How Does GIS Training Affect Turnover Intention of Highway and Bridge Industry Technicians? The Mediating Role of Career Growth and the Moderating Mechanism of Work Anxiety

Abstract

The highway and bridge industry is facing persistent challenges related to the high turnover of technical personnel, which poses risks to the continuity and sustainability of infrastructure development. Although Geographic Information System (GIS) training has increasingly been advocated as a strategy to stabilize the workforce, its practical application remains relatively limited across China. Drawing on the Conservation of Resources (COR) theory, this study examines whether GIS training is associated with lower turnover intention among technical staff, potentially through enhanced perceptions of career growth and reduced work-related anxiety. Based on 412 valid responses—primarily from technical personnel employed by major infrastructure enterprises such as regional subsidiaries of the China Communications Construction Group (CCCG) and China State Construction Engineering Corporation (CSCEC)—the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the proposed relationships. The findings indicate that GIS training is negatively associated with turnover intention, with career growth partially mediating this association. Additionally, work anxiety moderates the relationship, such that the link between GIS training and turnover intention appears weaker under higher levels of anxiety. This research contributes to bridging the gap between training practices and theoretical understanding, offering insights to inform workforce retention strategies in technology-intensive industries.

1. Introduction

Driven by the global push for infrastructure modernization, digital technologies are profoundly transforming the highway and bridge industry. China, in particular, is undergoing a critical stage of digital transformation [1]. The widespread application of information technologies has not only improved engineering quality and construction efficiency but has also exerted a profound impact on the career development paths of technical personnel. Among these technologies, Geographic Information Systems (GISs) play a vital role in the planning, construction, and maintenance of highways and bridges, thanks to their capabilities in spatial data analysis and visualization [2]. To accelerate digital upgrading in the sector, the Chinese government has issued several policy initiatives. For example, the Ministry of Transport released the Opinions on Promoting the Digital Transformation of Highways and Accelerating the Development of Smart Highways, emphasizing the use of GIS technology to enhance highway monitoring, intelligent maintenance, and construction management [3]. Additionally, the 14th Five-Year Plan for Highway Maintenance and Management Development proposes improvements in the highway and bridge maintenance system by leveraging GIS and other technologies to enhance infrastructure safety and durability [4].

Internationally, several countries are actively advancing the application of GIS in infrastructure development. For instance, the United States, through its “Smart Infrastructure” national initiative, emphasizes the role of GIS in enhancing the intelligent operation and maintenance of roads and bridges, thereby promoting the informatization of transportation management [5]. In Australia, VicRoads—the transportation authority in Victoria—has made GIS a core tool in its digital highway maintenance program, significantly improving inspection efficiency and risk warning capabilities [6]. These practical experiences indicate that GIS training has become a key approach to improving frontline technicians’ digital skills and fostering their career development. It also contributes to enhancing industry competitiveness and reducing employee turnover.

Despite clear national policies and industry direction highlighting the importance of GIS, how these macro-level initiatives influence individuals remains underexplored. As China promotes strategic initiatives like “Smart Transportation” and “New Infrastructure,” organizations are placing greater demands on the digital capabilities of technical personnel. Consequently, GIS training has been widely incorporated into talent development programs. However, this form of training is not merely a technical investment—it also poses a psychological challenge for employees. When organizations rapidly implement intensive training programs, employees may experience “knowledge update anxiety,” which can affect their professional confidence and perceived career growth and even increase their intention to leave. Therefore, it is essential to explore at the micro level how organizational training, mediated by individual cognition and emotional responses, shapes behavioral stability—thus building a theoretical bridge between macro policy and micro behavior.

In China, universities have gradually introduced GIS-related courses, particularly in majors such as surveying engineering, civil engineering, and geographic information science. Nevertheless, these courses often emphasize theoretical instruction, creating a disconnect from the complex, dynamic nature of engineering practice in the construction sector. Consequently, in-house training provided by enterprises has become a critical mechanism for applying GIS technologies on-site. This study focuses specifically on organizational-level training practices to bridge the gap between classroom knowledge and field demands and to examine how digital skills training can effectively support the career development of technical staff.

However, despite the increasing adoption of GIS technologies in infrastructure industries, the human support system underpinning this transformation faces significant challenges. With the deepening of China’s “New Infrastructure” strategy, the highway and bridge industry is experiencing a dual trend of technological intensification and a younger workforce. This places higher demands on frontline workers’ digital competencies and overall capabilities. Yet, this trend is accompanied by rising turnover rates among technical staff. Public data indicate that in some provinces and municipalities, the annual turnover rate among frontline technicians in grassroots highway departments reaches 15–20%, particularly in regions advancing digital transformation at a faster pace [3,7]. This underscores a structural disconnect between vocational training and employee retention.

Take, for example, a national expressway bridge project in the Guangdong–Hong Kong–Macao Greater Bay Area. To improve structural health monitoring and emergency response efficiency, the project team implemented an intelligent inspection system combining GIS and AI and organized centralized GIS training for frontline technicians to equip them with key skills such as data uploading and risk warning. While the system functioned effectively and training improved technical competence in the short term, the project team observed signs of professional burnout and voluntary resignations within six months after training. This case illustrates that if organizations intensify training efforts without addressing employee learning stress, mental state, and subjective perceptions of career growth, the intended benefits of training may be undermined.

This case highlights a practical dilemma organizations face in digital transformation: even with intensive, task-oriented skill training, neglecting employees’ psychological adaptation processes can significantly diminish training effectiveness and even trigger unintended turnover. Beyond institutional design, psychological factors at the individual level also play a crucial role in shaping the relationship between training effectiveness and career stability. Prior studies suggest that technical workers lacking foundational knowledge or facing dual pressures from performance assessments and skill updates are more prone to anxiety and self-doubt, which may hinder their ability to absorb and apply training content [8,9]. For instance, Vivin et al. found that employees with high anxiety tend to have weaker learning motivation and internalization capacity [10], while Muschalla’s research revealed that work-related anxiety not only reduces employees’ perception of developmental opportunities but also significantly increases their intention to leave [11].

Although the existing literature has extensively examined the effectiveness of general skill training and the relationship between training satisfaction and work attitudes, most studies focus on general-purpose training programs. Few have explored the unique mechanisms of professional technical training, such as GIS, within high-tech-intensive industries. Especially in the context of China’s infrastructure sector, limited empirical research has addressed how training affects perceived career growth and, in turn, reduces turnover intention. Moreover, there is a lack of empirical investigation into how employees’ psychological states—such as anxiety—moderate the effectiveness of training.

To address these research gaps and reconcile the disconnection between macro policy advocacy and micro individual behavior, this study adopts the Conservation of Resources (COR) theory as its theoretical foundation to construct a mediation model of “GIS training → career growth → turnover intention.” It further introduces work anxiety as a moderating variable to examine its role in the training-outcome mechanism. Specifically, this study aims to address the following three research questions: (1) Does GIS training directly influence technicians’ turnover intention? (2) Does career growth mediate the relationship between GIS training and turnover intention? (3) Does work anxiety moderate the effect of GIS training on turnover intention? To strengthen the theoretical framework, this study also draws on Training Transfer Theory [12], Self-Efficacy Theory [13], and Expectancy Theory [14] to explain how employees internalize training outcomes, develop motivation for growth, and adjust their retention intentions based on expected rewards. These theories complement COR theory and collectively support the proposed path relationships and moderation mechanisms.

