Exploring the Effect of Team Personality Traits, Role Behaviour and Job Performance on Project Success. A Case of Telecom Projects in Emerging Markets.

Exploring the Effect of Team Personality Traits, Role Behaviour and Job Performance on Project Success. 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A Case of Telecom Projects in Emerging Markets. Charles Okeyia, Charlotte Smith, Michail Koubouros This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3990907/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Team personality traits enable project practitioners to deliver project success through role behaviour and job performance. However, various team personality traits influence role behaviour and job performance. Project success is a challenging phenomenon for project practitioners, where different factors play a critical role in project success. The objective of this study is to explore the effect of team personality traits on project success, with moderating effects of role behaviour and job performance. The study includes participants from telecom organisations in Nigeria, an emerging market, using the survey-structured quantitative data collection technique. The five-factors personal traits model was used to assess the individual participants and personal traits and team role experience and orientation factors. The collected data was analysed using the SPSS AMOS v29, with the results indicating that personal trait factors of agreeableness and conscientiousness are positive predictors of project success. In contrast, openness, extraversion and neuroticism did not have a strong correlation with project success in this context. The findings also concluded that the team role experience and orientation supported the role behaviour impact on project success. The team role experience and orientation model factors as moderators to role behaviour and job performance, which are relevant to the theory and practice and provide in-depth insight that is valuable for project practitioners, decision-makers, individuals, and scholars. Management Personality Traits Role Behaviour Job Performance Project Success Figures Figure 1 Figure 2 Figure 3 1. The Background Story In Nigeria, telecom organisations currently deploy and execute infrastructure and asset strategies to help their customers access better internet connections, network coverage and penetration, and good quality of service - QoS (NCC, 2021; Danbatta et al., 2022). Therefore, to avoid customers porting their subscriptions and patronage because of QoS issues and competition, these organisations focus on improving business performance by creating value through their project teams to deliver their projects within scope, within budget, and on time (PMI, 2014). However, managing project teams comes with challenges from formation and membership, with what drives team performance and success a fundamental problem for team practitioners and scholars to address (Tannenbaum et al., 2012; Mathieu et al., 2015). From a practical perspective that involves operational business decisions, practitioners and managers should understand the rationale that certain work teams are more efficient than others. Conversely, extant literature noted personality traits (Costa & McCrae, 1992), method of project team compositions and roles behaviour (Belbin 1993, 2010) as significant factors for job performance (Boshoff & Arnolds, 1995), which results in project success. This assertion triggers the discussions and debates that project success depends on individual organisational and work commitment, which arises from work motivation or personality (Costa & McCrae, 1992; Dwivedula et al., 2016; Harahap, 2019; Wen et al., 2021). This theoretical perspective underlines that personalities in a project in various roles have distinct anticipation of being achieved; occasionally, they are used to comprehend other team diversity and team performance. That is why Beblin (2010) argued that structuring team roles to build a team was critical for the team's effectiveness and, thus, one of the most significant elements affecting team success. Thus, given the extant theoretical lens, this study hypothesis may be worth exploring. 1. 2. Study Objectives Guided by this paper's context and extant academic literature, this study aims to contribute to the understanding of project success through team management and collaboration based on personality traits, roles, behaviour and job performance. Thus, the paper investigates the correlation between personality traits driving project success through role behaviour and job performance. The study question is constructed to achieve this objective. 1. 3. Study Questions This study aims to explore the effect of personality traits driving project success through role behaviour and job performance and also address the identified study gaps on team member's personality traits, an approach likely to enhance the extant findings of personality traits in operative team formation and management (LePine et al., 2011). Accordingly, the specific study questions are stated below. Q1. Which team personality traits could affect project success? Q2. How do personality traits impact role behaviour and project success? Q3. Do team members influence job performance on project success? 1. 3.1 Hypotheses of the Study Given the identified constructs, the hypothesis below will guide the entire study, assist in finding the answer to the study questions and define the correlation between the variables. H1. Personality traits do not have a significant effect on driving project success. H2. Team role behaviour does not have a significant impact on project success. H3. Team member's work influence is not significant to job performance and project success. 1.4 Theoretical Framework Based on the extant literature and existing understanding of the five-factor (FFM) personality traits driving project success. This study applies the five-factor personality trait theory, with an identifiable feature on the theories (Costa & McCrae, 1992) (Judge et al., 1999) and (Wen et al., 2021), who claim that individuals with high conscientiousness (the trait of being disciplined, orderly and determined to ensure what is correct) performed better through their jobs, while individual high in neuroticism (the trait of an individual with anxiety or self-consciousness) were less effective in selected vital approaches. However, some extant study studies have argued about the team personality approach by embracing a micro-dynamic viewpoint in considering the link among individual personality traits, team role behaviour and performance. First, Stewart et al. (2005) employed a new approach to operationalising role behaviours as a multilevel relationship by individualising them from team role formations and linking them with dimensions of personality traits and team performance measures. Secondly, the study draws on employing role behaviours to connect individual personality traits and team outcomes through cross or directional-level techniques (Tasa et al., 2011) by combining the situational impacts of shared effectiveness when studying the correlation among team members' personality traits and role behaviours. In addition, the study will utilise Belbin's (1993; 2010) team roles and performance theory, which describes different personality characteristics and team behaviour grouped within three team roles: people-oriented, action-oriented, and problem-solving-oriented. These constructs are critical to project success, as they are quantifiable outputs or self-assessments of individuals concerning their performance (Senior, 1997; Partington et al., 1999; Belbin, 2010). However, the empirical result for this notion is broadly combined, as various studies stated a strong relationship between personality traits with job performance and project success (Belbin, 2010; LePine et al., 2011); job performance is influenced by both individual personality traits (Yun et a., 2007; Mihalcea 2013) individuals with positive behaviours towards job performance, but negative role behaviours create project failure. 1.5. Study Design and Methods This study considered the quantitative study design the most suitable method based on its characteristics. It is designed to find how several individuals think, act or feel in a particular method (Creswell et al., 2009). This includes a case study approach (Yin, 2014; 2016; Saunders et al., 2019), using a survey questionnaire to examine the correlation among the effect of personality traits driving project success through role behaviour and job performance. This assertion is centred on the understanding that quantitative study relates to theories with essential elements such as models, constructs, concepts, problems and hypotheses. Kerlinger (1986, p. 26) argued that a concept expresses an abstraction formed by a generalisation from particulars. It will be acknowledged that the quantitative method reflects the researcher's actual philosophical stance on the understanding and study, which aligns with the positivist philosophical stance (Crotty, 1998; Denzin & Lincoln, 2013; Saunders et al., 2019) to identify and comprehend the correlation among the key variables, such as personality, role behaviour and job performance to project success and to explain experiences linked with those variables by testing extant theories and concepts. 1.5.1 Data collection and analysis approaches The study applies a quantitative approach to data collection to address the study objectives and hypothesis by administrating an online structured questionnaire to the participants. The primary study data was collected from the field technicians and operations team members of various clusters in their south/south region. The study survey was designed using the five Likert scale to collect data. The Likert scale for quantitative measurement was created by Pearce and Porter (1985) and employed by Carmeli and Freund (2004) in their study on job performance and satisfaction. The online structured questionnaire was designed with the MS forms to reflect the study problem and the variables. 1.5.2 Study Sample Design The sample size for this study is one hundred and fifteen (115) employees of the studied organisation, focusing on the field operation team members who have worked one year and over in the studied organisation. The justification of this study sample size was based on Kumar et al. (2013) suggestion that includes the study approach, participants accessibility, and analytical approach – regression analysis using SPSS. Hair et al. (2018) noted that a minimum sample size of fifty (50) to one hundred (100) is appropriate for regression analysis. Likewise, the probability sampling technique will be applied for this study to test the hypotheses and make inferences regarding the variables and population. Kothari (2019) supported the use of probability sampling techniques in quantitative studies when determining the sample size from a known population, such as in this study context. 1.6. Data Analysis The data will be analysed using SPSS v29 and SPSS AMOS v28. The analysed data will be presented in a tables and figures format, using statistical tools such as multiple regression and correlation analysis regarding the variables. The designed hypotheses will be analysed using correlation analysis to find the model that is an appropriate fit for the sample size and data. 1.7 Significance of the Study This study is significant because it will resolve the delay in project success delivery and issues of negative team collaboration in job performance, which is related to various dimensions of personality traits. Primarily, a conceptual framework is used to justify and describe the role the dimensions of five-factor personality traits perform in the studied workplace and teams, a perspective well-grounded in literature (LePine et al., 2011; Barrick et al., 2013; Mathieu et al., 2015; Kendra, 2019; Erik, 2020; Mu et al., 2022; Zell and Lesick, 2022). Thus, focusing on the gap in the reviewed literature affects the study context and methodology on team formation and, in addition, assists in addressing the team miscommunication and collaborative issues that impact project success in the study organisation. 2. Literature Review This chapter shows a synthesised and critiqued evaluation of the extant literature on personality, role behaviour and job performance by exploring the theoretical and empirical arguments, critical discussions, opportunities and disagreements. The study focuses on team personality traits and underlines the key findings between team behaviour, performance, and project success. The study questions are illustrated with a multi-directional study model developed from the literature and conceptual framework. In the study situation, it is important to identify which features improve the team's job performance and project success. Previous studies into job performance and project success have proposed that specific factors impact performance and project management, as Hao et al. (2019) proposed personality, job design, and knowledge-sharing behaviour. However, the impact of team and individual personality on team job performance and project success has been generally studied in extant literature (LePine et al., 2011). Moreover, subsequent study offered a catalogue of personality traits, such as the big five-factors model, in which (Yilmaz et al. 2017; Joanna 2019) concluded that personality traits of extroversion and neuroticism are significant to team job performance influencing project success, which has been expanded by (Erik, 2020; Zell and Lesick, 2022). Thus, this study can be measured as one of the contributions to studying this significant subject matter. 2.1. Personality Personality traits have received significant attention in psychology studies (McCrae and Costa, 2013) and management studies (LePine et al., 2011; Barrick and Mount, 2013). Kendra, 2019) and Hogan agree with Gavoille and Hazans (2022) and Mu et al. (2022) that personality is a function of a human being's inclination to behave and act in a specific approach. Mayer (2005), Kramer (2014) and Kendra (2019) conclude that a personality is a steady form of psychological procedures, features and developments that generate cognitive outlooks and methods that could be applied to decide people's differences, resemblances, opinions, approaches, and actions. This insight is because personality traits influence and define individual behaviour in a work setting and impact team and job performance (Barrick et al., 1998; Raja, 2019; Maryam, 2020). However, this study has suggested various models in which personality, role behaviour, job performance and project success congregate. The leading model was the five-factors model (FFM) of extroversion, neuroticism, agreeableness, conscientiousness and openness (Goldberg, 1990; LePine et al., 2011; Judge, 2013; Barbee, 2020; Erik, 2020). However, in contrast, McCrae and Costa (2013) and Joanna (2019) maintained that FFM is not independent because two higher-order factors are extroversion and neuroticism. This is in disagreement with Goldberg's (1990) and Erick's (2020) view that described extroversion as the inclination to be sociable, enthusiastic, energetic and optimistic, associated with the extent and strength of people, interactive action (Bruck and Allen, 2003); related to project success that needs widespread social communication (Barrick and Munt, 2003); positively interrelated with involvement in teams (Barry et al., 1997; Kendra, 2019; Gavoille and Hazans, 2022). These extant perspectives remain relevant in this domain, as the descriptions indicate a relationship between particular dimensions of personality traits (FFM) and specific individual benchmarks of job performance (Barrick and Mount, 1991; Hough, 1992; Raja, 2019; Maryam, 2020; Neema, 2020). Therefore, personality has been broadly understood as a notably significant individual contribution to the team and job performance (LePine et al., Bradley et al., 2013; Colbert et al.,2014; Kendra, 2019). The explanations for this are diverse, as members' instincts, judgments, and behaviours affected by their personality traits are wholly connected to their job performance and affect collaborations and interactions with team members. 2.2 Role Behaviour Studies have offered various standard options to operationalise team members' role behaviours as these roles are grouped into task and social boundary-spanning roles (Mumford et al., 2006). However, this study focuses on task and social role behaviour based on the study's focus on job performance. Van Dick et al. (2011) support Mumford et al. (2006) earlier studies by finding task roles primarily imply a group of behaviours aiming at the accomplishment of jobs and the shared team aims, involving job involvement, establishing team objectives and working together toward the objectives (LePine et al., 2011), evaluating and resolving problems, (Kilumets et al., 2015) monitoring advancement and obeying to the cutoff date. Conversely, social roles include behaviour that is directed to interpersonal harmony within the team that encourages effective teamwork (Belbin, 1993; LePine et al., 2011; Barrick et al., 2013; Kilumets et al., 2015) such as caring for individual emotions and feelings, respect for each other, opinions, contributing to team objectives and open communication and collaborations. In disagreement with the studies by Belbin (1993), LePine et al. (2011) and Barrick et al. (2013), Mathieu et al., 2015) argued on the theory of team role experience and orientation (TREO), which is applied to indicate the wide task and social role classification for job performance. Based on these conclusions, this study selected the TREO theory because of Mathieu et al. (2015) perspective and the argument that organisers and doers were created from a job-oriented viewpoint, such as job completion. However, challengers and innovators were created from the change-oriented perspective, examining the team context or standing conduct of performing jobs and providing new methods. The connectors and team builders, inversely, were expressed as sub-sets of the social-emotional feature. From these arguments, employing the job and social role behaviour structure appears appropriate to team up the dimensions of the TREO theory. However, the change-oriented viewpoint that entails challengers and innovators is also associated with team jobs since questioning existing methods for job performance, pushing the team to examine all likely resolutions and developing creative concepts for resolving issues are about enhancing team job performance and attaining job-related objectives. This discussion synthesises the correlation among the FFM and TREO dimensions in Table 2.1 related to the extant literature-based study questions, methods and context. Table 2.1. The Relationship between Five Factors and The Team Role Experience and Orientation Sources: Mathieu et al., (2015). Given the correlation between FFM and the TREO dimensions, this study uses the FFM to recreate the individual and team personality traits and role behaviour to address job performance issues in the studied context. Following this line of investigation, the study aims to understand the influence of personality traits, roles, and job performance on project success and fill the gaps by integrating the previously described correlations between certain personality traits and project success. 