The Direct Negotiation Method in Human Talent Management

The Direct Negotiation Method in Human Talent Management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Direct Negotiation Method in Human Talent Management Carlos-Alberto Segura-Villarreal, Henry-Alberto Binns-Hernández, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6572727/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study assesses the relationship between the pillars of the Direct Negotiation Method (DNM) and the effectiveness of Human Talent Management (HTM). Using a quantitative exploratory approach, a 5-point Likert scale questionnaire was applied to a non-probabilistic sample of 91 subjects. Using Exploratory Factor Analysis (EFA) and Linear Regression techniques, our findings indicate that the dimensions: interests, options, and objective criteria explain 31.4% of the HTM effectiveness. While the dimension of people was not found directly significant, it is inferred to be embedded within the other three dimensions. Theoretical contribution lies in this being the first study to seek whether there is a significant relationship between the pillars of DNM and the effectiveness of HTM. The practical contribution of this study lies in providing organizations with empirical insights to optimize negotiation strategies, making HRM processes more structured and effective. By integrating negotiation principles into talent management, organizations can foster better agreements, leading to improved employee satisfaction, motivation, and retention. From a social perspective, the study underscores the role of structured negotiation in creating a more harmonious work environment, reducing workplace conflicts, and enhancing collaboration. Strengthening negotiation capabilities within HRM can lead to fairer, more transparent human resource practices, ultimately contributing to employee well-being and long-term organizational success. JEL codes (F00) Negotiations (J24) Human Capital and Skills (J53) Labor-Management Relations (M14) Corporate Culture Figures Figure 1 Figure 2 1. INTRODUCTION We find ourselves in a context, where globalization and technological advances have directly impacted how organizations develop their strategies (Teece, 2010; He et al., 2020 ), and where, it is essential to have human resources committed to the development and execution of effective strategies (Chiavenato, 2009 ; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024 ; d'Armagnac et al., 2022; Nicolás-Agustín et al., 2022 ). One of the main challenges that organizations face in this regard is the growing influence of multiculturalism, which refers to individuals with notable knowledge, skills, abilities and other cultural characteristics (Caputo et al., 2019 ; Hong & Minbaeva, 2022 ; Cabral & Martínez, 2022 ; Clouet et al., 2024 ) that requires firms to have more efficient skills and structures for the management of human talent, through which, it will be more feasible and motivating for collaborators (Ujma & Ingram, 2019 ) to absorb knowledge, process it and subsequently return it to their organization in the form of concrete results, i.e., through more innovative strategies (Cohen & Levinthal, 1990). Key skills include adaptability, emotional intelligence, understanding communication styles, norms for relationship building, conflict resolution approaches, awareness of power dynamics, and the cultural values (Brett, 2019 ). Such skills are especially important in negotiations. For van Kleef & Cote ( 2018 ), high-quality agreements or negotiations foster mutual satisfaction, maintain long-lasting relationships, create order and stability, and reduce the chances of future conflicts (Cabral & Martínez, 2022 ). Therefore, they stimulate personal and economic growth, directly or indirectly impacting the organization's operational results. The above is related to human talent management (HTM), which encompasses the set of guidelines and actions necessary for developing and directing activities such as recruitment, selection, training, rewards, and performance evaluation (Chiavenato, 2009 ; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024 ); then, the importance of achieving "high-quality agreements or negotiations" becomes clear. This can be facilitated through more structured and efficient negotiation processes such as the direct negotiation method (DNM). Based on key pillars, this approach provides a systematic framework for conducting negotiations and organizing discussions about four fundamental aspects: people, interests, options, and objective criteria (Fisher & Ury, 1985 ; Graham, 2018 ). The literature points out gaps corresponding to our study topic related to DNM and HTM (d'Armagnac et al., 2022). Some authors call for more empirical research in human resource management (Ferreira et al., 2017 ) and others mention that negotiation advantages and capabilities can moderate the relationship between workforce behaviors and human capital value creation. However, they comment that these negotiation capabilities or advantages are less frequently used in empirical research (Chadwick & Flinchbaugh, 2021 ). Following these arguments, this study sought to assess the relationship between the pillars of the direct negotiation method and the effectiveness of human talent management. In doing so, it addressed our research question: is there a significant relationship between the pillars of the direct negotiation method and human talent management? Notice that our study focuses on the negotiation literature (Fisher & Ury, 1985 ; Graham, 2018 ; Hart & Schweitzer, 2022 ) and seeks to integrate it with the corresponding human talent management literature (Chiavenato, 2009 ; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024 ; Chadwick & Flinchbaugh, 2021 ). Given the current situation faced by organizations, in which conflicts arise every day that threaten their effectiveness and productivity (Fisher & Ury, 1985 ; Graham, 2018 ), it is important to develop studies such as this one, which serve as a theoretical and practical basis for today's turbulent and conflictive environments. From a theoretical point of view, our study contributes with a valuable discussion of the literature about the direct link between the direct method of negotiation and human talent management, something that has not been specifically studied (Teece, 2010 ; van Kleef & Cote, 2018 ; Gaspar et al., 2022 ; Brett & Mitchell, 2019 ; Wang & Rajagopalan, 2015 ; Saorín-Iborra & Cubillo, 2019 ; Olekalns & Smith, 2018 ; Graham, 2018 ; Lewis et al., 2018 ; Fisher & Ury, 1985 ). Concerning its practical contribution, it lies in the fact that, empirically, it has been possible to identify key variables of the direct negotiation method that contribute to better explaining the effectiveness of human talent management, which will allow organizations to better structure their negotiation strategies and processes, thus facilitating the generation of more and better agreements with win-win results between the interested parties. On a social level, our work generates a positive impact because having better relationships within organizations can improve the work environment, thus impacting economic and social results (Applebaum et al., 2000; Schaufeli & Bakker, 2003 ; Leyva-Grijalva et al., 2024 ). Regarding the structure of this document, it is composed of five sections: the introduction, which follows the structure proposed by Plano & Creswell ( 2015 ); the theoretical framework, in which the theory that supports our research model and the hypothesis is presented; the methodology; the analysis of results; and the conclusions sections. 2. LITERATURE REVIEW Although a specific theoretical framework on the relationship between DNM and HTM has not been found in the current literature, we support our research with relevant studies on negotiation and human talent. 2.1. The Direct Negotiation Method Negotiation is the process by which conflicts or differences can be sought to be resolved in order to develop agreements (Munduate & Martínez, 1998; Pruitt , 1981; Pruitt & Carnevale , 1993) without affecting the relationships or emotions between the parties involved, thus increasing economic, social and innovation opportunities (Teece , 2010; van Kleef & Cote, 2018; Gaspar et al., 2022; Brett & Mitchell, 2019; Wang & Rajagopalan, 2015; Saorín-Iborra & Cubillo, 2019; Olekalns & Smith, 2018; Graham, 2018; Lewis et al., 2018; Fisher & Ury, 1985). Current literature indicates that negotiation advantages and capabilities are less frequently used in empirical research (Chadwick & Flinchbaugh, 2021), something that can be confirmed in the ability, motivation, and opportunity (AMO) theory, proposed by Applebaum et al. (2000), which focuses on helping to choose between human resource management practices that promote organizational performance, and in which a strong negotiation-related component cannot be identified. Furthermore, there is excessive heterogeneity concerning the conceptualization and use of the variables of the AMO Theory, which in turn leads to the proliferation of interpretations that hinder the development of a theoretical basis that can support studies such as this one (Bos-Nehles et al., 2023). On the other hand, Schaufeli & Bakker (2003) posit that employees who feel committed to their work have high levels of productivity, which concur with Mendoza Mera et al. (2023), that sustain that organizational development is a process through which the performance of an organization is sought. Similarly, Leyva-Grijalva et al. (2024) advocate the idea that good management of human talent has a greater impact on job performance. Other authors, such as Chiavenato (2009), consider that human talent management is directly linked to recruitment, selection, training, rewards and performance evaluation processes. In turn, these processes involve conflicts between the human talent manager and his or her collaborators, which requires finding and implementing more efficient and effective conflict resolution strategies or techniques (Cabral & Martinez, 2022). Negotiation is part of our lives, we negotiate every day and even if we do not recognize it, we are negotiators (Fisher & Ury, 1985; Graham, 2018; Hart & Schweitzer, 2022). Negotiation processes must be based on ethics, communication, and of course, emotional intelligence, through which the desired results can be achieved away from stereotypes or other beliefs that affect the relationship of the negotiators (Lewis et al., 2018; Gaspar et al., 2022; Caputo et al., 2019). To develop an effective negotiation, a process consisting of four stages is required (Fisher & Ury, 1985; Graham, 2018): people, interests, options, and objective criteria. Each of the stages or pillars of the direct negotiation method is detailed below: People . People should always be separated from the problem; in this way harmonious and long-term relationships can be maintained between the parties. Understanding people and establishing harmonious social relationships with them is essential for closing long-term agreements (Fisher & Ury, 1985; Lewis et al., 2018; Cuervo‐Cazurra et al., 2019; O'Reilly et al.,1991). Interests . We must be clear about our interests in the negotiation and those of our counterparts to increase the agreement’s potential benefits. Understanding the motivations, interests, and preferences of the parties is vital to reaching effective agreements (Albin, 2022; Wiblen & McDonnell, 2020; d'Armagnac et al., 2022; Nicolás-Agustín et al., 2022). Options. It is always necessary to propose multiple options for agreement and be willing to listen to proposals from the other party, you have to be creative (Fisher & Ury, 1985). As suggested by psychological literature, creative capacity and absorptive capacity are quite similar (Cohen & Levinthal, 1990). In addition, Zollo & Winter (2002), mention that incremental improvements can be achieved through the tacit accumulation of experience and sporadic acts of creativity. Any curious and creative individual can contribute to the development and implementation of innovative options (Scott & Bruce, 1994). Objective Criteria . All proposals presented must be based on objective criteria. Likewise, it is through the search for objective criteria that we increase our knowledge on a topic (Hamel, 1991., Phelps, 2010., Heirati et al., 2016). Undoubtedly, access to information is essential for decision-making. Without data or information, it is impossible to make objective and well-founded decisions. In addition, without information ignorance is generated, therefore, the storage and good management of information is crucial (Zollo & Winter, 2002., Sjodin et al., 2020., Villar et al., 2014). From all the above, it can be concluded that each pillar of the direct negotiation method, if effectively implemented, will have an impact on an integrative negotiation process, thus generating win-win results between the parties, thereby making the related processes more effective and efficient. For example, if the rewards process takes into consideration people and their interests and proposes various options based on objective criteria, the collaborators will likely agree with the agreement proposed by the person responsible for human talent management, resulting in an effective and efficient process. The same occurs with the other stages of the process. 