Innovative behaviour profile and its associated factors among nurses in China: a cross-sectional study based on latent profile analysis


In the increasingly competitive healthcare landscape, innovation has become paramount in addressing ever-changing global health challenges, including adapting to emerging technologies, resource constraints, ageing populations and integrated care needs.1 Innovative behaviour refers to the deliberate process through which individuals generate, introduce and implement new ideas, products, technologies and procedures within their professional roles, ultimately benefiting the relevant individual or organisation internally.2 Nurses comprise over half of the global health workforce and possess a distinctive opportunity to contribute, advocate and execute innovations as vital healthcare providers worldwide.3 4 Nurse innovative behaviours involve surmounting potential obstacles, seeking support and collaborating to develop and implement novel nursing philosophies, processes or delivery models that contribute to patient well-being, foster professional advancement and enhance the competitiveness of healthcare organisations.5 It is essential for ensuring safe and high-quality healthcare, preventing diseases, improving patient outcomes, optimising care processes and promoting innovation in the healthcare industry.6 7

With the rapid global growth of traditional Chinese medicine (TCM) and its crucial role in treating COVID-19, it has gained a unique position in international medicine.8 TCM has spread to 196 countries and territories, with over a third of the world’s population undergoing TCM-related therapies, particularly TCM nursing technology.9 TCM nursing is rooted in TCM theories and integrated with modern nursing science. It holds a critical role in various areas, including fundamental nursing care, chronic disease management, geriatric nursing, hospice care and family nursing.10 The implementation of TCM nursing takes place primarily in TCM hospitals, where TCM nurses are the key practitioners. The innovative behaviours of nurses in TCM hospitals significantly contribute to advancing TCM nursing technological innovation, reinforcing discipline establishment and enhancing the quality of TCM nursing services.11 The insights gained from studying the nurses’ innovative behaviour in TCM hospitals may be beneficial for countries worldwide aiming to develop tailored strategies for promoting TCM nursing and fostering nursing innovation. However, there is limited research on the innovative behaviours of nurse groups in TCM hospitals. Furthermore, TCM hospitals lack emphasis on the cultivation of nurses’ innovative capabilities, resulting in low motivation among nurses to innovate or face challenges in the sustainable promotion and implementation of innovative concepts.12 Given the existing era of artificial intelligence and emerging technologies, identifying the population characteristics of innovative nurses assumes greater significance in enhancing the innovative practices of nurses within TCM hospitals.

However, innovative behaviour is a dynamic, complex, multistage process.13 Research indicates that nurses’ innovative behaviours have remained at a moderate level in the current high-technology, high-risk, high-workload and high-stress nursing work environment.5 14–17 Drawing from a synthesis of prior studies, nurses’ innovative behaviours were influenced by both their internal characteristics and the external environment of the workplace and organisation. Significant individual-level factors in nurses’ innovative behaviours included numerous sociodemographic characteristics, such as education level, positions, professional title, pay, nursing experience and hospital size.5 18–20 Factors that reflect the individual’s internal positive states, such as psychological capital, positive coping styles and adversity quotient (AQ), have a positive impact on nurses’ innovative behaviour.21–23 AQ represents an individual’s inherent ability to overcome challenges, adversity and obstacles, emerging as a pivotal positive psychological element gaining traction in the nursing field.23 Research has demonstrated that nurses with a high level of AQ exhibited heightened engagement in their work and proclivity towards innovative behaviours.24 There is limited research addressing the influence of the AQ on innovative behaviour, warranting further investigation.

Studies19 25 investigating external environmental factors have found that work innovation support, organisational innovation climate and structural empowerment (SE) can enhance nurses’ innovative behaviours. A vital component of the nursing work environment is SE, which is defined as the extent to which an organisation equips its employees with the power, information, support, opportunities, resources and other necessary elements to accomplish their tasks.26 Research indicated that SE had a direct positive effect on nurses’ innovative behaviour, meaning that the higher the level of SE in the organisation, the more innovative behaviours of the nursing staff will be.19 27 Nevertheless, the impact of SE on different latent profiles of nurses’ innovative behaviour remains uncertain.

