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Analytical abilities and the performance of HR professionals

David Kryscynski1 | Cody Reeves1 | Ryan Stice-Lusvardi2 | Michael Ulrich3 |

Grant Russell4

1Marriott School of Management, Brigham

Young University, Provo, Utah

2School of Management Science and

Engineering, Stanford University, Stanford,


3Jon M. Huntsman School of Business, Utah

State University, Logan, Utah

4Google, Mountain View, California


David Kryscynski, Associate Professor of

Strategy, Management Department, Marriott

School of Business, Brigham Young University,

567 TNRB, Provo, UT 84606.


Recent years have shown an increased focus on workforce analytics and the importance of

workforce analytics in helping HR professionals to be more useful business partners. This sug-

gests that HR professionals may need to become more and more data savvy and develop bet-

ter analytical abilities if they hope to perform well and contribute meaningfully in the future.

Despite this emphasis, there has been no research explicitly connecting the individual level ana-

lytical abilities of HR professionals to their job performance. Using a proprietary sample of

360 feedback surveys from 1,117 HR professionals in 449 unique organizations we test this

general relationship. We also test whether the relationship varies by industry-, company-, and

job-level factors. We find support for our main hypotheses that HR professionals with higher

analytical abilities will also have higher perceived job performance. We also find that the

strength of this relationship varies by some job roles. We explore and discuss these empirical



ability, HR and technology, strategic HR


The role of HR professionals has evolved throughout history—from

“personnel administrators” and “industrial relations professionals” in

the 20th century to “HR managers” and “people managers” in the

21st century (Ferris et al., 2007; Kaufman, 2014, 2015). However,

HR professionals still struggle to get out from under their own his-

tory. The work of HR professionals continues to be perceived as

administrative (Lawler & Mohrman, 2003) in spite of a steady

decrease in the time spent on administrative tasks, as well as

increased involvement in executing and developing organizational

strategy (Lawler, 2005; Ulrich, Younger, & Brockbank, 2008). For

more than two decades, numerous scholars and practitioners have

demonstrated the importance of a strategic role for HR (Lawler,

2005; Ulrich et al., 2008) and further urged HR professionals to focus

on activities that place them in full strategic partnership with other

key decision makers within the business (Brockbank, 1999; Lawler &

Mohrman, 2000; Ulrich, 1997; Ulrich, Brockbank, & Johnson, 2009;

Ulrich, Younger, Brockbank, & Ulrich, 2012), but the profession as a

whole still struggles to establish itself as a strategic partner (Ferris

et al., 2007; Hammonds, 2005; Mundy, 2012). There are a number of

factors that may contribute to this struggle, but one prominent factor

is HR’s lack of evidence-based rigor in decision making. Workforce

analytics has been popularly identified as one means by which HR

can address this failing and enhance its empirical rigor, but HR may

not have the necessary capabilities (Boudreau & Ramstad, 2007;

Meinhert, 2011; Oehler, 2015; Roberts, 2009; Schramm, 2006; Zie-

linski, 2014a). With a growing consensus that the HR profession

needs greater analytical rigor, we might presume that HR profes-

sionals with greater analytical abilities will be better HR professionals

overall, ceteris paribus. They may make better decisions, have greater

influence, generate new insights, better communicate with business

leaders, and so forth.

Despite a growing sentiment that HR professionals with higher

analytical skill will be a better strategic resource and partner (Robb,

2003; Roberts, 2009; Zielinski, 2014b), we find no empirical evidence

supporting this general supposition at the individual level. We find

many claims that analytical skill may be critical for HR professionals

(e.g., Lawler, 2006; Roberts, 2007) and that increasing the analytical

skill of HR professionals holds “the potential for HR managers to

communicate HR’s value and further transform its image from a back

office administrative oriented function to a full-fledged strategic and

DOI: 10.1002/hrm.21854

Hum Resour Manage. 2018;57:715–738. © 2017 Wiley Periodicals, Inc. 715

business partner” (Dulebohn & Johnson, 2013). Unfortunately, how-

ever, these claims are primarily theoretical and/or based on anecdotal

observation. We also find evidence supporting a general positive cor-

relation between HR analytics and different dimensions of perfor-

mance at the HR department level (Lawler & Boudreau, 2015), but

we simply lack empirical evidence supporting the relationship

between an individual HR professional’s analytical abilities and that

person’s performance.

The purpose of this article, therefore, is to explicitly test whether

HR professionals who have better analytical skills also demonstrate

higher performance, as well as the contextual factors that may mod-

erate that relationship. Based on the research mentioned above, we

generally expect that higher analytical skills will relate to higher indi-

vidual performance. We also draw upon extant organizational

research to argue that the positive relationship between analytical

skills and individual performance will be stronger in certain industry,

company, and job contexts. Specifically, we expect this relationship

to be stronger in: (a) high-tech industries, (b) companies whose HR

departments engage in high levels of HR analytics, (c) lower job

levels, and (d) HR generalist job types.

We test our hypotheses using a unique and proprietary data set

from the Human Resource Competency Study (Ulrich, Brockbank,

Johnson, & Younger, 2007; Ulrich, Brockbank, Johnson, & Younger,

2010). This practitioner-oriented study recruited more than 4,000 HR

professionals in 2015 to participate in a 360 feedback process where

raters evaluated both the HR professionals’ individual competencies

as well as their performance. We leverage measures of the HR pro-

fessional’s perceived analytical skill along with measures of that HR

professional’s perceived performance for 1,117 of the HR profes-

sionals in the sample. The results support our main hypotheses, but

of our hypothesized interactions we only find partial support for our

expectation that analytical abilities will be more valuable for HR gen-

eralists than HR specialists. We thus contribute to the emerging

stream of academic research in workforce analytics in at least two

ways. First, we provide a large-scale empirical test of the taken-for-

granted assumption that HR professionals with higher analytical abil-

ities will also have higher individual performance. Second, we explore

the conditions under which analytical skill may have stronger or

weaker relationships with individual performance.


In the past decade, reports of HR analytics’ successes have spurred

discussions from both practitioners and scholars about the impor-

tance of utilizing workforce data and analytics to strengthen HR’s

contribution to organizations. Schramm (2006) reported that effective

use of workforce analytics was a key determinant of successful

human capital management. Since that time, a number of scholars

and consultants have championed workforce analytics

(e.g., Boudreau & Ramstad, 2007; Gibbons & Woock, 2007; Ulrich &

Dulebohn, 2015) and identified it as a key area for investment

(Mondore, Douthitt, & Carson, 2011; Ulrich & Dulebohn, 2015). The

result is that more and more HR professionals are using HR data to

provide legitimate and reliable foundations for decisions (Boudreau &

Ramstad, 2007).

Although many executives recognize the potential of HR to pro-

vide insights about human capital and shape the organization in mean-

ingful ways (Lawler, 2006; Zeidner, 2009), it is not entirely clear

whether HR professionals have the analytical skills and abilities to

realize this potential. Scholars argue that successful HR analysts

require substantial analytical skills (Levenson, 2005; Wolfe, Wright, &

Smart, 2006), and, historically, HR has not attracted individuals with

strong analytical and quantitative skills (Roberts, 2009; Ulrich & Dule-

bohn, 2015), which has led to a dearth of qualified analysts in HR

departments. Brockner and Flynn (2006) observed that “future HR

practitioners almost universally shy away from the more analytical

classes.” Given this reluctance toward analytics typical of most HR

professionals, it is not surprising that the decisions made about human

capital often lack evidence-based rigor. The stigma of HR as being low

on decision-making rigor and subsequently credibility has led many to

push for the adoption of evidence-based methods that validate HR

decisions and place it on equal footing with its other partners in busi-

ness (Lawler, 2006). Many have come to view workforce analytics as a

major means in achieving this goal. It makes use of “data, metrics, sta-

tistics and scientific methods, with the help of technology, to gauge

the impact of [human capital management] practices on business

goals” (Roberts, 2009). In using analytics, HR professionals are better

able to provide managers and executives with insights and recommen-

dations that are based on empirical evidence.

Many have expressed impatience with the often less-than-

empirical approach many HR professionals employ. Pfeffer and Sut-

ton (2006) capture the exasperation of many who have dealt with HR

professionals who have sought to employ logic and conventional wis-

dom in place of evidence-based claims: “Evidence-based management

is based on the belief that facing the hard facts about what works

and what doesn’t, understanding the dangerous half-truths that con-

stitute so much conventional wisdom about management, and reject-

ing the total nonsense that too often passes for sound advice will

help organizations perform better.” In light of such sentiments, we

would expect to find that an individual who utilizes analytical skills

and abilities would be a welcome contrast to other less empirically

grounded HR professionals, and approval for this evidence-based

approach would be reflected in the individual’s performance ratings.

Scholarly empirical investigation is needed to examine the expecta-

tion that an HR professional’s analytical skills and abilities positively

impacts individual performance.

We conceptualize an individual’s analytical ability following the

LAMP framework introduced by Boudreau and Ramstad (2007). They

introduce the LAMP framework to articulate how HR can move to

leverage rigorous principles of decision science in engaging workforce

management. LAMP stands for (a) logic—ensuring a clear causal logic

connecting measures and relevant business outcomes, (b) analytics—

engaging analysis that clearly tests relationships between measures

and outcomes, (c) measures—identifying the right data and ensuring

high-quality data, and (d) process—ensuring a process for incorporat-

ing the insights from rigorous analytics into business decision making.

While their framework focuses on how the HR function can

more fully engage HR analytics, these four components are clearly


applicable at the individual level. For the HR function to ensure

appropriate causal logic, the HR professionals involved must possess

the intellectual abilities to establish causal connections between ele-

ments in the system. For the HR function to engage appropriate ana-

lytics, HR professionals must have the abilities to perform the needed

analyses. For the HR function to ensure appropriate measures, HR

professionals must be able to individually identify appropriate data

and information. And for the HR function to ensure a process for

incorporating insights from analysis into decision making, HR profes-

sionals must have the ability to translate results into understandable

and actionable insights for managers.

Additionally, the LAMP framework maps closely to the analytical

capabilities of HR managers conceptualized by Becker, Huselid, and

Ulrich (2001). They conceptualized these individual level capabilities

as comprising (a) critical causal thinking—very similar to the logic

component articulated above, (b) understanding the principles of

good measurement (psychometrics and econometrics)—very similar to

the measurement component articulated above, (c) estimating causal

relationships—very similar to the analytics component articulated

above, and (d) communicating HR strategic performance results to

senior line managers—very similar to the process component articu-

lated above.

We thus define an individual’s analytical ability as that individual’s

ability to (a) develop causal “logic” connecting critical components of

the organizational system, (b) leverage appropriate “analytics” to test

causal relationships in the data, (c) ensure appropriate “measures” for

the components of the system, and (d) ensure a “process” for incor-

porating insights into organizational decision making.

Accordingly, individuals with strong analytical ability must both

get the right data and information (Dulebohn & Johnson, 2013; Robb,

2003; Roberts, 2007) as well as appropriately analyze and interpret

those data when generating insight (Levenson, 2011; Schramm,

2006). Not all data are useful data, and skilled data analysts are able

to identify which data will be most useful and discard data that may

not be useful or relevant for the important business issues at hand

(Robb, 2003; Roberts, 2009). Simply gathering the right data, how-

ever, is woefully insufficient, as is interpreting the wrong data. HR

professionals must also be able to translate the right data and/or

information into valuable insight (Robb, 2003; Schramm, 2006;

Ulrich & Dulebohn, 2015). This process may require a combination of

both qualitative and quantitative analysis of relevant data and infor-

mation (Levenson, 2005; Schramm, 2006).


