U.S. patent application number 10/410307 was filed with the patent office on 2003-10-09 for computer-implemented system for human resources management.
Invention is credited to Dewar, Katrina L..
Application Number | 20030191680 10/410307 |
Document ID | / |
Family ID | 22785382 |
Filed Date | 2003-10-09 |
United States Patent
Application |
20030191680 |
Kind Code |
A1 |
Dewar, Katrina L. |
October 9, 2003 |
Computer-implemented system for human resources management
Abstract
A system and method for testing and/or evaluating employees or
potential employees is disclosed. A computer arranges a plurality
of applicants in a stack ranked table. The table may rank or
re-rank applicants against each other, from best to worst, after
successive screening, selecting, and/or interviewing stages for a
particular job. Performance evaluations of hired workers may be fed
back to the computer for adjusting the system and method.
Competencies shown to be predictive of successful performance of a
given type of job are tested for at various stages in an online
testing system.
Inventors: |
Dewar, Katrina L.;
(Plymouth, MN) |
Correspondence
Address: |
MILES & STOCKBRIDGE P.C.
Suite 500
1751 Pinnacle Drive
McLean
VA
22102-3833
US
|
Family ID: |
22785382 |
Appl. No.: |
10/410307 |
Filed: |
April 10, 2003 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10410307 |
Apr 10, 2003 |
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09878245 |
Jun 12, 2001 |
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60211044 |
Jun 12, 2000 |
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Current U.S.
Class: |
706/45 |
Current CPC
Class: |
G06Q 10/1053 20130101;
G06Q 10/0639 20130101; G09B 7/02 20130101; G06Q 10/063112 20130101;
G06Q 30/0203 20130101; G06Q 10/10 20130101 |
Class at
Publication: |
705/8 |
International
Class: |
G06F 017/60 |
Claims
14. An electronic assessment system for assessing an individual
applicant for employment by an employer, the system comprising: an
electronic applicant terminal operable to present a plurality of
questions to the applicant and to receive electronically the
applicant's responses to the questions; an applicant screening
computer configured to provide applicant results automatically in
response to receiving the electronically received responses; and an
electronic report viewer operable to present to the employer a
viewable report containing the applicant results, characterized in
that the computer is configured to compare automatically the
electronically received responses with electronically stored rating
correlation data, the rating correlation data being indicative of
calculated correlations between actual job duty performance ratings
of a plurality of hired individuals and previous responses given by
the hired individuals.
15. The system of claim 14, the previous responses characterized by
having been collected from the individuals before the individuals
were hired into the job for which the actual job duty performance
ratings were collected.
16. The system of claim 14, the previous responses characterized by
having been collected from the individuals in response to the
plurality of questions.
17. An electronic prediction system for assessing an individual
applicant for employment by an employer, the system comprising: an
electronic applicant terminal operable to present a plurality of
questions to the applicant and to receive electronically the
applicant's responses to the questions; an applicant screening
computer responsive to the electronically received responses and
operable to predict expected performance for a candidate if the
candidate were to be employed by the employer, the computer
providing applicant results indicative of expected performance
based upon correlations of the electronically received answers with
answers to questions by other individuals for which job duty
performance information has been collected; and an electronic
report viewer operable to present to the employer a viewable report
containing the applicant results.
18. The apparatus of claim 17 wherein the job duty performance
information has been collected electronically.
19. The apparatus of claim 17 wherein the job duty performance
information has been stored electronically.
20. The apparatus of claim 17 characterized in that the applicant
terminal is configured to communicate with the applicant screening
computer over the Internet.
21. An electronic assessment system for assessing an individual for
a potential human resources action by an employer, the system
comprising: an electronic terminal operable to present a plurality
of questions to the individual and to receive electronically the
individual's responses to the questions; a computer configured to
provide results automatically in response to receiving the
electronically received responses; and an electronic report viewer
operable to present to the employer a viewable report containing
the results, characterized in that the computer is configured to
compare automatically the electronically received responses with
electronically stored rating correlation data, the rating
correlation data being indicative of calculated correlations
between actual job duty performance ratings of a plurality of hired
individuals and previous responses given by the hired
individuals.
22. The electronic assessment system of claim 21, characterized in
that the potential human resources action is hiring the individual
for a job.
23. The electronic assessment system of claim 21, characterized in
that the potential human resources action is promoting the
individual.
24. An apparatus for assisting in determining the suitability of an
individual for employment by an employer, the apparatus comprising:
an electronic data interrogator operable to present a first set of
a plurality of questions to the individual; an electronic answer
capturer operable to electronically store the individual's
responses to at least a selected plurality of the first set of
questions presented to the individual; an electronic predictor
responsive to the stored answers and operable to predict at least
one post-hire outcome if the individual were to be employed by the
employer, the predictor providing a prediction of the outcome based
upon correlations of the stored answers with answers to sets of
questions by other individuals for which post-hire information has
been collected; and an electronic results provider providing an
output indicative of the outcome to assist in determining the
suitability of the individual for employment by the employer.
25. An apparatus according to claim 24 wherein the post-hire
outcome indicates whether the individual is predicted to be
eligible for re-hire after termination.
26. An apparatus according to claim 24 wherein the post-hire
outcomes indicate whether the individual is predicted to be
involuntarily terminated and whether the individual is predicted to
be eligible for re-hire after termination.
27. An apparatus according to claim 24 wherein at least one of the
predicted outcomes is a predicted value for a continuous
variable.
28. An apparatus according to claim 24 wherein the predicted
outcome indicates whether the individual will belong to a
particular group.
29. An apparatus according to claim 24 wherein at least one of the
predicted outcomes is a predicted ranking of the individual for the
outcome.
30. An apparatus according to claim 24 wherein at least one of the
predicted outcomes indicates a predicted employment tenure for the
individual.
31. An apparatus according to claim 24 wherein at least one of the
predicted outcomes indicates a predicted number of accidents for
the individual.
32. An apparatus according to claim 24 wherein at least one of the
predicted outcomes indicates a predicted sales level for the
individual.
33. An apparatus according to claim 24 wherein the predictor
comprises an artificial intelligence-based prediction system.
34. An apparatus according to claim 24 wherein the data
interrogator is located at a first location and the predictor is
located at a second location which is remote from the first
location.
35. An apparatus according to claim 34 wherein the data
interrogator and the predictor are selectively electronically
interconnected through a network.
36. An apparatus according to claim 35 wherein the network is the
worldwide web.
37. An apparatus according to claim 35 wherein the network is a
telephone network.
38. An apparatus according to claim 35 wherein the network is an
electronic network.
39. An apparatus according to claim 24 wherein the first set of
questions may be varied.
40. An apparatus according to claim 39 wherein the predictor is
operable to determine and indicate a lack of a correlation between
one or more questions of the first set of questions and at least
one of the predicted outcomes, whereby questions which lack the
correlation may be discarded or modified.
41. An apparatus according to claim 24 wherein at least one of the
predicted outcomes is longevity with an employer and the answers to
sets of questions by other individuals comprise answers by
employees of the employer for whom longevity has been
determined.
42. An apparatus according to claim 24 in which the predictor
comprises at least one model which provides a predictor of the
probability of the individual exhibiting at least one of the
predicted outcomes, the model being based on correlations between
the at least one of the predicted outcomes and the answers to
questions by the other individuals, including answers by at least
some employees of the employer, the model taking at least selected
answers of the stored answers as inputs to the model, a probability
of the individual exhibiting the at least one of the predicted
outcomes being provided as an output of the model.
43. An apparatus according to claim 33 wherein the model comprises
at least one expert system.
44. An apparatus according to claim 24 wherein the predictor is
responsive to the stored answers and operable to predict plural
outcomes if the individual were to be employed by the employer.
45. A method for assessing suitability of persons for employment
based on information for hired employees, the method comprising:
collecting pre-hire applicant information for hired employees
before they are hired; collecting post-hire measures of the job
effectiveness of hired employees; constructing an artificial
intelligence model identifying associations of patterns within the
pre-hire data associated with patterns of job effectiveness in the
post-hire data; collecting pre-hire information for a new
applicant; and applying the artificial intelligence model to the
pre-hire information for the new applicant to provide a prediction
of the new applicant's suitability for employment.
46. The method of claim 45 further comprising: collecting post-hire
information for the new applicant; and using at least the pre-hire
and post-hire information for the new applicant to refine the
artificial intelligence model.
47. The method of claim 45 further comprising: constructing at
least one other artificial intelligence model of a different type;
and assessing the relative effectiveness of the artificial
intelligence models at predicting suitability of employees for
employment based on actual employment effectiveness of employees
hired based on the models.
48. An apparatus for assisting in determining the suitability of an
individual for employment by an employer, the apparatus comprising:
means for electronically presenting a first set of a plurality of
questions to the individual; means for electronically storing the
individual's responses to at least a selected plurality of the
first set of questions presented to the individual; responsive to
the stored answers, means for predicting at least one post-hire
outcome if the individual were to be employed by the employer, the
means for predicting providing a prediction of the outcome based
upon correlations of the at least one characteristic with answers
to sets of questions by other individuals and the closeness of the
stored answers to such correlations; and means for providing an
output indicative of the outcome to assist in determining the
suitability of the individual for employment by the employer.
49. An artificial intelligence-based system for predicting employee
behaviors based on pre-hire information collected for the employee,
the system comprising: an electronic device for presenting an
employment application comprising a set of questions to an
employment candidate, wherein the electronic device is operable to
transmit answers of the employment candidate to a central store of
employee information, wherein the central store of employee
information comprises information collected for a plurality of
candidate employees and a plurality of hired employees; an
artificial intelligence-based model constructed from information
collected from the hired employees based on answers provided by the
hired employees and employment behaviors observed for the hired
employees; a software system for supplying the answers of the
employment candidate to the artificial intelligence-based model to
produce predicted employment behaviors for the employment
candidate; and a report generator to produce a hiring
recommendation report for the employment candidate based on the
predicted employment behaviors of the employment candidate.