However, few studies have specifically investigated the innovative behaviour of nurses in TCM hospitals. Previous studies primarily focused on categorising innovative behaviours based on the total or mean scores, emphasising the overall level of innovative behaviours and their related factors while neglecting population heterogeneity. Person-centred approaches allow us to understand how variables work across people and identify subpopulations with heterogeneous characteristics, which can provide valuable insights when developing tailored interventions.28 Latent profile analysis (LPA) is a categorical latent variable modelling approach that focuses on identifying latent subpopulations within a population based on a given set of variables.29 It assumes that people can be classified with varying degrees of probability into categories that have different configurable profiles of personal or environmental attributes.30 Compared with traditional non-latent clustering methods, LPA can filter the number of categories based on more objective and rigorous fitness metrics and incorporate demographics and other covariates to construct a regression mixed model for examining complex variable relationships.29 LPA has been widely used in sociology, psychology and medicine.29

Kurt Lewin’s field dynamic theory31 states that individual behaviour results from the interplay between the individual and the environment. It is based on two key concepts: the ‘psychological tension system’, focusing on individual motivation, and the ‘life space’, emphasising the impact of the environment on behaviour.31 This theory supports the introduction of AQ and SE in investigating their influence on nurses’ innovative behaviours in TCM hospitals, contributing to the advancement of theory and research on innovative behaviour in healthcare.

Based on the above, the objectives of this study were (1) to identify latent profile characteristics of innovative behaviours of nurses in TCM hospitals using LPA and (2) to explore the influence of demographic-sociological factors, AQ factors and SE factors on the latent profile of innovative behaviours of nurses in TCM hospitals. This will help deepen the understanding of the innovative behaviours of nurses in TCM hospitals and develop targeted interventions to improve innovative behaviours.


Study design

A descriptive cross-sectional design was adopted, and the Strengthening the Reporting of Observational Studies in Epidemiology guidelines were followed to strengthen the reporting of observations.

Sample size estimation

G*power V.3.1 was used to calculate a minimum sample size of 415 based on multiple regression analysis (effect size of 0.10, a power of 0.95, a significance level of 0.05 and 36 predictors). Considering that 20% of questionnaires are invalid and that sample sizes of more than 500 are suitable for LPAs,30 a minimum sample size of 500 was required for this study.


From April 2023 to July 2023, a cross-sectional survey that included nurses from secondary and tertiary TCM hospitals in Anhui Province, China, was conducted using a multistage stratified sampling method. As of the time of sampling, there were a total of 53 secondary TCM hospitals and 20 tertiary TCM hospitals in Anhui. In the first stage, three secondary TCM hospitals and three tertiary TCM hospitals were randomly selected. For the second stage, 10 clinical departments were randomly selected from each TCM hospital. During the third stage, all eligible nurses in each department were surveyed. Study participants were selected based on the following criteria: (a) holding a Chinese registered nurse licence; (b) being regularly employed in the surveyed hospitals with at least 1 year of nursing experience in a front-line clinical department; and (c) providing informed consent and voluntary participating in this study. The exclusion criteria included (a) nurses from hospitals not included in the survey, such as trainee nurses and nurses on exchange studies; (b) nurses who were absent from work or on sick leave during the questionnaire collection period; and (c) nurses who had submitted their resignation notices.


Individual characteristics: demographics and job

Based on previous studies,5 18–20 the individual characteristics consisted of 23 questions on sociodemographic characteristics (age, gender, education level, family situation, marital status, monthly salary, etc) and work characteristics (professional title, working experience, position, department, hospital level, average number of night shifts per month, etc). Participants were also asked to report whether nurses had any funding projects and whether they had published scientific papers.

