As organizations navigate a rapidly changing and competitive business landscape, effective talent management and employee performance optimization have emerged as crucial determinants of sustained success. Thus, this study aimed to develop a novel model for Talent Management and Development, Integrating Training and Talent Management, and Employee Training and Performance that places a strong emphasis on the integral role of training and employee development in bolstering the sustainability of organizations. The proposed model integrates three contemporary theories–management and development theory, expectancy theory, and adult learning theory–to develop a novel model for talent management and employee performance and investigate the role of training and employee development in the sustainability of organizations in Saudi Arabia. A quantitative survey (questionnaire) was used as the primary method of data collection. Therefore, 273 employees completed the questionnaire, and the analysis research model was comprehensively assessed using Amos software for structural equation modeling (SEM). In this study, the model was developed along three main axes: talent management and development, integrating training and talent management, and employee training and performance. The findings revealed that talent management and development were positively correlated with employee training and performance, and integrating training and integrating training and talent management was also positively correlated with employee training and performance. The findings of this model acknowledge that investments in training and development not only enhance individual skills and competencies but also contribute to heightened employee engagement, job satisfaction, and overall organizational performance. Furthermore, Organizations that adopt this approach are better equipped to adapt to change, harness the full potential of their workforce, and secure enduring prosperity, thereby ensuring their ability to thrive in the face of continuous challenges and opportunities.
employee training and performanceintegrating trainingSEMsustainability of organizationstalent managementThe author(s) declared that financial support was received for this work and/or its publication. The author extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number (R-2026-25).section-at-acceptanceSustainable OrganizationsIntroduction
In today’s fast-paced and competitive business world, organizations recognize the critical importance of talent management and employee performance in achieving strategic objectives. Training and development initiatives are essential for enhancing employees’ skills and fostering a culture of continuous improvement. This study explores the latest practices in training, employee performance, and talent management, emphasizing the pivotal role of human resources (HR) in organizational success. Human resources are fundamental to business operations and the achievement of organizational goals. Effective HR ensures that tasks are completed on time and within budget, which is crucial for maintaining a competitive edge (Riyanto et al., 2021). In the era of globalization, HR remains a key focus for businesses aiming to thrive, as it significantly influences the successful implementation of organizational activities (Saluy et al., 2022). HR plays a strategic role in enhancing employee productivity and overall organizational performance. Ensuring that employees work effectively is a challenge for many organizations, impacting productivity growth (Zahrani, 2022a). Employees must perform their duties to the best of their abilities; however, not all possess the necessary skills and aptitudes, particularly in terms of performance training. Training is a crucial factor that influences employee performance. It is designed to improve employees’ abilities and performance by enhancing specific work-related knowledge, skills, attitudes, and behaviors (Saluy et al., 2022). Research shows that training can positively impact performance (Martins, 2021; McCrie and Lee, 2022), although some studies suggest that performance is unaffected by training (Pham et al., 2020). Overall, effective human resource management and the implementation of robust training programs are vital for boosting employee performance and achieving organizational success.
According to Liu et al. (2021), the primary objective of talent management is to develop employees into capable, highly skilled, and exceptional company leaders. Managers must conduct objective assessments of individuals’ strengths and weaknesses to identify potential candidates for the talent pool. During the recruitment process, HR personnel often use competence-based interviews to evaluate candidates’ competencies and familiarity with specific jobs, as detailed by Moore et al. (2020). The integration of training and talent management fosters the development of a skilled workforce and promotes a culture of continuous learning, adaptability and innovation. This approach aligns with the principles of sustainable organizations that prioritize employee growth and well-being as essential for long-term success. Recent studies by Liu et al. (2021) and Susanto and Sawitri (2023) emphasize the importance of training and development in enhancing employee performance. Investing in training programs that align with organizational goals equips employees to excel in their roles, leading to improved performance and a sustainable workforce. Contemporary talent management practices, as highlighted by Ast and Nyhuis (2022), recognize that sustainable success relies on a dynamic approach that continuously assesses and develops talent. This model aligns with sustainable business practices by focusing on nurturing internal talent to achieve the organization’s goals. Additionally, Zahrani (2022a,b,c) and Li et al. (2021) underscore the need for structured and goal-oriented talent development programs. These programs create pathways for employees to reach their potential, thereby driving organizational growth and sustainability.
Problem background and problem statement
The success of an organization heavily depends on employee performance and talent management in the fast-paced, competitive business world of today. Businesses are better positioned to achieve their strategic objectives when they attract, nurture, and retain top talent while maximizing employee performance (Collings and Mellahi, 2009; Zahrani, 2022b). Consequently, the role of training and development in talent management and sustainable employee performance has gained increasing importance in recent years. Despite the recognized importance of talent management, employee performance, and the role of training and development, a new model that aligns these factors with sustainability principles is needed. This model should address changing workforce dynamics, technological advancements, global competition, and sustainability imperatives to ensure that organizations can attract, retain, and enhance the performance of their employees sustainably. Current models may not adequately address these evolving requirements, necessitating the development of a new framework to guide organizations in achieving these objectives. The problem is to create a comprehensive and sustainable model for talent management and employee performance that considers the modern workforce landscape, technological advancements, global competition, and sustainability principles (Sonnenberg et al., 2014). This new model should harmonize talent management, training and development, employee performance, and sustainability, helping organizations navigate the challenges and opportunities of contemporary business environments. This study serves as a roadmap for organizations aiming to excel in attracting, nurturing, and retaining talent while promoting sustainability and optimizing employee performance. Addressing talent management issues is critical for enhancing training and employee performance. An organization’s competitive advantage and success rely heavily on its talent, making talent management a crucial component of business success. According to Coculova et al. (2020), effective talent management is key to business success. This study identifies the primary drivers of talent management, including support for creativity and innovation within the company, employee training and performance, talent management and development, and the integration of training and talent management related to performance. This study proposes a novel model for talent management and employee performance that emphasizes the pivotal role of training and development in achieving sustainability. This model recognizes that human capital is the lifeblood of any organization and that the ongoing growth and adaptation of this capital are fundamental to sustainability.
