Can Project Team Members’ Willingness to Disclose Past Performance During Procurement Improve Organizational Business Process Success?

Abstract

Projects continue to fail approximately half the time, both before and after the COVID-19 pandemic. While prior studies highlight the influence of project leadership and individual competencies, little is known about whether team members’ willingness to disclose past performance can improve team allocation decisions and enhance business process success. However, we do not know if team members’ willingness to disclose their past performance may improve teamwork allocation in projects, thereby increasing business process success while reducing the likelihood of the project failing. We applied a rigorous post-positivist research design using correlation, conditioned correlation, t-tests, and ordinary least squares (OLS) linear regression to test the hypotheses. Controlling established predictors including budget, end user community size, and certification, we found that team members’ willingness to share their past performance evaluations significantly improved project success, increasing explained variance from 9.6% to 18.8%. The results indicate that transparency factors—specifically, willingness to share past performance—outweigh traditional resource allocation variables in predicting Fintech project outcomes, explaining an additional 19% of the variance in project success.

1. Introduction

Although global political and environmental tensions, cybersecurity threats, and the COVID-19 pandemic have fundamentally altered business processes worldwide, particularly through reduced business travel and increased reliance on virtual collaboration—we know that the likelihood of project success remains unchanged at only 50% [1,2]. We recently examined the project failure factors [3], but the results were inconclusive. Other researchers identified significant causal factors of project success versus failure as prior experience [4], leadership skills [5], certification/education [6], quality [7], contextual elements like project type, organizational maturity, and risk management [8,9,10,11]. In this study, we distinguish between the project team—those individuals actively working on project deliverables—and the end user community, which represents the broader population of users who will be impacted by, trained on, or utilizing the project outputs. This distinction is important as these two metrics measure different aspects of project scope and complexity. Even the most rigorous quantitative studies of project success produced marginal effect sizes, commonly below 5% [3]. Consequently, this study aims to close the literature gap by identifying an additional causal factor that impacts project success, namely team member willingness to share past performance evaluations.

A significant factor impacting project success is unknown yet obvious: team member capability. It is difficult to know a team member’s capability without testing them or reviewing past performance evaluations. Aptitude testing is not a commonly accepted method for project team member selection, and aptitude tests may not indicate team member capability in the dynamic project context. Project managers can determine team members’ hard and soft skill capability by reviewing past performance evaluations [12].

There were some studies showing that the combined skillsets of team members at the group level of analysis impacted project success [13,14]. However, there were no studies about how team members’ individual past performance evaluations influences project success, which makes it a relatively unknown causal factor. It is logical to hypothesize that team member capability would significantly impact project success. Rarely does one person solely control a project’s success, so we argue that this is why no research has determined why projects are not always succeeding. Most prior studies focused on project managers or organizational context rather than the individual team members executing the project tasks.

We take a different perspective when studying team members’ impact on project success compared to most human resource management (HRM) literature. Studies of team member impact in the HRM literature commonly use individual dependent variables like performance rating evaluated by line managers rather than project managers. Additionally, we argue that it is already clear from the psychology and HRM literature that past performance predicts future performance, so studying past performance indicators as predictors of project success will not add new meaning to the body of knowledge.

Our perspective is that given today’s high data privacy standards, performance evaluations of new team members may not be openly available to project managers (PM). Since PMs are not formal line supervisors, they may not have authorization to access team members’ past performance records. We argue that PMs ought to have access to team members past performance. We posit that if team members volunteer to share their past performance evaluations with PMs, this will eliminate one of the severe unknowns in project management internal risk management. Subsequently, we posit that when team members refuse to disclose their past performance evaluations to PMs, this will create an unknown and subsequently, in the long run, reduce project success because employees are more likely to withhold unfavorable past performance evaluations, and this disclose/withhold behavior is an indicator of future behavior and indirectly project success. In simple terms, team members will want to hide bad behavior but be willing to disclose good prior performance evaluations voluntarily, and this disclosed/undisclosed indicator will impact project success. Our research question (RQ) is whether a team member’s willingness to disclose past performance impacts project success in the context of already known causal factors such as project size, complexity, and experience.

Organizational decision-makers and PMs need to know if a team member’s willingness to disclose their prior performance evaluations impacts project success. PMs can use that to inform their team member selection decision if they can choose team composition. On the other hand, if the PM does not have the authority to choose team members, they may still be able to use this indicator for work breakdown structure allocations. If the PM knows a proposed resource is unwilling to share and disclose their prior performance evaluation, it could assume the resource is high risk and allocate them to a less critical task where other team members can absorb weaker peer performance. While one indicator may not fix the high project failure rate, knowing such a significant predictor could influence future research and organizational culture to ensure PMs are given copies of team members’ prior performance evaluations. Access to a team member’s historical performance record would allow the PM to observe the employees’ apparent strengths and weaknesses and inform allocation and mentoring decisions in a project.

