Technophobia and Its Associated Factors Among Registered Nurses in China: A Cross‐Sectional Multicenter Study - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Nurs Manag . 2026 Apr 16;2026:9935996. doi: 10.1155/jonm/9935996 Search in PMC Search in PubMed View in NLM Catalog Add to search Technophobia and Its Associated Factors Among Registered Nurses in China: A Cross‐Sectional Multicenter Study Xuange Sun Xuange Sun 1 Department of Endocrinology and Metabolism, The First Hospital of China Medical University, Shenyang, China, cmu.edu.cn 2 School of Nursing, China Medical University, Shenyang, China, cmu.edu.tw Find articles by Xuange Sun 1, 2 , Xu Liu Xu Liu 2 School of Nursing, China Medical University, Shenyang, China, cmu.edu.tw 3 Transplantation and Hepatobiliary Department, The First Hospital of China Medical University, Shenyang, China, cmu.edu.cn Find articles by Xu Liu 2, 3 , Xiaolin Chang Xiaolin Chang 4 Outpatient Service by Famous Specialists, The First Hospital of China Medical University, Shenyang, China, cmu.edu.cn Find articles by Xiaolin Chang 4 , Na Hu Na Hu 3 Transplantation and Hepatobiliary Department, The First Hospital of China Medical University, Shenyang, China, cmu.edu.cn Find articles by Na Hu 3 , Xiangxiu Qi Xiangxiu Qi 2 School of Nursing, China Medical University, Shenyang, China, cmu.edu.tw 5 Hospital Infection Control Office, Shengjing Hospital of China Medical University, Shenyang, China, cmu.edu.cn Find articles by Xiangxiu Qi 2, 5, ✉ , Xiaofei Li Xiaofei Li 2 School of Nursing, China Medical University, Shenyang, China, cmu.edu.tw 3 Transplantation and Hepatobiliary Department, The First Hospital of China Medical University, Shenyang, China, cmu.edu.cn Find articles by Xiaofei Li 2, 3, ✉ Editor: Saba Noor Author information Article notes Copyright and License information 1 Department of Endocrinology and Metabolism, The First Hospital of China Medical University, Shenyang, China, cmu.edu.cn 2 School of Nursing, China Medical University, Shenyang, China, cmu.edu.tw 3 Transplantation and Hepatobiliary Department, The First Hospital of China Medical University, Shenyang, China, cmu.edu.cn 4 Outpatient Service by Famous Specialists, The First Hospital of China Medical University, Shenyang, China, cmu.edu.cn 5 Hospital Infection Control Office, Shengjing Hospital of China Medical University, Shenyang, China, cmu.edu.cn ✉ Corresponding author. Revised 2026 Mar 27; Received 2026 Jan 14; Accepted 2026 Mar 28; Collection date 2026. Copyright © 2026 Xuange Sun et al. Journal of Nursing Management published by John Wiley & Sons Ltd. This is an open access article under the terms of the https://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMC Copyright notice PMCID: PMC13087445 PMID: 41992642 Abstract Aim To examine the level of technophobia and identify its associated sociodemographic, occupational, and psychosocial factors among registered nurses in China. Background The rapid digital transformation of healthcare systems demands that nurses effectively adopt and use digital technologies. Technophobia—defined as negative emotional reactions toward technology—may impede nurses’ adaptation to technological innovations. However, no study is currently available on technophobia and its associated factors in registered nurses. Design A multicenter, cross‐sectional study. Methods A total of 1559 registered nurses from two university‐affiliated tertiary hospitals in Northeast China were surveyed between September and October 2025. Data were collected using a self‐administered questionnaire including sociodemographic factors, occupational factors, the validated Chinese version of the Technophobia Scale, the Multidimensional Scale of Perceived Social Support, and the Athens Insomnia Scale. A generalized linear model was fitted to identify independent predictors of technophobia. Results The nurses’ median technophobia score was 26.00 (P 25 –P 75 : 13.00–39.00), indicating a low‐to‐moderate level overall. The generalized linear model revealed that male gender ( β = 0.201), having one child ( β = 0.130), living alone ( β = 0.091), having a higher perceived workload ( β = 0.048), and poorer sleep quality ( β = 0.015) were independently associated with greater technophobia. Conversely, higher job satisfaction ( β = −0.081) and stronger social support ( β = −0.003) were independently associated with lower technophobia; however, the effect size for social support was relatively small, all p < 0.05. Conclusions Technophobia among registered nurses in China is at a moderately low level. Interventions targeting modifiable factors—such as workload management, sleep health, job satisfaction, and social support—through structured digital literacy training, peer support mechanisms, and other supportive workplace strategies