Effects of Personality Type Tools and Problem-Solving Methods on Engineering Company Project Success

Abstract

Personality type tools have been utilized to explain human behavior by organizing, classifying, and categorizing individuals into distinct personality types. Alternatively, problem-solving methods have been employed throughout project life cycles to enhance problem solving across all levels of the organizational workforce. This study evaluated the influence of personality tools and methods for problem solving on project success. The population comprised 29 active project managers employed in the engineering section of a large manufacturing firm, focusing on sustainable development projects. Quantitative study, through correlational analysis, demonstrates that personality tools facilitate the formation of effective project teams (p < 0.05) and that problem-solving methods significantly improve the likelihood of successful project completion (p < 0.05). This research presents a novel approach and a decision framework for projects that identify the preferred methods for solving problems associated with various MBTI profiles, and demonstrate that where MBTI is utilized as a guiding tool for effective project team formation, ESTJ, ENTJ, ISTJ, and INTJ are prioritized profiles. The research concludes that selecting methods for solving problems according to the nature of the problem impacts overall project success, with the Five Whys methodology contributing when used for root cause analysis. Therefore, it is essential to enhance project managers’ awareness that specific tools and methodologies can aid in the formation of teams that successfully complete projects.

1. Introduction and Literature Review

Project success is measured by meeting the triple constraint of concluding the project on time, within budget, and within the scope [1]. This approach places an emphasis on the technical aspects of project management at the expense of interpersonal, soft skills. Nonetheless, organizations that invest in both have a greater likelihood of project success [2]. For an organization to be able to form a project team, the use of personality assessment tools [3], such as the Myers–Briggs Type Indicator (MBTI^®), is a necessity. On the other hand, achieving successful projects by using effective problem-solving methods, and approaching this process with a systematic and logical methodology while focusing on positive interpersonal interaction, has become an important endeavor in organizations [4].

Personality is determined by two factors, genetics and hereditary factors, as well as the effects of upbringing, culture, environment, and experience, which contribute to the change in overall personality over time [5]. To be able to identify general personality styles in people, different models have been established throughout history. In recent years, researchers have used personality tools to gain a better understanding of how different personalities affect project success [6] and how they can be used in organizations worldwide [7,8]. It has been determined that the personalities of project managers (openness, neuroticism, agreeableness, conscientiousness, and extraversion) influence the success of software development projects [9]. In addition, research has been conducted on the impact of various personalities on teamwork [10], with the majority of studies focusing on software development projects [11], project managers [12], project team formation based on DISC (dominance, influence, stamina, and conscientiousness) profiles [13] or the ‘Big Five’ (openness, neuroticism, agreeableness, conscientiousness, and extraversion) [14], and rarely on more than one personality tool. One of the most popular tools for classifying personalities is the MBTI. The MBTI is a model based on the personality theory of Jung, and it is a tool for understanding personalities and preferred modes of behaving [12]. The MBTI categorizes individuals into distinct personality types along four dimensions: where they focus their energy (extraversion (E) vs. introversion (I)), how they gather information (sensing (S) vs. intuition (N)), how they make decisions (thinking (T) vs. feeling (F)), and how they deal with the external world (judging (J) vs. perceiving (P)) [15]. Previously, the MBTI tool was used to examine the interactions between members of student teams, with the conclusion that teams that communicate or interact less appear to produce a lower quality of work [13]. Also, teams that have undergone MBTI testing tend to perform better together, regardless of the diversity of the team members’ personality types [16]. The MBTI tool is generally used for the self-understanding of individual preferences and their relation and communication styles to others, as well as for team building and educational and carrier guidance [17,18]. On the other hand, the MBTI is also a widely criticized tool with moderately high levels of item homogeneity, where the psychometric limitations of the MBTI raise concerns about the validity of the instrument and the use of dichotomous forced-choice items greatly limits both the theoretical and statistical import [19]. Also, the tool is considered static, oversimplified, and is considered to have poor validity and reliability, as individual profiles and preferences change over time [20,21]. Alternatively, while the preferences do not change, human behavior adapts over time, ensuring that the initial MBTI profile remains permanent [22]. Even with these limitations, the MBTI provides a psychometrically simple description of Jungian personality types and as such is applicable for use in applied contexts [19], thus making it a usable tool for team formation [23] and determining the impact of various personalities on project success [17,24]. As such, the MBTI remains a user-friendly tool that categorizes individual personality preferences at a certain time [25,26]. According to the results of a specific MBTI study, project managers with the ESTJ profile were predicted to have superior project schedule performance [27]. Research based on the DISC profile shows that balanced teams that have a single leader produce better work products [28]. Additional research on team performance supports these findings, demonstrating that the individual personality types of team members have a significant impact on group processes, and that the most effective teams are those with a diversity of personality types [29]. Based on the literature, an analogy was drawn between the two groups of personality tools, where, in the first group, the most compatible tools were Jung, Myers–Briggs, and Keirsey’s DISC and the Four Temperaments; in the second group, compatibility was recognized between Belbin, the ‘Big Five’, and the Birkman Method [30]. To define personality profiles, personality analysis, which utilizes various methods such as self-report questionnaires and behavioral observations, is used to assess an individual’s traits, motivations, and behaviors. Traditional personality methods mainly include self-report personality tests in the form of questionnaires to obtain the personality traits of participants. The known limitations of these methods are self-cognition, social expectations, or the emotional state when filling out the questionnaires, which can result in high subjectivity [31], and which should be taken into account while forming conclusions in this study. This study focuses on the MBTI tool, which is the most utilized and widely recognized model in the researched organizations. Based on personalities and project outcomes, an investigation was conducted to determine the relationship between the project manager and team personalities and the project manager’s personal experience with the profiles, project management, and project success.

When it comes to problem solving, organizations seek tools and methods that can assist employees and projects. The issue behind problem solving is typically not the methodology itself but rather the ability of employees to apply the selected methodology, which emerges from the organizational structure. Methodologies and tools for problem solving represent a structure that describes a process that must be followed to address problems. In general, they are divided into five groups: general problem-solving tools, problem-solving approaches, methods centered on the root cause of the problem, diagram-based tools, and methods centered on improving business processes [32]. Problem-solving skill, on the other hand, refers to a team’s ability to anticipate problems, analyze them, devise viable solutions, and address them in a timely manner [33]. Problem-solving competency is one of the team competencies that aid in the accomplishment of a project’s objective. “Competence” is defined as a specialized system of abilities, cognitive skills, and behaviors used to perform tasks [34]. Problem solving is defined as the use of work processes to close the distance between the current state and the desired state [35]. Diverse studies have been conducted to investigate the potential effects of problem-solving methods on project performance. According to research that has adopted a problem-solving perspective, a team’s performance is determined by its ability to address problems efficiently and effectively [35]. On the one hand, a project team with a higher level of problem-solving skill or competency can make a concerted effort with its available resources and implement an effective strategy to facilitate a positive outcome [35]. On the other hand, the project manager’s experience and ability to identify a problem and select an appropriate solution are rarely questioned. In a previous study [36], an overview of problem-solving methods as well as their applicability and advantages were identified. The focus of this study will be on seven problem-solving methods (the Five Whys, Deming Cycle (PDCA), Six Sigma (DMAIC), Cause and Effect Analysis, The Eight Discipline Methodology (8D), Failure Mode and Effect Analysis (FMEA), and Kaizen, and Appreciative Inquiry [36] that were acknowledged and utilized in the projects under study. The Five Whys method is used for simple, single-root-cause (manufacturing) problems that require fast solutions [37]. The PDCA method is also used for solving simple problems and ensuring continuous improvement and iterative process optimization [38]. Six Sigma is a data-driven approach focusing on complex problems that require significant process optimization and improvement [39]. Cause and Effect Analysis is a visual tool focused on brainstorming to organize potential problems and causes into categories [37,40]. The Eight Discipline Methodology (8D) is a structured, team-based methodology that identifies, corrects, and prevents the recurrence of high-impact problems [41]. FMEA is a method for predicting problems before their occurrence, allowing for proactive risk mitigation [42]. Kaizen focuses on continuous improvement, implementing small incremental improvements in the organization [43]. Appreciative Inquiry is an unconventional people-oriented collaborative approach focusing on discovering and delivering the vision instead of problems [44].