2. Theoretical Foundation and Hypothesis Development

2.1. Theoretical Foundation

2.1.1. Conservation of Resources (COR) Theory

This study is grounded in the Conservation of Resources (COR) theory, which serves as the theoretical foundation for developing a moderated mediation model involving GIS training, career growth, turnover intention, and work anxiety. The model aims to explain how employee training under digital transformation influences behavioral stability. Proposed by Hobfoll (1989) [15], COR theory posits that individuals are inherently motivated to acquire, protect, and accumulate resources to enhance their ability to cope with stress and promote psychological well-being. Resources are broadly defined within this framework to include not only external assets such as skills, time, and job positions but also internal psychological resources like self-esteem, confidence, and a sense of control. The principle of primacy of resource loss suggests that the negative impact of resource loss outweighs the benefits of resource gain. Conversely, the resource gain spiral posits that initial resource acquisition can trigger a cycle of positive accumulation, leading to sustained personal development [15,16].

Within the digital infrastructure industry, GIS training can be viewed as a critical organizational intervention designed to empower employees by enhancing their technical competencies and job performance—resources categorized as high-value instrumental resources. Although some higher education institutions have begun offering GIS-related courses in disciplines such as geoinformatics and surveying engineering, systematic GIS training for frontline technical workers in the construction sector remains limited. Most employees do not receive formal GIS education during their academic training. Instead, their skills are primarily acquired through on-site project-based training, rotational programs, or employer-sponsored upskilling courses. These in-service training programs typically emphasize practical engineering applications and task-oriented content, covering key modules such as spatial data modeling, 3D visualization, and structural monitoring. In some enterprises, certification systems are also implemented to promote skill transfer.

From the perspective of COR theory, acquiring technical resources such as GIS skills not only improves employees’ job adaptability but also enhances their perception of career growth—a form of psychological resource. This perception may function as a mediating mechanism between GIS training and turnover intention. On one hand, the improvement of skills enhances employability and mobility, increasing employees’ competitiveness both within and beyond the organization. On the other hand, mastering new technologies helps employees establish a positive professional identity, reinforcing their self-perception as “high-value talent.” Additionally, GIS competencies signal adaptability and learning capabilities during digital transformation, thereby bolstering employees’ confidence in future career advancement and reducing their intention to leave.

However, COR theory also underscores that individuals experience stress and resource depletion when faced with resource threats or increased difficulty in acquiring resources. In the context of this study, work anxiety is conceptualized as a psychological state reflecting resource exhaustion. Existing research has indicated that during periods of digital transformation, employees lacking prior experience or facing elevated performance expectations are more likely to experience “knowledge updating anxiety” and resistance to skill internalization [17,18]. Such anxiety not only diminishes their ability to gain resources from training but also negatively affects their perception of career growth prospects, thereby weakening the positive impact of training on retention intentions. Thus, work anxiety may serve as a critical moderating variable, disrupting the resource conversion chain between GIS training and turnover intention. Specifically, the higher the employee’s anxiety level, the weaker the effect of training in supporting resource gain and retention.

2.1.2. Supplementary Theoretical Perspectives

While COR theory provides a robust macro-level framework for understanding resource dynamics in employee training, it pays relatively limited attention to the specific psychological mechanisms and individual differences involved in training transfer. To offer a more comprehensive explanation of how GIS training influences employees’ perceived career growth and, in turn, affects their turnover intention, this study incorporates three supplementary theoretical perspectives: Training Transfer Theory, Self-Efficacy Theory, and Expectancy Theory.

Training Transfer Theory emphasizes that the effectiveness of training lies in the successful application of learned skills to real-world tasks [12]. In COR terms, training becomes a real resource gain only when transfer occurs, reinforcing perceived competence and career progress. Conversely, failed transfer may result in resource stagnation or even perceived loss.

Self-Efficacy Theory contributes to the understanding of how resource appraisal influences motivation. Employees who believe they can master GIS tasks are more likely to persist in learning, overcome technical difficulties, and view the training as a source of future resource gain. This aligns with COR’s view that confidence and self-belief are core psychological resources. High self-efficacy thus serves as a cognitive filter that determines whether training is internalized as resource enrichment or stress [13].

Expectancy Theory complements COR by focusing on the valuation of future outcomes. It posits that employees’ motivation to engage with training depends on their expectations regarding its utility and reward potential [14]. When employees perceive GIS training as instrumental to achieving meaningful career advancement, the anticipated value of resource gain is amplified, increasing commitment and reducing turnover intention. If these expectations are unmet, perceived loss may occur—even in the presence of technical skill development.

This study constructs an integrated conceptual framework in which COR theory provides the overarching structure for understanding resource dynamics, while Training Transfer Theory, Self-Efficacy Theory, and Expectancy Theory elaborate the micro-level psychological mechanisms that shape how GIS training translates into perceived career growth and influences turnover intention. Specifically, these supplementary theories explain how acquired skills are applied (training transfer), how self-beliefs affect resource conversion (self-efficacy), and how expected outcomes drive engagement (expectancy). Work anxiety is introduced as a moderator that influences how employees cognitively and emotionally respond to training as a resource investment. Together, this multi-level framework enhances theoretical coherence and offers a more nuanced understanding of employee behavior in technology-intensive contexts.

2.2. Hypothesis Development

2.2.1. GIS Training and Turnover Intention

In the highway and bridge construction industry, Geographic Information System (GIS) training is widely regarded as an effective means to reduce employee turnover intention. Prior research has shown that GIS training enhances technical personnel’s ability to process spatial data, improves their work efficiency, and strengthens their project management skills [19]. Unlike traditional digital skills training, GIS programs demand not only proficiency in advanced techniques such as spatial modeling and data visualization but also a higher level of cognitive engagement and continuous learning capacity. Mastery of GIS technology enables better construction planning, facilitates efficient data flow, and enhances job adaptability and professional competence [20]. Specifically, systematic GIS training equips employees with the skills to analyze geospatial information accurately, optimize construction processes, and improve teamwork, thereby boosting individual job performance [21,22]. As employees develop stronger technical capabilities and respond more effectively to work challenges, they experience a greater sense of competence and job satisfaction, which in turn reduces their voluntary turnover intention [23,24].

From the perspective of Conservation of Resources (COR) theory, GIS training represents a crucial “resource injection” mechanism, providing employees with high-value skill resources that enhance their replaceability and job security within the organization. When employees perceive a growth in their resource reserves and recognize organizational investment in their development, they are more likely to develop organizational commitment and lower their turnover intention [25]. Furthermore, the resource gain may trigger a “positive resource spiral,” motivating employees to further invest in their work and pursue sustainable career development.