may help mitigate technophobia and enhance nurses’ readiness for digital transformation. Patient or Public Contribution Nurses voluntarily participated in the survey. The findings may indirectly benefit patients through improved nurse engagement with digital health systems and safer, more efficient care delivery. Keywords: digital health, job satisfaction, nurses, psychosocial factors, sleep quality, social support, technophobia, workload 1. Introduction The rapid advancement of digital technologies has profoundly transformed healthcare delivery worldwide. Electronic health records, telemedicine, artificial intelligence‐assisted diagnostics, and mobile health applications have become integral components of modern clinical practice, contributing to improved efficiency, accuracy, and patient safety [ 1 – 3 ]. In China, the national push for digital health and information systems has accelerated the digital transformation of healthcare institutions [ 4 ]. Recent reports indicate that over 90% of tertiary hospitals have completed deployment of basic information management systems, reflecting the government’s strong policy support for health informatization [ 5 ]. In nursing, technology integration has facilitated evidence‐based decision‐making, enhanced monitoring and documentation, and expanded patient education and communication channels [ 6 – 8 ]. Nurses in China are increasingly expected to acquire digital competencies and adapt to rapidly changing technological environments. In recent years, nurses have played a crucial role in guiding the integration of digital technologies into healthcare settings [ 9 ]. However, the mandatory and widespread adoption of digital technologies has significantly increased the pressure on nurses to learn and effectively use these systems [ 10 , 11 ]. The digital shift has made it increasingly difficult for nurses to balance traditional care duties with the demands of mastering new technology, creating a unique form of job‐related anxiety: technophobia [ 12 ]. Technophobia—defined as aversion or anxiety toward technologies and technology‐related products—represents a form of technology‐related anxiety [ 13 ]. In healthcare settings, technophobic tendencies among nurses may result in reduced engagement in technology‐based interventions, hinder the effective utilization of information systems, and lead to the avoidance of innovative digital tools [ 14 ], thereby reducing job satisfaction, compromising the quality of patient care, and impeding the advancement of nursing practice. Previous research has shown that technophobia is associated with a range of demographic and psychosocial factors. Age, living situation, health status, education level, monthly income, and e‐health literacy have all been identified as factors related to technophobia [ 15 , 16 ]. In addition, psychosocial factors may also play an important role in shaping individuals’ attitudes toward technology. Among these factors, social support has been identified as an important resource that facilitates adaptation to technological change by providing emotional encouragement and practical assistance [ 17 ]. Conversely, sleep problems such as insomnia may impair cognitive functioning, emotional regulation, and learning ability, which are critical for adapting to new technologies in healthcare environments [ 18 ]. However, evidence regarding technophobia has mainly focused on older adults [ 15 , 17 ], university students [ 19 , 20 ], and teachers [ 16 , 21 ]. Nurses—who constitute nearly half of the global health workforce [ 22 ], serve as the frontline of patient care, and are pivotal for the successful integration of digital technologies into clinical practice [ 9 ]—have been largely overlooked in research on technophobia. Addressing this gap is critical, as nurses’ readiness and confidence with digital tools directly impact patient safety, care quality, and the sustainable adoption of healthcare innovations. Therefore, this study aims to investigate the current status of technophobia and its associated factors among nurses in China using a cross‐sectional multicenter design. By identifying the demographic, occupational, and psychosocial determinants of technophobia, this study seeks to provide evidence to guide targeted interventions that enhance nurses’ digital readiness, reduce technology‐related anxiety, and thereby promote the sustainable integration of digital health innovations into nursing practice. 