50. A computer-implemented method of predicting employment
performance characteristics for a candidate employee based on
pre-hire information collected for hired employees, the method
comprising: collecting data indicating pre-hire information for a
plurality of the hired employees; collecting data indicating
post-hire outcomes for the hired employees; constructing an
artificial intelligence-based model from the pre-hire information
and the post-hire outcomes for the employees; from the candidate
employee, electronically collecting data indicating pre-hire
information of the candidate employee; and applying the model to
the collected pre-hire information of the candidate employee to
generate one or more predicted post-hire outcomes for the candidate
employee.
51. The method of claim 50 wherein collecting data from the
candidate employee comprises electronically presenting a set of
questions at an electronic device and electronically collecting
answers to the questions at the electronic device.
52. The method of claim 50 wherein the pre-hire information
comprises one or more pre-hire characteristics and constructing the
model comprises: identifying one or more pre-hire characteristics
as ineffective predictors; and responsive to identifying the
pre-hire characteristics as ineffective predictors, omitting the
ineffective predictors from the model.
53. The method of claim 50 further comprising: providing a report
indicating applicant flow.
54. The method of claim 50 wherein constructing the model
comprises: constructing a plurality of proposed models, wherein at
least two of the models are of different types; and selecting a
superior proposed model as the model to be used.
55. The method of claim 54 wherein at least two of the proposed
models are different expert models.
56. The method of claim 50 further comprising using the one or more
predicted post-hire outcomes to influence a hiring decision.
57. The method of claim 50 further comprising using the one or more
predicted post-hire outcomes to influence a promotion decision.
58. The method of claim 50 wherein at least one of the predicted
post-hire outcomes is denoted as a probability that a particular
value range of a job effective measure will be observed for a
candidate employee.
59. The method of claim 50 wherein at least one of the predicted
post-hire outcomes is denoted as a value for a continuous
variable.
60. The method of claim 50 wherein at least one of the predicted
post-hire outcomes is denoted as a relative ranking for an
outcome.
61. The method of claim 60 wherein the ranking is relative to other
employment candidates.
62. The method of claim 60 wherein the ranking is relative to the
hired employees.
63. The method of claim 50 further comprising: storing a relative
importance of one or more particular post-hire outcomes; and
generating automated hiring recommendations based on the predicted
post-hire outcomes for the candidate employees and the importance
of the post-hire outcomes.
64. The method of claim 50 further comprising: refining the model
based on newly-observed post-hire outcomes.
65. The method of claim 50 wherein the pre-hire information
comprises answers to questions on a job application, the method
further comprising: identifying one or more questions as
ineffective predictors; responsive to identifying the questions as
ineffective predictors, modifying the job application by removing
the questions; collecting new pre-hire information for additional
candidate employees based on the modified job application;
collecting new post-hire information for the additional candidate
employees; and constructing a refined artificial-intelligence model
based on the additional pre-hire and post-hire information for the
additional candidate employees.
66. The method of claim 65 further comprising: responsive to
determining pre-hire and post-hire information has been collected
for a sufficient number of additional employees, providing an
indication that a refined model can be constructed.
67. The method of claim 65 further comprising: providing a report
indicating the identified questions are ineffective predictors.
68. The method of claim 65 further comprising: adding one or more
new questions to the modified job application before collecting
additional pre-hire information.
69. The method of claim 68 wherein the new questions are composed
based on job skills appropriate for a particular job related to the
job application.
70. The method of claim 68 further comprising: evaluating the
effectiveness of the new questions.
71. An artificial intelligence-based employee performance
prediction system comprising: a set of pre-hire characteristic
identifiers; a set of post-hire outcome identifiers; a collection
of data for employees, wherein the data includes values associated
with the pre-hire identifiers and the post-hire identifiers; and an
artificial intelligence-based model chosen from a set of candidate
models, the artificial intelligence-based model exhibiting superior
ability at predicting values associated with the post-hire outcome
identifiers based on values associated with the pre-hire
characteristic identifiers in comparison to the other candidate
models.
72. A computer-readable medium having a collection of
employment-related data, the data comprising: pre-hire information
for a plurality of employees, wherein the pre-hire information
comprises information electronically-collected from an applicant,
wherein the information comprises a plurality of pre-hire
characteristics; post-hire information for at least some of the
plurality of employees, wherein the information comprises a
plurality of post-hire outcomes; and a data structure identifying
which of the pre-hire characteristics are effective in predicting a
set of one or more of the post-hire outcomes for a job
applicant.
73. A method for providing an automated hiring recommendation for a
new potential employee, the method comprising: collecting pre-hire
information for potential employees; storing the pre-hire
information for the potential employees in a database; after hiring
a plurality of the potential employees, collecting employment
performance information for at least some of the hired employees;
storing the employment performance information collected from the
hired employees; constructing an artificial intelligence-based
model based on correlations between the pre-hire information and
the employment performance information collected from one or more
of the hired employees; collecting pre-hire information for a new
potential employee; based on the artificial intelligence-based
model, providing an automated hiring recommendation for the new
potential employee; after hiring the new potential employee,
collecting employment performance information for the new potential
employee; adding the employment performance information for the new
potential employee to the database; and modifying the artificial
intelligence-based model based on the pre-hire and employment
performance information for the new potential employee.
74. A method for providing an automated hiring recommendation
service for an employer, the method comprising: stationing a
plurality of electronic devices at a plurality of employer sites,
wherein the electronic devices are operable to accept directly from
one or more job applicants answers to questions presented at the
electronic devices; sending the answers of at least one of the job
applicants to a remote site for analysis; applying an artificial
intelligence-based predictive model to the answers of the least one
of the job applicant to generate an automated hiring
recommendation; and automatically sending the hiring recommendation
to the employer.
75. A method of constructing a model generating one or more job
performance criteria predictors based on input pre-hire
information, the method comprising: from a plurality of applicants,
electronically collecting pre-hire information from the applicants;
collecting post-hire information for the applicants based on job
performance of the applicants after hire; and from the pre-hire
information and the post-hire information, generating an artificial
intelligence-based predictive model operable to generate one or
more job performance criteria predictors based on input pre-hire
information from new applicants.
76. A computer-readable medium comprising computer-executable
instructions for performing the method of claim 75.
77. The method of claim 75 further comprising: limiting the
applicants for the model to those providing a certain answer to a
knock-out question.
78. The method of claim 75 further comprising: limiting the
applicants for the model to those not providing a certain answer to
a knock-out question.
79. The method of claim 75 further comprising: limiting the
applicants for the model to those with a particular occupation; and
constructing the model as an occupationally-specialized model.
80. The method of claim 75 wherein the model accepts one or more
inputs, the method further comprising: identifying in the pre-hire
information one or more characteristics that are ineffective
predictors; and omitting the ineffective predictors as inputs to
the model.
81. The method of claim 75 wherein the pre-hire information
comprises one or more characteristics, the method further
comprising: identifying in the pre-hire information one or more
characteristics that are ineffective predictors; and providing an
indication that the characteristics no longer need to be
collected.
82. The method of claim 75 wherein job performance criteria
predictors comprise a predictor indicating whether a job candidate
will be voluntarily terminated.
83. The method of claim 75 wherein job performance criteria
predictors comprise a predictor indicating whether a job candidate
will be eligible for rehire after termination.
84. The method of claim 75 wherein the pre-hire information
comprises one or more characteristics, the method further
comprising: identifying in the pre-hire information one or more
characteristics that are ineffective predictors; and responsive to
identifying the ineffective predictors, collecting new pre-hire
information not including the ineffective predictors; and building
a refined model based on the new pre-hire information.
85. The method of claim 84 further comprising: adding one or more
new characteristics to be collected when collecting the new
pre-hire information.
86. The method of claim 85 further comprising: evaluating the
effectiveness of the new characteristics.
87. An electronic assessment system for assessing an individual for
a potential human resources action by an employer, the system
comprising: an electronic terminal operable to present a plurality
of questions to the individual and to receive electronically the
individual's responses to the questions; a computer configured to
provide results automatically in response to receiving the
electronically received responses; and an electronic report viewer
operable to present to the employer a viewable report containing
the results, characterized in that the computer is configured to
rate automatically the electronically received responses to provide
a rating for the individual and to rank automatically the
individual in order against other individuals based on the
rating.
88. An electronic assessment system for assessing an individual for
a potential human resources action by an employer, the system
comprising: an electronic terminal operable to present a plurality
of questions to the individual and to receive electronically the
individual's responses to the questions; a computer configured to
rate automatically the electronically received responses to provide
a rating for the individual and to rank automatically the
individual in order against other individuals based on the rating;
and an electronic report viewer operable to present to the employer
a viewable report containing the individual's rating and the
individual's rank order.
89. An electronic assessment system for assessing an individual for
a potential human resources action by an employer comprising: an
electronic terminal operable to present an abbreviated set of
questions to the individual and to receive electronically the
individual's responses to the questions; a computer configured to
provide results automatically in response to receiving the
electronically received responses; and an electronic report viewer
operable to present to the employer a viewable report containing
the results, characterized in that the computer is configured to
compare automatically the electronically received responses with
electronically stored rating correlation data, the rating
correlation data being indicative of calculated correlations
between actual job duty performance ratings of a plurality of hired
individuals and previous responses given by the hired individuals
to a full set of questions, the abbreviated set of questions being
selected from the full set of questions.
90. The system of claim 89, further characterized in that the
abbreviated set of questions are selected from the full set of
questions based on validated correlations between the abbreviated
set and the actual job duty performance ratings.
91. A computer-readable medium substantially as shown and
described.
92. A method substantially as shown and described.
93. An apparatus substantially as shown and described.
94. A method for constructing an artificial intelligence-based
employment selection process based on pre-hire information
comprising personal employee characteristics and post-hire
information comprising employee job performance observation
information, the method comprising: generating a plurality of
predictive artificial intelligence models based on the pre-hire and
post-hire information, wherein at least two of the artificial
intelligence models are of different types; testing effectiveness
of the models to select an effective model; and applying the
effective model to predict post-hire information not yet
observed.
95. The method of claim 94 characterized in that at least one of
the models is an expert system.
96. The method of claim 94 further comprising: identifying at least
one of the models as exhibiting impermissible bias; and avoiding
use of the models exhibiting impermissible bias.