While there seems to be some general intuition that HR professionals

with greater analytical ability will also be higher performing HR pro-

fessionals, it is useful to articulate why analytical ability may enhance

their performance. There are at least four reasons: (a) HR profes-

sionals with higher analytical abilities likely use their analytical skills

to make better business decisions; (b) leveraging insights from data

may make HR professionals more influential in creating momentum

toward needed changes; (c) abilities to use and interpret data and

information allow HR professionals to discover new insights that

other HR professionals may not see and (d) analytical ability allows

HR professionals to better communicate and coordinate with other

numbers-driven functions such as research and development (R&D),

sales, finance, and so forth. We discuss each of these in turn.

First, analytical abilities likely enhance decision making

(Boudreau & Ramstad, 2007; Roberts, 2007, 2009; Schramm, 2006).

A strong theme across domains and functions in recent years has

been the value of evidence-based decision making (Boudreau & Ram-

stad, 2007; Pfeffer & Sutton, 2006; Ulrich, Younger, Brockbank, &

Ulrich, 2013). We see across many disciplines the common theme

that individuals who are better able to analyze and interpret data and

information tend to make more informed and rigorous decisions

(Boyd & Crawford, 2012; Lawler, Levenson, & Boudreau, 2004;

McEntire, Dailey, Osburn, & Mumford, 2006). HR professionals are

tasked with many different decisions regarding the HR systems over

which they have responsibility. These decisions may focus on com-

pensation systems, hiring and selection systems, organizational

design, and so forth (Schramm, 2006). Each of these decisions may

have important implications for how the firm functions by linking HR

practices to business strategies (Brockbank, 1999). When HR profes-

sionals use insight from carefully analyzed data and information to

make these decisions, it is likely that the outcomes will be higher

quality (Mondore et al., 2011). In addition, when leveraging evidence

in decision making rather than opinions or anecdotal accounts, the

decision-making process may occur and resolve more quickly. In

other words, analytical ability may also facilitate faster decision


Second, and closely related to the points above, HR professionals

with greater analytical ability may have greater influence when a

change is needed within the organization. One of the classic roles of

HR is change management (Ulrich, Brockbank, Yeung, & Lake, 1995),

and one of the critical components to driving organizational change is

effectively building a business case for change (Ulrich et al., 2013;

Zaccaro & Banks, 2004). HR professionals who are better able to use

and leverage data and information may be able to build evidence-

based business cases for change that allow them to quickly and effec-

tively communicate the need for change within the organization.

Thus, we may expect these HR professionals to be more effective at

initiating important change initiatives.

Third, HR professionals who can generate insights from data may

be better able to see what other HR professionals do not see. Many

have argued that the real power of workforce analytics comes not in

its descriptive powers but in its predictive potential (Ulrich & Dule-

bohn, 2015). Through predictive analytics, capable HR analysts may

be able to identify concerning trends or issues on the horizon that

others may not see or recognize. This ability to better read the “tea

leaves” through superior data analysis abilities may make them partic-

ularly valuable at helping the organization identify and avert issues in


Fourth, HR professionals with higher analytical ability speak the

language of numbers and data. This means that these HR profes-

sionals are better able to communicate with and understand the busi-

ness realities faced by the more numbers-oriented, and often

strategic decision-making, functions in the business such as R&D,


accounting, finance, operations, and so forth. Rather than being seen

as touchy-feely like traditional HR (Burke, 2004), these HR profes-

sionals are more likely to build strong working relationships with their

numbers-oriented colleagues in these other functions. When these

HR people better understand the business realities that these func-

tions face, they are better able to integrate their HR work with the

business and better able to customize HR based on business needs.

When HR professionals make better decisions, appropriately ini-

tiate change, identify important upcoming trends, and integrate well

with the other business functions, they likely create more value for

the business. They are also likely to be seen as valuable business

partners not only within HR but outside of HR as well. Accordingly,

we expect that HR professionals with higher analytical ability will

receive higher job performance ratings.

Hypothesis 1: A positive relationship will exist between

HR professionals’ analytical ability and ratings of individ-

ual performance.


While we believe that, in general, HR professionals with higher ana-

lytical ability will exhibit higher individual performance, we also

wanted to explore how context might shape this relationship. Specifi-

cally, we examine industry-, company-, and job-level factors that may

moderate the relationship between an individual’s analytical ability

and performance.

We previously conceptualized an individual’s analytical ability as

that individual’s ability to engage in appropriate LAMP. This conceptu-

alization implicitly assumes that the HR professional has access to data

and empirical tools to engage in various forms of HR analytics. If there

are no HR data, then it is likely impossible to engage in any analytics.

If there is no software or analytical tools, then it is very difficult for an

HR professional to generate any meaningful insights from whatever

HR data may be available in the firm. Also, as Boudreau and Ramstad

(2007) emphasize, much of the success of HR analytics may actually

rest in the external receptiveness to HR analytics—that is, the extent

to which the broader organization expects and is open to insights from

HR analytics. Thus, in addition to having sufficient data and analytical

tools, it may be critically important to have an organizational environ-

ment that both expects and supports gaining insights from HR analyt-

ics. These insights suggest that contexts that are more likely to have

sufficient HR data, analytical support tools, and appropriate embedded

processes to expect and then incorporate insights from HR analytics

may benefit more from an individual HR professional’s analytical abil-

ities. In other words, the relationship between an individual HR pro-

fessional’s analytical ability and individual performance may be more

positive in these contexts than in contexts that lack HR data, analytical

tools, and processes that expect and support such analytics.

As we describe in detail below, we generally expect that (a) high-

tech industries, (b) companies that engage more heavily in HR

analytics, (c) lower level HR jobs, and (d) generalist HR jobs are more

likely to provide contexts that support data availability, tool availabil-

ity, and a context of expectations for HR analytics.

4.1 | Stronger positive relationship in high-techindustries

High-tech industries are generally those that are advancing both their

use of and engagement with the newest available technologies.

Heckler (2005) reports on work by the Bureau of Labor Statistics to

define high-tech industries as those that include the following four

factors: (a) they employ high proportions of scientists, engineers, and

technicians; (b) they employ high proportions of employees in the

research and development function; (c) they produce products that

rely on advanced technologies; and (d) they leverage advanced tech-

nologies in their production methods. In other words, these are

industries that are leveraging advanced technologies and people who

are uniquely positioned to use them. High-tech industries in recent

years have become increasingly focused on “big data”—a buzz term

used to describe the vast quantities of readily available information

available to firms and individuals in the digital marketplace

(McAfee & Brynjolfsson, 2012). Organizations have been leveraging

their volumes of data to better predict what movies you might like to

watch (e.g., Netflix), what products you might like to buy

(e.g., Amazon), or what music you may want to stream (e.g., Pandora).

Access to large volumes of data and advancements in algorithms

seem to be allowing companies to mass customize their products and

services to individual customers (Fogliatto, da Silveira, & Boren-

stein, 2012).

Given the trend toward big data analytics in these high-tech

industries, it seems likely that organizations within these industries

provide contexts that may enhance the value of an HR professional’s

analytical abilities. Many organizations in these high-tech industries

are applying data analytics to their consumer data to uncover prefer-

ences and patterns and to predict consumer buying behavior. These

predictions allow organizations to better tailor their products and ser-

vices and better position their products and services at times when

the consumer is likely to buy. In many cases, the core customer value

proposition rests upon the organization’s ability to leverage analytics

to make the buying experience better for the individual consumer.

In order to generate such insights, these organizations must

engage in gathering large volumes of data and then analyzing those

data. It thus seems likely that these organizations will have embedded

analytical tools and systems and processes for understanding and

interpreting analytics. In addition to being more likely to have analyti-

cal tools in place, these organizations may also be more likely to

gather and quantify HR metrics in the first place. Their emphasis on

data management and analytics may lead them to err on the side of

collecting and ensuring access to relevant HR information.

Given the centrality of these analyses to the core value proposi-

tion of many of these high-tech organizations, it is likely that man-

agers understand the intricacies of data analytics. They may even

have biases toward insights that come from big data and rigorous

analytics rather than anecdotal experiences or qualitative impres-

sions. Accordingly, managers in these high-tech industries may have a


higher level of expectation for analytical rigor from HR professionals.

They may expect HR professionals to be able to develop meaningful

HR-related insights from rigorous quantitative analysis of HR and

related information. Additionally, they are also more likely to under-

stand and appreciate analytical insights that come from HR profes-

sionals who can speak the language of analytics and put HR issues

into these analytical terms.

In contrast, organizations in relatively low-tech industries may be

less likely to have HR data in readily accessible formats, may be less

likely to have appropriate analytical tools, and may be less likely to

have managers who can appreciate and understand the insights

gained from HR analytics. We thus expect that the relationship

between an individual HR professional’s analytical abilities and indi-

vidual performance will be stronger in these high-tech industries than

in relatively low tech industries. Formally:

Hypothesis 2: The positive relationship between analyt-

ical ability and individual performance will be stronger

for HR professionals in high-tech than in low-tech


4.2 | Stronger positive relationship in companiesthat engage in higher levels of HR analytics

We also anticipate that companies that engage in higher levels of HR

analytics more generally will exhibit a stronger positive relationship

between analytics and performance based on similar logic to our

arguments for the prior hypothesis. Even companies in relatively low

tech industries may choose to engage in HR analytics. They may

identify opportunities to enhance the efficiency or effectiveness of

their workforce through applying the tools and insights of HR analyt-

ics (Boudreau & Ramstad, 2007). Companies that choose to engage

the tools and methodologies of HR analytics are more likely to ensure

that appropriate HR data is collected, effective analytical tools are

available, and that managers and key decision makers understand the

value of the insights gained from HR analytics. Thus, the more a com-

pany engages in HR analytics, the more likely that company has cre-

ated an internal context where the analytical abilities of the HR

professional contribute meaningfully to the performance of that pro-

fessional. Thus, we generally expect that as a company’s engagement

in HR analytics increases, the strength of the positive relationship

between an HR professional’s analytical abilities and that individuals’

performance increases. Formally:

Hypothesis 3: The positive relationship between analyt-

ical ability and individual performance will be stronger as

the extent to which a company engages in HR analytics


4.3 | Stronger positive relationship for lower joblevels

In addition to industry and company context, it is also likely that an

individual’s specific job context affects the extent to which analytical

ability impacts individual performance. Best practices for creating

organizational competency models suggests explicitly accounting for

how competencies may differ by job level because the expectations

may change as individuals progress through the organizational ranks

(e.g., Campion et al., 2011; Rodriguez, Patel, Bright, Gregory, & Gow-

ing, 2002). In particular, as people move from entry-level jobs into

management-level positions (i.e., as they move to higher job levels),

they are likely to shift from task executors to task overseers. Their

specific individual implementation skills become less important than

their overall task management skills.

In the context of HR analytics, it is likely that much of the heavy

lifting of HR analytics falls upon the shoulders of lower level employ-

ees who are tasked with projects to investigate certain HR trends or

concerns within the organization. We can imagine, for example, an

organizational scenario where the management team notices an

increased attrition rate in the leadership pipeline. Concerned with this

trend, the management team may ask the HR department to investi-

gate the trend and, accordingly, ask the highest ranking HR profes-

sional in the organization to look into the problem. The top HR

professional is, in many cases, unlikely to actually dig into the data

analytics personally, given the many diverse job demands of a man-

ager but, instead, may delegate certain aspects of the task to lower

level HR professionals. One person may be asked to explore the

training data, another may be asked to explore compensation and

benefits, and so on. Alternatively, one person may be tasked with the

entire project and asked to focus primarily on this project for a speci-

fied time period. In either case, the higher level HR manager is

responsible for ensuring the analytics work gets done but relies on

lower level HR professionals to actually perform the time-consuming

and detailed analysis.