Literature review and research theory
Talent management and development theory, such as the “War for Talent” theory by Ed Michaels, Helen Handfield-Jones, and Beth Axelrod, emphasizes the competitive advantage gained through attracting and developing top talent (Michaels et al., 2001). Additionally, the talent management framework developed by Bersin and Deloitte underscores strategic talent management, which involves recruitment, onboarding, learning and development, and performance management (Bersin, 2008). The “Continuous Learning Framework” by Armstrong and Taylor (2020) emphasizes continuous learning as an integral part of talent management and development. The “Strategic Alignment Model” by Noe and Winkler highlights the alignment of training with organizational objectives to achieve better talent management (Jehanzeb and Bashir, 2013). For employee training and performance, the “Expectancy Theory” developed by Victor Vroom (1964) emphasizes the relationship between training, employee motivation, and performance. Similarly, Knowles (1980) adult learning theory provides insights into how different training methods can impact adult learners’ performance. Therefore, with respect to the relevant theories and a theoretical framework, this study developed a new model for talent management and employee performance, focusing on the role of training and development and sustainability factors, which involves the integration of various theories and a solid theoretical framework (see Figure 1).
Research model with hypotheses development.
Flowchart illustrating relationships in talent management. "Talent Management and Development" connects to "Employee Training and Performance" and "Integrating Training and Talent Management." Various elements like TMD1 to TDSP3, and ITTM1 to EEIP4, connect with arrows, showing dependencies or pathways, labeled H(a1) to H(c12), indicating specific interactions or influences within the system.Talent management and development (TMD)
Sustainability and Talent Management and Development are two critical areas within organizations that, when strategically aligned, can have a profound impact on long-term success, resilience, and social responsibility (Al-Youbi et al., 2020). Developing a sustainable workforce involves nurturing a culture of continuous learning and skill development. This includes training programs that focus on environmental and ethical practices, diversity and inclusion, and social responsibility (Saleh and Atan, 2021). Sustainability education is an integral part of talent development.
Talent management strategies (TMS)
Talent management is crucial for identifying, nurturing, and retaining high potential employees. In a comprehensive study, Sonnenberg et al. (2014) presented a framework for talent management, emphasizing the need for strategic alignment and flexibility. As asserted by Susanto and Sawitri (2023), internal recruitment within a company serves as a powerful motivator for employees, signifying respect and demonstrating the existence of clear career progression within the organization. Similarly, leadership plays a pivotal role in setting the sustainability tone. Talent development programs should identify and nurture leaders who can drive sustainability initiatives, making them an integral part of leadership development (Al-Youbi et al., 2020; Zahrani, 2022c).
Talent development and succession planning (TDSP)
Effective talent development and succession planning are essential for long-term organizational success. Groves (2007) provides insights into talent development strategies and their role in succession planning, advocating a holistic approach to talent management. Research suggests that action-oriented development is crucial for improving competencies that are in line with an organization’s strategic plans when it comes to leaders’ development for succession planning (Gül et al., 2023). Organizations can identify sustainability-related skills and competencies and include them in their talent development frameworks. Performance management systems should consider sustainability goals, and performance evaluations should include metrics related to sustainability (Mater et al., 2024; Alserhan et al., 2023). By linking individual and team objectives to sustainability, employees are more likely to engage in sustainable practices (Mio et al., 2022).
Integrating training and talent management (ITTM)
Integrating sustainability into talent management and development is a strategic move that ensures a workforce capable of driving sustainability, innovation, and success. This integration aligns an organization’s human capital with its sustainability goals and positions it to thrive in an increasingly conscious and responsible business landscape (Al-Youbi et al., 2020). Therefore, sustainability and talent management and development are two critical areas within organizations that, when strategically aligned, can profoundly impact long-term success, resilience, and social responsibility.
Aligning training with talent management goals (ATTM)
An integrated approach that aligns training with talent management objectives has gained prominence in recent years. Shet and Bajpai (2023) proposed a model that emphasizes the strategic synchronization of training programs with talent management goals. As outlined by Ast and Nyhuis (2022), training plays a pivotal role in enabling employees to execute tasks effectively and to expedite task completion. In line with Susanto and Sawitri (2023), a training program is instrumental in fostering employee independence. This program typically involves a series of steps for mentees, including learning from a mentor and then applying that knowledge until they succeed. According to Zahrani (2022a,b,c), there are crucial pre-agreements to establish in the training and mentoring process. These agreements encompass the duration of the mentoring relationship and the specific targets to be attained upon the program’s completion.
Evaluating the effectiveness of integrated programs (EEIP)
Efforts to integrate training and talent management should be regularly evaluated for their impacts. Vural et al. (2012) present a case study that assesses the effectiveness of an integrated program, highlighting the significance of performance metrics in evaluating the success of such initiatives. Employee integration refers to the time and effort each person spends learning new skills, expanding their knowledge base, and becoming more creative (Jia et al., 2022). Employee integration and talent management, which seek to improve job satisfaction by acquiring and supporting higher skill and knowledge levels, have a direct relationship in the workplace (Jia et al., 2022).
Employee training and performance (ETP)
Talent development should incorporate data collection and analysis related to sustainability. Training programs can be designed to ensure that employees have the skills to meet sustainability reporting requirements and adhere to industry standards and regulations (Son-Turan, 2017; Zahrani, 2021). Therefore, sustainable practices contribute to environmental and social responsibility and lead to competitive advantages.
Role of training in employee performance (RTEP)
Numerous studies have emphasized the foundational role of training in improving employees’ performance. Al-Youbi et al. (2020) argues that training not only bridges skill gaps but also contributes to increased job satisfaction, motivation, and overall productivity. According to McCrie and Lee (2022), training is an activity designed to increase employees’ or members’ abilities and performance in carrying out their duties by developing their work-related knowledge, skills, attitudes, and behaviors. According to Pham et al. (2020), training helps people, groups, and organizations perform and behave better. According to the implementer’s goals, training exercises must be created in a way that will actually benefit the participants (Martins, 2021). Performance assessment is the process of reviewing and assessing an employee’s performance (Wang et al., 2022).