Recent empirical studies also support the positive link between technical training and employee retention. A cross-sectional study of frontline construction workers in China indicated that effective skill training significantly improves organizational identification and reduces turnover intention [26]. Similarly, a three-wave survey of public employees in Guangdong Province showed that digital training not only enhanced skill levels but also bolstered confidence in career ability and organizational belonging, leading to higher work engagement [27]. These studies provide empirical grounding closely aligned with the constructs of this research and highlight the practical value of technical training in managing employee attrition. Compared to prior research that primarily focused on manufacturing and energy sectors, this study extends the examination to the highway and bridge sector, exploring whether and how GIS training influences frontline technical employees’ turnover intention. Based on this, the following hypothesis is proposed:

H1. 

The level of GIS training is negatively associated with employee turnover intention.

2.2.2. GIS Training and Career Growth

In the context of ongoing digital transformation across infrastructure industries, GIS technology has become an essential skill for technical personnel, supporting both construction management and spatial decision-making. Effective GIS training not only enhances professional skills but also improves employees’ competitiveness and clarity in career progression. Existing studies suggest that structured technical training reinforces employees’ skill identification and role competence, thereby clarifying career orientation and increasing expectations for growth and development confidence [27]. Specifically, in the highway and bridge sector, GIS training strengthens practical capabilities in spatial data processing, 3D modeling, and engineering optimization, opening more pathways for career development [28,29]. As technical proficiency increases, employees are more likely to perceive value in themselves and form a positive outlook on their career future, thus enhancing their sense of career growth [30].

From the COR perspective, career growth can be understood as a form of psychological resource. When employees acquire valuable instrumental resources (e.g., technical skills) through GIS training, they are more likely to build confidence in their long-term development, fostering positive emotional responses and stronger organizational attachment. Complementing this with Self-Efficacy Theory, the successful transfer of training outcomes and the self-efficacy fostered during training contribute to enhanced perceptions of career growth [13,15,16].

Empirical evidence supports this mechanism. In smart construction sites or BIM-integrated projects, systematic internal training has improved employees’ capabilities in spatial modeling, visualization analysis, and on-site BIM system operations. This not only enhanced job competence but also boosted confidence in personal career development and clarity in career direction [31]. Although such studies may not focus directly on GISs, the underlying logic of how training affects psychological reactions aligns closely with the hypothesized mechanisms in this study. Based on this, the following hypothesis is proposed:

H2. 

GIS training is positively associated with employees’ career growth.

2.2.3. Career Growth and Turnover Intention

Career growth is widely recognized in the human resource management literature as a critical factor influencing employee turnover intention, especially in technology-intensive industries [32]. Research shows that positive career growth experiences enhance employees’ career identity and organizational commitment, thus reducing voluntary turnover [33,34]. In the highway and bridge sector, career growth contributes to job stability among technical staff and improves team cohesion and project continuity [35,36]. When employees perceive that the organization provides clear development pathways and sustained growth opportunities, they are more inclined to stay and grow with the organization rather than seek external options [37].

From the COR perspective, career growth represents a typical psychological resource that can buffer work-related stress, strengthen organizational belonging, and lower turnover motivation [15,16]. The resource gain principle posits that individuals prefer to retain existing resource systems and avoid the risks associated with rebuilding them in uncertain environments. As such, employees who experience career growth within the organization are more likely to “accumulate in place” rather than “reinvest elsewhere”.

Expectancy Theory offers a supplementary view: employees decide whether to remain in the organization based on their evaluation of whether continued effort will lead to desirable outcomes and career advancement [14]. When the growth trajectory is clear and the expectancy of effort-to-reward is high, the likelihood of retention increases significantly.

Recent empirical data supports this mechanism. According to McKinsey’s 2022 [38] “Greater China Tech Talent Retention Survey,” lack of clear career development ranked among the top three drivers of employee turnover in industries such as manufacturing, construction, and transportation, second only to compensation and workload. Among employees with clear growth paths, turnover rates were on average 13% lower than their counterparts [39]. Based on this, the following hypothesis is proposed:

H3. 

Career growth is negatively associated with employee turnover intention.

2.2.4. The Mediating Role of Career Growth

Career growth may serve as a critical mediator in the relationship between training and turnover intention. Prior research has shown that technical training not only improves job capabilities directly but also indirectly influences turnover intention by enhancing employees’ perceptions of career growth [40]. Specifically, GIS training provides employees with professional skills such as data analysis and spatial modeling, while simultaneously creating realistic promotion and development pathways within the organization, thereby enhancing perceptions of career growth [41]. This sense of growth can strengthen employees’ emotional bonds with the organization and reduce turnover intention [42]. In the technology-intensive highway and bridge sector, training-induced perceptions of career growth often become a key psychological driver for continued tenure [43].

From the COR perspective, this mediating process aligns with the mechanism of a “resource gain spiral.” That is, the initial investment in resources (e.g., GIS training) helps employees acquire new instrumental and psychological resources (e.g., technical skills, confidence), thereby reinforcing career growth expectations and generating a cumulative resource effect. When employees perceive themselves to be on a growth trajectory, they are more likely to invest in their current organization and avoid the disruption of resource accumulation that would come with leaving [15,16].

Supplementary theories further support this mediating mechanism. According to Self-Efficacy Theory, positive feedback and successful experiences during training enhance employees’ confidence in their future development and drive them to seek further growth [13]. Expectancy Theory suggests that employees are more willing to exert effort within the organization when they can clearly foresee the benefits of doing so [14]. Thus, GIS training can effectively reduce turnover intention primarily through its capacity to stimulate career growth expectations. Based on this, the following hypothesis is proposed:

H4. 

Career growth mediates the relationship between GIS training and employee turnover intention.

2.2.5. The Moderating Role of Job Anxiety

Although GIS training has demonstrated effectiveness in enhancing employee capabilities and improving job stability, its actual outcomes may vary depending on individual psychological conditions [44]. Particularly in high-pressure engineering environments, employees’ levels of job anxiety may interfere with their ability to absorb and apply training content, thereby affecting both training efficacy and behavioral outcomes such as retention [45]. Studies show that highly anxious employees often suffer from distractions, reduced achievement motivation, and negative expectations about the future, which undermine their ability to gain resources from training [46,47,48].

Within the COR framework, job anxiety is viewed as a manifestation of resource depletion. When individuals face threats of resource loss (e.g., lack of skills, performance pressure) but are unable to replenish these resources, they may enter a “resource defense” state—marked by skepticism or resistance to external interventions—which inhibits resource acquisition processes [15,16]. In the context of GIS training, such a state makes it harder for employees to convert training into tangible career advantages, weakening the intended resource gain mechanism.

Conversely, employees with lower levels of job anxiety possess more available resources and are more likely to engage in training with openness and initiative, which boosts learning motivation and self-efficacy. Self-Efficacy Theory highlights that individuals’ confidence in overcoming challenges strongly influences how they respond to external support [13]. Therefore, less anxious employees are more likely to view GIS training as an opportunity for career advancement, strengthening organizational commitment and retention intentions. Based on this, the following hypothesis is proposed:

H5. 

Job anxiety moderates the relationship between GIS training and employee turnover intention.

