[Excerpt] This article starts with a simple value proposition: that companies can save large sums and gain competitive advantage by utilizing more fully their own data on the determinants of profit. In this article, we describe how we used a variety of statistical methods (correlations, simple regressions, multiple regressions, and decompositions) to help one company, Borders, gain competitive advantage by learning from itself

DeVaro, Jed

Fields, Gary S

English

DigitalCommons@ILR

Cornell University ILR School 
DigitalCommons@ILR 
Working Papers ILR Collection 
10-1-2005 
Doing the Right Jobs Right: Managers’ Attributes and Activities at 
Borders 
Gary S. Fields 
Cornell University, gsf2@cornell.edu 
Jed DeVaro 
Cornell University, jed.devaro@csueastbay.edu 
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Doing the Right Jobs Right: Managers’ Attributes and Activities at Borders 
Abstract 
[Excerpt] This article starts with a simple value proposition: that companies can save large sums and gain 
competitive advantage by utilizing more fully their own data on the determinants of profit. In this article, 
we describe how we used a variety of statistical methods (correlations, simple regressions, multiple 
regressions, and decompositions) to help one company, Borders, gain competitive advantage by learning 
from itself. 
Keywords 
Borders, profit, manager attributes, competitive advantage 
Comments 
Suggested Citation 
Fields, G., & DeVaro, J. (2005). Doing the right jobs right: Managers’ attributes and activities at Borders. 
Retrieved [insert date], from Cornell University, ILR School site: http://digitalcommons.ilr.cornell.edu/
workingpapers/14/ 
Required Publishers Statement 
Copyright held by the authors. 
This article is available at DigitalCommons@ILR: https://digitalcommons.ilr.cornell.edu/workingpapers/14 
Doing the Right Jobs Right:   
Managers’ Attributes and Activities at Borders 
 
Gary Fields and Jed DeVaro* 
ILR School 
Cornell University 
Ithaca, NY 14853, U.S.A. 
gsf2@cornell.edu, devaro@cornell.edu 
October, 2005 
 
 
Missing the Boat? 
 
In a recent book Playing to Your Strengths, Haig Nalbantian, Rick Guzzo, Dave Kieffer, and Jay Doherty 
of Mercer Human Resources Consulting wrote: 
 
The reason executives know so little about their human assets isn’t a lack of interest or 
concern. Indeed, CEOs and senior managers spend most of their time dealing with people 
problems. The problem has been their inability to measure, assess, and predict the 
outcomes of workforce tactics in the same way they do with other parts of the business. 
The tools simply have not been available. They didn’t miss the boat. There wasn’t any 
boat. (Emphasis added) 
 
In fact, there was a boat that had been developed at Mercer and at Cornell. In this article, we’ll explain it to 
you and show you how we deployed it to help a Fortune 100 company learn about its human assets. 
  
 
The Approach We Took 
 
This article starts with a simple value proposition: that companies can save large sums and gain competitive 
advantage by utilizing more fully their own data on the determinants of profit. By analyzing what has 
worked to increase profits within their own organizations, managers can seek out and develop those 
competencies in current employees and new hires. Equally importantly, by knowing which factors make 
little or no difference in their particular context, managers can avoid wasting time on the factors that are 
unimportant for them.   
 
Does your company regularly analyze this kind of information and act on it? Or do you leave this potential 
source of competitive advantage lying untapped within your organization’s electronic and paper data 
archives?  
 
Tapping data in this way is very different from benchmarking. Organizations that benchmark learn from 
others. Here you learn from yourself. There are, undoubtedly, many contexts in which it is helpful to learn 
from your competitors and peers, imitating their successes and avoiding their failures. However, in other 
situations, you can do better by learning from your own experiences. What works for you is not necessarily 
what works for them.   
 
Tapping data in this way is also different from dashboarding. Organizations that dashboard gauge 
aggregate measures such as turnover cost, compensation as a percentage of operating expense, HR 
employment as a percentage of the total, and time to fill. But did you know that you can also use data to 
learn what differentiates your top performers from your middling ones from your poor ones? Imagine the 
time and money you can save in hiring and training by concentrating on the attributes that matter for you 
and ignoring those that make no demonstrable difference! 
 
                                                          
*
 We are grateful to Borders Group for access to the data and to Dan Smith and Jaime Tache for very 
helpful discussions about the data set and results. 
 2 
 
In this article, we describe how we used a variety of statistical methods (correlations, simple regressions, 
multiple regressions, and decompositions) to help one company, Borders, gain competitive advantage by 
learning from itself. 
 