Personality tools and problem-solving methods and their impacts can be observed through the soft skill theory, which provides the framework for understanding interpersonal attributes (communication, teamwork, and emotional intelligence), where problem solving is a soft skill that enables effective problem resolution, which has a direct impact on project outcomes and project success [2,45]. Problem solving can be perceived as a bridge between understanding soft skills and their application in projects [46]. When it comes to observing the overlap between team formation based on personality tools (namely, MBTI) and problem-solving methods, the current research indicates that the use of MBTI tools to form diverse teams can also improve the problem-solving capabilities of the teams due to the different perspectives and approaches of individual profiles and the ability of these profiles to address different issues and bring approaches to the table. However, the direct link between profiles and problem solving, mainly root cause analysis, has not been further explored [17]. Additionally, teams composed of diverse personality types may generate more innovative solutions and enhance overall team effectiveness [47], which could highlight the importance of defining preferred profiles within teams [48].

However, insufficient research has been conducted in these fields. Therefore, it is necessary to revisit and re-evaluate the impact that personality tools and problem-solving methods have on project success. This study seeks to determine if there is a relationship between personality tools and problem-solving methods and project success, as well as their applicability and impact. The literature review identified a knowledge gap concerning how personality type tools affect project managers and project teams, particularly regarding their personal experience with these tools, their project management skills, and their knowledge of problem-solving methods, all of which influence project success. The present research study aims to fill this gap by investigating the use of personality type tools and problem-solving methods and establishing their relationship with project success within a large matrix organization [49] in Denmark. This organization encourages the use of dedicated personality type models without exploring the possibilities of their use and the use of specific problem-solving methods, regardless of the problem origin. This research proposes a novel approach by creating a direct link between personality profiles and chosen problem-solving methods, their impact on team formation, and project success. The results of this study can assist in determining which profiles are preferred and unpreferred by project managers in specific project teams for successfully terminated projects, as well as how problem-solving methods are utilized to increase the success of projects, which is the main purpose of the research.

The research question is as follows: Is there a relationship between personality tools, problem-solving methods, and project success? The independent variables included personality tools and problem-solving methods, while the dependent variable was project success. The target population consisted of 29 active project managers from a large Danish manufacturing company’s engineering departments, focusing on sustainable development projects. According to the existing literature, these projects are defined as development that meets the needs of the present without jeopardizing future generations’ growth and prosperity and involves the consideration of social, environmental, and economic resources [50].

The paper is structured as follows. The introduction and literature review, as well as the purpose and methodology of this paper, are presented first. The main findings are then presented and discussed. The paper concludes with a summary of the principal findings and suggestions for future research.

2. Materials and Methods

The study’s dependent variable is project success, where a successful project is completed on time, within budget, and with all planned features [51], and its independent variables are personality tools and problem-solving methods. Project life cycle and sustainability factors (political, economic, and social) were considered as moderating variables. The conceptual framework for this study was adapted from the factors of the project implementation profile described in [52,53] and was summarized in the state of the art, with expected relationships (H1, H2) and gaps (Figure 1) [54]. Thus, this study investigated the impact of independent variables (personality tools and problem solving) on the dependent variable (project success) and their mutual impact, while taking the moderating role of the project life cycle and certain sustainability factors into consideration.

The research methodology adapted for this research is as follows. First, the relevant research articles were identified. The most relevant studies were then used to form research questions, gaps, and hypotheses. These were used to construct the latent variable questionnaire, which uses observed items to measure unobserved constructs, like personality traits, for quantitative data collection [55]. After the data were gathered and analyzed, conclusions and recommendations were formed while ensuring ethical considerations. The data were gathered using Google Forms and analyzed using the statistical analysis software Minitab Statistical Software 22. The participants in this research were project managers from a large Danish manufacturing company’s engineering departments, focusing on sustainable development. The intention of the researchers was to select participants who were representative of the study’s population, including all engineering teams within the organization. Participants were required to be at least 18 years old and actively employed as project managers. This study employed a quantitative research approach. Researchers used quantitative methods to discover explanations and make predictions [56]. Since the variables for this study are easily measurable, a quantitative method was appropriate for this study. The research was correlational in nature. Participants were selected from the representative sample based on their suitability for the cross-sectional (correlational) study [57]. This means that we used a non-probabilistic, purposive sampling technique, attempting to overcome the method’s limitations by ensuring diversity in terms of age, background, and knowledge. The target population consisted of 29 project managers (including project managers, project leaders, and manager specialists), who were selected based on the position profile they held in the organization. Consequently, the target population for the study was small. The company where the research was conducted is estimated to have less than 80 project manager positions in various departments, while the engineering departments have less than 45 project managers. Therefore, the used sampling procedure ensured a representative sample population of project managers in the observed organization, as study participants were believed to be acquainted with most of the personality tools and problem-solving methods, which was considered crucial for this research. However, their training and experience in both were reverified throughout the questionnaire. Based on the established use of personality tools, predominantly the MBTI, and problem-solving methods, a web-based structured questionnaire with 72 closed-ended questions was developed. To ensure the content validity of the questionnaire [58], the relevant aspects were defined as follows: personality tools, problem solving, and project success. Therefore, the survey was divided into two phases, with the first consisting of 20 questions about personality tools and the second consisting of 23 questions about problem-solving methods. Each query regarding an independent variable corresponded to a particular question regarding the dependent variable. Survey questions were structured to be unbiased and easy to understand, and closed-ended questions were chosen as they are useful for efficiently gathering specific, factual, and quantitative data [59]. Respondents indicated the extent to which they agree that each stage’s statement correlates with project success. The project success statements were derived from [60] but tailored to this study, forming 22 statements in total. A pilot study was conducted on a small sample group representative of the target population. Since the sample size for the study was small, the representative group was 5 participants. Based on the feedback collected from the pilot, only 2 issues were fed back, discussed, and corrected before the continuation of the study. These included questions regarding personal experience in both personality tools and problem solving, where a possibility for answering “none” was not provided from the start. A possibility of an “additional” choice, apart from the given methodologies, was included in the survey prior to continuation. The appropriate time frame for data collection, based on the research objectives, was defined as 20 min. Participants had the option of leaving their contact information if they were prepared to discuss the survey results with the authors or participate in in-person interviews, which will be used as the basis for future research. Six individuals expressed interest, and further investigation was taken into consideration. Except for the 12 descriptive questions, most of the items were rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) to measure participants’ perceptions and experiences. These were adapted from previous research in project management, with a specific focus on project manager and team capabilities, and were deemed valid and reliable [61]. The item example for questions measured on a Likert scale is as follows: “As a project leader, I give more importance to the team members’ relationships rather than managing the project tasks.” All variables and scales used in the research, together with item examples and references to validity and reliability, are listed in Table 1. The age distribution of the respondents fell into four categories: 18–24, 25–34, 35–44, and >45 years of age. Age is considered to influence an individual’s performance, as younger project managers place a greater emphasis on technical aptitude, whereas older project managers place a greater emphasis on project management and extra-organizational objectives [62]. This study’s age brackets were derived from research [63] examining the effect of project manager age on project success.

As the sample size was based on resource constraints [64], due to the organizational setup and adequate resource availability, to determine the sample size, a sample size calculator was utilized. A confidence level of 0.99 was selected, and the sample size was determined to be 29 individuals, with margin of error ±4.45%. If a level of confidence of 0.95 is selected, the calculated sample size is 28. Consequently, the study required between 28 and 29 participants. Due to the small sample size, a reliability test was performed. Cronbach’s alpha value for all non-descriptive questions in the questionnaire concerning personality tools and problem solving was α = 0.94, indicating positive results. Having established consistency and reliability, further statistical analysis was conducted.

Table 1. Operationalization of research variables.

Variable	Operational Definition	Items	Measurement Scale	Item Examples	References
Personality tools	Use of personality tools in teams, knowledge and experience in different tools; MBTI	20	5-point Likert scale	Personality tools can help project managers to establish good-performing teams. The Myers–Briggs® tool can be used as a guiding tool when forming teams in projects.	[18,65]
Problem solving	Use of problem-solving methods in teams, knowledge and experience in different tools	23	5-point Likert scale	When problems occur during the project, problem-solving methods are used. Problem-solving methods help identify and remove problems that have occurred during a project’s life cycle.	[66]
Project success	Impact of personality tools and problem-solving methods during project life cycle on termination of projects with success	22	5-point Likert scale	Teams created based on personality tools can help project managers to achieve greater success on projects. Using the right team members (team members with certain profiles) to help solve problems can increase the chances of successful realization of projects. Sharing the results (knowledge sharing) of the solved problems can help achieve project goals with success.	[13,14,35,61,67,68]

Table 1. Operationalization of research variables.