Impact of training methods on employee performance (TMEP)
London et al. (2004) explored the impact of different training methods. Their research suggests that e-learning and on-the-job training are significantly effective in improving performance, demonstrating the need for tailored approaches. The quality of workers’ work will increase with proper job training and assistance in enhancing their competencies in performing tasks (McCrie and Lee, 2022; Al-Muani et al., 2024). Employee attendance at training programs increases the likelihood that participants will perform well because they acquire the skills, knowledge, and abilities necessary to do their jobs. According to Wang et al. (2022), training is one way helps workers become more proficient at the tasks they are assigned and capable of handling the responsibilities that the company outlines for them.
Research methodology
This study adopted a quantitative approach, and the survey included both demographic data and questionnaires. Thus, this approach allows for a comprehensive exploration of the proposed model’s effectiveness. In this study, we began with a thorough literature review to understand the existing models, theories, and practices related to talent management, training and development, and sustainability in employee performance. Utilize academic databases, journals, books, and modern references to gather relevant information. The next step was primary data collection. After a literature review to understand the existing models, theories, and practices related to them, we developed a structured questionnaire to collect the quantitative data. 25 questionnaires were distributed to HR professionals, employees, and organizational leaders in various industries, including HR managers and executives, to gather in-depth insights into their experiences and perceptions regarding talent management, training, and sustainability in Saudi Arabia. The final step was secondary data collection. We collected data from organizations that successfully integrated training and development into their talent management practices. A total of 248 questionnaires were distributed to the participants.
Measurement instrument and analysis
This study modified the completed questionnaires to create a new model that describes how training and development can be integrated into talent management to improve employee performance, with a focus on sustainability. This model was developed using insights from the literature review and primary data collected for this study. Based on commonly used validity and reliability from additional reviews of the literature and primary data, the measurement scales for the variables were developed for this study; Hair et al. (2019) support this. The sample questionnaire was modified to include questions measuring the elements of the research model depicted in Figure 1, as well as basic demographic data, such as participant age, gender, and educational attainment. Three questions for the talent management and development (TDM) factor were adapted from (Michaels et al., 2001); Four questions for the talent management strategies (TMS) factor were adapted from (McDonnell et al., 2017); Three questions for the talent development and succession planning (TDSP) factor were adapted from (Groves, 2007); Three questions for the integrating training and talent management (ITTM) factor were adapted from (Al-Youbi et al., 2020); Three questions for the aligning training with talent management (ATTM) factor were adapted from (Shet and Bajpai, 2023); Four questions for the evaluating the effectiveness of integrated programs (EEIP) factor were adapted from (Vural et al., 2012); Four questions for the employee training and performance (ETP) factor were adapted from (Mather et al., 2011); Four questions for the role of training in employee performance (RTEP) factor were adapted from (McCrie and Lee, 2022); and finally, Four questions for the impact of training methods on employee performance (TMEP) factor were adapted from (Wang et al., 2022), see Appendix A (Questionnaires).
Data analysis, testing, and validation
As mentioned above, this study applied the newly developed model to a sample of organizations from different industries to assess its effectiveness by gathering feedback and insights from these organizations to validate the model. For data analysis, we used AMOS 24 statistical analysis software to analyze the quantitative data obtained from the questionnaires. Validity and reliability tests were conducted to provide information on the dependability of the data. Cronbach’s alpha coefficient (CA) and composite reliability (CR) were used to evaluate reliability; both required values to be higher than 0.700. The 35 hypotheses tested for the evaluation of the model using the path coefficients and t-value are also reported in this research analysis. In this study, descriptive statistics served as a fundamental tool for providing a concise and lucid overview of the demographic data. Therefore, this enhances the comprehension of the sample’s composition and contributes to the interpretation of the research findings (see Table 1).
Descriptive statistics.
Frequency
Percent
Gender
Male
165
60.4
Female
108
39.6
Total
273
100
Age
18–21
30
11.0
22–25
24
8.8
26–29
71
26.0
30–33
78
28.6
>34
70
25.6
Total
273
100
Level of education
High school
39
14.3
Diploma
83
30.4
University graduate
151
55.3
Total
273
100
Companies
Social Development Bank
50
18.3
King Saud University
122
44.7
Commission for Riyadh City
101
37
Total
273
100
Source: Author.
Results and data analysisMeasurement model fit
Using a variety of fit indices, including Chi-square, the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR), SEM assessed goodness of fit. Table 2 shows that all values are acceptable according to Hair et al. (2019).
Measurement model fit.
Model
NPAR
CMIN
DF
P
CMIN/DF
Default model
67
1848.148
461
0.000
4.009
Saturated model
528
0.000
0.000
0.000
0.000
Independence model
32
6627.491
496
0.000
13.362
Model
NFI
RFI
IFI
TLI
CFI
Default model
0.921
0.930
0.975
0.957
0.974
Saturated model
1.000
0.000
1.000
0.000
1.000
Independence model
0.000
0.000
0.000
0.000
0.000
Model
RMR
GFI
AGFI
PGFI
Default model
0.031
0.947
0.597
0.965
Saturated model
0.000
1.000
0.000
0.000
Independence model
0.316
0.135
0.080
0.127
Source: Author.
Reliability and validity of the latent variables
In this study, SEM was used to construct items, load them onto latent variables, and assess the reliability and validity of these latent variables. This involves calculating the composite reliability (CR) and average variance extracted (AVE) to determine the quality of the measurement model. Ideally, CR values should be above 0.70 to indicate good reliability. AVE values should ideally be above 0.50 to indicate good convergent validity; these results are consistent with those of Hair et al. (2019). Additionally, SEM was used to examine all factor loadings to assess the relationships between latent variables, and their indicator values should ideally be above 0.70 to indicate good reliability. The results show the overall fit of the measurement as a good model fit, which indicates that the model accurately represents the data (Hair et al., 2019) (see Figure 2 and Table 3).
Measurement model. Source: Author.