97. The method of claim 96 wherein the impermissible bias is
against a protected group of persons.
98. A computer-implemented method of refining an
artificial-intelligence based employee performance selection
system, the method comprising: collecting information via an
electronic device presenting a set of questions to employment
candidates, wherein the questions are stored in a computer-readable
medium; testing effectiveness of at least one of the questions in
predicting the post-hire information; and responsive to determining
the question is ineffective, deleting the question from the
computer-readable medium.
99. A computer-readable medium comprising a predictive model, the
model comprising: inputs for accepting one or more characteristics
based on pre-hire information for a job applicant; one or more
predictive outputs indicating one or more predicted job
effectiveness criteria based on the inputs, wherein the predictive
model is an artificial intelligence-based model constructed from
pre-hire data electronically collected from a plurality of
employees and post-hire data, and the model generates its
predictive outputs based on the similarity of the inputs to
pre-hire data collected for the plurality of employees and their
respective post-hire data.
100. The computer-readable medium of claim 99 wherein the
predictive model comprises a predictive output indicating a rank
for the job applicant.
101. The computer-readable medium of claim 100 wherein the rank is
relative to other applicants.
102. The computer-readable medium of claim 100 wherein the rank is
relative to the plurality of employees.
103. The computer-readable medium of claim 99 wherein the
predictive model comprises a predictive output indicating
probability of group membership for the job applicant.
104. The computer-readable medium of claim 99 wherein the
predictive model comprises a predictive output indicating predicted
tenure for the job applicant.
105. The computer-readable medium of claim 99 wherein the
predictive model comprises a predictive output indicating predicted
tenure for the job applicant.
106. The computer-readable medium of claim 99 wherein the
predictive model comprises a predictive output indicating predicted
number of accidents for the job applicant.
107. The computer-readable medium of claim 99 wherein the
predictive model comprises a predictive output indicating whether
the applicant will be involuntarily terminated.
108. The computer-readable medium of claim 99 wherein the
predictive model comprises a predictive output indicating whether
the applicant will be eligible for rehire after termination.
109. A computer-readable medium comprising a refined predictive
model, the model comprising: inputs for accepting one or more
characteristics based on pre-hire information for a job applicant;
one or more predictive outputs indicating one or more predicted job
effectiveness criteria based on the inputs, wherein the predictive
model is constructed from pre-hire data electronically collected
from a plurality of employees and post-hire data, wherein the
pre-hire data is based on a question set refined by having
identified and removed one or more questions as ineffective.
110. The computer-readable medium of claim 109 wherein the
ineffective questions are identified via an information transfer
technique.
111. The computer-readable medium of claim 109 wherein the model is
an artificial intelligence-based model.
112. A system for assessing an individual applicant for employment
by an employer, the system comprising: an electronic applicant
terminal logged on to a website and operable to present a plurality
of questions to the applicant and to receive electronically the
applicant's responses to the questions; an applicant screening
computer associated with the website and configured to provide
applicant results automatically in response to receiving the
electronically received responses; an electronic network connecting
the electronic applicant terminal to the applicant screening
computer in accordance with a uniform resource locator associated
with website, the uniform resource locator having been entered by
the applicant into the electronic applicant terminal; and an
electronic report viewer operable to present to the employer a
viewable report containing the applicant results, wherein the
applicant screening computer is configured to compare automatically
the electronically received responses with electronically stored
rating correlation data, the rating correlation data being
indicative of calculated correlations between actual job duty
performance ratings of a plurality of hired individuals and
previous responses given by the hired individuals.
113. A method for assessing an individual applicant for employment
by an employer, the method comprising: hosting an employer website
identified by a uniform resource locator; providing via the uniform
resource locator access to the website by the applicant at an
applicant terminal; transmitting questions from an applicant
screening computer to the applicant terminal; receiving at the
applicant screening computer responses from the applicant to the
questions transmitted over the Internet; comparing automatically
the received responses with electronically stored rating
correlation data, the rating correlation data being indicative of
calculated correlations between actual job duty performance ratings
of a plurality of hired individuals and previous responses given by
the hired individuals.
114. A method for assessing an individual applicant for employment
by an employer, the method comprising: making a connection over a
network between a job applicant telephone and a computer;
transmitting questions from the computer to the job applicant
telephone; receiving at the computer responses from the applicant
to the questions transmitted, the responses having been transmitted
over the network from the job applicant telephone; comparing
automatically the received responses with electronically stored
rating correlation data, the rating correlation data being
indicative of calculated correlations between actual job duty
performance ratings of a plurality of hired individuals and
previous responses given by the hired individuals.
115. A method of assessing the suitability of an individual for a
job action, the method comprising: making a connection between a
computer and a terminal; receiving at the computer responses
entered at the terminal by the individual in response to questions;
scoring the received responses according to correlations between
job duty performance ratings of a plurality of workers and previous
responses given by the workers.
116. The method of claim 115, wherein making the connection
comprises logging the individual on to a website.
117. The method of claim 115, wherein the scoring is performed
automatically.
118. The method of claim 115, wherein the scoring is performed in
real time.
119. The method of claim 115, further comprising displaying a rank
order of the individual.
120. The method of claim 115, wherein the previous responses were
given by the workers in response to said questions.
121. The method of claim 115, wherein the scoring predicts the
turnover potential.
122. The method of claim 115, wherein the scoring provides
information on a probability of not terminating early.
123. The method of claim 115, wherein the scoring is indicative of
a probability of successful job duty performance.
124. The method of claim 115, wherein the individual is a job
applicant and the job action is employment.
125. The method of claim 115, wherein the correlations are made
before the responses are received.
126. An apparatus for assessing the suitability of an individual
for a job action, the apparatus comprising: means for making a
connection between a computer and a terminal; means for receiving
at the computer responses entered at the terminal by the individual
in response to questions; means for scoring the received responses
according to correlations between job duty performance ratings of a
plurality of workers and previous responses given by the
workers.
127. The apparatus of claim 126, wherein the means for making a
connection comprises a website.
128. The apparatus of claim 126, wherein the previous responses
were given by the workers in response to said questions.
129. The apparatus of claim 126, wherein the terminal comprises a
telephone.
130. The apparatus of claim 126, wherein the means for making a
connection comprises an Internet and the responses are entered at
the terminal by pointing and clicking.
131. The apparatus of claim 126, wherein the computer comprises a
testing program.
132. The apparatus of claim 126, further comprising a scoring
database.
133. The apparatus of claim 126, wherein the correlations are made
before the responses are received.
134. A computer program capable of causing a computer to perform
the functions of: making a connection between a terminal and a
computer; receiving at the computer responses entered at the
terminal by an individual in response to questions; scoring the
received responses according to correlations between job duty
performance ratings of a plurality of workers and previous
responses given by the workers.
135. A method of constructing a computer model useful for deciding
whether a new job applicant would be suitable for employment, the
method comprising: collecting complete pre-hire information from a
plurality of original applicants in response to a complete set of
pre-hire information items; hiring the original applicants;
collecting post-hire information for the original applicants based
on job performance of the original applicants after hire; comparing
the complete pre-hire information to the post-hire information;
responsive to the comparing, selecting a sub-set of the complete
pre-hire information items, the sub-set being selected for a high
correlation to the post-hire information; generating from the
pre-hire information and the post-hire information a computerized
predictive model operable to generate an applicant suitability
indication based on newly input pre-hire information electronically
collected from a new applicant, the suitability indication
indicating whether the new applicant would be suitable for
employment and the newly input pre-hire information being limited
to the new applicant's responses to the selected subset of the
complete pre-hire information items.
136. The method of claim 135 wherein the computerized predictive
model reflects input from job incumbent experts.
137. The method of claim 135 further comprising: designing and
testing the pre-hire information to comply with EEOC guidelines and
to not be dependent on any group membership.
138. The method of claim 94 wherein the different types of
artificial intelligence models are at least two of the following
four different types of artificial intelligence models: application
questions; customer service inventory; working with information
test; and sales potential inventory.
139. An electronic prediction system for assessing the suitability
of job applicants for an employer, the electronic prediction system
comprising: a plurality of applicant terminals connected to the
Internet; an applicant screening server connected through the
Internet to the applicant terminals, the applicant screening server
having a testing computer program and storing test meta-data; an
employer website configured to present questions to the applicants
at the applicant terminals and to receive applicant responses
entered at the applicant terminals in response to presentation of
the questions, the questions having been validated by correlating
job duty performance ratings of a plurality of hired workers with
previous responses given by the workers to the questions; a scoring
system for automatically scoring the applicant responses, the
scoring system being validated to predict both performance and
turnover potential; a scoring database connected to the applicant
screening server; an applicant input system located on the
employer's premises and configured electronically to receive input
from an applicant at the employer's premises after the candidate
has come to the employer's premises and logged on; and a viewing
system for permitting the employer to view applicant results from
the electronic prediction system and the applicant's rank
order.
140. A method of constructing a computer model generating one or
more job performance criteria predictors based on input pre-hire
information, the method comprising: from a plurality of applicants,
collecting pre-hire information from the applicants; collecting
post-hire information for the applicants based on job performance
of the applicants after hire; and responsive to the pre-hire
information and the post-hire information, forming a computer model
operable to generate a plurality of job performance criteria
predictors based on input pre-hire information electronically
collected from new applicants.
141. An electronic prediction system for assessing the suitability
of job applicants for an employer, the electronic prediction system
comprising: an applicant screening server storing a testing
computer program and test data, the testing computer program
configured to present questions to the applicants over a network,
the questions having been validated by correlating job duty
performance ratings of a plurality of hired workers with responses
given by the workers; and means for receiving input from an
applicant at the employer's premises after the candidate has come
to the employer's premises and logged on.
142. A method of deciding whether a new job applicant would be
suitable for employment, the method comprising: collecting complete
pre-hire information from a plurality of original applicants in
response to a complete set of pre-hire information items; hiring
the original applicants; collecting post-hire information for the
original applicants based on job performance of the original
applicants after hire; comparing the complete pre-hire information
to the post-hire information; responsive to the comparing,
selecting a sub-set of the complete pre-hire information items, the
sub-set being selected for a correlation to the post-hire
information; preparing from the pre-hire information and the
post-hire information a computerized predictive model operable to
generate automatically an applicant suitability indication based on
newly input pre-hire information electronically collected from a
new applicant, the suitability indication indicating whether the
new applicant would be suitable for employment and the newly input
pre-hire information being limited to the new applicant's responses
to the selected subset of the complete pre-hire information items.