Since our RQ is a cause-effect type construct and references past performance in project management, we may assume this will be a quantitative study where historical or retrospective data are sought. We can also assume quantitative techniques will be used to test hypotheses to support the RQ. Accordingly, this study aims to determine whether team members’ willingness to disclose past performance evaluations improves project success, after accounting for known causal factors such as project size, complexity, and experience. The remaining sections of this manuscript review the literature, outline the methods, and present results followed by implications and conclusions.

2. Review of Literature

Several researchers have examined honesty in sharing performance appraisals, which implies willingness to disclose past performance, whether that may be good or bad. Past behavior predicts future project performance behavior [4,5]. Therefore, a PM could leverage past behavior in resource allocation decisions. Since projects are temporary endeavors, a PM may not have access to team members past performance evaluations due to organizational culture or company policy. Certainly, if past performance evaluations are available to the PM, they can be leveraged for allocation choices. Regardless of whether the PM has access to prior performance reports of team members, the PM may strategically ask the resource for a copy and base allocation decisions on that answer.

Trust and transparency are central to effective performance evaluation. Fair and open evaluation processes strengthen employee confidence and willingness to share feedback, fostering continuous improvement. Employees may sometimes undergo additional training or read materials to overcome the negative aspects identified in their performance evaluations. These elements carry through to the employees’ honesty and transparency to the new PM.

Motivation and morale are fundamental in performance evaluations. If the organization values integrity and if it is committed to helping employees grow, employees can be motivated by that and spread positive morale to other team members, even helping them to improve their performance in projects. Accountability is a well-known factor in performance and the evaluation process. Honest appraisals done by supervisors can hold team members accountable for their performance (good resulting in organizational praise or bad resulting in being overlooked for promotions). When employees know their performance is being evaluated truthfully, they are more likely to take responsibility for their work and strive for better project performance. Likewise, past performance sharing has a carry-forward effect on future projects. When team members know that a PM may request their past evaluations, they tend to perceive project performance as part of their long-term professional record, encouraging accountability and consistent effort across projects. In other words, this concept of accountability in project performance evaluation would increase project success if all team members knew their performance evaluation would be shared with future PMs.

Improvement and development are underlying reasons for conducting and sharing performance valuations across projects. Honest feedback identifies improvement areas, providing a clear path for promotions. This can lead to enhanced skills and better performance on future projects. When team members realize that sharing past performance evaluations could lead to skill improvement and promotions, they are more likely to be willing to disclose past performance evaluations so that a PM will gain an objective view of strengths and weaknesses.

Organizational culture impacts projects. If the organization has a work environment where honesty and transparency for performance evaluations are highly regarded, team members will see this as a benefit for improvement, not a hindrance. The organization can use open communication and promotional techniques to ensure team members are exposed to this culture. Once team members see the organization is committed to honesty and transparency, they may feel more willing to share their past performance evaluations even with new PMs they do not yet know. This increased transparency in performance evaluation will improve project success because knowledge is better than the unknown. This supports the notion that transparent performance information is preferable to uncertainty when managing project risk.

2.1. Factors That Impact Project Success

The literature is clear that several factors impact project success in most projects. The better-known factor impacting project success is the size of the project in terms of budget, which reflects complexity, meaning the as complexity and budget increases, this tends to have an inverse relationship with project success [8,15,16]. The size of the organization and end user community impacted by the project have also been commonly found to impact project outcome—generally, projects affecting larger end user communities experience more complexity and coordination challenges, which can lower overall performance [9,15,16]. More years of experience, sometimes described as seniority, has also been found to improve project success for leaders and team members [4,9]. Many researchers have found that gender does not predict project outcome, mainly because the discipline is dominated by males [4]. Therefore, we assert it will be of no value to measure gender unless the sample is approximately equally balanced. Certification has been found to increase project success while more education tends to have the opposite effect [4]. We argue the common factors discussed above are a priori meaning that we expect a sample population to follow these generally accepted characteristics.

To control established project characteristics documented in prior research with this dataset [17], we included project budget, end user community size, and professional certification as control variables. Previous analysis of this sample confirmed that higher budgets (r = −0.041, p < 0.01) and larger end user communities (r = −0.241, p < 0.001) negatively predict success, while certification shows modest positive effects (r = 0.099, p < 0.001) [17].

Leadership and project management skills are equally important for the success of projects [6,18]. For instance, effective leadership can ensure that projects are planned adequately and that risks are mitigated, helping to keep teams motivated [14]. Effective project leadership can ensure that clear direction is provided to the team members and conflicts among team members are resolved immediately ensuring collaborative work atmosphere in the team [19]. The composition and skillsets of the team members also contribute to the success of projects and having the right mix of skills and experience can ensure that the team can handle technical challenges as well as interpersonal dynamics that may come up during the project lifecycle [13,14]. We developed this hypothesis to assess if PM leadership skills positively increased project success:

H1: 

Better project leadership improves project success.

Resource availability, including time, money, and technology can determine the success of a project as well-funded projects are likely to have access to modern tools and technology [20]. Resource constraints can lead to delays or scope reductions negatively impacting the outcomes of projects [21,22]. Organizational culture can also influence the outcomes of projects as an organizational culture promoting innovation, flexibility, and accountability can create a conducive environment contributing to project success [23]. We developed this hypothesis to determine if a supportive organizational culture impacted and in fact improved project success:

H2: 

Supportive organizational culture adds to project success.