Accordingly, the analytical ability of the higher level HR profes-

sional may simply be less critical for individual performance. In con-

trast, the analytical abilities of these lower level HR professionals

who are often tasked with doing the detailed analyses may be far

more important. Thus:

Hypothesis 4: The positive relationship between analyt-

ical ability and individual performance will be weaker as

the job level of the HR professional increases.

4.4 | Stronger positive relationship for HRgeneralists

Just as job level may affect the relationship between analytical ability

and performance because lower level employees are more likely to

be tasked with projects and assignments that require analytics, so

might an individual’s job type moderate this relationship. Significant

organizational research has explored the fit between individuals and

vocations (Holland, 1973), jobs (e.g., Edwards, 1991; Kristof, 1996),

organizations (e.g., Chatman, 1989), groups (e.g., Judge & Ferris,

1993; Kristof, 1996), supervisors (Adkins, Russell, & Werbel, 1994;

Vianen, 2000), and so forth. One of the key insights from the

research on person–job fit is that higher fit between individual abil-

ities and job demands positively impacts individual performance


(Kristof-Brown, Zimmerman, & Johnson, 2005). Not surprisingly, peo-

ple who are better equipped for the job demands tend to perform at

a higher level. It thus seems reasonable that some HR job types will

have a greater need for analytical abilities than others, and, therefore,

these job types may have a stronger positive relationship between

analytical abilities and performance.

While there are many different HR job types, we draw upon the

distinction between HR generalists and HR specialists. HR generalists

are HR professionals who serve as business partners to business lead-

ers (Lawler, 2005). They bring HR expertise to business challenges

and help business managers and leaders navigate the critical HR

issues that face the organization as a whole. As a consequence, these

HR generalists tend to face somewhat ambiguous situations that may

require careful analysis to generate useful insights. They are not sol-

ving specific functional HR problems but, instead, are engaging their

HR toolkits to help solve specific business challenges. In contrast, HR

specialists are HR professionals whose work largely emphasizes a

specific HR function such as compensation, benefits, hiring, training,

and so forth. Rather than partnering with business managers and

leaders to address specific business challenges, these specialists tend

to focus on the HR systems and policies that undergird the effective

functioning of the HR department. Their specific expertise in these

functional areas allows them to implement specialist tasks such as

reviewing resumes for an open job position or developing a new pay


Since generalists tend to partner with the business to solve busi-

ness challenges with HR insights, we expect that analytical skills may

be more valuable to these HR professionals. Rather than engaging in

specific functional tasks, these HR professionals may have to discover

root causes of business challenges and leverage data, information,

and analytics to identify potential solutions. Additionally, managers

and business leaders may demand empirical support and evidence

from HR generalists who bring them insights and potential solutions.

Accordingly, we anticipate that the relationship between analytical

ability and performance will be stronger for HR generalists than HR

specialists. Thus:

Hypothesis 5: The positive relationship between analyt-

ical ability and individual performance will be stronger

for HR generalists than for HR specialists.


The goal of our study is to empirically explore the relationship

between an HR professional’s analytical skills and abilities and that

HR professional’s individual performance in a sample that provides a

high level of cross-context generalizability. Ideal data, accordingly,

would come from a global sample of HR professionals with access to

detailed data on an individual’s analytical abilities, individual perfor-

mance, individual demographics, and organizational context factors.

We find such a sample from the Human Resource Competency Study

(HRCS), a practitioner-oriented study examining the competencies of

HR professionals.

The HRCS is based on 360 survey methodology. The study

recruits practicing HR professionals to participate in a 360 survey to

measure their individual competencies in HR-specific domains. The

study has been conducted every four to five years since 1987 (Ulrich

et al., 2007, 2010; Ulrich, Brockbank, Ulrich, & Kryscynski, 2015;

Ulrich et al., 2008; Ulrich, Younger, et al., 2012), with the most recent

data collected between March and September 2015. This is one of

the longest running and most comprehensive studies of HR profes-

sionals and their competencies that we can find in the literature.

Approximately 18 months prior to each survey round, the study

directors recruit HR professional organizations worldwide to partner

in data collection—these are the regional partner organizations. These

regional partner organizations participate in at least three ways:

(a) they conduct focus groups and interviews with business leaders

and HR professionals to discover the new and emerging dimensions

of HR competencies that should be considered in any new HR com-

petency model; (b) they recruit HR professionals from their member-

ship and distribution lists to build a global sample of HR

professionals; and (c) they help to write and publish articles and

books that share the study’s findings.

This approach to data collection has several important implica-

tions for academic research. First, it is very difficult to track response

rates. The regional partner organizations use a combination of direct

invitations to individuals and organizations as well as broad advertis-

ing strategies at conferences and/or in local publications to recruit

HR professionals. This means that there is no accurate way of track-

ing how many individuals received invitations to participate in the

study. Second, since we are not able to track nonresponders, we have

no way of systematically comparing responders to nonresponders to

evaluate potential response bias in the data. The 360 methodology

makes the study extremely time intensive for both the HR profes-

sionals and their raters. It is possible that there is a selection bias

embedded in the process where only successful HR professionals

who are highly motivated by their own development goals and

opportunities will participate in the study.

Despite these limitations, the HRCS data is the largest single

database of global HR professionals and associate raters available.

The data collected by January 1, 2016, contains 4,324 HR profes-

sionals and 32,733 raters who evaluated those HR professionals

worldwide. Approximately 30% of the sample is from North America

and 25% from Asia, with the remaining divided among the other

major regions of the world. These data thus provide a unique and

proprietary large global sample of HR professionals to use in explor-

ing the relationship between an HR professional’s analytical abilities

and that HR professional’s performance.

5.1 | Sample

While the full 2015 HRCS data sample contains 4,324 HR profes-

sionals, we do not have full data for all of those professionals. Some

of those HR professionals only completed self-ratings, some received

ratings from others but did not complete their self-ratings, and so

forth. To be sure that the sample used for our study had representa-

tive ratings of the HR professional from multiple raters, we selected

only HR professionals who had (a) completed self-ratings, (b) a rating


from their direct supervisors, (c) at least two ratings from HR collea-

gues, and (d) at least two ratings from colleagues outside of HR

(e.g., marketing, line management, R&D, etc.). Applying this filter

reduced our usable sample to 1,465 HR professionals. Robustness

checks testing different cutoff levels are consistent with the main


A subset (approximately 30%) of the raters in the data also com-

pleted an organization survey where they answered questions about

the HR department and the company. In order to control for the

organizational context in our analysis, we incorporated these

organization-level surveys into the data as well. In order to ensure a

representative score for the organization level variables we filtered

out organizations with fewer than three organization surveys. After

merging in these data, the sample of HR professionals with qualified

organization-level data reduced to 1,117 HR professionals housed

within 449 unique organizations. Thus, our final sample for empirical

analysis is 1,117 HR professionals. Robustness checks with different

cutoff levels for organization surveys are consistent with the main


5.2 | Measurement approach

The HRCS survey consists of 123 competency items that were

designed to measure what HR professionals need to know, do,

and/or be in order to be effective in their jobs. In other words, the

competencies measure the knowledge, skills, abilities, and other char-

acteristics (KSAOs) of HR professionals. Respondents rate HR profes-

sionals on each of these 123 items using a 5-point Likert-type scale

(strongly disagree to strongly agree). The items from round 1 of the

HRCS survey in 1987 were developed through a thorough literature

review of the HR profession as well as detailed interviews and focus

groups with HR professionals and business leaders. In subsequent

rounds of the HRCS survey the core project team started with the

items from the prior round and engaged simultaneously in literature

review, interviews, and focus groups to refine and update the items.

Through the rounds of research approximately 50% to 60% of the

items from the prior round are retained and the remaining 40% to

50% of the items are new and/or modified based on the literature

reviews, interviews, and focus groups. The practical intent is that

each round of the survey captures the most current thinking in the

HR profession regarding the competencies and skills of HR profes-

sionals that are critical for job performance.

The 123-item survey administered in 2015 followed this same

general procedure. Over 20 regional partner organizations engaged in

focus groups and interviews to identify what HR professionals will

need to know, be, and do in the next several years to drive perfor-

mance. Simultaneously, the project directors conducted literature

reviews of the HR profession to identify themes and trends that were

underrepresented in prior study rounds. At the conclusion of the lit-

erature review and interviews, the HRCS project directors met with

the regional partners for a full-day workshop to identify common

themes and develop new items for the survey. The collaboration of

regional partners and the project directors then iterated electronically

to cut old questions, modify old questions, and add new questions to

the survey. Additionally, survey items were evaluated by other

academic professionals with expertise in strategic human resource

management (SHRM).

Our study focuses on the impact of an HR professional’s analyti-

cal ability on that professional’s performance. Accordingly, two mem-

bers of the co-author team carefully and systematically reviewed

each of the 123 items from the HRCS survey to identify items that

seemed to map conceptually to our theoretical construct of analytical

ability. These items provided a starting point for constructing our

measures of analytical ability.

We were concerned, however, that highly competent HR profes-

sionals may have both higher analytical ability and higher perfor-

mance, thus making it difficult to tease out the effect of analytical

ability rather than an individual’s general ability. Prior rounds of the

HRCS study suggest that an HR professional’s general business skills

and personal credibility both positively correlated with that individ-

ual’s performance (Ulrich et al., 2007, 2013; Ulrich, Brockbank,

Younger, & Ulrich, 2012), both providing rough proxies for an individ-

ual’s overall competence. We thus also reviewed the 123-item survey

to identify items that mapped conceptually onto an individual’s gen-

eral business skills and an individual’s personal credibility. This proc-

ess revealed 42 items that seemed to conceptually map to either

analytical ability, general business skills, or personal credibility.

We then followed the procedures of Fabrigar and Wegener

(2014) by performing an exploratory factor analysis (EFA) using a ran-

dom sample of 40% of the data followed by a confirmatory factor

analysis (CFA) using the other 60% of the data. We first aggregated

all data to the level of the HR participant so that the item-level scores

for each HR participant represented the average score in the eyes of

all non–self-raters, who evaluated the HR professional. We then per-

formed parallel analysis and found that the data suggest seven fac-

tors. We then conducted a seven-factor EFA using promax rotation

to allow for covariance between the latent factors. The results sug-

gested three clean factors representing analytical ability, general busi-

ness skills, and personal credibility along with four additional factors

that were eliminated due to weak loading, cross loading, or lack of

clear relevance for the present research (see appendix for EFA results

and comments on why items were included or excluded).

A survey with such a large number of items with only a 5-point

Likert-type scale may lead to highly correlated factors or may make it

difficult to cleanly identify factors (Costello & Osborne, 2005; Matsu-

naga, 2010). In order to maximize the discriminant validity of our fac-

tors and reduce the correlation between latent factors, we chose an

aggressive factor cutoff score of 0.80 with a cross loading maximum

of 0.40. Thus, any item with a factor loading below 0.80 or with a

cross loading of 0.40 or above was eliminated from scale construction

(Costello & Osborne, 2005). This stringent cutoff requirement left

13 items that loaded on the three latent factors. Robustness checks

with different cutoff levels were consistent with the main results.1

We then performed a CFA with the remaining 13 items and

leveraging the other 60% of the data and supported the three-factor

model suggested by the EFA above. The analysis suggests a strong

model fit (goodness of fit index [GFI] = 0.936, adjusted goodness of

fit index [AGFI] = 0.906, root mean square error of approximation

[RMSEA] = 0.074, and standardized root mean square residual

[SRMR] = 0.041; Hu & Bentler, 2009).