2. Methods 2.1. Study Setting and Participants This study employed a cross‐sectional design and collected questionnaires using the electronic “Questionnaire Star” tool ( https://www.wjx.cn/ ). The participants were recruited from two tertiary hospitals in China’s Northeast, namely the First Hospital of China Medical University and the Shengjing Hospital of China Medical University. All the nurses of these two hospitals who met the inclusion criteria and did not meet the exclusion criteria were invited to participate in this survey via WeChat between September 2025 and October. The inclusion criteria were (1) registered nurses employed at the respective hospital; (2) engaged in clinical or related nursing work for more than 6 months and possessing basic nursing experience; (3) voluntarily agreed to participate in the study; and (4) able to independently complete the electronic questionnaire. Nurses currently on extended leave (e.g., maternity leave, sick leave) and not in regular work were excluded. This study has been carried out in strict adherence to the Declaration of Helsinki and has passed the ethical approval of the Ethics Committee of the First Hospital of China Medical University, number [2025] 782. Informed consent from all participants was obtained before they participated in this study. 2.2. Sample Size Determination An a priori sample size estimation was conducted using G∗Power 3.1 for multiple linear regression (F tests, linear multiple regression: fixed model, R 2 deviation from zero). A small‐to‐moderate effect size ( f 2 = 0.02), α = 0.05, power = 0.95, and 16 predictors were assumed. The calculation indicated a minimum required sample size of 204 participants. Considering a possible 20% attrition rate, the final required sample size was 255. A total of 1561 nurses initially participated in the survey. After data screening, two questionnaires were excluded due to abnormal values in the age variable. Therefore, data from 1559 participants were included in the final analysis. 2.3. Measures 2.3.1. Questionnaire for General Information The general information of this study includes age (years), gender, marriage, education level, family per capita monthly income, number of children, living status, department, employment status, professional title, number of night shifts per week, perceived staffing adequacy status, perceived workload, and job satisfaction. 2.3.2. Technophobia The technophobia was measured using the validated Chinese version of the Technophobia Scale [ 23 ]. The original scale was compiled by Khasawneh [ 13 ] and included 16 items and five dimensions named techno paranoia, techno fear, techno anxiety, cybernetic revolt, and communication devices avoidance [ 13 ]. The original scale demonstrated good internal consistency, with a Cronbach’s α coefficient of 0.867. During the localization process, item 5 and item 8 were excluded due to low relevance ( r < 0.4), and item 13 was excluded due to multiple meanings [ 23 ]. The final Chinese‐language version of the scale comprises 13 items across three dimensions, including techno‐anxiety, techno‐paranoia, and privacy concerns. The Chinese version demonstrated good psychometric properties, with a Cronbach’s α of 0.911 for the total scale and 0.759–0.885 for the subscales. Each item was rated on a 5‐point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), yielding a total score between 13 and 65. Higher scores indicate greater levels of technophobia. The Technophobia Scale was initially validated among older adults and has subsequently been applied to university students [ 19 , 20 ] and teachers [ 21 ]. Its items capture general negative emotional reactions to technology, making the scale suitable for adult populations. Representative items include: “I feel anxious when I have to use new communication devices (e.g., a new mobile phone or a newly purchased computer),” “I try to avoid using new technologies, such as smartphones and smart wristbands, as much as possible,” and “I am very afraid that technology will change our way of life, communication patterns, and emotional relationships.” In the present study, the Chinese version demonstrated good internal consistency (Cronbach’s α = 0.984), supporting its reliability in the nurse sample. 2.3.3. Social Support Social support was measured using the Multidimensional Scale of Perceived Social Support, which includes 12 items divided into family support, friend support, and other support, three dimensions [ 24 ]. The original scale demonstrated good internal consistency, with Cronbach’s α coefficients ranging from 0.85 to 0.91 across subscales and 0.88 for the total scale. The Chinese version of the scale, translated by Jiang [ 25 ], has been widely used in Chinese nurse populations and has demonstrated good reliability and validity in previous studies [ 26 , 27 ]. Each item was rated on a 7‐point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), resulting in a total score ranging from 12 to 84. Higher scores indicate greater social support. In this study, it demonstrated high internal consistency, with a Cronbach’s alpha of 0.984. 