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American Interdisciplinary Journal of Business 

and Economics 
ISSN: 2837-1909| Impact Factor : 4.6 

Volume. 9, Number 3; July-Sept, 2022; 

Published By: Scientific and Academic Development Institute (SADI) 

8933 Willis Ave Los Angeles, California 

https://sadipub.com/Journals/index.php/aijbe 

 

 

32 
American Interdisciplinary Journal of Business and Economics | 

https://sadipub.com/Journals/index.php/aijbe 
 

FORCED RANKING GOES WRONG: EXAMINING THE DISASTROUS 

EFFECTS OF VITALITY CURVES 

 

 

 

van de Poll, Theo Kroese 

Managing Director at Transparency Lab BV  

Founder Moving-as-One  

Abstract: General Electric’s Jack Welch introduced the 20-70-10 rule, also known as the vitality curve, which 

has become a widely used employee performance management technique. However, it has faced criticism for 

its negative effects on teamwork and the lack of correlation between individual employee ratings and team 

performance. This study explores the possibility of extending the vitality curve to team performance 

management and addressing the criticisms of the vitality curve. The study surveyed over 1,600 teams with 

over 110,000 employees using questionnaires and divided the employees into three groups (Red, Amber, and 

Green) within each team, similar to the vitality curve. The results showed that 40% of the teams had 

predominantly green employees, 40% had mostly amber employees, and 20% had a large contingent of red 

employees. The study concludes that it is essential to evaluate teams separately to improve performance 

management. 

Keywords: performance management, vitality curve, team performance, employee performance, performance 

evaluations, rank-order evaluations, forced ranking, teamwork, employee morale.  

 

Introduction 

The introduction provides an overview of the origin and implementation of the vitality curve, including its 

strengths and weaknesses. Some studies argue that the controversy surrounding the vitality curve is due to 

poor implementation rather than flaws in its design. Others have explored the effects of rank-order evaluations 

on employee behavior and the relationships between employees and leaders. Companies are shifting away 

from ratings-based performance management, and there is uncertainty about what happens after stopping the 

practice. The pros and cons of using the vitality curve in performance evaluations have been discussed, with 

some arguing that its negative effects on teamwork and employee morale outweigh the benefits of identifying 

and removing lower-performing employees. 

Proof of the curve  

Dick Grote, a management consultant and former GE employee argues for the strengths of forced ranking 

as a performance management tool. He asserts that the strategy's controversy arises from poor 

implementation rather than flaws with its design. Grote's research and experiences demonstrate how 

managers can use forced ranking fairly and effectively (Grote, 2005). Gill et al. (2019) studied how 

organizations using rank-order evaluations responded to the rank they received, mainly through the effort 



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they put into their jobs after being ranked. They found that the rank response function is U-shaped, meaning 

the hardest working employees were ranked first or last. Kwak and Choi (2015) researched how forced 

ranking performance appraisals influence employees, leaders, and organizations' relationships and how 

discrepancies in ratings relate to turnover intention and leader-member exchange. The results showed 

asymmetrical and nonlinear relationships. Companies continue to shift away from ratings-based 

performance management, but there is uncertainty in what happens after the practice has been stopped. 

Rock and Jones (2015) researched 33 of the 52 largest companies that had thus far eliminated performance 

ratings to find out how those companies dealt with performance management afterward. Cappelli and 

Conyon (2018) recognize the disdain towards performance appraisals in general, but they argue their 

importance in determining merit pay and promotions, for example. Their evidence indicates performance 

appraisals being a significant part of a "relational, openended view of employment," rather than to "simply 

settle-up contractually based employment relationships." Discussing the advantages and disadvantages of 

using forced ranking to remove the lower performers in a company is futile if the process of identifying 

those performers is ineffective (Lawler, 2002a).  

