The effect of developmental electronic performance monitoring on employee innovative behavior

Abstract

In the context of the digital era, many enterprises have invested in the wave of digital transformation. Electronic performance monitoring (EPM), as an effective digital management tool for enterprise digital transformation, profoundly influences the innovative behavior of employees while improving enterprise management efficiency. Nowadays, an increasing number of organizations are actively adopting employee performance management systems to motivate employees and achieve organizational performance goals, fostering high-quality development. However, the influencing mechanism on how electronic performance monitoring affects organizational employee innovation is unclear. Based on the social exchange theory, this study conducted empirical analyses on 265 questionnaire surveys collected online from employees of Chinese enterprises in the financial, Internet, and manufacturing industries using SPSS 27.0 and MPLUS 8.3 software. Hierarchical regression analysis and the Bootstrap method were employed to verify the mechanisms through which developmental electronic performance monitoring influences employee innovative behavior. The study finds that: developmental electronic performance monitoring positively impacts employee innovative behavior. Leader-member exchange partially mediates the relationship between developmental electronic performance monitoring and employee innovative behavior. Power distance positively moderates the relationship between leader-member exchange and employee innovative behavior. The findings of this study expand the existing research on the influence mechanism of developmental electronic performance monitoring on employee innovative behavior and offer practical implications for enterprises aiming to effectively utilize electronic performance monitoring to foster employee innovation.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-12326-7.

Introduction

In the digital economy era, innovation is the key to maintaining competitive advantage and achieving high-quality development for enterprises. Employees are the primary agents through which enterprises implement innovation¹. Actively promoting employees’ innovative behaviors is of great significance to the high-quality development of enterprises². Consequently, this has emerged as a focal research topic in academia. Concurrently, the development of the digital economy has become an unstoppable force. According to the Global Digital Economy White Paper (2024), the aggregate digital economy of five countries, including China, surpassed 33 trillion dollars in 2023, with a year-on-year growth rate exceeding 8%. The digital economy accounted for 60% of GDP. The vigorous growth of the digital economy continues to drive the empowerment of organizations through digital technologies. Digital technologies such as big data, cloud computing, LOT, blockchain, and artificial intelligence are widely applied in the workplace. Enterprises are increasingly engaging in digital transformation to empower high-quality development through digitalization. With the advancement of digital technologies, electronic performance monitoring (EPM) has become widely adopted in the workplace. Companies utilize a variety of intelligent sensing devices, such as facial recognition, fingerprint attendance systems, video surveillance, email tracking, and smart desks and chairs, to obtain real-time data on employee information. EPM, as an effective tool and inevitable choice for enterprises undergoing digital transformation, and as a core component of digital human resource management, plays a significant role in driving continuous innovation and thereby achieving high-quality development within enterprises³. EPM systems are capable of continuously and effectively evaluating employees’ work performance and behavioral information, and further influencing employees’ innovative performance in the digital workplace⁴. However, research that deeply explains the relationship between EPM and organizational employee behavior from both theoretical and empirical perspectives remains limited. This gap hampers the effective application of EPM in enterprises and constrains the effectiveness of digital management. Against this backdrop, how to effectively utilize EPM has emerged as a key challenge for enterprises. Therefore, investigating how to leverage EPM to stimulate employees’ innovative behavior holds significant practical importance.

Employee innovative behavior refers to the actions taken by employees in generating innovative ideas or solutions to problems and subsequently implementing them. While the extant research on the drivers of employee innovative behavior is relatively extensive, it has predominantly focused on individual-level and organizational-level factors such as personal traits, AI perception, digital technology requirements and different leadership styles^5–8. However, these studies have largely overlooked the impact of organizational management practices under the digital context, such as EPM, on employee innovation.

EPM is a digital management approach that uses technology to observe, record, analyze, and report information about employee job performance⁹. Prior research has established that electronic performance monitoring can influence employee work performance, affective commitment and proactive behavior^10–14. However, studies on how it affects employee innovative behavior remain exceedingly scarce and are still subject to debate. On one hand, some scholars contend that clear performance indicators and timely feedback can stimulate employee motivation⁴ thereby fostering the emergence of innovative thinking; on the other hand, others argue that excessive monitoring may result in heightened employee stress and diminished autonomy¹² ultimately suppressing the potential for innovation. The ultimate outcome hinges significantly on the purpose of electronic performance monitoring used by enterprises and the actual perception of employees. Wells et al.¹¹ classify electronic performance monitoring into two dimensions based on employee-perceived purposes: preventive and developmental.

The primary purpose of preventive electronic performance monitoring (PEPM) is to effectively prevent behaviors that may negatively impact the organization through its deterrent and cautionary effects. Developmental electronic performance monitoring (DEPM) aims to provide constructive feedback to employees, helping them improve performance and enhance skill levels. Studies have indicated that when employees perceive the preventive purpose of organizational use of EPM, they experience increased work pressure and decreased job satisfaction, along with other negative emotional attitudes^4,10. Conversely, when employees perceive the use of EPM as a means to promote their personal growth and career development, it fosters positive emotional tendencies and work attitudes, such as enhanced self-efficacy, job satisfaction, and trust in management^15,16. These positive attitudes can, in turn, encourage employees to engage in proactive innovation. However, since innovation is inherently a high-risk behavior and failure is inevitable during the innovation process, the use of PEPM by organizations may exacerbate employee work pressure, which is not conducive to the conduct of innovation activities. In contrast, the adoption of DEPM by organizations can create a positive atmosphere and provide constructive feedback and improvement suggestions, thereby facilitating proactive employee innovation. Hence, to better stimulate employee innovation, adopting DEPM would be a more favorable choice. This study aims to further explain the mechanisms through which DEPM affects employee innovative behavior.