A path analysis diagram showing relationships among three main concepts: Talent Management and Development, Integrating Training and Talent Management, and Employee Training and Performance. Key paths with standardized coefficients connect various elements; for example, Talent Management and Development influences Employee Training and Performance. Each concept is linked to specific indicators, such as TMD1 to TMD3 and ETP1 to TMEP4, with respective weights ranging from 0.70 to 0.83. The diagram illustrates how integrating talent management impacts training outcomes and performance.
Reliability and validity of the latent variables.
Factors
Code
Items values
Composite reliability (CR)
Average variance extracted (AVE)
Cronbach’s alpha (CA)
Talent management and development
TMD1
0.741
0.937
0.732
0.983
TMD2
0.733
TMD3
0.792
Talent management strategies
TMS1
0.731
0.987
0.663
0.932
TMS2
0.711
TMS3
0.734
TMS4
0.721
Talent development and succession planning
TDSP1
0.760
0.973
0.579
0.911
TDSP2
0.732
TDSP3
0.725
Integrating training and talent management
ITTM1
0.790
0.899
0.712
0.927
ITTM2
0.722
ITTM3
0.771
Aligning training with talent management
ATTM1
0.722
0.937
0.604
0.931
ATTM2
0.754
ATTM3
0.713
Evaluating effectiveness of integrated programs
EEIP1
0.763
0.900
0.642
0.927
EEIP2
0.762
EEIP3
0.821
EEIP4
0.794
Employee training and performance
ETP1
0.833
0.960
0.702
0.886
ETP2
0.795
ETP3
0.704
ETP4
0.784
Role training in employee performance
RTEP1
0.793
0.843
0.669
0.839
RTEP2
0.704
RTEP3
0.701
RTEP4
0.794
Training methods employee performance
TMEP1
0.790
0.912
0.645
0.907
TMEP2
0.803
TMEP3
0.805
TMEP4
0.774
Source: Author.
Discriminant validity in structural equation modeling (SEM)
Discriminant validity in structural equation modeling (SEM): This study assessed whether different latent constructs (factors) in the developed model were distinct from each other. In other words, it checks whether the latent variables measure unique and separate concepts. Moreover, converging validity is a key component of structural equation modeling (SEM) analysis, and it assesses whether the observed indicators (items) of a latent variable are converging or measuring the same underlying construct (Table 4).
Discriminant validity.
Factors
TMD
TMS
TDSP
ITTM
ATTM
EEIP
ETP
RTEP
TMEP
TMD
0.900
TMS
0.660
0.884
TDSP
0.456
0.342
0.864
ITTM
0.660
0.345
0.402
0.843
ATTM
0.432
0.432
0.541
0.621
0.911
EEIP
0.543
0.399
0.347
0.314
0.543
0.852
ETP
0.433
0.478
0.503
0.400
0.321
0.456
0.843
RTEP
0.432
0.577
0.312
0.435
0.450
0.421
0.430
0.885
TMEP
0.535
0.436
0.324
0.354
0.543
0.443
0.329
0.540
0.894
Source: Author.
Structural model and hypotheses testing analysis
Structural equation modeling (SEM) allows for the testing of hypotheses about the relationships between latent variables and observed indicators in a research model. Therefore, this study used AMOS to test the hypotheses and structural models. After clearly defining all research hypotheses for each path and based on theoretical reasoning and prior research, this study specified the directionality of the relationships, such as positive or negative associations, for structural model and hypothesis testing analysis using AMOS. This study examined the estimates of the path coefficients (path loadings) in the research model output. According to the research model output, all hypotheses and relationships have accepted and significant values, and the path coefficients are statistically significant (more than p-values <0.05); these results are consistent with those of Hair et al. (2019); see Table 5 and Figure 3.
Results of hypotheses.
Factor
Hypothesis relationship
Estimate
S.E.
C.R.
p-values
Results
Talent management and development
H (a)
ETPa
←
TMDa
0.333
0.079
4.823
0.000
Accepted
H (b)
ITTMa
←
TMDa
0.735
0.122
6.898
0.000
Accepted
H (c)
ETPa
←
ITTMa
0.667
0.093
7.176
0.000
Accepted
H (a1)
TMD1
←
TMDa
0.744
0.122
5.748
0.000
Accepted
H (a2)
TMD2
←
TMDa
0.732
0.136
7.732
0.000
Accepted
H (a3)
TMD3
←
TMDa
0.793
0.142
8.041
0.000
Accepted
H (a4)
TMS1
←
TMDa
0.731
0.154
7.702
0.000
Accepted
H (a5)
TMS2
←
TMDa
0.714
0.153
7.760
0.000
Accepted
H (a6)
TMS3
←
TMDa
0.734
0.158
8.060
0.000
Accepted
H (a7)
TMS4
←
TMDa
0.721
0.169
8.118
0.000
Accepted
H (a8)
TDSP1
←
TMDa
0.762
0.159
7.986
0.000
Accepted
H (a9)
TDSP2
←
TMDa
0.731
0.162
8.204
0.000
Accepted
H (a10)
TDSP3
←
TMDa
0.724
0.163
8.264
0.000
Accepted
Integrating training and talent management
H (b1)
ITTM1
←
ITTMa
0.794
0.111
9.652
0.000
Accepted
H (b2)
ITTM2
←
ITTMa
0.722
0.120
11.016
0.000
Accepted
H (b3)
ITTM3
←
ITTMa
0.770
0.116
10.398
0.000
Accepted
H (b4)
ATTM1
←
ITTMa
0.720
0.116
10.374
0.000
Accepted
H (b5)
ATTM2
←
ITTMa
0.752
0.126
8.706
0.000
Accepted
H (b6)
ATTM3
←
ITTMa
0.723
0.108
10.340
0.000
Accepted
H (b7)
EEIP1
←
ITTMa
0.761
0.116
10.075
0.000
Accepted
H (b8)
EEIP2
←
ITTMa
0.764
0.114
9.517
0.000
Accepted
H (b9)
EEIP3
←
ITTMa
0.821
0.120
10.033
0.000
Accepted
H (b10)
EEIP4
←
ITTMa
0.791
0.117
10.811
0.000
Accepted
Employee training and performance
H (c1)
ETP1
←
ETPa
0.831
0.132
7.512
0.000
Accepted
H (c2)
ETP2
←
ETPa
0.793
0.120
9.429
0.000
Accepted
H (c3)
ETP3
←
ETPa
0.704
0.115
9.456
0.000
Accepted
H (c4)
ETP4
←
ETPa
0.781
0.126
9.936
0.000
Accepted
H (c5)
RTEP1
←
ETPa
0.794
0.126
9.960
0.000
Accepted
H (c6)
RTEP2
←
ETPa
0.703
0.119
10.062
0.000
Accepted
H (c7)
RTEP3
←
ETPa
0.704
0.127
9.997
0.000
Accepted
H (c8)
RTEP4
←
ETPa
0.790
0.117
9.777
0.000
Accepted
H (c9)
TMEP1
←
ETPa
0.792
0.129
9.270
0.000
Accepted
H (c10)
TMEP2
←
ETPa
0.804
0.114
9.407
0.000
Accepted
H (c10)
TMEP3
←
ETPa
0.802
0.108
9.219
0.000
Accepted
H (c10)
TMEP4
←
ETPa
0.771
0.130
9.053
0.000
Accepted
Source: Author.