2.3 Job Performance Job performance describes the effect of personality traits and team roles on project success. (Kanfer, 1990; Campbell et al., 1993; Roe, 1999). Raja (2019) supports earlier studies of Campbell et al., 1993 and Kramer (2014) that performance has been conceptualised from two viewpoints: procedure or activity and result. The process is about the behavioural aspect that denotes how individuals perform a job, the action itself. This proposition was supported by Campbell et al. (1993) with the outlined factors that account for all the behaviours, which included job performance (job-specific task proficiency, non-job-specific task proficiency, written and oral communication task proficiency, demonstrating effort, supporting individual discipline, enabling team performance, leadership and management. Conversely, Borman et al. (1993) asserted that the result is a pattern of effect characteristics that indicates the consequence of individual behaviour. Furthermore, Goldberg (1993) argues that behaviour considerably influences job performance, remarkably when individual personality traits conflict with team and job performance measures. This understanding explains the relationship between job performance and project success, which implies that approaches concerning a behaviour lead to targets to execute and to the real performance (result) of the behaviour. 2.4 Project Success Stakeholders view project success from various perspectives as the conditions that measure performance (Mir et al., 2014). However, in this study, the project success is operationalised as a concept of personality traits, role behaviour and job performance (LePine et al., 2011). This perspective makes project success a dependent variable and the FFM of personality traits, role behaviour, and job performance the independent variables. Barrick et al. (2014) agree with LePine et al. (2011) that one of the personality traits of conscientiousness provides coherent outcomes driving job performance based on resourceful, reliable, determined, thorough and diligent characteristics. In contrast to LePine et al. (2011), John and Srivastava (2013) remodelled the factors of the five-factor model. They described conscientiousness as submissive, orderly, and practical, which requires small leadership and discipline to deliver what is required. Cote and Miners (2006) also posit that conscientiousness has a negative relationship with driving job performance, inconsistent effects of conscientiousness driving performance or attaining expertise and skill (LePine et al., 2011; Judge, 2013). However, from this discussion, this study can conclude that conscientiousness influences project success. Thus, this study could argue that from job performance, extraversion and agreeableness are strong predictors but less specific for predicting individual job performance. 2.5 Conceptual Framework The conceptual framework presents the hypothetical five-factor model from the team and individual perspective. The study attempts to integrate individual factors and explain the relationship relating to personality traits, role behaviour, and job performance that drive project success. The study also explains cross-level moderating influences of the study context (personality traits with job role behaviour, job performance) on the relationship to project success. The correlation relating to job performance and the attributes of role behaviour was examined based on the differences in personality traits. The outcomes could offer a relationship between job performance with neuroticism and extraversion, while for consciousness, agreeableness and openness, the traits had an average job performance. 2.5 Study Gaps Drawing on the reviewed extant literature, the study observed the various contexts unrelated to this study context on how individual personality trait differences impact how individuals act and cooperate with other members toward project success. This observed gap is based on the operational activities, formation and conflict results about the relationships between certain five-factor personality traits such as openness, neuroticism and extraversion, role behaviour and job performance. Thus, much of the literature did not address the contextual factors of the individual and team aspects. For instance, LePine et al. (2011) noted the unpredicted findings concerning individual emotional issues' (Barrick et al. 2013) on virtual teams. Lastly, there is a lack of interpretivism (qualitative) approach to the correlation between the five factors of personality traits driving project success through role behaviour and job performance. The justification for adopting a quantitative study design provides a platform for future application of mixed method or qualitative approaches in the study. 2.6 Summary This chapter has examined the wider space of project success and team study that appropriate scholarly subjects of personality, role behaviour and job performance have been reviewed, synthesised and justified. Although the extant literature shows that the personality trait composition method has been thoroughly explored, the study on connecting individual personality, role behaviour and job performance to project success in the studied context still needs to be explored. Consequently, the study developed some hypotheses to address the study problems. 3. Methods The study sample is drawn from Nigeria's telecommunications infrastructure managed service organisation. Telecommunications services in Nigeria are known for their high infrastructure operations services because of the increase in subscribers' voice and internet usage (NCC, 2019). This telecommunication sector presented strategies and enabling regulations for network operators that tailored cutting-edge technologies that support their operations—consequently, the operations circles around the highly skilled team operations employees that manage and maintain their assets. Therefore, the study employed a case study method (Yin, 2014; 2016; Saunders et al., 2019) and quantitative data collection technique (Crotty, 1998; Denzin & Lincoln, 2013; Saunders et al., 2019) based on the positivist philosophical position of knowledge of facts that are measured and observed. 3.1 Case Study Study The applied case study method helped in obtaining comprehensive data for the big five factors of personality traits (LePine, 2011; Kendra, 2019) to analyse how these personality traits impact job performance (Neema and Ashish, 2020; Zell and Lesick, 2022) and project success, in which the objective will be to look empirical and theoretical validity to assist the predicting model. The case study will also support finding a more specified outcome on how individual personality traits potentially drive project success through job performance, notably in the telecommunication domain. 3.3.1 Quantitative Data Collection The quantitative technique aims to link the statistical correlations between the significant variables of personality, role behaviour, and job performance contexts. The justification for using this data collection approach of quantitative techniques is based on Bryman and Bell's (2015) proposition and the study's position about knowledge and access to knowledge that refers to creating the study assumptions concerning what is known and how it can be known. A structured questionnaire was sent to field team members and network operations centre teams to quantitatively measure the relationship among the various dimensions of the FFM of personality traits and project success, including roles, behaviours, and job performance. The questionnaire measured the participant's understanding, experience, personality traits, and roles in project success, as reflected in the study questions. 3.2 Study Participants and Procedures The survey participants were chosen from the field operation teams and teams employed in the study organisation. As this survey questionnaire needed embedded samples, individual team members and work teams nested in the teams. One reason for choosing field operations teams was that the individuals must have served for over one year. The justification for this duration requirement was that the survey questionnaire involved technical tasks, managerial and supervisory assessment of job performance and project success. This insight explains that the survey questionnaire data were collected in a process associated with the work of Teddlie et al. (2007) and Creswell et al. (2016). The first round was probability sampling of operational context to ascertain the accessibility of the survey. This procedure was followed by selecting individuals and members from the field operations teams chosen in the probability sampling. This choice of probability sampling was to increase the generalisability of the findings. The questionnaire was sent to one hundred and fifteen (115) field team members from the studied organisation through their email addresses. The one-hundred-and-fifteen sample size is based on the study approach, analytical methods, time and effective cost management related to the study context (Kumar et al., 2015; Hair et al., 2018). One hundred and five (105) responses from the survey questionnaire were obtained, with the overall response result at ninety (90) per cent sample size. At the same time, ten (10) per cent represents unusable, deleted responses or no response. The teams agreed to participate by completing the survey questionnaires. The questionnaires represent personality traits, role behaviour, job performance and project success. 3.3 Measurements of the Variables The study collected data sources from the field operations teams, technicians and supervisors to reduce standard method variance. Participants were asked to rate their personality traits, role behaviour and job performance. The study applied by Stewart et al. (2005), Li (2012), Yang et al. (2015) and Kwak et al. (2019) works by using corresponding individual responses to the questionnaire questions. Additionally, the collected data were from selected participants to confirm the reliability of matching up the individual scores to denote the various values of each construct, which is coherent with extant works (Li, 2012; Kendra, 2019). All scales were in the five-Likert point format. 3.3.1 Dependent Variables – Project Success Project Success is the key dependent variable, measured as a 5-point Likert scale - Strongly disagree – 1; Disagree – 2; Neutral – 3; Agree – 4 and Strongly agree – 5 (Blakeney 2017). Pearce and Porter (1986) developed this Likert scale for quantitative measurement, and it was also used by Carmeli and Freund (2004) in their study on job satisfaction and performance. The participants were requested to signify the level of their job experience and view with each assertion. For instance, the ability to work with other members, attain task objectives, and finish tasks on schedule, within scope, quality, and overall performance. 3.3.2 Independent Variables - Personality Traits The study adopted the five-Likert point scale for its high statistical reliability and validity across the various constructs related to the study problem. The participant's big five personality traits were measured by reflecting on the five-factor dimensions of the personality behaviours. For instance, openness – the individual has a vivid imagination; conscientiousness – the individual that pays attention to details; extraversion – the individual does not care about being the centre of discussion or attention; agreeableness – the individual identifies with other members feeling; neuroticism – the individual has frequent mood swings. All five-factor dimensions have entries that indicate positive or negative attributes of the specific personality trait. 3.4 Analysis Techniques Regression analysis using SPSS v29 and SPSS AMOS v28 to analyse the relationship between the observed variables closely (Szlachciuk et al., 2022). The first phase involves the R-square percentage representing the significance of all the variables, where a high percentage indicates an excellent acceptable level, additionally testing the hypotheses and analysing the correlation between the constructs and variables. This approach and analysis are consistent with the practices of extant study works (Barrick et al., 2012; Li, 2012; Gavoille and Hazans, 2022; Zell and Lesick, 2022). As a process earlier to hypothesis testing (Li 2012; Kendra, 2019), the multiple linear regression of Y = x + B(x1) + B(x2) + B(x3) + Σ was judged particularly valuable and significant because the identified constructs related to team personality traits and role behaviour might theoretically and conceptually cover. 3.5 Chapter Summary This chapter presented the implemented study methodology and data collection approach, which includes the case study with an explanatory sequential design and the quantitative data collection technique to attain a critical analytical depth. The phase of the quantitative data collection processes includes descriptive analysis using SPSS v 29 and SPSS AMOS, as well as regression and correlation analysis. 4. Data Analysis Chapter four describes the analytical methods used for the survey questionnaire data and presents the results. The analysis used a statistical package for the social sciences (SPSS v 29 and SPSS AMOS 28) to analyse the data. Firstly, descriptive statistics of the means and standard deviation samples and the multiple regression analysis using SPSS v29, whereby the multiple regression contrasts the planned hypotheses and also studies the correlations among the dependent variable y - and the independent variables x - (Blaž, Janez, et al. 2021) through the estimation of the regression r-square and coefficient that determine the influence that the variations of the (x) have on the (y) behaviour. The model uses structural equation modelling with SPSS AMOS to create a model that validates the distinctiveness and correlations of the various variables. This analysis includes the five factors model personality traits and team members (Belbin, 2010; Barrick et al., 2012; LePine et al., 2012; Kendra, 2019) and role behaviour (Mathieu et al., 2015), job performance (Raja 2019). Therefore, to determine if personality traits do not directly correlate with project success and job performance, the two models were tested separately, with various paths to hypothesise the entire model. Bollen and Long (1993) noted the correctness of hypothesis testing in model fitting, even as the necessary distributional assumptions are attained and consistently examined. 4. 1 Descriptive Analysis with SPSS Performing the correlation analysis between the variables shows a partial analysis with multiple regression analyses. The model tests the corresponding theory of personality traits to project success through role behaviour and job performance, measuring the fit of the regression model and the fit of the dataset. The model summary table 4.1 supported this output by indicating the hypothesis testing results, with a good observation of the data point. The R measures the strength of the linear relationship associating the independent variables and the dependent variables (Chin, 2012). R is also known as the correlation coefficient, whereby an R of one (1) value shows a strong linear relationship, and an R with a zero (0) value implies no linear relationship (Field et al., 2019). The R for this study model is .957, which implies a strong linear relationship relating to the independent variables of personality traits, role behaviour and job performance and the dependent variable of project success. 4.1.1 Evaluating the Fit of the Model. The r-square, which is also known as the coefficient of determination, is the proportion of the variance in the dependent variable - project success that could described by the independent variables – personality traits, role behaviour and job performance. The value for R-squared ranges from zero (0), which shows that the dependent variable cannot described by the independent variables, and the value of one (1), which signifies that the dependent variable can sufficiently be described deprived of error by the independent variables. The R-squared value for this study model is 0.916, which signifies that personality traits, role behaviour and job performance can describe 92% of the variance in the project's success. This r-square value means the regression model justifies the variability observed in the target variables. Thus, the r-square is employed to confirm the percentage of the influence of the independent variables on the dependent variable. Table 4.1. Regression Model Summary The adjusted R-squared is the revised version of the R-squared that is adjusted for the number of independent variables in the model (Van den Cruijce and Endres 2022), often lower than the R-squared. Nonetheless, it can be helpful in evaluating the fit of several regression models. This study model adjusted R-squared is 0.908. Lastly, the standard error of the regression is the average distance that the detected values decrease from the regression line (Van den Cruijce and Endres 2022), as the observed values in this model fall an average of 0.4051 units from the regression line. 4.1.2 Analysing the Overall Significance of the Regression Model. The ANOVA table presents the degrees of freedom, the sum of squares, mean squares, F statistics and the overall significance of the regression model. The regression degrees of freedom for this model are 10 – 1 = 9. These findings imply that the model has one intercept term, which is the dependent variable and nine (9) independent variables, making the model have a total of ten (10) regression coefficients. This computation is consistent with the proposition that regression degrees of freedom are the number that is equivalent to the figure of regression coefficients – 1. However, the total degrees of freedom df is the number to the number of observations – 1, which is 105 – 1 = 104. Observation in regression is the dataset, and the observation for this study dataset is one hundred and five (105). Table 4.2. ANOVA The residual degree of freedom is the number that is equal to the total df – regression df. For this output, residual degrees of freedom are 104 – 9 = 95. Meanwhile, the means squares are calculated by the regression sum of squares divided by regression degrees of freedom. Thus, in this regression, means squares = 170.097/9 = 18.899666. The residual means squares are computed by the residual sum of squares divided by the residual degree of freedoms, which in this model is 15.595/95 = 0.16415. The F statistics is computed as regression mean square divided by residual mean square, which is 18.900/.164 = 115.2439. These values are in alignment with the proposition that F statistics implies if the regression model presents an appropriate fit to the data than a model that includes no independent variables. In principle, F statistics assays if the regression model as an entire is helpful. Largely, if no independent variables in the model are statistically significant, the overall F statistics are also not statistically significant (Field 2019). Thus, a lower corresponding p-value of (p < .001 ) indicates a statistically significant value. We presumed that there was a statistically significant difference; thus, we rejected the null hypothesis of the ANOVA. Additionally, the p-value is correlated with F statistics to observe if the overall regression model is significant. Thus, in this model, the p-value of <0.001 is within the accepted value of 0.05 or 0.10, implying that the entire regression model is statistically significant and fits the data. 4.2. Regression Analysis The study examined the variables from the descriptive statistics, coefficient and correlation between variables. The coefficient matrix of the variables and determination analysis defines the accuracy of the analytical model (Field 2012). The value of the coefficient is a means to measure the influence of the observed and predictor-independent variables on the variation of the dependent variable of project success. Therefore, the regression analysis shows in (Appendix 1) that the p-values of job performance p < .001 and team members p < .001 as predictor variables are statistically significant based on the solid evidence against the null hypothesis favouring the alternative. This result is consistent with Tuckman (1990) and Belbin (2010) on team composition and performance. This understanding is because team members' management is characterised by Tuckman's (1990) theory and Belbin's (2010) theory on team formation and roles, including resources, investigators, team workers, coordinators, monitors, and implementers. This proposition provides an understanding of how employees function within their project teams. It also highlights the significance of insights and using various individual skill sets and strengths in a team. These skill sets and strengths could be linked to some dimension of personality traits. Conversely, personality traits have a p < .091, which could be seen as a borderline significant trend but more remarkable than the usual significance level of 0.05. This output of p < .091 means a probability of 9% of finding that result by chance when the variable has no actual impact. 