Variable	Operational Definition	Items	Measurement Scale	Item Examples	References
Personality tools	Use of personality tools in teams, knowledge and experience in different tools; MBTI	20	5-point Likert scale	Personality tools can help project managers to establish good-performing teams. The Myers–Briggs® tool can be used as a guiding tool when forming teams in projects.	[18,65]
Problem solving	Use of problem-solving methods in teams, knowledge and experience in different tools	23	5-point Likert scale	When problems occur during the project, problem-solving methods are used. Problem-solving methods help identify and remove problems that have occurred during a project’s life cycle.	[66]
Project success	Impact of personality tools and problem-solving methods during project life cycle on termination of projects with success	22	5-point Likert scale	Teams created based on personality tools can help project managers to achieve greater success on projects. Using the right team members (team members with certain profiles) to help solve problems can increase the chances of successful realization of projects. Sharing the results (knowledge sharing) of the solved problems can help achieve project goals with success.	[13,14,35,61,67,68]

Assumption checking in statistics is the process of verifying that the conditions required for a statistical test to be valid are met. Key assumptions include the normality of data, homogeneity of variance (equal variances across groups), and independence of observations [69]. The Shapiro–Wilk test, used for testing normality in data, was used since the sample size is small [70], where the p-value was greater than the significance level of 0.05 for all observed parameters. Therefore, the null hypothesis that the data follow a normal distribution is rejected. To determine the homogeneity of variance, Levene’s test, which does not assume the data are normally distributed [71], was conducted, where p > 0.05. Therefore, it can be concluded that the variances are equal, and thus data homogeneity is confirmed. The independence of observations was secured through the research study design, while ensuring each observation was collected independently. As the research used purposive sampling, the criteria and participant selection, as well as characteristics of the target population and limitations, were thoroughly investigated. Since the data were not normally distributed and the nature of the variables associated with the function and the perception of project success was ordinal, Spearman’s rho was utilized for correlation [72,73] by using p-values and Spearman’s rho values (ρ). Spearman’s rho is used to measure the strength and direction of a monotonic relationship between two ranked variables, and as such is suitable for use in research in project management [74]. A positive Spearman’s ρ value suggests a positive correlation, while a negative value implies an inverse relationship between the variables. All p-values < 0.05 are deemed statistically significant at the 95% confidence level.

Given that the sample size was small, multivariate models (linear regression analysis and factor and cluster analysis) and mixed-methods triangulation, using qualitative analysis, were considered to ensure improving the validity and credibility of the correlational study findings. Also, to ensure the robustness of the derived conclusions, effect sizes, confidence intervals, and multiplicity correction were used. Since there were only 6 participants willing to participate in further research, a regression study was instead deemed appropriate to verify correlational results for both personality and problem-solving tools and their impact on project success.

3. Results and Discussion

A research response rate of 96.6% was obtained from 28 questionnaire respondents, where one researcher was responsible for data collection and analysis. The study’s research questions were as follows: What is the relationship between personality assessment tools and project success? What is the relationship between problem-solving methods and project success? The null hypothesis (H0) for objective 1 was that personality tools used to form project teams have no significant impact on project success, whereas the alternative hypothesis (H1) was that personality tools used to form project teams have a significant impact on project success. Objective 2’s null hypothesis (H3) was that problem-solving methods do not have a significant impact on project success, while the alternative was that problem-solving methods can have a significant impact on project success (H4). The hypotheses, with their expected direction and theoretical reasoning, can be summarized as follows:

• Objective 1. Personality tools used for project team formation have a positive impact on project success. Expected direction: Positive (direct) relationship; project teams formed based on personality profiles contribute to an increase in successfully terminated projects.
• Objective 2. Problem-solving tools used in projects have a positive impact on project success. Expected direction: Positive relationships; projects and project teams that use problem-solving methods during the project’s life cycle will enable projects to terminate with success.

Theoretical reasoning: The potential of using personality tools and problem-solving methods in projects and for project team formation have been previously researched, and positive implications have been established. The influence of both their mutual codependence and impact on project success is indicated but not established.

The demographic profiles (

Table 2. Demographics.

Characteristic	n	%
Gender
Female	9	32.1
Male	19	67.9
Age bracket
>45	8	28.6
18–24	0	0.0
25–34	6	21.4
35–44	14	50.0
Number of projects
>10	20	71.4
2–3	2	7.1
3–5	5	17.9
5–10	1	3.6
Size of project team
>30 people	2	7.1
10 people	12	42.9
10–30 people	10	35.7
<10 people	3	10.8
Mixed	1	3.5
>30 people	2	7.1
Work experience
>10 years	19	67.9
3 to 5 years	1	3.6
5 to 10 years	8	28.6
>10 years	19	67.9
Experience in project management
<1 year	6	21.4
>10 years	7	25.0
1 to 3 years	4	14.3
3 to 5 years	1	3.6
5 to 10 years	10	35.7
Project duration
<1 year	10	35.7
>1 year	13	46.4
Both	5	17.9

) of the 28 respondents were analyzed using descriptive analysis. A total of seven questions were investigated in the demographic section. The results in Table 2 show that most respondents were male (67%, n = 19); therefore, gender equity among the respondents was not realized in this study. A total of 21.4% (n = 6) of respondents were in the age bracket of 25–34 years; 50% (n = 14) were in the age bracket of 35–44 years; and 28.6% (n = 8) were in the age bracket of above 45 years. The question on the respondents’ experience suggests that most of the respondents have been working in their position for more than 10 years (67.9%, n = 19), followed by those working between 5 and 10 years (28.6%, n = 8), and 3 to 5 years (3.6%, n = 1). This signified the respondents’ varied experiences in handling problems on projects inside the company. Results also show that 11 out of 28 respondents had some experience in project management, where 25% (n = 7) had more than 10 years of experience and 37% (n = 10) had 3 to 5 years of experience. A total of 71.4% (n = 20 respondents) were involved in more than 10 projects in their work life, and they were currently working in teams consisting of 10–30 people.

Furthermore, 27 respondents had personal experience with the MBTI (96.4%), which was the focus of this survey; 53.6% (n = 15) had experience with DiSC personality theory; 53.6% (n = 15) had experience with Belbin Team Roles and personality types theory; and most of them had personal experience with the problem-solving methods that are the focus of this survey, with most having experience with PDCA (89.3%, n = 25), Five Whys (89.3%, n = 25), and the 8D methodology (82.1%), as visualized in

Table 3. Personality tools.

	Acquaintance with Personality Methods		Use of Personality Tools in the Project		Training in Personality Tools
	n	%	n	%	n	%
Myers–Briggs® personality type theory	27	96.4%	20	71.4%	24	85.7%
Keirsey’s personality type theory	2	7.1%	0	0.0%	0	0.0%
Katherine Benziger’s Brain Type theory	1	3.6%	0	0.0%	0	0.0%
The ‘Big Five’ Factors personality model	2	7.1%	0	0.0%	0	0.0%
The Four Temperaments/Four Humors	1	3.6%	1	3.6%	0	0.0%
Carl Jung’s Psychological Types	6	25.0%	1	3.6%	1	3.6%
Hans Eysenck’s personality type theory	1	3.6%	0	0.0%	0	0.0%
William Moulton Marston’s DiSC 1 personality theory	14	53.6%	5	17.9%	10	35.7%
Belbin Team Roles and personality type theory	14	53.6%	3	10.7%	9	32.1%
The Birkman Method®	1	3.6%	0	0.0%	0	3.6%

¹ DiSC Dominance, Influence, Stamina, and Conscientiousness.

and

Table 4. Problem-solving methods.

	Acquaintance with Problem-Solving Methods		Use of Problem-Solving Methods in the Project		Training in Problem-Solving Methods
	n	%	n	%	n	%
The Deming Cycle (PDCA 1)	25	89.3%	20	71.4%	17	60.7%
The Eight Discipline Methodology (8D)	23	82.1%	17	60.7%	21	75.0%
Five Whys	25	89.3%	17	60.7%	18	64.3%
The Four-Step Innovation Process	0	0.0%	0	0.0%	0	0.0%
Failure Mode and Effects Analysis (FMEA)	21	75.0%	13	46.4%	10	35.7%
Appreciative Inquiry	3	10.7%	1	3.7%	2	7.1%
Cause and Effect Analysis	24	85.7%	14	50.0%	13	46.4%
Kepner–Tregoe Decision Analysis	0	0.0%	0	0.0%	0	0.0%
Kaizen	12	42.9%	4	14.3%	5	17.9%
Six Sigma–DMAIC 2	15	53.6%	10	35.7%	8	28.6%

¹ PDCA, Plan–Do–Check–Act; ² DiSC, Dominance, Influence, Stamina, and Conscientiousness.