Research hypotheses outcomes. Source: Author.
A structural equation model diagram shows three main latent variables: “Talent Management and Development,” “Employee Training and Performance,” and “Integrating Training and Talent Management.” Each latent variable connects to observed variables indicated by rectangles, such as “TMD1,” “ETP1,” and “ITTM1.” The paths between these variables are marked with coefficients, showing relationships and effect sizes. “Talent Management and Development” is linked to multiple elements labeled “e1” to “e10,” while “Employee Training and Performance” connects to “e21” to “e32.” The arrows and numbers signify the magnitude of influence among the variables.Discussion and implementations
This study presents a new model for talent management and employee performance, emphasizing the importance of employee training and development for sustained success. It emphasizes the dynamic nature of employee performance and the long-term perspective of talent management and performance. The model aims to create a virtuous cycle in which talent identification and development drive sustainable organizational success, fueling ongoing talent growth and enabling organizations to adapt and thrive in an ever-changing business landscape. To achieve organizational goals and objectives, high-potential employees must be strategically identified, recruited, developed, and retained (Kravariti et al., 2023). The identification and development of future leaders inside the organization is critical for continuity and stability, and talent management plays a critical role in succession planning (Kaewnaknaew et al., 2022). Emphasizing talent development communicates to staff members that the company supports their professional development and advancement, which raises workers’ engagement (Kumar, 2022). According to Yildiz and Esmer (2023), organizations should fund educational and developmental initiatives specifically designed to improve employees’ abilities, expertise, and skills. Examples of such programs include online courses, workshops, and training. As a result, as part of succession planning, talent management should be strongly associated with leadership development programs that train high-potential employees to assume important leadership roles (Kravariti et al., 2023). This is in line with the research findings, which showed a positive correlation between employee training and performance, talent management, and development, as well as between the two when training and talent management are integrated.
Strategic talent management is a method for attracting, nurturing, and retaining elite employees within a company, ensuring that the right personnel are in the right positions with the necessary skills (Hassanein and Özgit, 2022). This includes structured educational and growth initiatives, such as coaching, mentoring, training, and opportunities for skill improvement (Igweh and Kifordu, 2022). Talent management ensures that the workforce possesses the skills needed to achieve the organization’s strategic objectives (Yildiz and Esmer, 2023). Managers should conduct regular performance reviews and provide feedback to help employees identify their strengths and areas for growth (Kumar, 2022). The research indicates that strategic talent management is positively correlated with development and talent management outcomes. Talent development involves identifying high-potential employees and developing their skills and expertise to prepare them for leadership roles within the company (Zahrani, 2022a,b,c). Its main goals are performance improvement, knowledge transfer and professional growth. Knowledge transfer from experienced leaders to future leaders helps to retain essential institutional knowledge (Kravariti et al., 2023).
Combining Talent Management and Training: Training is a methodical process of providing staff members with the abilities, knowledge, and skills required to successfully carry out their existing responsibilities (Masenya, 2022). Therefore, integrating training into talent management ensures that high-potential employees receive targeted development opportunities to prepare them for leadership positions (Shet and Bajpai, 2023). Talent management extends beyond formal training, emphasizing ongoing learning and skill enhancement, which aligns with the dynamic needs of an organization (Al Awadhi and Muslim, 2023). Thus, organizations conduct a skills gap analysis to identify the disparities between the skills high-potential employees possess and the skills required for future leadership roles (Siambi, 2022). This is in line with research findings that show that training and talent management integration positively relates to both employee training and performance, and training and talent management integration positively relates to training and talent management itself. Aligning training with talent management ensures that training initiatives support an organization’s strategic objectives (Shet and Bajpai, 2023). Identifying an organization’s strategic objectives provides a foundation for aligning training (Masenya, 2022; Al-Rahmi et al., 2022; Masenya, 2022). Aligning training with talent management goals is a strategic approach that ensures that training initiatives are closely linked to an organization’s talent development and retention objectives (Al Jawali et al., 2022).
Assessing the efficacy of integrated programs: Integrated talent management is a holistic strategy that synchronizes multiple HR operations, including hiring, performance management, training, and succession planning, to accomplish corporate goals (Sinambela et al., 2022). To ensure that integrated talent management programs provide the desired results, it is essential to assess their efficacy (Javid and Amini, 2023). To improve talent management strategies, organizations must pinpoint areas for improvement and make informed choices (Collier, 2023). Provide systems for obtaining input from managers, staff members, and HR specialists to learn more about the program’s efficacy (Al Jawali et al., 2022; Al-Rahmi et al., 2022). To assess a program’s efficacy, most organizations set quantifiable goals, such as enhanced leadership development or decreased turnover (Sinambela et al., 2022). This is in line with the research findings, which indicate that integrating talent management and training has a positive correlation with determining the success of integrated programs.