Nurse Innovative Behaviour Scale

The Nurse Innovative Behaviour Scale (NIBS) was used to measure the level of innovative behaviour of nurses in TCM hospitals. Bao et al developed the Chinese version of NIBS, derived from Scott and Bruce’s Innovative Behaviour Scale, specifically tailored for the nursing population.32 It consists of 10 items with three dimensions: idea generation (3 items), obtaining support (4 items) and idea realisation (3 items). The participants responded using a 5-point Likert scale ranging from 1 (‘never’) to 5 (‘frequently’). Higher scores indicated better innovative behaviour at work. The instrument has demonstrated good psychometric properties and high internal consistency among Chinese nurses, as validated through various surveys and studies.17 33 34 In this study, the total Cronbach’s alpha was 0.908, and the Cronbach’s alpha for each subscale ranged from 0.816 to 0.898.

Nurse Adversity Quotient Self-Evaluation Scale

In this study, the AQ of nurses in TCM hospitals was measured using the Nurse Adversity Quotient Self-Evaluation Scale (NAQSE). The NAQSE, created by Liu et al,35 was designed to evaluate the level of nurse AQ based on the AQ theory by Stoltz23 as its conceptual framework. The scale contains four dimensions: adversity control (12 items), adversity attribution (8 items), adversity influence (14 items) and adversity endurance (10 items), totalling 44 items. Each item was rated on a 5-point Likert scale (ranging from 1=‘strongly disagree’ to 5=‘strongly agree’). The lower the score indicated, the higher the individual’s AQ. The Cronbach’s alpha for the total scale was 0.938, and the dimensions were 0.920, 0.897, 0.934 and 0.891, respectively. The NAQSE has been validated to have good reliability and validity in the Chinese nursing population.36 In this study, the overall Cronbach’s alpha was 0.962, and Cronbach’s alpha for each dimension ranged from 0.853 to 0.911.

Conditions for Work Effectiveness Questionnaire-II

SE levels were measured using the Chinese version of the Conditions for Work Effectiveness Questionnaire-II (CWEQ-II), designed by Laschinger37 based on Kanter’s theory of organisational SE and translated into Chinese by Jia.38 This tool is widely used among Chinese nurses, displaying robust reliability and validity.38 39 The CWEQ-II is a 19-item tool with six dimensions: opportunity (3 items), resources (3 items), information (3 items), support (3 items), formal power (3 items) and informal power (4 items), measured using a 5-point Likert scale (ranging from 1=‘none’ to 5=‘very a lot’). A higher score in this measurement indicates that subjects receive a higher level of SE in the clinical work environment. In this study, the overall Cronbach’s alpha was 0.934, and the Cronbach’s alpha for each subscale ranged from 0.736 to 0.875.

Data collection

The Questionnaire Star platform ( was used to develop an online questionnaire for data collection. A QR code was embedded in a poster for participants to scan using their mobile phones, allowing them to complete the survey. The completed questionnaires were submitted directly on the web platform. All questions on the online questionnaire were set as mandatory, with the restriction that each mobile phone number could only complete the questionnaire once. The initial page of the questionnaire clarified the research’s purpose, emphasised the voluntary nature of participation and highlighted that submitting the questionnaire constituted consent to take part in the study. The entire questionnaire was completed in approximately 10–15 min. Two researchers scrutinised the online questionnaire for rigour. The feasibility of the questionnaire was confirmed through a convenience sample of 30 nurses from a tertiary TCM hospital.

The sampling process was conducted separately, and each hospital was given a 1-week time limit to complete the questionnaire. Initially, permission was acquired from the chief of the nursing office and the head nurse of the department in the surveyed hospital. Subsequently, the researcher hand-picked the nurses who met the requirements on-site in the department. In the second step, the researcher explained the purpose, content and precautions of the survey to the participants. Following the acquisition of informed consent, they were instructed to complete the questionnaire anonymously based on their actual situation. Nurses were reassured that there were no right or wrong answers and that they could withdraw from the study at any time. During the final stage, the questionnaire data were extracted from the ‘Questionnaire Star’ system and cross-checked by the two researchers to complete the compilation of the data. Questionnaires completed in less than 180 s and with regular (consistent or wavy) answers were excluded. A total of 642 questionnaires were distributed in this study, and 529 valid questionnaires were recovered, presenting a validity rate of 82.40%. See the online supplemental file ‘Annex I—Questionary’ for details of data collection.

