The most consistent assessment of the effectiveness of cancer treatment interventions is the satisfaction of cancer patients with their care (Zimmermann et al., 2008), which is increasingly being included in cancer clinical trials (Lipscomb et al., 2007). Patient satisfaction is a subjective assessment that represents the cognitive and emotional responses caused by the interaction between patients’ expectations of ideal care and perceived actual care (Makhlouf et al., 2019). It is the most recognized and widely used medical services outcome item (Ong, 2000) and an important indicator to measure the quality of medical care (Donabedian, 1988). Measuring patient satisfaction also provides actionable insights for improving the healthcare system (Fontecha et al., 2019; Chung et al., 2020).

Cancer patient satisfaction is closely related to nursing care. Compared with other medical staff, nurses have the closest and longest contact with patients (Kim-Soon et al., 2017), and their nursing performance has an important influence on cancer patient satisfaction. Studies have shown that nurses’ performance and attitudes toward patients are related to their psychological capital. As a strategic resource, psychological capital has received increasing attention (Ardichvili, 2011). It includes self-efficacy, hope, resilience, and optimism (Newman et al., 2014; Luthans et al., 2015). Nurses’ psychological capital has a positive impact on the nursing performance of oncology nurses (Jung, 2019) and is conducive to improving the satisfaction of cancer patients. Specifically, when nurses have low psychological capital, they are more prone to empathy fatigue (Spence Laschinger and Nosko, 2015), work stress (Yin et al., 2018), and burnout (Gómez-Urquiza et al., 2017). These phenomena lead to negative states such as emotional exhaustion and decreased work enthusiasm (Aiken et al., 2002), which is not conducive to providing high-quality nursing services for patients. In contrast, high psychological capital is the motivation for nurses to work efficiently (Luthans et al., 2007), it significantly reduces burnout of nurses (Zhou et al., 2018) and promotes them to maintain a lasting work enthusiasm (Ren et al., 2021). Nurses with high psychological capital have stronger job embeddedness (Sun et al., 2012) and higher professional identity (Qiu et al., 2019). It can effectively improve nursing competence, achieve a high-quality nursing service supply, and ultimately improve patient satisfaction with medical services.

Most studies have examined the correlation between a single dimension of nurses’ psychological capital and patient satisfaction. For example, there is a positive correlation between the self-efficacy of nurses and patient satisfaction. Nurses need to have a high sense of self-efficacy to provide high-quality care for palliative care patients (Kurnia et al., 2018) and thus achieve high patient satisfaction (Aspinal et al., 2003). The emotional resilience of nurses is also positively correlated with patient satisfaction in hospice care, and patients cared for by high-resilient nurses tend to have higher satisfaction (Son and Park, 2021). In addition, optimistic nurses have a positive impact on patients. The optimism of nurses contributes to their work performance and increases patient satisfaction (Luthans et al., 2008). To comprehensively reflect the psychological state of nurses, this study selects four dimensions of self-efficacy, hope, resilience, and optimism to explore the relationship between the psychological capital of nurses and the satisfaction of cancer patients. Furthermore, there is a relative lack of research on elderly cancer patients in the cancer patient satisfaction literature. Elderly cancer patients have more intentions and opportunities to communicate with nurses, they are more sensitive to the emotions of nurses and more likely to be influenced by the nurses’ psychological capital. Accordingly, the following hypothesis is proposed.

Work engagement is a positive, fulfilling, and work-related psychological state, it consists of three aspects: dedication, focus, and vitality (Schaufeli and Bakker, 2004; Schaufeli et al., 2006). There are many factors affecting work engagement, including leadership style (Decuypere and Schaufeli, 2020), organizational climate (Rožman and Štrukelj, 2021), and diverse HR practices (Luu et al., 2019) at the organizational level. Psychological flexibility (Holmberg et al., 2020), proactive personality (Hu et al., 2021), self-efficacy, and resilience (Bakker and van Wingerden, 2021) at the individual level. According to the JD-R model, both personal and job resources can have a significant impact on work engagement (Bakker and Demerouti, 2017). Employees will generate work engagement if they have sufficient resources to handle job demands (Bakker and Demerouti, 2008). Psychological capital is a collection of positive psychological resources (Thompson et al., 2015), including hope, efficacy, resilience, and optimism (Luthans et al., 2015). Psychological capital has proved to be an effective personal resource (Newman et al., 2014) that can help employees maintain work engagement (Simons and Buitendach, 2013; Gupta et al., 2017).