Based on the social exchange theory, friendly interaction between the organization and employees facilitates the establishment of social exchange relationships that can further promote positive attitudes and work behaviors¹⁷. Drawing on the reciprocity perspective, Eisenberger et al.¹⁸ posited that employees can perceive organizational intentions from the established systems and procedures, and attribute these intentions based on their own situational contexts. When organizations implement beneficial systems and procedures, employees tend to attribute these actions to genuine organizational concern for and commitment to meeting their needs. In return for such perceived support, employees are motivated to engage actively in their work and take actions that are beneficial or valuable to the organization. Leader-member exchange (LMX) refers to the social exchange between leaders and members, reflecting the quality of the relationship between them. when employees perceive that EPM can effectively enhance their technical capabilities and knowledge levels, they are inclined to establish high-quality interactions with the organization and their supervisors. This, in turn, fosters the development of high-quality LMX relationships. Employees are motivated to maintain this good relationship and will actively engage in innovative behaviors in return to the organization. As a result, this study proposes that LMX serves as a potential mediating mechanism through which DEPM influences employee innovative behavior.

In addition, power distance, as a highly regarded cultural value, is closely related to the social exchange process between leaders and employees. It can influence employees’ perceptions of and reactions to their superiors¹⁹. At the individual level, power distance refers to employees’ perceptions of status and power distribution within an organization. Employees with different levels of power distance orientation may interpret organizational contexts and leadership behaviors differently, thereby eliciting divergent behavioral responses. As an important situational variable in organizational management for predicting employee behavior, the impact of power distance on employee innovative behavior warrants further investigation. Therefore, this study will further examine the moderating role of power distance orientation in the relationship between LMX and employee innovative behavior.

In summary, this study, grounded in social exchange theory, integrates DEPM, LMX, power distance, and employee innovative behavior into a cohesive research framework. It aims to thoroughly investigate the underlying mechanisms through which DEPM influences employee innovative behavior. In this study, empirical research was used to collect data by distributing questionnaires to the employees of the company online and analyzing the data using SPSS 27.0 and MPLUS 8.3 software to test the research hypotheses. This study not only enriches the research on the antecedents of employees’ innovative behavior, but also expands the research on the positive effect results of EPM, which fills the gap of existing research. Furthermore, in practice, it also provides guidance reference for enterprises to effectively utilize EPM to promote employee innovation, and then promote enterprises to achieve high-quality development.

Theoretical analysis and research hypothesis

EPM and employee innovative behavior

EPM as a digital management method can effectively assess the information related to employees’ performance¹⁴. EPM implemented for the purpose of employee development can signal to employees that the organization attaches great importance to their personal growth and career development³. DEPM aims to provide employees with constructive performance feedback, identify strengths, weaknesses, and training needs, and ultimately assist them in acquiring new skills and improve their performance levels over time¹². Indeed, innovation is a challenging and high-risk endeavor. Its successful realization hinges not only on individual employees’ comprehensive knowledge, superior abilities, and strong intrinsic motivation but also necessitates adequate resources to support and ensure its viability. Research has also shown that adequate material and psychological resources are more likely to promote innovative behavior²⁰.

DEPM provides employees with diverse organizational resources and potentially facilitates the construction of a social exchange relationship between the employee and the organization, building a bridge between the two parties based on care and trust, and its signals to employees that the organization cares for and trusts them, so that the employees perceive that they are valued²¹. In the case of sufficient resources, employees are more relaxed when facing work challenges, have the courage to explore the unknown, dare to take risks, and then continue to make new attempts and show more innovative behaviors²². On the one hand, DEPM can identify employees’ strengths, weaknesses, and training needs, thereby assisting organizations in conducting diversified training programs, such as online seminars and on-site simulations, to equip employees with the skills and knowledge necessary for their career development. This, in turn, enhances their working abilities and lays the foundation for innovative behaviors. On the other hand, DEPM provides employees with detailed, specific, and meaningful performance feedback, which aids them in understanding their own performance and future work direction. This feedback effectively reduces the uncertainty and ambiguity associated with their roles in the workplace, thereby alleviating psychological pressure. Such a positive atmosphere can significantly stimulate employees’ innovation potential, encouraging them to try new methods and engage in more innovative behaviors.

Jeske et al.²³ found that the training and information feedback provided by organizations to their employees through DEPM can enhance employees’ self-efficacy, so that they have the confidence to overcome challenges at work and take the initiative to innovate²⁴. DEPM can significantly enhance employees’ work effectiveness, and through material rewards and psychological incentives, it strengthens the close connection between employees and the organization, and builds a high-quality interactive relationship based on trust and respect. According to the social exchange theory, employees will spontaneously seek to innovate in their work in order to return to the organization for the motivation to maintain this relationship. In summary, the analysis shows that DEPM helps to promote employees’ innovative behaviors. Thus, this study proposes the following hypothesis:

Hypothesis 1: DEPM positively affects employee innovative behavior.