. It has also been recognized that respondents are familiarized with the FMEA to a great extent (75%, n = 21; Table 4), Kaizen (42.9%, n = 12), and Appreciative Inquiry (10.7%, n = 3). However, establishing correlation between these methods and project success was not set, since FMEA is used to predict problems before they happen, Kaizen focuses on small incremental improvements, and Appreciative Inquiry does not focus on conventional problem solving. However, these methods were considered when discussing the relationship between problem-solving methods and personality tools in projects. The Four-Step Innovation Process and Kepner–Tregoe Decision Analysis methods were not considered for future analysis, since none of the respondents were acquainted with those methods (Table 4).

Considering that the MBTI was the focus of this study, respondent profiles were determined (21.4%, n = 6, were ENTP, followed by 10.7%, n = 3, of ESTP, ENTJ, ISTJ, INTJ, and ESFJ), as well as to what extent they agree with the indicated profile: 25%, n = 7, strongly agree; 60.7%, n = 17, agree to some extent; and 14.3%, n = 4, neither agree nor disagree. The indicated profiles were identified prior to this research; therefore, the extent of agreement with the defined profile was considered pivotal for further analysis. In addition, respondents were asked to indicate which profile a successful project manager should possess, with ESTJ (78.6%, n = 22), ENTJ (75.0%, n = 21), and ESFJ (60.7%, n = 17) being the preferred profiles. Also, respondents were asked to identify the profiles they would use to establish more effective teams and, consequently, projects. A total of 81.5% (n = 22) selected ESTJ in this instance. Regardless of the nature of the project, 67.9% (n = 19) of project managers would always choose ISTJ and 64,3% (n = 18) would again choose ESTJ. On the other hand, 57.1% of respondents (n = 16) indicated that INFPs would not be chosen for initiatives requiring a rapid and successful outcome (

Table 5. MBTI profiles.

	Respondent MBTI Profiles		Choice of Project Manager MBTI for Successful Projects		Choice of Team Member MBTI for Better-Performing Projects		Choice of Team Member MBTI Regardless of Project Type		Undesired Team Member MBTI for Projects Requiring Fast and Successful Outcome
	n	%	n	%	n	%	n	%	n	%
ESTP *	3	10.7%	15	53.6%	15	55.6%	14	50.0%	10	35.7%
ESTJ	2	7.1%	22	78.6%	22	81.5%	18	64.3%	10	35.7%
ENFJ	0	0%	15	53.6%	16	59.3%	13	46.4%	8	28.6%
ENTJ	3	10.7%	21	75.0%	19	70.4%	16	57.1%	10	35.7%
ISTP	2	7.1%	12	42.9%	13	48.1%	11	39.3%	9	32.1%
ISTJ	3	10.7%	13	46.4%	19	70.4%	19	67.9%	7	25%
INFJ	0	0%	10	35.7%	15	55.6%	9	32.1%	9	32.1%
INTJ	3	10.7%	13	46.4%	18	66.7%	16	57.1%	8	28.6%
ESFP	0	0%	13	46.4%	14	51.9%	10	35.7%	9	32.1%
ESFJ	3	10.7%	17	60.7%	16	59.3%	12	42.9%	6	21.4%
ENFP	0	0%	11	39.3%	16	59.3%	13	46.4%	11	39.3%
ENTP	6	21.4%	13	46.4%	16	59.3%	13	46.4%	7	25.0%
ISFP	1	3.6%	9	32.1%	13	48.1%	8	28.6%	12	42.9%
ISFJ	0	0%	10	35.7%	14	51.9%	8	28.6%	8	28.6%
INFP	0	0%	11	39.3%	15	55.6%	9	32.1%	16	57.1%
INTP	2	7.1%	11	39.3%	16	59.3%	14	50.0%	10	35.7%

* Note: Profile abbreviations explained in Section 1, line 55.

). According to 57.1% (n = 16) of the respondents, team members with different personalities should be selected based on the type of project, and different tools should be combined and compared to achieve a better definition of distinct personality types (42.9%, n = 12).

3.1. Objective 1: Influence of Personality Tools on Project Success

Objective 1 seeks to establish the impact of personality tools on the success of projects. From these analyses, it can be concluded that personality tools are a good starting point for establishing good-performing project teams, as those teams can achieve greater success (p < 0.05). Also, educating employees about personalities and various personality tools can have a positive effect on the project’s outcome (p < 0.05), where the researched organizations have encouraged project managers to continuously develop new skills and deepen their knowledge regarding personalities and tools [75]. The Myers–Briggs tool is used as a guiding tool and the most qualified tool in team formation, which will result in more successful project completion (p < 0.05) (

Table 6. Pairwise correlation—personality tools and project success.

	n	Correlation	95% CI for ρ	p-Value
Personality tools useful for assessing and understanding individuals	28	0.283	(−0.108; 0.599)	0.144
Personality tools can help establish good-performing teams	28	0.665	(0.353; 0.844)	<0.001
Project leaders focus on interpersonal skills, technical skills, and administrative skills of team members	28	0.066	(−0.315; 0.429)	0.740
Project leaders give more importance to team member relationships	28	0.171	(−0.219; 0.514)	0.384
Educating employees regarding personalities	28	0.482	(0.111; 0.735)	0.009
Training in different methods	28	0.208	(−0.183; 0.542)	0.287
Myers–Briggs® is enough for complete understanding of personalities	28	0.186	(−0.204; 0.526)	0.342
Myers–Briggs® tool used as a guiding tool	28	0.407	(0.024; 0.686)	0.031
Myers–Briggs® profile is the most qualified tool	28	0.054	(−0.326; 0.419)	0.784
Choosing team members dependent on the project type	28	0.321	(−0.069; 0.626)	0.095
Combining tools for better definition of personality types	28	0.080	(−0.303; 0.440)	0.686

).

Based on the presented results, the null hypothesis (H0) was rejected, as a correlation between personality tools and project success could be established for certain considered variables (p < 0.05), while the alternate hypothesis (H1) was accepted. By focusing solely on the MBTI profiles, project managers, of whom 21.4% (n = 6) had an ENTP profile, claimed that, for a successful project, the project manager should have either an ENTJ or an ESTJ profile. Moreover, for the best-performing teams, there should be as many ISTJ profiles as possible and as few INFP profiles as possible. Therefore, contrary to previous research, diversity is neither required nor desired for these projects. In contrast, the research concludes that the ESTJ profile for project managers is the most dependable in terms of project performance, as previously seen in [27]. From the beginning of the study, it was anticipated, based on previous research, that the null hypothesis would be rejected. It is hypothesized that the reason for the contrary result, the accepted alternative hypothesis, is the project managers’ experience and knowledge background, the high concentration of ENTP profiles, and the complete absence of the following profiles: ESFP, ISFJ, ENFJ, INFJ, ENFP, and INFP. It is also believed that the combination of various personality tools in a project’s life cycle can lead to greater project success.

When observing the correlations between all variables related to personalities, the relationships with ρ > 0.60 are considered to have significant positive correlations (Figure 2). If personality tools are used to help project managers create teams, they become useful for assessing and understanding individuals (ρ = 0.65). Using personality tools for assessing and understanding individuals can lead to more successful realization of projects (ρ = 0.70) and can help project managers to establish good-performing teams (ρ = 0.70). This is especially the case where the MBTI tool is used as a guiding and chosen personality tool when forming teams in projects (ρ = 0.63) and for understanding personalities (ρ = 0.63). In successful teams, the team members with different personalities are chosen depending on the project type (ρ = 0.65). Teams created based on personality tools can help project managers to achieve greater success on projects (ρ = 0.61). Educating employees regarding different personality tools, and the MBTI specifically, can result in better understanding and thus the creation of more efficient teams (ρ = 0.68, ρ = 0.73). Combining tools to define personalities can result in more successful projects (ρ = 0.75) and training in different methods has given a deeper understanding of different personalities (ρ = 0.75).

To evaluate the data based on the control variables, such as gender, age, and project experience, the Mann–Whitney nonparametric test was performed. When observing the gender (male and female), age (>45 and 25–44), and project experience (>10 and <1–10), there is not enough evidence to conclude that the difference between the population medians (Table 2) is statistically significant (p < 0.05); therefore, the control variables remain grouped. When control variables are considered, the importance of personality tools and previous conclusions remains unchanged; however, Table 6’s findings significantly weaken the relationship between the variables in question.