The factor analyses were performed using all available ratings

aggregated to the level of the HR professional, but for the regression

analysis we were concerned with common source or common

method bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). We

thus leveraged our data from multiple raters to construct different

measures from different rater types. Our main analysis uses perfor-

mance ratings from supervisors, analytical ability ratings from HR

raters, and general business skill and personal credibility ratings from

non-HR raters. In robustness checks, we also test other potential

combinations of data aggregation methods and find general support

for the main results.

5.2.1 | Dependent variable: Individual performance

The survey contained eight items that were direct measures of indi-

vidual performance. One stand-alone item asked raters: “Overall,

compared with the other human resource professionals whom you

have known, how does [HR Professional Name] compare?” Raters

had six options on a Likert-type scale: (1) Well below average (bottom

10% of all HR professionals), (2) Below average (bottom 25% of all

HR professionals), (3) Average (top 50% of all HR professionals),

(4) Above average (top 25% of all HR professionals), (5) Well above

average (top 10% of all HR professionals), and (6) Exceptional (top

2% of all HR professionals).

The other seven individual performance items also ask the raters

to compare the HR professional to other HR professionals, but asks

the rater to judge the extent to which the HR professional creates

value for different stakeholders of the business. The question states:

“Overall, compared with the other human resource professionals

whom you have known, how does [HR Professional Name] compare

in creating value for [Insert Stakeholder].” This question used the

same rating scale mentioned above, but raters were asked to answer

the question for seven different stakeholders: (a) external customers,

(b) investors or owners, (c) communities where you operate, (d) line

managers in your organization, (e) your employees, (f ) other partners

in the value chain (e.g., suppliers, distributors, service providers), and

(g) government regulators.

We performed an exploratory factor analysis using both the

seven items that rated value created for stakeholders, as well as the

overall performance item described above. The results suggest two

clean factors that generally capture the value created for internal

stakeholders and the value created for external stakeholders. Value

created for external stakeholders includes the items that focus on

external customers, investors, communities, other partners in the

value chain, and government regulators. Value created for internal

stakeholders includes the items that focus on employees and line

managers as well as the overall performance measure. At first blush

the loading of the overall performance item with the two value crea-

tions for line managers and employees may seem unusual, but when

we consider the 360 survey context, it may make sense. The overall

performance rating comes from internal stakeholders—that is, line

managers and employees. Thus, the overall performance rating should

conceptually align with the perceived value that the HR professional

creates for line managers and employees.

In summary, then, we have two measures of individual perfor-

mance: the value created for internal stakeholders and the value cre-

ated for external stakeholders. As mentioned above, these measures

are constructed for the main analysis using only data from direct

supervisor ratings. Thus, our performance measures can be best sum-

marized as the perceived value the HR professional creates for inter-

nal or external stakeholders from the perspective of the direct


5.2.2 | Independent variable: Analytical ability

As described previously, analytical ability represents an individual’s

ability to engage in appropriate logic, analytics, measures, and pro-

cesses for HR analytics. The results of the factor analysis suggested

keeping six items for the analytical ability scale. Since the survey was

not originally designed with the LAMP model as a guiding framework,

the individual items do not directly arise from these components.

There is, however, strong conceptual overlap between the LAMP

components and the items in the six-item scale (see Table 1). The full

list of items is shown in Table 1, but example items include: “Trans-

lates data into useful insights for [Organization Name],” “Identifies

[Organization Name]’s problems that can be solved with data,” and

“Effectively uses HR analytics to create value for [Organization

Name].” In addition to scale support from the EFA, this six-item scale

has a Cronbach’s alpha of .94, suggesting strong internal reliability. As

mentioned above, the main results are based on the ratings from HR

raters. Thus, our measure of analytical ability can be best summarized

as the perceived analytical ability of the HR professional from the

perspective of his/her HR colleagues.

5.2.3 | Other individual variables

We need to control for variables that may explain both an individual’s

perceived performance as well as an individual’s perceived analytical

ability. The biggest overarching concern is that an individual’s overall

general competency could explain both, such that any observed

empirical relationship between analytical ability and performance

TABLE 1 Mapping survey measures to conceptual components of LAMP

Logic Analytics Measures Process

Translates data into useful insights for [Organization Name] M H L M

Identifies [Organization Name]’s problems that can be solved with data H L M L

Uses data to influence decision making in [Organization Name] M L L H

Effectively uses HR analytics to create value for [Organization Name] M H M M

Identifies important questions about the organization that can be answered with data H M H L

Accurately interprets statistics L H L L

Notes: H = high, M = medium, L = low mapping between item and LAMP component.


could be driven by the unmeasured general ability of the individual.

We thus seek to proxy for the individual’s general ability through

multiple individual-level control variables, as described below.

We measure an individual’s general business skills using three

items drawn from the competency portion of the HRCS survey.

These three items were selected from a stringent 0.80 EFA cutoff

and have a Cronbach’s alpha score of .84, suggesting high internal

scale reliability. These items include “Understands investor

expectations,” “Knows how investors value [Organization Name],”

and “Helps investors recognize the quality of leadership within

[Organization Name].” Close examination suggests that all three items

focus on the HR professional’s knowledge of investors, which sug-

gests a very narrow type of business acumen. If we use a more gen-

erous EFA cutoff of 0.60, however, more general business skills come

back into the measure (see appendix).

We measure an individual’s personal credibility using four items

from the competency portion of the HRCS survey. These four items

were selected using a stringent 0.80 EFA cutoff and have a Cron-

bach’s alpha score of .85, suggesting high internal scale reliability.

These items are shown in the appendix, but examples are: “Shows a

genuine interest in others” and “Acts with appropriate balance of

confidence and humility.”

In addition to the competency section of the survey, HR profes-

sionals also completed a personal demographics survey that provides

several additional objective proxies for that individual’s general com-

petence. Total years of work experience is a simple measure of the

total years the person has been in the workforce and organizational

experience (tenure) is a measure of the total years the person has

been employed at the current organization. These two experience

variables are complemented by a third experience variable, HR experi-

ence, that focuses on the total years the person has been employed

in an HR capacity. Highest education level is a categorical variable that

captures the highest education level achieved by the HR professional.

Primary role is a categorical variable that indicates the current job role

of the HR professional in the organization. HR generalists and HR

business partners from the primary role are designated as HR general-

ists for the interaction hypotheses; all other primary roles are consid-

ered HR specialist roles. Example categories include benefits,

compensation, HR strategy development, labor relations, and so

on. Job level is a categorical variable that indicates the individual’s

current job level in the organization. Job level options are entry level,

nonsupervisory employee, supervisor, management, executive, top

executive, other.

5.2.4 | Organization-level variables

In addition to measuring individual variables that may proxy for the

individual’s general competence and ability, we also worried that

the organizational context may affect the relationship between an

individual’s analytical ability and individual performance. In order to

account for this context, we also captured the extent to which

each organization’s HR department engages in HR Analytics. Exam-

ple measures include “Measures the impact of HR actions on busi-

ness outcomes” and “Uses HR analytics to improve decision


5.2.5 | Industry-level variables

In addition to the variables mentioned above for the main analysis,

we also tested for industry-level effects. Using the North American

Industry Classification System (NAICS) three-digit industry codes and

Heckler’s (2005) work on high-tech industries, we generated a

dummy code that indicates whether the HR practitioner’s industry is

considered a high-tech industry (1 = high-tech, 0 = not high-tech).


Means, standard deviations, and correlations are reported in Table 2.

Prior to performing OLS regression analysis, we tested for rater

agreement in order to determine whether justification existed to

aggregate the ratings provided by multiple raters. Given the multi-

item, multirater nature of the data, rater agreement was assessed

with an rwg(j) statistic. Analyses revealed sufficient agreement among

ratings of analytical abilities [rwg(j) = .97, p < .05)], personal credibility

[rwg(j) = .90, p < .05], and general business skill [rwg(j) = .87, p < .05]

to justify aggregation. Data for these competencies were aggregated

for all subsequent analyses.

6.1 | Testing Hypothesis 1

Hypothesis 1 suggests a positive relationship between an individual’s

analytical ability and performance. Our empirical approach to testing

this hypothesis is laid out in Models 1 through 5 in Table 3 for inter-

nal stakeholder performance and Models 1 through 5 in Table 4 for

external stakeholder performance. In both tables, Model 1 includes

the intercept only, Model 2 adds objective individual-level controls

such as educational background, Model 3 adds perceived general

business skills and personal credibility. As we might expect, we see

that general business skill positively and significantly relates to both

performance measures (internal β = 0.19, p < .01; external β = .25,

p < .01). Personal credibility, however, is not significant for either

dependent variable. Model 4 adds analytical skill so that we can see

the effect of analytical skill without contextual controls in order to

reduce concerns of over fitting the data to any specific context. Ana-

lytical ability is positive and significant for both dependent variables

(internal: β = .26, p < .01, external: β = 0.25, p < .01). Model 5 adds

contextual controls for the individual’s job context, company context,

and industry context. Analytical abilities remain positive and signifi-

cant after adding context specific controls. Thus, we see strong sup-

port for our main hypothesis that an HR professional’s analytical

abilities positively relate to that individual’s performance.

6.2 | Testing the moderation hypotheses

Hypotheses regarding whether the influence of analytical abilities on

performance ratings differs by industry, job level, department analytic

score, and job role were tested using OLS regression.

