International Journal of Industrial Engineering, 21(3), 168-17.docx

International Journal of Industrial Engineering, 21(3), 168-178,
2014
ISSN 1943-670X
©INTERNATIONAL JOURNAL OF INDUSTRIAL
ENGINEERING
A VARIANT PERSPECTIVE TO PERFORMANCE
APPRAISAL SYSTEM:
FUZZY C – MEANS ALGORITHM
Coskun Ozkana, Gulsen Aydin Keskinb,*, Sevinc Ilhan
Omurcac
[email protected], [email protected], [email protected]
a Yıldız Technical University, Mechanical Engineering Faculty,
Industrial Engineering Department, Istanbul – Turkey,
Tel: +90 212 383 2865, Fax: +90 212 383 2866
b Kocaeli University, Engineering Faculty, Industrial
Engineering Department, Umuttepe Campus, Kocaeli – Turkey
c Kocaeli University, Engineering Faculty, Computer
Engineering Department, Umuttepe Campus, Kocaeli – Turkey
Performance appraisal and evaluating the employees for
awarding is an important issue in human resource management.
In performance appraisal systems, ranking scales and 360
degree are the most commonly used types of evaluating
methods in which the evaluator gives a score for each criterion

to assess all employees. Ranking scales are relatively
simple assessment methods. Despite using ranking scales allows
the management to complete the evaluation process in
a short time, they have some disadvantages. In addition,
although, all the performance appraisal methods evaluated the
employees in different ways, the employees get scores for each
evaluation criteria and then their performances are
evaluated according to total scores.
In this paper, the fuzzy c – means (FCM) clustering algorithm
is applied as a new method to overcome the common
disadvantages of the classical appraisal methods and help
managers to make better decisions in a fuzzy environment.
FCM algorithm not only selects the most appropriate
employee(s), but also clusters them with respect to the
evaluation
criteria. To explain the FCM method clearly, a performance
appraisal problem is discussed and employees are clustered
both by the proposed method and the conventional method.
Finally, the results obtained by the current system and FCM
have been presented comparatively. This comparison concludes
that, in performance appraisal systems, FCM is more
flexible and satisfactory compared to conventional method.
Key words: Performance appraisal, fuzzy c – means algorithm,
fuzzy clustering, multi criteria decision making,
intelligent analysis.
1. INTRODUCTION
Employee performances such as capability, knowledge, skill,
and other abilities are significantly important for the
organizations (Gungor et al., 2009). Hence, accurate personnel
evaluation has a significant role in the success of an
organization. Evaluation techniques that allow companies to
identify the best employee from the personnel are the key
components of human resource management (Sanyal and

Guvenli, 2004). However, this process is so complicated due
to human nature. The objective of an evaluation process
depends on appraising the differences between employees, and
estimating their future performances. The main goal of a
manager is to attain ranked employees who have been
evaluated with regard to some criteria. Therefore, the
development of efficient performance appraisal methods has
become a main issue. Some authors define the performance
appraisal problem as an unstructured decision problem, that
is, no processes or rules have been defined for making decisions
(Canos and Liern, 2008).
Previous researches have shown that performance appraisal
information is used especially in making decisions
requiring interpersonal comparisons (salary determination,
promotion, etc.), decisions requiring personal comparison
(feedback, personal educational need, etc.), decisions orientated
to the continuation of the system (target determination,
human force planning, etc.) and documentation. It is clear that
in a conventional way, there are methods and tools to do
those tasks (Gürbüz and Albayrak, 2014); however, each
traditional method has certain drawbacks. In this paper, fuzzy
c – means (FCM) clustering algorithm is proposed to make a
more efficient performance evaluation by removing these
drawbacks.
The proposed method enables the managers group their
employees with respect to several criteria. Thus, managers can
determine the most appropriate employee(s), in case of
promotion, salary determination, and so on. In addition, in case
of personal educational requirement, they will know which
employee(s) needs training by the proposed method.
This paper proposes an alternative suggestion to performance
appraisal system. After a brief review of performance
appraisal in Section 2, FCM algorithm is described in Section 3.
A real-life problem is solved both by FCM and the
conventional method to evaluate their performances and the
findings are discussed in Section 4. Finally, this paper

concludes with a discussion and a conclusion.
A Variant Perspective to Performance Appraisal System
169
2. PERFORMANCE APPRAISAL
Long term success of an organization depends heavily on its
ability to measure the performances of its employees, and
then use that information to insure that performances meet
present standards and improve over time. This process is
mentioned as performance appraisal or performance evaluation.
It is a complex and challenging task. Several different
performance appraisal methods can be examined. If it is used
effectively, performance appraisal can improve the
motivation and performance of an employee. It can also define
the training needs of employees. If an employee is
unable to meet the expectations, a training program may enable
him/her to improve any skills or knowledge (Fisher et
al., 1990).
Since the appraisal process involves the evaluation of
employees, based on a variety of criteria, it is a typical multi
criteria decision making (MCDM) problem (Huang et al., 2009)
and researchers show that fuzziness could be
successfully applied to solve such problems (Chang et al.,
2007).
Various approaches have been developed to help
organizations improve the loyalty of the employees to their
work.
Some of these are conventional methods which are used at the