In order to further strengthen the research validity and recognize the factors driving the concepts in the researched data, confirmatory factor analysis [76] was included in the research. Principal component analysis was used as the extraction method, as the data were not normally distributed and the sample size was small. The number of factors for this analysis were determined using Kaiser’s criterion by analyzing the scree plot and focusing on eigenvalues greater than one; each factor was labeled based on interpreted associations. When observing personality tools, six factors were identified, where the first two had eigenvalues over three, and the remaining factor values were between one and two. As outer loadings are a critical aspect of evaluating the validity of constructs in a model, loadings in the range of 0.60 to 1.00 and −0.60 to −1.00 were chosen for further analysis.

Table 7. Confirmatory factor analysis—personality tools.

Variables	Items	Factor Loading
Personality tools	Personality tools useful for assessing and understanding individuals	0.742
	Personality tools can help establish good-performing teams	0.864
	Educating employees regarding personalities	0.709
	Myers–Briggs® is enough for complete understanding of personalities	0.850
	Myers–Briggs® tool used as a guiding tool	0.900
Tool combination	Combining tools for better definition of personality types	−0.796
	Training in different methods	−0.845
	Myers–Briggs® profile the most qualified tool	−0.575
Team formation	Training in different methods	−0.612
	Choosing team members dependent on the project type	−0.693
Education	Project leaders focus on interpersonal skills, technical skills, and administrative skills of team members	−0.618
	Educating employees regarding personalities	−0.893
Myers–Briggs® profiles Team relationships	Myers–Briggs® is enough for complete understanding of personalities	−0.964
	Project leaders give more importance to team member relationships	−0.864

indicates how the latent variables relate to each other and to individual characteristics.

For all six factors, high loadings were identified across multiple items, indicating a strong association of these items with the underlying construct. Figure 3 shows all of the coefficients of each variable for the first component versus the coefficients for the second component, where coefficients close to −1 or 1 indicate a strong influence on the component. The team tool, team success, education success, MBTI guide, team profile, MBTI success, individual success, and MBTI team coefficients all have a strong influence on the first component, which can be summarized as personality tools impact, training success, and tool success, and MBTI coefficient have a strong influence on the second component, which can be summarized as tools combination impact.

Since the sample size is small, regression analysis, with a 95% confidence level, was carried out to confirm the results of the correlation analysis (

Table 8. Regression analysis—effect of personality tools on project success.

Independent Variable	Coefficient	Hodge g (CI for η 1)	Adjusted CI for Difference
Personality tools can help establish good-performing teams	2.192 (0.411) *	0.431 [95% CI: 0.000, 0.500] *	[95% CI: −0.107, 0.857] *
Educating employees regarding personalities	2.961 (0.542) *	0.483 [95% CI: 0.000, 0.500] *	[95% CI: 0.000, 0.821] *
Myers–Briggs® tool used as a guiding tool	1.912 (0.687) *	0.186 [95% CI: 0.000, 0.500]	[95% CI: −0.285, 0.678]
Constant      0.958 R2 = 0.435 F-ratio = 4.430 * n = 27

* p < 0.05. Note: Coefficients are standardized regression slopes with standard errors in parentheses. ¹ η: median of difference.

) and focus specifically on the significant relationships between personality tools and project success (Table 6). Multilinear regression analysis was used to model the relationship between a continuous dependent variable and two or more independent variables by fitting a linear equation to the data [77].

The results presented in Table 8 indicate that all of the significant relationships from the correlational study can be verified through regression analysis, with p < 0.05. Namely, personality tools can help with good-performing team creation (p < 0.05) and educating employees about personalities, and various personality tools can have a positive effect on the project’s outcome (p < 0.05); moreover, if the MBTI is used as a guiding tool for team formation, projects will be more successful (p < 0.05). Sensitivity tests, alternative analysis, and multiplicity corrections have been conducted to ensure the validity of the research findings. Multiplicity corrections control the increased risk of finding a false positive when multiple statistical tests are performed [78], and sensitivity and alternative analyses are used to test the robustness of primary findings under different assumptions and to provide a more nuanced understanding of the results [78]. The effect sizes of Cohen’s d can be used in sensitivity analyses to determine how robust a test’s results are to changes in assumptions or data [79]. Since the data for this study are non-normal and the sample size is small, Hedges’ g was chosen instead of Cohens’ d. Hedges’ g values are reported in Table 8 alongside confidence intervals, which can be an alternative or a complementary approach to traditional hypothesis testing, as it provides a range of plausible values for a population parameter, rather than a simple reject/fail-to-reject decision [80]. To calculate confidence levels, the sample Wilcoxon method was used, since the data are non-normal and the sample size is small [81]. Since common multiplicity corrections like Bonferroni may be too conservative and power reducing for non-normal data with small samples, alternative approaches include the bootstrap method, which calculates new confidence levels and new adjusted p-values [78]. The effect size (Hedges ’g) is reported for all re-tested relationships, where the first two relationships have a medium (g = 0.5) effect and the third relationship has a small effect (g = 0.2), where the difference between the two-group means is statistically significant but the actual difference between the group means is trivial. When observing confidence levels, there is a 95% confidence that the method used to create the interval will capture the true population parameter. The bootstrap method was used to calculate the new confidence levels by using 1000 re-samples with 95% confidence, where relationships indicated with an asterisk remained statistically significant (p < 0.05) after correction (Table 8). The significance of variable “Myers–Briggs^® tool used as a guiding tool” became statistically insignificant after bootstrapping, which suggests that the initial significance may have been due to chance or specific characteristics rather than the true effect. However, this variable is still considered theoretically important, and collecting a larger dataset would be advisable in future. Even though the Myers–Briggs, as a guiding tool, has become insignificant, the significance of using personality tools in projects remains.

Subgroups and hierarchical models have been considered for future analysis, as they can be identified (age, gender, experience in methods and project management, and personality profiles), but statistical significance cannot be detected, as the sample size is small and homogeneous. As the assumption is that different subgroups have different impacts on project success, it is recommended to include them in future research. Variables that were concluded as not statistically significant, based on the correlational analysis, have not been considered for further analysis in the current research. However, variables such as use of personality tools when forming project teams and training in certain methodologies should be reframed and reconsidered when replicating the conducted research.

3.2. Objective 2: Influence of Problem-Solving Methods on Project Success

To establish the relationship between problem-solving methods and project success, a correlational nature has been observed. Based on the results presented in

Table 9. Pairwise correlation—problem-solving methods and project success.

	n	Correlation	95% CI for ρ	p-Value
Project team members know where to go for assistance	28	−0.149	(−0.496; 0.240)	0.450
Taking immediate actions	28	−0.097	(−0.455; 0.287)	0.622
Problem-solving methods are used in projects	28	0.371	(−0.016; 0.661)	0.052
Deming Cycle used for process improvement	24	−0.109	(−0.492; 0.309)	0.612
Deming Cycle is continuous process	24	0.069	(−0.344; 0.460)	0.749
The Eight Discipline Methodology used for recurrent problems	24	0.160	(−0.263; 0.531)	0.956
The Eight Discipline Methodology is a team-based approach	23	0.121	(−0.308; 0.509)	0.455
Five Whys is used for manufacturing problems	25	0.078	(−0.328; 0.459)	0.711
Five Whys make root cause definition	25	0.526	(0.137; 0.774)	0.007
Cause and Effect Analysis used as a quality control tool	21	−0.069	(−0.487; 0.374)	0.766
Cause and Effect Analysis identifies causes of problems	21	0.064	(−0.379; 0.482)	0.784
Six Sigma used for roadmap projects or quality improvements	17	0.364	(−0.158; 0.727)	0.151
Six Sigma is a synonym for root cause analysis	16	0.421	(−0.118; 0.768)	0.105
Problem-solving benefits through increasing efficiency or project effectiveness	28	0.569	(0.220; 0.789)	0.002
Right problem-solving methods chosen for solving problems	28	0.268	(−0.124; 0.587)	0.005
Problem-solving methods prevent the problems from occurring or reoccurring	28	0.231	(−0.160; 0.560)	0.153
Problem-solving methods identify and remove occurred problems	28	0.123	(−0.264; 0.475)	0.241
Problems are solved completely and in a given time	28	−0.009	(−0.381; 0.365)	0.963
Results published and distributed	28	0.252	(−0.139; 0.576)	0.195

, it can be concluded that problem-solving methods are generally used in projects and, when used, have a significant impact on project success (p < 0.05), as well as problem-solving benefits in increasing efficiency or project effectiveness (project outcomes) (p < 0.05). Also, it is crucial to recognize and choose the right problem-solving methods for solving problems (p < 0.05), since, in the researched organizations, cognitive fit [82] hinders project managers’ ability to solve problems effectively, as their focus and background knowledge on problem solving is limited. Out of all analyzed problem-solving methods, Five Whys has a positive impact on project success if used for root cause definition (p < 0.05) (Table 9).