Supplemental material

Ethical considerations

All study procedures adhered to the ethical standards outlined in the 1964 Helsinki Declaration. The purpose and significance of the study were explained via an online questionnaire. The privacy, anonymity, voluntariness and confidentiality of data were maintained and guaranteed. On reviewing the instructions, the participants are required to click a button to confirm their participation officially. The survey data were stored on a password-protected computer and kept strictly confidential.

Data analyses

This study conducted an LPA to classify the latent profiles of innovative behaviours of nurses in TCM hospitals. LPA, an ideal person-centred statistical analysis method, is commonly used for identifying the number of subgroups within a given sample. The method is applied to explain the association between external continuous variables through latent category variables and to establish local independence among exogenous variables. Compared with cluster analysis, the advantages of LPA are that: (1) individuals are classified directly based on the model-estimated membership probabilities; (2) variables can be continuous, categorical, count or any combination of these; and (3) demographics and other covariates can be used to describe the profile.30 Mplus V.8.3 software was used for LPA to identify subgroups of nurses’ innovative behaviours in TCM hospitals according to the scores of each item in the NIBS. First, one to six latent feature models were fitted separately. Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted BIC (aBIC), entropy, Lo-Mendell-Rubin likelihood ratio test (LMR) and bootstrap likelihood ratio test (BLRT) were selected as the fit indices of the model to determine the optimal number of profiles. AIC, BIC and aBIC consider model fit and parsimony, with lower values indicating better fitness. Entropy refers to individual classification accuracy, with values closer to 1 indicating higher classification accuracy. LMR and BLRT compare the k-class model with the k-1 class model, with significant p values suggesting better fitness for the k-class model. In addition to assessing model fit, it is essential to consider the optimal number of categories in conjunction with the clinical significance and interpretability of the study results.

Subsequently, SPSS V.25.0 was employed to conduct the subsequent statistical analysis to facilitate more in-depth interpretations of subgroups of innovative behaviours among nurses in TCM hospitals. The frequencies and component ratios were used to describe categorical variables, whereas the mean and SDs were used to describe continuous variables. The χ2 test or Fisher’s exact test was employed to compare the differences in sociodemographic characteristics across each latent profile of innovative behaviours. The one-way analysis of variance (ANOVA) was used to compare the differences in the NAQSE and CWEQ-II scores across each latent profile of innovative behaviours. Multivariate logistic regression was employed to examine the factors associated with different latent profiles of innovative behaviours among nurses in TCM hospitals. P<0.05 was considered statistically significant, and the test level was α=0.05.

Patient and public involvement

Neither patients nor the public were involved in the design, conduct, reporting or dissemination plans associated with this research.


Participant characteristics

A total of 529 TCM hospital nurses completed the questionnaire. Table 1 summarises their sociodemographic characteristics. The participants consisted of 510 females (96.41%) with an average age of 33.46 (5.83) years. Among the participants, 305 nurses (57.66%) were from tertiary TCM hospitals, while 224 (42.34%) were from secondary TCM hospitals. The majority of the participants (90.36%) had attended training related to TCM knowledge and skills, and 469 (88.66%) had participated in the prevention and control during the COVID-19 pandemic. More than four-fifths of the participants had obtained bachelor’s degree (85.44%), and 110 (20.79%) had published scientific papers as first or corresponding authors.

Table 1

Demographic and work-related characteristics of each latent profile (n=529)

Latent profiles of nurses’ innovative behaviour in TCM hospitals

In this study, LPA was performed to examine nurses’ innovative behaviours. We assumed equal variance between profiles but zero covariance profiles while estimating for two to six profile models. As shown in table 2, (a) the AIC and BIC values exhibited a decreasing trend as the number of profiles increased, with a gradual slowing down in the rate of decrease at the start of the three-profile model; (b) the three-profile model exhibited the highest entropy; (c) statistical significance was found in LMR and BLRT values (p<0.001) in the three-profile model; and (d) the three-profile model demonstrated the highest posterior probability compared with the other profiles: 96.7%, 96.8% and 96.9%, respectively. Combining all indicators, along with the simplicity, scientific validity and practical significance of the model, the three-profile model emerged as the optimal model for interpretation and further analysis.