Psychological capital has a positive impact on work attitudes, psychological state, and performance of employees (Peterson et al., 2011; Adil and Kamal, 2016). It is also a significant predictor of work engagement (Joo and Lee, 2017). Studies have found that employees with higher psychological capital are more likely to have positive emotions, which helps to increase their vitality, focus, and dedication at work, and thus generates higher work engagement (Gupta et al., 2017; Karatepe and Avci, 2017). In addition, psychological capital is considered a positive resource for coping with negative outcomes such as burnout and workplace violence (Sui et al., 2019). When facing difficult clinical problems, nurses with high psychological capital can immediately sense the positive personal resources they possess, and usually will not give up or be frustrated easily. They will actively think about solutions to problems and maintain the ambition to accomplish their goals (Yang et al., 2020), generating high work engagement. Relevant studies have also confirmed that nurses’ psychological capital is positively associated with their work engagement (Pan et al., 2017; Wang et al., 2017). Accordingly, the following hypothesis is proposed.

Patient satisfaction is a useful performance indicator for healthcare providers (Heidegger et al., 2006; Jha et al., 2008). In nursing practice, the work engagement of nurses has strategic importance. Because as the largest group of healthcare providers, the work engagement of nurses may affect the quality of care, work performance, patient satisfaction, and even patient safety (Keyko et al., 2016; Perreira et al., 2018). Work engagement is the prerequisite for positive work behaviors. Nurses with high work engagement have a strong sense of initiative that facilitates optimal nursing care (Yongxing et al., 2017; Lisbona et al., 2018). Highly engaged nurses can proactively respond to patient needs and improve patient satisfaction with care (Bacon and Mark, 2009). At the same time, the work engagement of nurses tends to be positively correlated with their job satisfaction, which is positively related to patient satisfaction (Mrayyan, 2006; Ferrara et al., 2013).

Dedicated nurses tend to have positive emotions and are more likely to have work-related pleasure, which will increase their occupational health (Gandhi et al., 2014; Khamisa et al., 2015). This state of well-being promotes successful performance (Ferrara et al., 2013) and positively affects patients’ perceptions of nursing quality (Papastavrou et al., 2014). Research has found that nurses with high work engagement are often energetic, they can respond effectively to job demands and have more efficient performance, which has a positive impact on patient satisfaction (Schaufeli et al., 2006). High levels of work engagement among nurses are associated with positive quality of care outcomes, including fewer medical errors (Tsiga et al., 2017), more professional cognition (Cao et al., 2020), and higher patient satisfaction (De Simone et al., 2018). Conversely, when nurses’ work engagement is low, they typically exhibit low morale, emotional exhaustion, and a lack of enthusiasm for their work. It will negatively affect patient experience and reduce the quality of care and patient satisfaction (Willard-Grace et al., 2021). Studies have explored the influencing factors of cancer patient satisfaction from the aspects of treatment outcome, hospital environment, medical equipment, nursing technology (Wiggers et al., 1990), and nursing accessibility (Sitzia and Wood, 1998). Few scholars have directly discussed the impact of nurses’ work engagement on patient satisfaction, and research on elderly cancer patients is rarer. Elderly cancer patients are a special patient group, their satisfaction studies have similarities with ordinary patients. Thus, this study proposes the hypothesis.

According to the analysis of hypothesis 2, nurses with high psychological capital will have positive work effects and can positively influence their work engagement. Based on the analysis of hypothesis 3, nurses’ work engagement has an important influence on their performance. Nurses with higher work engagement can give full play to their work potential and initiative, which can improve the quality of care and patient satisfaction. In summary, the following hypothesis is proposed.