Risk management is crucial to the success of projects because projects are inherently risk and the ability of the project manager to identify, assess, and mitigate risks will determine the success of a project [24]. Effective risk management will ensure that the risks are identified in time before they affect a project and that risks are handled in time [11]. Stakeholder engagement is also essential as this will ensure that projects are active and supportive stakeholders can ensure that projects face minimal roadblocks as stakeholders will help secure resources, facilitate communication and advocate for projects within the organization [25]. When combined, risk management and stakeholder management require regular communication. Therefore, stakeholder communication management is essential for project success. Stakeholder communication management requires regular updates, clear instructions, and open channels for feedback to ensure that minimal misunderstandings occur and are also required to ensure that teams can work effectively [26,27]. We developed this hypothesis to measure if using stakeholder communication management within a project would contribute to project success:

H3: 

Effectively applying stakeholder communication management helps a project to succeed.

Change management is also an important factor as projects undergo changes in scope or objectives. Hence, the ability to manage these changes without disrupting the project timeline or budget is essential for the project success [24]. Change management can be effectively managed through efficient monitoring and evaluation mechanisms that ensure that progress is tracked and deviations from the plan are identified early [28]. When deviations are identified early, project managers will have the opportunity to assess the performance against key metrics and make necessary adjustments increasing the chances of the project success. We developed this hypothesis to assess whether effectively managing change was critical for project success:

H4: 

Effectively managing change is critical for project success.

2.2. Team Members Past Performance Impact on Project Success

Experienced team members have a significant positive influence on the success of projects [15,29]. Team members with a history of strong performance bring with them valuable experience, skills, and problem-solving abilities contributing to better project outcomes [30,31]. The prior experience of the team members helps the teams’ meet deadlines and also ensures effective collaboration among the team members. Team member experience, in terms of years, has been proven to be a predictor of project success so this is a necessary control parameter. To test this a priori theory that a team member’s years of experience impact project success, as more experience will signify a resource with more capability, we developed this hypothesis:

H5: 

Higher team members’ years of experience increase project success.

Team members with a history of poor performance can become a liability to the project as past failures may be an indication of underlying issues, including lack of motivation, poor time management, and inability to handle pressure [12]. Such individuals may struggle to meet the expectations and could cause delays in the project timeline and ultimately lower the quality of the project deliverables. Experienced team members with a prior history of success can be role models for the new members of the team and can inspire confidence and boost the morale of the team [32]. These high-performing team members set standards for other members to follow by contributing innovative ideas and finding creative solutions to problems [33]. These factors ultimately contribute to the success of projects.

However, the challenge for project managers would be ensuring the willingness of team members to disclose their past project performance. Transparency is essential in performance evaluations to ensure that there is trust between the team members and the project members [34]. When there is trust within the teams, the members will voluntarily share their performance history which will allow project managers to make appropriate decisions about task assignments [35]. In this context the team dynamics are quite critical as the trust within the teams also depends on the internal team dynamics and culture [36]. Project managers can build trust among team members who openly acknowledge past weaknesses and demonstrate commitment to improvement by coaching and mentoring the team members. This approach will ensure that team members will be open to self-improvement and will be better equipped to handle the challenges of complex projects.

Trust, transparency, and willingness to share a performance evaluation represent interlocked constructs that can be tested as a single factor. The PM may or may not have access to all team members’ performance evaluations, so we propose that the PM can indicate if most team members consented to provide their last performance evaluation, which represents trust, transparency, and willingness to share performance evaluations. This willingness factor can then be tested as a predictor of project success alongside other established factors such as budget, end user community size, and certification.

We also believe willingness to show a performance evaluation may be an indirect mediator or moderator of other factors’ impact on project success. First. we argue that if a team member is willing to supply their last evaluation to inform project team member selection, this will allow the PM to choose only resources with past behavior most conducive to the context, thus decreasing the end user community size and improving project success—that would be a mediator of the end user community size factor. On the other hand, we argue willingness to share a past performance evaluation during the team selection process would divide the staffing pool into those with good past performance (those saying yes) and those with unfavorable items in their past (those saying no). In that situation we argue willingness is a moderator, a conditional factor representing resource capability, but if the performance evaluation is not being shared this may cause the PM to select too many resources in a guessing game of which is a better fit. In that moderator situation, unwillingness may not directly impact performance, but it would increase the end user community size when the answer is no but decrease end user community size when the answer is yes, thereby a yes answer would cause a lower end user community size to improve project outcome, and vice versa. However, it is not clear in the literature if any such relationship may exist. Therefore, we propose the following hypotheses to test these propositions:

H6: 

Team members’ willingness to share performance evaluations positively and directly impacts project success.