DEPM and LMX

LMX refers to the social exchange between leaders and employees, reflecting the quality of the relationship between leaders and members²⁵. DEPM can motivate employees to actively engage with and comprehend organizational goals by furnishing constructive feedback and offering comprehensive training. This, in turn, fosters a perception among employees that they are trusted and valued by the organization, thereby enhancing their trust in their leaders and cultivating a high-quality LMX relationship. Firstly, the constructive feedback imparted by leaders through DEPM facilitates employees’ understanding of their roles within the organizational hierarchy. This clarification effectively mitigates role ambiguity between supervisors and subordinates, delineating the responsibilities of both parties and reducing intra-organizational conflicts that may arise from such ambiguity. Consequently, the quality of the relationship between employees and their leaders is enhanced. Secondly, the organization’s provision of diversified training programs constitutes a pivotal supportive resource that aids employees in enhancing their professional competencies and work capabilities. Recognizing the benefits derived from these training initiatives, employees are incentivized to actively maintain and enhance the quality of their relationships with their supervisors, with the aim of accessing additional valuable resources and opportunities for personal and professional growth. Ahmed et al.²¹ found that employees who perceive the developmental purpose of EPM, or who are made aware of the positive intentions of using EPM through a thorough explanation by the leader, can increase trust in the leader through increased perceptions of interpersonal fairness and information fairness¹⁰. And trust helps both parties to establish a high quality LMX relationship. Tomczak et al.²⁶ also pointed out that the developmental purpose of EPM can positively influence employees’ perceptions of relationship quality. In summary, when leaders communicate the developmental purpose of using EPM to their employees, employees’ trust in their leaders is enhanced and the quality of the relationship between the two parties is higher. Thus, this study proposes the following hypotheses:

Hypotheses 2: DEPM positively affects the LMX relationship.

LMX and employee innovative behavior

It has been demonstrated that high-quality LMX enables members to perceive greater cultivation, trust, respect, empowerment, and valuable resources from their leaders^27,28. These resources can stimulate the inherent potential of employees and help them to carry out innovative activities. Moreover, positive social interaction between leaders and employees enhances cognitive thinking and flexibility, further stimulating creativity²⁹. A high-quality LMX not only affords leaders the opportunity to provide heightened support to their subordinates but also bestows empowerment and encouragement. This makes subordinates feel valued and appreciated, thereby enhancing their job satisfaction and proactivity³⁰. Furthermore, encouragement and support from leaders significantly enhance employees’ sense of self-worth and feeling of being valued. In such an environment, employees’ sense of belonging and identification with the organization is strengthened, leading them to be more inclined to engage in innovative attempts. When employees receive high-quality support from their leaders in the form of LMX, they feel more confident and exhibit positive behaviors and attitudes, i.e., creativity, work engagement, innovative performance and innovative behaviors^31–34. Besides, the trust, concern, and continuous encouragement demonstrated by leaders towards their employees foster a harmonious and cordial workplace atmosphere. When subordinates are able to interact with their leaders in a relatively casual environment, they benefit more from their supervisors’ encouragement and exhibit higher levels of innovative behavior. Thus, this study proposes the following hypothesis:

Hypothesis 3:LMX positively affects employee innovative behavior.

The mediating role of LMX

According to social exchange theory, when one party in an exchange relationship provides resources and benefits to the other, the recipient, in order to maintain and strengthen this relationship, will reciprocate by returning benefits to the issuer³⁵. DEPM provides employees with performance feedback that serves as a resource benefiting them. This feedback increases employee trust in their leader, promotes a positive relationship between the leader and the employee, and enhances the quality of their exchange relationship²¹. To consolidate this high-quality exchange relationship, employees may exceed their required in-role performance and engage in extra-role behaviors, such as innovative actions, thereby maintaining a stable social exchange. In addition, the quality of LMX relationships also depends on the reciprocity and emotional connection between the two parties in the exchange. DEPM provides leaders with performance data, enabling them to accurately express their care for their subordinates and convey signals of trust and concern. When employees perceive support, they develop trust and a sense of belonging toward their leaders, which in turn improves the quality of LMX. In high-quality LMX relationships, emotional and resource support from superiors can enhance employees’ sense of self-confidence³⁶. This further motivates them to actively explore new methods³⁷. In summary, DEPM can promote proactive innovation among employees by improving the quality of LMX relationships. Thus, this study proposes the following hypothesis:

Hypothesis 4:LMX mediates the relationship between DEPM and employee innovative behavior.

The moderating role of power distance

Power distance refers to an individual’s acceptance of unequal power distribution within systems and organizations³⁸. Power distance exerts a significant influence on individual innovative behavior. When employees conceive innovative ideas, they often require organizational resources to implement them. Employees with high power distance are more inclined to comply with organizational and leadership directives. Consequently, during organizational resource allocation, they are more likely to be allocated a greater quantity and higher quality of resources. With ample resources at their disposal, these employees are better positioned to actualize their innovative ideas, thereby manifesting higher levels of innovative behavior³⁹. Employees with varying power distance orientations exhibit different affective attitudes and behavioral responses to leadership behaviors⁴⁰.