Job resources refer to the physical, psychological, social, and organizational resources owned by employees in the work environment. These resources usually have functions of achieving work goals, reducing physical and psychological exertion associated with job demands, and motivating individual growth, learning, and development (Demerouti et al., 2001; Hobfoll, 2002). Common job resources include job control, performance feedback, support from leaders and colleagues, job autonomy, and career development opportunities (Schaufeli and Bakker, 2004; Bakker and Demerouti, 2007). The JD-R model suggests that job resources play an important role in initiating the motivational process. Individuals with sufficient job resources are more likely to be motivated to complete high-demand tasks, generate higher work engagement, and improve work performance (Bakker and Demerouti, 2007; Bakker and Demerouti, 2008). Job resources can stimulate individuals from both internal and external aspects to achieve positive outcomes. As an internal motivation factor, job resources can meet the basic needs of people and promote the growth and development of employees. As an external incentive factor, job resources can help employees to enhance their core self-concept and improve their well-being, which promotes their efforts to fulfill job demands and job roles and ultimately achieve job goals (Schaufeli and Bakker, 2004; Bakker and Demerouti, 2017).

According to the JD-R model, translating job demands into work engagement requires certain job and personal resources (Bakker and Demerouti, 2007). The work of nurses often includes stressful and difficult situations, it also has high emotional requirements. Therefore, to encourage high work engagement, nurses as boundary crossers in healthcare organizations need to be supported by more job and personal resources (Othman and Nasurdin, 2013). Nurses with high psychological capital tend to have more positive psychological states. When they have rich job resources at the same time, they are more likely to feel that work goals are highly consistent with their own. As a result, these nurses are intrinsically and extrinsically motivated to be more focused on their goals and drive themselves to generate greater work engagement (Xanthopoulou et al., 2009). When nurses are engaged in nursing services, they invest personal resources such as psychological capital. If they can get support from superiors and colleagues, and enjoy job autonomy, job control, and other job resources, the personal resources of nurses will be supplemented and their professional ability will be developed (Akkermans et al., 2013), which will generate stronger engagement. Conversely, when nurses invest inherent personal resources in their work but do not receive the appropriate job resources as supplements, they will reduce the supply of personal resources to prevent the threat of resource depletion due to deprivation of their resources (Ito and Brotheridge, 2003), thus decreasing their work engagement. In summary, the following hypothesis is proposed.

In the present study, the questionnaire survey was conducted in a tertiary first-class hospital in central China, which mainly focuses on oncology treatment. The hospital integrates medical treatment, research, teaching, prevention, and rehabilitation, it is a research center for oncology prevention and treatment in central China and has received 1.26 million inpatients. Patients are admitted from all provinces in China, with a wide geographical distribution and a representative sample. The study consisted of two groups: elderly cancer patients and their care nurses. The inclusion criteria for elderly cancer patients are as follows: (1) over 60 years old; (2) cancer diagnosed between July 2020 and July 2022 by pathological examination and combined with clinical signs and symptoms; (3) able to communicate effectively; and (4) voluntarily accepting the questionnaire survey. Exclusion criteria are as follows: (1) patients with visual or hearing impairment and (2) patients with other serious comorbidities (severe heart disease, etc.) that prevented them from participating in this study. The inclusion criteria for nurses are as follows: (1) have a professional qualification of nurse and within the valid registration period; (2) continuous work in the current department for more than 6 months; and (3) voluntarily participating in this study. Exclusion criteria are as follows: (1) nurses in training, or not working due to sickness or maternity leave and (2) advanced training, internship, or temporary nurses.

Investigators were uniformly trained before the formal survey to ensure that their instructional language was standardized and explanations were clear. With the consent of the nursing departments of the survey hospitals, paper questionnaires were first distributed to several patients and nurses in May 2022 for pre-survey. Based on the feedback from the pre-survey, the questionnaire was revised to ensure that there was no ambiguity in context and sentences. A formal study was conducted in July 2022 in the form of a paper questionnaire. Following the principle of informed consent, elderly cancer patients and nurses who met the sample selection criteria were surveyed. The investigator explained the purpose, significance, and method of completion of the study before filling out the questionnaire. Due to the age and knowledge limitations of the elderly cancer patient, the investigator explained the content of the questionnaire in detail and then answered the questions raised by the respondents exactly and objectively before the survey. In this study, nurses recovered 279 questionnaires and patients recovered 293 questionnaires. Two hundred and sixty-two matching questionnaires were finally obtained after screening, and invalid questionnaires with many missing values were deleted. Two hundred and thirty valid questionnaires were retained in total, with a valid return rate of 87.79%.