Compared with traditional digital skills training, GIS training demands not only proficiency in advanced techniques such as spatial modeling and data visualization but also higher levels of cognitive engagement and continuous learning ability. Such requirements may trigger unique psychological responses. For instance, under high cognitive load, employees may experience a greater sense of achievement or self-efficacy, thereby reinforcing their perceptions of career development. In addition, due to the cross-departmental and cross-project nature of GIS applications, employees may develop a heightened need for role integration or experience increased job anxiety. These psychological mechanisms have been underexplored in existing training models within IT or manufacturing contexts. Therefore, building upon established theoretical frameworks such as the Conservation of Resources (COR) theory, this study introduces career development as a mediating variable and incorporates job anxiety as a moderating factor. This approach aims to capture the distinctive mechanisms through which GIS training affects the mobility intentions of technical talent. Figure 1 presents the conceptual model proposed in this study.

3. Research Methodology

3.1. Research Design

This study adopts a quantitative research design grounded in Conservation of Resources (COR) theory to explore how Geographic Information System (GIS) training affects turnover intention among technical personnel in the road and bridge construction industry. A cross-sectional survey approach was employed to investigate the structural relationships among key variables, including GIS training, career growth, work anxiety, and turnover intention, within actual industrial contexts.

Data were collected via a structured questionnaire targeting frontline technical employees with practical GIS experience in construction and transportation sectors. To examine the proposed model, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed. This method is well-suited for analyzing complex models involving both mediation and moderation effects, especially under conditions of non-normal data distribution and theory development.

Distinct from prior research that typically centers on general IT or manufacturing training, this study focuses on GIS training—a cognitively demanding process requiring advanced skills in spatial analysis and data modeling. Such training not only enhances employees’ technical competencies and professional identity but may also lead to psychological strain. Accordingly, the research model incorporates career growth as a mediating variable and work anxiety as a moderating factor. By integrating theory-driven modeling with empirical data, this study aims to test the proposed hypotheses and offer practical insights into talent development and retention in the construction industry.

3.2. Questionnaire Design

All variables in this study were measured using validated scales adapted from prior research. Each construct was assessed using a five-point Likert scale, where respondents indicated their level of agreement with each item from 1 (“Strongly disagree”) to 5 (“Strongly agree”). To ensure contextual relevance and linguistic accuracy, the original English scales underwent a back-translation process and were reviewed by subject matter experts. Minor modifications were made to the wording of items to align them with the specific context of GIS training in the road and bridge construction industry, without altering the core meaning of the constructs.

A summary of the measurement scales used in this study is presented in

Table 1. The scales selected in this study.

Scale	Source	Cronbach’s Alpha
Turnover Intention	Bothma and Roodt (2013) [49]	α > 0.70
GIS Training	Lapierre et al. (2016) [50]	α = 0.90
Career Growth	Weng et al. (2010) [51]	α > 0.70
Work Anxiety	McCarthy et al. (2016) [52]	α = 0.94

. Turnover intention was assessed using the scale developed by Bothma and Roodt (2013) [49]. GIS training was measured using the scale by Lapierre et al. (2016) [50], career growth using the scale by Weng et al. (2010) [51], and work anxiety using the scale by McCarthy et al. (2016) [52]. All scales have been validated in previous research and demonstrated satisfactory internal consistency, with Cronbach’s alpha values exceeding 0.70. A Cronbach’s alpha above 0.70 is generally considered acceptable, indicating that the items within each scale are sufficiently correlated and reliably measure the same underlying construct [53].

Prior to the formal survey, a pilot test was conducted with 30 participants from the target population to assess the clarity and suitability of the revised items. Based on their feedback, minor refinements were made to ensure that the questions accurately captured the intended constructs within the Chinese linguistic and cultural context.

3.3. Survey Participants and Sampling Method

The target population of this study consisted of technical personnel holding various positions within the road and bridge construction industry, including roles in road surveying and design, on-site project management, and bridge inspection. To ensure the relevance of responses to the research objectives, all participants were required to have practical experience with Geographic Information System (GIS)-related tasks.

To meet the requirements for statistical analysis, the study followed the “five-times rule” proposed by Hair et al. [54], which recommends that the minimum sample size should be five times the number of measurement items. Given that the questionnaire included 27 measurement items, the minimum required sample size was calculated as 27 × 5 = 135. Considering the characteristics of technical personnel in the road and bridge industry, along with typical questionnaire response rates in related studies (ranging from 30% to 50%), the study estimated the number of questionnaires to be distributed based on the lower bound of a 30% response rate. Accordingly, at least 450 questionnaires were required. In total, 500 questionnaires were distributed.

According to Hair et al. [54], adhering to this sample-to-item ratio is essential for ensuring sufficient statistical power and the reliability of Partial Least Squares Structural Equation Modeling (PLS-SEM), thereby confirming the appropriateness of the sampling design in this study.

The final valid sample comprised 412 respondents, primarily drawn from large infrastructure companies, including but not limited to regional subsidiaries of the China Communications Construction Company (CCCC) and China State Construction Engineering Corporation (CSCEC). Respondents were affiliated with various engineering units and project teams, enhancing the heterogeneity and representativeness of the data across diverse organizational settings.

3.4. Data Collection Procedure

Data for this study were collected between November and December 2024. To improve coverage and representativeness, both online and offline survey methods were employed. A total of 438 responses were received. After eliminating invalid responses due to excessively short completion times, missing data, or outliers, 412 valid questionnaires remained, resulting in a valid response rate of approximately 82.4%, which aligns well with industry expectations.

Moreover, Baruch and Holtom [55] suggest that acceptable response rates for organizational survey research typically range from 30% to 50%. The 82.4% response rate achieved in this study significantly exceeds this benchmark, enhancing the credibility and robustness of the dataset.

All participants were informed of the purpose of the study and assured that their responses would remain anonymous and confidential. Participation was entirely voluntary, and no personally identifiable information was collected at any stage. The study adhered to ethical principles of social science research and complied with the Declaration of Helsinki. All procedures involving participants were conducted in accordance with established ethical standards. Given the non-intrusive nature of the survey and the anonymous data collection method, formal ethical approval was not required—consistent with standard practices in similar studies in this field.

This study collected data from technical personnel in the road and bridge sector, resulting in 412 valid responses. The sample was predominantly male (91.3%), with the majority aged between 26 and 45 years (67.7%). Regarding educational background, 45.1% held a vocational college diploma, while 35.2% possessed a bachelor’s degree or higher, indicating a relatively high level of professional qualification. In terms of income, 54.3% reported a monthly salary between RMB 5001 and 9000, and 10.4% earned over RMB 12,000 per month. As for work experience, the largest group had between 3 and 5 years of experience (37.4%), followed by those with less than 3 years (27.4%), reflecting high labor mobility in the industry. The sample covered technical personnel from diverse backgrounds, providing a solid empirical basis for analyzing the impact of GIS training on job stability. Detailed demographic information is presented in

Table 2. Demographic information form.