To achieve project success, it is advantageous to use the right problem-solving methods that ensure complete, on-time solutions and secure progression; therefore, hypothesis H3 was rejected, as a statistically significant relationship was found in certain examined relationships (p < 0.05), while hypothesis H4, which states that problem-solving methods can have a significant effect on project success, was accepted. Based on the problem solving expertise and experience of project managers, it is not surprising that the chosen problem-solving methodologies for solving all problems will be the same regardless of project type, namely, the 8D methodology, Five Whys, and PDCA, as the majority have the most experience in the chosen methodologies. On the other hand, it is anticipated and speculated that, in the future, based on the analysis of the descriptive “other” option in questions related to problem-solving methods, this option will gradually shift to Six Sigma, which is regarded as the solution to all problems. A prerequisite for the successful completion of a project is unmistakably the project manager’s ability to guide the team towards the most effective problem-solving approach.

When observing the correlations between all variables related to problem solving, the following relationships have significant positive correlations (ρ > 0.60) (Figure 4).

Based on results shown in Figure 4, the Eight Discipline Methodology (8D) is preferred in cases of project difficulties where problems have been identified (ρ = 0.62) and have a direct impact on project success (ρ = 0.66). Also, problem solving directly benefited the intended users either through increasing efficiency or project effectiveness, thus securing successful outcomes on projects (ρ = 0.65). Choosing the right problem-solving methods will help prevent problems from occurring or reoccurring, thereby aiding in successfully reaching project goals (ρ = 0.66). The use of the Deming Cycle (PDCA) methodology has a direct impact on problem prevention (ρ = 0.62); the Six Sigma–DMAIC methodology should be used for roadmap projects or quality improvements (ρ = 0.65), and has a direct impact on successful problem solutions (ρ = 0.65) and project success (ρ = 0.62). And the use of the Cause and Effect method to successfully identify problems has a direct impact on project success (ρ = 0.64). Problem solving has directly benefited the intended users by removing occurred problems (ρ = 0.77), and if the problems are approached immediately, the impact on project success is higher (ρ = 0.61).

In order to evaluate the data based on the control variables, such as gender, age, and project experience, the Mann–Whitney nonparametric test was performed again. When observing the gender (male and female), age (>45 and 25–44), and project experience (>10 and <1–10), there is not enough evidence to conclude that the difference between the population medians (Table 2) is statistically significant (p < 0.05); therefore, the control variables remain grouped. When observing the control groups, the significance of problem-solving methods and previous conclusions remains unchanged.

When observing problem-solving methods, four factors have been identified, where the first had an eigenvalue of 4.74, the second had an eigenvalue of 2.30, and the remaining two had values between 1 and 2. Again, as outer loadings are a critical aspect of evaluating the validity of constructs in a model, loadings in the range of 0.60 to 1.00 and −0.60 to −1.00 have been taken into consideration for further analysis (

Table 10. Confirmatory factor analysis—problem-solving.

Variables	Items	Factor Loading
Problem solving impact	Problem solving benefits through increasing efficiency or project effectiveness	0.880
	Right problem-solving methods chosen for solving problems	0.622
	Problem-solving methods prevent the problems from occurring or reoccurring	0.792
	Problem-solving methods identify and remove occurred problems	0.794
Problem-solving methods	Project team members know where to go for assistance	−0.798
	Taking immediate actions	−0.817
	Problem-solving methods are used in projects	−0.652
Project success	Taking immediate actions	0.827
	Problem-solving methods are used in projects	0.644
	Problems are solved completely and in a given time	0.564
Learning culture	Project team members know where to go for assistance	−0.778
	Results published and distributed	−0.776

).

High loadings have been identified across multiple items, indicating a strong association of these items with the underlying construct, as seen in Table 10. Figure 5 shows the loading plot, where the project triangle, project effectiveness, and project success coefficients have a strong impact on the first component, problem solving impact (>0.6), and the problem removal, prevention, and solution coefficients have a strong influence on the second component, problem-solving methods (>0.6).

Regression analysis focused on the significant relationships (Table 9) between problem solving and project success. The results presented in

Table 11. Regression analysis—effect of problem-solving methods on project success.

Independent Variable	Coefficient	Hodge g (CI for η 1)	Adjusted CI for Difference
Problem-solving methods are used in Projects	0.170 (1.350) *	1.360 [95% CI: −1.500, −1.000] *	[95% CI: −1.821, −0.821] *
Five Whys make root cause definition	2.144 (0.719) *	0.544 [95% CI: −0.000, 0.500] *	[95% CI: −0.014, 0.894] *
Problem solving benefits through increasing efficiency or project effectiveness	1.320 (0.930) *	0.424 [95% CI: −0.500, 0.000] *	[95% CI: −0.821, 0.071]
Right problem-solving methods chosen for solving problems	0.730 (1.090) *	1.522 [95% CI: −2.000, −1.000] *	[95% CI: −2.035, −1.000] *
Constant      0.380 R2 = 0.314 F-ratio = 2.63 * n = 27

* p < 0.05. Note: Coefficients are standardized regression slopes with standard errors in parentheses. ¹ η: median of difference.

indicate that all of the significant relationships from the correlational study can be verified with regression analysis, with p < 0.05; problem-solving methods are generally used in projects and have a significant impact on project success (p < 0.05), as well as increase efficiency or project effectiveness (p < 0.05). If the right methods are chosen, where Five Whys is used for root cause analysis, they will have a positive impact on project success (p < 0.05).

Hedges’ g values are reported in Table 11 alongside confidence intervals, new and adjusted confidence levels, and p-values. The effect size is indicated together with the confidence levels together for all re-tested relationships, where the first and fourth relationships have a large (g = 0.8) effect, and the two middle relationships have a medium effect (g = 0.5) (Table 11). The bootstrap method was used to calculate the new confidence levels, where the asterisk-marked relationships remained statistically significant after the correction (p < 0.05). The significance of problem solving impact on project effectiveness became statistically insignificant after bootstrapping; however, this variable is contained in other statistically significant relationships, and is still considered theoretically and practically important in this research. As the assumption is that different subgroups have different impacts on project success, as previously discussed, it is recommended to include them in future research. Variables focusing on problem-solving methods other than Five Whys, due to the small sample size, or the variables that were concluded as not statistically significant, have not been considered for further analysis. The missing data on different problem-solving methods is “Missing Not At Random” (MNAR), as the survey replies were based on personal experience in these methods [83]. These variables could potentially be artificially recreated [83], which could be considered prior to replication of the current study. The variable focusing on the relationship between problem solving and project success should also be considered, with potential redefinition, when replicating the conducted research.

To further understand the similarities between the observations from all 28 respondents, a cluster analysis has been made, resulting in a dendrogram (Figure 6) that can be grouped in four clusters, excluding respondent 28. Cluster analysis is generally used to identify groups of similar objects or data points, segment datasets, and uncover hidden structures [84]. For determining the number of clusters and the robustness of the analysis, Cluster K analysis [85] was conducted, and the dendrogram was examined and recreated, classifying 28 observations into four clusters. Cluster 1 has the least variability of the four clusters, with the smallest value for the average distance from centroid 2.449. In the first cluster, the following respondents share the most similar views on the attributes in question: 2 and 10 (both males, age >45, with >10 years in work and project experience, similar knowledge regarding personality and problem-solving tools, and personality profiles of ENTP and ESFJ) and 11 and 23 (male and female, with similar knowledge regarding personality and problem-solving tools, and with ENTP and ESFJ profiles). From the first cluster, it can be observed that ENTP and ESFJ profiles share similar observations on subjects’ usability of personality and problem-solving tools for achieving project success. In the second cluster are respondents 4 and 8 (both males, with similar age, work experience, project experience, and knowledge regarding personality and problem-solving tools, with personality profiles of ENTP and ISTJ); 6 and 15 (male and female, with similar knowledge regarding personality and problem-solving tools, and ESFJ and ISTP profiles); and 27 (ESTP). From the second cluster, it can be observed that ENTP and ESFJ profiles also share similar observations on subjects’ usability of personality and problem-solving tools for achieving project success, with ISTJ and ISTP profiles.