Description
[0001] This application claims the benefit of U.S. Provisional
Patent Application No. 60/211,044, filed Jun. 12, 2000.
BRIEF DESCRIPTION OF THE DRAWINGS
[0002] FIG. 1 provides a block diagram of an exemplary system in
accordance with the present invention.
[0003] FIG. 2 illustrates a process for testing and evaluating job
applicants in accordance with an embodiment of the present
invention.
[0004] FIG. 3 depicts a hiring procedure in accordance with one
embodiment of the invention.
[0005] FIG. 4 is a block diagram of a process employing
feedback.
[0006] FIG. 5 diagrams an online system in accordance with one
embodiment of the invention.
[0007] FIG. 6 shows an example of a web-based presentation for a
screening solution.
[0008] FIG. 7 shows an example of a stack ranked table.
[0009] FIG. 8 shows an example of a screening solution question
presented to an applicant taking a screening solution test over the
Internet.
[0010] FIG. 9 shows an example of a structured interview guide for
use in an interview solution.
[0011] FIG. 10 illustrates procedural steps that may be followed in
a web-based applicant system according to an embodiment of the
present invention.
[0012] FIG. 11 illustrates procedural steps that may be followed in
a web-based selection solution according to an embodiment of the
present invention.
[0013] FIG. 12 illustrates procedural steps that may be followed by
an employer according to an embodiment of the present
invention.
[0014] FIG. 13 illustrates a human capital management
life-cycle.
DETAILED DESCRIPTION
[0015] A system for testing a job applicant provides a computerized
stack ranking of multiple applicants, predictive of the comparative
levels of successful job performance. The predictive stack ranking
may be used as a dynamic interactive filter with a pool of
applicants over the course of the evaluation or employment process.
The system may utilize a communications network to communicate
between an applicant terminal and a system server.
[0016] The system may be used for example for screening, selecting,
retaining, assigning, or analyzing the job applicant. The job
applicant can for example be a new job applicant, an employee
seeking to retain a job, an employee seeking a different job in the
same organization, or an employee being evaluated for retention,
re-assignment, or promotion. Applicants may or may not know they
are being evaluated.
[0017] Once an applicant becomes an employee, the system may
collect data regarding the employee for use in a feedback loop
informing the online hiring process and improving the accuracy of
the predictive stack ranking. For example, the data may indicate
the employer's rating of the employee's actual job performance.
Such a rating can be cross-checked against the answers that the
employee gave during the application process. The cross-checking
can be used as feedback to refine the questions and evaluation
criteria used at each stage of the hiring process. For example, the
cross-checking may be analyzed to select from among many questions
a small subset having high predictive value. The small subset can
then be used in a quick initial screening stage. Or, the small
subset can be given greater weight than other questions in a
computerized stack ranking of candidates.
[0018] FIG. 1 provides a block diagram of an exemplary system in
accordance with the present invention. A job applicant can use
applicant terminal 102 to communicate over network 104 with system
server 106. Applicant terminal 102 may for example be a telephone
handset, a personal computer, a workstation, a handheld wireless
device such as those marketed under the trademarks PALM or
HANDSPRING, or a Wireless Application Protocol enabled device such
as a mobile phone. Network 104 may for example be the Internet, the
World Wide Web, a wide area network, a local area network, a
telephone network, a wireless communication network, a combination
thereof, or any other link capable of carrying communications
between an applicant terminal and a server.
[0019] System server 106 employs a testing computer program 108 and
has access to a scoring database 110. System server 106
communicates with applicant terminal 102 in accordance with
instructions from testing computer program 108.
[0020] System server 106 may communicate with employer server 112
over network 104 or over direct link 114. System server 106 is
shown as a unitary server, but may be a distributed computing
platform.
[0021] An applicant terminal may be remote from, or co-located
with, system server 106 and/or employer server 112. For example,
applicant terminal 102 may be located at a job applicant's home,
applicant terminal 116 may be located at a job fair or employment
office, and applicant terminal 120 may be located at an employer's
location.
[0022] Partner server 121 may be linked to network 104 and system
server 106 to facilitate integration of a business partner seeking
to participate in the system of FIG. 1.
[0023] System server 106 may pose questions to a job applicant
located at an applicant terminal, receive responses from the job
applicant, and score the answers in accordance with scoring
database 110. The scoring may take place in real time, i.e., while
the applicant is still online, and may be reported in the form of a
comparative stack ranking of multiple applicants. The stack ranking
may be delivered from system server 106, over either network 104 or
direct link 114, to employer server 112.
[0024] Scoring of each answer by system server 106 may be instant,
i.e., before the next question is answered. Thus, adaptive testing
techniques may be implemented over network 104. For example, the
answers given by an applicant at applicant terminal 102 to
questions propounded early in a test may determine which questions
are propounded by system server 106 to the applicant later in the
same test. In addition, if an applicant at terminal 102 provides an
unacceptable answer to a disqualifying "knock-out" question, server
106 may immediately terminate the test.
[0025] These same adaptive testing principles may be applied to a
software program used to support a real time interview, either in
person or over a communications network. For example, an employer
conducting an oral interview in person or over a telephone can
enter a candidate's oral answer into employer terminal 124, which
then communicates the answer to system server 106, which in turn
suggests via employer terminal 124 the next question for the
employer to ask the interviewee.
[0026] The system may test an online applicant for any competency
desired, in any sequence. The tested competencies may be abilities,
traits, knowledge, skills, etc., that have been proven relevant to
and predictive of successful job performance. By way of example and
not limitation, the following competencies may be tested:
[0027] 1. dependability
[0028] 2. agreeableness
[0029] 3. critical thinking
[0030] 4. problem solving ability
[0031] 5. talkativeness
[0032] 6. assertiveness
[0033] 7. gregariousness
[0034] 8. persuasiveness
[0035] 9. achievement
[0036] 10. education
[0037] 11. experience
[0038] 12. customer service orientation
[0039] 13. customer focus
[0040] 14. conscientiousness
[0041] 15. self-confidence
[0042] 16. motivation
[0043] 17. revenue focus
[0044] 18. cognitive ability
[0045] 19. leadership
[0046] 20. decision making
[0047] 21. flexibility
[0048] 22. commitment
[0049] 23. learning ability
[0050] 24. dedication
[0051] 25. tenacity
[0052] 26. number of jobs held
[0053] 27. length of time in job(s)
[0054] 28. working with information
[0055] 29. supervisory potential
[0056] 30. judgment
[0057] 31. leadership
[0058] 32. coaching skills
[0059] 33. teamwork
[0060] 34. interpersonal skills
[0061] 35. business leadership
[0062] 36. leadership motivation
[0063] 37. self-leadership
[0064] 38. interpersonal leadership
[0065] 39. communication skills
[0066] 40. management potential
[0067] 41. likelihood of retention
[0068] 42. self-control
[0069] 43. energy
[0070] 44. executive potential
[0071] 45. listening orientation
[0072] 46. language skills (English, etc.)
[0073] 47. verbal reasoning
[0074] 48. spatial ability
[0075] 49. interest
[0076] 50. motivation
[0077] Typically, system server 106 tests for certain ones of the
competencies that have been proven to be predictive of successful
performance of the type of job for which the applicant is being
considered. The results of the testing are tabulated in a stack
ranked table. The stack ranked table may rank a number of
applicants against each other and list them in order, from first to
last. The table may also present other information for each
applicant. The other information may include, by way of example and
not limitation:
[0078] 1. Name
[0079] 2. Identifying number (e.g. social security number).
[0080] 3. Score achieved at various stages for various
competencies.
[0081] 4. Recommendation (or not) to continue the hiring process
beyond each stage
[0082] 5. Link to application information (e.g. address, resume
details)
[0083] 6. Contact information (phone number, e-mail address,
mailing address, etc.)
[0084] 7. Date of application
[0085] 8. Success or failure in complying with knockout
requirements for the job
[0086] 9. Screening solution scores, presented as percentiles
[0087] 10. A calculated recommendation to proceed or not to proceed
with the applicant
[0088] 11. Results (by competency) of the selection solution
[0089] 12. Link to allow manual entry of the test answers if not
done on computer directly by the applicant
[0090] 13. A calculated recommendation to hire or not hire based on
a weighted overall score of selection competencies (or other
factors the hiring company wishes to use and that are approved as
statistically valid and legally defensible)
[0091] 14. Additional columns for storage of data from a structured
behavioral interview
[0092] 15. Additional columns for storage of data from other
decision-making processes such as drug testing, reference checks,
or medical exams.
[0093] A process for testing and evaluating job applicants may be
described with reference to FIG. 2. Generally, applicant testing
201 includes providing a test to a job applicant and scoring the
applicant's answers. The test may be administered online or it may
be administered manually off-line. Scores are entered into a system
for calculating a stack ranked table. Predictive stack ranking 202
generally includes ranking a job applicant against other job
applicants in order from first to last or other comparative
ranking. The other job applicants may be current job applicants,
past job applicants, or fictional job applicants.
[0094] FIG. 3 depicts a hiring procedure in accordance with one
embodiment of the invention. Announcement 302 may be an online job
announcement such as a web page with an "apply now" hyperlink icon.
The web page may reside on an employer's website or an employment
agency website, for example. Or, an online job announcement may be
a recorded announcement on a menu-driven telephone voice processing
system. Alternatively, announcement 302 may be an offline job
announcement such as a newspaper advertisement.
[0095] In response to announcement 302, an interested job applicant
requests administration of screening test 304. Screening test 304
may be remotely administered and scored online, with the scores
being automatically provided to predictive stack ranking 306.
Alternatively, screening test 304 may be administered manually with
paper and pencil, and then graded by hand or machine, with the
scores being provided to predictive stack ranking 306. The
predictive stack ranking may for example be constructed by system
server 106 or employer server 112.