2.2. Effectiveness in Human Talent Management (HTM) Effectiveness in Human Talent Management will be understood as how activities related to recruitment, selection, training, rewards and performance evaluation of staff are executed efficiently and harmoniously, guaranteeing satisfaction among those responsible for human talent management and collaborators concerning the processes required at each stage (Chiavenato, 2009; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024; d'Armagnac et al., 2022). In other words, we are talking about the development of integrative agreements with win-win results between the parties (Fisher & Ury, 1985; Covey, 2003), which favors and encourages long-term relationships between those involved, thus generating a better work environment which impacts on better operational performance (Schaufeli & Bakker, 2003; Leyva-Grijalva et al., 2024). Related to this last point, several investigations mention the importance of understanding the feelings of the parties involved in a negotiation in order to guarantee harmonious relations that will ultimately result in better agreements, thus bringing benefits to the interested parties (Teece, 2010; van Kleef & Cote, 2018; Gaspar et al., 2022; Brett & Mitchell, 2019; Wang & Rajagopalan, 2015; Saorín-Iborra & Cubillo, 2019; Olekalns & Smith, 2018; Graham, 2018; Lewis et al., 2018; Fisher & Ury, 1985). When there is a pleasant organizational climate within organizations that encourages cooperation, the results of its collaborators, and therefore the organization, will increase, thus generating greater benefits (Chiavenato, 2009; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024; d'Armagnac et al., 2022). Based on the above, the research hypothesis is structured: H1: There is a significant relationship between the pillars of the Direct Negotiation Method and the effectiveness of Human Talent Management . On the other hand, Figure 1 presents the proposed research model. It is structured based on the literature review and empirical experience of the authors. As can be seen in Figure 1, the dependent variable corresponds to the Effectiveness in Human Talent Management, and the independent variable corresponds to the Pillars of the Direct Negotiation Method, which will be measured through four dimensions: people, interests, options and objective criteria (Fisher & Ury, 1985; Graham, 2018). In the following section, the methodological design of our research is presented. 3. RESEARCH METHODOLOGY This cross-sectional study applies a quantitative and exploratory approach (Hernández et al., 2014), because the aim is to explore whether there is a significant relationship between the pillars of the Direct Negotiation Method and the effectiveness in Human Talent Management. The main aspects of the methodological design are presented below. 3.1. Measures We used a survey questionnaire as the research instrument (Nicolás-Agustín et al., 2022), which meets the essential requirements corresponding to reliability, validity and objectivity (Hernández et al., 2014) (See Appendix 1). Since this study is exploratory and there are no previously validated scales in other research directly related to our topic, we were tasked with establishing them based on the study of current literature and the empirical knowledge of the authors (Portocarrero-Ramos & Bonifaz de Portocarrero, 2021). The scales were subsequently evaluated by a panel of experts on the subject of study, thus verifying the logic and clarity of the items that measure the dimensions. The questionnaire is composed of five sections, one for each dimension, which measure our independent variable (See Figure 1). All dimensions are composed of seven items. These were measured on a 5-point Likert scale to indicate the degree of importance of the factors (Hair et al., 2017). It is worth remembering that a Likert scale is a set of items presented in the form of statements or judgments, and the participants' reaction is requested (Hernández et al., 2014). This scale ranges from 5 (Strongly agree) to 1 (Strongly disagree), except for the control variables, which make up the final section of the instrument. These details can be seen in Table 1 (sample description). We also carried out a pre-test (Munerah et al., 2021), meaning that the instrument was tested on 30 participants from different countries (including experts in the subject under study). They completed the questionnaire and provided feedback on the clarity and difficulty of the questions. The results confirmed the reliability, validity and objectivity of the scales used in the final questionnaire (Hernández et al., 2014) (See Appendix 1). 3.2. Data collection By applying the G*Power 3.1 software, which takes into consideration Cohen's tables (1992), the ideal sample size for the current study was determined (Reyes-Menendez et al., 2018). The parameters considered were effect size ( f 2 = 0.15)., a a=0.05., a statistical power = 95% (Hair et al., 2017), and as predictors 4 (See Figure 1) (Marcoulides & Saunders, 2006). In this way, it was obtained that the ideal sample size was 89 participants. However, our final sample size was 91 participants, which is exceeding the minimum size recommended by the G*Power 3.1 software. Data were collected using Google Forms between February 16 and February 28, 2025. The sample description is presented in Table 1. As it was an online form, subjects from various countries and with heterogeneous profiles participated. The questionnaire design followed several recommendations to avoid the common method bias associated with responses given to a series of questionnaire questions (Kock et al., 2021; Podsakoff et al., 2003). Questions were asked clearly and concisely using terms familiar to respondents. The design and presentation of the questions was also a factor taken into consideration. As a common method for bias control, Harman's one-factor test (Kock et al., 2021) was applied, which did not detect a single factor that could explain most of the total variation, suggesting that bias is very unlikely. Other tests that were applied to the data were the Shapiro-Wilk test (because n>50), the Bartlett sphericity test, and the Kaiser-Meyer Olkin (KMO) adequacy test, since it is required to verify that the data structure is adequate for the EFA, the linearity test, homoscedasticity and the Durbin-Watson test, among others that will be presented in the following section. Data preparation and analysis followed the recommendations and parameters recommended by recognized authors in the field of analysis, as well as taking into consideration the practices implemented by other cited authors. 3.3. Data analysis method To analyze the data and test the hypothesis of the model, this research was divided into two parts. The first part corresponded to the application of the Exploratory Factor Analysis (EFA) technique because the object of study is relatively new, and the theory on the subject has not yet been consolidated (Lloret-Segura et al., 2014; Méndez-Martínez & Rondón-Sepúlveda, 2012; Hair et al., 2017). Also, because the aim was to reduce the number of variables and force them to fall into a single factor, through which the dependent variable could be explained (Pérez-López, 2004; Field, 2024; Fávero & Belfiore, 2019; Anderson et al., 2012; Hair et al., 2017; López-Aguado & Gutiérrez-Provecho, 2019). Once the EFA was performed, the degree of prediction of the independent variable on the dependent variable was evaluated (See Figure 1). For this, linear regression was used as a statistical technique, which helps decision-makers make better decisions (Field, 2024). In the second stage of the analysis method, we proceeded to identify the variables that presented a greater predictive capacity of the dependent variable, for this the decision tree technique was implemented, using the Classification and Regression Trees method (CRT) (Berlanga Silvente et al., 2013). Having identified that the EFA was not the best option for the analysis of our data, we proceeded to apply the Automatic Linear Modeling technique, a technique through which, once the atypical data have been eliminated, the perceptual variables that contribute most to the explanation of our dependent variable are calculated. After this, having already identified the main variables, we proceeded to run the forced linear regression. Regarding the application of these techniques, SPSS 30 software was used. 4. ANALYSIS OF RESULTS AND DISCUSSION 4.1. Sample description. Next, the results obtained after applying the different statistical tests are presented. Table 1 shows the characteristics of the research sample. Table 1. Sample description (n = 91) Gender Frequency % Country of Origin Frequency % Man 39 42.86% Costa Rica 69 75.82% Woman 51 56.04% Argentina 4 4.40% Other 1 1.10% Brasil 3 3.30% Total 91 100% Colombia 2 2.20% Age Frequency % USA 1 1.10% 18 to 30 years old 11 12.09% Ecuador 1 1.10% 31 to 40 years old 32 35.16% El Salvador 3 3.30% 41 to 50 years old 29 31.87% Honduras 3 3.30% 51 to 60 years old 8 8.79% Mexico 1 1.10% 61 years old 11 12.09% Mongolia 1 1.10% Total 91 100% Paraguay 1 1.10% Current Situation Frequency % Venezuela 2 2.20% Salaried worker (I depend on a boss) 52 57.14% Total 91 100% Unemployed 5 5.49% Negotiation Experience Frequency % Self-employed (Entrepreneur) 25 27.47% Yes 65 71.43% Housewife/housekeeper 2 2.20% No 26 28.57% Student 5 5.49% Total 91 100% Retired 2 2.20% Experience in HTM Frequency % Total 91 100% Yes 65 71.43% Level of education Frequency % No 26 28.57% University Career 71 78.02% Total 91 100.00% Technical Career 4 4.40% Economic sector Frequency % High school 11 12.09% Services (Tertiary) 69 75.82% Elementary school 4 4.40% Manufacturing (Secondary) 16 17.58% Without studies 1 1.10% Agriculture (Primary) 6 6.59% Total 91 100% Total 91 100% Source: Prepared by the authors From Table 1, we can highlight some characteristics of our sample. As can be seen, the participants had a high academic level. For example, 78.02% of the subjects had a university degree. The main participating country was Costa Rica with 75.82%. In addition, 71.43% of the sample had experience in negotiations and human talent management. 4.2. Evaluation of the measurement model The reliability of the construct was measured by its internal consistency (Götz et al., 2010). Nunnally and Bernstein (1994) and Hair et al., (2017) suggest validating these indicators with a value of at least 0.7, considered as an acceptable level mainly for exploratory research, and values of 0.8 or 0.9 for more advanced stages of the research. The results of these tests can be observed in Appendix 1. As can be seen, the values obtained comply with the recommendations (Nunnally & Bernstein, 1994., Hair et al., 2017). Similarly, the variance inflation factor (VIF) indicates that there are no serious multicollinearity problems given that the VIFs in our instrument are less than 10 (Kutner et al., 2004; Field, 2024) (See Appendix 1). 4.3. (First Stage) Exploratory Factor Analysis (EFA) In the case of the Shapiro-Wilk test, in this first stage it was significant, interpreting in this way that our data are not normal, however this does not influence the execution of the EFA (López-Aguado & Gutiérrez-Provecho, 2019; Pérez-López, 2004; Field, 2024; Fávero & Belfiore, 2019; Anderson et al., 2012; Hair et al., 2017). For the development of the EFA, in the extraction, the maximum likelihood method was used with a fixed number of factors and with maximum interactions for convergence of 100 (Lloret-Segura et al., 2014) (Méndez-Martínez & Rondón-Sepúlveda, 2012). Kaiser-Meyer Olkin (KMO) adequacy test (0.902) and the Bartlett sphericity test (p < 0.001) offer adequate values for using this analysis (Pérez-López, 2004; Field, 2024; Fávero & Belfiore, 2019; Anderson et al., 2012; Hair et al., 2017; Lloret-Segura et al., 2014; Méndez-Martínez & Rondón-Sepúlveda, 2012). Similarly, the correlations were adequate and proved to be significant. In this method there was no reduction of variables, the same 28 variables (items) were loaded into the created factor, assuming that this explains 46.44% of the total variance. If we perform the extraction exercise using the Eigenvalue Criterion, we get five factors, however, these do not generate a significant model. Something that can be seen here is that in the first factor there is a strong mix of variables, which, as will be seen later, is affected by the characteristics of the sample. 4.4. (First Stage) Linear Regression In this first stage, when performing the regression, the factor obtained from the factorization was used as an independent variable. From the results obtained, none of them are significant and the F statistic is not significant, which makes us think about rejecting H1. In other words, with this model it is not possible to explain the dependent variable. Regarding the regression assumptions, the linearity and homoscedasticity tests do not give an ideal pattern. However, the Durbin-Watson test is within adequate values indicating that there is no autocorrelation. The value of this test corresponds to 1.929, which according to Field (2024), is between the accepted parameters of 1 and 3, which means that some of our calculations are not affected (López-Aguado & Gutiérrez-Provecho, 2019; Pérez-López, 2004; Field, 2024; Fávero & Belfiore, 2019; Anderson et al., 2012; Hair et al., 2017). Not satisfied with the situation presented above, a second method of analysis was used. This is presented below. 4.5. (Second Stage) Decision Trees and Automatic Linear Modeling Due to the results obtained with our analysis method in the first stage, we wanted to better evaluate the data to understand its behavior and the best possible solution to apply. The above led us to implement the decision tree technique using the Classification and Regression Trees method (CRT). This is because decision trees are a data mining technique that prepares, probes and explores data to extract the information hidden in them. The solution to prediction, classification and segmentation problems is addressed (Berlanga Silvente et al., 2013). In Appendix 3 we can see how, broadly speaking, the qualitative variables are being mixed with the quantitative variables, thus affecting our data. In other words, the sociodemographic variables are also influencing the explanation of our dependent variable. This situation is what is causing excessive dispersion in our data, which is reflected in the results of the analysis methods of the first stage, mainly in the fact that the variables are being mixed and do not load on the construct that they should theoretically load (Hair et al., 2017). Given this situation and the limited availability of resources, we decided to identify the main variables that explain our dependent variable using the automatic linear modeling method, which, after eliminating outliers, provides the list of variables that best explain the phenomenon under study. In Figure 2, after having separated our qualitative variables, we can see the variables that this technique shows as the main ones. As we can see, the main variables that best help in explaining the model correspond to: LI2, LO2, LO7, LI5 and LCO5 . In other words, in this case, interests, options and objective criteria are the dimensions that best explain the effectiveness in human talent management. That said, we continue with the multiple linear regression technique. 