Table 2

Fit statistics for profile structure (n=529)

Figure 1 illustrates the mean scores of the three profiles on the NIBS’s 10 items. The first profile (35.3% of participants) exhibited the lowest score for all items and was therefore labelled as ‘low innovative behaviour’. The second profile (48.4% of participants) exhibited a medium score and was therefore labelled as ‘moderate innovative behaviour’. The third profile (16.3% of participants) achieved the highest score and was therefore labelled as ‘high innovative behaviour’. The results of the ANOVA indicated significant differences in the scores of each profile on the NIBS and its dimensions (p<0.001). The mean scores of the NIBS of all nurses in profiles 1, 2 and 3 were 2.14 (SD=0.32), 2.95 (SD=0.24) and 3.87 (SD=0.32), respectively (table 3).

Table 3

Profile differences in innovation behaviour and the results of post hoc analysis (n=529)

Figure 1
Figure 1

Mean scores of the three latent profiles on the 10 items of the Nurse Innovative Behaviour Scale.

Sociodemographic, AQ and SE characteristics of each latent profile of nurses’ innovative behaviour in TCM hospitals

The frequencies and percentages of the sociodemographic and work-related characteristics for each profile are shown in table 1. The results indicated that the average age of all nurses in profiles 1, 2 and 3 was 33.30 (SD=5.29), 33.08 (SD=5.61) and 34.95 (SD=7.30), respectively. The low innovative behaviour group had the largest proportion of nurses from secondary TCM hospitals (50.8% vs 43.0% and 35.9%). The high innovative behaviour group had the largest percentage of positions as head nurses (30.2% vs 9.4% and 3.7%). The results of χ2 tests and ANOVA indicated that there were statistically significant differences among the three profiles in terms of gender, average monthly income, department, hospital level, nurse competency level, position, employment methods, number of night shifts per month, participation in pandemic prevention and control of COVID-19, attended training related to TCM knowledge and skills, attended research course training, organised nursing research and academic activities, chaired/participated in research on foundation projects and published scientific papers as the first or corresponding author. Moreover, the differences in levels of AQ and SE across each profile were analysed. A post hoc analysis revealed that the high innovative behaviour group exhibited the highest AQ and SE levels, followed by the moderate innovative behaviour group and then the low innovative behaviour group.

Multiple logistic regression analysis of different latent profiles of nurses’ innovative behaviour in TCM hospitals

A multivariate logistic regression model was constructed to identify the factors influencing profile membership, using the variables with statistically significant differences as determined by ANOVA and χ2 tests. First, the high innovative behaviour group was compared with other profiles. The TCM nurses who had a monthly income of ¥5001 and ¥7000 (OR=2.960, 95% CI 1.130, 7.751) and did not attend training related to TCM knowledge and skills (OR=6.925, 95% CI 1.123, 42.705) had a greater likelihood of belonging to the low innovative behaviour group. Nurses working in surgery (OR=4.057, 95% CI 1.111, 14.818) (OR=3.824, 95% CI 1.179, 12.399) and obstetrics and gynaecology (OR=7.137, 95% CI 1.336, 38.125) (OR=6.258, 95% CI 1.337, 29.305) were more likely to belong to the low innovative behaviour group and moderate innovative behaviour group. Second, when referring to the moderate innovative behaviour group, nurses working in the secondary TCM hospital (OR=2.206, 95% CI 1.187, 4.100) were more likely to be in the low innovative behaviour group. Third, as compared with the position of head nurse, nurses whose position was nurse staff (OR=6.298, 95% CI 1.274, 31.129), clinical nursing teacher (OR=5.792, 95% CI 1.011, 33.180) or nursing group leader (OR=6.960, 95% CI 1.013, 47.803) had a greater probability of belonging to the low innovative behaviour group rather than high innovative behaviour group and moderate innovative behaviour group. In addition, nurses with lower AQ scores (OR=2.247, 95% CI 1.312, 3.849) (OR=1.815, 95% CI 1.240, 2.657) and higher SE scores (OR=0.117, 95% CI 0.061, 0.222) (OR=0.338, 95% CI 0.215, 0.532) had a greater probability of belonging to the high innovative behaviour group and the moderate innovative behaviour group rather than the low innovative behaviour group. (table 4)

