In the digital age and knowledge-based economy, organisations are looking for the proper resources to achieve a sustainable competitive advantage, and the theory of the firm based on TM hypothesises that talent is the only asset of firms that may provide a basis for sustainable competitive advantage. Therefore, organisations should focus on talent and the capabilities derived from talent (Rabbi, Ahad, Kousar, & Ali, 2015).

The TM process is a new approach to HR that allows organisations to focus seriously on hiring, engaging and developing employees who can contribute to the company’s growth. In essence, this means that the company’s needs are assessed and experts are found who can help with organisational goals to maintain and develop the organisation to meet current and future business requirements. Talent management ensures that the right people with the right skills are located at the right organisational functions (Baba, 2018).

In recent years, with the advent of cloud-based information technology and the TM concept, more terms have emerged, including references to digitalised talent management (DTM), human capital technology, e-talent management and, more broadly, electronic human resource management (HRM), while there is no significant difference between the concepts (Wiblen & Marler, 2021).

What has been proven in studies (Table 1) is that the use of technology in TM improves the performance of the organisation. Other studies have also confirmed the impact of using technology in TM through talent analysis, which improves strategic HRM and the development of high-performance talent (Sivathanu & Pillai, 2020).

Digitalisation is entirely distinct from digital transformation. An organisation might engage in digitalisation activities and projects, including automating processes over IT solutions, but digitisation describes the transformation of information into a digital representation (Gökalp & Veronica, 2020). Digital transformation, in fact, is not something that an organisation can implement as a project. Instead, this term addresses the special business strategic transformation that requires organisational changes in all aspects through the implementation of digital technologies.

In practice, digital transformation depends on the way the organisation copes with change. The organisation should consider change as a core competency because the company evolves towards end-to-end management of its operational processes.

The TM Forum’s Digital Maturity Model and Rabbi et al. (2015) offer a practical approach to transformation. It has been designed over the course of many months by industries as follows:

The main objective of every organisational strategy is to achieve the effectiveness and efficiency of the operation, which could lead the organisation to its mission (Dzimbiri & Molefi, 2021). In culture, people and organisational approach, TM is one of the pivotal aspects that mainly focuses on how digital transformation views TM and what actions should be undertaken to cover strategy planning, workforce planning, talent acquisition, talent development, digital learning and employee engagement to improve organisational performance. The issue is that digital technology may be the enabler, but it is people and their inherent need to connect that really speed things up.

Industry 4.0 is a gradual set of traditional products and industrial practices within the growing world of information technology. This includes using M2M and the IoT to help manufacturers and buyers alike to increase automation and improve communication and monitoring, along with self-diagnosis and new levels of analysis for future supply of production loads. Service providers and factories are typically increasingly automated and self-monitoring. The fifth generation of mobile infrastructure is a gateway to the digital era, which introduces a new capability to manage everything over the mobile network. The rapid change caused by Industry 4.0 resulted in a considerable gap between the existing ability of staff and the evolving necessities of their roles, so the need to find a more innovative approach to TM has become vital (Maisiri & Van Dyk, 2021; Schultz, 2021). Are machines able to analyse and communicate with their human and colleagues? Simultaneously, Industry 5.0 predicts the potential of quantum computing that brings humans and machines closer together in the workplace. The Fifth Industrial Revolution is about the use of unique features of artificial intelligence (Xun, Yuqian, Birgit, & Lihui, 2020) by employers and employees, who will have more equipment for sensible and well-informed decisions. The Fourth and Fifth Industrial Revolutions release HR teams from the bulk of day-to-day management as well. This freedom gives them enough time to adapt to the needs of talent identification and also allows them to focus on the growth and productivity of their organisation. The study hypothesised that:

Different definitions of organisational performance have been proposed in the previous studies, which applied the construct. Through a meta-analysis, it has been suggested that organisational performance should be assessed in financial and operational terms (Luo, Ying, & Stephanie, 2012). Organisational performance is sophisticated, empowering the organisation to execute strategies to meet institutional objectives (Frank & Kweku, 2018). Economic performance primarily has financial and market outcomes that assess the profits, sales, return on investment (ROI) for shareholders, Earning Per Share (EPS) and other financial metrics. Operational performance, on the other hand, focuses on customer satisfaction (Gong & Yi, 2018) and the firm’s HR indexes like retention (Maake, Harmse, & Schultz, 2021), productivity and ROI of HR.

As can be investigated in the given studies (Table 1), most articles address the issues of TM processes with organisational performance in a fragmented manner or assess digitalisation’s impact on TM without a conceptual model. On the other hand, because of the digital evolution in the present age, it is possible to examine new variables that play a key role in the discussion of process management that influence the administration of TM processes. Thus, to fill the existing gap, the relationship between TM and organisational performance through digitalisation mediation can be highlighted with several dimensions. Therefore, the hypotheses are as follows:

Based on the preceding hypothesises, a conceptual model is proposed (Figure 1).