Specifically, individuals with high power distance are more inclined to accept status differences, defer to authority figures, and demonstrate greater appreciation, trust, and identification with their leaders⁴¹. In the context of high-quality LMX relationships, employees with high power distance are more capable of perceiving support and respect from their leaders, enabling them to obtain additional psychological resources such as satisfaction and self-worth. Consequently, these employees are more likely to engage in innovative activities based on reciprocal norms⁴². Therefore, high power distance tends to strengthen the relationship between LMX and employees’ innovative behavior. Conversely, employees with low power distance are more concerned with establishing equal relationships with their leaders and may exhibit insufficient gratitude, loyalty, and obedience toward them. As a result, the trust and resources provided by superior leaders are often taken for granted, which can hinder the stimulation of their sense of obligation to reciprocate¹⁹. Consequently, low power distance does not enhance the positive effects of LMX on employees’ innovative behavior. Thus, this study proposes the following hypothesis:

Hypothesis 5:The power distance positively moderates the relationship between LMX and employee innovative behavior. That is, the higher the level of power distance, the stronger the positive relationship between LMX and employee innovative behavior.

In summary, this study constructs a theoretical model, as shown in Fig. 1.

Fig. 1.

Theoretical model.

Methods

Participants

The survey participants of this study are all employees of companies in the financial, Internet, and manufacturing industries, and the sample data are mainly from various provinces and cities in China, such as Beijing, Shanghai, and Henan. This study obtained a total of 265 valid questionnaires. The study participants included 187 women (70.6%) and 78 men (29.4%). And the specific descriptive statistical results are presented in Table 1.

Table 1.

Sample characteristics.

Variables		Percent (%)
Gender	Male	29.4
	Female	70.6
Age	Below 25 years	17.7
	26 ~ 35 years	61.9
	36 ~ 45 years	15.1
	Above 46 years	5.3
Education level	High school or below	1.9
	Associate degree	9.4
	Bachelor’s degree	65.7
	Master’s degree	20.4
	Doctoral degree	2.6
Working tenure	Below 1 years	6.0
	1 ~ 3 years	16.6
	3 ~ 5 years	18.9
	5 ~ 10 years	38.9
	Above 10 years	19.6
Work position	Non-managerial Staff	40.0
	First-line managers	23.0
	Middle managers	21.1
	Senior managers	15.9

Procedure

The sample data for this study were collected entirely through an online platform, utilizing the professional data collection platform credamo to distribute questionnaires to employees of enterprises. The advantages of online data collection are twofold. First, it allows for a broad coverage of sample size, transcending the limitations of specific regions and thus not being confined to enterprises or employees within a particular area. Instead, the scope of sample collection is expanded nationwide, thereby enhancing the representativeness and coverage of the sample data. Second, through a series of targeted option settings, the questionnaire can be specifically distributed to the target user group, which allows for the collection of more representative survey data.

To ensure the validity of sample collection, this study conducted sample characteristic settings, quality control, and response settings on the platform. Prior to the dissemination of the questionnaire, sample characteristic settings were implemented. The platform allows for pre-settings based on age, gender, city, industry, and type of enterprise, enabling the questionnaire to be distributed to target employees according to the research requirements. In this study, only the industry was specified, with selections made from industries with significant involvement in digital technologies, such as finance, the internet and manufacturing. Meanwhile, to encourage participants to respond truthfully, this study did not strictly require the disclosure of the name of the participant’s organization, thereby further protecting participant privacy. Secondly, quality control was implemented. The platform evaluates the credibility of participants based on their response situations, with a total score of 100. In this study, participants with a credibility score of 70 or above and a historical acceptance rate of 70% or above were selected to ensure data quality. Lastly, response settings were conducted. To prevent multiple responses from the same user, this study restricted the IP addresses of participants. Each IP address was permitted to respond only once, thereby avoiding homogenization in data collection.

Participants access the credamo platform with a unique username and password and voluntarily click on the link to fill out the questionnaire. Informed consent is deemed to have been given when clicking on the “Start Answering” button, and participants can withdraw at any time. Prior to the survey, participants were informed that the survey was solely for academic research purposes, that their responses would be anonymous, and that the results would be kept strictly confidential to ensure the authenticity of the survey data. Participants are considered to have completed the survey by clicking “Submit Questionnaire” after completing their responses. In addition, this study tracked the time it took participants to complete the questionnaire and required a minimum response time versus a maximum response time to ensure that participants were attentive.

The sample selection collected for this study occurred between November 2023 and January 2024. The target population of this study is employees of enterprises. Most of the participants are located in Beijing, Shanghai, Henan and other provinces and cities, which are also more developed in the digital economy and the application of digital technology in the workplace is more common and representative. To eliminate unqualified responses and to ensure the overall reliability of the survey, screening items were incorporated into the questionnaire. These items included questions such as “Which business operations of your company involve data statistical analysis through computer networks?” and “Does your company frequently utilize electronic technologies (such as office computer monitoring or location tracking) to monitor your performance at work?” All variables involved in this study were self-assessed by the employees.

A total of 330 questionnaires were distributed, with 276 being returned. After rigorous screening and exclusion criteria were applied, 265 valid questionnaires were obtained, resulting in an effective response rate of 80.3%.The criteria were the following: questionnaires that did not pass the screening questions, contradictory results of pre and post responses, choosing the same answer for all questions, and questionnaires that took too long or too short to answer.