4.6. (Second Stage) Multiple Linear Regression As we saw previously, through Automatic Linear Modeling it was possible to identify the main variables that explain our dependent variable (HTM). Once identified, the linear regression technique was executed. To begin with, in Table 2 we can see that the variables are significantly correlated with each other. Table 2. Correlations GTH LI2 LI5 LO2 LO7 LCO5 Sig. (1-tailed) GTH LI2 0.418 LI5 0.012 0.000 LO2 0.000 0.000 0.001 LO7 0.005 0.000 0.000 0.000 LCO5 0.292 0.001 0.025 0.002 0.000 Source: Prepared by the authors Table 2 shows that the variables LI2 and LCO5 are not significantly correlated with the dependent variable, however, they are kept in the model because they do have a significant correlation with the other variables and contribute to a better explanation of the model as a whole. Regarding the regression assumptions, the residuals follow an normal distribution, and the linearity and homoscedasticity tests, in this second stage, present an acceptable pattern (López-Aguado & Gutiérrez-Provecho, 2019; Pérez-López, 2004; Field, 2024; Fávero & Belfiore, 2019; Anderson et al., 2012; Hair et al., 2017). Residuals present homoscedasticity, which speaks of the constancy of the variance of the residuals along the explanatory variable. The scatter plot does not present a marked pattern, and its residuals are randomly distributed (Fávero & Belfiore, 2019; Díaz-Mata, 2013; Field, 2024). Let us remember that the data are not yielding more suitable results given the strong influence that exists due to the characteristics of the sample. Entering the model, the Durbin-Watson test is within adequate values indicating no autocorrelation. In Table 3 the value of this test corresponds to 2.197, which according to Field (2024) is between the accepted parameters of 1 and 3, so some of our calculations will not be affected. Other authors who agree with this result are Lind et al. (2017) and Marôco (2003). On the other hand, the R 2 gives us a 31.4% explanatory capacity (See Table 3). At this point it is difficult to provide empirical rules for acceptable R² values, since this depends on the complexity of the model and the research discipline (Hair et al., 2017). However, considering what Chin & Marcoulides (1998) said, we could say that the explanatory capacity of the model is moderate. Table 3. Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 .560 a 0.314 0.274 19.335 2.197 a. Predictors: (Constant), LI2, LI5, LO2, LO7, LCO5 b. Dependent Variable: HTM Source: Prepared by the authors Continuing with the analysis, we see that in Table 4 the F test gives us a significant result, which means that we can effectively accept our alternative hypothesis (H1), indicating that there is a significant relationship between three of the four pillars of the Direct Negotiation Method and the effectiveness in Human Talent Management. Table 4. ANOVA a Model Sum of Squares df Mean Square F Sig. 1 Regression 14546.586 5 2909.317 7.782 <.001 b Residual 31777.875 85 373.857 Total 46324.462 90 a. Dependent Variable: HTM b. Predictors: (Constant), LI2, LI5, LO2, LO7, LCO5 Source: Prepared by the authors On the other hand, Table 5 shows that our VIF values were less than 3.3, which is following Diamantopoulos and Siguaw (2006), who consider that there is high multicollinearity when the VIF is greater than 3.3. In addition, we can observe that all the independent variables of our model are considered statistically significant with p-value < 0.05, which tells us about a good predictive capacity of our model. Table 5. Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0% Confidence Interval for B Collinearity Statistics B Std. Error Beta Lower Bound Upper Bound Tolerance VIF 1 (Constant) 47.864 11.952 4.005 0.000 24.100 71.627 LI2 -10.776 2.824 -0.485 -3.815 0.000 -16.391 -5.160 0.500 2.002 LI5 5.522 2.253 0.265 2.451 0.016 1.043 10.001 0.692 1.445 LO2 7.918 2.455 0.358 3.225 0.002 3.037 12.800 0.655 1.527 LO7 8.759 3.398 0.360 2.578 0.012 2.004 15.514 0.413 2.422 LCO5 -5.507 2.399 -0.232 -2.295 0.024 -10.277 -0.737 0.788 1.269 a. Dependent Variable: HTM Source: Prepared by the authors Finally, the regression line equation for the tested model is: Effectiveness in HTM = 47,864 – 10,776 (LI2) + 5,522 (LI5) + 7,918 (LO2) + 8,759 (LO7) – 5,507 (LCO5). By interpreting this equation, we can say that the more and greater variety of interests there are, the more difficult it will be to develop effective agreements related to human talent management (LI2). However, considering the employee's interest about the performance evaluation process that will be applied to him/her (LI5), having a wide number of options to select the ideal candidate for a position (LO2) and to close agreements (LO7), increase the effectiveness of human talent management, while if the performance evaluation is developed based on objective criteria (LCO5) this will have a negative impact on HTM. The latter is interesting given that we can indirectly associate it with people, since objective criteria can measure hard skills but not soft skills, which often determine the values and behaviors of human talent. Possibly focusing on objective criteria leaving aside other aspects of the collaborators could generate discontent and demotivation, thus affecting management processes (Chiavenato, 2009; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024; Applebaum et al., 2000). In fact, this can also be seen in the same equation, where considering the interest of the collaborator in relation to the performance evaluation process that will be applied to him or her (LI5) exerts a positive effect on the effectiveness of human talent management. 4.7. Discussion Our study aimed to assess the relationship between the pillars of the Direct Negotiation Method (DNM) and the effectiveness of Human Talent Management (HTM). The results highlight that there is indeed a significant relationship between three of the pillars/dimensions of the direct negotiation method and the effectiveness in the management of human talent, these correspond to interests, options, and objective criteria. However, the “People” dimension is indirectly embedded in these three dimensions (Fisher & Ury, 1985; Lewis et al., 2018; Cuervo-Cazurra et al., 2019; O'Reilly et al.,1991). As can be seen in the regression equation, the LI2 variable has a negative correlation with our dependent variable, meaning that the greater the number of interests exposed, the lower the effectiveness in human talent management. While it is true that authors such as Nicolás-Agustín et al., (2022) talk about how organizations would align the interests of the employer and the employee to achieve better organizational performance, in no case is it mentioned that excessive interests hinder or harm effectiveness in human talent management. The same occurs with other authors such as Albin (2022), Strutzenberger and Ambos (2014), and Cabral and Martínez (2022), who mention the importance of taking into account the interests of the parties to reach agreements, but it is not said that excessive interests distort a negotiation and harm the closing of agreements. The importance of considering the employees’ interest in performance (L15 variable, Interests dimension) has a positive correlation with the effectiveness in human talent management, which means that the greater the openness of the organization to understand the interests of the collaborators concerning the process of evaluating their performance, the more effective the human talent management process will become. This situation could occur due to effects related to motivation (Applebaum et al., 2000), since if a worker perceives that their interests are heard and taken into account, there could be a better work environment, impacting the other stages of the process, thus remembering that understanding the motivations, interests and preferences of the parties is vital to reaching effective agreements (Albin, 2022; Wiblen & McDonnell, 2020; d'Armagnac et al., 2022; Nicolás-Agustín et al., 2022). About the number of options available to select the ideal candidate for a position (L02 variable, options dimension) means that the greater the number of options available to select the ideal candidate for a position, the greater the possibilities of agreements in different areas, stages, and processes. These options also involve other ones related to the environment. For example, if it is remote work, hybrid work, or work performed under special conditions. The above is in line with Fisher and Ury (1985), and Graham (2018) who suggest that it is always necessary to have a wide variety of options for agreements and to be attentive to the counterparty's proposals. In this sense, a greater number of options for selecting the ideal candidate for a position will reflect greater efficiency in HTM. Complementarily, this variable (L07, options dimension) also presents a positive relationship with the dependent variable. This indicates that the greater the variety of options to reach agreements, the greater the effectiveness in human talent management. This can be interpreted from the point of view of van Kleef & Cote (2018) and Covey (2003), who mention that good agreements contribute to the creation of better relationships in the present and future, thus generating a better organizational climate. Seen another way, if multiple options are available for possible agreements related to the recruitment, selection, training, rewards and performance evaluation process (Chiavenato, 2009; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024), it will be easier for the parties involved to select the best option that allows them to reach a win-win result, which will generate a harmonious and lasting environment between the parties (Teece, 2010; van Kleef & Cote, 2018; Gaspar et al., 2022; Brett & Mitchell, 2019; Wang & Rajagopalan, 2015; Saorín-Iborra & Cubillo, 2019; Olekalns & Smith, 2018; Graham, 2018; Lewis et al., 2018; Fisher & Ury, 1985). About the performance evaluation that should be developed based on objective criteria (LCO5), it is true that through the search and preparation of objective criteria, we develop our understanding of a topic (Hamel, 1991., Phelps, 2010., Heirati et al., 2016), there are often hidden components in people that cannot be contemplated so easily. If by pursuing objective criteria we neglect the feeling or other soft aspects of people, we could be generating a negative environment in our work team (van Kleef & Cote, 2018; Gaspar et al., 2022; Brett & Mitchell, 2019; Wang & Rajagopalan, 2015; Saorín-Iborra & Cubillo, 2019; Olekalns & Smith, 2018; Graham, 2018; Lewis et al., 2018; Fisher & Ury, 1985), thus undermining the effectiveness of HTM processes. The results showed that this variable (LC05, objective criteria dimension) presented a negative relationship to HTM effectiveness. Therefore, it can be interpreted that the more objective criteria are applied to the performance evaluation process, the level of effectiveness in human talent management will decline. Unlike what was mentioned by Albin (2022), who indicated that external criteria are needed to evaluate the severity of claims for justice to eliminate subjective and selfish notions, in this case, it can generate an adverse effect on collaborators and therefore, on human talent management. Along these lines, people and their opinions should not be neglected, which could have an impact on positive options for agreements related to performance evaluation or other human talent management (Graham, 2018; Fisher & Ury, 1985; Quinn & Hilmer, 1994). 5. CONCLUSIONS As we have seen, excessive interests between the parties can generate distortion and difficulty in closing agreements. Considering the interests of employees in their performance evaluation processes, having multiple options for closing agreements, and taking into account the opinions and soft criteria of employees beyond objective criteria, is crucial for the effectiveness of human talent management. Another important factor is that, although the dimension corresponding to “People” does not appear clearly identified as an explanatory variable, it can be indirectly linked to each of the three dimensions. This makes us think that, beyond being an independent dimension, “People” should play an integrating role in each of the other dimensions in an intra- and inter-relational manner. On the other hand, given that the needs of society are great and growing as companies' existing business models will increasingly be based on data, and new business models will quickly emerge to redefine the way companies create and deliver or share value, it is essential to carry out more studies that can provide practical value, with which organizations can put the results obtained into practice in order to achieve more and better economic, social and technological benefits (Porter & Kramer, 2011; He et al., 2020 ). Undoubtedly, a study related to the Direct Negotiation Method in Human Talent Management is very useful. Our study offers some contributions. Theoretically, this is one of the first studies to take into consideration the pillars of the Direct Negotiation Method and the effectiveness in Human Talent Management. As Chadwick and Flinchbaugh ( 2021 ) comment, in empirical research, the references to the capabilities or advantages of negotiation are used less frequently, for this reason, our study is of great value since it lays the theoretical and empirical foundations for future research on this topic. Based on the results obtained in this research, academics will be able to rely on laying the foundations that will allow them to develop further research related mainly to negotiation and effectiveness in Human Talent Management. Likewise, scales can be perfected that allow for a better measurement of the impact of a structured negotiation on the effectiveness in Human Talent Management. In a practical sense, based on this study, organizations will be able to design strategies based on the pillars of the Direct Negotiation Method that influence the effectiveness of Human Talent Management, allowing companies to have more efficient negotiation processes when managing their human talent. This is important because if there is no harmonious and motivating environment, good results are not achieved (Applebaum et al. 2000, Ujma & Ingram, 2019 ). If efficient and well-structured negotiation processes are not developed, this will affect the success