Index
A
ABM (agent-based modeling), 127, 129
advanced analytics
defined, 13
expertise, combining with, 20-21
adversarial nature of humans, 124
agency theory, 3
cooperation, 33
agent-based modeling (ABM), 127, 129
Agpar, Dr. Virginia, 20
AI (artificial intelligence), 22-23, 130
software applications, 25
algorithms, 23
genetic algorithms, 131
analytical thinking versus intuition, 4-5
anchoring, 7
appraisal politics, impact on performance management, 112
approaches to HCM practices, 67-69
argumentative reasoning, 11-12
artificial intelligence (AI), 22-23, 130
software applications, 25
asymmetric information, 37-38, 83-84
B
Barton, Richard, 93
Bebchuk, Lucian, 119
Besse, Tim, 93
BI (business intelligence)
biases
anchoring, 7
appraisal politics, 112
confirmation bias, 7
in empirical research, 61
impact on performance management, 112-113
loss aversion, 7
removing from decision making, xvii-xviii, 81-82
similar to me bias, 112
status quo, 7
big data, 9
bio data, as employee selection tool, 93-98
BizX, optimal HCM practice selection, 74-75
Bloomberg, Michael, 42
Boisjoly, Roger, 28
Boston Scientific, as model for collaboration, 48
Buffet, Warren, 11
C
CDM (collaborative decision making) software, 51-53
CEP (Center for Economic Performance), 59
certitude, 10
challenges with forecasting, 90-92
Boston Scientific, 48
incentive contracts for, 44-45
participative decision making, 42-43, 49-50
and tournament compensation, 107
collaborative decision making (CDM) software, 51-53
collecting human capital data, 59-61
collective intelligence, 36-37
collusion, 34
combinatorics, 127
combining expert intuition and analytics, 20-21
commoditizing human capital, 56
comparing analytical thinking and intuition, 4-5
compensation packages, 104-105, 107-108
executive compensation, applying to human sciences, 118-120
piece rates, 106
complexity theory, 115
configurational approach to HR practices, 67
confirmation bias, 7
contingency approach to HR practices, 67
sharing, 126
critical information, importance of sharing, 126
D
data mining, 130
Davenport, Thomas, 4
Dawes, Robyn, 2
Dawes formula, 18
decision making
AI, software applications, 25
analytical thinking versus intuition, 4-5
biases, 6
anchoring, 7
confirmation bias, 7
loss aversion, 7
status quo, 7
certitude, 10
critical information, importance of sharing, 126
descriptive, 12
“framing effect,” 2
human nature, 6
normative, 12
prescriptive, 12
decision trees, 130
applying to incentive issues, 117
employee selection, applying to, 99-100
DecisionAnalyticsInc.com, xx
deep Q&A expert systems, 99
descriptive decision making, 12
deterministic world view, 125
diagnosing problems with HCM, 124
E
ECM (enterprise content management) software, 49
econometrics, applying to incentive issues, 117-118
economic impact of collaboration, 42-43
Edgar database, 119
efficiency wage, 108
EMC, as model for collaboration, 47-48
empirical research
bias in, 61
generalizability, 126
human sciences, applying to
deep Q&A expert systems, 99
expert intuition, 98
game theory, 99
predictive modeling, 99
compensation packages, 104-105
piece rates, 106
motivations of individuals, identifying, 103-107
compensation packages, 104-105
and predictive analytics, 88-89
enterprise content management (ECM) software, 49
enterprise resource planning (ERP) software
optimal HCM practices, selecting, 75-76
equity in decision making, xviii-xix
ERP (enterprise resource planning) software
optimal HCM practices, selecting, 75-76
evaluating performance, 112-113
executive compensation
applying human sciences to, 118-120
indexation, 120
experimental philosophy, 6
expert intuition, applying to incentive issues, 115-116