In the third cluster are 24 and 20 (similar age, work experience, project experience, and knowledge regarding personality and problem-solving tools, with ESTJ and INTJ profiles) and 17 (ISTJ); in the fourth, 16 and 18 (both males, with similar age, work experience, project experience, knowledge regarding personality and problem-solving tools, and ESTP profiles) and respondent 28, with substantially different observations (ESTJ). From the third and fourth clusters, it can be observed that ESTJ profiles also share similar observations on subjects’ usability of personality and problem-solving tools for achieving project success, with ESTJ, INTJ, and ISTJ profiles; ESTP profiles share the similarities amongst themselves. To further strengthen the findings on profile observation similarities, it is recommended that the research be broadened to encompass a larger, non-homogeneous research sample.

4. Conclusions

The study identified the MBTI tool as the most used and recognized in the researched area. Regarding problem-solving methods, eight of the most prevalent have been investigated to determine their specific application and benefits. To highlight the objectives of this study—by identifying the personality tools that should be used for a specific project and comprehending the impact of selected problem-solving methods on overall project success—emphasis was placed on the tools and methodologies that have already been recognized and utilized in the researched area, and their relationship with project success was observed. From the research findings, the following conclusions can be drawn:

• Personality tools have a distinct and significant relationship with project success;
• Problem-solving methods have an observed significant influence on project success.

Regarding the limitations of the research, since a positive influence of personality tools on project success has been identified, it is recommended that the study be upgraded based on a larger number of participants, as a deeper look into personality tools and their influence on project success would be of great benefit to the organizations, project managers, and teams when initiating new projects and forming new teams. Also, according to this study, the preferred profile type for the project manager is either ESTJ or ENTJ, and the preferred profile types (>50%) for project team members are ESTJ, INTJ, ENTJ, and ISTJ. ISFP and INFP are the least preferred profiles (>40%) to be used on projects if a successful outcome is desired (Table 5). To further understand the results of the study, when it comes to the 16 personality types, it was hypothesized that all profiles chose the same profiles to secure the project’s success; a chi-square test was performed, and there was not enough evidence to conclude that the variables were associated (p > 0.05) (Figure 7).

Regarding problem-solving methods, based on this research, it is difficult to determine their actual significance, given that the respondents lack a clear, adequate background. Regardless, the observations made on both personality tools and problem-solving methods can be used as decision guidelines, while keeping in mind that the following guidelines were derived based on current research in a highly isolated environment. To summarize the research findings, and to achieve better-performing teams and more successful projects, a combination of tools, for which an adequate education and training background has been established, should be used to describe personalities and achieve a better definition of distinct personality types. The MBTI tool can be used as a guiding tool when forming teams in projects where team members with different personalities are considered crucial for achieving project success. When using the MBTI, the profile of the project manager should be either ESTJ, ENTJ, or ESFJ; for the teams to be more successful, regardless of project type, ESTJ, ENTJ, ISTJ, and INTJ profiles should be prioritized. Since the sample size of the conducted research is small, only the relevant MBTI profiles have been used in the interpretation of results (n = 15, >50%; Table 5).

The problem-solving methods should be used in projects and chosen based on the type of problem if project success is to be achieved. The Eight Discipline Methodology (8D) should be applied to recurrent problems; Cause and Effect Analysis should be utilized to identify the causes of problems and resolve them thoroughly within a specified timeframe; the Deming Cycle (PDCA) should be implemented for process improvement, while Six Sigma–DMAIC is recommended for roadmap projects, quality enhancements, and root cause analysis. It is generally considered that the presence of diverse personalities in teams can yield more successful project outcomes, as different profiles contribute to problem solving and innovation, while homogeneity in teams contributes to faster actions and high performance [86]. The conducted research supports both theories, as it suggests the use of the MBTI tool for team formation to ensure, based on the project type, diversity among profiles, while indicating several prioritized profiles when it comes to project managers and project teams. The current research also recognized the importance of the use of the right problem-solving tools in projects, which suggests adaptation of current problem-solving methods and practices to the assigned team, as well as the use of the MBTI methodology at the moment of team formation, as personal preferences change over time [21].

Recognizing a gap between different profiles and their use of problem-solving methods,

Table 12. Novel framework, profiles, and problem solving ¹.

Respondents MBTI Profile	Use of Problem-Solving Methods in Teams								Training in Problem-Solving Methods								Choice of Method that Solves all Problems
	PDCA	8D	Five Whys	FMEA	Appreciative Inquiry	Cause and Effect Analysis	Kaizen	DMAIC	PDCA	8D	Five Whys	FMEA	Appreciative Inquiry	Cause and Effect Analysis	Kaizen	DMAIC	PDCA	8D	Five Whys	FMEA	Appreciative Inquiry	Cause and Effect Analysis	Kaizen	DMAIC
ESTP n = 3	n = 2 70%	n = 1 30%	n = 2 70%	n = 1 30%		n = 1 30%		n = 2 70%	n = 2 70%	n = 3 100%	n = 2 70%	n = 1 30%		n = 2 70%		n = 2 70%	n = 1 30%	n = 1 30%
ESTJ n = 2	n = 1 50%	n = 1 50%	n = 1 50%	n = 1 50%					n = 1 50%	n = 2 100%	n = 2 100%	n = 1 50%					n = 1 50%	n = 1 50%	n = 2 100%	n = 1 50%		n = 1 50%
ENTJ n = 3	n = 2 70%	n = 3 100%	n = 2 70%	n = 1 30%		n = 3 100%			n = 2 70%	n = 3 100%	n = 2 70%	n = 1 30%		n = 3 100%			n = 1 30%	n = 2 70%	n = 1 30%	n = 1 30%		n = 1 30%		n = 1 30%
ISTP n = 2	n = 2 100%	n = 1 50%	n = 2 100%	n = 1 50%		n = 1 50%		n = 1 50%	n = 1 50%	n = 1 50%	n = 2 100%			n = 1 50%	n = 1 50%	n = 2 100%		n = 1 50%	n = 1 50%	n = 1 50%
ISTJ n = 3	n = 3 100%	n = 3 100%	n = 3 100%		n = 1 30%	n = 2 70%	n = 1 30%	n = 1 30%	n = 3 100%	n = 3 100%	n = 3 100%	n = 1 30%	n = 1 30%	n = 1 30%	n = 2 70%	n = 1 30%	n = 3 100%	n = 3 100%	n = 3 100%			n = 2 70%	n = 1 30%	n = 1 30%
INTJ n = 3	n = 1 30%	n = 1 30%	n = 1 30%	n = 2 70%		n = 2 70%		n = 1 30%	n = 2 70%	n = 2 70%	n = 2 70%	n = 2 70%		n = 2 70%		n = 1 30%	n = 2 70%	n = 2 70%	n = 1 30%	n = 2 70%		n = 1 30%		n = 1 30%
ESFJ n = 3	n = 1 30%	n = 3 100%	n = 1 30%	n = 2 70%		n = 1 30%		n = 1 30%		n = 2 70%	n = 1 30%	n = 1 30%		n = 1 30%		n = 1 30%	n = 2 70%	n = 2 70%	n = 2 70%					n = 2 70%
ENTP n = 6	n = 5 80%	n = 3 50%	n = 3 50%	n = 4 70%		n = 2 30%	n = 1 30%	n = 2 30%	n = 4 70%	n = 3 50%	n = 3 50%	n = 2 30%	n = 1 30%	n = 2 30%	n = 2 30%	n = 2 30%	n = 2 30%	n = 3 50%	n = 1 30%	n = 2 30%		n = 1 30%		n = 3 50%
ISFP n = 1	n = 1 100%	n = 1 100%		n = 1 100%		n = 1 100%												n = 1 100%				n = 1 100%
INTP n = 2	n = 2 100%	n = 2 100%	n = 1 50%	n = 1 50%			n = 1 50%		n = 2 100%	n = 2 100%		n = 1 50%				n = 1 50%	n = 2 100%

¹ Note: MBTI profile and problem-solving methods abbreviations explained in Section 3, Table 4 and Table 5.

illustrates how various team profiles (based on MBTI team formation) approach and utilize different problem-solving methods to ensure successful project completion, considering only those profiles with multiple representations (n > 1) and a response frequency greater than 70% in the interpretation of results. Based on the results represented in Table 12, when an ESTP profile is chosen for the project team, the chosen problem-solving methodology to address problems in the project will be the PDCA, Five Whys, and Six Sigma methods. For ESTJs, the Five Whys methodology is chosen regardless of the problem type. The ENTJ profile in projects will choose the 8D, Cause and Effect, PDCA, and Five Whys methods, and the ISTP profile will choose PDCA and Five Whys. In contrast, the ISTJ profile will select PDCA, 8D, Five Whys, and Cause and Effect methodologies, while the INTJ profile will opt for FMEA and Cause and Effect. ESFJ profiles will choose the 8D and FMEA methods, while ENTP profiles will select PDCA and FMEA. Finally, the INTP profile will use the PDCA and 8D methodologies in teams formed based on the MBTI personality tool. Preference in choice of problem-solving methods could suggest the most compatible profiles during team formation if the projects in question are to have a successful outcome, which should be explored in future research. The current research reveals compatibility between ESTP, ENTJ, ISTP, and ISTJ profiles based on the chosen methodologies. Since ESTJ, ENTJ, ISTJ, and INTJ are profiles that should be prioritized in teams regardless of the project type if the projects are to be successful, these profiles should utilize the following problem-solving methods in the team: 8D and Five Whys, PDCA, and Cause and Effect Analysis, which are also contained in the 8D methodology [87]. The INTJ profile should introduce the FMEA methodology, which is also a methodology of choice for most profiles with the letter “N” included, but also a methodology that is used for problem prevention. Also, the potential of the Appreciative Inquiry method, its use in the project team, and its potential impact on project success should be further explored due to its unconventional nature.