of talent management processes, which could lead to staff being dissatisfied with some of the key elements of the management process, thus generating negativity in work teams. At a social level, our work intends to create a positive impact by fostering better relationships within organizations, which enhances the work environment and, in turn, influences economic and social outcomes. Our work also has some limitations. The main possible limitation is the non-probabilistic sample, which does not allow the results to be generalized to the population. Although the sample recommended by the sample power technique was indeed exceeded, and sufficient to run the model, it would be good for future research to use a probabilistic sample. Another factor that could be understood as a limitation is that 75% of the sample belonged to the tertiary economic sector (services). It would be important for future research to strive for a more homogeneous distribution, or to analyze a broader sample belonging to the primary (agricultural) and secondary (manufacturing) economic sectors to understand the behavior of these sectors and thus ultimately be able to compare results. Future research should continue this study by adding more indicators or restructuring the sample, thereby increasing the explanatory power of this phenomenon. Our study gives an idea of the variables that explain the effectiveness of Human Talent Management. This will serve as a guide for theory development that will allow a better comprehension of the phenomenon. Declarations Acknowledgements: The authors wish to acknowledge the support provided by the Postgraduate Office of Costa Rica Institute of Technology [ITCR] for this publication. In addition, the authors thank The Advanced Science and Technology Education Center for their support in this process. Consent to Publish declaration: not applicable The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. No funding was received for conducting this study. No funds, grants, or other support was received. Ethics declarations Ethics approval and consent to participate: The research strictly followed Brazilian ethical guidelines for research involving human subjects, particularly Resolutions No. 466/2012 and No. 510/2016 of the Brazilian National Health Council (CNS). Considering that the study involved only non-invasive interviews with adult participants, did not collect sensitive or biomedical data, and presented minimal risk, it was exempt from submission to the CEP/CONEP system, as stipulated by Official Letter No.17/2022/CONEP/SECNS/MS. Internationally, this exemption corresponds to IRB exemption protocols for minimal-risk studies. All participants were fully informed about the aims and procedures of the research and signed a written informed consent form prior to participating. Participation was voluntary, anonymity was guaranteed through coding, and data were securely stored in protected digital environments. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6572727","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468855850,"identity":"dfa2bf45-015e-443d-9329-aba70176c2d9","order_by":0,"name":"Carlos-Alberto Segura-Villarreal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie3QsUrEMBjA8S8cpMsnt0ZK6Ss0BM7BE18lRdCluAhON0QKuUW99QTBhxCcC4U6nY8gFodbbrgOQg4RTDsept4omD+BBMKPLwTA5/vTBXnRbkTvTrCSALIjRO1GWJZ0RP1Ghiqo6w/9GgNkTb0xZXQbLJ7f1hOIDxyQFShEpC+4gsWjQFkKjefp1bwCfl/8TBJAGu5rSRS5eQpBlqmGjOd7CiRzPCyBYPlpybEa4HJjWjJc8fyrl8CINFqmiiIFbAmzU0gPYSWKEF7kiUYqQjw9E5qt+N11xfjc9WPTad2YS3k0i9/tYXwYPcyyZG0m49g1BQZ2od3p9ngX6CKm99rn8/n+fd95EFTmffa80gAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Carlos-Alberto","middleName":"","lastName":"Segura-Villarreal","suffix":""},{"id":468855851,"identity":"a19768e0-1bb9-471e-b91d-fe6ac0b709be","order_by":1,"name":"Henry-Alberto Binns-Hernández","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Henry-Alberto","middleName":"","lastName":"Binns-Hernández","suffix":""},{"id":468855852,"identity":"892d8b2f-c996-4f64-89a4-8cd67399025a","order_by":2,"name":"Linda Jessica De Montreuil Carmona","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Linda","middleName":"Jessica De Montreuil","lastName":"Carmona","suffix":""}],"badges":[],"createdAt":"2025-05-01 15:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6572727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6572727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84301216,"identity":"3ec40357-4a80-475b-887a-b44afe50b513","added_by":"auto","created_at":"2025-06-10 10:39:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: \u003c/strong\u003eOwn elaboration.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6572727/v1/b7285050aed2fa871980eff7.png"},{"id":84301249,"identity":"b3030546-b538-4663-97fc-904e4f6f1567","added_by":"auto","created_at":"2025-06-10 10:39:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMain predictors of the dependent variable HTM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Prepared by the authors\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6572727/v1/8a2f74486da762ec44a45f10.png"},{"id":84301582,"identity":"60a1afc1-0cd2-48f5-b428-379b41e94719","added_by":"auto","created_at":"2025-06-10 10:47:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1441256,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6572727/v1/0a729539-7067-45c3-8d6a-602ace314659.pdf"},{"id":84301259,"identity":"97ad1a44-37d5-41ba-99a4-100bc93124fc","added_by":"auto","created_at":"2025-06-10 10:39:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":358720,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6572727/v1/09b3cd465e046df9da0476ee.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Direct Negotiation Method in Human Talent Management","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eWe find ourselves in a context, where globalization and technological advances have directly impacted how organizations develop their strategies (Teece, 2010; He et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and where, it is essential to have human resources committed to the development and execution of effective strategies (Chiavenato, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; d'Armagnac et al., 2022; Nicol\u0026aacute;s-Agust\u0026iacute;n et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the main challenges that organizations face in this regard is the growing influence of multiculturalism, which refers to individuals with notable knowledge, skills, abilities and other cultural characteristics (Caputo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hong \u0026amp; Minbaeva, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cabral \u0026amp; Mart\u0026iacute;nez, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Clouet et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) that requires firms to have more efficient skills and structures for the management of human talent, through which, it will be more feasible and motivating for collaborators (Ujma \u0026amp; Ingram, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to absorb knowledge, process it and subsequently return it to their organization in the form of concrete results, i.e., through more innovative strategies (Cohen \u0026amp; Levinthal, 1990).\u003c/p\u003e \u003cp\u003eKey skills include adaptability, emotional intelligence, understanding communication styles, norms for relationship building, conflict resolution approaches, awareness of power dynamics, and the cultural values (Brett, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such skills are especially important in negotiations. For van Kleef \u0026amp; Cote (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), high-quality agreements or negotiations foster mutual satisfaction, maintain long-lasting relationships, create order and stability, and reduce the chances of future conflicts (Cabral \u0026amp; Mart\u0026iacute;nez, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, they stimulate personal and economic growth, directly or indirectly impacting the organization's operational results.\u003c/p\u003e \u003cp\u003eThe above is related to human talent management (HTM), which encompasses the set of guidelines and actions necessary for developing and directing activities such as recruitment, selection, training, rewards, and performance evaluation (Chiavenato, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); then, the importance of achieving \"high-quality agreements or negotiations\" becomes clear. This can be facilitated through more structured and efficient negotiation processes such as the direct negotiation method (DNM). Based on key pillars, this approach provides a systematic framework for conducting negotiations and organizing discussions about four fundamental aspects: people, interests, options, and objective criteria (Fisher \u0026amp; Ury, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Graham, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe literature points out gaps corresponding to our study topic related to DNM and HTM (d'Armagnac et al., 2022). Some authors call for more empirical research in human resource management (Ferreira et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and others mention that negotiation advantages and capabilities can moderate the relationship between workforce behaviors and human capital value creation. However, they comment that these negotiation capabilities or advantages are less frequently used in empirical research (Chadwick \u0026amp; Flinchbaugh, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFollowing these arguments, this study sought to assess the relationship between the pillars of the direct negotiation method and the effectiveness of human talent management. In doing so, it addressed our research question: is there a significant relationship between the pillars of the direct negotiation method and human talent management?\u003c/p\u003e \u003cp\u003eNotice that our study focuses on the negotiation literature (Fisher \u0026amp; Ury, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Graham, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hart \u0026amp; Schweitzer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and seeks to integrate it with the corresponding human talent management literature (Chiavenato, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chadwick \u0026amp; Flinchbaugh, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the current situation faced by organizations, in which conflicts arise every day that threaten their effectiveness and productivity (Fisher \u0026amp; Ury, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Graham, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), it is important to develop studies such as this one, which serve as a theoretical and practical basis for today's turbulent and conflictive environments.\u003c/p\u003e \u003cp\u003eFrom a theoretical point of view, our study contributes with a valuable discussion of the literature about the direct link between the direct method of negotiation and human talent management, something that has not been specifically studied (Teece, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; van Kleef \u0026amp; Cote, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gaspar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Brett \u0026amp; Mitchell, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang \u0026amp; Rajagopalan, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Saor\u0026iacute;n-Iborra \u0026amp; Cubillo, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Olekalns \u0026amp; Smith, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Graham, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lewis et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fisher \u0026amp; Ury, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConcerning its practical contribution, it lies in the fact that, empirically, it has been possible to identify key variables of the direct negotiation method that contribute to better explaining the effectiveness of human talent management, which will allow organizations to better structure their negotiation strategies and processes, thus facilitating the generation of more and better agreements with win-win results between the interested parties. On a social level, our work generates a positive impact because having better relationships within organizations can improve the work environment, thus impacting economic and social results (Applebaum et al., 2000; Schaufeli \u0026amp; Bakker, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Leyva-Grijalva et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the structure of this document, it is composed of five sections: the introduction, which follows the structure proposed by Plano \u0026amp; Creswell (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); the theoretical framework, in which the theory that supports our research model and the hypothesis is presented; the methodology; the analysis of results; and the conclusions sections.\u003c/p\u003e"},{"header":"2. LITERATURE REVIEW","content":"\u003cp\u003eAlthough a specific theoretical framework on the relationship between DNM and HTM has not been found in the current literature, we support our research with relevant studies on negotiation and human talent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1. The Direct Negotiation Method\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNegotiation is the process by which conflicts or differences can be sought to be resolved in order to develop agreements (Munduate \u0026amp; Mart\u0026iacute;nez, 1998; Pruitt , 1981; Pruitt \u0026amp; Carnevale , 1993) without affecting the relationships or emotions between the parties involved, thus increasing economic, social and innovation opportunities (Teece , 2010; van Kleef \u0026amp; Cote, 2018; Gaspar et al., 2022; Brett \u0026amp; Mitchell, 2019; Wang \u0026amp; Rajagopalan, 2015; Saor\u0026iacute;n-Iborra \u0026amp; Cubillo, 2019; Olekalns \u0026amp; Smith, 2018; Graham, 2018; Lewis et al., 2018; Fisher \u0026amp; Ury, 1985).