2.3.4. Insomnia Status The sleep quality was measured using the Athens Insomnia Scale (AIS) [ 28 ]. The original scale demonstrated good reliability with Cronbach’s α = 0.89. The Chinese version of the scale has been widely used in Chinese populations and has demonstrated good reliability and validity in previous research [ 29 , 30 ]. It covers eight items and two dimensions, including nocturnal sleep difficulties and daytime consequences of disturbed sleep. Each item is rated on a 0‐3 scale, with 0 indicating no impact and 3 indicating significant impact, yielding a total score ranging from 0 to 24, with higher scores indicating more severe insomnia symptoms, with a Cronbach’s alpha of 0.903. 2.4. Statistical Analysis Data were analyzed using SPSS version 25.0. A two‐sided p value < 0.05 was considered statistically significant. Normality of the sample distribution was tested using qq‐plots and the Kolmogorov–Smirnov test. Median and quartile spacing were used to describe the continuous variables in this study because the variables were not normally distributed, and the percentages (constituent ratios) were used to describe the qualitative data. For univariate comparisons, the Mann–Whitney U test or Kruskal–Wallis test was used. Spearman correlation analysis was used to test for the relationship between technophobia and the continuous variables. To identify factors associated with technophobia, a generalized linear model (GLM) with a gamma distribution and log link function was fitted. The assessment of multicollinearity potential was conducted using tolerance and the variance inflation factor (VIF). Noncollinearity was accepted if the tolerance exceeded 0.10 and the VIF was below 10.0. 3. Results 3.1. Characteristics of the Study Sample In total, 1559 participants were included in this study. The median technophobia score of Chinese nurses was 26.00 (P 25 : 13.00, P 75 : 39.00). The median age of participants was 37.0 (P 25 : 30.00, P 75 : 40.00). The median perceived workload scores were 3.00 (P 25 :2.00, P 75 : 3.00), and the median job satisfaction scores were 4.00 (P 25 : 4.00, P 75 : 4.00). The descriptive statistics of variables are shown in Table 1 . TABLE 1. Characteristics of the study sample. Variable M (P 25 , P 75 )/frequency (%) Technophobia 26.00 (13.00, 39.00) Age 37.00 (30.00, 40.00) Perceived workload 3.00 (2.00, 3.00) Job satisfaction 4.00 (4.00, 4.00) Sleep quality 6.00 (2.00, 9.00) Social support 72.00 (60.00, 82.00) Gender Male 47 (3.01) Female 1512 (96.99) Marriage have a spouse 1079 (69.21) have no spouse 480 (30.79) Education level college and below 90 (5.77) undergraduate 1435 (92.05) Master’s and above 34 (2.18) Family per capita monthly income (yuan) < 8000 202 (12.96) 8000–10000 485 (31.11) > 10,000 872 (55.93) Number of children 0 531 (34.06) 1 804 (51.57) > 1 224 (14.37) Living status living with others 1257 (80.63) living alone 302 (19.37) Department outpatient 107 (6.86) ICU 81 (5.20) medical ward 274 (17.58) surgical ward 319 (20.46) operating room 39 (2.50) emergency 52 (3.34) pediatrics/neonatology 320 (20.53) others 367 (23.54) Employment status permanent staff with official establishment (bianzhi) 113 (7.25) contract staff 1439 (92.30) agency hired/dispatched staff 7 (0.45) Professional title staff nurse 1055 (67.67) supervisor nurse 494 (31.69) associate senior nurse/chief nurse 10 (0.64) Number of night shifts per week 0 483 (30.98) 1 457 (29.31) 2 521 (33.42) 3 98 (6.29) Perceived staffing adequacy adequate 827 (53.05) moderate 625 (40.09) inadequate 107 (6.86) Open in a new tab 3.2. Univariate Analysis of Technophobia in Nurses There were significant differences in the technophobia in terms of the number of children and perceived staffing adequacy ( p < 0.05) (Table 2 ). TABLE 2. Comparison of technophobia scores among nurses with different sociodemographic and work‐related characteristics. Variable M (P 25 , P 75 ) Z/H p Gender Male 27.00 (15.50, 43.50) Z = −1.327 0.185 Female 26.00 (13.00, 39.00) Marriage have a spouse 26.00 (13.00, 39.00) Z = −1.313 0.189 have no spouse 26.00 (13.75, 37.00) Education level college and below 26.00 (14.25, 39.00) H = 1.549 0.461 undergraduate 26.00 (13.00, 39.00) Master’s and above 22.00 (13.00, 34.75) Family per capita monthly income (yuan) < 8000 26.00 (15.00, 39.00) H = 2.408 0.300 8000–10000 26.00 (14.00, 39.00) > 10,000 26.00 (13.00, 39.00) Number of children 0 26.00 (13.00, 35.00) H = 10.191 0.006 1 26.00 (14.00, 39.00) > 1 26.00 (13.00, 38.00) Living status living with others 26.00 (13.00, 39.00) Z = −0.358 0.720 living alone 26.00 (14.00, 39.00) Department outpatient 26.00 (14.00, 39.00) H = 3.511 0.834 ICU 26.00 (13.00, 39.00) medical ward 26.00 (13.00, 34.00) surgical ward 26.00 (13.00, 37.00) operating room 24.00 (14.00, 37.00) emergency 26.00 (17.00, 