4.3 Correlations Analysis The outcome of this correlation analysis on these variables expresses the strong point and direction of the linear relationship involving the two variables. Pearson's r for the correlation between the personality traits value of .276 to project success shows that the correlation is positively significant at the 0.01 level (2-tailed) with a p-value of .004; job performance value of .909 to project success shows a strong significant correlation at the 0.01 level (2-tailed) with a p-value of < .001; team members value of .912 to project success, which means that the correlation is significant at the 0.01 level (2-tailed) with a p-value of < .001; conscientiousness value of -.201 to project success that the correlation is strong significant at the 0.05 level (2-tailed) with a p-value of .040, and agreeableness value of -.229 to project success that signifies a correlation that is significant at 0.05 level (2-tailed) with a p-value of .019. These independent variables' values are positive and mean a strong relationship exists between team members, job performance, personality traits and dimensions of agreeableness and conscientiousness with the project success variable. A change in these variables strongly correlates with changes in the project's success. The sig (2-tailed ) value for team members and job performance is less than .05. Thus, we can conclude that there is a positive statistically significant correlation between these variables and project success. Table 4.3. Correlation between Variables Additionally, role behaviour value of .241 to personality traits shows that the positive correlation is significant at the 0.05 level (2-tailed) with a p-value of 0.013, team members value of .223 to personality traits presents that the correlation is strongly significant at the 0.05 level (2-tailed) with a p-value of 0.022, job performance value of .250 to personality traits indicates that the correlation is significant at the 0.05 level (2-tailed) with a p-value of 0.10. However, the correlation concerning personality traits and job performance p-value is above the 0.05 threshold, which in some cases is acceptable based on the direction with the higher statistical power, such as the 2-tailed that doubles the significance level. Agreeableness has a value of -.336 to role behaviour, showing that the correlation is a strong significant value at the 0.01 level (2-tailed) with a p-value of < 0.001. In contrast, team members' value of .826 to job performance indicates that the correlation is significant at the 0.01 level (2-tailed) with a p-value < 0.001. Again, agreeableness with a value of .197 to neuroticism indicates a significant correlation at 0.05 level (2-tailed) and a p-value of 0.04. Openness with a value of .918 to neuroticism has a significant correlation at 0.01 level (2-tailed) and p-value of < 0.001. Lastly, the conscientiousness value of -.266 to job performance signifies a significant correlation at 0.01 level (2-tailed) with a p-value of 0.006. Again, a conscientiousness value of -.212 to team members indicates a significant correlation at the 0.05 level (2-tailed) with a p-value of 0.03. These independent (predictors) variables that the correlation is statistically significant in this model are consistent with the proposition by Lepine et al. (2011), Barbee (2020) and Wen et al. (2021) stated in chapter 2.1 on the impact of personality traits on job performance. Likewise, Barrick et al. (1998), Raja (2019) and Maryam (2020) argue and suggest that some dimensions of personality traits influence job performance and project success. Mathieu et al. (2015) TREO theory also supported the role behaviour impact on project success, as Belbin's (1993, 2010) contribution of team members' roles and formation in project success. For these reasons, we can conclude that there is a strong correlation between team members and some dimensions of personality traits and job performance to project success. Given the findings, we assumed that the direction of the relationship that extraversion as one dimension of personality traits has a p-value of .137, which is not significant to project success, trigged a concern because of its characteristics that comprise sociability, assertiveness, active, and positive emotionality. These extraversion features, according to Barry and Stewart (1997), enhance performance and process (Mu et al., 2022), drive the work environment and are akin to team formation and collaboration. 4.4 Coefficient Using the coefficient p-values to decide the inclusion of variables in the final model is an acceptable practice. This proposition explains the linear regression coefficient insight about the model as the positive coefficient shows that the increase in value of the independent variables also implies an increase in the dependent variable. The coefficient provides the number that is essential to report the estimated regression equation: Y=b0+b1x1+b2x2. For this model, the estimated regression equation is project success = -6.192+.600+-.132+.810+5.440+.106+-.147+.117+-.863+-.329 = -0.59. This value shows that each coefficient is explained as the average increase in the dependent variable for each one-unit rise in a given independent variable, believing that all other independent variables are constant. For instance, for each personality trait, the average likely increases in value by .600 points, assuming that the number of other variables and personality trait dimensions are constant. On the other hand, the intercept (constant) is interpreted as the expected project success for team member's personality traits, role behaviour, and job performance aligned with the team collaboration and delivery. For instance, the project success score is -6.192 if the independent variables are statistically significant based on the expected p-values. Table 4.4. Coefficient Therefore, we can resolve that the pathway of the correlation that team members and job performance variables have on project success has a strong correlation pathway indicated by the positive value (b). This means a positive correlation exists between team members, job performance, and project success. These empirical findings are consistent with Kaplan et al. (2010) and Belbin (2010) studies that enable team members' participation and direct collaboration in an organisation. In contrast, the negative coefficient implies that the dependent variables reduce as the independent variable increases. Additionally, Hair et al. (2010) and Pallant (2010) argued that when the maximum scale of variance inflation factors (VIFs) is less than the value of 10, that indicates no multicollinearity. Multicollinearity is the condition of very high inter-relationships between the independent variables (x), which signifies a data distribution. However, when this happens in the data, the statistical assumption decision concerning the data could not be consistent. Therefore, to measure multicollinearity, probing tolerance and the variance inflation factors are based on the r-square value discovered by regressing a predictor on all the other predictors in the model or analysis. All the independent variables are within the accepted scale for this coefficient table, indicating no multicollinearity according to Durbin and Watson's (1971) scale range. This study has no problem with the VIF values. 4.5 Model Results with SPSS AMOS v 28. The path diagram for this model presents the unstandardised coefficients with the covariance and slopes and the standardised coefficients of correlation and beta weights. The Chi-tests the null hypothesis that the overidentified and just-identified model fits the data. In the just-identified model, there is a direct path from each variable to each other variable but not through an intervening variable. This action could have made the Chi-square have a value of zero. For instance, the nonsignificant Chi-square shows that the fit concerning the overidentified model and the data is not significantly flawed compared to the fit relating to the just-identified mode and the data. Thus, we can conclude that this model is a good fit because the model produces the variance-covariance matrix – correlation matrix – from the path coefficients. However, the minimum result for this study analysis was achieved with Chi-square = 12.774, degrees of freedom = 14 and probability level = .544 (See Appendix 1). 4.5.1 The Maximum Likelihood Estimates The parameters for this model are estimated by the maximum likelihood (ML) methods rather than by ordinary least squares (OLS) methods. Bollen and Long (1993) noted that OLS methods reduce the squared deviations between the values of the standard variable and those predicted by the model. For instance, in ML, an iterative process endeavours to maximise the probability that attained values of the standard variable will be adequately predicted (See Appendix 1). 4.5.2 Standardised Regression Weights: (Group number 1 – Default model). The path coefficients below correspond with the earlier ones obtained by multiple regression. Table 4.2 estimates of standardised regressions present that when team members go up by one (1) standard deviation, project success goes up by .543 standard deviations. When team members go up by one (1) standard deviation, job performance increases by .823 standard deviations. When team members go up by one (1) standard deviation, roles behaviour goes up by .045 standard deviations (See Appendix 1). Additionally, when personality traits increase by one (1) standard deviation, project success increases by .046 standard deviations. These values are accepted in model fit indices of between 0.05 and 0.08, according to Hu and Bentler (1998) and Kline (2005) on AMOS confirmatory factors analysis and standard equation modelling. 4.5.3 The squared multiple correlations: (Group number 1 – Default model) The below squared multiple correlation coefficient is in the five multiple regressions. The total impact of one variable on another can be divided into direct impacts – no intervening variables involved – and indirect impacts, that is, through one of the intervening (team members) variables (See Appendix 1). The impact of job performance on project success. The direct impact is .447 – the path coefficient from job performance to project success. The indirect impact, through team members, is calculated (Kline 2005) as the product of the path coefficient from job performance to team members and the path coefficient from team members to project success. 4.6 Testing the Hypotheses The regression models were applied to test the hypotheses that concentrate on the individual and team correlations with the variables, as the regression analyses also support the illustration of each dimension of the personality traits on role behaviour and job performance. Remarkably, job performance and role behaviour were regressed on the constructs related to individual and team factors to test the scope to which it connected to personality traits and project success. 4.6.1 Personality Traits and Project Success Multiple regression was run to predict project success from the dimensions of personality traits. The study combined the sample test outcomes into one table and marked them appropriately. However, as shown in Table 4.3, the model using the personality dimensions to predict project success did not indicate a suitable model fit r-square .015, indicating that these personality traits contribute to individual and team members' project success concerns. Hypothesis 1 is linked with driving project success by predicting the dimensions of the five factors' personality traits related to project success. To test the hypothesis, the estimated regression models with results reported in Table 4.4, coefficient with a 95.0% confidence interval (CI) bound. The sample team members were significantly linked with project success (β = .5.440, 95%, CI is 6.586 > 4.294; β = .577, 95%). These lower-level CI and upper-level CI show a high positive relationship between these variables as the accurate population correlation because the percentages demoted a high confidence level. Thus, explain the valid population parameter of the attributes of the entire sample size from the drawn sample. This result supported hypothesis 3, indicating that team member performance predicts project success. This result aligns with job performance that conceptualised two team members' perspectives of process or action and outcome (Belbin, 2010; Kramer, 2014). that signifies how individuals perform a job, the action itself (Campbell et al., 1993; Maryam, 2020). 4.6.2 Roles Behaviour and Project Success Multiple regression analysis was performed to predict project success from role behaviour, and the result is presented in Tables 4.3 and 4.4. Correlation and coefficient. The regression discovered that using role behaviour to predict project success created an unaccepted model fit for the sample of p < .142, which is above the standard value of 0.05. This value shows the no fit on the amount of variance in the dependent variable described by the independent variable. However, role behaviour is assumed to predict individual and team role behaviour in project success, as indicated in Table 4.4 coefficient. The standard beta coefficient range β = -.048 and p-v .142 is no statistically significant difference to project success because the value exceeds the accepted p-value range of 0.05. 4.6.3 Job Performance Configuration and Project Success Hypothesis 3 suggested that job performance configuration is operationalised by the mean value of team members' composition, skill sets, and teamwork, which is significantly associated with project success. The study included the mean value of job performance into the model using team members to predict project success and perceived a significant increase in the adjusted r-square value of .908. This explains that adding control variables through the mean value of job performance into the model could increase its predictive power to project success. Table 4.4 also presents a standardised coefficient beta β = .487 and (sig) p <.001 on the job performance influence on project success. This result indicates a substantial impact on the dependent variable. Additionally, the regression coefficient implies that job performance configuration operationalised through the value of the team members' characteristics significantly predicts project success. 4.7 Chapter Summary The analysis of the quantitative approach discovered many applicable results concerning the theorised hypotheses. This study analysis indicates that team members' characteristics with some personality traits significantly predict job performance and project success. Additionally, the results revealed that some dimensions of personality traits supported the project success relationship. When these team members' contextual factors were low, personality traits could be expressed as moderated job performance; the personality traits and team members' relationship improved when the individual contextual factors were high. The quantitative outcome established that team members and job performance configuration predicted project success, although the data did not support some hypotheses, such as role behaviour. 5. Findings and Discussion In this context, the telecommunication domain entails actions and tasks associated with asset delivery, planning, implementation, operations and maintenance activities. In existing studies, various practitioners and scholars have investigated and explored the problems that impact project success. However, the critical literature gap intended to address was the insufficient academic study on how personality traits and contrasts between individuals impact the performance of project teams. 5.1 Study Questions Q1. Which individual personality traits could influence project success? From the data, the most influential dimension of personality traits to the least based on the individual levels were arranged (Barrick, 1998; LePine et al., 2011 ; Raja, 2019; Maryam, 2020 ; Wen et al., 2021 ), openness, conscientiousness, agreeableness, extraversion and neuroticism. Q2. How do personality traits influence role behaviour and project success? The data revealed that individuals' interactions and personality traits influence role behaviour and project success (LePine et al., 2011 ; Mathieu et al., 2015 ; Raja, 2019; Barbee, 2020 , Maryam) either positively or negatively. The reviewed literature provided a clear understanding of the influence of the various personality traits on role behaviour and how they can be applied in delivering project success. Q3. Does team members' work influence job performance on project success? The data revealed that team members' characteristics were one of the critical factors influencing project success in telecommunication assets managed projects. However, job performance has an impact on project success. 