Employee training and performance improvement are essential for an organization’s success and competitiveness (Siambi, 2022). Training and development programs should align with organizational goals and objectives to maximize their impact (Collier, 2023). Moreover, employee training provides opportunities for skill enhancement, leading to improved performance (Nguyen et al., 2023), and investing in employee development fosters a sense of value, leading to increased job satisfaction and motivation (Salim et al., 2022). Thus, most organizations include training impact as a part of regular performance appraisals to track progress and identify further training needs (Esthi, 2022). This is consistent with the results of this study, as it was found that there is a positive relationship between employee training and performance with training and performance itself. Organizations that prioritize training encourage a culture of continuous learning, adaptation, and innovation (Al-kharabsheh et al., 2023) and perform a thorough needs assessment to identify the specific skills and knowledge gaps that hinder employee performance (Idris et al., 2023; Abbas et al., 2021). This is in line with the study’s findings, which showed that employee performance and training have a positive relationship between them. Training also plays a role in employee performance. Therefore, most organizations understand the specific learning objectives and performance goals that training aims to achieve employees’ needs (Nguyen et al., 2023). This is in line with the study’s findings, which showed that employee performance and training have a positive relationship and that training techniques affect performance. This study was analyzed, and the hypothesis suggested that talent management and development programs positively impact an organization’s ability to attract, retain, and develop talented individuals. Strategic talent management also enhances overall workforce capabilities and performance. The hypothesis further suggests that effective talent development programs prepare employees for leadership roles, aligning with the objectives of succession planning. The integration of training and talent management strategies leads to better performance. Aligning training programs with talent management goals also improves employees’ performance. Continuous evaluation and adjustment lead to more effective programs. Training positively impacts employee performance by increasing their skills, motivation, and satisfaction. The text emphasizes the importance of talent management, development, training, and evaluation in enhancing employee performance and organizational success, emphasizing the need to align talent-related strategies. Here are the scientific contributions:
Innovative talent management model: This study contributes to the scientific community by presenting a novel model of talent management and employee performance. This model emphasizes the strategic importance of training and employee development in enhancing the sustainability of organizations. It provides a fresh perspective on the integration of training and development into talent management, offering a comprehensive framework for addressing critical issues.
Empirical insights: Through empirical research and case studies, this study offers valuable scientific insights into the actual impact of the proposed talent-management model on organizational sustainability. It provides evidence of how aligning individual employee development with organizational goals and values can improve employee performance and overall organizational success.
Interdisciplinary integration: This research bridges various disciplines by incorporating elements from human resource management, organizational psychology, and performance management. By combining insights from these fields, it enriches the scientific understanding of talent management and employee performance.
Data-driven decision-making: The talent management model places a strong emphasizes the value of data-driven decision-making. By emphasizing the necessity for businesses to adopt a more analytical and evidence-based method for efficiently managing their workforce, this study benefits the scientific community.
This study introduces an innovative talent management model, provides empirical insights, promotes data-driven decision-making, and develops a new framework. It explores alignment theory, cultural factors, and human capital development, enriching the scientific understanding of talent management. The theoretical contributions are as follows:
Talent management framework: The study contributes to the theoretical underpinning of talent management by introducing a comprehensive framework that emphasizes the role of training and employee development. This study advances existing theories by proposing a more holistic approach to talent management.
Alignment theory: The model incorporates the concept of aligning individual employee development with broader organizational missions and values. This theoretical contribution enriches our understanding of how alignment theory can be applied to enhance employee performance and organizational sustainability.
Cultural and contextual considerations: By recognizing the impact of diverse cultural and global contexts on talent management, this study extends existing theoretical frameworks. This highlights the need for organizations to consider cultural factors when implementing talent management strategies, thereby contributing to cross-cultural management theories.
Human capital development theory: This study reinforces and extends the human capital development theory by emphasizing the significance of continuous training and development in building and nurturing human capital. This study provides a theoretical foundation for the role of education and skill enhancement in achieving sustainable organizational success.
Conclusion, limitations, and future research