[0096] Predictive stack ranking 306 totals the graded answers
according to particular competencies known to be relevant to
successful job performance. Predictive stack ranking 306 may be
administered by a computer processor located at system server 106,
for example. Predictive stack ranking 306 may give different weight
to different questions, and may at any stage immediately disqualify
an applicant providing an unacceptable answer to a "knock-out"
question. Predictive stack ranking 306 may rank the applicant in
order against other job applicants in a table. Predictive stack
ranking 306 may be used to decide which applicants to invite for
the next stage, selection test 308.
[0097] Selection test 308 is preferably conducted under supervised
conditions. For example, selection test 308 may be administered in
person. An in-person test may take place at a job fair, an
employer's location, a job site, or an employment agency. An
in-person test may include verification of the job applicant's
identity, such as by examination of a photo identification document
produced by a test-taker. Selection test 308 may be administered
online or manually. Supervised conditions typically include
observation of the test-taker during administration of the test.
The answers to selection test 308 are graded and the results are
incorporated in predictive stack ranking 306.
[0098] Predictive stack ranking 306 may then update a previously
created entry for the applicant and rank or re-rank the applicant
in order against other job applicants. After this is accomplished,
the highest ranking applicants may be invited for interview
310.
[0099] Interview 310 may be structured or unstructured, online or
in person. If interview 310 is structured, a program leads the
interviewer through the interview by suggesting questions one at a
time. The program may be a list of questions written on paper or it
may be a computer program resident for example in system server
106. The program suggests questions that are predetermined to be
valid, i.e., proven to be associated with successful job
performance and legally permitted. The interviewer can input the
answers and/or a score for the answers, either after each answer or
at the conclusion of the interview. This can be done via employer
terminal 124, for example.
[0100] Interview 310 results in an interview score being provided
to predictive stack ranking 306. Predictive stack ranking 306 is
revised to reflect the interview score. In particular, the relative
rank of the job applicants is reassessed.
[0101] FIG. 4 is a block diagram of a process employing feedback.
Test design 402 is initially performed using industry-accepted
standards. Test administration 404 tests and scores job applicants
and/or incumbents. Employee performance evaluation 406 measures
actual job performance of the applicant or incumbent after holding
the job for a period of time. This information is fed back to test
design 402 and/or test administration 404. Test design 402 may be
revised to delete questions which were not predictive of successful
job performance. This can be done for example by deleting questions
whose answers bore no relation to performance evaluation 406 for a
statistically valid sample. Test administration 404 may be revised
by adjusting the weight given to certain questions or answers that
showed an especially strong correlation to employee performance
evaluation 406. For example, if test administration 404 is
associated with predictive stack ranking 306, feedback from
employee performance evaluation 406 may help determine how various
job applicants are comparatively ranked against each other.
[0102] FIG. 5 diagrams an online computer based system 500 in
accordance with one embodiment of the invention. Box 502 represents
a job vacancy with a requirement for an online screening and
selection solution. The vacancy can come to the attention of a
potential job applicant in a number of ways.
[0103] For example, box 504 represents an online application via a
hiring company's own website. A company offering a job may post a
vacancy announcement on the company's website and invite job
seekers to apply by clicking on an icon labeled "apply here" or the
like. Box 506 represents a similar posting on an online job board.
Box 508 represents candidates given a Uniform Resource Locator
(URL) directly by the company. This may occur when the company
offering a job identifies a potential candidate. Box 510 represents
a media advertisement including a URL for a job. Thus, job seekers
observing the advertisement can direct their browsers to the
indicated URL.
[0104] At job fair 512, job seekers may be provided a URL
associated with the company or the particular vacancy.
Paper-and-pencil measures could also be used at job fairs and
entered into the system. A computer terminal may be provided for
use of job seekers at job fair 512, enabling job seekers to
participate in the online system. Box 514 represents an executive
search via a recruiter network. Job seekers relevant to the search
are identified in recruitment firm applicant database 516. Database
516 can link to a URL associated with the job.
[0105] Preferably, no matter how a potential applicant becomes
aware of or identified for a job opening in system 500, the
potential applicant is considered at decision 520. Decision 520
asks whether applicant has completed the required screening
solution 524. If not, the applicant at box 522 is given via e-mail,
mail, or in person, a URL for assessment. For example, system 500
may send an e-mail message to a potential applicant, the e-mail
message inviting the potential applicant to apply for vacancy 502
by directing a browser to a screening solution URL provided in the
e-mail message. Alternatively, when a potential applicant is
visiting a website at which decision 520 determines that the
required screening solution has not been completed, the website
host can provide a link to a web page identified by the screening
solution URL. Decision 520 may be based on a potential applicant's
name, e-mail address, and/or other identifying information.
[0106] Screening solution 524 is administered via the Internet and
is hosted at the screening solution URL mentioned above. Screening
solution 524 asks screening questions to ascertain if the applicant
has the basic qualifications to do the job. These are based on
questions typically asked by recruiters but which are statistically
validated over time to ensure they are legally defensible and
predictive. The questions may include a combination of biodata and
personality measures. They may include self-assessments of skill
levels appropriate to the job requirements. Screening solution 524
requires applicants to transmit elicited information over the
Internet. A possible example of a web-based presentation for
screening solution 524 is illustrated in FIG. 6. Screen shot 600
shows a portion of the presentation.
[0107] Once completed, screening solution 524 provides applicant
feedback 540 and conveys applicant details and screening scores to
stack ranked table of applicants 530. Applicant feedback 540 may
provide a message to the online applicant indicating that the
screening solution is complete, that the applicant has passed or
failed the screening stage, and that the applicant may or may not
be contacted in due course. Other information may also be provided
to the applicant in the feedback pages, like a realistic job
preview, recruiter phone number, scheduling information, etc.
[0108] Once an applicant has completed the screening solution,
system 500 ranks the applicant in comparative order against other
applicants in stack ranked table of applicants 530. A certain
number or percentage of applicants in table 530 will be chosen for
further consideration. For example, the applicants ranking among
the top five of all applicants ranked in table 530 may be chosen
for advancement in the system at this juncture. Information
identifying the chosen applicants will be included on a "short
list" as indicated by box 536.
[0109] The short list chosen at box 536 is transmitted to selection
solution 538, at which the advancing applicants are invited to
answer selection questions. Selection solution 538 asks additional
questions and requires an advancing applicant to input answers.
Preferably, the applicant completes selection solution 538 while
sitting at a terminal located at one of the company's locations.
The terminal communicates over the Internet with a website set up
to administer the selection solution.
[0110] At the conclusion of selection solution 538, applicant
feedback 540 is provided from the website to the applicant, and
applicant details and scores 541 are incorporated in stack ranked
table 530. Feedback 540 may optionally include a sophisticated
report on the applicant's strengths and weakness. The applicant may
then be directed to an appropriate web page chosen by the hiring
company. One page may indicated successful completion and a second
page may indicate failure. The appropriate web page may suggest
other openings appropriate to the applicant's test responses and
may provide hyperlinks the applicant can use to initiate the
application process for these other openings.
[0111] Once stack ranked table 530 re-ranks the applicants as a
result of selection solution 538, some applicants are invited to
participate in interview solution 542. For example, the top three
applicants as ranked by table 530 after selection solution 538 may
be invited for an in-person interview. Because the selection
solution is preferably in instant communication with stack ranked
table 530, the interview invitation may be extended immediately at
the conclusion of the selection solution.
[0112] Interview solution 542 is preferably a structured interview,
with questions provided via the Internet to the interviewer at the
company's location. The interviewer reads the provided questions
and reports a score over the Internet from the company's location
for incorporation in stack ranked table 530. Benchmark performance
anchors may assist the interviewer in grading the applicant's
responses.
[0113] Interview solution 542 can be designed according two
exemplary models. In a first model, an employer is provided with
standard interview guides for several job types as well as the
competency templates for these types so that the employer can build
variations to meet specific needs. In a second model, an employer
can build new interview guides and new competency templates. In the
second model, the employer has access to the full array of
work-related competencies and associated questions in a
comprehensive question bank.
[0114] In ranking applicants, stack ranked table 530 may consider a
combination of different biographical, personality, behavioral, and
other appropriate information and competencies. In addition to the
comparative ranking, table 530 may indicate for each applicant a
yes/no recommendation, a percentage likelihood of successful job
performance, biographical information not used for evaluative
purposes, and so forth.
[0115] Stack ranked table 530 may be developed by grading the
various solution stages with a computer implementing the following
algorithm. First, search for disqualifying answers to "knock-out"
questions. Second, give points for answers matching those of the
previously hired candidates who achieved a successful performance
evaluation. Third, deduct points for answers matching those of the
previously hired candidates who received an unsuccessful
performance rating. Fourth, multiply the added or subtracted points
by any weighting assigned each question. Fifth, sum the points for
all questions related to a given competency. Sixth, compare the
summed points for each competency to norms of either the
job-holders in the company or a wider population. Seventh, predict
performance of the applicant as a worker in the job, based on the
business outcomes identified by the hiring company and the
competencies that contribute to those outcomes.
[0116] A final selection is made based on stack ranked table 530.
Preferably, the selection is transmitted over the Internet to the
company, enabling the company to make an offer to the selected
applicant(s). For example, if there is only one opening, an offer
may be extended to the applicant ranked highest by stack ranked
table 530. If the applicant accepts the offer, the applicant is
employed by the company. If the applicant declines, the next
highest ranked applicant in stack ranked table 530 is offered the
job. If a plural number of openings exist, that number of
applicants may be selected off the top of stack ranked table 530
and offered the job. If one of the applicants declines, the next
highest ranked applicant in stack ranked table 530 is offered the
job. Data from stack ranked table 530 is forwarded to data
warehouse 534.
[0117] The performance of successful applicants is monitored during
their employment. At box 550, performance data for successful
applicants are collected at a later date, and sent to data
warehouse 534.
[0118] Data collected at data warehouse 534 are used for research
and development and for reporting purposes. For example, functions
enabled by storing comprehensive data generated by system 500 may
include:
[0119] a. Storage of question level responses from applicants for
jobs. This can be used for re-checking of applicant information
(auditing etc.) and for research to develop new solutions and
questions.