5.2 Discussions The study context was conceptualised as the capability of individual personality traits to accomplish project success through role behaviour and job performance (LePine et al., 2012 ; Barrick et al., 2014; Mathiew et al., 2015; Kendra 2019 , Raja 2020; Erick, 2020). Noteworthy, role behaviour contrasts with job performance, which implies the project delivery of values or metrics such as project cost, time to project completion and quality of deliverables (Goldberg, 1993 ; Campbell et al., 1993 ; Mathiew et al., 2015; Raja 2019; Maryam 2020 ). Fundamentally, job performance is focused on characteristics of individuals and team members' creative and working ability, their fulfilment with the team as its actions and collaboration between them; thus, individual dimensions of personality traits were used as the leading indicators to measure job performance. Additionally, job performance is affected by individual and team standards scores, which influences the project success position of the telecommunication assets managed team. Openness was observed as sufficient to create positive project success through job performance. Also, openness predicted role behaviour, while neuroticism, on the other hand, was observed to be negatively related to role behaviour. Thus, high scores of openness were also noticed in the extant literature, with job performance driving project success, as it is categorised as team members who create originality and noble thoughts, do not evade conflict, are flexible to modification, own high levels of adaptableness, compliance and encourage open communication (Neuman et al., 2014; Ghani et al., 2016; Raja, 2019; Maryam 2020 ). The quantitative data provided supporting evidence of openness features that supported individuals through collaborating during conflict and were flexible when issues occurred. The results from openness were consistent with Raja (2019) and Juhasz (2010), who established a strong relationship relating to team-oriented communication and learning new-found entities, and Bradley et al. ( 2013 ), who established team members who scored high on openness assumed conflict in their team better and thus had a significant effect on job performance and project success. In contrast, in this study, some dimensions of personality traits, such as conscientiousness, were not discovered to be adequate to create better job performance. This insight from this study is incompatible with the extant literature, which indicates that high conscientiousness scores are categorised by individual and team members involved in project success (Neuman et al., 2014; Raja, 2019; Maryman, 2020). Other studies perceived high conscientiousness scores as high performance and above-average conscientiousness. Conscientiousness, according to Bell (2007), allows team members to allocate tasks efficiently and successfully, supervise progress, and report performance in project implementation. Principally, conscientiousness represents the inclination of team members to be structured in their behaviour through appropriate tasks, responsibilities, and assignments. Specifically, job performance was discovered to be rational with the correlation relating conscientiousness and role behaviour. This was because conscientiousness was more positively related to individual work role behaviour when team members' work traits were low, while the correlation was mitigated when high. However, the hypothesised interaction of the individual work characteristic and other dimensions of personality traits on role behaviour was not discovered. Likewise, it was discovered that neuroticism was more negatively correlated with individual role behaviour when team members' interrelationships were low, although this correlation was reduced when team interdependence was high. This understanding is because team interdependence regulated the correlation relating to neuroticism and individual role behaviour. Thus, the hypothesised interaction of individual and team interdependency and other dimensions of five-factor personality traits on role behaviour was not established. 5.3. Practical Implication. This study illustrates the significance of considering team members' personality traits when assessing job performance (Debusscher et al., 2016 ) and project success. Given the critical effects of the FFM dimensions of personality traits, exploring a more consolidative, dynamic approach to personality traits assessment can contribute to a deep insight into team members' personalities and, therefore, reinforce the dimension of each individual. However, such consolidative and dynamic approaches by (Belbin 2010 , LePine et al., 2011 , Barrick et al., 2013, Raja 2019) can be challenging to use in certain situations, Mathieu et al., 2015 ; Kendra) explained how the TREO approach can be modified and extended to assess additional dimensions of personality concepts. 5.4 Contribution to Knowledge The contribution of this study to the body of knowledge is stated accordingly. The results discovered that the study effectively examined and unified the concepts of five-factor dimensions of personality traits with role behaviour and job performance and investigated their influence on project success for actual telecommunication asset-managed projects. Interpreting the correlation relating to personality traits and role behaviour indicates that the statistical results on some FFM dimensions of personality traits significantly predicted individual and team role behaviour in the workplace. Thus, the outcome of this study has created outcomes that increase earlier findings in this study area through the link relating to openness and individual and team members' role behaviours. Barrick et al. (2011) argued that openness was negatively associated with individual and team role behaviour in social and at-work activities. In contrast, Mathieu et al. ( 2015 ) opined that openness was strongly related to components of individual and team task role behaviour such as connector, challenger and innovator. This insight differs from Barrick et al. (2011) but is consistent and aligns with Mathieu et al. ( 2015 ). Thus, these conclusions contribute to the literature by exhibiting the correctness of using the Mathieu et al. ( 2015 ) TREO role dimensions model to operationalise role behaviour in driving project success based on various individual personality traits. 5.5 Study Limitations The five-factors model dimension of personality traits is probably specific based on the logic that the impacts of its factors and the interfaces among the factors vary based on the individual personality dimensions under reflection. For instance, conscientiousness relates to rigidity and organisation. Thus, it is perhaps that a strong level of conscientiousness correlates with low conscientiousness adaptability, while high conscientiousness attracts vigour and intensity. Therefore, to explore these impacts, additional study is required on the analytical dynamics of the other dimensions of personality traits and their impacts on role behaviour, job performance and project success. Another limitation is the concern about how extraversion was theorised to predict individual and team members' role behaviour and job performance. The quantitative data did not support this influence; thus, it is problematic to determine the role that extraversion played in project success. Lastly, the five-factor model does not separate inward and outward prompts of transformation in team personality positions; rather, it captures the developing path. Further study is, however, required to examine the methods that highlight these shifts. 6. Conclusions The study finding requires adjusting the conceptual framework model, presented in Chap. 2 sub 2.5, Fig. 2.1 . The adjusted conceptual framework model presented in Chap. 6, Fig. 6.1 , is distinctive in various approaches from the primary study model developed by the study and supported by the extant literature and hypotheses. To start with, in contrast with the original conceptual framework model – the strength of the personality traits relationship to role behaviour, job performance driving project success and adjusted study model indicates that the job performance and personality traits relationship stands out, while role behaviour is at the borderline as indicated in Fig. 6.1 . The personality dimension of openness is correlated to job performance. Nevertheless, conscientiousness, agreeableness, extraversion and neuroticism were reformed to a compact concept in the adjusted conceptual framework of how this dimension of the five-factor personality traits predicts role behaviour and job performance in driving project success. This understanding is because the hypothesised correlation relating to role behaviour and project success was insignificant. The adjustments made to this study model echo the contributions of this study to the literature on five-factor personality traits and role behaviour and job performance, based on identifying the most significant predictors of personality traits driving project success by leaving out the less significant personality traits. Thus, this study has helped to resolve the ambiguous correlation relating to the FFM of personality traits and individual role behaviour and job performance. This is because the study indicates how team members' contextual factors such as skill sets, teamwork and commitment act on individual positionality about the dimensions of five-factor personality traits with job performance driving project success. 6.1 Recommendations The study recommends several essential effects of team personality traits driving project success. Firstly, the results justify advancement from previously favoured prevalent team personality traits relationship between role behaviour and job performance that encourages team members' openness, commitment and collaborative teamwork. Again, the results denote that various FFM dimensions of the personality traits model affect individuals' and team members' behaviour inversely. These contrasts that affect team behaviour differently may draw from either the individual who possesses those personality traits or the personality traits themselves. Openness was observed to contribute more to job performance, while conscientiousness and agreeableness were discovered to be essential to individual role behaviour. The study recommends that individual and team job performance and project success indicators differ, with several subjective indicators, such as role behaviour, which are more problematic to explore than objective indicators, and the overall team performance that is objective. 6.3 Summary The study has clarified how the study answers addressed each study question and how the adjusted study conceptual framework model explained the correlation among the individual and team members' personality traits driving project success through role behaviour and job performance indicators. Primarily, the study illustrates that team members and job performance are significant predictors of project success and are slightly related to personality traits such as openness. At the same time, conscientiousness and agreeableness were significant predictors of role behaviour. However, the adjusted conceptual framework model also exhibits that neuroticism, a dimension of the FFM personality traits, is insignificantly correlated to role behaviour or job performance driving project success. Furthermore, the adjusted conceptual framework model proposes the presence of individual team members' contextual factors, including team interdependence, which interact with individual personality traits, driving project success through job performance. However, several constructs concerning personality traits, individual behaviour, and performance are unconnected to the adjusted conceptual model, indicating an opportunity for further study, which may include qualitative data to support the new study. Declarations The study was approved by the University of Lincoln ethics committee. References Alias, Z., Zawawi, E.M.A., Yusof, K., and Aris, N.M. (2014) Determining critical success factors of project management practices: A conceptual framework. 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Teddlie, C., and Tashakkori, A. (2009) Foundations of Mixed Methods Study: Integrating Quantitative and Qualitative Approaches In the Social and Behavioural Sciences . Sage Publications. Waude, A. (2017) Hans Eysenck's PEN Model of Personality . Available from: https://www.psychologistworld.com/personality/pen-model-personality-eysenck [accessed: July 10 2023]. Wen, X., Zhao, Y., Yang, Y. T., Wang, S., and Cao, X. (2021) Do Students with Different Majors Have Different Personality Traits? Evidence from Two Chinese Agricultural Universities. Frontiers in Psychology, p. 12, Article 1460. Available from: https://doi.org/10.3389/fpsyg.2021.641333 [accessed: April 20, 2023]. Yang, X., Tong, Y., and Teo, H. (2015) Fostering Fast-response Spontaneous Virtual Team: Effects of Member Skill Awareness and Shared Governance on Team Cohesion and Outcomes. Journal of the Association for Information Systems, 16(11), 919. Yilmaz, M., O'Connor, R., Colomo-Palacios, R. & Clarke, P. (2017) An examination of personality traits and how they impact on software development teams. Information and Software Technology. Available from: https://86.10.1016/j.infsof.2017.01.005 [accessed: June 06, 2023]. Yin, R. K. (2014) Case Study Study Design and Methods (5 th ed.). Thousand Oaks, CA: Sage, 282 pages. Yin, R. K. (2016) Qualitative Study from Start to Finish (2nd ed.). New York, NY: Guilford Publications. Zell, E., and Lesick, T. L. (2022) Big Five Personality Traits and Performance: A quantitative synthesis of 50+ Meta-analyses. Journal of Personality, 90 (4), 559–573. Available from: https://doi.org/10.1111/jopy.12683 [accessed: June 06, 2023]. Additional Declarations The authors declare no competing interests. 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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-3990907","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":274953659,"identity":"7f9d9b84-e82e-4e30-965d-434df153e893","order_by":0,"name":"Charles Okeyia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYDACCSB+wMAgB+EZgAjmBsJaEhgYjJG0MBKnJRFJGQEt/LObn31IqKhLnz+7+Zg0T4FN4vz+gw0MPyq24bbkzjHjGQlnDuduuHMsTZrHIC1xw43EBsaeM7dxajGQSDBmSGw7kLtBIsfs5gwDoF4JxgZmxjZ8WtI/MyT+q0uXnwHW8j8X5DACWnKAtjQwJzDcyDG78cHgQG7DgUT8WiRu5BQzJBw7bLjhRlr6jw8GyfUgvxzE5xf+GembGT7U1MnLz0g+bJDwx85Yvv/wwQc/KnBrwQ4OkKh+FIyCUTAKRgEaAACTVFv+x6x1CAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4828-8245","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Charles","middleName":"","lastName":"Okeyia","suffix":""},{"id":274954444,"identity":"c90b0aaa-6b15-4616-b584-131f2391970e","order_by":1,"name":"Charlotte Smith","email":"","orcid":"","institution":"University of Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Charlotte","middleName":"","lastName":"Smith","suffix":""},{"id":274954445,"identity":"debdb942-3901-4c4f-bc9f-d31fb06a8af7","order_by":2,"name":"Michail Koubouros","email":"","orcid":"","institution":"University of Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Michail","middleName":"","lastName":"Koubouros","suffix":""}],"badges":[],"createdAt":"2024-02-26 12:31:17","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3990907/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3990907/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51770660,"identity":"c582dd7a-178b-4edf-b959-e01ed45a3382","added_by":"auto","created_at":"2024-02-28 19:28:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17683,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2.1 Conceptual Framework on the Five-Factors Model\u003c/p\u003e","description":"","filename":"2.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3990907/v1/f027ce9973e553ed39184302.png"},{"id":51769625,"identity":"c1df12f7-e803-48b3-9aaa-68979a0419e7","added_by":"auto","created_at":"2024-02-28 19:20:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124637,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4.1a. Model Path Unstandardised Estimates Diagram\u003c/p\u003e\n\u003cp\u003eFigure 4.1b. Model Path Standardised Estimates Diagram\u003c/p\u003e","description":"","filename":"4.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3990907/v1/ef2523a149058e0013bc2e6c.png"},{"id":51769623,"identity":"149dcc0c-70d3-4308-ab5a-7dfdb65a4419","added_by":"auto","created_at":"2024-02-28 19:20:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25210,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6.1. Adjusted Conceptual Framework\u003c/p\u003e","description":"","filename":"6.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3990907/v1/99d0b412234c3c4809d1b2b4.png"},{"id":51771730,"identity":"803b6569-a351-4ad7-acbd-28602f11685a","added_by":"auto","created_at":"2024-02-28 19:36:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":990214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3990907/v1/01af4ab4-d551-4f9e-bcbc-0384769782dc.pdf"},{"id":51769626,"identity":"81e894d2-aa96-4cb3-92f1-9d41022346e4","added_by":"auto","created_at":"2024-02-28 19:20:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":100129,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3990907/v1/f8a6679799ee2498e37a1452.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExploring the Effect of Team Personality Traits, Role Behaviour and Job Performance on Project Success. A Case of Telecom Projects in Emerging Markets.\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. The Background Story","content":"\u003cp\u003eIn Nigeria, telecom organisations currently deploy and execute infrastructure and asset strategies to help their customers access better internet connections, network coverage and penetration, and good quality of service - QoS (NCC, 2021; Danbatta et al., 2022). Therefore, to avoid customers porting their subscriptions and patronage because of QoS issues and competition, these organisations focus on improving business performance by creating value through their project teams to deliver their projects within scope, within budget, and on time (PMI, 2014). However, managing project teams comes with challenges from formation and membership, with what drives team performance and success a fundamental problem for team practitioners and scholars to address (Tannenbaum et al., 2012; Mathieu et al., 2015). From a practical perspective that involves operational business decisions, practitioners and managers should understand the rationale that certain work teams are more efficient than others.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConversely, extant literature noted personality traits (Costa \u0026amp; McCrae, 1992), method of project team compositions and roles behaviour (Belbin 1993, 2010) as significant factors for job performance (Boshoff \u0026amp; Arnolds, 1995), which results in project success. This assertion triggers the discussions and debates that project success depends on individual organisational and work commitment, which arises from work motivation or personality (Costa \u0026amp; McCrae, 1992; Dwivedula et al., 2016; Harahap, 2019; Wen et al., 2021). This theoretical perspective underlines that personalities in a project in various roles have distinct anticipation of being achieved; occasionally, they are used to comprehend other team diversity and team performance. That is why Beblin (2010) argued that structuring team roles to build a team was critical for the team\u0026apos;s effectiveness and, thus, one of the most significant elements affecting team success. Thus, given the extant theoretical lens, this study hypothesis may be worth exploring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. 