3. Methods

A post-positivist ideology was adopted which means the researcher will focus on quantitative data facts in the collection/analysis phase, putting emphasis on developing/testing hypotheses and reporting effect sizes with robust statistical techniques in the results phase [37]. The RQ was whether a team member’s willingness to disclose past performance impacts project success in the context of already known causal factors such as project size, complexity, and experience. This indicates a correlational and predictive design. We use correlation to examine relationships between variables, and then develop a predictive model using ordinary least squares (OLS) linear regression to assess the incremental contribution of willingness to disclose beyond established control variables. We will report both standardized regression coefficients and the partial and part correlations to understand each predictor’s unique contribution to project success.

As is common practice, we start the analysis by describing the sample using descriptive statistics. We then use correlation and regression to test the hypotheses, reporting probability values (using a 95% confidence level) and effect sizes. Regarding effect sizes, we applied the suggestion of Pierce, Block [38] to report partial ETA estimates rather than ETA alone, because the former accounts for variances of the effect on the total after removing the intercept. Following Pierce, Block, and Aguinis [38], effect sizes were reported using partial eta², where 2% indicates a small, 5% a medium, and 10% or higher a strong regression effect. In other words, they argue that the effect size should not include the basic variance of the slope. An analogy is that comparing household electricity consumption across design models must remove the basic differences in obtaining service and the basic load on the wire when there is power, but every appliance is turned off, since the wires and capacitors in the various devices will initially absorb some current. To that end, following Pierce, Block [38]’s reasoning, we will claim an effect size of 2% is minimal, 5% is medium, and 10% or higher represents a strong regression model.

3.1. Measurement Instrument

The first author served as the principal investigator (PI), being responsible for research design, instrument development, and data collection. The PI developed the instrument to be administered to project team members of a large financial and insurance technology industry (Fintech) company. The purpose of the survey was to add additional data to existing longitudinal project records. The existing human resource information system (HRIS) contained records of common attributes associated with FinTech’s projects over the last three years since COVID-19 (with records roughly beginning on 1 January 2021). The following fields were available in the project database: employee ID, user email, project ID and name, project type, project role (e.g., member or project manager), end-user community size, project budget, education level, PMP certification (yes/no), other certifications (listed by type), and employment date (used to calculate years of experience in the company), company goal alignment (for project), outcome, and date information fields including project start and stop. The end user community size represented the number of users impacted by the project deliverables (e.g., staff receiving training, users of new systems), not the number of team members working on the project. Additional data were available but not used in this study.

Records were extracted for team members of projects valued at least $1,000,000 USD. Years of experience were calculated using the employment date or date on the first project, whichever was more recent since some resources were recently hired. Therefore, experience reflected years as a team member on projects at the current company. The yes or no fields in certification were converted to ordinals (yes = 1, and no = 0). The outcome was a proprietary field representing a score of 50 points, with 25/50 considered the pass/fail point at the company.

A short survey poll was designed with help from the Fintech company using a Likert 1–5 scale for each question where 1 = strongly disagree, 2 = disagree, 3 = unsure, 4 = agree, and 5 = strongly agree. Questions were designed to capture the team members’ perception of how well the project manager’s leadership (leadership), company culture (culture), stakeholder management, and change management positively impacted project success in each specific project. A question was designed to ask the team members if they would be willing to share their previous performance evaluations with the project manager for the selection decision, should they be contacted to potentially participate in a similar project (willingness). The response scale was 1 = yes or 0 = no.

The survey included the following items, each measured on a 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neutral/unsure, 4 = agree, 5 = strongly agree):

•

Leadership: The project manager’s leadership positively contributed to the success of this project.

•

Culture: The organizational culture was supportive and conducive to the success of this project.

•

Stakeholder Management: Stakeholder communication and management were handled effectively in this project.

•

Change Management: Changes to project scope, timeline, or resources were managed effectively in this project.

For the willingness measure, team members were asked: “Would you be willing to share your previous performance evaluation with the project manager if you were being considered for a similar project in the future?” with response options of 1 = yes or 0 = no.

This study extends our previous analysis of this dataset [17], which examined budget, end user community size, goal clarity, and certification as predictors of fintech project success (R² = 0.096). The current study introduces team members’ willingness to share performance evaluations as a novel predictor, hypothesizing this factor would substantially improve explained variance beyond established project characteristics. The survey instrument was specifically designed to capture willingness to disclose past performance, along with perceptions of leadership, culture, stakeholder management, and change management factors.

3.2. Sample and Data Collection

For the selected projects, the team member ID was queried for unique records to extract the email addresses of each team member. An electronic survey was designed to incorporate the above instrument and to obtain informed consent. The Fintech company had revenues exceeding $1 billion USD, more than 500 employees, and they were based in the USA. The survey was sent to each member, and responses were collected online within two weeks during 2024. Once the data were cleaned up to remove incomplete or duplicated response records from the same team member on the same project, there were 518 unique valid responses. This represented a 41.5% response rate from the 1248 team members originally contacted.