[0120] b. Reporting on Equal Employment Opportunity Commission
requirements. Data on ethnicity etc. can be stored to enable an
employer to comply with reporting requirements to government
agencies.
[0121] c. Source of data for designing new solutions including data
on the nature of the job and the competencies that are required by
the role (job analysis). This data is collected using online
assessments.
[0122] d. Source of data for statistical research on correlation
between the solutions and their predicted outcomes for applicants,
and the actual outcomes for employees who were hired (validation
studies).
[0123] e. Design of solutions other than recruitment related
solutions.
[0124] f. Reporting of usage volumes for billing and financing
accounting purposes.
[0125] Because system 500 preferably uses instant communications,
adaptive testing techniques may be implemented online. An
applicant's failure to overcome hurdles in a given solution will
deliver a different path through the solution than that of a
successful applicant. The degree of advancement of a given
applicant through system 500 may result in different charges to the
company from a solutions provider. For example, a solutions
provider that hosts a website supporting screening solution 524,
selection solution 538, and interview solution 542 may charge the
hiring company the following amounts: one dollar for every
applicant completing only the screening solution, five dollars for
every applicant advancing only to the end of the selection
solution, ten dollars for every applicant rejected after the
interview solution, twenty dollars for every applicant offered a
job, and fifty dollars for every applicant accepting an offer.
[0126] In practice, any of the various stages (screening solution
524, selection solution 538, and interview solution 542) may be
skipped, re-ordered, combined with other stages, or eliminated. Or,
a short telephone interview may be structured early in the process
to quickly screen applicants.
[0127] In a preferred embodiment, the questions to be asked at the
various stages are selected for a particular type of job being
offered in accordance with a proven relationship with desired
business outcomes. Business outcomes can for example include: level
of sales, customer satisfaction, quality measures such as fault
rates, retention and tenure of employment, time keeping, learning
ability, progression to more senior roles over time, and supervisor
ratings of behavioral success. The particular type of job is
defined in conjunction with the U.S. Department of Labor "O*NET"
classification system. Some types of jobs might include customer
service, technical, professional, or managerial. Various
competencies are determined to be associated with desired business
outcomes for a given type of job. These competencies are tested for
at various solution stages with appropriate questions.
[0128] The appropriate competencies, questions, scoring, weighting,
and ranking factors for a new job can be designed from historical
tests for existing jobs, by applying statistical techniques and
using the gathering of data on the Internet to ensure rapid
validation of the new assessment solution. Confirmatory job
analysis is used to determine the appropriateness of solutions for
a particular job.
[0129] FIG. 7 shows an example of a stack ranked table. Computer
screen shot 700 illustrates a sample stack ranked table 730 for a
customer service job. Various tabs permit viewing of data generated
by each solution stage. Tab 702 reveals data 703 from a screening
solution, tab 704 reveals data 705 from a selection solution, tab
706 reveals data 707 from an interview solution, and tab 708
reveals all results. In screen shot 700, tab 708 is selected.
[0130] Section 709 of screen shot 700 shows general information
about each applicant, including current rank 710, a link 712 to
application information (not shown), last name 714, first name 716,
and application date 718.
[0131] Screening solution data 703 includes an indication 720 of
whether each applicant successfully passed the knockout
requirements for the job. Data 703 also includes scores on certain
competencies such as educational and work related experience 722,
customer service orientation 724, and self-confidence 726. Column
728 indicates whether each applicant is recommended to advance
beyond the screening stage.
[0132] Selection solution data 705 includes scores on certain
competencies such as customer focus 732, conscientiousness 734, and
problem solving 736. Column 738 indicates whether each applicant is
recommended to advance beyond the selection stage.
[0133] Additional information (not shown) may include columns for
storage of data from other decision-making processes such as drug
testing, reference checks, or medical exams.
[0134] FIG. 8 shows an example of a screening solution question
presented to an applicant taking a screening solution test over the
Internet. In screen shot 800, simulated customer contact record 802
is presented to the applicant. The applicant is asked question 804,
and is required to click on a circle next to one of the answers.
Question 804 may test for a competency in working with information,
for example.
[0135] FIG. 9 shows an example of a structured interview guide for
use in an interview solution. As illustrated, the interview guide
is being presented online on a computer screen to an interviewer
conducting an interview with an applicant. Screen shot 900 shows
interview item 902 for a sample customer service job. The customer
service job opening is for a call center position, and revenue
focus has been identified as a relevant and predictive competency.
Item 902 elicits from the applicant a situation 904, the
applicant's behavior 906 in the situation, and the outcome 908
reported by the applicant. The interviewer can grade the
applicant's responses to item 902 by marking a score 910 from 1 to
10.
[0136] FIG. 10 illustrates procedural steps that may be followed in
a web-based applicant system according to an embodiment of the
present invention.
[0137] FIG. 11 illustrates procedural steps that may be followed in
a web-based selection solution according to an embodiment of the
present invention. For example, these steps may follow those
illustrated in FIG. 10.
[0138] FIG. 12 illustrates procedural steps that may be followed by
an employer according to an embodiment of the present
invention.
[0139] The following tables provide examples of screening solutions
and selection solutions designed for different types of jobs. The
tables show components (competencies) shown to be relevant to
successful performance of each job type. In the tables, some
components are considered required, and others are considered
optional.
[0140] Table One may be used for entry level and general skill
jobs:
1TABLE ONE Entry/General Skilled Solutions Solution Component
Definition Items Screening 7-10 Minutes Required Educational and
Measures potential for success in 15 Work-Related entry-level jobs
across industry Experience type and functional area. Scores on
Education and Work-Related Experience are derived from candidates'
responses to questions regarding developmental influences, self-
esteem, work history and work- related values and attitudes.
Self-Confidence This component references: be- 7 lief in one's own
abilities and skills and a tendency to feel competent in several
areas. Optional Decision Making/ Measures potential for success in
8 Flexibility entry level positions. Scores on Decision Making and
Flexibility are derived from candidates' responses to questions
regarding developmental influences, self- esteem, work history and
work- related values and attitudes. Selection 23-35 Minutes
Required Conscientiousness This component is designed to 65 predict
the likelihood that candidates will follow company policies
exactly, work in an organized manner, return from meals and breaks
in the allotted time, and keep working, even when coworkers are not
working. Retention Measures commitment, 44 Predictor impulsiveness,
responsibility, and motivation. It predicts the likelihood that a
new hire will remain on the job for at least three months. Optional
Learning Ability This component measures the 54 tendency to
efficiently and (12 effectively use numerical and minute analytical
reasoning. This timer) competency is characterized by the ability
to learn work-related tasks, processes, and policies.
[0141] Table Two may be used for customer service jobs:
2TABLE TWO Customer Service Solution Solution Component Definition
Items Screening 8-10 Minutes Required Educational and Measures
potential for success in 15 Work-Related customer service jobs.
Scores on Experience Education and Work-Related Experience are
derived from candidates responses to questions regarding develop-
mental influences, self-esteem, work history and work-related
values and attitudes. Customer Service Designed to predict the
likeli- 20 Orientation hood that candidates will show persistent
enthusiasm in customer interaction, apologize sincerely for
inconveniences to customers, be patient with customers, tolerate
rude customers calmly, and search for information or products for
customers. Optional Self-Confidence This component references: be-
7 lief in one's own abilities and skills and a tendency to feel
competent in several areas. Selection 17-29-37 Minutes Required
Customer Focus Designed to predict the likeli- 32 hood that
candidates will show persistent enthusiasm in customer interaction,
apologize sincerely for inconveniences to customers, be patient
with customers, tolerate rude customers calmly, and search for
information or products for customers. Conscientiousness This
component is designed to 65 predict the likelihood that candidates
will follow company policies exactly, work in an or- ganized
manner, return from meals and breaks in the allotted time, and keep
working, even when coworkers are not working. Optional Learning
Ability This component measures the 54 tendency to efficiently and
ef- (12 fectively use numerical and minute analytical reasoning.
This com- timer) petency is characterized by the ability to learn
work-related tasks, processes, and policies. Optional Retention
Measures commitment, im- 44 Predictor pulsiveness, responsibility,
and motivation. It predicts the likelihood that a new hire will
remain on the job for at least three months.
[0142] Table Three may be used for customer service jobs involving
sales:
3TABLE THREE Customer Service Solution: Sales Positions Solution
Component Definition Items Screening 9-15 Minutes Required
Educational and Measures potential for success in 15 Work-Related
customer service jobs. Scores on Experience Education and
Work-Related Experience are derived from candidates responses to
questions regarding develop- mental influences, self-esteem, work
history and work-related values and attitudes. Customer This
component is designed to 20 Service predict the likelihood that
Orientation candidates will show persistent enthusiasm in customer
inter- action, apologize sincerely for inconveniences to customers,
be patient with customers, tolerate rude customers calmly, and
search for information or products for customers. Optional Sales
Potential Designed to predict the likeli- 23 hood that candidates
will suggest or show alternative solutions based on customer needs,
direct conversation toward a commitment/order/sale, show confidence
even after a hard refusal/rejection, and strive to close a
transaction every time. Selection 15-27 Minutes Required Sales
Potential Designed to predict the likeli- 60 hood that candidates
will suggest or show alternative solutions based on customer needs,
direct conversation toward a commitment/order/sale, show confidence
even after a hard refusal/rejection, and strive to close a
transaction every time. Customer Focus Designed to predict the
likeli- 32 hood that candidates will show persistent enthusiasm in
customer interaction, apologize sincerely for inconveniences to
customers, be patient with customers, tolerate rude customers
calmly, and search for information or products for customers.
Optional Learning Ability This component measures the 54 tendency
to efficiently and ef- (12 fectively use numerical and minute
analytical reasoning. This com- timer) petency is characterized by
the ability to learn work-related tasks, processes, and
policies.