2. Study Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuided by this paper\u0026apos;s context and extant academic literature, this study aims to contribute to the understanding of project success through team management and collaboration based on personality traits, roles, behaviour and job performance. Thus, the paper investigates the correlation between personality traits driving project success through role behaviour and job performance. The study question is constructed to achieve this objective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. 3. Study Questions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to explore the effect of personality traits driving project success through role behaviour and job performance and also address the identified study gaps on team member\u0026apos;s personality traits, an approach likely to enhance the extant findings of personality traits in operative team formation and management (LePine et al., 2011). Accordingly, the specific study questions are stated below.\u003c/p\u003e\n\u003cp\u003eQ1. Which team personality traits could affect project success?\u003c/p\u003e\n\u003cp\u003eQ2. How do personality traits impact role behaviour and project success?\u003c/p\u003e\n\u003cp\u003eQ3. Do team members influence job performance on project success?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. 3.1 Hypotheses of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the identified constructs, the hypothesis below will guide the entire study, assist in finding the answer to the study questions and define the correlation between the variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH1. Personality traits do not have a significant effect on driving project success.\u003c/p\u003e\n\u003cp\u003eH2. Team role behaviour does not have a significant impact on project success.\u003c/p\u003e\n\u003cp\u003eH3. Team member\u0026apos;s work influence is not significant to job performance and project success.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Theoretical Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the extant literature and existing understanding of the five-factor (FFM) personality traits driving project success. This study applies the five-factor personality trait theory, with an identifiable feature on the theories (Costa \u0026amp; McCrae, 1992) (Judge et al., 1999) and (Wen et al., 2021), who claim that individuals with high conscientiousness (the trait of being disciplined, orderly and determined to ensure what is correct) performed better through their jobs, while individual high in neuroticism (the trait of an individual with anxiety or self-consciousness) were less effective in selected vital approaches.\u003c/p\u003e\n\u003cp\u003eHowever, some extant study studies have argued about the team personality approach by embracing a micro-dynamic viewpoint in considering the link among individual personality traits, team role behaviour and performance. First, Stewart et al. (2005) employed a new approach to operationalising role behaviours as a multilevel relationship by individualising them from team role formations and linking them with dimensions of personality traits and team performance measures. Secondly, the study draws on employing role behaviours to connect individual personality traits and team outcomes through cross or directional-level techniques (Tasa et al., 2011) by combining the situational impacts of shared effectiveness when studying the correlation among team members\u0026apos; personality traits and role behaviours.\u003c/p\u003e\n\u003cp\u003eIn addition, the study will utilise Belbin\u0026apos;s (1993; 2010) team roles and performance theory, which describes different personality characteristics and team behaviour grouped within three team roles: people-oriented, action-oriented, and problem-solving-oriented. These constructs are critical to project success, as they are quantifiable outputs or self-assessments of individuals concerning their performance (Senior, 1997; Partington et al., 1999; Belbin, 2010). However, the empirical result for this notion is broadly combined, as various studies stated a strong relationship between personality traits with job performance and project success (Belbin, 2010; LePine et al., 2011); job performance is influenced by both individual personality traits (Yun et a., 2007; Mihalcea 2013) individuals with positive behaviours towards job performance, but negative role behaviours create project failure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5. Study Design and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study considered the quantitative study design the most suitable method based on its characteristics. It is designed to find how several individuals think, act or feel in a particular method (Creswell et al., 2009). This includes a case study approach (Yin, 2014; 2016; Saunders et al., 2019), using a survey questionnaire to examine the correlation among the effect of personality traits driving project success through role behaviour and job performance. This assertion is centred on the understanding that quantitative study relates to theories with essential elements such as models, constructs, concepts, problems and hypotheses. Kerlinger (1986, p. 26) argued that a concept expresses an abstraction formed by a generalisation from particulars.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt will be acknowledged that the quantitative method reflects the researcher\u0026apos;s actual philosophical stance on the understanding and study, which aligns with the positivist philosophical stance (Crotty, 1998; Denzin \u0026amp; Lincoln, 2013; Saunders et al., 2019) to identify and comprehend the correlation among the key variables, such as personality, role behaviour and job performance to project success and to explain experiences linked with those variables by testing extant theories and concepts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5.1 Data collection and analysis approaches\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study applies a quantitative approach to data collection to address the study objectives and hypothesis by administrating an online structured questionnaire to the participants. The primary study data was collected from the field technicians and operations team members of various clusters in their south/south region. The study survey was designed using the five Likert scale to collect data. The Likert scale for quantitative measurement was created by Pearce and Porter (1985) and employed by Carmeli and Freund (2004) in their study on job performance and satisfaction.\u0026nbsp;The online structured questionnaire was designed with the MS forms to reflect the study problem and the variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5.2 Study Sample Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size for this study is one hundred and fifteen (115) employees of the studied organisation, focusing on the field operation team members who have worked one year and over in the studied organisation. The justification of this study sample size was based on Kumar et al. (2013) suggestion that includes the study approach, participants accessibility, and analytical approach \u0026ndash; regression analysis using SPSS. Hair et al. (2018) noted that a minimum sample size of fifty (50) to one hundred (100) is appropriate for regression analysis. Likewise, the probability sampling technique will be applied for this study to test the hypotheses and make inferences regarding the variables and population. Kothari (2019) supported the use of probability sampling techniques in quantitative studies when determining the sample size from a known population, such as in this study context.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.6. Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data will be analysed using SPSS v29 and SPSS AMOS v28. The analysed data will be presented in a tables and figures format, using statistical tools such as multiple regression and correlation analysis regarding the variables. The designed hypotheses will be analysed using correlation analysis to find the model that is an appropriate fit for the sample size and data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.7 Significance of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is significant because it will resolve the delay in project success delivery and issues of negative team collaboration in job performance, which is related to various dimensions of personality traits. Primarily, a conceptual framework is used to justify and describe the role the dimensions of five-factor personality traits perform in the studied workplace and teams, a perspective well-grounded in literature (LePine et al., 2011; Barrick et al., 2013; Mathieu et al., 2015; Kendra, 2019; Erik, 2020; Mu et al., 2022; Zell and Lesick, 2022). Thus, focusing on the gap in the reviewed literature affects the study context and methodology on team formation and, in addition, assists in addressing the team miscommunication and collaborative issues that impact project success in the study organisation.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThis chapter shows a synthesised and critiqued evaluation of the extant literature on personality, role behaviour and job performance by exploring the theoretical and empirical arguments, critical discussions, opportunities and disagreements. The study focuses on team personality traits and underlines the key findings between team behaviour, performance, and project success. The study questions are illustrated with a multi-directional study model developed from the literature and conceptual framework. In the study situation, it is important to identify which features improve the team\u0026apos;s job performance and project success.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies into job performance and project success have proposed that specific factors impact performance and project management, as Hao et al. (2019) proposed personality, job design, and knowledge-sharing behaviour. However, the impact of team and individual personality on team job performance and project success has been generally studied in extant literature (LePine et al., 2011). Moreover, subsequent study offered a catalogue of personality traits, such as the big five-factors model, in which (Yilmaz et al. 2017; Joanna 2019) concluded that personality traits of extroversion and neuroticism are significant to team job performance influencing project success, which has been expanded by (Erik, 2020; Zell and Lesick, 2022). Thus, this study can be measured as one of the contributions to studying this significant subject matter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Personality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePersonality traits have received significant attention in psychology studies (McCrae and Costa, 2013) and management studies (LePine et al., 2011; Barrick and Mount, 2013). Kendra, 2019) and Hogan agree with Gavoille and Hazans (2022) and Mu et al. (2022) that personality is a function of a human being\u0026apos;s inclination to behave and act in a specific approach. Mayer (2005), Kramer (2014) and \u0026nbsp;Kendra (2019) conclude that a personality is a steady form of psychological procedures, features and developments that generate cognitive outlooks and methods that could be applied to decide people\u0026apos;s differences, resemblances, opinions, approaches, and actions. This insight is because personality traits influence and define individual behaviour in a work setting and impact team and job performance (Barrick et al., 1998; Raja, 2019; Maryam, 2020). However, this study has suggested various models in which personality, role behaviour, job performance and project success congregate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe leading model was the five-factors model (FFM) of extroversion, neuroticism, agreeableness, conscientiousness and openness (Goldberg, 1990; LePine et al., 2011; Judge, 2013; Barbee, 2020; Erik, 2020). However, in contrast, McCrae and Costa (2013) and Joanna (2019) maintained that FFM is not independent because two higher-order factors are extroversion and neuroticism. This is in disagreement with Goldberg\u0026apos;s (1990) and Erick\u0026apos;s (2020) view that described extroversion as the inclination to be sociable, enthusiastic, energetic and optimistic, associated with the extent and strength of people, interactive action (Bruck and Allen, 2003); related to project success that needs widespread social communication (Barrick and Munt, 2003); positively interrelated with involvement in teams (Barry et al., 1997; Kendra, 2019; Gavoille and Hazans, 2022). These extant perspectives remain relevant in this domain, as the descriptions indicate a relationship between particular dimensions of personality traits (FFM) and specific individual benchmarks of job performance (Barrick and Mount, 1991; Hough, 1992; Raja, 2019; Maryam, 2020; Neema, 2020). Therefore, personality has been broadly understood as a notably significant individual contribution to the team and job performance (LePine et al., Bradley et al., 2013; Colbert et al.,2014; Kendra, 2019). The explanations for this are diverse, as members\u0026apos; instincts, judgments, and behaviours affected by their personality traits are wholly connected to their job performance and affect collaborations and interactions with team members.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.2 Role Behaviour\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eStudies have offered various standard options to operationalise team members\u0026apos; role behaviours as these roles are grouped into task and social boundary-spanning roles (Mumford et al., 2006). However, this study focuses on task and social role behaviour based on the study\u0026apos;s focus on job performance. Van Dick et al. (2011) support Mumford et al. (2006) earlier studies by finding task roles primarily imply a group of behaviours aiming at the accomplishment of jobs and the shared team aims, involving job involvement, establishing team objectives and working together toward the objectives (LePine et al., 2011), evaluating and resolving problems, (Kilumets et al., 2015) monitoring advancement and obeying to the cutoff date. Conversely, social roles include behaviour that is directed to interpersonal harmony within the team that encourages effective teamwork (Belbin, 1993; LePine et al., 2011; Barrick et al., 2013; Kilumets et al., 2015) such as caring for individual emotions and feelings, respect for each other, opinions, contributing to team objectives and open communication and collaborations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn disagreement with the studies by Belbin (1993), LePine et al. (2011) and Barrick et al. (2013), Mathieu et al., 2015) argued on the theory of team role experience and orientation (TREO), which is applied to indicate the wide task and social role classification for job performance. Based on these conclusions, this study selected the TREO theory because of Mathieu et al. (2015) perspective and the argument that organisers and doers were created from a job-oriented viewpoint, such as job completion. However, challengers and innovators were created from the change-oriented perspective, examining the team context or standing conduct of performing jobs and providing new methods. The connectors and team builders, inversely, were expressed as sub-sets of the social-emotional feature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom these arguments, employing the job and social role behaviour structure appears appropriate to team up the dimensions of the TREO theory. However, the change-oriented viewpoint that entails challengers and innovators is also associated with team jobs since questioning existing methods for job performance, pushing the team to examine all likely resolutions and developing creative concepts for resolving issues are about enhancing team job performance and attaining job-related objectives. This discussion synthesises the correlation among the FFM and TREO dimensions in Table 2.1 related to the extant literature-based study questions, methods and context.\u003c/p\u003e\n\u003cp\u003eTable 2.1. The Relationship between Five Factors and The Team Role Experience and Orientation\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eSources: Mathieu et al., (2015).\u003c/p\u003e\n\u003cp\u003eGiven the correlation between FFM and the TREO dimensions, this study uses the FFM to recreate the individual and team personality traits and role behaviour to address job performance issues in the studied context. Following this line of investigation, the study aims to understand the influence of personality traits, roles, and job performance on project success and fill the gaps by integrating the previously described correlations between certain personality traits and project success.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.3 Job Performance\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eJob performance describes the effect of personality traits and team roles on project success. (Kanfer, 1990; Campbell et al., 1993; Roe, 1999). Raja (2019) supports earlier studies of Campbell et al., 1993 and Kramer (2014) that performance has been conceptualised from two viewpoints: procedure or activity and result. The process is about the behavioural aspect that denotes how individuals perform a job, the action itself. This proposition was supported by Campbell et al. (1993) with the outlined factors that account for all the behaviours, which included job performance (job-specific task proficiency, non-job-specific task proficiency, written and oral communication task proficiency, demonstrating effort, supporting individual discipline, enabling team performance, leadership and management.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConversely, Borman et al. (1993) asserted that the result is a pattern of effect characteristics that indicates the consequence of individual behaviour. Furthermore, Goldberg (1993) argues that behaviour considerably influences job performance, remarkably when individual personality traits conflict with team and job performance measures. This understanding explains the relationship between job performance and project success, which implies that approaches concerning a behaviour lead to targets to execute and to the real performance (result) of the behaviour.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.4 Project Success\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eStakeholders view project success from various perspectives as the conditions that measure performance (Mir et al., 2014). However, in this study, the project success is operationalised as a concept of personality traits, role behaviour and job performance (LePine et al., 2011). This perspective makes project success a dependent variable and the FFM of personality traits, role behaviour, and job performance the independent variables. Barrick et al. (2014) agree with LePine et al. (2011) that one of the personality traits of conscientiousness provides coherent outcomes driving job performance based on resourceful, reliable, determined, thorough and diligent characteristics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to LePine et al. (2011), John and Srivastava (2013) remodelled the factors of the five-factor model. They described conscientiousness as submissive, orderly, and practical, which requires small leadership and discipline to deliver what is required. Cote and Miners (2006) also posit that conscientiousness has a negative relationship with driving job performance, inconsistent effects of conscientiousness driving performance or attaining expertise and skill (LePine et al., 2011; Judge, 2013). However, from this discussion, this study can conclude that conscientiousness influences project success. Thus, this study could argue that from job performance, extraversion and agreeableness are strong predictors but less specific for predicting individual job performance.