Expert Maker, 25
applying to incentive issues, 116-117
deep Q&A expert systems, 99
Expert Maker, 25
expertise, combining with advanced analytics, 20-21
F
Fehr, Ernst, 31
financial rewards to incentive contracts, 114-115
“The Firm’s Choice of HRM Practices: Economics Meets Strategic Human Resource Managementy,” 67
Flyvbjerg, Bent, 91
forecasting
inside view, 91
outside view, 91
reference class forecasting, 91-92
Forrester Research, 51
“framing effect” on decision making, 2
Fried, Jesse, 119
functions of performance management, 111-112
fuzzy logic, 131
G
applying to employee selection, 99
Gartner Research, 51
generalizability, 126
genetic algorithms, 131
Goodnight, James, 46
Google, xvii
H
halo, 113
Harris, Jeanne, 4
HCM (human capital management) decision making, xvii-xx, 56
agency theory, 3
AI, software applications, 25
biases
anchoring, 7
confirmation bias, 7
loss aversion, 7
status quo, 7
certitude, 10
descriptive decision making, 12
diagnosing problems with, 124
expert systems, 23
“framing effect” on decision making, 2
HR practices, selecting, 65-73
machine learning, 23
metrics, 14
normative decision making, 12
optimal HCM practice choices
selecting with ERP software, 75-76
selecting with SAS HCM software, 77
selecting with SAS Talent Scorecard, 77-78
selecting with Talent Analytics, 76
selecting with talent management suites, 79
prescriptive decision making, 12
workforce planning and predictive analytics, 88-89
high performance work practices (HPWP), 68
Hoch, Stephen, 21
Hohman, Robert, 93
homo economicus, 1
horn effect, 113
HPWP (high performance work practices), 68
human capital
difficulty of commoditizing, 56
human capital management (HCM) decision making. See HCM (human capital management) decision making
human nature, 6
adversarial nature of, 124
incentive contracts for, 44-45
participative decision making, 42-43
SAS Institute as model for, 46-47
“the tragedy of commons,” 31-32
I
incentive contracts, 56, 105-107
and complexity theory, 115
econometrics, applying, 117-118
executive compensation
human sciences, applying to, 118-120
indexation, 120
expert systems, applying, 116-117
low-wage workers, applying human sciences to incentives, 120
machine learning techniques, applying, 117-118
merit pay, applying human sciences to incentives, 121
for physicians, applying human sciences to incentives, 121
piece rates, 106
predictive modeling, applying, 117
for teachers, applying human sciences to incentives, 121
compensation packages, 104-105
tournament model incentive schemes, 106-107
indexation, 120
information overload, 9
inside view of forecasting, 91
intuition
versus analytical thinking, 4-5
expert intuition
applying to incentive issues, 115-116
combining with analytics, 20-21
impact on decision making, xvi-xvii
importance of in statistics, 5-6
thinking fast, 4
J-K
James, William, 19
Journal of Behavioral and Brain Sciences, 11
Kahneman, Daniel, xiv, 2, 5-6, 90-92
Kasparov, Gary, xix
Kaufman, Bruce, 67
Klein, Gary, 12
knowledge management, 36
ECM software, 49
L
Lean In (Sandberg), 81
Learning from Data (Abu-Mostofa), 22
Lev, Baruch, 62
Lewis, Michael, 19
linear programming, applying to incentive issues, 118
loss aversion, 7
low-wage workers, applying human sciences to incentives, 120
M
applying to incentive issues, 117-118
HCM practices, selecting, 72-73
Malone, Thomas, 35
Maude, Isabel, 124
“maverick” research, 24
Mayer, Marissa, 86
merit pay, applying human sciences to incentives, 121
metrics in HCM, 14
Miller, Ben, 67
modeling optimal HCM practice choices, 68-69
Moneyball (Lewis), 19
Monte Carlo simulation
applying to employee selection, 99-100
applying to incentive issues, 118
Moore, Gary, 84