The partial least squares structural equation modeling (PLS-SEM) was conducted to address the research questions. The reasons are as follows: first, the sample in this study included both nurses and patients, and each nurse was required to be matched with a patient they care for. This resulted in a small effective sample size, whereas PLS-SEM has higher inclusiveness for sample size (Ringle et al., 2012). Secondly, the sample data in this study tended to be distributed non-normally, and PLS-SEM does not force the data to obey a normal distribution and handles non-normal distributions relatively well. Finally, PLS-SEM can better test the interaction effects among continuous variables as proposed in the theoretical model (Hair et al., 2017), such as the test of moderating effects in this study.

Data analysis was mainly completed by SPSS 26.0 and SmartPLS 3.3.9 software. SPSS was used for sample descriptive statistics and Harman’s one-factor test of common method bias test. The analysis in SmartPLS 3.3.9 was conducted through two stages (Barclay et al., 1995). The analysis of the measurement model tested the reliability and validity of all latent construct measurements. The analysis of the structural model assessed the relationships among the latent constructs for hypothesis testing.

The demographic information of the participants is shown in Table 1. Most nurses are female (99.57%), and only one male nurse is interviewed. In terms of age, 23.48% of nurses are 30 years old or below, and 42.17% are 31–35 years old. Most nurses have a bachelor’s degree (91.74%) and are married (80.43%). Regarding tenure, 33.04% of nurses are 6–10 years and 21.74% are 11–15 years. The majority of the nurses are staff nurses (70.87%). In terms of title, 24.35% are nurse practitioners and 59.13% are nurse-in-charge. 83.48% of nurses have a monthly income of 7,000 yuan or above. Among the patient samples, 68.26% of patients are female and 31.74% are male. Regarding age, 57.39% of patients are 60–64 years old and 37.39% are 65–69 years old. Most patients have elementary school education or below (81.30%), get married (97.83%), and are not currently involved in work (88.26%). In terms of tumor stage, 35.65% of patients are diagnosed with UICC stage II and 26.52% are diagnosed with UICC stage III.

Since the three key variables of psychological capital, work engagement, and job resources were self-reported by nurses, the relationships between variables were inevitably subject to common method bias, and statistical tests for common method bias needed to be conducted. In Harman’s one-factor test (Podsakoff et al., 2003), all items of psychological capital, work engagement, and job resources of nurses were subjected to the unrotated principal component analysis. It was found that the first principal component explained only 35.879% of the total variance, which did not exceed the recommended value of 40%.

In addition, a common method factor was further added to the PLS environment for testing based on previous studies. According to the standardization process of Liang et al. (2007), a common method factor containing all latent variable indicators was added to the model. The presence of severe common method bias can be determined by comparing the variance of the actual factor loadings and the method factor loadings for each item. The results in Table 2 showed that the mean–variance of the actual factor loadings of the measurement items was 0.651, which was much larger than the mean–variance of the method factor loadings of 0.014. In summary, the common method bias is not a severe problem in this study.

The validity of the measurement model was evaluated by testing the reliability and validity of the measurement instrument. As shown in Table 3, in terms of reliability, Cronbach’s alpha coefficient and the composite reliability of each construct are above 0.9, which exceeds the 0.70 threshold, thus confirming the reliability of the measurement instrument. Regarding validity, convergent validity is examined by the factor loadings for each item and the average variance extracted (AVE) of the constructs. The results in Table 3 suggest that the factor loadings (range 0.717–0.930) of each item reach the 0.70 threshold (Hair et al., 2019), and the AVE of each construct is above 0.6, exceeding the acceptable threshold value of 0.50 (Fornell and Larcker, 1981). It indicates that the measurement instrument has sufficient convergent validity. Finally, the discriminant validity is tested based on the Fornell-Larcker criterion and the heterotrait-monotrait (HTMT) ratio of correlations. As given in Table 4, the correlation coefficients between the constructs are less than the square root of the AVE (the value on the diagonal). According to the Fornell-Larcker criterion (Fornell and Larcker, 1981), there is good discriminant validity between the constructs. Furthermore, in Table 5, the heterotrait-monotrait (HTMT) ratio of the correlation are below the 0.90 threshold (Hair et al., 2017), which also verifies the discriminant validity. In summary, the reliability and validity of the measurement instrument are satisfactory and can be used for subsequent structural model analysis.