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.5 Conceptual Framework\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe conceptual framework presents the hypothetical five-factor model from the team and individual perspective. The study attempts to integrate individual factors and explain the relationship relating to personality traits, role behaviour, and job performance that drive project success.\u003c/p\u003e\n\u003cp\u003eThe study also explains cross-level moderating influences of the study context (personality traits with job role behaviour, job performance) on the relationship to project success. The correlation relating to job performance and the attributes of role behaviour was examined based on the differences in personality traits. The outcomes could offer a relationship between job performance with neuroticism and extraversion, while for consciousness, agreeableness and openness, the traits had an average job performance.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.5 Study Gaps\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eDrawing on the reviewed extant literature, the study observed the various contexts unrelated to this study context on how individual personality trait differences impact how individuals act and cooperate with other members toward project success. This observed gap is based on the operational activities, formation and conflict results about the relationships between certain five-factor personality traits such as openness, neuroticism and extraversion, role behaviour and job performance. Thus, much of the literature did not address the contextual factors of the individual and team aspects. For instance, LePine et al. (2011) noted the unpredicted findings concerning individual emotional issues\u0026apos; (Barrick et al. 2013) on virtual teams. Lastly, there is a lack of interpretivism (qualitative) approach to the correlation between the five factors of personality traits driving project success through role behaviour and job performance. The justification for adopting a quantitative study design provides a platform for future application of mixed method or qualitative approaches in the study.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.6 Summary\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis chapter has examined the wider space of project success and team study that appropriate scholarly subjects of personality, role behaviour and job performance have been reviewed, synthesised and justified. Although the extant literature shows that the personality trait composition method has been thoroughly explored, the study on connecting individual personality, role behaviour and job performance to project success in the studied context still needs to be explored. Consequently, the study developed some hypotheses to address the study problems.\u003c/p\u003e"},{"header":"3. Methods","content":"\u003cp\u003eThe study sample is drawn from Nigeria's telecommunications infrastructure managed service organisation. Telecommunications services in Nigeria are known for their high infrastructure operations services because of the increase in subscribers' voice and internet usage (NCC, 2019). This telecommunication sector presented strategies and enabling regulations for network operators that tailored cutting-edge technologies that support their operations—consequently, the operations circles around the highly skilled team operations employees that manage and maintain their assets. Therefore, the study employed a case study method (Yin, 2014; 2016; Saunders et al., 2019) and quantitative data collection technique (Crotty, 1998; Denzin \u0026amp; Lincoln, 2013; Saunders et al., 2019) based on the positivist philosophical position of knowledge of facts that are measured and observed.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.1 Case Study Study\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe applied case study method helped in obtaining comprehensive data for the big five factors of personality traits (LePine, 2011; Kendra, 2019) to analyse how these personality traits impact job performance (Neema and Ashish, 2020; Zell and Lesick, 2022) and project success, in which the objective will be to look empirical and theoretical validity to assist the predicting model. The case study will also support finding a more specified outcome on how individual personality traits potentially drive project success through job performance, notably in the telecommunication domain.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.3.1 Quantitative Data Collection\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe quantitative technique aims to link the statistical correlations between the significant variables of personality, role behaviour, and job performance contexts. The justification for using this data collection approach of quantitative techniques is based on Bryman and Bell's (2015) proposition and the study's position about knowledge and access to knowledge that refers to creating the study assumptions concerning what is known and how it can be known. A structured questionnaire was sent to field team members and network operations centre teams to quantitatively measure the relationship among the various dimensions of the FFM of personality traits and project success, including roles, behaviours, and job performance. The questionnaire measured the participant's understanding, experience, personality traits, and roles in project success, as reflected in the study questions.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.2 Study Participants and Procedures\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe survey participants were chosen from the field operation teams and teams employed in the study organisation. As this survey questionnaire needed embedded samples, individual team members and work teams nested in the teams. One reason for choosing field operations teams was that the individuals must have served for over one year. The justification for this duration requirement was that the survey questionnaire involved technical tasks, managerial and supervisory assessment of job performance and project success.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis insight explains that the survey questionnaire data were collected in a process associated with the work of Teddlie et al. (2007) and Creswell et al. (2016). The first round was probability sampling of operational context to ascertain the accessibility of the survey. This procedure was followed by selecting individuals and members from the field operations teams chosen in the probability sampling. This choice of probability sampling was to increase the generalisability of the findings.\u003c/p\u003e\n\u003cp\u003eThe questionnaire was sent to one hundred and fifteen (115) field team members from the studied organisation through their email addresses. The one-hundred-and-fifteen sample size is based on the study approach, analytical methods, time and effective cost management related to the study context (Kumar et al., 2015; Hair et al., 2018). One hundred and five (105) responses from the survey questionnaire were obtained, with the overall response result at ninety (90) per cent sample size. At the same time, ten (10) per cent represents unusable, deleted responses or no response. The teams agreed to participate by completing the survey questionnaires. The questionnaires represent personality traits, role behaviour, job performance and project success.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.3 Measurements of the Variables\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe study collected data sources from the field operations teams, technicians and supervisors to reduce standard method variance. Participants were asked to rate their personality traits, role behaviour and job performance. The study applied by Stewart et al. (2005), Li (2012), Yang et al. (2015) and Kwak et al. (2019) works by using corresponding individual responses to the questionnaire questions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, the collected data were from selected participants to confirm the reliability of matching up the individual scores to denote the various values of each construct, which is coherent with extant works (Li, 2012; Kendra, 2019). All scales were in the five-Likert point format.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.3.1 Dependent Variables – Project Success\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eProject Success is the key dependent variable, measured as a 5-point Likert scale - Strongly disagree – 1; Disagree – 2; Neutral – 3; Agree – 4 and Strongly agree – 5 (Blakeney 2017). Pearce and Porter (1986) developed this Likert scale for quantitative measurement, and it was also used by Carmeli and Freund (2004) in their study on job satisfaction and performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe participants were requested to signify the level of their job experience and view with each assertion. For instance, the ability to work with other members, attain task objectives, and finish tasks on schedule, within scope, quality, and overall performance.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.3.2 Independent Variables - Personality Traits\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe study adopted the five-Likert point scale for its high statistical reliability and validity across the various constructs related to the study problem. The participant's big five personality traits were measured by reflecting on the five-factor dimensions of the personality behaviours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor instance, openness – the individual has a vivid imagination; conscientiousness – the individual that pays attention to details; extraversion – the individual does not care about being the centre of discussion or attention; agreeableness – the individual identifies with other members feeling; neuroticism – the individual has frequent mood swings. All five-factor dimensions have entries that indicate positive or negative attributes of the specific personality trait.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.4 Analysis Techniques\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eRegression analysis using SPSS v29 and SPSS AMOS v28 to analyse the relationship between the observed variables closely (Szlachciuk et al., 2022). The first phase involves the R-square percentage representing the significance of all the variables, where a high percentage indicates an excellent acceptable level, additionally testing the hypotheses and analysing the correlation between the constructs and variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis approach and analysis are consistent with the practices of extant study works (Barrick et al., 2012; Li, 2012; Gavoille and Hazans, 2022; Zell and Lesick, 2022). As a process earlier to hypothesis testing (Li 2012; Kendra, 2019), the multiple linear regression of Y = x + B(x1) + B(x2) + B(x3) + Σ was judged particularly valuable and significant because the identified constructs related to team personality traits and role behaviour might theoretically and conceptually cover.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.5 Chapter Summary\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis chapter presented the implemented study methodology and data collection approach, which includes the case study with an explanatory sequential design and the quantitative data collection technique to attain a critical analytical depth. The phase of the quantitative data collection processes includes descriptive analysis using SPSS v 29 and SPSS AMOS, as well as regression and correlation analysis.\u003c/p\u003e"},{"header":"4. Data Analysis","content":"\u003cp\u003eChapter four describes the analytical methods used for the survey questionnaire data and presents the results. The analysis used a statistical package for the social sciences (SPSS v 29 and SPSS AMOS 28) to analyse the data. Firstly, descriptive statistics of the means and standard deviation samples and the multiple regression analysis using SPSS v29, whereby the multiple regression contrasts the planned hypotheses and also studies the correlations among the dependent variable y - and the independent variables x - (Blaž, Janez, et al. 2021) through the estimation of the regression r-square and coefficient that determine the influence that the variations of the (x) have on the (y) behaviour.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe model uses structural equation modelling with SPSS AMOS to create a model that validates the distinctiveness and correlations of the various variables. This analysis includes the five factors model personality traits and team members (Belbin, 2010; Barrick et al., 2012; LePine et al., 2012; Kendra, 2019) and role behaviour (Mathieu et al., 2015), job performance (Raja 2019). Therefore, to determine if personality traits do not directly correlate with project success and job performance, the two models were tested separately, with various paths to hypothesise the entire model. Bollen and Long (1993) noted the correctness of hypothesis testing in model fitting, even as the necessary distributional assumptions are attained and consistently examined.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4. 1 Descriptive Analysis with SPSS\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003ePerforming the correlation analysis between the variables shows a partial analysis with multiple regression analyses. The model tests the corresponding theory of personality traits to project success through role behaviour and job performance, measuring the fit of the regression model and the fit of the dataset. The model summary table 4.1 supported this output by indicating the hypothesis testing results, with a good observation of the data point. The R measures the strength of the linear relationship associating the independent variables and the dependent variables (Chin, 2012). R is also known as the correlation coefficient, whereby an R of one (1) value shows a strong linear relationship, and an R with a zero (0) value implies no linear relationship (Field et al., 2019). The R for this study model is .957, which implies a strong linear relationship relating to the independent variables of personality traits, role behaviour and job performance and the dependent variable of project success.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.1.1 Evaluating the Fit of the Model.\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe r-square, which is also known as the coefficient of determination, is the proportion of the variance in the dependent variable - project success that could described by the independent variables \u0026ndash; personality traits, role behaviour and job performance. The value for R-squared ranges from zero (0), which shows that the dependent variable cannot described by the independent variables, and the value of one (1), which signifies that the dependent variable can sufficiently be described deprived of error by the independent variables. The R-squared value for this study model is 0.916, which signifies that personality traits, role behaviour and job performance can describe 92% of the variance in the project\u0026apos;s success. This r-square value means the regression model justifies the variability observed in the target variables. Thus, the r-square is employed to confirm the percentage of the influence of the independent variables on the dependent variable.\u003c/p\u003e\n\u003cp\u003eTable 4.1. Regression Model Summary\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe adjusted R-squared is the revised version of the R-squared that is adjusted for the number of independent variables in the model (Van den Cruijce and Endres 2022), often lower than the R-squared. Nonetheless, it can be helpful in evaluating the fit of several regression models. This study model adjusted R-squared is 0.908. Lastly, the standard error of the regression is the average distance that the detected values decrease from the regression line (Van den Cruijce and Endres 2022), as the observed values in this model fall an average of 0.4051 units from the regression line.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.1.2 Analysing the Overall Significance of the Regression Model.\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe ANOVA table presents the degrees of freedom, the sum of squares, mean squares, F statistics and the overall significance of the regression model. The regression degrees of freedom for this model are 10 \u0026ndash; 1 = 9. These findings imply that the model has one intercept term, which is the dependent variable and nine (9) independent variables, making the model have a total of ten (10) regression coefficients. This computation is consistent with the proposition that regression degrees of freedom are the number that is equivalent to the figure of regression coefficients \u0026ndash; 1. However, the total degrees of freedom df is the number to the number of observations \u0026ndash; 1, which is 105 \u0026ndash; 1 = 104. Observation in regression is the dataset, and the observation for this study dataset is one hundred and five (105).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4.2. ANOVA\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe residual degree of freedom is the number that is equal to the total df \u0026ndash; regression df. For this output, residual degrees of freedom are 104 \u0026ndash; 9 = 95. Meanwhile, the means squares are calculated by the regression sum of squares divided by regression degrees of freedom. Thus, in this regression, means squares = 170.097/9 = 18.899666. The residual means squares are computed by the residual sum of squares divided by the residual degree of freedoms, which in this model is 15.595/95 = 0.16415. The F statistics is computed as regression mean square divided by residual mean square, which is 18.900/.164 = 115.2439. These values are in alignment with the proposition that F statistics implies if the regression model presents an appropriate fit to the data than a model that includes no independent variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn principle, F statistics assays if the regression model as an entire is helpful. Largely, if no independent variables in the model are statistically significant, the overall F statistics are also not statistically significant (Field 2019). Thus, a lower corresponding p-value of (p \u0026lt; .001 ) indicates a statistically significant value. We presumed that there was a statistically significant difference; thus, we rejected the null hypothesis of the ANOVA.