Item	Question	Percentage	Number of People
Gender	Male	91.3%	376
	Female	8.7%	36
Age	Less than 25 years old	9%	37
	26–35 years old	36.4%	150
	36–45 years old	31.3%	129
	46–55 years old	10.7%	44
	56 years old and above	12.6%	52
Educational Background	High school or below	9%	37
	Technical school	10.7%	44
	Vocational college/Diploma	45.1%	186
	Bachelor’s degree or above	35.2%	145
Monthly Income	RMB ≤ 3000	4.6%	19
	RMB 3001–5000	23.1%	95
	RMB 5001–7000	30.3%	125
	RMB 7001–9000	24%	99
	RMB 9001–12,000	7.5%	31
	RMB >12,000	10.4%	43
Work Experience	<3 years	27.4%	113
	3–5 years	37.4%	154
	6–10 years	16.7%	69
	11–15 years	10%	41
	>15 years	8.5%	35

.

3.5. Data Analysis Methods

This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the data, test the research hypotheses, and assess the model’s fit. PLS-SEM is well-suited for small sample sizes, non-normal data distributions, and the analysis of complex path relationships. It also enables the simultaneous examination of both mediation and moderation effects [56,57]. Data analysis was conducted using Smart-PLS 4, focusing on the evaluation of both the measurement model and the structural model to ensure the reliability and validity of the research framework.

3.5.1. Measurement Model Evaluation Method

The purpose of evaluating the measurement model is to verify the reliability and validity of the measurement indicators and to ensure the quality of variable measurement. Reliability was assessed through Composite Reliability (CR) and Cronbach’s alpha, both of which should exceed 0.70 to confirm internal consistency [58,59]. Convergent validity was assessed using Average Variance Extracted (AVE) and outer loadings. AVE values should exceed 0.50, and outer loadings are generally expected to be above 0.70. However, in applied research, if an item’s loading falls between 0.60 and 0.70, and removing it does not significantly improve AVE or CR while the item holds theoretical significance, it may be retained [60].

Discriminant validity was evaluated using the Fornell–Larcker criterion and the Heterotrait–Monotrait (HTMT) ratio. According to the Fornell–Larcker criterion, the square root of the AVE for each latent construct should be greater than its correlations with other constructs. The HTMT ratio should be less than 0.90 to confirm satisfactory discriminant validity [61].

3.5.2. Structural Model Evaluation Method

Structural model evaluation examines the relationships between variables and tests the research hypotheses. Model fit was assessed using R² (coefficient of determination), f² (effect size), and Q² (predictive relevance). R² evaluates the explanatory power of the model for endogenous variables, f² reflects the strength of the influence of exogenous variables, and Q² indicates the model’s predictive accuracy [62,63].

Path coefficients were analyzed using bootstrapping to calculate T-values and p-values, with a p-value less than 0.05 indicating statistical significance [64]. Additionally, to ensure the robustness of regression analysis, the variance inflation factor (VIF) was used to assess multicollinearity. A VIF value below 3.3 is required to avoid high multicollinearity problems [65].

In the path analysis, gender, education level, and work experience were included as control variables. Their paths to the dependent variable (turnover intention) were modeled to test their significance and influence on the main relationships, thereby enhancing the accuracy and explanatory power of the model estimation. Including control variables helps eliminate potential confounding effects from demographic characteristics, allowing the study to focus more clearly on the theoretical relationships among the core variables.

4. Results

4.1. Common Method Bias (CMB) Test

To examine the potential impact of Common Method Bias (CMB) on the study results, Harman’s single-factor test was conducted. The results showed that the first common factor accounted for 29.247% of the total variance, which is significantly below the critical threshold of 50% [66], indicating that CMB is unlikely to pose a serious threat to the measurement results. Furthermore, the cumulative variance explained by the first two factors was 53.540%, providing additional evidence that no single factor dominates the data structure, thereby enhancing the credibility of the findings.

In addition, the full collinearity test proposed by Kock was applied. The variance inflation factor (VIF) values for all latent variables ranged between 1 and 1.424, all well below the threshold of 3.3, further confirming that CMB does not constitute a significant concern in this study [65].

4.2. Measurement Model Evaluation

4.2.1. Convergent Validity

The convergent validity of the measurement model was assessed through Average Variance Extracted (AVE) and outer loadings. The results showed that all standardized loadings exceeded 0.70, except for CG1 (0.671), which was slightly below the recommended threshold. Although CG1 did not meet the 0.70 benchmark, it was retained due to its theoretical significance in measuring the construct of career growth and its established validity in the prior literature. Removing this item could compromise the conceptual coverage of the construct [67]. Further analysis revealed that eliminating CG1 did not significantly improve either the AVE or the Composite Reliability (CR). Therefore, in order to preserve content validity and theoretical completeness, the item was retained.

To ensure a rigorous evaluation of the measurement model and structural relationships—particularly for the moderation effect—this study adopted the two-stage approach, as recommended by Hair et al. [68]. This method is particularly suitable for reflective measurement models, as it helps reduce model complexity and mitigate multicollinearity issues. The two-stage approach enables the construction of interaction terms based on latent variable scores derived from the measurement model, allowing moderation effects to be assessed more robustly. Accordingly, all measurement items—including those involved in the interaction term—were first validated for reliability and validity before the structural model estimation.

It is worth noting that within the PLS-SEM framework, outer loadings between 0.50 and 0.70 are generally considered acceptable when there is strong theoretical justification [68]. The standardized loadings for all measurement items are presented in

Table 3. Outer loadings of each item.

	CG	GT	ITL	WA	WA × GT
CG1	0.671
CG2	0.774
CG3	0.765
CG4	0.789
CG5	0.771
CG6	0.795
GT1		0.754
GT2		0.805
GT3		0.738
GT4		0.740
GT5		0.735
GT6		0.748
GT7		0.761
ITL1			0.792
ITL2			0.767
ITL3			0.796
ITL4			0.711
ITL5			0.778
ITL6			0.736
WA1				0.935
WA2				0.896
WA3				0.923
WA4				0.943
WA5				0.937
WA6				0.917
WA7				0.911
WA8				0.921

.

Meanwhile, the Average Variance Extracted (AVE) values for all latent variables exceeded the threshold of 0.50 (ranging from 0.570 to 0.852), indicating satisfactory convergent validity for each construct. In addition, the reliability analysis showed that all variables had Cronbach’s alpha coefficients above 0.85 and Composite Reliability (CR) values exceeding 0.89, suggesting a high level of internal consistency. Among them, work anxiety (WA) had the highest AVE value (0.852), indicating that its measurement items effectively explained the variance (as shown in

Table 4. Scale reliability and convergent validity analysis.

Variable	Cronbach’s Alpha	Composite Reliability (CR)	AVE
CG	0.855	0.892	0.580
GT	0.874	0.903	0.570
ITL	0.857	0.893	0.583
WA	0.975	0.979	0.852

).

In summary, the measurement model meets high standards in terms of reliability and convergent validity, thereby providing a solid foundation for the subsequent structural model analysis.

4.2.2. Discriminant Validity

The study employed the Fornell–Larcker criterion and the Heterotrait–Monotrait ratio (HTMT) to assess the discriminant validity of the measurement model.