Table 4

Multinomial logistic regression analysis of factors affecting profile membership (n=529)


The level of innovative behaviour of nurses in TCM hospitals needs to be further improved

The results of the study showed that nurses in TCM hospitals exhibited a lower mean score for innovative behaviour (2.81±0.66), compared with the study results (3.05±0.67) of Wei et al
11 in tertiary TCM hospitals. The inclusion of 42.34% of nurses from secondary TCM hospitals in this study could account for this disparity. Additionally, the absence of standardisation of TCM nursing techniques hindered the objective evaluation of TCM nursing care effectiveness and the ability to conduct further operational research, posing challenges to the implementation of innovative ideas.40 It is recommended that TCM nursing experts conduct evidence-based studies to standardise TCM nursing techniques, enhance the TCM nursing model, improve operational procedures and guidelines and establish a solid scientific foundation for TCM nursing innovations. This will incentivise nurses in TCM hospitals to adopt best practices and continuous improvement to respond effectively to changing healthcare needs and promote sustainable development in TCM nursing practice.

The innovative behaviour of nurses in TCM hospitals can be divided into three potential profiles

In this study, LPA confirmed the group heterogeneity in the types of innovative behaviour of nurses in TCM hospitals, primarily classified into three categories: low innovative behaviour, moderate innovative behaviour and high innovative behaviour. The low innovative behaviour group consisted of 35.3% of the participants, the moderate innovative behaviour group had the highest proportion (48.4%) of the participants and the high innovative behaviour group had the lowest proportion (16.3%). These findings aligned with previous LPA studies on nurse innovation.41 The three potential profiles of innovative behaviours of nurses in TCM hospitals contribute to a more comprehensive understanding of the characteristics and patterns of nurses’ innovative behaviours to identify innovative nurses. This advancement enriches the healthcare innovation theory at a microscopic level, proposing new research directions for future innovation research in healthcare.

The low innovative behaviour group was characterised by low positions, high numbers of night shifts and insufficient training opportunities. These nurses performed poorly at all stages of innovative behaviour and urgently necessitate targeted support. It is suggested that nurse managers create an innovation-oriented flat team structure, coupled with enhanced training and learning opportunities, to stimulate critical thinking, creative development and risk-taking behaviour among nurses, thereby promoting nurses’ innovative behaviour in TCM hospitals.42 The moderate innovative behaviour group demonstrated moderate performance at all stages of innovative behaviour. This group excels at generating ideas but lacks adequate organisational support, indicating that not all ideas conceived by nurses are practical and that obtaining support may be challenging in resource-constrained settings. This group of nurses should be encouraged to enhance their self-assessment skills and generate innovative and practically feasible ideas through thorough research and analysis. Meanwhile, rational allocation and effective use of limited resources can be achieved by optimising work processes, improving work efficiency and reducing waste, thereby creating maximum support for the implementation of innovative ideas. The high innovative behaviour group exhibited an overall high level of innovative behaviour, characterised by high levels of education, income, title, position and innovative research output. Team roles are crucial for organisational innovation.42 Nurse managers should continue to prioritise and support this group to prevent their transformation into a group with low innovative behaviour. By encouraging interdisciplinary teamwork and promoting knowledge sharing between different generations, this group could take the lead in TCM nursing research, education and management, thereby enhancing organisational innovative performance and fostering innovation and sustainable development of TCM nursing.