\u003c/p\u003e\n\u003cp\u003eAdditionally, the p-value is correlated with F statistics to observe if the overall regression model is significant. Thus, in this model, the p-value of \u0026lt;0.001 is within the accepted value of 0.05 or 0.10, implying that the entire regression model is statistically significant and fits the data.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.2. Regression Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe study examined the variables from the descriptive statistics, coefficient and correlation between variables. The coefficient matrix of the variables and determination analysis defines the accuracy of the analytical model (Field 2012). The value of the coefficient is a means to measure the influence of the observed and predictor-independent variables on the variation of the dependent variable of project success. Therefore, the regression analysis shows in (Appendix 1) that the p-values of job performance p \u0026lt; .001 and team members p \u0026lt; .001 as predictor variables are statistically significant based on the solid evidence against the null hypothesis favouring the alternative. This result is consistent with Tuckman (1990) and Belbin (2010) on team composition and performance. This understanding is because team members\u0026apos; management is characterised by Tuckman\u0026apos;s (1990) theory and Belbin\u0026apos;s (2010) theory on team formation and roles, including resources, investigators, team workers, coordinators, monitors, and implementers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis proposition provides an understanding of how employees function within their project teams. It also highlights the significance of insights and using various individual skill sets and strengths in a team. These skill sets and strengths could be linked to some dimension of personality traits. Conversely, personality traits have a p \u0026lt; .091, which could be seen as a borderline significant trend but more remarkable than the usual significance level of 0.05. This output of p \u0026lt; .091 means a probability of 9% of finding that result by chance when the variable has no actual impact.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.3 Correlations Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe outcome of this correlation analysis on these variables expresses the strong point and direction of the linear relationship involving the two variables. Pearson\u0026apos;s r for the correlation between the personality traits value of .276 to project success shows that the correlation is positively significant at the 0.01 level (2-tailed) with a p-value of .004; job performance value of .909 to project success shows a strong significant correlation at the 0.01 level (2-tailed) with a p-value of \u0026lt; .001; team members value of .912 to project success, which means that the correlation is significant at the 0.01 level (2-tailed) with a p-value of \u0026lt; .001; conscientiousness value of -.201 to project success that the correlation is strong significant at the 0.05 level (2-tailed) with a p-value of .040, and agreeableness value of -.229 to project success that signifies a correlation that is significant at 0.05 level (2-tailed) with a p-value of .019. These independent variables\u0026apos; values are positive and mean a strong relationship exists between team members, job performance, personality traits and dimensions of agreeableness and conscientiousness with the project success variable. A change in these variables strongly correlates with changes in the project\u0026apos;s success. The sig (2-tailed ) value for team members and job performance is less than .05. Thus, we can conclude that there is a positive statistically significant correlation between these variables and project success.\u003c/p\u003e\n\u003cp\u003eTable 4.3. Correlation between Variables\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, role behaviour value of .241 to personality traits shows that the positive correlation is significant at the 0.05 level (2-tailed) with a p-value of 0.013, team members value of .223 to personality traits presents that the correlation is strongly significant at the 0.05 level (2-tailed) with a p-value of 0.022, job performance value of .250 to personality traits indicates that the correlation is significant at the 0.05 level (2-tailed) with a p-value of 0.10. However, the correlation concerning personality traits and job performance p-value is above the 0.05 threshold, which in some cases is acceptable based on the direction with the higher statistical power, such as the 2-tailed that doubles the significance level.\u003c/p\u003e\n\u003cp\u003eAgreeableness has a value of -.336 to role behaviour, showing that the correlation is a strong significant value at the 0.01 level (2-tailed) with a p-value of \u0026lt; 0.001. In contrast, team members\u0026apos; value of .826 to job performance indicates that the correlation is significant at the 0.01 level (2-tailed) with a p-value \u0026lt; 0.001. Again, agreeableness with a value of .197 to neuroticism indicates a significant correlation at 0.05 level (2-tailed) and a p-value of 0.04. Openness with a value of .918 to neuroticism has a significant correlation at 0.01 level (2-tailed) and p-value of \u0026lt; 0.001. Lastly, the conscientiousness value of -.266 to job performance signifies a significant correlation at 0.01 level (2-tailed) with a p-value of 0.006. Again, a conscientiousness value of -.212 to team members indicates a significant correlation at the 0.05 level (2-tailed) with a p-value of 0.03. These independent (predictors) variables that the correlation is statistically significant in this model are consistent with the proposition by Lepine et al. (2011), Barbee (2020) and Wen et al. (2021) stated in chapter 2.1 on the impact of personality traits on job performance. Likewise, Barrick et al. (1998), Raja (2019) and Maryam (2020) argue and suggest that some dimensions of personality traits influence job performance and project success. Mathieu et al. (2015) TREO theory also supported the role behaviour impact on project success, as Belbin\u0026apos;s (1993, 2010) contribution of team members\u0026apos; roles and formation in project success. For these reasons, we can conclude that there is a strong correlation between team members and some dimensions of personality traits and job performance to project success.\u003c/p\u003e\n\u003cp\u003eGiven the findings, we assumed that the direction of the relationship that extraversion as one dimension of personality traits has a p-value of .137, which is not significant to project success, trigged a concern because of its characteristics that comprise sociability, assertiveness, active, and positive emotionality. These extraversion features, according to Barry and Stewart (1997), enhance performance and process (Mu et al., 2022), drive the work environment and are akin to team formation and collaboration.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.4 Coefficient\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eUsing the coefficient p-values to decide the inclusion of variables in the final model is an acceptable practice. This proposition explains the linear regression coefficient insight about the model as the positive coefficient shows that the increase in value of the independent variables also implies an increase in the dependent variable. The coefficient provides the number that is essential to report the estimated regression equation: Y=b0+b1x1+b2x2. For this model, the estimated regression equation is project success = -6.192+.600+-.132+.810+5.440+.106+-.147+.117+-.863+-.329 = -0.59. This value shows that each coefficient is explained as the average increase in the dependent variable for each one-unit rise in a given independent variable, believing that all other independent variables are constant. For instance, for each personality trait, the average likely increases in value by .600 points, assuming that the number of other variables and personality trait dimensions are constant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, the intercept (constant) is interpreted as the expected project success for team member\u0026apos;s personality traits, role behaviour, and job performance aligned with the team collaboration and delivery. For instance, the project success score is -6.192 if the independent variables are statistically significant based on the expected p-values.\u003c/p\u003e\n\u003cp\u003eTable 4.4. Coefficient\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTherefore, we can resolve that the pathway of the correlation that team members and job performance variables have on project success has a strong correlation pathway indicated by the positive value (b). This means a positive correlation exists between team members, job performance, and project success. These empirical findings are consistent with Kaplan et al. (2010) and Belbin (2010) studies that enable team members\u0026apos; participation and direct collaboration in an organisation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, the negative coefficient implies that the dependent variables reduce as the independent variable increases. Additionally, Hair et al. (2010) and Pallant (2010) argued that when the maximum scale of variance inflation factors (VIFs) is less than the value of 10, that indicates no multicollinearity. Multicollinearity is the condition of very high inter-relationships between the independent variables (x), which signifies a data distribution. However, when this happens in the data, the statistical assumption decision concerning the data could not be consistent.\u003c/p\u003e\n\u003cp\u003eTherefore, to measure multicollinearity, probing tolerance and the variance inflation factors are based on the r-square value discovered by regressing a predictor on all the other predictors in the model or analysis. All the independent variables are within the accepted scale for this coefficient table, indicating no multicollinearity according to Durbin and Watson\u0026apos;s (1971) scale range. This study has no problem with the VIF values.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.5 Model Results with SPSS AMOS v 28.\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe path diagram for this model presents the unstandardised coefficients with the covariance and slopes and the standardised coefficients of correlation and beta weights. The Chi-tests the null hypothesis that the overidentified and just-identified model fits the data. In the just-identified model, there is a direct path from each variable to each other variable but not through an intervening variable. This action could have made the Chi-square have a value of zero.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor instance, the nonsignificant Chi-square shows that the fit concerning the overidentified model and the data is not significantly flawed compared to the fit relating to the just-identified mode and the data. Thus, we can conclude that this model is a good fit because the model produces the variance-covariance matrix \u0026ndash; correlation matrix \u0026ndash; from the path coefficients. However, the minimum result for this study analysis was achieved with Chi-square = 12.774, degrees of freedom = 14 and probability level = .544 (See Appendix 1).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.5.1 The Maximum Likelihood Estimates\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe parameters for this model are estimated by the maximum likelihood (ML) methods rather than by ordinary least squares (OLS) methods. Bollen and Long (1993) noted that OLS methods reduce the squared deviations between the values of the standard variable and those predicted by the model. For instance, in ML, an iterative process endeavours to maximise the probability that attained values of the standard variable will be adequately predicted (See Appendix 1).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.5.2 Standardised Regression Weights: (Group number 1 \u0026ndash; Default model).\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe path coefficients below correspond with the earlier ones obtained by multiple regression. Table 4.2 estimates of standardised regressions present that when team members go up by one (1) standard deviation, project success goes up by .543 standard deviations. When team members go up by one (1) standard deviation, job performance increases by .823 standard deviations. When team members go up by one (1) standard deviation, roles behaviour goes up by .045 standard deviations (See Appendix 1).\u003c/p\u003e\n\u003cp\u003eAdditionally, when personality traits increase by one (1) standard deviation, project success increases by .046 standard deviations. These values are accepted in model fit indices of between 0.05 and 0.08, according to Hu and Bentler (1998) and Kline (2005) on AMOS confirmatory factors analysis and standard equation modelling.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.5.3 The squared multiple correlations: (Group number 1 \u0026ndash; Default model)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe below squared multiple correlation coefficient is in the five multiple regressions. The total impact of one variable on another can be divided into direct impacts \u0026ndash; no intervening variables involved \u0026ndash; and indirect impacts, that is, through one of the intervening (team members) variables (See Appendix 1).\u003c/p\u003e\n\u003cp\u003eThe impact of job performance on project success. The direct impact is .447 \u0026ndash; the path coefficient from job performance to project success. The indirect impact, through team members, is calculated (Kline 2005) as the product of the path coefficient from job performance to team members and the path coefficient from team members to project success.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.6 Testing the Hypotheses\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe regression models were applied to test the hypotheses that concentrate on the individual and team correlations with the variables, as the regression analyses also support the illustration of each dimension of the personality traits on role behaviour and job performance. Remarkably, job performance and role behaviour were regressed on the constructs related to individual and team factors to test the scope to which it connected to personality traits and project success.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.6.1 Personality Traits and Project Success\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eMultiple regression was run to predict project success from the dimensions of personality traits. The study combined the sample test outcomes into one table and marked them appropriately. However, as shown in Table 4.3, the model using the personality dimensions to predict project success did not indicate a suitable model fit r-square .015, indicating that these personality traits contribute to individual and team members\u0026apos; project success concerns.\u003c/p\u003e\n\u003cp\u003eHypothesis 1 is linked with driving project success by predicting the dimensions of the five factors\u0026apos; personality traits related to project success. To test the hypothesis, the estimated regression models with results reported in Table 4.4, coefficient with a 95.0% confidence interval (CI) bound. The sample team members were significantly linked with project success (\u0026beta; = .5.440, 95%, CI is \u0026nbsp;6.586 \u0026gt; 4.294; \u0026nbsp;\u0026beta; = .577, 95%). These lower-level CI and upper-level CI show a high positive relationship between these variables as the accurate population correlation because the percentages demoted a high confidence level. Thus, explain the valid population parameter of the attributes of the entire sample size from the drawn sample.\u003c/p\u003e\n\u003cp\u003eThis result supported hypothesis 3, indicating that team member performance predicts project success. This result aligns with job performance that conceptualised two team members\u0026apos; perspectives of process or action and outcome (Belbin, 2010; Kramer, 2014). that signifies how individuals perform a job, the action itself (Campbell et al., 1993; Maryam, 2020).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.6.2 Roles Behaviour and Project Success\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eMultiple regression analysis was performed to predict project success from role behaviour, and the result is presented in Tables 4.3 and 4.4. Correlation and coefficient. The regression discovered that using role behaviour to predict project success created an unaccepted model fit for the sample of p \u0026lt; .142, which is above the standard value of 0.05. This value shows the no fit on the amount of variance in the dependent variable described by the independent variable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, role behaviour is assumed to predict individual and team role behaviour in project success, as indicated in Table 4.4 coefficient. The standard beta coefficient range \u0026beta; = -.048 and p-v .142 is no statistically significant difference to project success because the value exceeds the accepted p-value range of 0.05.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.6.3 Job Performance Configuration and Project Success\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eHypothesis 3 suggested that job performance configuration is operationalised by the mean value of team members\u0026apos; composition, skill sets, and teamwork, which is significantly associated with project success. The study included the mean value of job performance into the model using team members to predict project success and perceived a significant increase in the adjusted r-square value of .908. This explains that adding control variables through the mean value of job performance into the model could increase its predictive power to project success.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4.4 also presents a standardised coefficient beta \u0026beta; = .487 and (sig) p \u0026lt;.001 on the job performance influence on project success. This result indicates a substantial impact on the dependent variable. Additionally, the regression coefficient implies that job performance configuration operationalised through the value of the team members\u0026apos; characteristics significantly predicts project success.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4.7 Chapter Summary\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe analysis of the quantitative approach discovered many applicable results concerning the theorised hypotheses. This study analysis indicates that team members\u0026apos; characteristics with some personality traits significantly predict job performance and project success. Additionally, the results revealed that some dimensions of personality traits supported the project success relationship. When these team members\u0026apos; contextual factors were low, personality traits could be expressed as moderated job performance; the personality traits and team members\u0026apos; relationship improved when the individual contextual factors were high. The quantitative outcome established that team members and job performance configuration predicted project success, although the data did not support some hypotheses, such as role behaviour.