6.2.1 | Testing Hypothesis 2

Hypothesis 2 predicted that the positive relationship between analyt-

ical ability and individual performance will be stronger for HR


TABLE 2 Means, standard deviations, and correlations

Variable Mean Std. dev. 1 2 3 4 5

1. Perf (Ext) 3.76 0.76 —

2. Perf (Int) 4.28 0.86 0.78** —

3. Exp (Total) 13.23 7.67 0.13** 0.12** —

4. Exp (Org) 7.65 6.77 0.07* 0.05 0.39** —

5. Exp (HR) 16.10 7.93 0.13** 0.13** 0.81** 0.45** —

6. Ed NoDgr 0.04 0.20 –0.03 –0.03 0.07 0.11** 0.13**

7. Ed Assc 0.03 0.18 –0.03 –0.03 –0.01 0.08* 0.02

8. Ed Td/Tech 0.01 0.09 0.01 0.01 0.04 –0.02 0.07*

9. Ed Pro 0.04 0.21 0.01 –0.01 0.05 0.00 0.06

10. Ed Bach 0.46 0.50 –0.07* –0.05 –0.06 –0.02 –0.11**

11. Ed Master 0.39 0.49 0.08* 0.06 0.00 –0.06 –0.01

12. Ed Dctr 0.01 0.10 0.05 0.08* 0.02 –0.01 0.06

13. Pers Cred 4.42 0.46 0.11** 0.12** –0.06 –0.03 –0.07*

14. Gen Bus Skl 4.07 0.54 0.19** 0.14** –0.01 –0.04 –0.03

15. Analyt Skl 4.03 0.50 0.20** 0.18** –0.02 0.02 0.01

16. High-Tech 0.43 0.50 –0.02 0.01 –0.03 0.01 –0.04

17. Job Entry 0.01 0.10 –0.03 –0.03 –0.10** –0.09* –0.13**

18. Job NonSup 0.24 0.43 –0.07* –0.03 –0.26** –0.13** –0.31**

19. Job Sup 0.10 0.30 –0.02 –0.06 –0.15** –0.06 –0.13**

20. Job Mgmt 0.46 0.50 –0.01 0.01 0.17** 0.10** 0.22**

21. Job Exec 0.14 0.35 0.10** 0.08* 0.20** 0.08* 0.18**

22. Job TopExec 0.01 0.12 0.13** 0.08* 0.16** 0.01 0.14**

23. Job Other 0.03 0.17 –0.04 –0.06 –0.06 0.02 –0.02

24. Dept Analyt 3.57 0.82 –0.01 –0.04 –0.18** –0.04 –0.19**

25. Role General 0.45 0.50 –0.05 0.02 0.04 –0.08* –0.08*

26. Role Benefit 0.01 0.10 0.03 0.00 0.07 0.10** 0.07*

27. Role Comp 0.05 0.21 –0.03 –0.04 –0.06 –0.01 –0.07*

28. Role HRStrat 0.05 0.22 0.15** 0.11** 0.14** –0.03 0.15**

29. Role Lab Rel 0.03 0.17 0.05 0.03 0.10** 0.10** 0.05

30. Role OrgDev 0.03 0.17 0.03 0.01 0.01 0.02 0.01

31. Role Staffing 0.06 0.24 –0.01 –0.05 –0.14** 0.01 –0.10**

32. Role Talent 0.03 0.18 0.04 0.03 0.00 –0.04 0.02

33. Role TrnDev 0.06 0.23 0.00 –0.01 –0.12** –0.02 –0.04

Variable 6 7 8 9 10 11 12

1. Perf (Ext)

2. Perf (Int)

3. Exp (Total)

4. Exp (Org)

5. Exp (HR)

6. Ed NoDgr —

7. Ed Assc –0.05 —

8. Ed Td/Tech –0.02 –0.02 —

9. Ed Pro –0.05 –0.04 –0.02 —

10. Ed Bach –0.22** –0.18** –0.08* –0.20** —

11. Ed Master –0.19** –0.15** –0.07* –0.17** –0.74** —

12. Ed Dctr –0.02 –0.02 –0.01 –0.02 –0.09** –0.08* —

13. Pers Cred 0.04 0.02 0.00 –0.10** 0.02 0.00 0.01

14. Gen Bus Skl –0.01 –0.01 –0.01 –0.05 –0.02 0.05 0.02

15. Analyt Skl –0.07* –0.12** 0.01 –0.01 0.02 0.06 0.01

16. High-Tech 0.00 –0.02 0.02 0.02 0.01 0.00 –0.04

17. Job Entry –0.02 –0.02 –0.01 –0.02 –0.05 0.08* –0.01



TABLE 2 (Continued)

Variable Mean Std. dev. 1 2 3 4 5

18. Job NonSup 0.08* –0.02 0.01 –0.08* 0.06 –0.06 –0.01

19. Job Sup 0.00 0.10** 0.01 0.05 –0.01 –0.05 –0.03

20. Job Mgmt –0.01 –0.01 –0.03 0.05 –0.02 0.01 0.00

21. Job Exec –0.06 –0.01 0.03 0.00 –0.04 0.05 0.05

22. Job TopExec 0.01 –0.02 –0.01 –0.03 0.00 0.02 –0.01

23. Job Other –0.04 –0.03 –0.01 0.00 0.02 0.02 –0.02

24. Dept Analyt –0.05 0.00 –0.05 –0.01 0.00 0.04 0.00

25. Role General 0.04 0.00 –0.05 –0.08* 0.05 0.00 –0.06

26. Role Benefit 0.08* –0.02 –0.01 –0.02 –0.02 0.00 –0.01

27. Role Comp 0.00 –0.04 0.05 –0.01 0.01 0.01 –0.02

28. Role HRStrat –0.01 –0.01 –0.02 0.07 –0.06 0.04 0.04

29. Role Lab Rel –0.01 –0.03 0.07* –0.03 0.09* –0.07* –0.02

30. Role OrgDev –0.04 –0.03 0.06 0.03 –0.08* 0.08* 0.06

31. Role Staffing 0.00 0.01 –0.02 0.00 –0.03 0.03 0.03

32. Role Talent –0.02 0.00 0.06 –0.04 –0.01 0.03 –0.02

33. Role TrnDev –0.01 0.07* –0.02 0.08* –0.03 –0.01 –0.02

Variable 13 14 15 16 17 18 19

1. Perf (Ext)

2. Perf (Int)

3. Exp (Total)

4. Exp (Org)

5. Exp (HR)

6. Ed NoDgr

7. Ed Assc

8. Ed Td/Tech

9. Ed Pro

10. Ed Bach

11. Ed Master

12. Ed Dctr

13. Pers Cred —

14. Gen Bus Skl 0.54** —

15. Analyt Skl 0.14** 0.18** —

16. High-Tech –0.12** –0.14** –0.13** —

17. Job Entry 0.05 0.08* 0.05 0.00 —

18. Job NonSup 0.10** 0.00 0.07* 0.05 –0.06 —

19. Job Sup –0.04 0.02 –0.02 0.00 –0.03 –0.19** —

20. Job Mgmt –0.08* –0.08* –0.06 0.00 –0.09** –0.52** –0.31**

21. Job Exec –0.01 0.03 0.03 –0.04 –0.04 –0.23** –0.14**

22. Job TopExec 0.03 0.07* 0.03 –0.01 –0.01 –0.07* –0.04

23. Job Other 0.04 0.06 –0.06 –0.03 –0.02 –0.10** –0.06

24. Dept Analyt 0.11** 0.12** 0.09** 0.05 0.03 0.09** 0.05

25. Role General 0.01 –0.03 –0.08** 0.03 –0.02 0.08* –0.04

26. Role Benefit 0.08* 0.02 –0.01 –0.01 –0.01 0.05 –0.03

27. Role Comp 0.00 –0.05 0.02 0.01 0.04 –0.01 0.00

28. Role HRStrat –0.04 0.03 0.10** 0.04 –0.02 –0.11** –0.06

29. Role Lab Rel 0.04 0.08* 0.09* –0.04 –0.02 0.05 –0.01

30. Role OrgDev 0.00 0.00 0.06 –0.10** –0.02 –0.06 0.01

31. Role Staffing –0.01 0.03 0.00 0.01 0.07* 0.09** 0.00

32. Role Talent 0.02 0.02 0.00 –0.01 –0.02 –0.05 0.05

33. Role TrnDev 0.03 –0.01 –0.04 0.02 0.03 0.00 0.04



TABLE 2 (Continued)

Variable Mean Std. dev. 1 2 3 4 5

Variable 20 21 22 23 24 25 26

1. Perf (Ext)

2. Perf (Int)

3. Exp (Total)

4. Exp (Org)

5. Exp (HR)

6. Ed NoDgr

7. Ed Assc

8. Ed Td/Tech

9. Ed Pro

10. Ed Bach

11. Ed Master

12. Ed Dctr

13. Pers Cred

14. Gen Bus Skl

15. Analyt Skl

16. High-Tech

17. Job Entry

18. Job NonSup

19. Job Sup

20. Job Mgmt —

21. Job Exec –0.38** —

22. Job TopExec –0.11** –0.05 —

23. Job Other –0.16** –0.07* –0.02 —

24. Dept Analyt –0.10** –0.04 0.02 0.04 —

25. Role General 0.00 –0.05 –0.02 –0.02 0.05 —

26. Role Benefit –0.04 0.00 –0.01 0.06 0.01 –0.11** —

27. Role Comp 0.04 –0.04 –0.03 0.00 0.00 –0.26** –0.02

28. Role HRStrat 0.01 0.13** 0.19** –0.04 –0.01 –0.28** –0.02

29. Role Lab Rel –0.02 –0.02 –0.02 0.01 –0.04 –0.21** –0.02

30. Role OrgDev 0.02 0.00 0.03 0.05 0.00 –0.21** –0.02

31. Role Staffing –0.06 –0.05 –0.03 0.04 0.06 –0.31** –0.03

32. Role Talent 0.00 0.05 –0.02 –0.03 0.00 –0.23** –0.02

33. Role TrnDev –0.02 0.00 –0.03 –0.01 –0.01 –0.29** –0.02

Variable 27 28 29 30 31 32 33

1. Perf (Ext)

2. Perf (Int)

3. Exp (Total)

4. Exp (Org)

5. Exp (HR)

6. Ed NoDgr

7. Ed Assc

8. Ed Td/Tech

9. Ed Pro

10. Ed Bach

11. Ed Master

12. Ed Dctr

13. Pers Cred

14. Gen Bus Skl

15. Analyt Skl

16. High-Tech



professionals in high-tech than in low-tech industries. As indicated in

model 6 in Tables 3 and 4, the interaction between HR professional

analytical abilities and high-tech industry was nonsignificant for per-

formance directed toward both internal stakeholders (β = –.07, n.s.)

and external stakeholders (β = .11, n.s.). Thus, we find no empirical

support for the industry effects predicted by Hypothesis 2.

6.2.2 | Testing Hypothesis 3

Hypothesis 3 predicted that the positive relationship between analyt-

ical ability and individual performance will be stronger as the extent

to which a company engages in HR analytics increases. As indicated

in Model 8 in Tables 3 and 4, the interaction between HR profes-

sional analytical abilities and the extent to which the company

engages with HR analytics was nonsignificant for both dimensions of

performance (internal stakeholders β = –.07, n.s.; external stake-

holders β = –.04, n.s.). Hypothesis 3 was therefore not supported.

6.2.3 | Testing Hypothesis 4

Hypothesis 4 predicted that the positive relationship between analyt-

ical ability and individual performance will be weaker as the job level

of the HR professional increases. As indicated in Model 7 of Tables 3

and 4, the interactions between HR professional analytical abilities

and job level were nonsignificant for both dimensions of perfor-

mance. As the relationship between analytical abilities and job perfor-

mance did not significantly vary across job levels, we find no support

for Hypothesis 4.

6.2.4 | Testing Hypothesis 5

Hypothesis 5 predicted that the positive relationship between analyt-

ical ability and individual performance will be stronger for HR gener-

alists than for HR specialists. As indicated in Model 9 of Tables 3 and

4, some interactions between HR professional analytical abilities and

job role were significant. For performance directed toward internal

stakeholders, negative interactions existed between analytical ability

and compensation roles (β = –.98, p < .05) and labor relations roles (β

= –1.12, p < .05). For performance directed toward external stake-

holders, negative interactions existed between analytical ability and

compensation roles (β = –.84, p < .05) as well as analytical abilities

and labor relations roles (β = –.92, p < .05). A positive interaction

existed between analytical abilities and talent management roles (β =

.63, p < .05). These results suggests that the importance of analytic

ability does vary across roles, although the relationship between ana-

lytic ability and performance is not consistently stronger for HR gen-

eralists. Indeed, in the case of those in talent management roles, the

relationship between analytical ability and job performance toward

external stakeholders was stronger than it was for those in HR gener-

alist roles. As the relationship between analytic ability and job perfor-

mance was weaker for multiple, but not all HR specialist roles, we

find partial support for Hypothesis 5.

We also show a fully specified model using all controls and all

interactions in Model 10 of Tables 3 and 4. Results of the fully speci-

fied model are consistent with all prior models with one exception—

the interaction between analytical abilities and the HR strategy role.

For HR strategist the relationship between analytical abilities and

internally directed performance is less positive than for other HR

generalists (β = –.72, p < .05).

6.3 | Robustness checks

Our research question focuses on understanding the main effect rela-

tionship between an individual’s analytical abilities and performance.

We thus control for many other factors at the individual, job, organi-

zation, and industry levels that may affect this relationship. But many

TABLE 2 (Continued)

Variable Mean Std. dev. 1 2 3 4 5

17. Job Entry

18. Job NonSup

19. Job Sup

20. Job Mgmt

21. Job Exec

22. Job TopExec

23. Job Other

24. Dept Analyt

25. Role General

26. Role Benefit

27. Role Comp —

28. Role HRStrat –0.05 —

29. Role Lab Rel –0.04 –0.04 —

30. Role OrgDev –0.04 –0.04 –0.03 —

31. Role Staffing –0.06 –0.06 –0.05 –0.05 —

32. Role Talent –0.04 –0.04 –0.03 –0.03 –0.05 —

33. Role TrnDev –0.05 –0.06 –0.04 –0.04 –0.06 –0.05 —

**p < .01, *p < .05. Note: n = 1,117. Variables 1 and 2 indicate performance toward external/internal stakeholders. Variables 3–5 indicate work experi-ence (in years). Variables 6–12 indicate highest education obtained. Variables 13–15 indicate three focal HR competencies. Variable 16 indicates whetherthe industry is considered high tech. Variables 17–23 indicate job level. Variable 24 indicates ratings of department-level analytic performance. Variables25–33 indicate current HR role.