 
Our Value Added 
 
Before our work began, a team of Cornell students had analyzed store outcomes, manager competencies, 
and activity ratings for each of Borders’ forty districts in the U.S. The student team had been given a 
database compiled by Borders from several sources: competency ratings (ten components), a psychological 
assessment tool known as psibase (twelve components), a 360-degree feedback index (six components), 
and a number of district activity measures (eight components). They began by interviewing general 
managers and district managers to find out which activities and competencies they believed were the 
important drivers of organizational outcomes. The managers identified communication skills, consistency 
skills, and the ability to develop others as the essential competencies needed for district managers to 
positively influence the success of their districts. The students then restricted their attention to the 
following indicators of the skills that had been identified by the managers: 
• Communication: informing score from the competency score, informing score from psibase, 
feedback from the competency score, feedback from psibase, respectful/supportive from 360, 
leadership from competency score, leadership from psibase, vision/strategic leadership from 360, 
persuasive from competency score, persuasive from psibase. 
• Consistency: results-oriented from competency score, results-driven from psibase, results-oriented 
from 360, persistent from competency score, persistent from psibase, persuasive from competency 
score, and persuasive from psibase. 
• Developing others: growth/development from 360, respectful/supportive from 360, general 
manager class training, general manager field training. 
Their research on these variables showed that a number of these variables made a statistically significant 
difference for a variety of organizational outcomes.  
 
Following the students’ work, we were engaged by Borders to analyze the data further. Our analysis, 
summarized here, advanced Borders’ understanding of itself in a number of respects.  
 
First and foremost, although the data set included measures of many district-level outcomes, we focused on 
only one of these, profit. Profit is literally the bottom line on Borders’ profit and loss statement. Borders, 
like most other companies, tries to achieve the maximum possible profits, now and in the future. Other 
outcome variables such as sales, attainment of goals, and the like are intermediate variables that help drive 
profits. Because the focus of managerial efforts is presumably to raise profits ever higher, district 
profitability was the focal point of our statistical analysis. 
 
Second, we worked with all of the performance variables contained in the data set.  When we looked at all 
of the available variables rather than some of them, we found many variables that were significantly 
associated with profits.  (By contrast, only one of the variables identified by the managers and analyzed by 
the students fell into this category.) This shows the value of doing comprehensive quantitative analysis.  
 
Third, having identified the significant drivers of profits, we then examined all of these variables at the 
same time, allowing us to determine whether one variable is an important determinant of profits after taking 
account of the effects of other variables. To illustrate, one of our findings is that profits are significantly 
associated with the district’s shrinkage (theft and damage) rating, but the effect of shrinkage disappears 
once we account for “managerial talent,” which is comprised mostly of the district manager’s ability to 
manage conflict. Had we considered only the simple correlations of each performance measure with profit, 
we would not have been able to tell which variables are important in the presence of others. 
 
Our principal findings and interpretations of them follow. 
 
 
 3 
 
Principal Findings 
 
We find many individual variables that were significantly associated with profits and make millions of 
dollars of difference.  Note carefully that some of these variables are associated with higher profits and 
some with lower profits.  In particular: 
 
For those variables that are scaled from 0 to 100: 
• A 50-point increase in informing on the psibase assessment is associated with an $8.1 million 
increase in profits.  
• A 50-point increase in feedback from the psibase assessment is associated with a $2.5 million 
increase in profits. 
• A 50-point increase in optimist on the psibase assessment is associated with a $2.5 million 
increase in profits. 
• A 50-point increase in results-driven on the psibase assessment is associated with a $1.8 million 
increase in profits. 
• A 50-point increase in conflict management from the psibase assessment is associated with a $1.6 
million increase in profits. 
 
And for those variables that are scaled from 1 to 5: 
• A one-point increase in optimism on the performance appraisal is associated with a $1.3 million 
increase in profits. 
• A one-point increase in informing on the performance appraisal is associated with a $1.2 million 
increase in profits. 
• A one-point increase in shrink on the balanced scorecared is associated with a $0.6 million 
increase in profits. 
• A one-point increase in position-specific goal from the balanced scorecard is associated with a 
$0.8 million decrease in profits. 
• A one-point increase in customer service from the balanced scorecard is associated with a $1.5 
million decrease in profits. 
• A one-point increase in consistency from the balanced scorecard is associated with a $1.5 million 
decrease in profits. 
 