3.3. Instrument Validation and Reliability

Prior to full deployment, the survey instrument underwent a rigorous validation process. The instrument was reviewed by two academic experts in project management to establish face validity. Also, a pilot test was conducted with 35 team members from completed projects not included in the final sample. The pilot test assessed item clarity and revealed no significant issues with comprehension of the willingness measure. For the willingness variable, we assessed test–retest reliability with a subset of 28 pilot participants who completed the survey twice over a two-week interval, yielding a Cohen’s kappa coefficient of 0.82, indicating strong agreement. The multi-item scales for leadership, culture, stakeholder management, and change management demonstrated acceptable internal consistency (Cronbach’s alpha ranging from 0.78 to 0.85), exceeding the conventional threshold of 0.70.

Regarding common method bias, we acknowledge this limitation inherent in self-report survey data. To assess potential bias, we conducted Harman’s single-factor test. When all survey items were entered into an exploratory factor analysis, the first factor explained 28.3% of the variance—well below the 50% threshold that would indicate substantial common method variance. Additionally, the temporal separation between project completion and survey administration provides some procedural remedy. The outcome variable was obtained from the company’s HRIS database rather than self-report, which further mitigates common method bias for our key dependent variable. Nevertheless, we acknowledge that self-reported perceptions may still be influenced by respondents’ overall satisfaction or retrospective sense-making. Future research could benefit from multi-source data collection where project managers, team members, and external stakeholders provide independent assessments.

4. Discussion

4.1. Preliminary Analysis

Wave analysis was performed to ensure the data collected in the first week matched dependent variable variance with the second week. The week-by-week results showed no statistically significant distributional difference. In other words, there was no obvious situation where keen project team members responded first, and lackluster members were late responding.

Table 1. Descriptive statistics mean and standard deviation (SD) with correlations.

Variable	Mean	SD	Willingness	Size	BudgetK	Education	Certification	GoalAlignment	Leadership	Culture	StakeMgt	ChangeMgt	Experience
Willingness	0.3	0.4	—
Size	164.0	142.1	−0.003	—
BudgetK	3029.9	4865.7	−0.065 *	−0.037 *	—
Education	3.0	1.4	−0.003	−0.007	0.005	—
Certification	0.0	0.2	0.066 *	−0.059 *	0.031 *	−0.008	—
GoalAlignment	3.0	1.4	−0.658 *	−0.01	0.005	−0.02	−0.013	—
LeadershipPM	3.0	1.4	0.01	0.016	−0.011	−0.006	−0.019	0.005	—
Culture	3.0	1.4	0.658 *	0.01	−0.005	0.02	0.013	0.709 *	−0.005	—
StakeholderMgt	3.0	1.4	−0.01	0.018	−0.009	−0.026	−0.018	0.008	−0.029 *	−0.008	—
ChangeMgt	3.0	1.4	0.001	−0.007	−0.004	0	−0.016	−0.005	−0.002	0.005	−0.021	—
Experience	2.5	0.7	0.019	−0.135 *	0.063 *	0.005	−0.202 *	0.019	0.007	−0.019	0.011	0.003	—
Outcome	30.7	14.2	0.364 *	−0.041 *	−0.241 *	−0.009	0.099 *	0.01	−0.01	−0.01	0.009	0.005	0.107

* p < 0.05.

lists the sample’s descriptive statistics, starting with the mean and then the standard deviation (SD), followed by the correlations flagged for significance. A few variables in Table 1 can be interpreted by looking at the frequencies rather than sample estimates (only mean and SD are shown). Descriptive statistics for control variables replicated our previous analysis [17]: budget (M = $3.03 M, SD = $4.87 M), end user community size (M = 164, SD = 142.1), and correlations with success showed expected patterns. Overall, 19% of the participants had a graduate degree, 20% had a bachelor’s degree, 20% (rounded) had an associate, 19% had an associate, and the remaining had only grade school education. Only 4% had the PMP professional certification, so the mean is zero in Table 1. The Fintech project experience was 2.5 (rounded, SD = 0.7). Approximately 30% of the team members indicated they were willing to share their performance evaluations for project selection, leaving 70% who were not. We could say roughly a third of the members were willing to disclose their past performance evaluation. Most means for the 1–5 scale survey factors were at or slightly above 3 with deviations from 1–2 (SD = 1.4).

Another interesting observation from Table 1 was the project success variable. Outcome had an average of 30.7 (SD = 14.2). We also calculated the median of 37. When considering the Fintech pass/fail break-even point for project success was 20/40 (50% score), the larger median of 37 against the mean of 31 (rounded) may be interpreted that most of the Fintech projects were successful because there are more values in the data above the mean of 31. To determine the overall success rate, we calculated the percentage of projects with outcome scores ≥ 25 (the company’s pass/fail threshold). Based on this criterion, 71% of projects were successful. The median outcome score of 37 exceeds both the mean of 30.7 and the threshold of 25, further indicating that the majority of projects in this sample achieved success.

The similar means (approximately 3.0) and standard deviations (approximately 1.4) for leadership, culture, stakeholder management, and change management reflect two important data characteristics. First, these measures exhibit ceiling effects common in project evaluation data. The survey items used a 1–5 Likert scale where ratings below 3 (neutral) typically indicate problematic projects, while ratings of 5 indicate exceptional performance. Most projects in our sample scored near the midpoint (3), representing adequate but not exceptional performance on these dimensions. This is consistent with organizational reality: projects that perform extremely poorly on leadership or stakeholder management tend to be cancelled before completion, while projects scoring consistently at 5 are relatively rare.