The study received ethical approval from the Business School of Zhengzhou University of Aeronautics, adhered to national and international ethical guidelines, and complied with the Declaration of Helsinki. Furthermore, we confirm that the methods were performed in accordance with applicable guidelines and regulations, informed consent was obtained from all participants, and they were informed of their right to withdraw at any time. In addition, we are committed to ensuring the confidentiality and anonymity of the data related to the study.

Measures

To ensure the reliability and validity of the questionnaire measurements, this study employs established scales from both domestic and international sources for variable measurement. For the English-language scales involved, a translation-retranslation method was utilized to assess the standardization and precision of the wording in the questionnaire, ensuring consistency before finalizing the questionnaire. Specifically, we first translated the English scales directly into Chinese. After consulting with several teachers, we revised any inappropriate terms to ensure that the items of the scales were coherent and comprehensible. Thereafter, we presented the translated and revised complete questionnaire scales to two MBA students for their feedback and further refinement. Ultimately, the final version of the complete questionnaire scale was established. All scales in this study adopt a 5-point Likert scale (1 = “Strongly Disagree”, 5 = “Strongly Agree”).

DEPM

Draws upon the scale developed by Wells et al.⁴ and consists of three items, with representative items such as “The organization uses electronic monitoring to help me better accomplish job tasks”. The Cronbach’s α value for this scale was 0.781.

LMX

Draws upon the scale developed by Wang et al.⁴³ and consists of seven items, with representative items such as “Generally, I am clear about whether my supervisor is satisfied with my job performance”. The Cronbach’s α value for this scale was 0.853.

Power distance

Draws upon the scale developed by Dorfman et al.⁴⁴ and consists of six items, with representative items such as “I don’t think managers need to consult their subordinates when making most decisions”. The Cronbach’s α value for this scale was 0.771.

Innovative behavior

Draws upon the scale developed by Scott et al.⁴⁵ and consists of six items, with representative items such as “I will proactively seek to apply new processes, techniques and methods”. The Cronbach’s α value for this scale was 0.867.

Control variables

Drawing on previous research on innovative behavior, gender, age, education level, hierarchical position, and work experience, have been found to be associated with innovative behavior. Therefore, this study controlled for these demographic variables.

Data analysis

We mainly conducted data testing following the following procedures: Firstly, we analyzed the data for reliability and validity using SPSS 27.0 and MPLUS 8.3 software. Secondly, we conducted a common method bias. Thirdly, descriptive statistics and correlation analysis were performed. Finally, hypothesis testing was performed by regression analysis.

Results

Reliability and validity analysis

Reliability analysis

This study used SPSS software to calculate Cronbach’s α coefficients for each variable to assess the internal consistency reliability of the questionnaire scales. According to the reliability test criteria, when the Cronbach’s α coefficient is greater than 0.7 it means that the variable has good reliability. The results indicate that the reliability coefficients for DEPM, LMX, employee innovative behavior, and power distance all exceeded 0.7 (as shown in Table 2), meeting the criteria for reliability assessment. This suggests that the variables examined in this study possess satisfactory levels of reliability.

Table 2.

Results of reliability analysis.

Variable	Entry	Cronbach’s α
DEPM	3	0.781
LMX	7	0.853
Employee innovative behavior	6	0.867
Power distance	6	0.771

Confirmatory factor analysis

This study conducted KMO and Bartlett’s spherical test using SPSS 27.0 software to assess the suitability of the research data for factor analysis. The results of the tests are shown in Table 3.The KMO values of DEPM, LMX, employee innovative behavior and power distance were higher than 0.6 and the Bartlett’s spherical test values reached the level of significance, which proves that the data of this study can be subjected to factor analysis.

Table 3.

Results of KMO and bartlett’s test of spherical.

KMO and Bartlett test		DEPM	LMX	Employee innovative behavior	Power distance
KMO		0.696	0.887	0.882	0.796
Bartlett’s test of spherical		225.522	675.864	665.613	369.792
	df	3	21	15	15
	Sig.	0.000	0.000	0.000	0.000

Subsequently, this study conducted CFA analysis using MPLUS 8.3 software to verify the discriminant validity of the four variables DEPM, LMX, employee innovative behavior and power distance. The results of the analysis are shown in Table 4. The four-factor model has the best fit (/df = 2.064, CFI = 0.910, TLI = 0.897, RMSEA = 0.063), which indicates a good discriminant validity among the variables in this study.

Table 4.

Results of confirmatory factor analysis.

Model		df		CFI	TLI	RMSEA
Four-factor model (X, M, W, Y)	419.007	203	2.064	0.910	0.897	0.063
Three-factor model (X, M + W, Y)	643.494	206	3.123	0.817	0.795	0.090
Two-factor model (X, M + W + Y)	718.536	208	3.454	0.787	0.763	0.096
One-factor model (X + M + W + Y)	825.473	209	3.949	0.742	0.715	0.106

X = DEPM, M = LMX, W = power distance, Y = employee innovative behavior。.

Common method bias test

In this study, Harman single-factor method was used to test the existence of common method bias. The results showed that the variance explored by the first factor was 35.625%, which was lower than 40%. It indicated that there is no severe common method bias in the questionnaire. Furthermore, after incorporating a common method variance (CMV) factor into the Four-Factor Model, the fit indices for the model were as follows: /df = 2.064, CFI = 0.910, TLI = 0.897, RMSEA = 0.063. The results show that the model fit did not improve significantly with the incorporation of CMV compared to the baseline model. Therefore, there is no significant presence of serious common method bias in our data.

