including potential biases and limitations. One notable limitation stems from the geographical location of the researcher. Participants in the qualitative study will be exclusively recruited from Tehran, the capital of Iran. This narrow selection might restrict exploring additional aspects of NIBs in rural and underserved clinical settings. Nevertheless, in the quantitative phase of the study, particularly during the construct validity step, questionnaires will be distributed through online platforms nationwide. This approach aims to engage more than 500 nurses from various regions, enriching the study’s insights. The results from this phase will inform adjustments to the qualitative stage of the study. Another potential limitation of the study could be the small number of nurses with the experience of innovation in patient care. Therefore, efforts will be made to use the inductive-deductive method and literature review to complete the pool of items in the initial phase of the study.76 We also may encounter limitations in the selection of experts in the field of innovation in nursing to determine content validity. Hence, leveraging expertise from other disciplines in innovative behaviours can be beneficial. Another issue is that the innovative behaviours construct, which will be presented as measurable items, may not elicit a consistent perception in nurses’ responses, and the KMO index is deemed unacceptable.77 In response to this concern, an accurate face validity assessment will be done to determine the comprehensibility of the items, as well as content validity before construct validity.

Despite the challenges, the diverse methodologies employed aim to provide valuable insights into the field of nursing.

Ethics and dissemination

This study originates from a doctoral dissertation in nursing. Permission and ethical approval from Semnan University of Medical Sciences have been obtained with reference code IR.SEMUMS.1401.226. Commencing in November 2022, the project is slated for completion by the end of 2024.

Before becoming part of the study, potential participants will be required to endorse a written informed consent form. This document will encompass details regarding the study’s purpose, voluntary involvement, data protection, security measures and the confidentiality of information.

Our objective is to furnish nursing managers with the final instrument derived from this study. This instrument will enable them to assess the innovative behaviours of nurses in health-oriented organisations, and if needed, implement necessary interventions.

The study’s findings will be disseminated through esteemed journals and presentations at both national and international conferences.

Ethics statements

Patient consent for publication


We extend our sincere gratitude to the participants, research team, ethics committee, and the administrators of Semnan University of Medical Sciences for their invaluable support in developing this study.


Supplementary materials

  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.


  • Contributors All authors have taken ownership of the entire content of this manuscript and have given their approval for its submission. ESD: conceptualisation, methodology, writing–original draft, writing–review and editing. MS: conceptualisation, methodology, writing–review and editing. AZ: conceptualisation, methodology, writing–review and editing. AAG: conceptualisation, methodology, writing–review and editing.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.