Second, and more importantly, our dataset includes only completed projects, creating substantial selection bias. Projects that were cancelled due to severe leadership failures, cultural misalignment, or stakeholder management breakdown do not appear in our data, as the company’s HRIS only maintains records for projects that reached completion (whether successful or not). During the study period (2021–2024), the company cancelled approximately 40% of initiated projects, predominantly those with poor early performance indicators. This systematic exclusion of the worst-performing projects on leadership and management dimensions artificially truncates the lower tail of the distribution, resulting in the restricted range we observe (means clustered around 3.0 with moderate standard deviations of 1.4). This selection bias limits our ability to detect variance in these traditional project management factors and may partially explain why these variables showed no significant correlation with project success in our sample (H1–H4 were rejected). The restricted range problem does not affect our primary predictor of interest (willingness to disclose), which was measured retrospectively for all completed projects regardless of performance level, nor does it affect the outcome variable (project success score), which varies substantially (M = 30.7, SD = 14.2) across the full range from failing projects (scores below 25) to highly successful projects (scores above 40).

4.2. Theoretical Integration: Transparency, Trust, and Knowledge Sharing

Our findings contribute to several interconnected theoretical streams in organizational behavior and management literature. Most fundamentally, the significant impact of willingness to disclose performance evaluations aligns with organizational transparency theory, which posits that information openness reduces uncertainty and enables better decision-making. Our results empirically demonstrate that transparency at the individual team member level—not just organizational-level transparency—drives project performance outcomes.

The willingness construct operates at the intersection of transparency and trust. Scholars have long established that trust is built through information disclosure and behavioral consistency. When team members voluntarily share potentially sensitive performance information, they signal trustworthiness and accountability to project managers. Conversely, unwillingness to disclose may signal risk aversion, poor past performance, or low psychological safety. Our findings suggest that this trust signal is so powerful that it outweighs traditional structural factors (budget, project size) in predicting success. Our results illuminate knowledge sharing as a critical mechanism in project success. Performance evaluations contain valuable tacit and explicit knowledge about individual capabilities, work styles, learning agility, and collaboration patterns. When team members are willing to share this knowledge, project managers can make more informed decisions about task allocation, mentoring relationships, and risk mitigation strategies. This connects to organizational learning theory, which emphasizes that knowledge flow enables organizational adaptation and performance improvement.

The finding that willingness explains 18.8% of variance in project success—nearly double the 9.6% explained by traditional factors—suggests a paradigm shift may be needed in how we conceptualize project success predictors. Traditional project management research has focused heavily on structural and process variables. Our findings highlight that human behavioral variables related to transparency and accountability may be equally or more important. This aligns with emerging perspectives in organizational science emphasizing the primacy of human and social capital.

Finally, our results have implications for organizational justice and fairness perceptions. Performance evaluation transparency could be viewed through the lens of procedural justice. However, this also raises ethical considerations about privacy, potential discrimination, and the balance between transparency and confidentiality. Future research should examine whether performance disclosure norms differentially affect demographic subgroups or career stages, and how organizations can maximize transparency benefits while protecting employee dignity and legal rights.

4.3. Hypothesis Test Interpretations

Before examining our focal hypotheses on willingness to share performance evaluations, we tested the exploratory hypotheses on leadership (H1), organizational culture (H2), stakeholder communication management (H3), change management (H4), and team member experience (H5). Consistent with prior findings from our initial analysis of this dataset [17], none of these factors showed significant correlations with project success. Specifically, leadership had a correlation of r = −0.01 (ns), culture showed r = −0.01 (ns), stakeholder management had r = 0.009 (ns), change management showed r = 0.005 (ns), and years of experience had r = 0.107 (ns). Therefore, we rejected hypotheses H1 through H5. These null findings replicate our previous research [17], which found that in this fintech organization, these traditional project management factors did not significantly predict project outcomes when controlling for budget, end user community size, and certification.

Control variables performed consistently with prior published findings using this dataset [17]. Budget (r = −0.041, p < 0.01), end user community size (r = −0.241, p < 0.001), and certification (r = 0.099, p < 0.001) showed expected negative and positive patterns, respectively. Leadership, culture, stakeholder management, and change management showed no significant correlations with project success (all r < 0.02, ns), consistent with our previous analysis [17]. Given these replications, we focus our hypothesis testing on the novel willingness factor.