35.00) pediatrics/neonatology 26.00 (15.00, 39.00) others 26.00 (13.00, 39.00) Employment status permanent staff with official establishment (bianzhi) 30.00 (17.00, 39.00) H = 5.288 0.071 contract staff 26.00 (13.00, 39.00) agency hired/dispatched staff 19.00 (13.00, 30.50) Professional title staff nurse 26.00 (13.00, 39.00) H = 0.361 0.835 supervisor nurse 26.00 (13.00, 39.00) associate senior nurse/chief nurse 26.50 (14.75, 37.00) Number of night shifts per week 0 26.00 (13.00, 39.00) H = 4.455 0.216 1 26.00 (13.00, 38.00) 2 26.00 (13.00, 39.00) 3 27.00 (18.00, 39.00) Perceived staffing adequacy adequate 24.00 (13.00, 35.00) H = 31.194 < 0.001 moderate 27.00 (17.00, 39.00) inadequate 26.00 (13.00, 41.50) Open in a new tab Spearman correlation analysis was conducted to examine the associations between continuous variables and technophobia among nurses (Table 3 ). The results showed that age ( r = 0.058, p = 0.021) was weakly but significantly positively correlated with technophobia. Perceived workload ( r = 0.194, p < 0.001) and sleep quality ( r = 0.252, p < 0.001) were also positively correlated with technophobia. Job satisfaction ( r = −0.263, p < 0.001) and social support ( r = −0.300, p < 0.001) were negatively correlated with technophobia. TABLE 3. Correlation analysis between continuous variables and technophobia in nurses. Variable Technophobia r p Age 0.058 0.021 Perceived workload 0.194 < 0.001 Job satisfaction −0.263 < 0.001 Sleep quality 0.252 < 0.001 Social support −0.300 < 0.001 Open in a new tab 3.3. Multivariate Analysis of Technophobia in Nurses A GLM was performed to identify factors associated with nurses’ technophobia (Table 4 ). The results indicated that male nurses reported a higher technophobia level than female nurses ( β = 0.201, 95% Cl: 0.054, 0.348, p = 0.007), although the magnitude of this association was modest. Nurses with one child had significantly severe technophobia compared with those without children ( β = 0.130, 95% Cl: 0.037, 0.223, p = 0.006). In addition, living alone was positively associated with technophobia ( β = 0.091, 95% Cl: 0.004, 0.179, p = 0.040), although the effect size was small. Higher perceived workload was positively associated with technophobia ( β = 0.048, 95% Cl: 0.015, 0.081, p = 0.005), whereas higher job satisfaction was negatively associated with technophobia ( β = −0.081, 95% Cl: −0.112, −0.050, p < 0.001). Sleep quality problems were also a significant associated factor of technophobia ( β = 0.015, 95% Cl: 0.009, 0.020, p < 0.001). In addition, greater social support was associated with lower levels of technophobia ( β = −0.003, 95% Cl: −0.005, −0.001, p = 0.008), although the magnitude of this association was very small, suggesting limited practical significance. TABLE 4. Multiple linear regression analysis of technophobia. Variables β estimate with 95% CI p Tolerance VIF Gender (Ref: Female) 0.201 (0.054,0.348) 0.007 0.901 1.110 Marriage (Ref: have no spouse) −0.005 (−0.099, 0.088) 0.912 0.301 3.318 Education level (Ref: college and below) undergraduate 0.033 (−0.079, 0.145) 0.562 0.622 1.608 postgraduate and above −0.098 (−0.300, 0.104) 0.342 0.653 1.532 Number of children (Ref: 0) 1 0.130 (0.037, 0.223) 0.006 0.267 3.751 > 1 0.093 (−0.017, 0.204) 0.096 0.385 2.595 Family per capita monthly income (yuan) (Ref: < 5000) 5000–8000 −0.015 (−0.096, 0.067) 0.721 0.399 2.506 > 8000 −0.020 (−0.100, 0.059) 0.613 0.368 2.717 Living status (Ref: living with others) 0.091 (0.004, 0.179) 0.040 0.489 2.046 Department (Ref: outpatient) ICU −0.111 (−0.255, 0.034) 0.134 0.551 1.814 medical ward −0.014 (−0.127, 0.098) 0.802 0.311 3.219 surgical ward 0.007 (−0.106, 0.119) 0.909 0.275 3.641 operating room −0.010 (−0.188, 0.169) 0.914 0.736 1.359 emergency −0.132 (−0.299, 0.036) 0.123 0.625 1.600 pediatrics/neonatology −0.029 (−0.138, 0.079) 0.594 0.297 3.369 others 0.057 (−0.049, 0.163) 0.294 0.281 3.558 Employment status (Ref: permanent staff with official establishment (bianzhi)) contract staff −0.096 (−0.208, 0.017) 0.096 0.619 1.615 agency hired/dispatched staff −0.256 (−0.625, 0.112) 0.173 0.929 1.076 Professional title (Ref: staff nurse) supervisor nurse −0.034 (−0.099, 0.030) 0.299 0.630 1.588 associate senior nurse/chief nurse −0.038 (−0.354,0.278) 0.814 0.876 1.141 Number of night shifts per week (Ref: 0) 1 −0.011 (−0.077, 0.055) 0.741 0.625 1.600 2 −0.021 (−0.090, 0.048) 0.546 0.536 1.864 3 0.077 (−0.032, 0.185) 0.167 0.804 1.244 Perceived staffing adequacy (Ref: adequate) moderate 0.044 (−0.009, 0.097) 0.102 0.837 1.195 inadequate 0.009 (−0.093, 0.112) 0.857 0.841 1.188 Age 0.000 (−0.006, 0.007) 0.903 0.282 3.544 Perceived workload 0.048 (0.015, 0.081) 0.005 0.774 1.293 Job satisfaction −0.081 (−0.112, −0.050) < 0.001 0.767 1.303 Sleep quality 0.015 (0.009, 0.020) < 0.001 0.790 1.267 Social support −0.003 (−0.005, −0.001) 0.008 0.786 1.272 Open in a new tab Note: β , standardized regression coefficient. Abbreviations: CI, confidence interval; VIF, variance inflation factor. 