Pros & cons; why companies have turned away from the curve  

While forced ranking may seem appealing, Hazels and Sasse (2008) point out that there are also 

consequences to using the system that may not be right for every company. In 2004, it was estimated that 

around one-third of employers use a forced ranking system. While it seems like a fair way to make cuts, 

Bob Rogers, president of Development Dimensions International, asserts that using forced ranking "has a 

tremendous downside in terms of teamwork, culture, competitiveness, and legal problems" (Johnson, 

2004). Lipman, a Mass Mutual Financial Group manager, adds to the discourse around forced rankings as 

a leader who followed the system. However, he recognizes that while forced ranking has its benefits, the 

negative issues such as lowered employee morale were not worth it (Lipman, 2012). Stewart et al. (2010) 

took a closer look at the advantages and disadvantages of using a forced distribution system in performance 

evaluations. They concluded that an organization should assess whether such a system would be compatible 

with its organizational culture and be fully aware of the downsides that go along with its use. Welch's 

performance management system that eliminates the bottom 10 percent of employees annually has been 

praised and implemented by many large corporations. On the surface, this seems like the most effective 

way to ensure a company consistently has the best employees, and Welch reasons that those employees are 

also better off not staying in a company that is "bad for them."   

However, according to Lawler's research, creating higher performance cannot be achieved through firing 

those at the bottom (Lawler, 2002b). Buckingham (2013) is vocal about forced rating systems not fulfilling 

the role they are intended to. He argues that forced ranking instead allows for fair compensation and value-

alignment between employees and companies, but it is still a poor way of accomplishing these things. After 

compiling evidence that companies are dissatisfied with their ratings-based performance management 

systems, Rock et al. (2014) conclude that these systems are overly convoluted and counter-productive. 

Instead of improving practices, neuroscience research suggests that these performance management 

systems are grounded in a misconstrued understanding of human responses and damage employee 

performance. At the turn of the 21st century, a forced ranking created quite a controversy, with various 

lawsuits filed against large companies such as Microsoft and Conoco (Boyle, 2001).  

Stack ranking is widely discussed, with most people agreeing it has more negative attributes than positive 

ones. However, companies like IBM and Amazon continue to use stack rankings. There are significant 

disadvantages to using the system in performance appraisals, primarily if an organization relies on 

creativity and innovation. However, positive outcomes depend on the type of company (PerformYard, 

2019). Forced ranking systems have been called out in various lawsuits as discriminatory, but Gary (2001) 

makes a case for how forced rankings can be used more effectually.  Mulligan and Schaefer (2011) stated 



van de Poll, Theo Kroese (2022)  
 

34 Interdisciplinary Journal of Educational Practice | https://sadipub.com/Journals/index.php/ijep 
 

that "systems with probationary periods before termination may realize some of the gains in workforce 

performance potential that traditional 'rank and yank' systems pose, while also having the potential to 

increase fairness perceptions." At the Society for Industrial and Organizational Psychology 2015 

conference (https://www.siop.org/), a lively debate was held on performance ratings. The panelists outlined 

the main points for eliminating the rating systems, including discrepancies between multiple raters on the 

same performance and inconsistencies in the appraisals' effects on performance. But they also indicated 

reasons for continuing the practice – such as understanding the need to improve performance management 

and recognizing that evaluating performance in some way is still necessary (Adler et al., 2016).  

The future of performance management  

Regardless of whether companies have official performance reviews or not, employees are still being rated 

one way or another. Without a formal system, there is no transparent or fair way to decide whom to give 

raises and promotions. With data from Facebook stating that 87% of their employees approve of keeping 

the rating system, Goler et al. (2016) offer a defense for the ranked performance reviews, arguing that 

people want to know their place in a company. Among employees in the Australian Public Service, anxiety 

is the main feeling about performance evaluations. The research done by Blackman et al. (2015) 

demonstrates the need to rethink how performance management is handled in these organizations to reduce 

anxiety among workers. Even though the performance management revolution began years ago, many 

organizations still use traditional performance management like rankings and annual reviews. There are 

improvements, but according to PerformYard (2020), "What defines performance management as 'modern' 

is not your process, but your approach." Various attempts have been made to improve performance 

management systems, but many still see it as a nuisance and ineffective towards what it is supposed to 

achieve. Pulakos et al. (2015) outlined their reform to performance management, focusing on everyday 

practices and experiential learning. Annual employee performance evaluations continue despite ongoing 

evidence of their ineffectiveness and the organizational shift in companies wanting different skill-sets that 

do not mesh with industrial-era performance management systems. According to Ewenstein et al. (2016), 

the problem in adapting these systems lies with the uncertainty of what would come next. They offer 

insights into the origins of ranked performance management systems and suggestions on how companies 

may adapt to the future - warning that it is necessary to do so quickly.  

Can the vitality curve be extended to teams?  