In this research, in the design stage of the initial conceptual model, a quantitative research strategy has been used. In later stages, by using the method of quantitative content analysis, the conceptual model of the impact of intellectual capital on organisational performance has been determined. The field method has been used to collect data related to the confirmation or refutation of research hypotheses. In the first stage, data collection was performed by tools including books, scientific journals and articles published in this field, and in the second stage, the data were collected through a questionnaire.

Using a quantitative design, this research investigated the mediating impact of digitalised process management on the relationship between TM and organisational performance.

Reliability and validity must be considered for outer model evaluation. In exploratory researches, all factor loadings are higher than 0.5, which are acceptable, and the value of the alpha coefficient should be 0.7 or more (Moazenzadeha & Hamidi, 2018). The alpha coefficient values for all variables are above 0.7. Similarly, the composite reliability (CR) should be more than 0.7 to be acceptable (Mohammadi, 2018). All constructs have a CR of 0.7 or more, which means this condition is satisfied in this study. To check average variance extracted (AVE), if each construct has an AVE value greater than 0.5, this condition will pass (Fornell & Larcker, 1981). It has been suggested that the CR value should be greater than the AVE to corroborate convergent validity (Chavoshi & Hamidi, 2019). The AVE values for all constructs are greater than 0.5, and for all of them, the CR value is greater than the AVE value. Based on the results, the convergent validity and reliability of the questionnaire constructs have been concluded.

In the next step, discriminant validity was tested through the heterotrait–monotrait (HTMT) ratio of correlation and the Fornell–Larcker criterion by Smart PLS by using the Fornell–Larcker method; the square root of the AVE compares with the correlation of latent variables. Based on this method, the square root of each construct’s AVE needed to be greater than the correlations with other latent variables (Ab Hamid, Sami, & Sidek, 2017). It has been shown that this condition is met in this research. To confirm the discriminant validity of the model, all ratios inside the HTMT matrix should be less than 0.9, and if HTMT has a value near 1, there will be a lack of discriminant validity. All ratios in the HTMT matrix had a value of less than 0.9.

It has been suggested that β (path coefficient), R² (determination coefficient), f² (effect size) and Q² (predictive relevance) should be assessed to evaluate the structural model. The path coefficients (β) between the constructs, calculated by Smart PLS, are summarised in Table 3. The existence of a relationship, intensity and the direction of the relationship between two latent constructs is determined through the path coefficient. The bootstrapping method was performed using Smart PLS 3.2.6 to analyse the path correlations, and based on Table 3, all t-statistics range from ±1.96 and p-values are less than 0.05, which means all research hypotheses are confirmed.

In the literature review, the R² value application is to make a connection between the measurement and structural components of structural equation modelling, which demonstrates the effect of an independent variable on a dependent variable. The R² should have a value between 0 and 1, and the higher R² value indicates the more accurate prediction of the independent variable for the dependent one. If R² has the value of 0.75, 0.5 and 0.25, the model, respectively, is called substantial, moderate and weak. The R² values greater than 0.35 indicate a substantial model as well (Hair, 2011). Figure 2 demonstrates values of β and R², which imply that the proposed model is substantial. The next step after assessing the hypothesis is to calculate the influence of every independent variable on the dependent ones. The effect of an independent variable on a dependent variable can be computed through the f² criterion. If f² takes the values of 0.02, 0.15 and 0.35, the effect size is, respectively, small, medium and large. The Q² criterion, which evaluates the predictiveness power of model relationships, is computed through the blindfolding process of Smart PLS. The values of 0.02, 0.15 and 0.35 for Q² reveal that the predictive relevance is, respectively, low, medium and high. The value of Q² larger than zero implies the dependent variable has predictive relevance (Hair et al., 2014). Based on Table 4, all predictive relationships are higher than zero.

The Sobel test is used to test the effect of a mediating variable. In the Sobel test, a Z-value is obtained through the following formula: if this value is greater than 1.96, it can be confirmed at a 95% confidence level that the mediating effect of a variable is significant (Sobel, 1982). The results are summarised in Table 5: Z−value=a×b(b2×sa2)+(a2×sb2)+(sa2×sb2)[Eqn 2]

a= The value of the path coefficient between the independent variable and the mediator.

b = The value of the path coefficient between the mediator and the dependent variable.

s_a = Standard error for the path between the independent and mediator variables.

s_b = The standard error for the path between the mediator and the dependent variable.

The relationship between TM structures and organisational performance is confirmed by the output of the articles listed in Table 1. Also, the relationship between digitalised process management and organisational performance was proved. It is also mentioned in Martínez-Morán, Urgoiti, Díez, and Solabarrieta (2021), and the outcome of this study confirms the impact of digitalised process management as a mediator between TM and organisational performance.

In the present century and in recent years, digital transformation has caused a great change in working processes, employee competencies and these issues in the mobile telecommunications industry; given these factors and considering how the new generations of telecommunications are opening the door to digitalisation, these companies need to take action to adapt to rapid changes in the ratio of review and digitalisation of TM processes. Therefore, considering the approval of digitalised process management as a mediator in the relationship between TM and organisational performance, it can be seen that companies operating in this industry in Iran can update the relevant processes.