4. Discussions This study examined the level of technophobia among nurses in China and explored its associated factors. The findings showed that technophobia among nurses was at a lower‐middle level, suggesting that there is still room for improvement in nurses’ adaptation to digital technologies. Several demographic, occupational, and psychosocial factors were associated with technophobia. These findings provide insight into potential factors influencing nurses’ adaptation to rapidly evolving healthcare technologies. The results of this study showed that the median technophobia score of Chinese nurses was 26.00, which was at a lower‐middle level compared with the total score of 65.00 on the scale. This finding differed from the results of the study examining older Chinese individuals [ 15 ] and older Chinese patients with ischemic stroke [ 17 ], where technophobia scores were at the upper‐middle level. The possible reasons for the variation may be partly due to differences in age groups and different education levels. In our study, the median age of the nurses was 37.00 years. Younger individuals are often referred to as “digital natives”; they generally tend to be more comfortable with technology and may have had more exposure to digital technology throughout their education and early career [ 31 ]. In addition, a total of 94.22% of nurses had a bachelor’s degree or higher in this study, which suggests that they may have had more opportunities to engage in technology‐related training and knowledge acquisition during their academic years. Higher educational levels are also typically associated with greater cognitive resources, enabling individuals to process and adapt to new information more effectively [ 32 , 33 ]. Still, technophobia remains an important issue within the nursing population, particularly when confronted with complex medical technologies and information systems. The study revealed that male nurses were more likely to experience higher levels of technophobia compared to their female counterparts. This result is different from some previous studies, such as a study of 176 South African university students, which found no significant association between gender and technophobia [ 34 ]. Kotzé et al. also reported that, women historically exhibited higher levels of anxiety toward technology adoption, but the gender gap has narrowed in recent years [ 35 ]. However, there are also studies that have found that women were more positive than men about the impact of technology on people and their work environments, and women also reflected greater comfort in using computers than men [ 36 ]. These discrepancies may be explained by the differing contexts and populations studied. It is possible that specific professional settings, such as nursing, introduce unique stressors that exacerbate technophobia for male nurses. In many nursing work environments, when technical issues arise—such as system updates, troubleshooting, or the implementation of new technologies—male nurses may be more likely to be expected to assist with managing these issues due to societal expectations or perceived expertise in handling technology [ 37 , 38 ]. Although this possibility was not directly measured in the present study, such expectations might contribute to additional role‐related pressure when new systems or technologies are introduced quickly, without sufficient time for familiarization [ 39 ]. Therefore, nursing managers should consider implementing a more balanced distribution of technical tasks and provide targeted support and training for all nursing staff, regardless of gender, to foster a more inclusive and effective work environment. Family responsibilities, particularly parenthood, may also be associated with technophobia [ 40 ]. In this study, nurses with one child showed more intention to experience significantly higher levels of technophobia compared to their childless counterparts. This is in agreement with the findings of Agota, who uncovered that teleworkers with children experience more negative impacts on their work performance and emotional well‐being compared to those without children [ 40 ]. Nursing is often characterized by shift work and high‐intensity demands, which limit the time available for professional development [ 41 ]. This is particularly true for nurses with children, as their time for learning and adapting to new technologies is further restricted by family responsibilities, exacerbating feelings of inadequacy when confronted with new technologies. Additionally, the dual pressures of parenting and professional duties