In the evaluation of the structural model, the bootstrap method based on deviation correction was used to calculate standard errors and significance (Hair et al., 2017). The sample size was set to 5,000, and 95% CI was obtained. The path coefficient test of the structural model is shown in Figure 2 and Table 6. The results indicate that nurses’ psychological capital has a significant positive effect on patient satisfaction (β = 0.447, t = 8.629, p < 0.05) and their work engagement (β = 0.421, t = 7.061, p < 0.05). Therefore, H1 and H2 are supported. In addition, the work engagement of nurses positively influences patient satisfaction (β = 0.417, t = 7.954, p < 0.05), and H3 is supported.

The Sobel test and variance accounted for (VAF) index were employed to examine the mediating effect (Sobel, 1982). As shown in Table 6, the Sobel test indicates that nurses’ work engagement plays a mediating role between nurses’ psychological capital and patient satisfaction (Z = 5.281, p < 0.05). The strength of the indirect effect relative to the total effect is determined by the variance accounted for (VAF) index. The criteria for mediating effect are as follows: VAF > 80% is complete mediation, 20% ≤ VAF ≤ 80% is partial mediation, and VAF < 20% has no mediation (Hair et al., 2021). The VAF for nurses’ work engagement is 28.203%. Therefore, nurses’ work engagement plays a partial mediation role between their psychological capital and patient satisfaction, and H4 is supported.

To test the moderating effect, nurses’ psychological capital was set as the predictor variable and job resources as the moderating variable in the PLS-SEM, and the interaction term was constructed. The significance of the interaction term is used as the criterion to determine the existence of moderating effect. As shown in Table 7, the interaction of nurses’ psychological capital and job resources (β = 0.396, t = 3.008, p < 0.05) has a significant positive effect on work engagement. It implies that nurses’ job resources play a positive moderating role in the relationship between psychological capital and the work engagement of nurses.

To reveal the moderating effect of nurses’ job resources more clearly, the moderating effect diagram is plotted (Figure 3). The simple slope test is conducted for the moderating effect of nurses’ job resources. The analysis results show that for nurses with high job resources, their psychological capital has a significant positive effect on work engagement (β = 0.817, p < 0.05). For nurses with low job resources, their psychological capital has a relatively small effect on work engagement (β = 0.025, p < 0.05). Thus, H5 is supported.

The measurement model was evaluated by calculating R², effect sizes (f²), and Q². As shown in Table 8, R² of the endogenous constructs are examined to assess the explanatory power of the model. The R² of nurses’ work engagement and patient satisfaction are 0.609 and 0.315, respectively, thus, the model is moderately strong (Chin, 1998). The f² is used to evaluate the effect size of each predictor construct, which according to Cohen (2013) f² values of 0.35, 0.15, and 0.02 are considered large, medium, and small. The effect sizes of nurses’ psychological capital and job resources on work engagement are 0.247 and 0.020. It indicates that the predictive contribution of nurses’ psychological capital to work engagement is at a medium level, while the predictive effect size of job resources on work engagement is small. The effect sizes of nurses’ psychological capital and work engagement on patient satisfaction are 0.400 and 0.345, which indicates a large predictive contribution of nurses’ psychological capital to patient satisfaction and a medium predictive contribution of nurses’ work engagement to patient satisfaction. To further assess the predictive capability of the model, Q² values are calculated. The Q² for the endogenous constructs nurses’ work engagement and patient satisfaction are 0.217 and 0.468. Because both of the values are greater than 0, it indicates that the model has an acceptable predictive relevance (Peng and Lai, 2012).