Descriptive statistics and correlation analysis of variables

The means, standard deviations, correlation coefficients, and levels of significance for each variable are presented in Table 5. DEPM is significantly and positively correlated with LMX (r = 0.501, p < 0.01) and employee innovative behavior (r = 0.550, p < 0.01). Similarly, LMX is also significantly and positively correlated with employee innovative behavior (r = 0.571, p < 0.01). These findings provide preliminary support for the hypotheses tested in this study.

Table 5.

Descriptive results of variables and correlation matrix.

Variables	M	SD	1	2	3	4	5	6	7	8
1 Gender	0.290	0.457
2 Age	31.853	5.607	0.048
3 Education level	3.120	0.682	−0.094	−0.063
4 Work tenure	6.025	2.995	0.049	0.740**	0.028
5 Work position	2.130	1.111	−0.045	0.368**	0.354**	0.504**
6 DEPM	3.743	0.861	0.010	0.122*	0.027	0.251**	0.280**
7 LMX	3.831	0.666	0.086	0.197**	0.112	0.354**	0.311**	0.501**
8 Power distance	2.177	0.698	−0.087	−0.087	0.037	−0.195**	−0.161**	−0.293**	−0.299**
9 Employee innovation behavior	4.013	0.649	0.096	0.170**	0.152*	0.281**	0.346**	0.550**	0.571**	−0.347**

M is the mean value, SD is the standard deviation, *p < 0.05、**p < 0.01、***p < 0.001.

Hypothesis testing

Direct effect test

This study utilized SPSS 27.0 to investigate the relationship between DEPM and employee innovative behavior by constructing multiple regression models, with the results presented in Table 4. Employee innovative behavior was designated as the dependent variable, while DEPM was included as an independent variable, with other variables held constant as controls. Model M4 revealed a significant positive relationship between DEPM and employee innovative behavior (β = 0.488, p < 0.001). Consequently, Hypothesis H1 is supported.

Indirect effect test

Utilizing the “three-step” method for testing mediation effects, as proposed by Baron et al.⁴⁶ this study examines whether LMX mediates the relationship between DEPM and employee innovative behavior. The results of the analysis are presented in Table 6. Initially, the main effect test confirmed that DEPM positively influences employee innovative behavior. Subsequently, the relationship between DEPM and LMX was examined, and Model M2 revealed that DEPM is significantly and positively correlated with LMX (β = 0.423, p < 0.001), thereby supporting Hypothesis H2.Additionally, Model M5 shows that LMX has a significant positive impact on employee innovative behavior (β = 0.678, p < 0.001), thereby supporting Hypothesis H3.

Table 6.

Results of regression Analysis.

variable	LMX		Employee innovation behavior
	M1	M2	M3	M4	M5	M6	M7	M8	M9
Control varibale
gender	0.086	0.083	0.107	0.102	0.048	0.055	0.084	0.040	0.054
age	−0.136	−0.071	−0.073	0.001	0.019	0.042	−0.032	0.035	0.024
Education level	0.043	0.074	0.061	0.097	0.032	0.055	0.087	0.046	0.048
Work tenure	0.365	0.262	0.199	0.079	−0.049	−0.069	0.132	−0.070	−0.059
Work position	0.166	0.065	0.257	0.140	0.144	0.103	0.219	0.131	0.132
Independent variable
DEPM		0.423***		0.488***		0.248***
Mediating variable
LMX					0.678***	0.566***		0.643***	0.621***
Moderator variable
Power distance							−0.284***	−0.142**	−0.146***
LMX x Power distance									0.113**
	0.166	0.326	0.152	0.364	0.535	0.579	0.227	0.553	0.565
	0.166	0.16	0.152	0.212	0.383	0.216	0.076	0.018	0.012
F	10.338***	20.825***	9.259***	24.576***	49.486***	50.574***	12.647***	45.415***	41.582***

Note: N = 265, *p < 0.05、**p < 0.01、***p < 0.001.

Finally, the analysis of the significant impact of DEPM and LMX on employee innovative behavior was conducted, and Model M6 reveals that the relationship between LMX and employee innovative behavior is significant (β = 0.566, p < 0.001), and the relationship between DEPM and employee innovative behavior remains significant (β = 0.248, p < 0.001). However, compared to Model M4, the inclusion of the LMX variable reduces the regression coefficient of DEPM on employee innovative behavior (β value decreases from 0.488 to 0.248). Therefore, the mediating effect of LMX is significant, and it plays a partial mediating role in the relationship between DEPM and employee innovative behavior, confirming Hypothesis H4.

Bootstrap test for mediating effect

To further assess the robustness of the mediation effect, this study utilized the PROCESS 4.0 macro in SPSS 27.0 to conduct a Bootstrap analysis. The data were randomly resampled 5,000 times with a 95% confidence interval, as shown in Table 7. The results indicated that the direct effect of DEPM on employee innovative behavior was 0.187, with a confidence interval of [0.116, 0.257], which excluded zero. Similarly, the indirect effect of DEPM on employee innovative behavior through LMX was 0.180, with a confidence interval of [0.117, 0.261], also excluding zero. These findings suggest that DEPM positively influences employee innovative behavior through the mediating role of LMX, thereby providing additional support for Hypothesis H4.

Table 7.

Test results of mediating effect based on Bootstrapping.