TABLE 3 OLS regression results for HR practitioner performance toward internal stakeholders

Model 1 Model 2 Model 3 Model 4 Model 5

Variable β SE β SE β SE β SE β SE

Intercept 4.28** 0.03 4.19** 0.16 3.24** 0.41 2.35** 0.48 2.43** 0.50

Ed/Exp Controls … … … … … … … …

Pers Cred 0.02 0.09 0.01 0.09 0.03 0.09

Gen Bus Skl 0.19** 0.07 0.16* 0.16 0.16* 0.07

Analyt Skl 0.26** 0.08 0.25** 0.08

HighTech 0.08 0.07

JobEntry 0.62* 0.28

JobSup –0.05 0.11

JobMgmt 0.05 0.09

JobExec 0.07 0.12

JobTopExec 0.82* 0.32

JobOther –0.43* 0.21

Dept Analyt –0.08 0.04

RoleBenefit 0.49 0.60

RoleComp –0.14 0.17

RoleHRStrat 0.20 0.15

RoleLabRel –0.02 0.25

RoleOrgDev –0.04 0.20

RoleStaffing –0.11 0.15

RoleTalent 0.20 0.21

RoleTrnDev 0.07 0.15





Model 1 Model 2 Model 3 Model 4 Model 5

Variable β SE β SE β SE β SE β SE




AS*Dept Analyt









Residual SE 0.86 0.85 0.86 0.85 0.85

df 1111 687 648 613 592

n 1117 697 660 626 621

Mult r2 0.03 0.05 0.07 0.11

Adj r2 0.02 0.03 0.05 0.06

F-Stat 2.46 2.99 3.83 2.87

p-val < .01 < .001 < .001 < .001

Model 6 Model 7 Model 8 Model 9 Model 10

Variable β SE β SE β SE β SE β SE

Intercept 2.29** 0.59 2.18** 0.75 1.46 1.30 2.14** 0.53 0.97 1.49

Ed/Exp Controls … … … … … … … … … …



of our individual-level variables such as education and experience

may actually affect performance through the individual’s abilities. For

example, education may affect performance by increasing an indivi-

dual’s general business acumen. Accordingly, it is possible that a

superior way to model our data involves a path model that allows us

to specify the pathways through which each variable may affect per-

formance directly and/or indirectly.

To test this, we leveraged structural equation modeling using

lavaan package (Rosseel, 2012) in the R software package. We first

assessed the fit of the measurement model using four measures:

TABLE 3 (Continued)

Model 6 Model 7 Model 8 Model 9 Model 10

Variable β SE β SE β SE β SE β SE

Pers Cred 0.03 0.09 0.04 0.09 0.04 0.09 0.04 0.09 0.05 0.09

Gen Bus Skl 0.16* 0.07 0.16* 0.07 0.16* 0.07 0.15* 0.07 0.14* 0.07

Analyt Skl 0.28** 0.10 0.32* 0.15 0.49 0.30 0.31** 0.08 0.60 0.35

HighTech 0.38 0.59 0.09 0.07 0.08 0.07 0.06 0.07 0.70 0.60

JobEntry 0.63* 0.29 –2.25 2.08 0.65* 0.28 0.61* 0.28 –2.25 2.26

JobSup –0.04 0.11 0.53 1.06 –0.05 0.11 –0.02 0.12 0.91 1.24

JobMgmt 0.05 0.09 0.15 0.77 0.05 0.09 0.07 0.09 –0.02 0.76

JobExec 0.07 0.12 0.53 0.98 0.06 0.12 0.06 0.12 0.55 0.92

JobTopExec 0.83* 0.33 3.38** 1.26 0.81* 0.32 0.75* 0.34 3.50* 1.41

JobOther –0.43* 0.12 3.28 2.28 –0.43* 0.21 –0.43* 0.18 2.03 1.90

Dept Analyt –0.07 0.04 –0.08 0.04 0.20 0.34 –0.07 0.04 0.15 0.36

RoleBenefit 0.50 0.60 0.42 0.58 0.49 0.60 4.23 2.94 3.53 2.54

RoleComp –0.14 0.17 –0.16 0.17 –0.14 0.17 3.85* 1.85 3.76* 1.67

RoleHRStrat 0.20 0.15 0.18 0.15 0.20 0.15 3.13* 1.50 3.20* 1.48

RoleLabRel –0.02 0.25 0.04 0.25 –0.01 0.24 4.82* 2.05 4.78* 2.10

RoleOrgDev –0.04 0.20 –0.03 0.20 –0.05 0.20 1.57 2.56 1.91 2.54

RoleStaffing –0.11 0.15 –0.09 0.15 –0.11 0.15 –0.74 1.45 –0.98 1.34

RoleTalent 0.21 0.21 0.20 0.21 0.19 0.21 –1.69 1.14 –1.57 1.25

RoleTrnDev 0.06 0.15 0.04 0.16 0.06 0.15 –1.35 1.21 –1.64 1.24

AS*HighTech –0.07 0.14 –0.16 0.15

AS*JobEntry 0.63 0.47 0.64 0.52

AS*JobSup –0.14 0.26 –0.23 0.30

AS*JobMgmt –0.02 0.19 0.02 0.18

Model 6 Model 7 Model 8 Model 9 Model 10

Variable β SE β SE β SE β SE β SE

AS*JobExec –0.11 0.24 –0.12 0.22

AS*JobTopExec –0.64 0.35 –0.68 0.39

AS*JobOther –0.95 0.55 –0.63 0.45

AS*Dept Analyt –0.07 0.08 –0.05 0.09

AS*RoleBenefit –0.96 0.69 –0.79 0.59

AS*RoleComp –0.98* 0.45 –0.96* 0.41

AS*RoleHRStrat –0.70 0.36 –0.72* 0.35

AS*RoleLabRel –1.12* 0.45 –1.10* 0.47

AS*RoleOrgDev –0.39 0.59 –0.47 0.58

AS*RoleStaffing 0.16 0.35 0.22 0.33

AS*RoleTalent 0.46 0.29 0.43 0.32

AS*RoleTrnDev 0.36 0.31 0.43 0.31

Residual SE 0.85 0.85 0.85 0.84 0.84

df 591 586 591 584 576

n 621 621 621 621 621

Mult r2 0.11 0.11 0.11 0.14 0.15

Adj r2 0.06 0.06 0.06 0.09 0.08

F-Stat 2.77 4.37 2.80 3.11 4.28

p-val < .001 < .001 < .001 < .001 < 0.001

**p < .01, *p < .05. Ed contrast is no degree. Job contrast is nonsupervisory. Role contrast is Generalist. AS = Analytic Skill.


TABLE 4 OLS regression results for HR practitioner performance toward external stakeholders

Model 1 Model 2 Model 3 Model 4 Model 5

Variable β SE β SE β SE β SE β SE

Intercept 3.76** 0.02 3.65** .015 2.78** 0.39 1.82** 0.43 1.88** 0.45

Ed/Exp Controls … … … … … … … …

Pers Cred –0.07 0.08 –0.05 0.08 –0.04 0.08

Gen Bus Skl 0.25** 0.06 0.24** 0.06 0.23** 0.06

Analyt Skl 0.25** 0.06 0.24** 0.06

HighTech 0.03 0.06

JobEntry 0.14 0.41

JobSup –0.03 0.12

JobMgmt 0.03 0.08

JobExec 0.11 0.11

JobTopExec 0.93** 0.34

JobOther –0.22 0.21

Dept Analyt –0.03 0.04

RoleBenefit 0.66 0.43

RoleComp –0.03 0.17

RoleHRStrat 0.25* 0.12

RoleLabRel 0.08 0.20

RoleOrgDev 0.03 0.17

RoleStaffing 0.01 0.14

RoleTalent 0.21 0.21

RoleTrnDev 0.21 0.14





Model 1 Model 2 Model 3 Model 4 Model 5

Variable β SE β SE β SE β SE β SE




AS*Dept Analyt









Residual SE 0.76 0.75 0.75 0.74 0.73

df 1111 687 648 613 592

n 1117 697 660 626 621

Mult r2 0.03 0.06 0.09 0.13

Adj r2 0.02 0.04 0.07 0.09

F-Stat 2.49 3.64 4.75 3.13

p-val <.01 <.001 <.001 <.001

Model 6 Model 7 Model 8 Model 9 Model 10

Variable β SE β SE β SE β SE β SE

Intercept 2.08** 0.54 1.78** 0.67 1.38 1.03 1.65** 0.47 1.46 1.24



chi-square (χ2), comparative fit index (CFI), standardized RMSEA, and

SRMR. Based on standards presented by Hu & Bentler (1999), rela-

tively good model fit is indicated by a nonsignificant χ2, a CFI value

close to .95 or higher, an RMSEA value close to .06 or lower, and an

SRMR value close to .08 or lower, although χ2 values are sensitive to

sample size. The measurement model demonstrated reasonably good

TABLE 4 (Continued)