Firstly, according to the Fornell–Larcker criterion, the square root of the Average Variance Extracted (AVE) for each latent variable is greater than its correlations with other latent variables, indicating good theoretical distinctiveness. Specifically, the square roots of the AVE for career growth (CG), GIS training (GT), intention to leave (ITL), and work anxiety (WA) are 0.762, 0.755, 0.764, and 0.923, respectively, all of which exceed their correlations with other constructs. This meets the criteria for discriminant validity, suggesting that each latent variable is conceptually distinct and that there is no substantial cross-loading, as shown in

Table 5. Fornell–Larcker.

	CG	GT	ITL	WA
CG	0.762
GT	0.496	0.755
ITL	−0.527	−0.573	0.764
WA	0.095	0.008	−0.122	0.923

below:

In addition, the Heterotrait–Monotrait ratio (HTMT) further validated the discriminant validity of the measurement model. The results indicated that all HTMT values were below the threshold of 0.90 (ranging from 0.014 to 0.662), suggesting that the discriminant validity among the latent variables was at an acceptable level. The highest HTMT value was observed between ITL and GT (0.662), which, although indicating a certain degree of correlation, still falls below the 0.90 threshold, thus supporting their conceptual distinctiveness. Moreover, the HTMT value for the interaction term WA × GT (maximum value of 0.479) was also relatively low, further supporting the model’s discriminant validity. Detailed results are shown in

Table 6. HTMT.

	CG	GT	ITL	WA	WA × GT
CG
GT	0.571
ITL	0.614	0.662
WA	0.117	0.056	0.131
WA × GT	0.379	0.370	0.479	0.014

:

In summary, the assessment results based on the Fornell–Larcker criterion and the HTMT ratio both support the discriminant validity of the measurement model, indicating that the latent constructs can be effectively distinguished. This provides a solid foundation for the subsequent structural model analysis.

4.3. Structural Model Evaluation

4.3.1. Path Coefficients and Significance Tests

This study employed the bootstrapping method (with 5000 resamples) to evaluate the significance of the path coefficients. The results indicate that most path coefficients are statistically significant (T > 1.96, p < 0.05), thus supporting the five proposed hypotheses. The key path relationships are as follows:

There is a significant negative relationship between GIS training (GT) and intention to leave (ITL) (β = −0.370, p < 0.001), supporting Hypothesis H1.

GIS training (GT) is significantly positively associated with career growth (CG) (β = 0.496, p < 0.001), supporting Hypothesis H2.

Career growth (CG) is significantly negatively related to intention to leave (ITL) (β = −0.257, p < 0.001), supporting Hypothesis H3.

Mediation analysis revealed that career growth (CG) plays a significant mediating role between GIS training (GT) and intention to leave (ITL) (β = −0.128, p < 0.001), supporting Hypothesis H4.

Moderation analysis showed that work anxiety (WA) significantly moderates the relationship between GIS training (GT) and intention to leave (ITL) (β = −0.200, p < 0.001), supporting Hypothesis H5.

To further illustrate the moderating role of work anxiety in the relationship between GIS training and turnover intention, an interaction plot was generated (see Figure 2). As shown in the figure, GIS training is negatively associated with turnover intention, and this association varies across levels of work anxiety. Specifically, the negative association is stronger among employees with higher levels of work anxiety (i.e., the slope is steeper), while the same negative pattern is observed among those with lower anxiety, but to a lesser extent. These results provide support for Hypothesis 5.

In addition, this study included gender, education level, and work experience as control variables to examine their effects on intention to leave (ITL). However, the results indicate that none of these control variables had a significant path coefficient, gender (β = −0.031, p = 0.813), education level (β = −0.013, p = 0.726), and work experience (β = −0.038, p = 0.334), with all T-values falling below the threshold for statistical significance. This suggests that, within the context of this research model, gender, education level, and work experience have limited explanatory power regarding turnover intention. Instead, employees’ intentions to leave, formed through their experiences with GIS training and perceived career growth opportunities, are primarily influenced by the core variables.

Detailed statistical results for the above path relationships are presented in

Table 7. Main path coefficients and significance test results.

Relationship	Original Sample (O)	Standard Deviation (STDEV)	T Values	p Values	Significance
GT → ITL	−0.370	0.041	9.053	0.000	Significant
GT → CG	0.496	0.038	13.088	0.000	Significant
CG → ITL	−0.257	0.047	5.480	0.000	Significant
GT → CG → ITL	−0.128	0.025	5.045	0.000	Significant
WA × GT → ITL	−0.200	0.039	5.135	0.000	Significant
Gender → ITL	−0.031	0.130	0.236	0.813	No Significant
Education → ITL	−0.013	0.038	0.350	0.726	No Significant
Work Experience → ITL	−0.038	0.039	0.967	0.334	No Significant

.

To provide a more intuitive representation of the relationships between variables, Figure 3 further illustrates the path coefficients and their directions in the structural model.

4.3.2. Effect Size and Predictive Power

1.

Coefficient of Determination (R²): Model’s Explanatory Power

To assess the explanatory power of the structural model, this study reports the R² values of two endogenous variables. The results indicate that the R² for career growth (CG) is 0.246, and the R² for intention to leave (ITL) is 0.458. According to Hair’s guidelines, R² values in social science research are typically classified into three categories: values above 0.75 indicate substantial explanatory power, 0.50 indicates moderate, and 0.25 indicates weak explanatory power [67,68]. Accordingly, the model in this study demonstrates a “weak to moderate” level of explanatory power for career growth and a “moderate” level for intention to leave. These findings suggest that the proposed model holds theoretical and practical value in explaining employees’ intention to leave. Detailed results are presented in

Table 8. Predictive Power (R²) of endogenous variables.

Endogenous Variable	R2 Value	Level of Explanatory Power
Career Growth (CG)	0.246	Weak to Moderate
Intention to Leave (ITL)	0.458	Moderate

.

2.

Effect Size (f²): Contribution of Independent Variables

To further evaluate the explanatory power of the structural model, the effect size (f²) for each path was calculated. According to the criteria proposed by Cohen and Hair et al., f² values are interpreted as follows: 0.02 ≤ f² < 0.15 indicates a small effect, 0.15 ≤ f² < 0.35 indicates a medium effect, and f² ≥ 0.35 indicates a large effect [67,68]. The results show that the effect size of GIS training on career growth is 0.325, indicating a medium effect, which suggests that training has a substantial impact on employees’ career growth. The direct effect size of GIS training on turnover intention is 0.181, also reflecting a medium-level effect. The f² value for career growth’s impact on turnover intention is 0.086, indicating a small effect. Additionally, the moderating effect of work anxiety has an f² value of 0.076, which also falls within the small effect range. Overall, the core paths in the model demonstrate good explanatory power. Detailed results are presented in

Table 9. Effect sizes (f²) of key paths.

Structural Path	Effect Size (f2)	Level of Influence
CG → ITL	0.086	Small
GT → CG	0.325	Moderate to Large
GT → ITL	0.181	Moderate
WA × GT → ITL	0.076	Small

.

3.