\u003c/p\u003e\n\u003cp\u003eCurrent literature indicates that negotiation advantages and capabilities are less frequently used in empirical research\u0026nbsp;(Chadwick \u0026amp; Flinchbaugh, 2021), something that can be confirmed in\u0026nbsp;the ability, motivation, and opportunity (AMO) theory, proposed by Applebaum et al. (2000), which focuses on helping to choose between human resource management practices that promote organizational performance, and in which a strong negotiation-related component cannot be identified. Furthermore, there is excessive heterogeneity concerning the conceptualization and use of the variables of the AMO Theory, which in turn leads to the proliferation of interpretations that hinder the development of a theoretical basis that can support studies such as this one (Bos-Nehles et al., 2023).\u003c/p\u003e\n\u003cp\u003eOn the other hand, Schaufeli \u0026amp; Bakker (2003) posit that employees who feel committed to their work have high levels of productivity, which concur with Mendoza Mera et al. (2023), that sustain that organizational development is a process through which the performance of an organization is sought. Similarly, Leyva-Grijalva et al. (2024) advocate the idea that good management of human talent has a greater impact on job performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOther authors, such as Chiavenato (2009), consider that human talent management is directly linked to recruitment, selection, training, rewards and performance evaluation processes. In turn, these processes involve conflicts between the human talent manager and his or her collaborators, which requires finding and implementing more efficient and effective conflict resolution strategies or techniques (Cabral \u0026amp; Martinez, 2022).\u003c/p\u003e\n\u003cp\u003eNegotiation is part of our lives, we negotiate every day and even if we do not recognize it, we are negotiators (Fisher \u0026amp; Ury, 1985;\u0026nbsp;Graham, 2018; Hart \u0026amp; Schweitzer, 2022). Negotiation processes must be based on ethics, communication, and of course, emotional intelligence, through which the desired results can be achieved away from stereotypes or other beliefs that affect the relationship of the negotiators (Lewis et al., 2018; Gaspar et al., 2022; Caputo et al., 2019).\u003c/p\u003e\n\u003cp\u003eTo develop an effective negotiation, a process consisting of four stages is required (Fisher \u0026amp; Ury, 1985; Graham, 2018): people, interests, options, and objective criteria.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach of the stages or pillars of the direct negotiation method is detailed below:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePeople\u003c/strong\u003e. People should always be separated from the problem; in this way harmonious and long-term relationships can be maintained between the parties. Understanding people and establishing harmonious social relationships with them is essential for closing long-term agreements (Fisher \u0026amp; Ury, 1985; Lewis et al., 2018; Cuervo‐Cazurra et al., 2019; O\u0026apos;Reilly et al.,1991).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterests\u003c/strong\u003e. We must be clear about our interests in the negotiation and those of our counterparts to increase the agreement\u0026rsquo;s potential benefits. Understanding the motivations, interests, and preferences of the parties is vital to reaching effective agreements (Albin, 2022; Wiblen \u0026amp; McDonnell, 2020; d\u0026apos;Armagnac et al., 2022; Nicol\u0026aacute;s-Agust\u0026iacute;n et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptions.\u003c/strong\u003e It is always necessary to propose multiple options for agreement and be willing to listen to proposals from the other party, you have to be creative (Fisher \u0026amp; Ury, 1985). As suggested by psychological literature, creative capacity and absorptive capacity are quite similar (Cohen \u0026amp; Levinthal, 1990). In addition, Zollo \u0026amp; Winter (2002), mention that incremental improvements can be achieved through the tacit accumulation of experience and sporadic acts of creativity. Any curious and creative individual can contribute to the development and implementation of innovative options (Scott \u0026amp; Bruce, 1994).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective Criteria\u003c/strong\u003e. All proposals presented must be based on objective criteria. Likewise, it is through the search for objective criteria that we increase our knowledge on a topic (Hamel, 1991., Phelps, 2010., Heirati et al., 2016). Undoubtedly, access to information is essential for decision-making. Without data or information, it is impossible to make objective and well-founded decisions. In addition, without information ignorance is generated, therefore, the storage and good management of information is crucial (Zollo \u0026amp; Winter, 2002., Sjodin et al., 2020., Villar et al., 2014).\u003c/p\u003e\n\u003cp\u003eFrom all the above, it can be concluded that each pillar of the direct negotiation method, if effectively implemented, will have an impact on an integrative negotiation process, thus generating win-win results between the parties, thereby making the related processes more effective and efficient. For example, if the rewards process takes into consideration people and their interests and proposes various options based on objective criteria, the collaborators will likely agree with the agreement proposed by the person responsible for human talent management, resulting in an effective and efficient process. The same occurs with the other stages of the process.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Effectiveness in Human Talent Management (HTM)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEffectiveness in Human Talent Management will be understood as how activities related to recruitment, selection, training, rewards and performance evaluation of staff are executed efficiently and harmoniously, guaranteeing satisfaction among those responsible for human talent management and collaborators concerning the processes required at each stage (Chiavenato, 2009; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024; d\u0026apos;Armagnac et al., 2022).\u003c/p\u003e\n\u003cp\u003eIn other words, we are talking about the development of integrative agreements with win-win results between the parties (Fisher \u0026amp; Ury, 1985;\u0026nbsp;Covey, 2003), which favors and encourages long-term relationships between those involved, thus generating a better work environment which impacts on better operational performance (Schaufeli \u0026amp; Bakker, 2003; Leyva-Grijalva et al., 2024).\u003c/p\u003e\n\u003cp\u003eRelated to this last point, several investigations mention the importance of understanding the feelings of the parties involved in a negotiation in order to guarantee harmonious relations that will ultimately result in better agreements, thus bringing benefits to the interested parties (Teece, 2010; van Kleef \u0026amp; Cote, 2018; Gaspar et al., 2022; Brett \u0026amp; Mitchell, 2019; Wang \u0026amp; Rajagopalan, 2015; Saor\u0026iacute;n-Iborra \u0026amp; Cubillo, 2019; Olekalns \u0026amp; Smith, 2018; Graham, 2018; Lewis et al., 2018; Fisher \u0026amp; Ury, 1985).\u003c/p\u003e\n\u003cp\u003eWhen there is a pleasant organizational climate within organizations that encourages cooperation, the results of its collaborators, and therefore the organization, will increase, thus generating greater benefits\u0026nbsp;(Chiavenato, 2009; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024; d\u0026apos;Armagnac et al., 2022).\u003c/p\u003e\n\u003cp\u003eBased on the above, the research hypothesis is structured:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u003c/strong\u003e \u003cem\u003eThere is a significant relationship between the pillars of the Direct Negotiation Method and the effectiveness of Human Talent Management\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eOn the other hand, Figure 1 presents the proposed research model. It is structured based on the literature review and empirical experience of the authors.\u003c/p\u003e\n\u003cp\u003eAs can be seen in Figure 1, the dependent variable corresponds to the Effectiveness in Human Talent Management, and the independent variable corresponds to the Pillars of the Direct Negotiation Method, which will be measured through four dimensions: people, interests, options and objective criteria (Fisher \u0026amp; Ury, 1985; Graham, 2018).\u003c/p\u003e\n\u003cp\u003eIn the following section, the methodological design of our research is presented.\u003c/p\u003e"},{"header":"3. RESEARCH METHODOLOGY","content":"\u003cp\u003eThis cross-sectional study applies a quantitative and exploratory approach (Hern\u0026aacute;ndez et al., 2014), because the aim is to explore whether there is a significant relationship between the pillars of the Direct Negotiation Method and the effectiveness in Human Talent Management. The main aspects of the methodological design are presented below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1. Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used a survey questionnaire as the research instrument\u0026nbsp;(Nicol\u0026aacute;s-Agust\u0026iacute;n et al., 2022), which meets the essential requirements corresponding to reliability, validity and objectivity (Hern\u0026aacute;ndez et al., 2014) (See Appendix 1).\u003c/p\u003e\n\u003cp\u003eSince this study is exploratory and there are no previously validated scales in other research directly related to our topic, we were tasked with establishing them based on the study of current literature and the empirical knowledge of the authors\u0026nbsp;(Portocarrero-Ramos \u0026amp; Bonifaz de Portocarrero, 2021). The scales were subsequently evaluated by a panel of experts on the subject of study, thus verifying the logic and clarity of the items that measure the dimensions.\u003c/p\u003e\n\u003cp\u003eThe questionnaire is composed of five sections, one for each dimension, which measure our independent variable (See Figure 1). All dimensions are composed\u0026nbsp;of seven items.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThese were measured on a 5-point Likert scale to indicate the degree of importance of the factors (Hair et al., 2017). It is worth remembering that a Likert scale is a set of items presented in the form of statements or judgments, and the participants\u0026apos; reaction is requested (Hern\u0026aacute;ndez et al., 2014). This scale ranges from 5 (Strongly agree) to 1 (Strongly disagree), except for the control variables, which make up the final section of the instrument. These details can be seen in Table 1 (sample description).\u003c/p\u003e\n\u003cp\u003eWe also carried out a pre-test (Munerah et al., 2021), meaning that the instrument was tested on 30 participants from different countries (including experts in the subject under study). They completed the questionnaire and provided feedback on the clarity and difficulty of the questions. The results confirmed the reliability, validity and objectivity of the scales used in the final questionnaire (Hern\u0026aacute;ndez et al., 2014) (See\u0026nbsp;Appendix 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy applying the G*Power 3.1 software, which takes into consideration Cohen\u0026apos;s tables (1992), the ideal sample size for the current study was determined\u0026nbsp;(Reyes-Menendez et al., 2018). The parameters considered were effect size (\u003cem\u003ef\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/em\u003e= 0.15)., a\u0026nbsp;a=0.05., a statistical power = 95%\u0026nbsp;(Hair et al., 2017), and as predictors 4 (See Figure 1) (Marcoulides \u0026amp; Saunders, 2006). In this way, it was obtained that the ideal sample size was \u003cstrong\u003e89\u0026nbsp;\u003c/strong\u003eparticipants. However, our final sample size was \u003cstrong\u003e91\u0026nbsp;\u003c/strong\u003eparticipants, which is exceeding the minimum size recommended by the G*Power 3.1 software.\u003c/p\u003e\n\u003cp\u003eData were collected using Google Forms between February 16 and February 28, 2025. The sample description is presented in Table 1. As it was an online form, subjects from various countries and with heterogeneous profiles participated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe questionnaire design followed several recommendations to avoid the common method bias associated with responses given to a series of questionnaire questions (Kock et al., 2021;\u0026nbsp;Podsakoff et al., 2003). Questions were asked clearly and concisely using terms familiar to respondents. The design and presentation of the questions was also a factor taken into consideration.\u003c/p\u003e\n\u003cp\u003eAs a common method for bias control, Harman\u0026apos;s one-factor test (Kock et al., 2021) was applied, which did not detect a single factor that could explain most of the total variation, suggesting that bias is very unlikely.\u003c/p\u003e\n\u003cp\u003eOther tests that were applied to the data were the Shapiro-Wilk test (because n\u0026gt;50),\u0026nbsp;the Bartlett sphericity test, and the Kaiser-Meyer Olkin (KMO) adequacy test, since it is required to verify that the data structure is adequate for the EFA,\u0026nbsp;the linearity test, homoscedasticity and the Durbin-Watson test, among others that will be presented in the following section.\u003c/p\u003e\n\u003cp\u003eData preparation and analysis followed the recommendations and parameters recommended by recognized authors in the field of analysis, as well as taking into consideration the practices implemented by other cited authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Data analysis method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze the data and test the hypothesis of the model, this research was divided into two parts. The first part corresponded to the application of the Exploratory Factor Analysis \u003cstrong\u003e(EFA)\u0026nbsp;\u003c/strong\u003etechnique\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ebecause the object of study is relatively new, and the theory on the subject has not yet been consolidated (Lloret-Segura et al., 2014; M\u0026eacute;ndez-Mart\u0026iacute;nez \u0026amp; Rond\u0026oacute;n-Sep\u0026uacute;lveda, 2012; Hair et al., 2017). Also, because\u0026nbsp;the aim was to reduce the number of variables and force them to fall into a single factor, through which the dependent variable could be explained (P\u0026eacute;rez-L\u0026oacute;pez, 2004; Field, 2024; F\u0026aacute;vero \u0026amp; Belfiore, 2019; Anderson et al., 2012; Hair et al., 2017; L\u0026oacute;pez-Aguado \u0026amp; Guti\u0026eacute;rrez-Provecho, 2019).