In their new book Teams That Work (2020), Tannenbaum and Salas share how in a modern workplace that 

is all about teams, there are ways to drive a team's effectiveness - whether a person is a team leader or a 

team member. In a study done by Scott and Einstein (2001) involving team-based organizations, they found 

that effective leadership is needed to make performance systems work in teams after identifying three types 

of teams and analyzing them based on performance appraisal characteristics. While many companies today 

praise teamwork, few have performance management systems that assess those teams. Darino and 

Johnson's (2020) research suggests that evaluating teams as a unit could improve performance management 

systems that organizations need. In a workplace study by Google, it was found that when working in teams, 

individual ratings did not correlate to the team's performance as a whole (Duhigg, 2016). Deloitte's 2016 

study showed that today's workplace is no longer based on hierarchy but a "network of teams." Things have 

changed rapidly, with companies modifying everything from job descriptions to the role of leaders. 

Moreover, companies' main issues revolve around these new work methods (Bersin, 2016). In team-based 

organizations, forced ranking performance systems should be terminated, and instead, the adoption of new 

performance appraisals is needed that emphasize and promote the new team-based structures (Dulebohn & 

Murray, 2019). Despite teams being far more prevalent in the workplace, they are often ineffective.  

Aguinis et al. (2013) propose six ways performance management systems should be redesigned to fit both 

an individual- and team-centered workplace - such as measuring and rewarding both individual and team 



van de Poll, Theo Kroese (2022)  
 

35 Interdisciplinary Journal of Educational Practice | https://sadipub.com/Journals/index.php/ijep 
 

performance and methods for implementation. Summarizing the abovementioned authors, the vitality 

curve's positive contribution is its rule-of-thumb to segment the workforce in high-, medium-, and low-

performers. The main criticism is the unintended consequences of taking this rough 20-70-10 guideline as 

an iron principle for rewards and' punishments.' And then, management attention to teams is not only about 

rewarding. Predominantly not. It's about understanding which team is moving well towards particular 

management objectives and which teams aren't. There is no firing of teams; there is an intervention, 

coaching, and, if needed, turn-around management. A performance rule-of-thumb for teams would be 

handy for dividing teams into high-, medium, or low-performing teams and focusing management 

attention. However, none of the abovementioned authors mention specific percentages as a basis for such 

a rule-of-thumb.  

OBJECTIVE  

This paper aims to derive a new rule-of-thumb percentage for dividing high-, medium- and low-performing 

teams: a vitality curve for teams. We based the division among teams on their relative position within 

strategic topics in their organization. And we tallied verifiable facts and behavior about the team's actual 

situation.  

METHOD  

Procedure and participants  

We deemed measuring team performance on financial indicators too tricky. A five percent increase in some 

indicator might be a low performance for one team but a stellar performance for another team. Moreover,  

every team likely had its specific history and context. Hence, we wanted to objectively compare teams on 

their  activity towards a particular management objective rather than achieving it. So, we first designed an 

alternative scale based on the Guttman scale (Gutman, 1950), specifically designed for employee polling 

(Van de Poll, 2018 and 2021).  

Next, we researched 328 relatively strategic assessments that would require focusing on management's 

attention. These assessments included topics on - among others - employee engagement, innovation, 

technology adoption, digital transformation, work pressure, value adoption, team effectiveness, diversity, 

purpose, IT security, work processes, competencies, creative agency management, and marketing 

excellence. These assessments involved 1,671 teams from various industries (both profit and non-profit) 

in 18 countries. These teams comprised 113,454 employees, answering close to 5.8 million questions.  

Measures  

Comparing teams on their progress towards a management target require tallying verifiable facts or 

behavior, not gathering opinions or agreements with statements. Hence, we replaced the traditional Likert 

survey format in favor of a survey based on a Guttman scale. Guttman scaling works with "current-status 

data" (Diamond, McDonald, and Shah, 1986): every following answer shows more progress than the 

previous answer. It's a scale from not so good to better to even better, so-called breaking points (Uhlaner, 

2002). Q. How do you celebrate successes?  