Stone-Geisser’s Predictive Relevance (Q²): Model’s Predictive Power

Predictive relevance (Q²) is used to assess the model’s predictive capability, where Q² > 0 indicates that the model has predictive relevance. The results show that the Q² values for career growth (CG) and intention to leave (ITL) are 0.238 and 0.399, respectively, both exceeding 0. This indicates that the model constructed in this study demonstrates strong predictive capability across different constructs, particularly for intention to leave (ITL). Furthermore, although the Q² value for career growth (CG) is relatively lower, considering the multifaceted nature of career growth, the model still provides valuable predictive insights, affirming its research value. For detailed results, see

Table 10. Predictive relevance (Q²) of endogenous variables.

Endogenous Variable	Q2 Predict	Predictive Capability
Career Growth (CG)	0.238	Predictive
Intention to Leave (ITL)	0.399	Strongly Predictive

.

In summary, the structural model based on Partial Least Squares Structural Equation Modeling (PLS-SEM) in this study demonstrates good reliability, validity, and predictive capability, providing strong empirical support for the proposed hypotheses. Although some R² values are relatively low, the model still exhibits substantial explanatory power in the complex domain of career growth, thereby ensuring both its theoretical and practical significance.

5. Discussion

Hypothesis 1. 

GIS training (GT) is significantly negatively correlated with intention to leave (ITL).

The path analysis revealed a statistically significant negative association between Geographic Information System (GIS) training and intention to leave (β = −0.370, t = 9.053, p < 0.001), supporting Hypothesis H1. However, the corresponding effect size (f² = 0.086) falls within the small range, indicating that although GIS training is associated with reduced turnover intentions, its actual explanatory power is limited.

This finding aligns with the existing literature suggesting that participation in technical training is often linked to lower intention to leave, potentially due to enhanced job-relevant competencies, perceived organizational investment, and increased employee engagement [69,70]. Under the Conservation of Resources (COR) theory, GIS training may act as an external resource that signals organizational support, prompting employees to reciprocate with greater affective attachment and reduced turnover intention.

Nevertheless, the small effect size suggests that GIS training, while directionally beneficial, may only play a minor role in reducing turnover intention. Therefore, organizations should avoid over-relying on training programs as a primary retention tool. Instead, it is advisable to integrate such initiatives with broader human resource strategies that foster career advancement opportunities, emotional well-being, and a supportive work environment.

In summary, the results confirm a statistically significant but practically limited link between GIS training and turnover intention, highlighting the need to contextualize training within a multifaceted retention framework.

Hypothesis 2. 

GIS training (GT) is significantly positively correlated with career growth (CG).

Path analysis revealed a significant positive association between GIS training and perceived career growth (β = 0.496, t = 13.088, p < 0.001), supporting Hypothesis H2. This suggests that individuals who participate more actively in GIS training tend to perceive greater advancement in their career development.

This result is in line with prior findings that emphasize the role of professional training in enhancing skill sets and reinforcing employees’ sense of career efficacy and direction [71]. Within the framework of Conservation of Resources (COR) theory, GIS training may be interpreted as an external resource that contributes to employees’ personal and professional reserves, potentially improving their adaptability to changing job demands.

However, while the relationship is statistically strong, it is essential to recognize that perceptions of career growth are also shaped by broader contextual factors such as mentorship availability, promotion systems, and organizational support beyond training. Therefore, organizations should avoid assuming that training alone will ensure career advancement, but rather embed it within a more comprehensive talent development strategy.

Hypothesis 3. 

Career growth (CG) is significantly negatively correlated with intention to leave (ITL).

Path analysis revealed a significant negative association between career growth and intention to leave (β = −0.257, t = 5.480, p < 0.001), supporting Hypothesis H3. This indicates that employees who perceive higher levels of career growth tend to report lower intentions to leave the organization.

This finding echoes previous research suggesting that perceived career development opportunities are associated with greater organizational loyalty and lower turnover tendencies [72]. Under the COR theory, career growth can be regarded as a form of accumulated personal resource, contributing to employees’ psychological security and their motivation to remain within the organization.

Nonetheless, the observed effect size is moderate, suggesting that while career growth is relevant, it may not be the sole or dominant factor influencing turnover intentions. Factors such as compensation fairness, work–life balance, and leadership quality could also play a significant role. Therefore, retention strategies should be multifaceted rather than solely focused on career advancement pathways.

Hypothesis 4. 

Career growth (CG) mediates the relationship between GIS training (GT) and intention to leave (ITL).

The results indicate a significant indirect association between GIS training and intention to leave through career growth (β = −0.128, t = 5.045, p = 0.000), thereby providing support for Hypothesis H4. This suggests that employees who participate in GIS training tend to report stronger perceptions of career development opportunities, which are in turn related to lower levels of turnover intention.

This observation is consistent with prior research highlighting career growth as a key correlate of employees’ organizational attachment [73]. Rather than suggesting that technical training alone is linked to reduced turnover intention, the findings point to employees’ perception of career advancement opportunities as a relevant explanatory pathway. It is worth noting that while the indirect association is statistically significant, its magnitude may vary among different employee groups. For example, early-career workers might experience a stronger association between perceived growth and reduced turnover intention compared to their more experienced counterparts, underscoring the importance of context-specific talent development approaches.

Within the framework of Conservation of Resources theory, GIS training may be interpreted as a potential initiator of a resource gain spiral: newly acquired technical competencies are associated with enhanced self-evaluations regarding one’s career prospects, which in turn relate to stronger psychological ties to the organization. These findings highlight the value of not only offering training opportunities but also aligning them with visible, personalized career development pathways. Future research could further explore how organizational signals related to internal advancement moderate this perceived growth process [74].

Hypothesis 5. 

Work anxiety (WA) moderates the relationship between GIS training (GT) and intention to leave (ITL).

Although the moderating effect of work anxiety reached statistical significance (β = −0.200), its practical effect size was relatively small (f² = 0.076). This indicates that while differences in work anxiety levels may be associated with variations in how GIS training relates to turnover intention, the strength of this association remains modest. Thus, the observed moderation may be more reflective of nuanced psychological differences rather than a strong differentiating mechanism. In particular, the association between GIS training and reduced turnover intention appeared more evident among employees experiencing lower anxiety. This suggests that psychological factors such as anxiety may shape the interpretation or internalization of training experiences rather than dramatically altering their outcomes.

From a theoretical perspective, this finding aligns with the resource conservation logic of COR theory [15,16]. According to COR theory, employees strive to acquire and protect resources to cope with stress and maintain well-being. GIS training, as a potential resource gain, can support these efforts. However, when employees experience high levels of work-related anxiety, their ability to absorb and leverage training resources may be constrained. Anxiety may impair cognitive flexibility, narrow attention, and reduce learning motivation, thereby limiting the perceived value or applicability of training in real work settings.

Moreover, anxiety can distort how training is interpreted. Highly anxious employees may perceive training as a stressor or a signal of increased performance pressure, reducing their engagement and dampening the training’s potential association with career optimism or organizational commitment. In contrast, employees with lower anxiety levels may be more receptive to viewing training as an organizational investment in their development, which could correlate with stronger organizational ties and lower turnover intentions.