Most of the variables that were found to affect profit when we considered them one at a time continued to 
affect profit when we analyzed them simultaneously, and they remained million dollar variables. For 
example, in both analyses a 50-point increase in feedback was associated with a $2.5 million increase in 
profits. Similarly, a one-point increase in informing was associated with a $1.2 million increase in profit 
when we considered this variable individually. When we analyzed it in the presence of other variables the 
effect increased to $1.6 million. On the other hand, in the presence of other variables, neither optimism nor 
customer service has a statistically significant effect on profit. Given the millions of dollars of profits 
involved, we would expect that the benefits of assessing and developing managers’ competencies at 
conflict management, drive for results, and the other statistically significant factors far exceed the costs. 
 
Otherwise, the remaining variables were not significantly correlated with profit. This is important for 
telling Borders what not to pay serious attention to. 
 
 
Interpreting the Findings 
 
As outsiders to Borders, when we entered this research project we had little idea of which district manager 
characteristics would affect profits. Though the managers had identified nineteen managerial attributes and 
characteristics that they believed were important drivers of store success, the data set contained metrics on 
a total of thirty-six characteristics, all of which we used. 
 
Two features of the comparison between managers’ hypotheses and our findings stand out. One is that a 
number of factors believed by general managers and district managers to be important made no 
 4 
 
demonstrable difference to district profits. The other is that some factors not mentioned by managers did 
affect district profits, and these are the characteristics that Borders should manage, both in terms of its 
hiring and in terms of its training. In this way, our research has taught Borders some things it apparently did 
not know about itself. 
 
It is also worth highlighting again that we found three variables that were associated with lower profits: 
one-point increases in customer service and in consistency each reduced profits by about $1.5 million and a 
one-point increase in position-specific goal reduced profit by about $0.8 million. Why might this be? 
Customer service is assessed by means of mystery shoppers and the like. Consistency is an amalgamation 
of performance on e-mail name collection, café exception reports, customer service evaluations, customer 
service phone shop, preferred programs, special orders, corporate sales, and cost control. Position-specific 
goals are set by the regional director for each district manager. Although it seems plausible that excellence 
in these areas might not produce discernible benefits in terms of profit, it is surprising that such excellences 
could actually lower profits, which is what we have found in the data. Our best interpretation of these 
findings is that districts that focus on achieving excellence in areas such as e-mail name collection may be 
doing so at the expense of other more profitable activities. Districts that spend less time worrying about 
these specific goals and more time focusing on sales on the floor and on minimizing losses due to theft and 
damage may be more profitable in the long run. 
 
This discussion illustrates that it is not necessarily wise to do the job right, so that the district gets higher 
marks on customer service, consistency, and position-specific goals. What matters is to do the right jobs 
right. 
 
  
How Borders Acted on the Findings 
 
Upon being presented with these findings, Borders took three steps. First, they became aware that the current 
definition of “consistency” has limitations, which they have since addressed. Second, they found that many 
of the manager attributes that made a positive difference to profits had been obtained from an assessment tool 
administered by a psychological testing service. Accordingly, they decided that the costs of using this tool to 
help select managers were well worth it, and so they are continuing with this practice. And third, they 
reconfirmed the potential benefits of training programs for district managers and store managers around the 
six items (shrinkage, informing, conflict management, results-driven, feedback, and optimism) that made a 
significant positive difference to profits. The first of these programs has already been launched.  
 
 
In Conclusion 
 
Our study has demonstrated the great value of workplace metrics in helping organizations learn what 
improves outcomes for them and what does not. Borders learned from itself what matters for it. No amount 
of benchmarking could have produced such insights.  
 
In the future, more could be done at Borders. One task would be to explore why the factors identified here 
as important influences on profits have the effects they do – not only those that were found to be positively 
associated with profits but also those that have been found to be negatively associated with profits. Another 
task would be to perform additional statistical analysis at the store rather than the district level. The best 
policy recommendations are those that are decided by combining detailed company knowledge with 
rigorous statistical analysis. 
 
These methods could also be fruitfully applied to analyze other outcome variables in other organizations. 
For example, one of us has studied the drivers of retention, performance, and potential of talent in an 
engineering firm.  That analysis showed the client which characteristics of the engineers made a major 
difference, which made a minor difference, and which made no difference at all. 
 
Organizations can learn tremendous amounts from their own databases. We hope that this article has 
suggested useful ways that you can tap data in your organization to gain competitive advantage. 


Doing the Right Jobs Right:  Managers’ Attributes and Activities at Borders

[Excerpt] This article starts with a simple value proposition: that companies can save large sums and gain competitive advantage by utilizing more fully their own data on the determinants of profit. In this article, we describe how we used a variety of statistical methods (correlations, simple regressions, multiple regressions, and decompositions) to help one company, Borders, gain competitive advantage by learning from itself.Fields6_rightjobsright_oct051.pdf: 426 downloads, before Oct. 1, 2020

Fields, Gary S.

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