[0143] Table Four may be used for customer service jobs in a call
center:
4TABLE FOUR Customer Service Solution: Call Center Positions
Solution Component Definition Items Screening 9-11 minutes Required
Educational and Measures potential for success in 15 Work-Related
customer service jobs. Scores on Experience Education and
Work-Related Experience are derived from candidates responses to
questions regarding develop- mental influences, self-esteem, work
history and work-related values and attitudes. Customer Service
Designed to predict the likeli- 20 Orientation hood that candidates
will show persistent enthusiasm in customer interaction, apologize
sincerely for inconveniences to customers, be patient with
customers, tolerate rude customers calmly, and search for
information or products for customers. Optional Self-Confidence
This component references: be- 7 lief in one's own abilities and
skills and a tendency to feel competent in several areas. Selection
16-31-39 Minutes Required Customer Focus This component is designed
to 32 predict the likelihood that candidates will show persistent
enthusiasm in customer inter- action, apologize sincerely for
inconveniences to customers, be patient with customers, tolerate
rude customers calmly, and search for information or products for
customers. Conscientiousness This component is designed to 65
predict the likelihood that candidates will follow company policies
exactly, work in an organized manner, return from meals and breaks
in the allotted time, and keep working, even when coworkers are not
working. Working with This component is designed to 30 Information
predict success in customer (15 service call-center jobs by minute
assessing a candidate's ability timer) to retrieve information and
use it in order to solve problems. Optional Retention Measures
commitment, impul- 44 Predictor siveness, responsibility, and
motivation. It predicts the likelihood that a new hire will remain
on the job for at least three months.
[0144] Table Five may be used for customer service jobs in a call
center involving sales:
5TABLE FIVE Customer Service Solution: Call Center Sales Positions
Solution Component Definition Items Screening 9-15 Minutes Required
Educational and Measures potential for success in 15 Work-Related
customer service jobs. Scores on Experience Education and
Work-Related Experience are derived from candidates' responses to
questions regarding develop- mental influences, self-esteem, work
history and work-related values and attitudes. Customer Designed to
predict the likeli- 20 Service hood that candidates will show
Orientation persistent enthusiasm in customer interaction,
apologize sincerely for inconveniences to customers, be patient
with customers, tolerate rude customers calmly, and search for
information or products for customers. Optional Sales Potential
Designed to predict the likeli- 23 hood that candidates will
suggest or show alternative solutions based on customer needs,
direct conversation toward a commitment/order/sale, show confidence
even after a hard refusal/rejection, and strive to close a
transaction every time. Selection 30 Minutes Required Sales Focus
Designed to predict the likeli- 60 hood that candidates will
suggest or show alternative solutions based on customer needs,
direct conversation toward a commitment/order/sale, show confidence
even after a hard refusal/rejection, and strive to close a
transaction every time. Customer Focus Designed to predict the
likeli- 32 hood that candidates will show persistent enthusiasm in
customer interaction, apologize sincerely for inconveniences to
customers, be patient with customers, tolerate rude customers
calmly, and search for information or products for customers.
Working with This component is designed to 30 Information predict
success in customer (15 service call-center jobs by minute
assessing a candidate's ability timer) to retrieve information and
use it in order to solve problems.
[0145] Table Six may be used for jobs in sales:
6TABLE SIX Sales Solutions Solution Component Definition Items
Screening 10-14 minutes Required Educational Measures potential for
success in 15 and Work- customer service jobs. Scores on Related
Education and Work-Related Experience Experience are derived from
candidates responses to questions regarding developmental
influences, self-esteem, work history and work- related values and
attitudes. Sales Potential Designed to predict the likelihood 23
that candidates will suggest or show alternative solutions based on
customer needs, direct conversation toward a commitment/order/sale,
show confidence even after a hard refusal/rejection, and strive to
close a transaction every time. Optional Customer Designed to
predict the likelihood 20 Service that candidates will show
persistent Orientation enthusiasm in customer interaction,
apologize sincerely for incon- veniences to customers, be patient
with customers, tolerate rude customers calmly, and search for in-
formation or products for customers. Selection 10-25-40 Minutes
Required Sales Focus Designed to predict the likelihood 60 that
candidates will suggest or show alternative solutions based on
customer needs, direct conversation toward a commitment/order/sale,
show confidence even after a hard refusal/rejection, and strive to
close a transaction every time. Optional Problem Measures the
tendency to efficiently 10 Solving and effectively use numerical
and analytical reasoning. This com- petency is characterized by the
ability to solve complex problems, and make reasoned decisions.
Optional Communi- Measures the tendency to efficiently 10 cation
and effectively use verbal reasoning. This competency is
characterized by the ability to verbally explain complex
information to others.
[0146] Table Seven may be used for supervisory jobs:
7TABLE SEVEN Supervisory Solutions Solution Component Definition
Items Screening 10-20 Minutes Required Supervisory Measures
potential for supervisory 10 Potential success across industry type
and functional area. Scores on Supervisory Potential are derived
from candidates' responses to questions regarding academic and
social background, and aspirations concerning work. Judgment
Measures potential for making good 10 judgments about how to
effectively respond to work situations. Scores on Judgment are
derived from candidates' responses to questions regarding
situations one would likely encounter as a manager/ supervisor.
Optional Leadership/ Measures potential for success as a 19
Coaching supervisor. This is done by having Teamwork/ applicants'
make judgments about Interpersonal the most effective teamwork and
Skills leadership behaviors in specific work situations. Scores are
determined by comparing their response profiles to the profiles of
supervisors who are known to be successful. Selection 22-37-52 Mins
Required Business Measures the candidate's thinking 28 Leadership
styles. High scorers are likely to have or learn good planning and
organizing skills, be innovative, consider issues from multiple
perspectives, and create strategies to build their business.
Required Leadership Measures the candidate's desire for 23
Motivation achievement, drive, initiative, energy level,
willingness to take charge, and persistence. High scorers are
likely to be highly motivated to succeed and to set challenging
goals for themselves and others. Self- Measures the candidate's
ability to 32 Leadership control emotions, act with integrity, take
responsibility for actions, and tolerate stress. High scorers are
also likely to have a positive attitude, be optimistic about the
future, and demonstrate high levels of professionalism.
Interpersonal Measures the candidate's 30 Leadership interpersonal
characteristics. High scorers are likely to persuade and influence
others, gain commitment, and build effective interpersonal
relationships. They also have potential to develop skills in the
areas of employee relations, coaching, motivating, and leading a
team. Optional Decision Measures the tendency to efficiently 10
Making/ and effectively use numerical and Problem analytical
reasoning. This Solving competency is characterized by the ability
to solve complex problems, and make reasoned decisions. Optional
Communi- Measures the tendency to efficiently 10 cation and
effectively use verbal reasoning. This competency is characterized
by the ability to verbally explain complex information to
others.
[0147] Table Eight may be used for professional jobs:
8TABLE EIGHT Professional Solutions Solution Component Definition
Items Screening 7 - Minutes Required Dependa- This competency is
characterized by: a 40 bility willingness to behave in expected and
agree upon ways; following through on assignments and commitments;
keep promises; and accept the consequences of one's own actions.
Interpersonal This competency is indexed by a Skills tendency to be
pleasant, cooperative, and helpful when working with others, as
well as flexible in conflict resolution situations. Self-Control
This competency is characterized by the ability to: stay calm and
collected when confronted with adversity, frustration, or other
difficult situations; and avoid defensive reactions or hurt
feelings as a result of others' comments. Energy This competency is
characterized by a preference to stay busy, active, and avoid
inactive events or situations. Selection 35-50 Minutes Required
Business Measures the candidate's thinking 32 Leadership styles.
High scorers are likely to have or learn good planning and
organizing skills, be innovative, consider issues from multiple
perspectives, and create strategies to build their business.
Leadership Measures the candidate's desire for 35 Motivation
achievement, drive, initiative, energy level, willingness to take
charge, and persistence. High scorers are likely to be highly
motivated to succeed and to set challenging goals for themselves
and others. Self- Measures the candidate's ability to 34 Leadership
control emotions, act with integrity, take responsibility for
actions, and tolerate stress. High scorers are also likely to have
a positive attitude, be optimistic about the future, and
demonstrate high levels of professionalism. Interpersonal Measures
the candidate's 41 Leadership interpersonal characteristics. High
scorers are likely to persuade and influence others, gain
commitment, and build effective interpersonal relationships. They
also have potential to develop skills in the areas of employee
relations, coaching, motivating, and leading a team. Decision
Measures the tendency to efficiently 10 Making/ and effectively use
numerical and Problem analytical reasoning. This competency Solving
is characterized by the ability to solve complex problems, and make
reasoned decisions. Optional Communi- Measures the tendency to
efficiently 10 cation and effectively use verbal reasoning. This
competency is characterized by the ability to verbally explain
complex information to others.
[0148] Table Nine may be used for managerial jobs:
9TABLE NINE Managerial Solutions Solution Component Definition
Items Screening 10-20 Minutes Required Management Measures
potential for managerial 10 Potential success across industry type
and functional area. Scores on Management Potential are derived
from candidates' responses to questions regarding academic and
social background, and aspirations concerning work. Judgment
Measures potential for making good 10 judgments about how to
effectively respond to work situations. Scores on Judgment are
derived from candidates' responses to questions regarding
situations one would likely encounter as a manager/supervisor.
Optional Self- This component references: belief in 10 Confidence
one's own abilities and skills and a tendency to feel competent in
several areas. Decision Measures potential for success as a Making
manager. This is done by having applicants' make judgments about
the most effective decisions in specific work situations. Their
potential is de- termined by comparing their response profiles to
the profiles of successful managers. Selection 20-35-50 Mins
Required Business Measures the candidate's thinking 32 Leadership
styles. High scorers are likely to have or learn good planning and
organizing skills, be innovative, consider issues from multiple
perspectives, and create strategies to build their business.