Practically, these results highlight the importance of considering emotional readiness when planning training interventions. While GIS training may be associated with positive career-related perceptions and lower turnover intention, its outcomes may vary depending on employees’ emotional states. Organizations might consider assessing work anxiety prior to training and offering complementary resources such as psychological support or stress-management sessions. For example, the Shanghai Urban Transport Planning Research Institute integrated GIS training into its professional development framework. Internal assessments found that younger technical staff who completed the training tended to express greater optimism about their career paths and lower intention to leave. However, among those who eventually resigned, elevated work anxiety was frequently reported. This case underscores the potential moderating role of anxiety and illustrates the benefits of combining skill development initiatives with attention to employee well-being.

Overall, while the statistical moderation effect was limited, the findings offer useful insights into the complex interplay between technical training and employee attitudes. They suggest that training strategies should be complemented by efforts to create emotionally supportive environments, particularly in high-turnover, high-pressure industries. This perspective also contributes to broader theoretical discussions on how emotional states may shape resource-related perceptions and behaviors in the workplace.

6. Conclusions

6.1. Research Summary and Theoretical Contributions

This study focuses on GIS training in the highway and bridge construction industry and explores its relationship with employee retention. By introducing perceived career growth as a potential mediating variable and job anxiety as a moderating factor, a more comprehensive theoretical model was developed. Based on the results of PLS-SEM analysis, GIS training was found to be significantly and negatively associated with turnover intention, and this relationship may be indirectly explained through employees’ perceived career growth. Furthermore, job anxiety was identified as a potential moderator—when anxiety levels were lower, the negative association between GIS training and turnover intention was stronger. These findings suggest a potential pathway linking GIS training, perceived career growth, and turnover intention, highlighting the critical role of employees’ psychological states. This contributes a new perspective to human resource management in technology-intensive industries.

At the theoretical level, this study expands the scope of research on the relationship between professional training and employee retention. While the prior literature has extensively examined general training and turnover intention, few studies have systematically modeled and empirically tested how specialized skill training—such as GIS training—affects employee behavior through psychological mechanisms. Compared with conventional IT training, GIS training incorporates complex tasks such as spatial analysis, cross-system data integration, and data visualization, which may uniquely influence employees’ learning motivation, role competence, and perceptions of long-term career development. Most existing studies focus on the technical application of GIS, with limited exploration of its theoretical value in employee development and organizational behavior.

To address this gap, this study, grounded in Conservation of Resources (COR) theory, constructs a mediating path model of “GIS training → career growth → turnover intention” and introduces job anxiety as a psychological moderator. This contributes to a deeper understanding of the boundary conditions of training effectiveness. Unlike traditional views that consider training as a static motivational tool, this study emphasizes the dynamic interaction between training and employees’ psychological resources, thereby enriching the applicability of COR theory in career development and organizational behavior.

Additionally, the study is situated within the Chinese highway and bridge sector, responding to the sector’s pressing challenges such as high talent turnover and fragmented training systems. Although leading organizations like China Communications Construction Company and Shanghai Urban Traffic Design Institute have piloted GIS training with preliminary success, these efforts are often based on anecdotal evidence without theoretical modeling or empirical validation. By applying PLS-SEM for hypothesis testing, this study addresses this theoretical gap and provides a scientific foundation and actionable roadmap for organizations aiming to design high-quality GIS training programs and improve talent retention.

6.2. Practical Implications and Managerial Recommendations

From a practical standpoint, the findings suggest that organizations designing GIS training programs should not only focus on technical skills development but also address employees’ career development needs and implement effective measures to alleviate job anxiety. This can enhance training outcomes, reduce turnover, and promote long-term organizational stability.

Specifically, this study offers the following recommendations for designing and delivering digital skills training:

Tailored content by role: Training modules should be stratified according to job responsibilities and GIS application demands. For example, frontline technical staff may benefit from basic map processing and spatial data input, whereas senior engineers and planners may require advanced modules such as spatial modeling or BIM-GIS integration. Training intensity can be optimized at a frequency of once every 6 to 8 months, with each cycle including 8–12 h of hands-on instruction to ensure practical absorption.

Project-based training: Programs should incorporate real-life project cases (e.g., bridge inspection digitization, land-use simulation) and be delivered in short cycles of 2–3 weeks, using iterative, feedback-rich approaches. This enhances engagement and supports immediate skill transfer to the workplace.

Industry-academic partnerships: Organizations may collaborate with universities or vocational institutes to embed GIS training into structured certification systems or cooperative education pipelines. New hires from such programs tend to show higher digital readiness scores (measured via pre-hire assessments or training entry tests) and better long-term retention.

Considering the finding that high job anxiety may weaken the positive impact of GIS training on career development and retention intention, organizations should establish a comprehensive psychological support system throughout the training process to mitigate anxiety and optimize outcomes. Suggested strategies include the following:

Pre-training assessment: Administer brief psychometric tools such as the Job Anxiety Scale (JAS) or Work Anxiety Inventory (WAI) to screen for elevated anxiety levels. Employees scoring in the top 25% percentile may be prioritized for early intervention, including personalized coaching, workload adjustments, or preparatory sessions to boost training confidence.

Supportive training environment: Avoid overly competitive or evaluative formats during instruction. Instead, emphasize cooperative learning, peer support, and low-pressure environments, where trainees feel safe to experiment and ask questions.

Post-training support: Within 4–6 weeks after training completion, organizations should offer application workshops, mentoring, or peer-sharing sessions to help employees translate skills into measurable work outputs. Tracking short-term outcomes (e.g., successful GIS task completion, confidence ratings) can reinforce competence, self-efficacy, and organizational commitment.

6.3. Limitations and Future Research Directions

Despite the meaningful insights provided by this study, several limitations should be acknowledged. First, the use of a cross-sectional design limits the ability to draw causal conclusions. Although theoretical logic and structural modeling were employed to infer possible relationships, future research should adopt longitudinal designs to track employees’ career trajectories and turnover intentions after GIS training, allowing for a more accurate assessment of the lasting effects and mitigating the biases associated with cross-sectional data.

Second, future studies could incorporate additional organizational and individual-level variables—such as organizational commitment and work engagement—to develop more comprehensive models and further uncover the dynamic mechanisms linking professional training, career growth, and employee retention.

Lastly, while this study focuses on technical personnel in China’s highway and bridge sector, the proposed framework and findings may offer valuable insights for other countries and technology-intensive industries. With the global acceleration of infrastructure digitalization, GIS training is increasingly adopted in sectors such as energy, construction, mining, and water resources. Comparative empirical studies across countries or industries are encouraged to validate the cross-cultural applicability of these findings, thereby enhancing their external validity and global relevance.

In conclusion, this study sheds light on the potential of GIS training to reduce turnover intention among technical staff. This effect may be partially mediated by perceived career growth and moderated by job anxiety. When designing training strategies, organizations should take into account employees’ career trajectories and psychological conditions and develop adaptive, human-centered training and support systems to maximize training effectiveness and strengthen workforce stability—ultimately building a solid talent foundation for sustainable industry development.