Model 6 Model 7 Model 8 Model 9 Model 10

Variable β SE β SE β SE β SE β SE

Ed/Exp Controls … … … … … … … … … …

Pers Cred –0.04 0.08 –0.03 0.08 –0.04 0.08 –0.04 0.08 –0.03 0.08

Gen Bus Skl 0.23** 0.06 0.22** 0.06 0.22** 0.06 0.22** 0.06 0.22** 0.06

Analyt Skl 0.19* 0.09 0.26 0.14 0.36 0.24 0.29** 0.07 0.34 0.29

HighTech –0.40 0.51 0.03 0.06 0.03 0.06 0.01 0.06 –0.25 0.51

JobEntry 0.14 0.39 –2.15 3.29 0.16 0.40 0.12 0.41 –2.07 3.25

JobSup –0.03 0.12 0.70 1.08 –0.03 0.12 –0.01 0.12 0.66 1.10

JobMgmt 0.03 0.08 0.00 0.67 0.03 0.08 0.03 0.08 –0.13 0.66

JobExec 0.10 0.11 0.12 0.88 0.11 0.11 0.09 0.11 0.12 0.84

JobTopExec 0.93** 0.34 2.30 1.48 0.93** 0.34 0.89* 0.35 2.11 1.55

JobOther –0.23 0.21 2.03 2.44 –0.23 0.21 –0.23 0.19 1.30 2.23

Dept Analyt –0.03 0.04 –0.03 0.04 0.11 0.27 –0.02 0.04 0.08 0.28

RoleBenefit 0.65 0.43 0.61 0.42 0.66 0.42 –0.08 2.64 –0.30 2.50

RoleComp –0.03 0.17 –0.05 0.16 –0.03 0.17 3.39* 1.65 3.28* 1.54

RoleHRStrat 0.25* 0.12 0.24 0.13 0.25* 0.12 1.45 1.34 1.48 1.31

RoleLabRel 0.09 0.20 0.12 0.20 0.08 0.20 4.05* 1.73 3.76* 1.77

RoleOrgDev 0.03 0.17 0.04 0.17 0.03 0.17 1.14 1.83 1.02 1.89

RoleStaffing 0.01 0.14 0.02 0.14 0.01 0.14 –0.27 1.38 –0.46 1.28

RoleTalent 0.21 0.22 0.20 0.81 0.21 0.21 –2.36* 1.09 –2.43* 1.13

RoleTrnDev 0.21 0.14 0.20 0.27 0.21 0.14 0.44 1.01 0.26 1.02

AS*HighTech 0.11 0.12 0.07 0.13

AS*JobEntry 0.50 0.81 0.49 0.80

AS*JobSup –0.18 0.27 –0.17 0.27

AS*JobMgmt 0.01 0.16 0.04 0.16

Model 6 Model 7 Model 8 Model 9 Model 10

Variable β SE β SE β SE β SE β SE

AS*JobExec 0.00 0.21 –0.01 0.20

AS*JobTopExec –0.34 0.42 –0.31 0.44

AS*JobOther –0.58 0.59 –0.39 0.54

AS*Dept Analyt –0.04 0.07 –0.02 0.07

AS*RoleBenefit 0.19 0.62 0.24 0.59

AS*RoleComp –0.84* 0.41 –0.82* 0.38

AS*RoleHRStrat –0.29 0.32 –0.29 0.31

AS*RoleLabRel –0.92* 0.38 –0.94* 0.39

AS*RoleOrgDev –0.27 0.44 –0.24 0.46

AS*RoleStaffing 0.07 0.35 0.12 0.32

AS*RoleTalent 0.63* 0.28 0.64* 0.29

AS*RoleTrnDev –0.06 0.26 –0.01 0.26

Residual SE 0.73 0.73 0.73 0.73 0.73

df 591 586 591 584 576

n 621 621 621 621 621

Mult r2 0.13 0.14 0.13 0.16 0.16

Adj r2 0.09 0.09 0.09 0.10 0.10

F-Stat 3.16 3.36 3.03 3.29 3.36

p-val <.001 <.001 <.001 <.001 <0.001

**p < .01, *p < .05. Ed contrast is no degree. Job contrast is non–supervisory. Role contrast is Generalist. AS = Analytic Skill.


fit overall [χ2(62) = 335.12, p < .001, CFI = .97, RMSEA = .06, SRMR

= .04].

We next proceeded to test a partial mediation model in which

the work experience and education variables were included as exoge-

nous predictors of the three HR competencies (analytic skill, personal

credibility, and general business skill), which subsequently predicted

internally and externally focused performance. This initial structure

model demonstrated adequate fit [χ2(298) = 871.62, p < .001, CFI =

.95, RMSEA = .06, SRMR = .08].

The next step of our analysis involved comparing the fit of the

partial mediation model to possible alternative models, including a full

mediation model, a model with no mediation, and models that

allowed for partial mediation through only one of the HR competency

variables. Table 5 contains fit statistics for each model in addition to

the chi-square differences between each alternative model and the

partial mediation model. Unexpectedly, the model with no mediation

demonstrated significantly better fit with the data than the partial

mediation model [Δχ2(3) = 66.35, p < .001, CFI = .97, RMSEA =.05,

SRMR = .03]. A subsequent comparison to the remaining alternative

models indicated that the model with no mediation fit the data signif-

icantly better than all other alternative models (p < .001 in all cases).

Thus, path estimates were examined using the no mediation model.

This supports our use of OLS regression for the main results.


We find strong support for our hypothesis that HR professionals with

higher analytical skills have higher overall individual performance.

Our results show that they create more value both for external and

internal stakeholders of the business. The strengths of our large-scale

global data give us confidence that we may have uncovered an

empirical relationship that likely holds across geographies and con-

texts. We thus interpret our results as strong support for the positive

hypothesized relationship.

We also found partial support for our hypothesis that analytical

abilities would be more important for HR generalists than HR specia-

lists. The one exception to this, however, is for talent specialists. The

relationship between analytical ability and external stakeholder per-

formance is more positive for talent specialists than for HR general-

ists. This may be due to the strong emphasis on talent management

in modern organizations often referred to as the “War for Talent.” It

is possible that the war for talent puts great emphasis on applying

analytics to the talent challenges in organizations. Many CEOs

bemoan talent challenges and shortages, and the increased practical

emphasis on talent may make analytical abilities more relevant for tal-

ent specialists who are trying to respond to C-suite-level pressure to

better manage organizational talent. Additionally, data on individual

backgrounds, experiences, past performance, and the like may repre-

sent low-hanging fruit measurement opportunities in HR analytics.

Thus, this may be an area where individuals with strong analytical

skills can make a quick impact.

We were surprised to find no interaction effects between

industry-level, company–level, and job-level variables and analytical

skill. Despite having a large global sample, we certainly cannot fairly

interpret nonsignificant results, but our lack of findings suggests that

further research may help to uncover whether analytical skill is gener-

ally applicable across organizational contexts or whether it is more

valuable in certain specific organizational contexts.

7.1 | Practical implications

The clearest practical implication from our research is that an individ-

ual HR professional’s analytical ability contributes to perceptions of

individual performance. HR professionals with greater levels of ana-

lytical ability also receive higher performance ratings from their

supervisors. While we cannot observe the actual value these HR pro-

fessionals create for their companies, we assume that supervisor per-

formance ratings are at least a weak indicator of this value. Thus, HR

professionals who have higher analytical abilities likely create more

value for their companies. We can then reasonably expect that as

these individual professionals become more capable with HR analyt-

ics, their HR departments will be more capable of delivering value to

the organization through their analytical initiatives. Thinking back to

the observations of scholars and practitioners cited earlier, we see

how generating an HR department with these capabilities is challeng-

ing, given that HR has historically attracted individuals with minimal

training in quantitative methods, and a seemingly common distaste

for such methods (Roberts, 2009; Ulrich & Dulebohn, 2015).

There are many impediments associated with cultivating HR

departments driven by strong analytical rigor, and each one is com-

plex, pervasive, and deserving of its own comprehensive exploration.

In offering recommendations for action, we hope to avoid casting a

veneer of simplicity over these challenges. Nevertheless, we

endeavor to assemble a few approaches to cultivating analytical skills

advocated by scholars and/or practitioners, as well as suggestions

TABLE 5 SEM model fit and model comparisons

Model χ2 (df ) Δχ2 (df ) CFI RMSEA SRMR

Model 1: partial mediation model 871.62 (298) .949 .055 .075

Model 2: full mediation model 24.74 (18) .949 .054 .078

Model 3: no mediation model 66.35 (3)* .965 .046 .027

Model 4: analytic skill partial mediation only 23.45 (1)* .963 .048 .046

Model 5: personal credibility partial mediation only 34.94 (1)* .951 .054 .068

Model 6: general business partial skill mediation only 30.82 (1)* .950 .055 .072

Note: Each model compared to the partial mediation model.

*p < .05.


supported by our own research and experiences. In brief, these

include training, practicing, and recruiting.

7.1.1 | Training and practice

HR leaders in a number of organizations have begun to institute regu-

lar trainings to build the analytical skills of HR professionals. These

trainings vary greatly in terms of content, regularity, and intensity,

but for the most part endeavor to familiarize HR professionals

throughout the organization with (a) HR systems; (b) performance of

basic descriptive statistics (generating reports, etc.); (c) interpretation

of descriptive statistics and, in more advanced firms, in HR analytics;

and (d) performance of prescriptive and predictive statistics.

We give an example of the approach used in a large retail firm, which

captures similar approaches used in other organizations. In speaking with

the director of workforce analytics for this firm, he outlined his organiza-

tion’s approach to building the analytical skills of HR professionals, and

he emphasized the importance of making training sessions both bite-sized

(one hour, once a week, for three weeks, and repeated three times a

year), as well as applicable (having numerous opportunities to practice skill

in their everyday work). As trainees implemented their training, they were

also provided opportunities to reach to out to trainers for clarification, or

to extend their learning. In this case, the bite-sized and applicable nature

of the trainings were cited as perceived reasons for the dramatic growth

in both success and popularity of such trainings—measured by the

increased use of analytics among HR professionals, and the growing

demand for training among volunteer participants.

Training in many organizations can be an expensive and disrup-

tive undertaking. This model of providing a few annual trainings that

are highly relevant and manageable (and introduce trainees to experts

who can be resources as they develop their analytical skills) might be

a useful way for many organizations to train their HR personnel—and

also signals a growing expectation of such skills.

While such training provides a crucial opportunity for HR profes-

sionals to gain exposure to new analytical methods, it is important that

they then have regular opportunities to exercise these analytical abil-

ities. Levenson (2005) recommends using developmental assignments

to help build analytical skills. Another means by which this could be

accomplished is through ensuring that individuals are learning analytical

skills they can use to accomplish required work tasks. One problem with

this is that some HR roles require more analytical skills, while others

seemingly require little. This has enabled many HR professionals to

avoid analytical rigor almost entirely. Additionally, HR, like many organi-

zational functions, tends to be tradition bound and resistant to change

(Hammonds, 2005; Wolfe et al., 2006). In the case of training HR pro-

fessionals on analytical skills, we expect this will be exacerbated when

training addresses peripheral tasks, or worse, nonessential tasks that

increase workload. To address this issue, trainings should target the

analytical skills that are relevant to specific HR roles. This requires that

prior to trainings, HR leaders identify specific tasks and processes in

need of analytical rigor. When HR professionals are taught how to

employ analytical rigor to essential tasks specific to their role, they will

have regular opportunities to practice and reinforce these skills.

To facilitate such training and mentored practice, some scholars have

advocated creating a workforce analytics center of excellence (Levenson,

2005; Roberts, 2009). Many organizations have taken this approach, and

researchers have found that these organizations experienced greater

comfort and superior results from workforce analytics than other organi-

zations without centers of excellence (Roberts, 2009).

Finally, as HR leaders identify specific tasks and processes in need

of analytical rigor, they should clearly communicate to HR professionals

the expectation that analytical rigor be employed in these cases moving

forward. Doing so further increases the likelihood that HR professionals

will incorporate and practice analytical skills learned in trainings.

7.1.2 | Recruiting

The previous section on training and ensuring opportunities for exercis-

ing analytical skills addresses possible approaches for cultivating analyti-

cal skills in the population of existing HR professionals. An alternative or

complementary approach for increasing the overall analytical skill in HR

departments is through recruiting practices aimed at attracting and

selecting applicants with strong analytical skills and/or aptitude.

Even with the growing emphasis on evidence-based HR practices

and the need for HR professionals with training in quantitative meth-

ods, HR continues to attract individuals lacking strong quantitative

and analytical skills (Ulrich & Dulebohn, 2015). The responsibility for

rectifying this falls partly on the shoulders of educators who need to

more diligently ensure that aspiring HR professionals are required to

take classes that enhance their analytic abilities, such as statistics,

strategy, operations (Levenson, 2005), and dedicated analytics

courses, and also clearly emphasize to students the importance of

analytical skills for HR professionals. Additionally, HR recruiters need

to clearly communicate through job postings, interviews, and informa-

tional sessions that analytical abilities are of key importance.

HR leaders and recruiters then need to carefully screen job candi-

dates on the basis of their mastery of relevant quantitative methods

(Levenson, 2005) and analytic thinking abilities. In light of the multifac-

eted nature of analytical ability adopted in this paper and utilized by

others (Becker et al., 2001; Boudreau & Ramstad, 2007), this may involve

separate or combined tests measuring how well a candidate logically

assesses organizational problems (e.g., analytic reasoning), gathers or

identifies high-quality data (e.g., psychometrics and econometrics), ana-

lyzes the data systematically (e.g., statistics), and incorporates insights

into subsequent decision making (e.g., communication and process man-

agement). Although this recommendation may seem straightforward in

light of prior work on HR analytics, data suggest that in spite of many HR

practitioners emphasizing the importance of recruiting with “quantitative

skills” as a crucial prerequisite, HR continues to regularly hire candidates

with weak analytical abilities (Ulrich & Dulebohn, 2015). Anecdotal evi-

dence suggests that many HR leaders still view such skills as

unnecessary—often citing their own lack of quantitative skills and appar-

ent career success as supporting evidence. However, for HR departments

to successfully build up a workforce with strong analytical abilities, HR

leaders need to be diligent in insisting on analytic competence in appli-

cants. This is understandably difficult when an applicant is in all other

ways an excellent fit for the role, but lacks adequate analytical training. A

possible means of addressing these cases would be to offer and require

completion of an analytic-focused online training program before starting

at the company, with the option of testing out of the course.