This study clarified that all three categories of nurses in TCM hospitals had the lowest mean scores in the dimension of obtaining support, specifically on item 6 ‘Seeking funding for new approaches’ through the latent category line graph. This finding supported the previous study in a more nuanced perspective.11 It is suggested that hospital management should focus on enhancing innovation support, such as implementing appropriate reward systems, increasing research funding and fostering innovative thinking.43 Additionally, instrumental support can be provided by offering nurses a comprehensive web-based knowledge and information-sharing system, establishing an intelligent learning environment and reinforcing learning and training initiatives.42 These measures would enable TCM hospital nurses to proactively explore innovative strategies to tackle the challenges of TCM nursing technology in the present information-digital era by combining traditional treatment methods with modern healthcare through multidisciplinary cooperation.

Factors associated with three latent profiles of nurses in TCM hospitals

Demographic predictors of profile membership include gender, income per month, department, hospital level, positions, nurse competency level and attended training related to TCM knowledge and skills.

The study revealed that male nurses in TCM hospitals were more inclined to be in the medium innovative behaviour group than the low innovative behaviour group, aligning with previous research findings.44 This may be because societal expectations of men emphasise traits such as initiative, risk taking and innovation, potentially leading men to demonstrate more innovative behaviours.45 Notably, male nurses were under-represented in this study, necessitating further comprehensive research to determine the relationship between gender and different profiles of nurses’ innovative behaviour. Nurses in TCM hospitals with a monthly income between ¥5001 and ¥7000 were more likely to belong to the low innovative behaviour group than those with over ¥7000, aligning with previous research indicating that higher compensation may promote innovative behaviour.46 Income reflects the rewards for one’s work, and higher remuneration serves as an incentive to boost professional identity and job satisfaction among nurses, thereby fostering positive innovative behaviours. Conversely, inadequate compensation may lead to negative innovative behaviour.47 48

Compared with other departments, the study found that TCM nurses in surgery, obstetrics and gynaecology were more likely to belong to the low and moderate innovative behaviour groups. The challenging nature of nursing in these departments, characterised by uncertainties, fast-paced environments and heavy workloads, may lead to increased pressure and burnout, ultimately impacting their innovative behaviour negatively.49 Hospitals should assign work based on nurses’ abilities and efficiency to match workload with available resources, maintaining an optimal environment for innovation and stimulating enthusiasm.

This study’s findings indicated that nurses working in tertiary TCM hospitals, nurses with competency level 4 and above or nurses holding the head nurse position showed a greater tendency to belong to the high innovative behaviour group or the moderate innovative behaviour group compared with the low innovative behaviour group. These results were consistent with previous research.5 11 Tertiary public hospitals are better equipped with funds, resources and opportunities, focusing on developing innovative skills.5 Nurses with high positions, titles and competency levels are better able to leverage resources to stay updated on new technologies and advances in nursing, enhancing their innovation ability and fostering a culture of innovation within the nursing team.44 This reduces the risk of developing low-innovation behaviours. This suggests that nursing managers should optimise the allocation of resources to provide nurses with rich learning and practice opportunities to ensure the effective use of resources. They should also introduce personalised innovation incentives, such as promotions and bonuses, to increase enthusiasm for innovation. Additionally, establishing an exchange platform and organising regular academic activities could promote experience sharing and idea collision, thereby stimulating innovation. These measures are crucial to fully use the distinct role and potential of nurses in fostering the sustainable advancement of healthcare innovation.

The logistic regression analysis results showed that TCM hospital nurses who did not receive TCM knowledge and skills training were seven times more likely to be in the low innovative behaviour group. This issue may stem from the scarcity of skilled TCM nursing professionals in China, the overall inability of the TCM nursing team to identify evidence and provide care effectively and the absence of standardised TCM nursing techniques.12 To tackle these challenges, medical schools should offer more elective courses focusing on TCM theory to train professionals with knowledge of TCM theory and nursing skills. Hospitals should strengthen the continuing education programmes for current TCM nursing staff and enhance their ability to identify evidence and provide care and technical skills through training courses and seminars.