\u003c/p\u003e\n\u003cp\u003eOnce the EFA was performed, the degree of prediction of the independent variable on the dependent variable was evaluated (See Figure 1). For this, linear regression was used as a statistical technique, which helps decision-makers make better decisions (Field, 2024).\u003c/p\u003e\n\u003cp\u003eIn the second stage of the analysis method, we proceeded to identify the variables that presented a greater predictive capacity of the dependent variable, for this the decision tree technique was implemented, using the Classification and Regression Trees method (CRT) (Berlanga Silvente et al., 2013). Having identified that the EFA was not the best option for the analysis of our data, we proceeded to apply the Automatic Linear Modeling technique, a technique through which, once the atypical data have been eliminated, the perceptual variables that contribute most to the explanation of our dependent variable are calculated. After this, having already identified the main variables, we proceeded to run the forced linear regression.\u003c/p\u003e\n\u003cp\u003eRegarding the application of these techniques, SPSS 30 software was used.\u003c/p\u003e"},{"header":"4. ANALYSIS OF RESULTS AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003e4.1. Sample description.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, the results obtained after applying the different statistical tests are presented. Table 1 shows the characteristics of the research sample.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Sample description (n = 91)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry of Origin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e42.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eCosta Rica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e75.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eWoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e56.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eArgentina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eBrasil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eColombia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;18 to 30 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e12.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eEcuador\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;31 to 40 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e35.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eEl Salvador\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;41 to 50 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e31.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;51 to 60 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e8.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eMexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;61 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e12.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eMongolia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eParaguay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent Situation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eVenezuela\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eSalaried worker (I depend on a boss)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e57.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegotiation Experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eSelf-employed (Entrepreneur)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e27.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e71.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eHousewife/housekeeper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e28.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperience in HTM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e71.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e28.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eUniversity Career\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e78.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eTechnical Career\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic sector\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e12.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eServices (Tertiary)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e75.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eElementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eManufacturing (Secondary)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e17.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eWithout studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAgriculture (Primary)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Source:\u0026nbsp;\u003c/strong\u003ePrepared by the authors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFrom Table 1, we can highlight some characteristics of our sample. As can be seen, the participants had a high academic level. For example, 78.02% of the subjects had a university degree. The main participating country was Costa Rica with 75.82%. In addition, 71.43% of the sample had experience in negotiations and human talent management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEvaluation of the measurement model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reliability of the construct was measured by its internal consistency (G\u0026ouml;tz et al., 2010). Nunnally and Bernstein (1994) and Hair et al., (2017) suggest validating these indicators with a value of at least 0.7, considered as an acceptable level mainly for exploratory research, and values of 0.8 or 0.9 for more advanced stages of the research.\u003c/p\u003e\n\u003cp\u003eThe results of these tests can be\u0026nbsp;observed in Appendix 1. As can be seen, the values obtained comply with the recommendations (Nunnally \u0026amp; Bernstein, 1994., Hair et al., 2017). Similarly, the variance inflation factor (VIF) indicates that there are no serious multicollinearity problems given that the VIFs in our instrument are less than 10 (Kutner et al., 2004; Field, 2024) (See Appendix 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3. (First Stage) Exploratory Factor Analysis (EFA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the case of the Shapiro-Wilk test, in this first stage it was significant, interpreting in this way that our data are not normal, however this does not influence the execution of the EFA (L\u0026oacute;pez-Aguado \u0026amp; Guti\u0026eacute;rrez-Provecho, 2019; P\u0026eacute;rez-L\u0026oacute;pez, 2004; Field, 2024; F\u0026aacute;vero \u0026amp; Belfiore, 2019; Anderson et al., 2012; Hair et al., 2017).\u003c/p\u003e\n\u003cp\u003eFor the development of the EFA, in the extraction, the maximum likelihood method was used with a fixed number of factors and with maximum interactions for convergence of 100 (Lloret-Segura et al., 2014) (M\u0026eacute;ndez-Mart\u0026iacute;nez \u0026amp; Rond\u0026oacute;n-Sep\u0026uacute;lveda, 2012).\u003c/p\u003e\n\u003cp\u003eKaiser-Meyer Olkin (KMO) adequacy test (0.902) and the Bartlett sphericity test (p \u0026lt; 0.001) offer adequate values for using this analysis\u0026nbsp;(P\u0026eacute;rez-L\u0026oacute;pez, 2004; Field, 2024; F\u0026aacute;vero \u0026amp; Belfiore, 2019; Anderson et al., 2012; Hair et al., 2017; Lloret-Segura et al., 2014; M\u0026eacute;ndez-Mart\u0026iacute;nez \u0026amp; Rond\u0026oacute;n-Sep\u0026uacute;lveda, 2012). Similarly, the correlations were adequate and proved to be significant.\u003c/p\u003e\n\u003cp\u003eIn this method there was no reduction of variables, the same 28 variables (items) were loaded into the created factor, assuming that this explains 46.44% of the total variance.\u003c/p\u003e\n\u003cp\u003eIf we perform the extraction exercise using the Eigenvalue Criterion, we get five factors, however, these do not generate a significant model. Something that can be seen here is that in the first factor there is a strong mix of variables, which, as will be seen later, is affected by the characteristics of the sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4. (First Stage) Linear Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this first stage, when performing the regression, the factor obtained from the factorization was used as an independent variable.\u003c/p\u003e\n\u003cp\u003eFrom the results obtained, none of them are significant and the F statistic is not significant, which makes us think about rejecting H1. In other words, with this model it is not possible to explain the dependent variable.\u003c/p\u003e\n\u003cp\u003eRegarding the regression assumptions, the linearity and homoscedasticity tests do not give an ideal pattern. However, the Durbin-Watson test is within adequate values indicating that there is no autocorrelation. The value of this test corresponds to 1.929, which according to Field (2024), is between the accepted parameters of 1 and 3, which means that some of our calculations are not affected (L\u0026oacute;pez-Aguado \u0026amp; Guti\u0026eacute;rrez-Provecho, 2019; P\u0026eacute;rez-L\u0026oacute;pez, 2004; Field, 2024; F\u0026aacute;vero \u0026amp; Belfiore, 2019; Anderson et al., 2012; Hair et al., 2017).\u003c/p\u003e\n\u003cp\u003eNot satisfied with the situation presented above, a second method of analysis was used. This is presented below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5. (Second Stage) Decision Trees and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAutomatic Linear Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the results obtained with our analysis method in the first stage, we wanted to better evaluate the data to understand its behavior and the best possible solution to apply.\u003c/p\u003e\n\u003cp\u003eThe above led us to implement the decision tree technique using the Classification and Regression Trees method (CRT). This is because decision trees are a data mining technique that prepares, probes and explores data to extract the information hidden in them. The solution to prediction, classification and segmentation problems is addressed (Berlanga Silvente et al., 2013).\u003c/p\u003e\n\u003cp\u003eIn Appendix 3 we can see how, broadly speaking, the qualitative variables are being mixed with the quantitative variables, thus affecting our data. In other words, the sociodemographic variables are also influencing the explanation of our dependent variable. This situation is what is causing excessive dispersion in our data, which is reflected in the results of the analysis methods of the first stage, mainly in the fact that the variables are being mixed and do not load on the construct that they should theoretically load (Hair et al., 2017).\u003c/p\u003e\n\u003cp\u003eGiven this situation and the limited availability of resources, we decided to identify the main variables that explain our dependent variable using the automatic linear modeling method, which, after eliminating outliers, provides the list of variables that best explain the phenomenon under study. In Figure 2, after having separated our qualitative variables, we can see the variables that this technique shows as the main ones.\u003c/p\u003e\n\u003cp\u003eAs we can see, the main variables that best help in explaining the model correspond to: \u003cstrong\u003eLI2, LO2, LO7, LI5\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eLCO5\u003c/strong\u003e. In other words, in this case, interests, options and objective criteria are the dimensions that best explain the effectiveness in human talent management. That said, we continue with the multiple linear regression technique.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6. (Second Stage) Multiple Linear Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs we saw previously, through Automatic Linear Modeling it was possible to identify the main variables that explain our dependent variable (HTM).\u003c/p\u003e\n\u003cp\u003eOnce identified, the linear regression technique was executed. To begin with, in Table 2 we can see that the variables are significantly correlated with each other.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"350\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 350px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Correlations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGTH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLI2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLI5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLO2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLO7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCO5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig. (1-tailed)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGTH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLI2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLI5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLO2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLO7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCO5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 350px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Source:\u0026nbsp;\u003c/strong\u003ePrepared by the authors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2 shows that the variables LI2 and LCO5 are not significantly correlated with the dependent variable, however, they are kept in the model because they do have a significant correlation with the other variables and contribute to a better explanation of the model as a whole.\u003c/p\u003e\n\u003cp\u003eRegarding the regression assumptions, the residuals follow an normal distribution, and the linearity and homoscedasticity tests, in this second stage, present an acceptable pattern (L\u0026oacute;pez-Aguado \u0026amp; Guti\u0026eacute;rrez-Provecho, 2019; P\u0026eacute;rez-L\u0026oacute;pez, 2004; Field, 2024; F\u0026aacute;vero \u0026amp; Belfiore, 2019; Anderson et al., 2012; Hair et al., 2017).\u003c/p\u003e\n\u003cp\u003eResiduals present homoscedasticity, which speaks of the constancy of the variance of the residuals along the explanatory variable. The scatter plot does not present a marked pattern, and its residuals are randomly distributed (F\u0026aacute;vero \u0026amp; Belfiore, 2019; D\u0026iacute;az-Mata, 2013; Field, 2024).\u003c/p\u003e\n\u003cp\u003eLet us remember that the data are not yielding more suitable results given the strong influence that exists due to the characteristics of the sample.