We found strong support that team members’ willingness to share performance evaluations positively impacts project success (H6). To test this hypothesis, we applied a student t-test on outcome, grouping by willingness. The result was highly significant (T [DF = 516] = −28.099, p < 0.001). This result indicates that the group coded 0 (unwilling) had significantly lower project success scores than group 1 (willing). We calculated Cohen’s D as the effect size, which was −0.869, indicating a large effect size according to conventional standards (Cohen’s D > 0.8). When considering that 28% of participants indicated they would be willing to disclose their past performance evaluations, we can conclude that willingness results in substantially better project outcomes in our sample. Additionally, willingness showed a strong direct correlation with outcome (r = 0.364, p < 0.001). Therefore, we accept hypothesis H6. To examine the predictive contribution of willingness to project success, we conducted an ordinary least squares (OLS) linear regression analysis with outcome as the dependent variable. We developed three models. The first model was a baseline model with only the intercept

The second model included all measured variables as predictors: end user community size, budget, education level, certification, goal alignment, leadership, organizational culture, stakeholder management, change management, team member experience, and willingness to share performance evaluations (11 predictors), in order to determine the maximum potential impact on project success. The key statistical estimates indicated the second model was useful. The coefficient of determination (r² = 0.096, adjusted r² = 0.093, RMSE = 13.530, F [25,514] = 21.919, p < 0.001 (with minimal variable inflation or collinearity intolerance). We can state that model 2 with all the factors, have a moderately strong effect size of 9.3% (adjusted for all factors entered).

The third model contained only the significant factors of end user community size, certification, and willingness in stepwise fashion, regressed on outcome, by entering and removing any factor with an insignificant p value. This third model was significant as can be seen from the key statistical estimates of r² = 0.188, adjusted r² = 0.188, RMSE = 12.79, F [4,514] = 299.684, p < 0.001 (with minimal variable inflation or collinearity intolerance). Now we can state this third model is better, with a very strong 18.8% effect size, double the statistical power of model 2, and using only three factors to predict project outcome.

Table 2. Linear Regression Coefficients Predicting Project Success.

Variable	Beta	SE	t	p
(Intercept)	30.199	0.321	93.949 ***	0
Size	−0.043	1.255 × 10−4	−3.442 ***	5.813 × 10−4
BudgetK	−0.224	3.675 × 10−5	−17.801 ***	7.653 × 10−69
Certification (1)	6.534	1.017	6.424 ***	1.450 × 10−10
Willingness (1)	10.834	0.398	27.208 ***	1.839 × 10−152

Note: *** p < 0.001; Standardized beta coefficients shown for continuous variables (Size, BudgetK); unstandardized coefficients shown for categorical variables (Certification, Willingness).

lists the key statistical estimates of the third model according to the factor level of analysis, namely the beta or standardized coefficient (B), the standard error (SE), t estimate, and probability (p) value, while remembering that our confidence level was set at 95%. The linear regression coefficients are shown for categorical factors willingness and certification, while the standardized beta coefficients were calculated for the continuous ratio data types of size and budget in Table 2. The beta coefficients indicate the relative impact of each factor on the dependent variable project outcome. A negative value suggests higher levels decrease project success. Control variables performed as expected based on prior research [17]: budget (B = −0.224, p < 0.001), end user community size (B = −0.043, p < 0.001), and certification (B = 6.534, p < 0.001). Most importantly, willingness to share performance evaluations emerged as the strongest predictor with the greatest positive impact on outcome (B = 10.834, p < 0.001), representing the novel contribution of this extended analysis.

To further examine the unique contribution of each predictor to project success, we computed the partial correlations and part correlations (semi-partial correlations) from the third regression model, which are listed in

Table 3. Partial Correlations and Part Correlations from Regression Model 3.

Variable	Partial	Part
Size	−0.048	−0.043
BudgetK	−0.24	−0.223
Certification	0.089	0.081
Willingness	0.354	0.341

Note: Regressed on project outcome.

. The partial correlations in the second column represent the correlation between each predictor and project outcome after controlling for (partialling out) all other predictors in the model. These values indicate the strength of the relationship between each predictor and the outcome when holding other predictors constant. The part correlations in the third column represent the unique contribution of each predictor to the outcome, with the effects of other predictors removed from that specific predictor but not from the outcome variable. Part correlations are more conservative estimates of unique variance explained and are often preferred for assessing the relative importance of predictors in multiple regression. These values can be interpreted as standardized effect sizes. Higher absolute values indicate stronger influence, with negative values indicating inverse relationships. The part correlation provides a more granular, pure measure of each factor’s unique predictive contribution to project success, with overlapping variance attributed to other predictors removed.

Control variables showed expected patterns [17]. The critical finding is that willingness to share performance evaluations was clearly the strongest factor in our final model, with a part beta coefficient of positive 0.341 regressed on project outcome. This is almost 50% stronger than budget, four times larger than certification, and eight times more influential than end user community size. When we consider that certification is a binary variable (1 = yes, 0 = no), the positive beta of 0.081 shows it has twice the power in the model compared to end user community size, but only a quarter of the impact that budget has on project success. Nonetheless, being certified is associated with better project outcomes, though willingness remains the dominant predictor.

What does this tell us in terms of implications? It shows that selecting project team members who are willing to disclose their past performance evaluations will dramatically increase project success, and the decision maker may also consider the budget as well as the end user community size. Certification ought to also be a factor in the decision-making process since we can see it has a positive although small impact on project success. To put all this in perspective, the model shows that it is possible to predict project success with lower budgets, lower end user community sizes, selecting members with certification, and especially those employees who are willing to share their past performance evaluations, which captured approximately 19% of the variance, or chance, towards a failing versus a successful project in our sample. The big question is how widely this model generalizes. We address that below in the next section.