\u003c/p\u003e"},{"header":"5. Findings and Discussion","content":"\u003cp\u003eIn this context, the telecommunication domain entails actions and tasks associated with asset delivery, planning, implementation, operations and maintenance activities. In existing studies, various practitioners and scholars have investigated and explored the problems that impact project success. However, the critical literature gap intended to address was the insufficient academic study on how personality traits and contrasts between individuals impact the performance of project teams.\u003c/p\u003e \u003cdiv id=\"Sec45\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Study Questions\u003c/h2\u003e \u003cp\u003eQ1. Which individual personality traits could influence project success?\u003c/p\u003e \u003cp\u003eFrom the data, the most influential dimension of personality traits to the least based on the individual levels were arranged (Barrick, 1998; LePine et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Raja, 2019; Maryam, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), openness, conscientiousness, agreeableness, extraversion and neuroticism.\u003c/p\u003e \u003cp\u003eQ2. How do personality traits influence role behaviour and project success?\u003c/p\u003e \u003cp\u003eThe data revealed that individuals' interactions and personality traits influence role behaviour and project success (LePine et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mathieu et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Raja, 2019; Barbee, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Maryam) either positively or negatively. The reviewed literature provided a clear understanding of the influence of the various personality traits on role behaviour and how they can be applied in delivering project success.\u003c/p\u003e \u003cp\u003eQ3. Does team members' work influence job performance on project success?\u003c/p\u003e \u003cp\u003eThe data revealed that team members' characteristics were one of the critical factors influencing project success in telecommunication assets managed projects. However, job performance has an impact on project success.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec46\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Discussions\u003c/h2\u003e \u003cp\u003eThe study context was conceptualised as the capability of individual personality traits to accomplish project success through role behaviour and job performance (LePine et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Barrick et al., 2014; Mathiew et al., 2015; Kendra \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Raja 2020; Erick, 2020). Noteworthy, role behaviour contrasts with job performance, which implies the project delivery of values or metrics such as project cost, time to project completion and quality of deliverables (Goldberg, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Campbell et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Mathiew et al., 2015; Raja 2019; Maryam \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFundamentally, job performance is focused on characteristics of individuals and team members' creative and working ability, their fulfilment with the team as its actions and collaboration between them; thus, individual dimensions of personality traits were used as the leading indicators to measure job performance. Additionally, job performance is affected by individual and team standards scores, which influences the project success position of the telecommunication assets managed team.\u003c/p\u003e \u003cp\u003eOpenness was observed as sufficient to create positive project success through job performance. Also, openness predicted role behaviour, while neuroticism, on the other hand, was observed to be negatively related to role behaviour. Thus, high scores of openness were also noticed in the extant literature, with job performance driving project success, as it is categorised as team members who create originality and noble thoughts, do not evade conflict, are flexible to modification, own high levels of adaptableness, compliance and encourage open communication (Neuman et al., 2014; Ghani et al., 2016; Raja, 2019; Maryam \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The quantitative data provided supporting evidence of openness features that supported individuals through collaborating during conflict and were flexible when issues occurred. The results from openness were consistent with Raja (2019) and Juhasz (2010), who established a strong relationship relating to team-oriented communication and learning new-found entities, and Bradley et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), who established team members who scored high on openness assumed conflict in their team better and thus had a significant effect on job performance and project success.\u003c/p\u003e \u003cp\u003eIn contrast, in this study, some dimensions of personality traits, such as conscientiousness, were not discovered to be adequate to create better job performance. This insight from this study is incompatible with the extant literature, which indicates that high conscientiousness scores are categorised by individual and team members involved in project success (Neuman et al., 2014; Raja, 2019; Maryman, 2020). Other studies perceived high conscientiousness scores as high performance and above-average conscientiousness. Conscientiousness, according to Bell (2007), allows team members to allocate tasks efficiently and successfully, supervise progress, and report performance in project implementation. Principally, conscientiousness represents the inclination of team members to be structured in their behaviour through appropriate tasks, responsibilities, and assignments.\u003c/p\u003e \u003cp\u003eSpecifically, job performance was discovered to be rational with the correlation relating conscientiousness and role behaviour. This was because conscientiousness was more positively related to individual work role behaviour when team members' work traits were low, while the correlation was mitigated when high. However, the hypothesised interaction of the individual work characteristic and other dimensions of personality traits on role behaviour was not discovered. Likewise, it was discovered that neuroticism was more negatively correlated with individual role behaviour when team members' interrelationships were low, although this correlation was reduced when team interdependence was high. This understanding is because team interdependence regulated the correlation relating to neuroticism and individual role behaviour. Thus, the hypothesised interaction of individual and team interdependency and other dimensions of five-factor personality traits on role behaviour was not established.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec47\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Practical Implication.\u003c/h2\u003e \u003cp\u003eThis study illustrates the significance of considering team members' personality traits when assessing job performance (Debusscher et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and project success. Given the critical effects of the FFM dimensions of personality traits, exploring a more consolidative, dynamic approach to personality traits assessment can contribute to a deep insight into team members' personalities and, therefore, reinforce the dimension of each individual. However, such consolidative and dynamic approaches by (Belbin \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, LePine et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Barrick et al., 2013, Raja 2019) can be challenging to use in certain situations, Mathieu et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kendra) explained how the TREO approach can be modified and extended to assess additional dimensions of personality concepts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec48\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Contribution to Knowledge\u003c/h2\u003e \u003cp\u003eThe contribution of this study to the body of knowledge is stated accordingly. The results discovered that the study effectively examined and unified the concepts of five-factor dimensions of personality traits with role behaviour and job performance and investigated their influence on project success for actual telecommunication asset-managed projects. Interpreting the correlation relating to personality traits and role behaviour indicates that the statistical results on some FFM dimensions of personality traits significantly predicted individual and team role behaviour in the workplace. Thus, the outcome of this study has created outcomes that increase earlier findings in this study area through the link relating to openness and individual and team members' role behaviours.\u003c/p\u003e \u003cp\u003eBarrick et al. (2011) argued that openness was negatively associated with individual and team role behaviour in social and at-work activities. In contrast, Mathieu et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) opined that openness was strongly related to components of individual and team task role behaviour such as connector, challenger and innovator. This insight differs from Barrick et al. (2011) but is consistent and aligns with Mathieu et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Thus, these conclusions contribute to the literature by exhibiting the correctness of using the Mathieu et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) TREO role dimensions model to operationalise role behaviour in driving project success based on various individual personality traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec49\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Study Limitations\u003c/h2\u003e \u003cp\u003eThe five-factors model dimension of personality traits is probably specific based on the logic that the impacts of its factors and the interfaces among the factors vary based on the individual personality dimensions under reflection. For instance, conscientiousness relates to rigidity and organisation. Thus, it is perhaps that a strong level of conscientiousness correlates with low conscientiousness adaptability, while high conscientiousness attracts vigour and intensity. Therefore, to explore these impacts, additional study is required on the analytical dynamics of the other dimensions of personality traits and their impacts on role behaviour, job performance and project success.\u003c/p\u003e \u003cp\u003eAnother limitation is the concern about how extraversion was theorised to predict individual and team members' role behaviour and job performance. The quantitative data did not support this influence; thus, it is problematic to determine the role that extraversion played in project success. Lastly, the five-factor model does not separate inward and outward prompts of transformation in team personality positions; rather, it captures the developing path. Further study is, however, required to examine the methods that highlight these shifts.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThe study finding requires adjusting the conceptual framework model, presented in Chap.\u0026nbsp;2 sub 2.5, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e. The adjusted conceptual framework model presented in Chap.\u0026nbsp;6, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6.1\u003c/span\u003e, is distinctive in various approaches from the primary study model developed by the study and supported by the extant literature and hypotheses. To start with, in contrast with the original conceptual framework model \u0026ndash; the strength of the personality traits relationship to role behaviour, job performance driving project success and adjusted study model indicates that the job performance and personality traits relationship stands out, while role behaviour is at the borderline as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6.1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe personality dimension of openness is correlated to job performance. Nevertheless, conscientiousness, agreeableness, extraversion and neuroticism were reformed to a compact concept in the adjusted conceptual framework of how this dimension of the five-factor personality traits predicts role behaviour and job performance in driving project success. This understanding is because the hypothesised correlation relating to role behaviour and project success was insignificant.\u003c/p\u003e \u003cp\u003eThe adjustments made to this study model echo the contributions of this study to the literature on five-factor personality traits and role behaviour and job performance, based on identifying the most significant predictors of personality traits driving project success by leaving out the less significant personality traits. Thus, this study has helped to resolve the ambiguous correlation relating to the FFM of personality traits and individual role behaviour and job performance. This is because the study indicates how team members' contextual factors such as skill sets, teamwork and commitment act on individual positionality about the dimensions of five-factor personality traits with job performance driving project success.\u003c/p\u003e \u003cdiv id=\"Sec51\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Recommendations\u003c/h2\u003e \u003cp\u003eThe study recommends several essential effects of team personality traits driving project success. Firstly, the results justify advancement from previously favoured prevalent team personality traits relationship between role behaviour and job performance that encourages team members' openness, commitment and collaborative teamwork. Again, the results denote that various FFM dimensions of the personality traits model affect individuals' and team members' behaviour inversely. These contrasts that affect team behaviour differently may draw from either the individual who possesses those personality traits or the personality traits themselves. Openness was observed to contribute more to job performance, while conscientiousness and agreeableness were discovered to be essential to individual role behaviour.\u003c/p\u003e \u003cp\u003eThe study recommends that individual and team job performance and project success indicators differ, with several subjective indicators, such as role behaviour, which are more problematic to explore than objective indicators, and the overall team performance that is objective.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec52\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Summary\u003c/h2\u003e \u003cp\u003eThe study has clarified how the study answers addressed each study question and how the adjusted study conceptual framework model explained the correlation among the individual and team members' personality traits driving project success through role behaviour and job performance indicators. Primarily, the study illustrates that team members and job performance are significant predictors of project success and are slightly related to personality traits such as openness. At the same time, conscientiousness and agreeableness were significant predictors of role behaviour. However, the adjusted conceptual framework model also exhibits that neuroticism, a dimension of the FFM personality traits, is insignificantly correlated to role behaviour or job performance driving project success.\u003c/p\u003e \u003cp\u003eFurthermore, the adjusted conceptual framework model proposes the presence of individual team members' contextual factors, including team interdependence, which interact with individual personality traits, driving project success through job performance. However, several constructs concerning personality traits, individual behaviour, and performance are unconnected to the adjusted conceptual model, indicating an opportunity for further study, which may include qualitative data to support the new study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe study was approved by the University of Lincoln ethics committee.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlias, Z., Zawawi, E.M.A., Yusof, K., and Aris, N.M. (2014) Determining critical success factors of project management practices: A conceptual framework. \u003cem\u003eProcedia-Social and Behavioural Sciences\u003c/em\u003e, \u003cem\u003epp. 153\u003c/em\u003e, 61\u0026ndash;69. \u003c/li\u003e\n\u003cli\u003eAllport, G. (1937) \u003cem\u003ePersonality: A Psychological Interpretation\u003c/em\u003e. New York: Henry Holt.\u003c/li\u003e\n\u003cli\u003eAshton, M.C. 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Available from: https://doi.org/10.1111/jopy.12683 [accessed: June 06, 2023].\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Personality Traits, Role Behaviour, Job Performance, Project Success","lastPublishedDoi":"10.21203/rs.3.rs-3990907/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3990907/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTeam personality traits enable project practitioners to deliver project success through role behaviour and job performance. However, various team personality traits influence role behaviour and job performance. Project success is a challenging phenomenon for project practitioners, where different factors play a critical role in project success. The objective of this study is to explore the effect of team personality traits on project success, with moderating effects of role behaviour and job performance. The study includes participants from telecom organisations in Nigeria, an emerging market, using the survey-structured quantitative data collection technique. The five-factors personal traits model was used to assess the individual participants and personal traits and team role experience and orientation factors.\u003c/p\u003e \u003cp\u003e The collected data was analysed using the SPSS AMOS v29, with the results indicating that personal trait factors of agreeableness and conscientiousness are positive predictors of project success. In contrast, openness, extraversion and neuroticism did not have a strong correlation with project success in this context. The findings also concluded that the team role experience and orientation supported the role behaviour impact on project success. The team role experience and orientation model factors as moderators to role behaviour and job performance, which are relevant to the theory and practice and provide in-depth insight that is valuable for project practitioners, decision-makers, individuals, and scholars.\u003c/p\u003e","manuscriptTitle":"Exploring the Effect of Team Personality Traits, Role Behaviour and Job Performance on Project Success. A Case of Telecom Projects in Emerging Markets.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 19:20:18","doi":"10.21203/rs.3.rs-3990907/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e9f6f0c-dbcd-4a30-aae7-96133d35efc4","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28974726,"name":"Management"}],"tags":[],"updatedAt":"2024-02-28T19:20:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-28 19:20:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3990907","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3990907","identity":"rs-3990907","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}