This study’s results showed no significant differences in the three profiles of innovative behaviour among education level, professional title, years of experience and marital status. Conversely, a previous study41 showed that being unmarried, possessing higher education levels, holding advanced professional titles and having more years of work experience led to higher levels of innovative behaviour among nurses. This disparity may be due to differences in research topics, the same sampling strategy and different settings. In addition, the inclusion of other sociodemographic variables in this study did not result in significant differences. Further research is required to confirm the relationship between these factors and the potential profiles of innovative behaviour of nurses in TCM hospitals.

Inspired by Levin’s theory of field dynamics, this study incorporated AQ and SE to analyse their association with latent profiles of innovative behaviours of nurses in TCM hospitals. This has contributed to deepening the understanding of healthcare innovation and promoting the application and development of field dynamics theory. It was found that the AQ, a positive psychological factor, reduced the probability of belonging to the low innovative behaviour group, similar to previous findings.50 Nurses with a high AQ demonstrate greater positive coping abilities and self-efficacy, enabling them to effectively tackle challenges, thereby boosting work performance motivation and fostering innovation. Therefore, increasing the level of AQ through scientific and rational intervention programmes, such as conducting adversity-themed discussions, clinical case teaching and mindfulness-based stress reduction, emerges as a crucial strategy to promote innovative behaviour among nurses in TCM hospitals. This study further clarified that nurses in TCM hospitals were more likely to develop into moderate and high innovative behaviour groups when SE in a supportive organisational environment was high, similar to the findings of previous studies.17 19 27 When nurses are fully empowered by the organisation, they perceive a strong sense of control and organisational support for innovation in their work,17 which helps them maximise their potential and increase motivation for innovative behaviour. To improve nurses’ innovation in TCM hospitals, it is recommended that nurse managers focus on improving SE and creating a supportive environment through comprehensive measures such as providing resource support, building effective communication platforms, optimising work processes and providing training and career development opportunities. Meanwhile, promoting TCM nursing concepts, encouraging research and developing nursing models that are consistent with TCM characteristics will further promote the sustainable advancement and innovation of the TCM nursing heritage.

Practice implications

This study has theoretical and practical implications for future intervention research on the innovative behaviours of nurses in TCM hospitals. Nursing managers and educators should identify the potential characteristics of innovative behaviour among nurses in TCM hospitals. For nurses exhibiting low innovative behaviours, nursing managers should comprehensively analyse their salary satisfaction, perception of career development plans, SE and AQ. Subsequently, targeted interventions can be devised to enhance nurses’ innovative behaviours in TCM hospitals. First, rational staff allocation and innovation rewards are worth considering. Regarding career development planning, continuing education in TCM nursing can be integrated with nurses’ career development paths to foster advanced TCM nursing specialists who possess evidence-based medical thinking and practical nursing skills. To achieve SE, it is essential to implement a shared governance nursing management model, a well-developed intelligent learning environment, tools, knowledge-sharing systems and effective communication channels. In addition, offering occupational mental health programmes and providing relaxation space can alleviate nurses’ psychological burdens and improve their AQ.

Nursing educators should establish targeted curriculum design and training programmes based on the requirements and characteristics of nursing students, trainee nurses, new nurses and senior nurses to ensure the sustainability and effectiveness of nursing education. Simultaneously, the training programme should be optimised through continuous assessments and feedback to improve the professional quality, psychological well-being and overall competence of nurses in TCM hospitals.

Strengths and limitations

To the best of our knowledge, this is the first large sample size study to use the LPA approach to explore a heterogeneous subgroup of nurses’ innovative behaviours in TCM hospitals, providing a unique perspective and laying the groundwork for future research into the innovative behaviours of nurses in TCM hospitals. Although the study was diligently planned and executed, several limitations must be acknowledged. First, this study recruited only TCM hospital nurses from Anhui Province, which may reduce the generalisability of the findings. Second, the web-based cross-sectional study design and self-reported data collection instruments may have led to misleading results and limited the establishment of causal relationships. Future research requires more rigorously designed studies across geographical regions, hospital levels and hospital types are needed to explore this issue in depth. In addition, cultural and national differences may lead to different study results. Readers are advised to evaluate study results in conjunction with sampling and data collection methods.

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