can deplete cognitive and emotional resources [ 42 ]. Nurses with children may experience increased stress from balancing childcare with the high demands of their work, including mastering new technologies. This emotional burden can diminish their motivation to engage with new technological tools. This finding underscores the need for nursing managers to consider the unique challenges faced by nurses with children when implementing technology adoption strategies. Providing flexible training schedules, childcare support, and targeted resources for these nurses may help alleviate technophobia and promote more effective engagement with new technologies, ultimately improving their professional development. The study also found that living alone is associated with higher levels of technophobia, while higher levels of social support are linked to lower levels of technophobia among nurses. However, the observed effect sizes were relatively small, especially for social support, suggesting that these associations may have limited practical implications. This finding is consistent with previous studies that not living alone and social support represent important means of alleviating technophobia among Chinese older adults [ 15 ]. In nurse populations, the role of social support in alleviating work‐related stress and enhancing work engagement has been well established [ 43 , 44 ], and these are vital prerequisites to learn new technologies. Nursing requires a high level of concentration and precision, and this constant vigilance demands not only technical skills but also significant psychological and emotional focus. Due to the lack of immediate social support, nurses who live alone may experience greater feelings of isolation, which can exacerbate the stress and anxiety related to the adoption of new technologies, particularly in high‐pressure environments like healthcare [ 45 ]. This finding also echoes the stress‐buffering model, which posits that social support can reduce or eliminate the stress reaction [ 46 ]. Furthermore, living alone may limit nurses’ opportunities for collaborative learning, which is often a key aspect of professional development in healthcare settings. Nurses with strong social support networks, whether from family, friends, or colleagues, are more likely to benefit from emotional encouragement [ 47 ]. They are also more likely to have opportunities to share knowledge, discuss challenges, and build confidence in using new technologies through informal social networks. These interactions can enhance nurses’ sense of security and competence when engaging with new technological tools, thereby improving their ability to cope with the demands of new technologies. The study found that heavier workloads are associated with higher levels of technophobia, while higher job satisfaction is linked to lower levels of technophobia among nurses. These findings underscore the significant influence of work‐related factors on nurses’ attitudes toward technology. A recent study indicated that in major general hospitals in China, the nurse‐to‐patient ratio averages 1:8.0 during daytime hours, with each nurse responsible for the care of eight patients, and this ratio increases to 1:23 at night [ 48 ]. Nurses with heavy workloads often experience increased stress and cognitive overload, leaving them with fewer resources—both emotional and cognitive—to engage with new technologies, as new technologies are perceived as an additional burden in an already demanding work environment [ 49 ]. In contrast, job satisfaction is defined as “the pleasurable emotional state resulting from the appraisal of one’s job as achieving or facilitating the achievement of one’s job values [ 50 ]”. Previous studies have shown that job satisfaction mediated the relationship between occupational stressors and burnout among Chinese nurses [ 51 ]. Higher job satisfaction often reflects a supportive work environment, strong social networks, and a sense of accomplishment, all of which can reduce technophobia by enhancing self‐efficacy and fostering a positive attitude toward professional growth [ 52 ]. Nurses who are satisfied with their work environment are more likely to view technological challenges as opportunities for growth, rather than sources of stress; thus, these nurses may have more energy and motivation to engage with new technologies. Insomnia status was an independent factor associated with technophobia. Especially for nurses working long‐term night shifts or rotating shifts, irregular work hours may disrupt sleep patterns and contribute to poor sleep quality [ 53 ]. Poor sleep quality and sleep deprivation may impair cognitive performance, including attention, memory, and emotional regulation, which are important for learning and adapting to new technologies [ 54 ]. Previous studies have shown that cognitive function in nurses significantly declines during consecutive night shifts, reaching levels similar to those of being drunk (> 0.08 blood alcohol content) by the end of a 12‐h shift [ 18 ]. These cognitive challenges may increase the perceived difficulty of using unfamiliar technological systems and thereby contribute to technophobia. Therefore, nursing managers should implement strategies such as organizing shift schedules and ensuring adequate rest and recovery time to help improve nurses’ insomnia [ 55 ], thereby enhancing their ability to learn and adapt to new technologies and reducing technophobia. There are also some limitations in the current study. First, the cross‐sectional design of this study makes it difficult to establish cause‐and‐effect relationships. Second, although the study includes multiple centers, they are all concentrated in authoritative third‐class hospitals within the province, and the findings may not be generalizable to all healthcare institutions, particularly in rural or less technologically advanced areas. Third, data were collected through online questionnaires distributed via WeChat, which may introduce potential self‐report bias, such as social desirability bias or recall bias. Fourth, the Technophobia Scale was originally validated among older adults rather than nurses. However, it has subsequently been applied in several adult populations and demonstrated excellent internal consistency in the present study (Cronbach’s α = 0.984). Moreover, the scale items capture general negative emotional reactions to technology and are not specifically tailored to older adults, suggesting their applicability to broader adult populations. Fifth, the sample consisted predominantly of female nurses (96.99%), which may limit the robustness of gender comparisons. Future studies may consider recruiting a more balanced sample to better explore potential gender differences. Sixth, this study did not assess nurses’ digital literacy or technological competence, which may also influence technophobia and should be considered in future research. Finally, although several factors were statistically associated with technophobia, some associations showed relatively small effect sizes (e.g., social support), which may limit their practical significance. 5. Conclusion This multicenter cross‐sectional study demonstrated that technophobia among nurses in China was at a low‐to‐moderate level. Male nurses and those who have a child or live alone, as well as nurses reporting heavier workloads, poorer sleep quality, lower job satisfaction, and weaker social support, tended to exhibit greater technology‐related fear. These findings highlight the need for nursing managers to create supportive work environments and implement practical interventions such as structured digital literacy training programs, flexible scheduling arrangements for nurses with childcare responsibilities, and peer mentoring initiatives to strengthen nurses’ digital competence, facilitate their adaptation to technological change, and ultimately enhance the quality of patient care. Author Contributions Xuange Sun: methodology, conceptualization, validation, formal analysis, data curation, visualization, writing–original draft, writing–review and editing, and software. Xu Liu: conceptualization, validation, formal analysis, data curation, visualization, writing–original draft, writing–review and editing, and software. Xiaolin Chang: validation, investigation, resources, and writing–review and editing. Na Hu: writing–review and editing. Xiangxiu Qi: investigation, resources, writing–review and editing, and supervision. Xiaofei Li: investigation, resources, writing–review and editing, and supervision. Funding No funding was received for this manuscript. Disclosure The authors reviewed and assume full responsibility for the manuscript’s final version. Ethics Statement Ethical approval for this study was obtained from the Ethics Committee of the First Hospital of China Medical University, number [2025] 782. The study was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study. Consent Please see the Ethics Statement. Conflicts of Interest The authors declare no conflicts of interest. Acknowledgments The authors would like to express their sincere gratitude to all nurses who participated in this study and contributed their time and effort to the data collection process. Generative AI Statement . The authors used DeepSeek (an AI language polishing tool) to improve the manuscript’s clarity. This AI tool was only used for language refinement, having no role in the conceptual work or data interpretation. Sun, Xuange ,
Liu, Xu ,
Chang, Xiaolin ,
Hu, Na ,
Qi, Xiangxiu ,
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