Route		Effect value	standard error	95% confidence interval
				Lower limit	Higher limit
Total effect	DEPM—employee innovation behavior	0.367	0.040	0.289	0.445
Direct effect	DEPM—employee innovation behavior	0.187	0.036	0.116	0.257
Indirect effect	DEPM—LMX—employee innovation behavior	0.180	0.037	0.117	0.261

Test of moderating effect

Before conducting the regression test for moderating effects, the variables were centered to mitigate potential multicollinearity. Employee innovative behavior was designated as the dependent variable, with control variables, mediator variables, moderating variables, and interaction terms sequentially incorporated into the model. The results are shown in Table 8. M9 indicates that power distance has a positive moderating effect on the relationship between LMX and employee innovative behavior (β = 0.113, p < 0.01).That is, the higher the level of power distance is, the stronger the positive effect of LMX on employee innovative behavior is, thereby confirming Hypothesis H5.

Table 8.

Moderated effects test.

Power distance	Effect value	standard error	95% confidence interval
+SD	0.599***	0.054	[0.492,0.706]
SD	0.517***	0.048	[0.422,0.611]
-SD	0.434***	0.062	[0.312,0.556]

Furthermore, to further investigate the moderating effect, this study adopted the method proposed by Aiken et al.⁴⁷ Power distance was categorized into high and low groups based on values one standard deviation above and below the mean, respectively. A simple slope analysis was then conducted. The results of this analysis are presented in Table 6, while the moderating effect is graphically illustrated in Fig. 2. When power distance is relatively low, the relationship between LMX and employee innovative behavior is significant (β = 0.434, p < 0.001, 95% CI [0.312, 0.556]). And, when power distance is high, the positive influence of LMX on employee innovative behavior becomes even more pronounced (β = 0.599, p < 0.001, 95% CI [0.492, 0.706]), with a steeper regression line. These findings further confirming Hypothesis H5.

Fig. 2.

The moderating role of power distance.

Discussion

In the context of digitization, enterprises are confronted with the challenge of continuous transformation and upgrading to achieve high-quality development. Digital technologies such as big data, artificial intelligence, and machine algorithms are rapidly empowering organizations, precipitating a digital transformation in the field of human resource management⁴⁸. EPM, a tool that enables continuous management of employees, has been derived. EPM influences the transition of performance management from a traditional, cyclical assessment model to a continuous, technology-driven control mechanism, exerting a profound impact on employees’ work attitudes and behaviors. Based on social exchange theory, this study investigates the impact of developmental EPM on employee innovative behavior, thereby not only filling a gap in the literature to some extent but also enriching the body of research on EPM. In practical terms, this study provides actionable guidance for enterprises to effectively utilize EPM to stimulate employee innovation, thus holding significant practical implications.

Theoretical contributions

This study reveals the mechanism through which DEPM influences employee innovative behavior, thereby not only enriching the antecedent research on employee innovation but also extending the investigation of the outcomes associated with developmental electronic performance monitoring. Previous research on employee innovative behavior has primarily focused on individual traits, AI perception, digital technology requirements, and leadership styles^5–8. In contrast, studies examining the relationship between electronic performance monitoring and employee innovative behavior from the perspective of EPM are exceedingly rare. Moreover, while existing research has confirmed the positive impact of developmental electronic performance monitoring on employee work performance, affective commitment, and organizational citizenship behavior^10–14few studies have explored how it affects employee innovative behavior. Grounded in social exchange theory, this study elucidates the positive influence of developmental electronic performance monitoring on employee innovative behavior. It not only fills a gap in the extant literature but also provides a new perspective for stimulating employee innovation in the digital age.

This study expands the research on the positive effects of EPM and reshapes the perception of the impacts of traditional EPM. Historically, EPM has been associated with a range of negative effects, such as increased employee work stress and the induction of counterproductive work behaviors^50,51. In management practice, EPM has often been characterized as ‘monitoring,’ ‘controlling,’ or even ‘threatening.’ However, these negative perceptions are primarily rooted in misconceptions about the purpose and implementation of EPM. This study reveals the positive effects of DEPM on employees’ innovative behaviors and demonstrates that the actual impact of EPM is not unidimensional and negative, but rather depends on the purpose and manner in which it is applied. Thus, this study provides support for the positive potential of EPM when implemented thoughtfully and developmentally. It also verified the mediating effect of LMX on the relationship between DEPM and employee innovative behavior, as well as the moderating effect of power distance. It further uncovers the ‘black box’ regarding the influence of DEPM on employees’ innovative behaviors.

Practical implications

First, enterprises should uphold the concept of long-term development when utilizing EPM, with a particular focus on the feedback and improvement functionalities of digital performance management. The advantages of implementing EPM for employees are numerous; however, the crucial factor lies in utilizing this tool effectively and reasonably to foster the mutual development of both employees and enterprises. The findings of our study indicate that DEPM positively influences employees’ innovative behavior. Consequently, when introducing EPM in the workplace, organizations and managers should promptly communicate the rationale behind its use to employees. Additionally, they should provide adequate feedback and resource support to ensure that employees recognize the trust and significance placed upon them by the organization, thereby motivating them to exhibit more innovative behaviors.