1. We don't  

2. When there is an apparent reason to do so, with whoever is involved  

3. We make it a habit to celebrate successes with the entire team  

As in this example, such answers can be considered 'objectively real' or 'a testable proposition' (Ahrens & 

Chapman, 2006). We eliminated adjectives and adverbs that cannot be verified (e.g., "good") to reduce 

interpretation bias. And we added proof-words" like, e.g., 'periodically,' 'formally,' 'measurable,' 

'documented,' and 'described' to reduce self-reporting bias by the respondent (Donaldson and Grans-

Vallone, 2002). Additionally, such "proof-words" help with verification and prevent employees from 

adding cognitive or emotional meaning (Frese & Zapf, 1988). For tallying a team's progress, we need 



van de Poll, Theo Kroese (2022)  
 

36 Interdisciplinary Journal of Educational Practice | https://sadipub.com/Journals/index.php/ijep 
 

'binary (no/yes), numerical or categorical representations' for our intended clustering (Plewis & Mason, 

2007).   

Data analysis  

Each Guttman-Poll question had three answers. The 'worst' answer of three (the current situation) was rated 

with 0—the 'middle' answer (the intermediate step) with a score of 5. And the 'best' answer (reflecting the 

content of the strategy that needed to be achieved) with a score of 10. An average score for a (part of a) 

team required averaging the respondents' scores on the individual questions. We refrained from weights 

among questions and answers.  

Where Welch's vitality curve focused on the ends of the performance Bell-curve (the top 20% and the 

bottom 10%), we postulated that high performing individuals ('Green') scoring 6.0 or higher on a scale of 

0 to 10 (on average, slightly above the middle answer of three). Low-performing employees ('Red') scored 

3.0 or lower (roughly halfway between the worst and middle answer), and the remainder of the team was 

'Amber' (scoring between 3.0 and 6.0). We described each team in three percentages (the % green, amber, 

and red employees). Next, we clustered the 1,671 teams for these three percentages via a K-means 

algorithm (randomly initialized, 20,000 iterations) and repeated this for three, four, five, and ten clusters. 

We correlated some questionnaire specifics (e.g., number of respondents and questionnaire length) to verify 

whether these control variables influence the cluster scores. Finally, we compared several scenarios to see 

which % green and % red would require management attention (to identify the leading and lagging teams, 

respectively).  

RESULTS  

Table 1 shows the sample size, the division in green, amber, and red respondents, and the correlation of 

some questionnaire specifics that might have been influencing that division. In contrast with Welch's 20-

70-10 vitality curve division, we tallied 32% green respondents, 51% amber respondents, and 17% red 

respondents. We then performed the K-means clustering on the 1,671 teams to see whether that would lead 

percentages to make up a vitality curve for teams.  

Table 1 : Sample size and correlations  

 
                                                                                                                                        Number of clusters  

                                                                     

N     Min         Max       Avg.  StDev.       3       4  5  10 Sample size  

Number of questionnaires  328 Number of teams 

 1,671  

Number of employees  113,454  

    

Teams per questionnaire  1  44  5.1  7.4    

Number of employees per team  3  834  68  71.2    

% respondents per team            

 % Green respondents  1%  96%  32%  19%  

  

         See table 2    

  



van de Poll, Theo Kroese (2022)  
 

37 Interdisciplinary Journal of Educational Practice | https://sadipub.com/Journals/index.php/ijep 
 

 % Amber respondents  2%  93%  51%  16%  

 % Red respondents  1%  82%  17%  13%  

Correlation      

Number of respondents in a team  -0.02  -0.05  -0.05   0.06  

Number of questions in the questionnaire   0.01   0.02   0.02  -0.19  

% anonymous respondents   0.02  -0.01  -0.01   0.12  

 % of respondents willing to share knowledge about the questionnaire  0.06  0.05  0.05 -0.09  

 
Min.: lowest number. Max: highest number. Avg: average number. StDev: standard deviation.  

Note: this table has been submitted separately as an editable Excel file  

The number of respondents fuels whether larger teams (with more diverse competencies) would perform 

better. The number of questions could indicate whether more extensive questionnaires make it difficult for 

respondents to achieve a 'green' score (as there is so much work to complete). Furthermore, a high 

percentage of anonymous respondents and/or a low percentage of respondents willing to share knowledge 

about the questionnaire topics could indicate a culture of fear or a similar situation. Such a situation would 

likely create more underperforming 'red' teams. However, these four assessment specifics did not correlate 

with the clusters. We have detailed the clusters in Table 2, which shows for each cluster the class centroids. 

We have estimated a 'color verdict' per cluster based on these centroids.   