Leadership Measures the candidate's desire for 35 Motivation
achievement, drive, initiative, energy level, willingness to take
charge, and persistence. High scorers are likely to be highly
motivated to succeed and to set challenging goals for themselves
and others. Self- Measures the candidate's ability to 34 Leadership
control emotions, act with integrity, take responsibility for
actions, and toler- ate stress. High scorers are also likely to
have a positive attitude, be optimistic about the future, and
demonstrate high levels of professionalism. Interpersonal Measures
the candidate's 41 Leadership interpersonal characteristics. High
scorers are likely to persuade and influence others, gain
commitment, and build effective interpersonal relationships. They
also have potential to develop skills in the areas of employee
relations, coaching, motivating, and leading a team. Optional
Decision Measures the tendency to efficiently 10 Making/ and
effectively use numerical and Problem analytical reasoning. This
competency Solving is characterized by the ability to solve complex
problems, and make reasoned decisions. Optional Communi- Measures
the tendency to efficiently 10 cation and effectively use verbal
reasoning. This competency is characterized by the ability to
verbally explain complex information to others.
[0149] Table Ten may be used for technical/professional jobs:
10TABLE TEN Technical-Professional Solutions Solution Component
Definition Items Screening 8 Minutes Required Dependa- This
competency is characterized by: a 40 bility willingness to behave
in expected and agree upon ways; following through on assignments
and commitments; keeping promises; and accepting the consequences
of one's own actions. Interpersonal This competency is indexed by a
Skills tendency to be pleasant, cooperative, and helpful when
working with others, as well as flexible in conflict resolution
situations. Self-Control This competency is characterized by the
ability to: stay calm and collected when confronted with adversity,
frustration, or other difficult situations; and avoid defensive
reactions or hurt feelings as a result of others' comments. Energy
This competency is characterized by a preference to stay busy,
active, and avoid inactive events or situations. Selection 35-50
Minutes Required Business Measures the candidate's thinking 32
Leadership styles. High scorers are likely to have or learn good
planning and organizing skills, be innovative, consider issues from
multiple perspectives, and create strategies to build their
business. Leadership Measures the candidate's desire for 35
Motivation achievement, drive, initiative, energy level,
willingness to take charge, and persistence. High scorers are
likely to be highly motivated to succeed and to set challenging
goals for themselves and others. Self- Measures the candidate's
ability to 34 Leadership control emotions, act with integrity, take
responsibility for actions, and tolerate stress. High scorers are
also likely to have a positive attitude, be optimistic about the
future, and demonstrate high levels of professionalism,
Interpersonal Measures the candidate's 41 Leadership interpersonal
characteristics. High scorers are likely to persuade and influence
others, gain commitment, and build effective interpersonal
relationships. They also have potential to develop skills in the
areas of employee relations, coaching, motivating, and leading a
team. Decision Measures the tendency to efficiently 10 Making/ and
effectively use numerical and Problem analytical reasoning. This
competency Solving is characterized by the ability to solve complex
problems, and make reasoned decisions. Optional Communi- Measures
the tendency to efficiently 10 cation and effectively use verbal
reasoning. This competency is characterized by the ability to
verbally explain complex information to others.
[0150] Table Eleven may be used for executive positions:
11TABLE ELEVEN Executive Solutions Solution Component Definition
Items Screening 20 Minutes Required Executive Measures potential
for success in 53 Potential high-level organizational positions
across industry type and functional area. Scores on Executive
Potential are derived from candidates' responses to questions
regarding work background, accomplishments, and career aspirations.
Selection 35-50 Minutes Required Business Measures the candidate's
thinking 32 Leadership styles. High scorers are likely to have or
learn good planning and organizing skills, be innovative, consider
issues from multiple perspectives, and create strategies to build
their business. Leadership Measures the candidate's desire for 35
Motivation achievement, drive, initiative, energy level,
willingness to take charge, and persistence. High scorers are
likely to be highly motivated to succeed and to set challenging
goals for themselves and others. Self- Measures the candidate's
ability to 34 Leadership control emotions, act with integrity, take
responsibility for actions, and tolerate stress. High scorers are
also likely to have a positive attitude, be optimistic about the
future, and demonstrate high levels of professionalism.
Interpersonal Measures the candidate's 41 Leadership interpersonal
characteristics. High scorers are likely to persuade and influence
others, gain commitment, and build effective interpersonal
relationships. They also have potential to develop skills in the
areas of employee relations, coaching, motivating, and leading a
team. Decision Measures the tendency to efficiently 10 Making/ and
effectively use numerical and Problem analytical reasoning. This
competency Solving is characterized by the ability to solve complex
problems, and make reasoned decisions. Optional Communi- Measures
the tendency to efficiently 10 cation and effectively use verbal
reasoning. This competency is characterized by the ability to
verbally explain complex information to others.
[0151] Table Twelve may be used for jobs involving campus
recruiting:
12TABLE TWELVE Campus Recruiting Solutions Solution Component
Definition Items Screening 12 Minutes Required Supervisory Measures
potential for supervisory 26 Potential success across industry type
and functional area. Scores on Supervisory Potential are derived
from candidates' responses to questions regarding academic and
social background, and aspirations concerning work. Judgment
Measures potential for making good judgments about how to
effectively respond to work situations. Scores on Judgment are
derived from candidates' responses to questions regarding
situations one would likely encounter as a manager/supervisor.
Management Measures potential for managerial Potential success
across industry type and functional area. Scores on Management
Potential are derived from candidates' responses to questions
regarding academic and social background, and aspirations
concerning work. Selection 20-35-50 Mins Required Business Measures
the candidate's thinking 32 Leadership styles. High scorers are
likely to have or learn good planning and organizing skills, be
innovative, consider issues from multiple perspectives, and create
strategies to build their business. Leadership Measures the
candidate's desire for 35 Motivation achievement, drive,
initiative, energy level, willingness to take charge, and
persistence. High scorers are likely to be highly motivated to
succeed and to set challenging goals for themselves and others.
Self- Measures the candidate's ability to 34 Leadership control
emotions, act with integrity, take responsibility for actions, and
tolerate stress. High scorers are also likely to have a positive
attitude, be optimistic about the future, and demonstrate high
levels of professionalism. Interpersonal Measures the candidate's
41 Leadership interpersonal characteristics. High scorers are
likely to persuade and influence others, gain commitment, and build
effective interpersonal relationships. They also have potential to
develop skills in the areas of employee relations, coaching,
motivating, and leading a team. Optional Decision Measures the
tendency to efficiently 10 Making/ and effectively use numerical
and Problem analytical reasoning. This Solving competency is
characterized by the ability to solve complex problems, and make
reasoned decisions. Optional Communi- Measures the tendency to
efficiently 10 cation and effectively use verbal reasoning. This
competency is characterized by the ability to verbally explain
complex information to others.
[0152] Table Thirteen may be used for a selection solution for a
job involving communication:
13TABLE THIRTEEN Communication Solution Solution Component
Definition Items Selection 37 Minutes Required Listening Measure of
the tendency to listen to 73 Orientation and understand others'
perspectives, to care for others, to accept and respect the
individual differences of people, and to be open both to multiple
ideas and to using alternative modes of thinking. English Measures
usage of verb tense and Language sentence construction. Scores on
Skills English Language Skills are derived from candidates
responses to grammar questions. Verbal Measures verbal reasoning
skills and Reasoning/ critical thinking/reasoning skills. Critical
Scores on Verbal Reasoning Ability Thinking are derived from
candidates' responses to analogies and questions about information
provided in brief reading passages.
[0153] Table Fourteen may be used for a selection solution for a
job involving financial services jobs referred to series
six/seven:
14TABLE FOURTEEN Series Six/Seven Success Solution Solution
Component Definition Items Selection 36 Minutes Required Problem
Measures the ability to analyze and 20 Solving evaluate
information. Scores on Problem Solving are derived from candidates'
responses to mathematical and analytical reasoning items, requiring
candidates to respond to facts and figures presented in various
formats. Verbal Measures verbal reasoning skills and Reasoning/
critical thinking/reasoning skills. Critical Scores on Verbal
Reasoning Ability Thinking are derived from candidates' responses
to analogies and involves making inferences from information
provided in the form of brief passages
[0154] Table Fifteen may be used for a selection solution for a job
requiring information technology aptitude:
15TABLE FIFTEEN Information Technology Aptitude Solution Solution
Component Definition Items Selection 18 Minutes Required Critical
Measure reasoning and critical thinking 58 Thinking skills. Scores
on Critical Thinking are derived from candidates' responses to
information provided in the form of brief passages. Problem Measure
the ability to analyze and Solving evaluate information. Scores on
Problem Solving are derived from candidates' responses to
mathematical and analytical reasoning items, requiring candidates
to respond to facts and figures presented in various scenarios.
Communi- Measures the ability to efficiently use cation verbal
information. Scores on Communication are derived from candidates'
ability to identify synonyms. Spatial Measure the ability to
visually Ability manipulate objects. Scores on Spatial Ability are
derived from candidates' ability to correctly identify the number
of blocks in progressively difficult figures.
[0155] Although the above disclosure has focused on recruiting
applications, the generated data may be used in other human capital
applications. FIG. 13 illustrates a human capital management
life-cycle. Measurement and data 1301 is initially used in the
context of recruiting 1302. For recruiting 1302, screening,
selection, and interview solutions measure applicants' competencies
and predict on-the-job performance and thus contribution to
business outcomes.
[0156] For compensation 1303, data about potential can be weighed
against performance data to ensure that high potential employees
who are on difficult assignments where they are structurally
constrained from succeeding are not underpaid by pure focus on
performance. For example, structural constraints may include
business environment, poor staff, unreliable equipment, etc.
[0157] For retention 1304, business with jobs that have high
turnover use the system to ensure that applicants have qualities
that contribute to longer tenure in roles.
[0158] For performance management 1305, the system can be used to
enhance the validity of employee performance evaluation.
[0159] For training and development 1306, a company may test
current employees in order to design executive training programs
addressing each individual's strengths and weaknesses. Or, for
employees that took a test and were hired despite weaknesses, the
data can be used to structure appropriate training.
[0160] For succession 1307, data on employees may be collected in
the process of organization mergers to assist planning for
retrenchment or change. Also, by measuring competencies and mapping
them between roles, it is possible to assess the potential that an
individual may have for a role other than the job they are
currently holding, such as for a promotion or a transfer to another
area.
[0161] The foregoing description is to be considered as
illustrative only. The skilled artisan will recognize many
variations and permutations within the spirit of the
disclosure.
* * * * *