Specifically, Firstly, in the implementation of EPM, it is crucial to adhere to the principles of timeliness, specificity, and constructiveness in providing performance feedback. This ensures that employees receive timely, specific, and meaningful information about their performance. Moreover, making the monitoring process transparent and facilitating information sharing is essential, as it enables employees to genuinely perceive that the EPM system is intended to encourage and support their positive work behaviors. Additionally, establishing performance-linked incentive mechanisms, such as rewarding innovative achievements and providing advancement opportunities, is vital for stimulating employees’ motivation and creativity. Secondly, based on the data collected through EPM, improvement plans and measures are actively formulated. These plans aim to address employees’ shortcomings and areas for improvement, along with providing specific suggestions for enhancement. Management encourage employees to continually learn and strive for self-transcendence, thereby fostering the mutual growth of both individuals and the organization. Finally, employees are provided with the necessary training, counseling, advanced technology, and other resources to support their development. The training programs encompass innovative thinking, innovative methods, and cutting-edge industry knowledge, aimed at broadening employees’ knowledge base and enhancing their work skills. These efforts not only instill trust in the organization among employees but also equip them with the abilities to overcome the difficulties and challenges they encounter in their work, ultimately enabling them to engage in higher-level innovation activities.

Second, managers should maintain a positive and interactive relationship with their employees. Serving as a crucial conduit for conveying organizational intentions, managers function as an important bridge for information transfer and communication between the organization and its workforce. Research findings indicate that DEPM can positively influence the LMX relationship, which subsequently fosters innovative behaviors among employees. Consequently, managers should not only fulfill their role of communicating the organization’s developmental intentions through EPM but also cultivate a harmonious working relationship with their team members. By actively engaging in close interactions with employees, such as demonstrating care, addressing concerns, and providing necessary resources, managers can ensure that employees feel supported, respected, and trusted, ultimately stimulating positive attitudes and fostering innovative behaviors in the workplace.

Third, when organizations adopt EPM, they must give particular attention to differences in employees’ cultural values, such as power distance. Employees who exhibit higher levels of power distance tend to experience a more pronounced facilitating effect of LMX on their innovative behavior. In the realm of organizational management, managers should pay attention to employees’ power distance orientation and adjust their management strategies accordingly. When dealing with employees who have a high power distance, managers should afford them special attention and provide both effective guidance and supportive resources. These employees, recognizing the goodwill of their leaders, are likely to develop a greater sense of trust and loyalty, ultimately contributing to beneficial organizational outcomes. Conversely, for employees with a low power distance orientation, organizations can mitigate the adverse effects of power distance through strategic culture building and system design. Leaders should also be mindful of their management approach when dealing with such employees, aiming to alleviate any negative attitudes they may hold towards the hierarchical structure within the organization.

Limitations and future research

This study explains the mechanism of action of DEPM in prompting employees’ innovative behaviors, albeit with certain limitations. Firstly, the time interval for data collection in this study is relatively short, which may, to a certain extent, affect the inference of causality among variables. To address this limitation, future research could utilize longitudinal tracking methods and experimental designs to provide more rigorous and sufficient evidence of causality. Secondly, this study examines the impact of DEPM on employees’ innovative behavior based on social exchange theory. However, it has been demonstrated that DEPM also influences employees’ intrinsic motivation and job satisfaction, among other factors. Therefore, further research could be conducted from other theoretical perspectives, such as self-determination theory or social information processing theory, to gain a more comprehensive understanding of DEPM’s effects. Lastly, this study focuses solely on the individual-level impact of DEPM on employees. Future research could extend the analysis to the organizational level to explain the effects of DEPM on teams and the organization as a whole. In summary, while this study contributes to the understanding of DEPM’s impact on innovative behavior, there is room for further exploration in terms of causality, theoretical frameworks, and organizational-level effects.

Conclusion

This study constructs a theoretical model based on social exchange theory. Through empirical analysis of 265 enterprise employee data, it explores the influence mechanism of DEPM on employees’ innovative behavior. The following principal conclusions are drawn:

DEPM positively influences on employee innovative behavior. On the one hand, DEPM can provide employees with rich material resources, such as constructive performance feedback information and diversified training. It lays the foundation for innovative behavior by helping employees to acquire the knowledge, skills and abilities required for innovation; on the other hand, DEPM can give employees psychological incentives, enhancing their sense of self-efficacy and affective commitment, which in turn encourages them to engage actively in innovation.

DEPM positively affects LMX. LMX positively influences employee innovation behavior. LMX serves as a mediator between DEPM and employee innovative behavior. Specifically, developmental EPM fosters high-quality LMX relationships by increasing employees’ trust in leadership. Based on maintaining this high-quality relationship, employees will actively engage in extra-role behaviors such as innovation.

Power distance positively moderates the relationship between LMX and employee innovative behavior. Within the context of a high-quality LMX relationship, employees with high power distance are more likely to perceive trust, respect, and valued resources from their leaders. Based on reciprocity norms, employees will initiate innovative behaviors in return.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

Z.Y. made Conceptualization, Data Curation, Formal Analysis, Formal Analysis, Project administration, Supervision, Validation, Writing—review and editing.R.F. made Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Validation, Writing—original draft, Writing—review and editing.F.M. made Investigation, Software, Writing—original draft.

Data availability

The data that support the findings of this study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethical statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Business School of Zhengzhou University of Aeronautics. This study did not involve any ethical issues.

Informed consent

Informed consent was obtained from all individual participants included in the study.