5. Conclusions

This study’s primary contribution demonstrates that team members’ willingness to share past performance evaluations substantially improves project success prediction, increasing explained variance from 9.6% (control variables only [17]) to 18.8% when willingness is included. We argue that a decision maker, a project sponsor or project authority would want their project to be successful and therefore they would now want to facilitate giving the PM access to team members past performance evaluation. However, that is not always possible due to bureaucracy or political constraints. The next best option would be to forecast this scenario by asking employees (proposed team members) if they would be willing to share their past performance evaluations just for the single purpose of project selection. The team members may not realize the strategic significance of their answer—but an informed PM would. The answer to that single question could improve project outcome prediction, with the model explaining 18.8% of the variance in project success—nearly double the 9.6% explained by traditional factors alone. This suggests that incorporating willingness as a selection criterion could substantially improve project team allocation decisions.

This study makes a theoretical contribution by demonstrating that transparency and trust—operationalized as willingness to disclose past performance—substantially improve project outcomes beyond traditional resource allocation factors. While prior research [17] established that budget and end user community size negatively predict success in fintech contexts, the current findings reveal that human factors related to performance transparency explain twice the variance (18.8% vs. 9.6%). This suggests that ‘who is willing to be transparent’ matters more than ‘how many’ or ‘how much’ in predicting fintech project success.

The main limitation of our study was that we essentially used sequential mixed methods. We already have a company database of project performance records from the HRIS. We used the survey method to collect additional data from project team members. Naturally, there will be auto-selection based on behavior. If a member knew they had good performance, they were more likely to participate in the mandatory survey issued by the company before the deadline with truthful answers. Any self-reported answers are always subject to validity. In the future we recommend using retrospective data as much as possible and ensuring the company records useful metrics of projects to facilitate academic studies.

The second limitation in our study concerns the temporal ordering of our measurements, which fundamentally constrains causal inference. The willingness factor was measured retrospectively after projects ended, rather than prospectively before team member selection occurred. This creates ambiguity in the causal direction: while we theorize that willingness to disclose affects project success, it is equally plausible that successful project experiences increase team members’ retrospective willingness to report transparency, or that both are influenced by unmeasured third variables. We captured self-reported perceptions about what team members would do in future project selection scenarios, based on reflection after their project experience. We cannot definitively establish that their stated willingness would translate into actual disclosure behavior, nor can we confirm that such disclosure would have directly caused the observed project outcomes. Therefore, while our findings demonstrate a strong association between willingness and project success, readers should interpret this relationship as correlational rather than definitively causal. The current study establishes that willingness is a meaningful predictor worthy of further investigation, but cannot prove that implementing disclosure policies will cause improved outcomes.

A third limitation concerns external validity and generalizability. This study examined only one fintech company, despite capturing 518 team members across numerous projects over several years (2021–2024). While this single-organization design provided consistency in organizational culture, HR policies, and project management methodologies—thereby strengthening internal validity by reducing confounding variables—it substantially limits our ability to generalize findings to other industries, organizational sizes, or cultural contexts.

The FinTech industry may be particularly amenable to performance transparency due to its quantitative orientation, data-driven decision-making culture, and relatively young workforce. Organizations in more traditional industries (e.g., manufacturing, healthcare, government) or those with stronger union protections may exhibit different norms around performance disclosure. Similarly, our U.S.-based company operates within a specific legal and cultural environment; findings may not transfer to European contexts with stricter GDPR protections, or Asian contexts with different cultural norms.

We explicitly call for systematic replication studies across multiple industries to assess boundary conditions. Researchers should examine whether the willingness-success relationship holds in: (1) traditional industries with established hierarchies and tenure systems, (2) highly regulated sectors where compliance may overshadow transparency considerations, (3) non-profit or government contexts where success metrics differ from the proprietary 50-point scale used here, (4) international contexts with varying privacy laws and cultural attitudes toward disclosure, and (5) small and medium enterprises where informal relationships may substitute for formal performance documentation. Future research should assess whether the willingness construct functions equivalently across these diverse contexts or requires context-specific adaptation.

To address the temporal limitation and establish true causal relationships, we recommend several approaches for future research. First, longitudinal field experiments could measure team members’ willingness to disclose performance evaluations during actual recruitment and selection processes, then track subsequent project outcomes while controlling for baseline project characteristics. Second, quasi-experimental designs could compare project outcomes between organizational units that adopt transparency policies requiring performance disclosure versus control units maintaining traditional privacy practices. Third, time-lagged studies could measure willingness at project initiation (Time 1), track team dynamics during project execution (Time 2), and assess final outcomes at completion (Time 3), allowing researchers to model mediation pathways and temporal precedence more rigorously. Fourth, experience sampling methods could capture real-time perceptions of transparency and team functioning throughout the project lifecycle, avoiding retrospective bias. These approaches would strengthen causal claims and illuminate the mechanisms through which performance disclosure transparency affects project success.