Ulrich and Dulebohn (2015) argue that “it is no longer possible to

sidestep data, evidence, and analytics that bring rigor and discipline

to HR” (p. 202). We believe that through revised recruiting and train-

ing practices, analytical skills can be steadily built up within HR

departments: Simply put, educators and recruiters need to clearly sig-

nal to prospective applicants an expectation of quantitative abilities,

and then HR leaders need to follow through by hiring applicants with

these skills. As illustrated by our findings, this becomes an even more

critical imperative when developing and hiring for certain HR roles

(e.g., generalists and talent specialists).

Supported by insights from long-time scholars and practitioners

in the fields of HR and analytics, as well as insights gleaned from our

own research and experience, we expect organizations that facilitate

relevant training in analytical skills, ensure regular opportunities to

exercise analytical abilities, and enforce recruiting criteria that require

relevant quantitative training can better cultivate analytical abilities

within existing HR departments. We further anticipate that doing so

will enable them to better leverage analytics, and as others have

argued well, when HR leverages analytics to generate business

insights HR will help move the organization toward increased overall

performance (Boudreau & Ramstad, 2007).


1For robustness, we also used a less stringent requirement of 0.60factor loading with cross loading lower than 0.20. The results are sub-stantively unchanged with the less stringent model, but the model fitstatistics of the CFA for that less stringent model suggest a marginalfit. We thus maintained the more stringent cut for the main analysis.


David Kryscynski

Michael Ulrich


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DAVID KRYSCYNSKI is Assistant Professor of Strategy at

Brigham Young University’s Marriott School of Management.

Professor Kryscynski received his PhD from Emory University’s

Goizueta Business School and researches human capital as a

source of competitive advantage. His research has been pub-

lished in outlets such as Academy of Management Journal, Acad-

emy of Management Review, Academy of Management Perspectives,

Strategic Management Journal, Management Science, and Journal of


CODY J. REEVES is an Assistant Professor of Organizational

Leadership and Strategy in the Marriott School of Management,

Brigham Young University. He received his PhD in management

and organizations from the Tippie College of Business, University

of Iowa. His research focuses on team effectiveness, organiza-

tional/team entry, recruiter judgments, and employee selection.

He has articles appearing in the Academy of Management Journal,

the Journal of Applied Psychology, and the Leadership Quarterly.

RYAN STICE-LUSVARDI is a Research Assistant in the School

of Management Science and Engineering at Stanford University.

Through her various research collaborations she has examined a

number of phenomena including gender inequality in equity pay,

innovation in brainstorm teams, and the emergence of the HR

analytics profession. Her primary research interest is studying

how data analytics are shaping work and organizations. She is a

graduate of Brigham Young University.

MIKE ULRICH is an Assistant Professor of Management at the

Jon M. Huntsman School of Business, Utah State University. He

received his PhD in management from the Moore School of Busi-

ness at the University of South Carolina. His research focuses on

human capital, executive succession, and organizational capabil-

ities. He has published three books and numerous articles.

GRANT RUSSELL is an HR Operations Specialist at Google

where he is responsible for the strategic development, service

delivery and continuous improvement of key people processes.

Since joining Google in May 2015, he has improved onboarding

system integrations and metrics, optimized compensation pro-

grams, and consulted on mobility processes. Grant received a

degree in management from Brigham Young University.

How to cite this article: Kryscynski D, Reeves C, Stice-

Lusvardi R, Ulrich M, Russell G. Analytical abilities and the

performance of HR professionals. Hum Resour Manage.




Appendix. EFA loadings of individual-level items






Understandslocal context

Understandsinternalpolitics Comments

Translates data into usefulinsights for [OrganizationName]

0.978 −0.026 −0.064 −0.090 0.136 −0.055 0.065

Identifies [OrganizationName]’s problems that canbe solved with data

0.939 −0.026 0.063 −0.127 0.114 −0.049 −0.026

Uses data to influence decisionmaking in [OrganizationName]

0.932 −0.075 −0.165 0.009 0.153 0.037 0.054

Effectively uses HR analytics tocreate value for[Organization Name]

0.861 0.017 0.113 −0.053 0.013 −0.050 −0.035

Identifies important questionsabout the organization thatcan be answered with data

0.825 −0.028 0.007 0.035 0.059 0.022 −0.039

Accurately interprets statistics 0.822 0.065 0.132 0.071 −0.203 0.007 −0.019

Incorporates rigorous dataanalysis when interpretinginformation

0.774 0.023 −0.025 0.140 −0.049 0.033 −0.061 Below 0.80, but included inrobustness

Excludes low quality data fromdecision processes

0.759 0.042 0.032 0.112 −0.141 0.074 −0.013 Below 0.80, but included inrobustness

Understands the limitations ofdata in ambiguous situations

0.698 0.096 0.207 0.010 −0.107 −0.007 −0.008 Below 0.80, but included inrobustness

Is receptive to feedback 0.021 1.146 0.014 −0.342 −0.054 −0.067 0.038

Acts with appropriate balanceof confidence and humility

0.039 1.064 −0.057 −0.294 0.013 0.047 0.070

Is aware of how he or shecomes across to others

−0.012 0.924 0.013 −0.229 0.062 0.039 −0.010

Shows a genuine interest inothers

−0.128 0.821 0.081 0.122 −0.013 0.022 −0.141

Works effectively withindividuals at all levels of[Organization Name]

−0.025 0.784 0.069 0.237 −0.139 −0.012 −0.051 Below 0.80, but included inrobustness

Demonstrates personalintegrity and ethics

0.031 0.724 −0.220 0.118 0.059 0.120 0.032 Below 0.80, but included inrobustness

Seeks to learn from bothsuccesses and failures

−0.021 0.621 0.092 0.076 0.154 −0.012 −0.030 Below 0.80, but included inrobustness

Has earned trust with keyinternal stakeholders

0.040 0.614 −0.176 0.471 −0.105 −0.024 0.119 Excluded due to crossloading

Frames complex ideas in simpleand useful ways

0.109 0.412 0.118 0.244 0.041 −0.005 0.011 Excluded, below 0.60 cutoff

Knows how investors value[Organization Name]

0.003 −0.079 0.977 −0.093 0.006 −0.093 0.037

Helps investors recognize thequality of leadership within[Organization Name]

0.032 −0.032 0.892 −0.092 0.109 −0.125 −0.092

Understands investorexpectations

0.064 −0.033 0.868 −0.270 −0.046 0.155 0.127

Aligns organizational brandwith customers,shareholders, and employees

−0.014 0.127 0.685 0.202 −0.076 −0.044 −0.057 Below 0.80, but included inrobustness

Understands expectations ofexternal customers

−0.006 0.110 0.660 0.138 −0.076 0.040 −0.023 Below 0.80, but included inrobustness

0.016 −0.095 0.615 0.058 0.021 0.253 0.039 Below 0.80, but included inrobustness








Understandslocal context

Understandsinternalpolitics Comments

Understands how to competeagainst other organizationsin your market

Recognizes local opportunitiesfor [Organization Name]’ssuccess

−0.049 0.026 0.610 −0.005 0.110 0.292 −0.094 Below 0.80, but included inrobustness

Helps employees understandhow [Organization Name]’sstrategy impacts their work

−0.030 0.233 0.495 0.059 0.199 −0.047 −0.037 Excluded, below 0.60 cutoff

Focuses internal organizationalactions on creating value forcustomers

−0.004 0.173 0.490 0.273 0.074 −0.136 −0.018 Excluded, below 0.60 cutoff

Understands how[Organization Name] makesmoney (e.g., who, where,how)

0.015 0.004 0.475 0.038 −0.080 0.017 0.437 Excluded, below 0.60 cutoff

Understands changes in[Organization Name]’sexternal environment(e.g., social, technological,economic, political,environmental, demographic,etc.)

0.061 −0.049 0.468 −0.039 0.145 0.211 0.173 Excluded, below 0.60 cutoff

Has history of deliveringresults

0.014 0.081 −0.093 0.919 −0.118 0.003 0.100 Excluded, single itemloading after cutoff

Persists through adversecircumstances

−0.017 0.193 −0.045 0.498 0.211 −0.012 0.022 Excluded, below 0.60 cutoff

Identifies problems that arecentral to [OrganizationName]’s strategy

0.059 −0.016 0.307 −0.242 0.792 0.040 0.041 Excluded, not germane tocurrent study

Contributes to creating[Organization Name]’sstrategy (e.g., help shape thevision of the future of theorganization)

0.084 −0.029 0.360 −0.240 0.768 −0.066 0.007 Excluded, not germane tocurrent study

Accurately anticipates[Organization Name]’s risks

0.120 −0.014 0.333 0.025 0.415 0.035 −0.034 Excluded due to crossloading

Understands local politicalenvironment (e.g., potentialobstacles in the localenvironment)

−0.022 0.004 0.239 −0.120 0.105 0.779 −0.084 Excluded, not germane tocurrent study

Is familiar with the local labormarket (e.g., labor shortages,localization, demographics,local universities, and othereducational institutions)

0.006 0.041 0.013 0.147 −0.127 0.744 0.003 Excluded, not germane tocurrent study

Understands who makes keydecisions in yourorganization (e.g., peoplewho control importantresources)

−0.025 0.081 0.041 0.183 0.076 −0.071 0.695 Excluded, not germane tocurrent study

Understands howorganizational units within[Organization Name] shouldwork together (e.g., acrossunits, departments, andfunctions)

0.014 0.229 0.132 0.222 0.102 −0.037 0.332 Excluded, not germane tocurrent study

Displays a passion forunderstanding how and whythings work in [OrganizationName]

0.039 0.265 0.178 0.230 0.228 −0.070 0.066 Excluded, did not load









Understandslocal context

Understandsinternalpolitics Comments

Stands up to senior leaderswhen appropriate

−0.049 −0.022 −0.042 0.359 0.392 0.085 0.066 Excluded, did not load

Provides alternative insights onoperational challenges

0.079 0.027 0.214 0.305 0.307 0.019 −0.040 Excluded, did not load

Takes appropriate risks inpersonal workresponsibilities

0.172 0.249 0.067 0.226 0.156 0.056 −0.044 Excluded, did not load



  • Analytical abilities and the performance of HR professionals
      • 4.1 Stronger positive relationship in high-tech industries
      • 4.2 Stronger positive relationship in companies that engage in higher levels of HR analytics
      • 4.3 Stronger positive relationship for lower job levels
      • 4.4 Stronger positive relationship for HR generalists
    • 5 METHODS
      • 5.1 Sample
      • 5.2 Measurement approach
        • 5.2.1 Dependent variable: Individual performance
        • 5.2.2 Independent variable: Analytical ability
        • 5.2.3 Other individual variables
        • 5.2.4 Organization-level variables
        • 5.2.5 Industry-level variables
    • 6 RESULTS
      • 6.1 Testing Hypothesis 1
      • 6.2 Testing the moderation hypotheses
        • 6.2.1 Testing Hypothesis 2
        • 6.2.2 Testing Hypothesis 3
        • 6.2.3 Testing Hypothesis 4
        • 6.2.4 Testing Hypothesis 5
      • 6.3 Robustness checks
      • 7.1 Practical implications
        • 7.1.1 Training and practice
        • 7.1.2 Recruiting

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