motivation
agency theory, 3
incentive contracts, 56
piece rates, 106
tournament model incentive schemes, 106-107
multiple regression techniques
applying to employee selection, 99-100
incentive issues, applying to, 117
mutual monitoring, 33
N
natural language, 22
applying to incentive issues, 118
neuroeconomics, 127
“The New Human Science,” xiv, xix, 123
non-linear programming, applying to incentive issues, 118
nonPareil Institute, 84
normative decision making, 12
Nowak, Michael, 29
Nudge (Thayler and Sunstein), 1
O
OLS (ordinary leased squared), 127
optimal HCM practice choices
selecting with software applications
SAS HCM software, 77
talent management suites, 79
ordinary leased squared (OLS), 127
Boston Scientific, 48
organizational success equation, 67
performance management, 111-114
organizational success equation, 67
outcomes, predicting with Sundem-Tierney equation, 17-20
outside view of forecasting, 91
P
panel data analysis, 59
participative decision making, 42-43, 49-50
Pay Without Performance (Bebchuk and Fried), 119
performance management, 111-114
physicians, applying human sciences to incentives, 121
piece rates, 106
policies (HCM), selecting, 56-57
practices (HCM)
HPWP, 68
optimal HCM practice choices, modeling, 68-69
selecting through machine learning, 72-73
inside view, 91
outside view, 91
reference class forecasting, 91-92
Predictably Irrational (Ariely), 101
predictive analytics, xvii-xviii
forecasting
statistics, importance of intuition in, 5-6
Sundem-Tierney equation, 17-20
predictive modeling, applying to incentive issues, 117
prescriptive decision making, 12
probabilistic world view, 125
prosperity, the Scandinavian model, 39-43
Q-R
Race Against the Machine (Brynjolfsson and McAfee), 24
Raiders of the Lost Ark, 10
rationality, theory of argumentative reasoning, 11-12
Rayner, Nigel, 24
and mutual monitoring, 33
recruiting with social analytics, 92-93
reference class forecasting, 91-92
removing
bias from decision making, xvii-xviii, 84
sharing, 126
Russo, J. Edward, 7
S
sabermetrics, 19
SAP, xviii
SAS HCM software, optimal HCM practice selection, 77
SAS Institute, as model for collaboration, 46-47
SAS Talent Scorecard, optimal HCM practice selection, 77-78
Schoemaker, Paul J. H., 7
selecting
employees. See employee selection
HCM practices
with SAS HCM software, 77
with SAS Talent Scorecard, 77-78
with Talent Analytics, 76
with talent management suites, 79
Selic, Dan, 84
similar-to-me bias, 112
Smith, Adam, 30
software applications
BizX, selecting optimal HCM practices, 74-75
ECM software, 49
ERP software, selecting optimal HCM practices, 75-76
talent management suites, 79
statistics
importance of intuition in, 5-6
multiple regression techniques, applying to employee selection, 99-100
status quo, 7
Streetlights and Shadows (Klein), 12
success
individual contribution to organizational success, identifying, 105-106
organizational success equation, 67
Sundem-Tierney equation, 17-20
T
Talent Analytics, selecting optimal HCM practices, 76
talent management suites, selecting optimal HCM practices, 79
Taylor, Frederick, 71
teachers, applying human sciences to incentives, 121
Tetlock, Philip, 12
theory of argumentative reasoning, 11-12
thinking fast, 4
thinking slow, 4
tournament model incentive schemes, 106-107
and collaboration, 107
“the tragedy of commons,” 31-32
“transaction cost” literature, 64
U-V
universalistic approach to HR practices, 67
uRiKA, 51
value of organizational capital, 62-65
vendors of expert systems, 51
W
wage inequality, applying human sciences to incentives, 122
websites, DecisionAnalyticsInc.com, xx
Wilson, E.O., 34
and predictive analytics, 88-89
X-Y-Z
Xerox, xvii