Analytics vs. Attention in Dynamic
Environment: Using Major League
Baseball Teams
Jaemin Kim, Ph.D.
Ellen Kraft, Ph.D.
Stockton University
Research Questions
 Does data-driven decision making lead to an increase in an
organization’s performance? Under what condition is the impact of
analytical capability on the performance greater?
 In the external environment where vicarious learning is dominant, is
HR management closely linked to statistical results foster the
benefits from the capability?
 Is HR management decision making that uses intuition helpful?
Hypotheses
 Data-driven decision making is the organization-wide outcome. It occurs
after task conflicts among various functional and hierarchical units are
coordinated in the whole organization (Davenport, 2014; McAfee et al.,
2013).
 These characteristics make it difficult for competitors to understand the
value-creating strategy that goes beyond simple addition of analytical
resources such as data scientists, data servers and statistical programs
(Miller, 2003).
 Hypothesis 1: Data-driven decision making will increase organizational
performance.
Hypotheses
 Analytics makes it possible for managers to allocate their equal attention
to various aspects of performance without being biased by a few most
salient aspects (Cyert & March, 1963).
 Analytic-based HR management accelerates the rational decisionmaking process that considers all available information. Thus, this method
of decision making helps managers reevaluate overlooked employees by
managers who had relied on their biased attention based on experience,
intuition, and emotion.
 Hypothesis 2: Analytics-based management of HR will increase an
organization’s performance
Hypotheses
 When individuals are embedded in capabilities and interact one another to solve
organization-level problems, social complexity is created (Argote & Ren, 2012).
 Causal ambiguous occurs when it is hard to identify the link between resources
and competitive advantage (King & Zeithaml, 2001).
 When the firms assign strategic tasks to a certain group of HRs who fit the
analytical framework while overlooking those who do not fit, the firms face a
limited range of available strategies, and their analytical capability turns out to
be socially simplistic (i.e., social simplicity), as only the HRs who fit into the
framework have more opportunities to engage in limited strategic works. As the
number of available strategies decreases, the logic of value creation becomes
causally clearer (i.e., causal clarity).
 Hypothesis 3: Analytic-based HR management will negatively moderate the
relationship between data-driven decision making and an organization’s
performance
Hypothesized Model
Conceptual Relations between Analytical
Capability & Analytic-Based Management
Data & Analytical Model
 30 MLB Teams for three years from 2013 to 2015
 Sean Lahman’s Baseball Achive
 Random-effects model
 Multi-level linear model & Multiple linear regression model for robustness tests
Measures: Data-driven decision making
MLB Teams
All-In
Boston Red Sox
Chicago Cubs
Cleveland
Indians
Houston Astros
New York
Yankees
Oakland A’s
Pittsburgh
Pirates
ST. Louis
Cardinals
Tampa Bay
Rays
Believers
Baltimore
Orioles
Kansas City
Royals
Los Angeles
Dodgers
New York Mets
San Diego
Padres
Toronto Blue
Jays
Washington
Nationals
One Foot In
Chicago White
Sox
Los Angeles
Angels
Milwaukee
Brewers
San Francisco
Giants
Seattle Mariners
Texas Rangers
Skeptics
Arizona
Diamondbacks
Atlanta Braves
Cincinnati Reds
Colorado
Rockies
Detroit Tigers
Minnesota
Twins
Non-Believers
Miami Marlins
Philadelphia
Phillies
Measures: Analytics-base management

Measures: Winning Percentage

Statistical Results of Hypothesis Tests
Model 1
Model 2
Model 3
Control Model
Direct Effect
Interaction Effect
Coef.
Control
Intercept
Batting Average
Fielding Percentage
Fielding-Independent Pitching
Salary
Three-year Park Factor for Batters
Three-year Park Factor for Pitcher
Attendance
League Affiliation
Division Affiliation
S.E.
-2.86
2.05
1.07 *
0.51
+
3.66
2.08
-0.10 ***
0.02
<-0.01
<0.01
*
0.01
<0.01
*
-0.01
<0.01
<0.01 +
<0.1
Included
Included
Direct Effect
Data-driven decision making
Analytic-Based HR Management
Coef.
S.E.
-4.79 *
1.90
1.29 **
0.45
**
5.44
1.91
-0.09 ***
0.01
<0.01
<0.01
*
0.01
<0.01
*
-0.01
<0.01
<0.01
<0.01
Included
Included
0.01
<-0.01
***
Interaction Effect
Data-Driven x HR Management
No. of obs.
No. of groups
Adj. R 2
F-value
<0.01
0.02
Coef.
-5.14 **
1.90
1.14 *
0.04
**
5.83
1.92
-0.09 ***
0.01
<0.01
<0.01
*
0.01
<0.01
*
<-0.01
<0.01
<0.01 +
<0.01
Included
Included
0.01
0.08
-0.02
90
30
0.55
13.59
***
90
30
0.61
16.53
***
S.E.
90
30
0.61
15.52
***
+
+
***
<0.01
0.05
0.01
Robustness Tests
Multiple Regression Model
Direct Model
Interaction Model
Data-driven
decision making
Analytic-based HR
MGMT
0.01(<0.01)***
-0.01(<0.01)
0.11(0.04)**
0.01 (<0.01)***
<-0.01(<0.01)
-0.03(0.01)**
Interaction Effect
Results
0.02(<0.01)***
Multilevel Linear Model
Direct Model
Interaction Model
H1 Supported
H2 Not supported
H3 Supported
0.02 (<0.01)***
0.11(0.04)**
-0.03(0.01)**
H1 Supported
H2 Not supported
H3 Supported
Discussion
 We found that data-driven decision making enabled MLB teams to overcome their limited
attention to a few salient events.
 The analytic capability in concert with attention-based management makes it possible for
firms to develop socially complex and causally ambiguous strategies, thus discouraging
competitors to imitate their strategy.
 The analytic capability combined with analytic-based management possibly makes the
underlying algorithm of developing and implementing strategies scanty that competitors
can understand the HRs and strategies that contributed to improving an organization’s
performance.