Patient satisfaction is an important performance indicator for nurses, and measuring satisfaction levels also provides the basis for improving healthcare systems (Chung et al., 2020). This study explores the mechanism of nurses’ psychological capital on the satisfaction of elderly cancer patients. It provides practical implications for healthcare organizations to carry out effective medical staff management and obtain competitive advantages.

Firstly, healthcare organizations should realize that when nurses have positive psychological capital, elderly cancer patients will receive higher-quality medical services. Therefore, the psychological capital of nurses can be an important means to enhance the competitive advantage of healthcare organizations. As psychological capital is learnable, it can be measured, developed, and managed effectively, and it is relatively lasting and stable after being formed. Through targeted training, healthcare organizations can develop and manage the psychological capital of nurses to obtain high patient satisfaction and ultimately enhance their competitive advantage. The specific measures include: first, the physical and mental health of nurses should be put in the first place in healthcare organization management to ensure the development of positive psychological capital. Second, cultivate nurses’ positive expectations of job performance, career development and role orientation by formulating reasonable career development goals, and career planning. Third, healthcare organizations should fully endow nurses with work autonomy to cultivate their professional self-confidence and optimism. Fourth, it is necessary to build a learning and development platform for nurses to improve their self-efficacy.

Secondly, healthcare organizations can encourage nurses to make reasonable use of psychological capital to improve work engagement, to achieve good work performance. Healthcare organizations should fully investigate and understand the psychological capital of nurses and take personalized intervention measures to help nurses develop positive psychological capital. Intervention measures can be provided through traditional classroom training, video discussion, self-centered exercise, and reading intervention. By learning how to enhance self-efficacy, hope, optimism, and resilience, nurses can more effectively protect, increase, and maintain psychological capital, which translates into a series of positive outcomes such as engagement. In addition to training, healthcare organizations should take psychological capital as an important assessment indicator when recruiting medical staff. It should select and hire employees with high psychological capital characteristics so that they can quickly put into work and achieve high performance.

Finally, healthcare organizations should be aware of the importance of providing sufficient work resources for nurses, it can maximize the role of nurses’ resources and promote them to produce high work engagement. Nurses have a huge workload and high job demands. It is difficult for nurses to efficiently complete work objectives only by relying on their resources. Healthcare organizations need to provide supporting job resources such as work autonomy, career development opportunities, and social support to help nurses perform their duties. With the effective supply of these job resources, the personal psychological capital of nurses can be strengthened, so that nurses can respond to high-intensity nursing work with a positive attitude. It can also help nurses alleviate the negative impact of work pressure and be competent for higher job demands. Healthcare organizations can use job resources to effectively stimulate the work enthusiasm, initiative, and potential of nurses, which will improve their work engagement and achieve personal and organizational goals.

There are three shortcomings in this study that can be addressed in future studies. Firstly, we collected data on nurses’ psychological capital, job resources, work engagement, and patient satisfaction by questionnaire at the same time point. Although this method has good external validity, the cross-sectional study design has relatively insufficient internal validity and does not allow us to explore whether the relationship between nurses’ psychological capital and the satisfaction of elderly cancer patients has changed over time. Therefore, to further confirm the relationship between the variables revealed in this study, future studies can incorporate experimental methods and longitudinal follow-up surveys to enhance the validity of the study and the findings. Secondly, for sampling convenience, the sample of this study was sourced from a tertiary first-class hospital in China. Although the findings were consistent with theoretical predictions, the generalizability of the findings was limited and the applicability of the results of this study to other countries needs to be further tested. Therefore, future studies can be conducted in different countries or cultural contexts to verify the generalizability of the study. Finally, this study focused on the impact of nurses’ psychological capital on patient satisfaction at the individual level. Some scholars recently have extended psychological capital to the collective and team level, they borrowed the concept of an agency vehicle and found that psychological capital at the collective level can act as a mediator between team behavior and team performance. In conjunction with current trends in multi-level and cross-level research, future research can examine the impact of the collective psychological capital of nurses on patient satisfaction.