However, giving such a verdict remains a difficult task. For example, cluster 3 in the 10-cluster analysis 

has 35% green respondents, 30% amber, and 34% red respondents. Would that be a draw? Aware of a risk 

of overfitting, we assume the rule-of-thumb division of individual respondents as 30-50-20 (rather than 32-

5117), as depicted in Table 3.   

Table 2:  Management scenarios, based on RAG cut-offs  

Scenario name  
Employees %   Division of teams (%)   

Green  Amber  Red  Green  Amber  Red  Double  

Database  

Actual average  

  

32%  

  

51%  

  

17%  

  

35%  

  

38%  

  

21%  

  

5%  

Rule-of-thumb  30%  50%  20%  40%  40%  20%    

Management scenario  

Ensure top-end  

Alert on low-end  

  

50%  

  

  

  

30%  

  

19%  

  

  

  

18%  

  

  

Both ends, heavier  25%    25%  32%    12%  4%  

Both ends, lighter  15%    15%  31%    18%  11%  

Employees %, green: the minium percentage of'green' respondents in a team, 

given a scenario. Ditto for % Amber and % Red.  

Double: the employee mix tags this percentage to more than one category, e.g., 

Amber and Red.  

Note: this table has been submitted separately as an editable Excel file  

We then counted the teams with at least 30% green respondents, the teams with at least 50% amber 

respondents, and those with at least 20% red respondents. We then came to a 35% green team division, 

38% amber teams, and 21% red teams. Another 5% of teams would qualify for more than one color. E.g., 

a team with 60% amber respondents and 40% red respondents would be eligible as both amber and red. 

Further analyzing the 5% double-count and wanting to achieve a rule-of-thumb division (rather than 

overfitting our model), we arrive at our 40-40-20 division. The original vitality curve (for individuals) was 

developed to focus management attention (i.e., promoting and firing). A variety of reasons can drive 

management attention for teams. Table 3 shows four different scenarios of how management would like to 



van de Poll, Theo Kroese (2022)  
 

38 Interdisciplinary Journal of Educational Practice | https://sadipub.com/Journals/index.php/ijep 
 

focus their attention, scenarios that help deviate from the 'standard' 40-40-20 rule-of-thumb. For instance, 

management may focus on the teams where at least 50% of the respondents are green. In such a situation, 

they have to focus on 19% (say, a rule-of-thumb of 20%) of their teams. Figure 1 helps identify the 

approximate percentage of teams given a starting percentage of either green, amber, or red respondents.  

 
Figure 1. Linking employees to teams  

DISCUSSION  

Individual employees' percentual differences in the original vitality curve are based on a composite index 

of hard performance figures (say, reaching a sales target) and softer ones (e.g., client satisfaction or 

comparison with peers). As mentioned in the introduction, this has led to criticism about the ineffective 

identification of performers due to, among others, discrepancies between multiple raters on the same 

performance. Hence, our use of a survey scale focused on verifiable facts and behavior comparable within 

and among teams.  

The consequences of this 40-40-20 rule-of-thumb are different from the original vitality curve. The main 

reason: teams as a whole – contrary to employees – do not get fired. Firing the bottom 20% of the teams 

makes no sense. As mentioned before, individual ratings did not correlate to the team's performance as a 

whole (Duhigg, 2016). Maybe a manager gets replaced. In the long run, bad performing functions can be 

outsourced. Yet, in all cases, this 40-40-20 rule-of-thumb helps to focus management attention, whatever 

their attention scenario.  

CONCLUSIONS  

The original 20-70-10 vitality curve, developed for employee performance management, has done many 

good things (from an efficiency perspective) and many bad things (from a human perspective). Although 

there is abundant literature suggesting that evaluating teams as a unit could improve performance 

management systems that organizations need, specific percentages to drive a team's effectiveness have not 

been proposed so far. In this study, the vitality curve for teams helps to focus management attention further. 

Similar to Jack Welch's vitality curve for employee performance, we divide team performance into three 

groups as Red, Amber, Green. Roughly 40% of the teams had predominantly green employees, 40% mostly 

amber employees, and 20% had a large contingent of red employees. The number of teams, respondents, 

and countries represented in our database could indicate sufficiently broad applicability of this 40-40-20 

rule-ofthumb. Future studies will investigate whether technology-related questionnaires usually had 

'greener' teams than process- or people-related topics.   

ACKNOWLEDGEMENT  

We would like to thank Dr. Jasna Duricic for her constructive comments.   



van de Poll, Theo Kroese (2022)  
 

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