\u003c/p\u003e\n\u003cp\u003eEntering the model, the Durbin-Watson test is within adequate values indicating no autocorrelation. In Table 3 the value of this test corresponds to 2.197, which according to Field (2024) is between the accepted parameters of 1 and 3, so some of our calculations will not be affected. Other authors who agree with this result are Lind et al. (2017) and Mar\u0026ocirc;co (2003).\u003c/p\u003e\n\u003cp\u003eOn the other hand, the \u003cstrong\u003eR\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003egives\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eus a \u003cstrong\u003e31.4%\u0026nbsp;\u003c/strong\u003eexplanatory capacity (See Table 3). At this point it is difficult to provide empirical rules for acceptable R\u0026sup2; values, since this depends on the complexity of the model and the research discipline (Hair et al., 2017). However, considering what Chin \u0026amp; Marcoulides (1998) said, we could say that the explanatory capacity of the model is moderate.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"445\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 445px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. Model Summary\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error of the Estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDurbin-Watson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.560\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e19.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 445px;\"\u003e\n \u003cp\u003ea. Predictors: (Constant), LI2, LI5, LO2, LO7, LCO5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 445px;\"\u003e\n \u003cp\u003eb. Dependent Variable: HTM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 445px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Source:\u0026nbsp;\u003c/strong\u003ePrepared by the authors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eContinuing with the analysis, we see that in Table 4 the F test gives us a significant result, which means that we can effectively accept our alternative hypothesis (H1), indicating that there is a significant relationship between three of the four pillars of the Direct Negotiation Method and the effectiveness in Human Talent Management.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"450\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 450px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4. ANOVA\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 27.9999%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.889%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.4444%;\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.8889%;\"\u003e\n \u003cp\u003e14546.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2909.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e7.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.4444%;\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.8889%;\"\u003e\n \u003cp\u003e31777.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e373.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.4444%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20.8889%;\"\u003e\n \u003cp\u003e46324.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 450px;\"\u003e\n \u003cp\u003ea. Dependent Variable: HTM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 450px;\"\u003e\n \u003cp\u003eb. Predictors: (Constant), LI2, LI5, LO2, LO7, LCO5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 450px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Prepared by the authors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOn the other hand, Table 5 shows that our VIF values were less than 3.3, which is following Diamantopoulos and Siguaw (2006), who consider that there is high multicollinearity when the VIF is greater than 3.3. In addition, we can observe that all the independent variables of our model are considered statistically significant with p-value \u0026lt; 0.05, which tells us about a good predictive capacity of our model.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" style=\"width: 17.6991%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5. Coefficients\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 2.4702%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.1486%;\" colspan=\"3\"\u003e\u003cstrong\u003eUnstandardized Coefficients\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 2.9452%;\" colspan=\"2\"\u003e\u003cstrong\u003eStandardized Coefficients\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1.3301%;\" colspan=\"2\"\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1.2351%;\" colspan=\"2\"\u003e\u003cbr\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.1352%;\" colspan=\"2\"\u003e\u003cstrong\u003e95.0% Confidence Interval for B\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.3886%;\" colspan=\"3\"\u003e\u003cstrong\u003eCollinearity Statistics\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1.6785%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.4702%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.9452%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.3301%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.2351%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1.5834%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower Bound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5201%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper Bound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.1852%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTolerance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 0.285%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.1852%;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.6785%;\"\u003e\n \u003cp\u003e47.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.4702%;\"\u003e\n \u003cp\u003e11.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.9452%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.3301%;\"\u003e\n \u003cp\u003e4.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5834%;\"\u003e\n \u003cp\u003e24.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5201%;\"\u003e\n \u003cp\u003e71.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.1852%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2.1852%;\"\u003e\n \u003cp\u003eLI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.6785%;\"\u003e\n \u003cp\u003e-10.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.4702%;\"\u003e\n \u003cp\u003e2.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.9452%;\"\u003e\n \u003cp\u003e-0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.3301%;\"\u003e\n \u003cp\u003e-3.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5834%;\"\u003e\n \u003cp\u003e-16.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5201%;\"\u003e\n \u003cp\u003e-5.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.1852%;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e2.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2.1852%;\"\u003e\n \u003cp\u003eLI5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.6785%;\"\u003e\n \u003cp\u003e5.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.4702%;\"\u003e\n \u003cp\u003e2.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.9452%;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.3301%;\"\u003e\n \u003cp\u003e2.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5834%;\"\u003e\n \u003cp\u003e1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5201%;\"\u003e\n \u003cp\u003e10.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.1852%;\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e1.445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2.1852%;\"\u003e\n \u003cp\u003eLO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.6785%;\"\u003e\n \u003cp\u003e7.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.4702%;\"\u003e\n \u003cp\u003e2.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.9452%;\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.3301%;\"\u003e\n \u003cp\u003e3.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5834%;\"\u003e\n \u003cp\u003e3.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5201%;\"\u003e\n \u003cp\u003e12.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.1852%;\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e1.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2.1852%;\"\u003e\n \u003cp\u003eLO7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.6785%;\"\u003e\n \u003cp\u003e8.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.4702%;\"\u003e\n \u003cp\u003e3.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.9452%;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.3301%;\"\u003e\n \u003cp\u003e2.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5834%;\"\u003e\n \u003cp\u003e2.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5201%;\"\u003e\n \u003cp\u003e15.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.1852%;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e2.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2.1852%;\"\u003e\n \u003cp\u003eLCO5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.6785%;\"\u003e\n \u003cp\u003e-5.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.4702%;\"\u003e\n \u003cp\u003e2.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.9452%;\"\u003e\n \u003cp\u003e-0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.3301%;\"\u003e\n \u003cp\u003e-2.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5834%;\"\u003e\n \u003cp\u003e-10.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.5201%;\"\u003e\n \u003cp\u003e-0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 2.1852%;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.2351%;\"\u003e\n \u003cp\u003e1.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" style=\"width: 18.843%;\"\u003e\n \u003cp\u003ea. Dependent Variable: HTM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" style=\"width: 18.843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Source:\u0026nbsp;\u003c/strong\u003ePrepared by the authors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFinally, the regression line equation for the tested model is:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEffectiveness in HTM = 47,864 \u0026ndash; 10,776 (LI2) + 5,522 (LI5) + 7,918 (LO2) + 8,759 (LO7)\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026ndash; 5,507 (LCO5).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBy interpreting this equation, we can say that the more and greater variety of interests there are, the more difficult it will be to develop effective agreements related to human talent management (LI2). However, considering the employee\u0026apos;s interest about the performance evaluation process that will be applied to him/her (LI5), having a wide number of options to select the ideal candidate for a position (LO2) and to close agreements (LO7), increase the effectiveness of human talent management, while if the performance evaluation is developed based on objective criteria (LCO5) this will have a negative impact on HTM.\u003c/p\u003e\n\u003cp\u003eThe latter is interesting given that we can indirectly associate it with people, since objective criteria can measure hard skills but not soft skills, which often determine the values and behaviors of human talent. Possibly focusing on objective criteria leaving aside other aspects of the collaborators could generate discontent and demotivation, thus affecting management processes (Chiavenato, 2009; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024; Applebaum et al., 2000). In fact, this can also be seen in the same equation, where considering the interest of the collaborator in relation to the performance evaluation process that will be applied to him or her (LI5) exerts a positive effect on the effectiveness of human talent management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7. Discussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study aimed to assess\u0026nbsp;the relationship between the pillars of the Direct Negotiation Method (DNM) and the effectiveness of Human Talent Management (HTM). The results highlight that\u0026nbsp;there is indeed a significant relationship between three of the pillars/dimensions of the direct negotiation method and the effectiveness in the management of human talent, these correspond to interests, options, and objective criteria. However, the \u0026ldquo;People\u0026rdquo; dimension is indirectly embedded in these three dimensions\u0026nbsp;(Fisher \u0026amp; Ury, 1985; Lewis et al., 2018; Cuervo-Cazurra et al., 2019; O\u0026apos;Reilly et al.,1991).\u003c/p\u003e\n\u003cp\u003eAs can be seen in the regression equation, the LI2 variable has a negative correlation with our dependent variable, meaning that the greater the number of interests exposed, the lower the effectiveness in human talent management. While it is true that authors such as Nicol\u0026aacute;s-Agust\u0026iacute;n et al., (2022) talk about how organizations\u0026nbsp;would align the interests of the employer and the employee to achieve better organizational performance, in no case is it mentioned that excessive interests hinder or harm effectiveness in human talent management. The same occurs with other authors such as Albin (2022),\u0026nbsp;Strutzenberger and Ambos (2014),\u0026nbsp;and\u0026nbsp;Cabral and Mart\u0026iacute;nez (2022), who mention the importance of taking into account the interests of the parties to reach agreements, but it is not said that excessive interests distort a negotiation and harm the closing of agreements.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;importance of considering the employees\u0026rsquo;\u0026nbsp;interest in performance\u0026nbsp;(L15 variable, Interests dimension) has a positive correlation with the effectiveness in human talent management, which means that the greater the openness of the organization to understand the interests of the collaborators concerning the process of evaluating their performance, the more effective the human talent management process will become. This situation could occur due to effects related to motivation (Applebaum et al., 2000), since if a worker perceives that their interests are heard and taken into account, there could be a better work environment, impacting the other stages of the process, thus remembering that understanding the motivations, interests and preferences of the parties is vital to reaching effective agreements (Albin, 2022; Wiblen \u0026amp; McDonnell, 2020; d\u0026apos;Armagnac et al., 2022; Nicol\u0026aacute;s-Agust\u0026iacute;n et al., 2022).