Based on results presented in Table 12, operational guidelines in the form of decision frameworks for projects, project managers, and project teams have been developed (Figure 8). The framework encompasses research findings into easy-to-understand and highly usable guidelines when a new project is being planned. Project managers, along with project participants, need to acknowledge the use of personality tools and problem-solving methods in the project. However, the framework also supports the exclusive use of both or their suggested combination. Based on the profiles chosen for a certain team, problem-solving methods that could be utilized during the project’s life cycle, in case of problems, are recognized and established. By being able to recognize the usability of certain problem-solving methods, the capability gap analysis in terms of education and training can be encouraged early in projects, thus securing adequate project progression. This is also valid in the case of the personality tools, as adequate education and training are crucial for full comprehension of all aspects of the chosen personality profiles in the newly formed project team. After team formation, MBTI profile preferences can be used to understand how different profiles learn [88], thereby ensuring adequate training and education for project teams. Also, the framework advocates for selecting profiles for project managers and team members according to project type, better-performing projects, and impact of social factors on the choice on both personality profiles and problem-solving methods, emphasizing profiles characterized by resilience and adeptness in managing challenges [89]. When it comes to problem-solving methods, the impact of sustainability gradually shifts focus from more well-known problem-solving methods towards more proactive ones like FMEA [90]. Also, effective integration of stakeholder engagement [91] and collaborative efforts enhances co-learning between projects and organizations in the pursuit of sustainability across levels. The suggested framework is applicable in sustainable development projects; however, is suspected that it could be used on projects in general, regardless of the project focus, if the project recognizes the use of both personality tools and problem-solving methods, and acknowledges that both have a positive impact on project progression and the termination of projects with success.

4.1. Sustainable Development Projects, Sustainability, and Project Success

Generally, it is considered that sustainable development projects impact project success through the recognition of stakeholder engagement [92], increased brand reputation, innovation, and long-term cost savings [93], where the high level of focus on soft, interpersonal skills was observed in the researched teams. Furthermore, the importance of integrating sustainability into project management, along with its expected positive effects on project success and its relationship with the acknowledged benefits of personality tools and problem-solving methods, should be investigated further, as this research primarily focused on sustainable development projects. When it comes to project success, a successful sustainable project includes, beyond the traditional success criteria, criteria such as business strategic goals and objectives, fostering new technology (innovation), markets, satisfying stakeholders, and generating positive environmental and social impacts that emphasize the assessment of project outcome success or its effectiveness over time [94,95,96]. ESG (Environmental, Social, and Governance) considerations are used to evaluate a company’s performance on sustainability and ethical impact, and have recently become central to corporate strategy and investment decision making, reflecting a global shift toward sustainability and responsible business conduct [97]. ESG provides a structured framework to measure sustainability performance and behavior by assessing organizational impact on environmental systems, societal well-being, and governance practices, and has evolved into a core evaluative tool for investors seeking long-term value [98]. Integrating sustainability practices into current projects and determining their impact on project success requires inclusion of a three-dimensional approach (environmental, social, and economic) into current research [99,100]. By observing solely social metrics, project success is highly impacted by stakeholder management, social equity, and health and safety, and these metrics alongside sustainability-related frameworks and tools should be integrated into project life cycles to generalize current research on sustainable practices [101]. Incorporation of sustainability in project management can, based on the previous research, be grouped into five key practices: environmental efficiency, compliance, social responsibility, continuous improvement and lessons learned, and project success [102]. From the sustainability theory perspective, the integration of personality tools and problem-solving methods can be carried out through consideration of psychological aspects like resilience, motivation, and strategic thinking, and concepts like sustainable mindset, which encompasses emotional intelligence, systemic thinking, and collaboration [103]. In this regard, problem solving competences should be broadened to encompass systems and future thinking and strategic competences, while the choice of profiles for team formation is shifted towards profiles that exhibit both resilience and handling challenges as primary traits.

The impact of external environmental factors on project success and their implementation into the project life cycle, together with personality tools and problem-solving methods, has been partially incorporated into this study by observing the social factors of sustainable development projects, while interpreting the impact of personality tools and problem-solving methods on project success. Social factors are focused on cultural aspects, welfare, quality of life, and the wellbeing of project participants throughout the project life cycle [104] and sustainable leadership [105], and they enhance corporate reputation and stakeholder trust, as well as increase employee satisfaction and customer loyalty [106]. In this research, the impact of social factors depicts how certain social factors, perceived through personality tools and their combination with problem-solving methods in projects, influence project outcomes. Stakeholder relationships, project team dynamics, communication and collaboration, motivation, knowledge sharing, training and education, and continuous improvement were observed throughout the project life cycle. Observing sustainable project success requires consideration of variables such as project efficiency, stakeholders, teams, business success, future preparation, and sustainability [107]. Sustainable project success refers to the development that meets the needs of the present; therefore, for this study, we considered project efficiency, stakeholders, teams, and solving and improving customers’ problems. The results of this research can therefore be linked to the sustainable project success as well, and contribute to organizational sustainability by implicitly addressing the underestimated social impact [108] of project teams on the overall success. The impact of sustainability factors on the relationships observed in this research is limited due to the sole integration of the social aspects of sustainability; therefore, it is recommended to broaden the current research to incorporate all sustainability factors. However, it is believed that implied limitations do not diminish the overall research conclusions.

4.2. Future Recommendations

It is recommended that further research be conducted in a less homogeneous context and in a broader research area, as it is believed that problem-solving methods have a significant impact on a project’s life cycle and could, therefore, alter its outcome. Regarding sample homogeneity, the research utilized purposive sampling to select participants with specific knowledge of personality tools, problem solving, and project management; this approach could introduce certain biases related to both sampling and selection. Selection biases were imposed due to convenience sampling, while sample homogeneity was a deliberate research strategy, as the research was focusing on a uniform group. Recognized biases were mitigated throughout the research by examining the study’s hypothesis, statements and justifications, as well as scrutinizing data collection and analysis methods. The current research thus expands the scope of knowledge and enables direct replication, as well as generalization.

Following the research findings, future suggestions and recommendations are as follows:

General recommendations:

• Similar research should be conducted where the influence of the matrix organization on the project outcome is also observed.
• Similar research should be conducted on personality tools, focusing on how different identified personality tools affect project success in the observed research area, while also mitigating subjectivity during personality profile assessments.
• Similar research should be conducted on problem-solving methods, with a greater emphasis on enabling problem-solving methods to influence the outcome of projects than on examining their impact, enabling organizations to gain a competitive advantage by investing in the resource dynamic capability building [109].
• Sustainability-related recommendations:
• Similar research should be conducted on individual personality types and their influence on success, with a focus on identifying internal unique resources and capabilities that contribute to achieving sustainable competitive advantage [110].
• Current research could be broadened to take all sustainability factors (environmental, social, and governmental) into consideration to understand the full impact of sustainable practices, personality tools, and problem-solving methods on project success.
• Similar research could be conducted to investigate the impact of personality tools and problem-solving methods on sustainable project success.
• Replication and generalization:
• The current research should be directly replicated using the original methods on a larger sample with diverse personality traits across various countries with differing project management practices. Additionally, the existing hypothesis should be expanded to encompass multiple problems to enhance generalizability and advance scientific knowledge. In evaluating replication across different sectors and nations, the reliability of the original study, its methodologies, materials, and sample size were considered. Consequently, the country’s status, cultural and economic development, impact of consumer and industrial goods sectors, and use of global project management practices should be considered essential prerequisites for direct replication. The research should also be replicated to validate the novel frameworks presented in this paper.