The development of a new model for talent management and employee performance in Saudi Arabia highlights the importance of training and development for organizational sustainability. This model promotes the alignment of individual employee development with organizational goals, fosters a culture of learning, and empowers employees to reach their full potential. It encourages data-driven decision-making and a strategic approach to talent acquisition and retention in the hospitality industry. However, the model may face resistance in organizations with traditional practices and cultures, and its effectiveness may vary depending on industry-specific factors and organizational size. Additionally, the model may require substantial financial and time investments, potentially presenting challenges for smaller organizations to implement it. Future research should focus on longitudinal studies, adaptability, and integrating technology and digital learning platforms. This will ensure the effectiveness and applicability of the model in diverse contexts.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frsus.2026.1741640/full#supplementary-material
ReferencesAbbasJ.KumariK.Al-RahmiW. M. (2021). Quality management system in higher education institutions and its impact on students’ employability with the mediating effect of industry–academia collaboration. J. Econ. Adm. Sci.40, 325–343. doi: 10.1108/jeas-07-2021-0135Al AwadhiT. H.MuslimS. B. (2023). Reviewing the relation between human resources practices, talent management and succession planning in UAE public sector. Inf. Sci. Lett.12, 1271–1283. doi: 10.18576/isl/120442Al JawaliH.DarwishT. K.ScullionH.Haak-SaheemW. (2022). Talent management in the public sector: empirical evidence from the emerging economy of Dubai. Int. J. Hum. Resour. Manag.33, 2256–2284. doi: 10.1080/09585192.2021.2001764Al-kharabshehS.AttianyM.AlshawabkehR.HamadnehS.AlshuridehM. (2023). The impact of digital HRM on employee performance through employee motivation. Int. J. Data Netw. Sci.7, 275–282. doi: 10.5267/j.ijdns.2022.10.006Al-MuaniL.Al-MomaniM.AmayrehA.AladwanS.Al-RahmiW. (2024). The effect of logistics and policy service quality on customer trust, satisfaction, and loyalty in quick commerce: a multigroup analysis of generation Y and generation Z. Uncertain Supply Chain Manag.12, 1417–1432. doi: 10.5267/j.uscm.2024.4.009Al-RahmiW. M.UddinM.AlkhalafS.Al-DhlanK. A.Cifuentes-FauraJ.Al-RahmiA. M.. (2022). Validation of an integrated is success model in the study of e-government. Mob. Inf. Syst.2022, 1–16. doi: 10.1155/2022/8909724AlserhanS.AlqahtaniT. M.YahayaN.Al-RahmiW. M.AbuhassnaH. (2023). Personal learning environments: modeling students’ self-regulation enhancement through a learning management system platform. IEEE Access11, 5464–5482. doi: 10.1109/access.2023.3236504Al-YoubiA. O.Al-HayaniA.RizwanA.ChoudhryH. (2020). Implications of COVID-19 on the labor market of Saudi Arabia: the role of universities for a sustainable workforce. Sustainability12:7090. doi: 10.3390/su12177090ArmstrongM.TaylorS. (2020). Armstrong’s handbook of human resource management practice. London: Kogan Page Publishers.AstJ.NyhuisP. (2022). Approach for determining functional flexibility of the workforce based on training losses and employee specific risks. Procedia CIRP107, 839–844. doi: 10.1016/j.procir.2022.05.072BersinJ. (2008). The blended learning book: best practices, proven methodologies, and lessons learned. Zürich: Pfeiffer.CoculovaJ.SvetozarovovaN.BertovaD. (2020). Analysis of factors determining the implementation of talent management. Mark. Manag. Innov.3, 249–256. doi: 10.21272/mmi.2020.3-18CollierP. J. (2023). Developing effective student peer mentoring programs: a practitioner’s guide to program design, delivery, evaluation, and training. New York, NY: Taylor & Francis.CollingsD. G.MellahiK. (2009). Strategic talent management: a review and research agenda. Hum. Resour. Manag. Rev.19, 304–313. doi: 10.1016/j.hrmr.2009.04.001EsthiR. B. (2022). The effect of competence and job training on the performance of crab meat agro-industry employees in West Java. J. Ilm. Agrineca22, 1–9. doi: 10.36728/afp.v22i2.2005GrovesK. S. (2007). Integrating leadership development and succession planning best practices. J. Manag. Dev.26, 239–260. doi: 10.1108/02621710710732146GülD.AkkayaG.YildirimA. (2023). The effect of talent management on the job satisfaction and organizational commitment of nurses. Int. Nurs. Rev.70, 329–337. doi: 10.1111/inr.12796, 36037319HairJ. F.RisherJ. J.SarstedtM.RingleC. M. (2019). When to use and how to report the results of PLS-SEM. Eur. Bus. Rev.31, 2–24. doi: 10.1108/EBR-11-2018-0203HassaneinF.ÖzgitH. (2022). Sustaining human resources through talent management strategies and employee engagement in the Middle East hotel industry. Sustainability14:15365. doi: 10.3390/su142215365IdrisB.SaridakisG.JohnstoneS. (2023). Training and performance in SMEs: empirical evidence from large-scale data from the UK. J. Small Bus. Manag.61, 769–801. doi: 10.1080/00472778.2020.1816431IgwehF. K.KiforduA. A. (2022). Talent management strategies, a panacea for stemming Nigeria youth exodus in the health sector: federal medical centres in focus. J. Glob. Econ. Bus.3, 1–15. doi: 10.31039/jgeb.v3i10.46JavidN. J.AminiM. (2023). Evaluating the effect of supply chain management practice on implementation of halal agroindustry and competitive advantage for small and medium enterprises. Int. J. Comput. Sci. Inf. Technol.15, 8997–9008. Available at: https://ssrn.com/abstract=4348136JehanzebK.BashirN. A. (2013). Training and development program and its benefits to employee organization: a conceptual study. Euro. J. Bus. Manag.5, 27–31. Available at: https://www.iiste.org/Journals/index.php/EJBM/article/view/3947JiaK.ZhuT.ZhangW.RasoolS. F.AsgharA.ChinT. (2022). The linkage between ethical leadership, well-being, work engagement, and innovative work behavior: the empirical evidence from the higher education sector of China. Int. J. Environ. Res. Public Health19:5414. doi: 10.3390/ijerph19095414, 35564809KaewnaknaewC.SiripipatthanakulS.PhayaphromB.LimnaP. (2022). Modelling of talent management on construction companies’ performance: a model of business analytics in Bangkok. Int. J. Behav. Anal.2, 1–17. Available at: https://ijbmcjournals.org/wp-content/uploads/2022/01/IJBA-Vol-2_1_14.pdf?utm_source=chatgpt.comKnowlesM. S. (1980). The modern practice of adult education: from pedagogy to andragogy. Englewood Cliffs, NJ: Prentice Hall Regents.KravaritiF.TasoulisK.ScullionH.AlaliM. K. (2023). Talent management and performance in the public sector: the role of organisational and line managerial support for development. Int. J. Hum. Resour. Manag.34, 1782–1807. doi: 10.1080/09585192.2022.2032265KumarS. (2022). The impact of talent management practices on employee turnover and retention intentions. Glob. Bus. Organ. Excell.41, 