\u003c/p\u003e\n\u003cp\u003eAbout the\u0026nbsp;number of options available to select the ideal candidate for a position\u0026nbsp;(L02 variable, options dimension) means that the greater the number of options available to select the ideal candidate for a position, the greater the possibilities of agreements in different areas, stages, and processes. These options also involve other ones related to the environment. For example, if it is remote work, hybrid work, or work performed under special conditions. The above is in line with Fisher and Ury (1985), and Graham (2018) who suggest that it is always necessary to have a wide variety of options for agreements and to be attentive to the counterparty\u0026apos;s proposals. In this sense, a greater number of options for selecting the ideal candidate for a position will reflect greater efficiency in HTM.\u003c/p\u003e\n\u003cp\u003eComplementarily, this variable (L07, options dimension) also presents a positive relationship with the dependent variable. This indicates that the greater the variety of options to reach agreements, the greater the effectiveness in human talent management. This can be interpreted from the point of view of van Kleef \u0026amp; Cote (2018) and Covey (2003),\u0026nbsp;who mention that good agreements contribute to the creation of better relationships in the present and future, thus generating a better organizational climate.\u003c/p\u003e\n\u003cp\u003eSeen another way, if multiple options are available for possible agreements related to the recruitment, selection, training, rewards and performance evaluation process (Chiavenato, 2009; Mendoza Mera., et al., 2023; Leyva-Grijalva et al., 2024), it will be easier for the parties involved to select the best option that allows them to reach a win-win result, which will generate a harmonious and lasting environment between the parties (Teece, 2010; van Kleef \u0026amp; Cote, 2018; Gaspar et al., 2022; Brett \u0026amp; Mitchell, 2019; Wang \u0026amp; Rajagopalan, 2015; Saor\u0026iacute;n-Iborra \u0026amp; Cubillo, 2019; Olekalns \u0026amp; Smith, 2018; Graham, 2018; Lewis et al., 2018; Fisher \u0026amp; Ury, 1985).\u003c/p\u003e\n\u003cp\u003eAbout the performance evaluation that should be developed based on objective criteria (LCO5), it\u0026nbsp;is true that through the search and preparation of objective criteria, we develop our understanding of a topic (Hamel, 1991., Phelps, 2010., Heirati et al., 2016), there are often hidden components in people that cannot be contemplated so easily. If by pursuing objective criteria we neglect the feeling or other soft aspects of people, we could be generating a negative environment in our work team (van Kleef \u0026amp; Cote, 2018; Gaspar et al., 2022; Brett \u0026amp; Mitchell, 2019; Wang \u0026amp; Rajagopalan, 2015; Saor\u0026iacute;n-Iborra \u0026amp; Cubillo, 2019; Olekalns \u0026amp; Smith, 2018; Graham, 2018; Lewis et al., 2018; Fisher \u0026amp; Ury, 1985), thus undermining the effectiveness of HTM processes.\u003c/p\u003e\n\u003cp\u003eThe results showed that this variable (LC05, objective criteria dimension) presented a negative relationship to HTM effectiveness. Therefore, it can be interpreted that the more objective criteria are applied to the performance evaluation process, the level of effectiveness in human talent management will decline. Unlike what was mentioned by Albin (2022), who indicated that external criteria are needed to evaluate the severity of claims for justice to eliminate subjective and selfish notions, in this case, it can generate an adverse effect on collaborators and therefore, on human talent management. Along these lines, people and their opinions should not be neglected, which could have an impact on positive options for agreements related to performance evaluation or other human talent management (Graham, 2018; Fisher \u0026amp; Ury, 1985; Quinn \u0026amp; Hilmer, 1994).\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eAs we have seen, excessive interests between the parties can generate distortion and difficulty in closing agreements. Considering the interests of employees in their performance evaluation processes, having multiple options for closing agreements, and taking into account the opinions and soft criteria of employees beyond objective criteria, is crucial for the effectiveness of human talent management.\u003c/p\u003e \u003cp\u003eAnother important factor is that, although the dimension corresponding to \u0026ldquo;People\u0026rdquo; does not appear clearly identified as an explanatory variable, it can be indirectly linked to each of the three dimensions. This makes us think that, beyond being an independent dimension, \u0026ldquo;People\u0026rdquo; should play an integrating role in each of the other dimensions in an intra- and inter-relational manner.\u003c/p\u003e \u003cp\u003eOn the other hand, given that the needs of society are great and growing as companies' existing business models will increasingly be based on data, and new business models will quickly emerge to redefine the way companies create and deliver or share value, it is essential to carry out more studies that can provide practical value, with which organizations can put the results obtained into practice in order to achieve more and better economic, social and technological benefits (Porter \u0026amp; Kramer, 2011; He et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Undoubtedly, a study related to the Direct Negotiation Method in Human Talent Management is very useful.\u003c/p\u003e \u003cp\u003eOur study offers some contributions. Theoretically, this is one of the first studies to take into consideration the pillars of the Direct Negotiation Method and the effectiveness in Human Talent Management. As Chadwick and Flinchbaugh (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) comment, in empirical research, the references to the capabilities or advantages of negotiation are used less frequently, for this reason, our study is of great value since it lays the theoretical and empirical foundations for future research on this topic.\u003c/p\u003e \u003cp\u003eBased on the results obtained in this research, academics will be able to rely on laying the foundations that will allow them to develop further research related mainly to negotiation and effectiveness in Human Talent Management. Likewise, scales can be perfected that allow for a better measurement of the impact of a structured negotiation on the effectiveness in Human Talent Management.\u003c/p\u003e \u003cp\u003eIn a practical sense, based on this study, organizations will be able to design strategies based on the pillars of the Direct Negotiation Method that influence the effectiveness of Human Talent Management, allowing companies to have more efficient negotiation processes when managing their human talent. This is important because if there is no harmonious and motivating environment, good results are not achieved (Applebaum et al. 2000, Ujma \u0026amp; Ingram, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). If efficient and well-structured negotiation processes are not developed, this will affect the success of talent management processes, which could lead to staff being dissatisfied with some of the key elements of the management process, thus generating negativity in work teams. At a social level, our work intends to create a positive impact by fostering better relationships within organizations, which enhances the work environment and, in turn, influences economic and social outcomes.\u003c/p\u003e \u003cp\u003eOur work also has some limitations. The main possible limitation is the non-probabilistic sample, which does not allow the results to be generalized to the population. Although the sample recommended by the sample power technique was indeed exceeded, and sufficient to run the model, it would be good for future research to use a probabilistic sample. Another factor that could be understood as a limitation is that 75% of the sample belonged to the tertiary economic sector (services). It would be important for future research to strive for a more homogeneous distribution, or to analyze a broader sample belonging to the primary (agricultural) and secondary (manufacturing) economic sectors to understand the behavior of these sectors and thus ultimately be able to compare results.\u003c/p\u003e \u003cp\u003eFuture research should continue this study by adding more indicators or restructuring the sample, thereby increasing the explanatory power of this phenomenon. Our study gives an idea of the variables that explain the effectiveness of Human Talent Management. This will serve as a guide for theory development that will allow a better comprehension of the phenomenon.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge the support provided by the Postgraduate Office of Costa Rica Institute of Technology\u0026nbsp;[ITCR]\u0026nbsp;for this publication. In addition, the authors thank The Advanced Science and Technology Education Center for their support in this process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe authors did not receive support from any organization for the submitted work.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo funding was received for conducting this study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo funds, grants, or other support was received.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research strictly followed Brazilian ethical guidelines for research involving human subjects, particularly Resolutions No. 466/2012 and No. 510/2016 of the Brazilian National Health Council (CNS). Considering that the study involved only non-invasive interviews with adult participants, did not collect sensitive or biomedical data, and presented minimal risk, it was exempt from submission to the CEP/CONEP system, as stipulated by Official Letter No.17/2022/CONEP/SECNS/MS. Internationally, this exemption corresponds to IRB exemption protocols for minimal-risk studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants were fully informed about the aims and procedures of the research and signed a written informed consent form prior to participating. Participation was voluntary, anonymity was guaranteed through coding, and data were securely stored in protected digital environments. The study also followed the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp skip=\"true\"\u003eTherefore, all methods were carried out in accordance with relevant international and Brazilian guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The data used for the development of the current study are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAppelbaum, E., Berg, P. B., Kalleberg, A. L., \u0026amp; Bailey, T. A. (2000). \u003cem\u003eManufacturing Advantage: Why High-Performance Work Systems Pay Off.\u003c/em\u003e Ithaca, London: Cornell University Press.\u003c/li\u003e\n\u003cli\u003eAnderson, D., Sweeney, D., \u0026amp; Williams, T. 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Deliberate learning and the evolution of dynamic capabilities. \u003cem\u003eOrganization Science, 13\u003c/em\u003e, 339\u0026ndash;351. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"JEL codes (F00) Negotiations, (J24) Human Capital and Skills, (J53) Labor-Management Relations, (M14) Corporate Culture","lastPublishedDoi":"10.21203/rs.3.rs-6572727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6572727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assesses the relationship between the pillars of the Direct Negotiation Method (DNM) and the effectiveness of Human Talent Management (HTM). Using a quantitative exploratory approach, a 5-point Likert scale questionnaire was applied to a non-probabilistic sample of 91 subjects. Using Exploratory Factor Analysis (EFA) and Linear Regression techniques, our findings indicate that the dimensions: interests, options, and objective criteria explain 31.4% of the HTM effectiveness. While the dimension of people was not found directly significant, it is inferred to be embedded within the other three dimensions. Theoretical contribution lies in this being the first study to seek whether there is a significant relationship between the pillars of DNM and the effectiveness of HTM. The practical contribution of this study lies in providing organizations with empirical insights to optimize negotiation strategies, making HRM processes more structured and effective. By integrating negotiation principles into talent management, organizations can foster better agreements, leading to improved employee satisfaction, motivation, and retention. From a social perspective, the study underscores the role of structured negotiation in creating a more harmonious work environment, reducing workplace conflicts, and enhancing collaboration. Strengthening negotiation capabilities within HRM can lead to fairer, more transparent human resource practices, ultimately contributing to employee well-being and long-term organizational success.\u003c/p\u003e","manuscriptTitle":"The Direct Negotiation Method in Human Talent Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 10:38:45","doi":"10.21203/rs.3.rs-6572727/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-20T16:49:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T22:29:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T23:16:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23004946690213310335050566726647025466","date":"2025-06-09T20:29:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108936083189469051531415419175708189318","date":"2025-06-04T23:06:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-04T17:42:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-04T17:39:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-31T12:11:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-29T23:49:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Psychology","date":"2025-05-29T23:46:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c507459b-1c2c-4e11-8bef-a6a94dded506","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-20T05:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 10:38:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6572727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6572727","identity":"rs-6572727","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}