21–34. doi: 10.1002/joe.22130LiF.LiuB.LinW.WeiX.XuZ. (2021). How and when servant leadership promotes service innovation: a moderated mediation model. Tour. Manag.86:104358. doi: 10.1016/j.tourman.2021.104358LiuY.VrontisD.VisserM.StokesP.SmithS.MooreN.. (2021). Talent management and the HR function in cross-cultural mergers and acquisitions: the role and impact of bi-cultural identity. Hum. Resour. Manag. Rev.31:100744. doi: 10.1016/j.hrmr.2020.100744LondonM.MoneE. M.ScottJ. C. (2004). Performance management and assessment: methods for improved rater accuracy and employee goal setting. Hum. Resour. Manag.43, 319–336. doi: 10.1002/hrm.20027MartinsP. S. (2021). Employee training and firm performance: evidence from ESF grant applications. Labour Econ.72:102056. doi: 10.1016/j.labeco.2021.102056MasenyaT. M. (2022). “Integrating talent and knowledge management practices in the new normal business environment: developing future leaders in public sector organizations” in Navigating the new normal of business with enhanced human resource management strategies (Palmdale, PA: IGI Global), 113–144.MaterW.AldwairiM.MatarN.Al-RahmiW. M. (2024). Developing a success model of a social student relationship management system. Heliyon10:e25941. doi: 10.1016/j.heliyon.2024.e25941MatherG.DenbyL.WoodL. N.HarrisonB. (2011). Business graduate skills in sustainability. J. Glob. Responsib.2, 188–205. doi: 10.1108/20412561111166049McCrieR.LeeS. (2022). “Training and development for high performance” in Security operations management (Oxford: Butterworth–Heinemann).McDonnellA.CollingsD. G.MellahiK.SchulerR. (2017). Talent management: a systematic review and future prospects. Eur. J. Int. Manag.11, 86–128. doi: 10.1504/ejim.2017.081253MichaelsE.Handfield-JonesH.AxelrodB. (2001). The war for talent. Brighton, MA: Harvard Business Press.MioC.CostantiniA.PanfiloS. (2022). Performance measurement tools for sustainable business: a systematic literature review on the sustainability balanced scorecard use. Corp. Soc. Responsib. Environ. Manage.29, 367–384. doi: 10.1002/csr.2206MooreS. J.DurstP. T.RitterC.NobregaD.BarkemaH. W. (2020). Effects of employer management on employee recruitment, satisfaction, engagement, and retention on large US dairy farms. J. Dairy Sci.103, 8482–8493. doi: 10.3168/jds.2019-18025, 32622598NguyenC.JensenM.DayE. (2023). Learning not to take the bait: a longitudinal examination of digital training methods and overlearning on phishing susceptibility. Eur. J. Inf. Syst.32, 238–262. doi: 10.1080/0960085X.2021.1931494PhamN. T.Vo-ThanhT.ShahbazM.Duc HuynhT. L.UsmanM. (2020). Managing environmental challenges: training as a solution to improve employee green performance. J. Environ. Manag.269:110781. doi: 10.1016/j.jenvman.2020.110781, 32560999RiyantoS.EndriE.HerlishaN. (2021). Effect of work motivation and job satisfaction on employee performance: mediating role of employee engagement. Probl. Perspect. Manage.19, 162–174. doi: 10.21511/ppm.19(3).2021.14SalehR.AtanT. (2021). The involvement of sustainable talent management practices on employee’s job satisfaction: mediating effect of organizational culture. Sustainability13:13320. doi: 10.3390/su132313320SalimN. A.SutrisnoS.MaangoH.YusufM.HaryonoA. (2022). Employee performance and the effects of training and the workplace. J. Darma Agung30, 549–558. Available at: https://www.scribd.com/document/871974978/2267-1-5103-1-10-20221114?utm_source=chatgpt.comSaluyA. B.ArmansyahS.DjamilM.MulyanaB.PramudenaS. M.RindaR. T.. (2022). Motivation moderating the influence of organizational culture and leadership on employment performance. WSEAS Trans. Environ. Dev.18, 662–670. doi: 10.37394/232015.2022.18.63ShetS. V.BajpaiA. (2023). Integrating competency modeling in talent management: framework for implications in a disruptive environment. Thunderbird Int. Bus. Rev.65, 177–187. doi: 10.1002/tie.22246SiambiJ. K. (2022). Leadership succession planning and organization transition: a review of literature. Int. J. Manag. Stud. Res.10, 16–30. doi: 10.20431/2349-0349.1003003SinambelaE. A.DarmawanD.MendrikaV. (2022). Effectiveness of efforts to establish quality human resources in the organization. J. Mark. Bus. Res.2, 47–58. Available at: https://www.scirp.org/reference/referencespapers?referenceid=393706SonnenbergM.van ZijderveldV.BrinksM. (2014). The role of talent-perception incongruence in effective talent management. J. World Bus.49, 272–280. doi: 10.1016/j.jwb.2013.11.011Son-TuranS. (2017). “Compliance and reporting trends: essential strategies” in Risk management, strategic thinking and leadership in the financial services industry: a proactive approach to strategic thinking (Cham: Springer).SusantoP. C.SawitriN. N. (2023). Coaching, mentoring, leadership transformation and employee engagement: a review of the literature. Dinasti Int. J. Educ. Manag. Soc. Sci.4, 297–308. doi: 10.31933/dijemss.v4i2.1591VroomV. H. (1964). Work and motivation. New York, NY: Wiley.VuralY.VardarlierP.AykirA. (2012). The effects of using talent management with performance evaluation system over employee commitment. PRO58, 340–349. doi: 10.1016/j.sbspro.2012.09.1009WangX.GuchaitP.KhoaD. T.PaşamehmetoğluA.WenX. (2022). Hospitality employees’ affective experience of shame, self-efficacy beliefs and job behaviors: the alleviating role of error tolerance. Int. J. Hosp. Manag.102:103162. doi: 10.1016/j.ijhm.2022.103162YildizR. O.EsmerS. (2023). Talent management strategies and functions: a systematic review. Ind. Commer. Train.55, 93–111. doi: 10.1108/ICT-01-2022-0007ZahraniA. A. (2021). Consumers’ perceptions of intention to use a credit card: perceived risk and security. Entrep. Sustain. Issues9, 37–49. doi: 10.9770/jesi.2021.9.2(2)ZahraniA. A. (2022a). Team creativity and green human resource management practices’ mediating roles in organizational sustainability. Sustainability14:12827. doi: 10.3390/su141912827ZahraniA. A. (2022b). Promoting sustainable entrepreneurship in training and education: the role of entrepreneurial culture. Front. Environ. Sci.10:963549. doi: 10.3389/fenvs.2022.963549ZahraniA. A. (2022c). Developing a new model of ethical leadership. Entrep. Sustain. Issues9, 198–211. doi: 10.9770/jesi.2022.9.3(12)
Edited by: Alina Badulescu, University of Oradea, Romania
Reviewed by: Wioletta Pomaranik, Warsaw University of Technology, Poland
Ali Almashyakhi, Institute of Public Administration, Saudi Arabia