first practices of performance appraisal concept. Some
others include developed modern methods to solve practical
problems and to make more objective appraisal of
conventional evaluation methods. The common methods used
for performance appraisal are forced distribution, mixed
standard scales, weighted checklist, critical incident technique,
behaviorally anchored rating scale, self-evaluation,
graphic rating scales, 360 degree and ranking and paired
comparison ranking. When the related literature is examined in
depth, ranking and 360 degree methods are found to be the most
commonly used techniques.
In ranking and paired comparison ranking, the evaluator ranks
the employees in order from best to worst, with respect
to their overall performances. Hence, the most preferred
employee takes place on the top. This method requires the
comparison of many pairs and it is easy to explain, understand
and use. Also it is generally not time consuming and less
expensive than other evaluation techniques; however, it has
some disadvantages. The comparisons are highly subjective
opinions, which the evaluator may have difficulty in supporting
evidence. The ordering of employees depends on the
size and character of the particular work group. Also, the
method requires that one evaluator knows the performance of
each employee and only one person can receive the top ranking.
In large groups, this may not be possible (Fisher et al.,
1990).
In mixed standard scales, the manager marks the grade that
describes the evaluated employee better for each category
(the quantity and quality of the work, the attention to the work,
decision making capability, etc). The method does not
encourage an assessment to the employees. Instead, it can
strengthen the emotion of “finishing the job as soon as
possible”. While the assessment of a person is easy, the
interpersonal comparisons can be difficult.
Behaviorally anchored rating scale method requires greater
attention since a behaviorally anchored rating scale is

necessary for each job type. This method depends on the
observable behavior of employees. Thus, judgments made
during the evaluation still play a major role. A work analysis is
required for a sensitive behavior based rating scale.
Therefore, all the work analysis has to be updated. The cost of
performing this method is high.
In graphic rating scales, even though it is easy to assess the
employees individually, interpersonal evaluations can be
difficult.
Using forced distribution method, performance of an
employee is determined with respect to the other employees. To
determine the performance of an employee, firstly the
arithmetic mean and the standard deviation of the scores of the
evaluated employees are computed. This is a time consuming
method.
In critical incident technique, the manager notes the critical
incidents of the behaviours of each employee. Since it
requires the examination of each employee in detail, it is time
consuming, too. Furthermore, it is difficult to quantify the
effects of critical incidents on the performances of employees,
and hence interpersonal performance differences cannot
be determined easily with this method.
Using weighted checklist method, it is hard to develop the
performance appraisal system. The preparation and
application phases of the method also take a long time. Besides,
the weights cannot be computed easily.
In 360 degree method, performance appraisal process is based
on the opinion of different groups of reviewers who
socialize with the evaluated employees since they can truly
respond to how an employee develops his/her job. This
method has some limitations as: Businesses willing to
implement this comprehensive method of assessment should be
willing to spend the time and effort to train each anonymous
evaluator in the process as well as correct ways to interpret
questions. Besides, although quite a few information have been
obtained related to employees, there is not a certain

method how to evaluate this information (Espinilla et al., 2013).
In the literature, there are several studies realized to
overcome the drawbacks of traditional methods to evaluate the
performances of the employees. Shaout and Al-Shammari
(1998) present a proposed application of the fuzzy set theory
to a personnel performance evaluation system. Aguinis et al.
(1998) present a new procedure for computing equivalence
bands to implement banding procedures in staffing decision
making for employee evaluation. Capaldo and Zollo (2001)
focus on the reliability of rating scales in employee assessment
by applying fuzzy logic. Chang et al. (2007) develop a
Ozkan et al.
170
fuzzy group decision support system including three ranking
methods to help making better decision under fuzzy
circumstances. Golec and Kahya (2007) present a
comprehensive hierarchical structure for evaluating an
employee by
fuzzy model. Kuo and Chen (2008) apply fuzzy delphi method
to construct key performance appraisal indicators for
mobility of the service industries. Secme et al. (2009) use
integrated fuzzy analytic hierarchy process and TOPSIS for
performance evaluation of banks. Moon et al. (2010) use a fuzzy
set theory, electronic nominal group technique and
TOPSIS for ranking decisions through the multi criteria
performance appraisal process for the promotion screening of
employees. Wu and Hou (2010) develop an integrated model for
employee performance estimation and reduced the
work load of 3PL (third party logistics) decision makers.
Özdaban and Özkan (2010) suggest a fuzzy model on

determining of job and personnel evaluation. Moon et al. (2010)
discussed an approach based on fuzzy set theory and
nominal group technique for the promotion screening of
candidates applying for a particular commission in a military
organization. Kelemenis et al. (2011) present a fuzzy TOPSIS
for the ranking of the personnel alternatives. Özdaban
and Özkan (2011) study to evaluate personnel and jobs jointly
with fuzzy distance sets. Sepehrirad et al. (2012) aim to
develop a mathematical model for 360 degree performance
appraisal in which subjective assessments are weighted and
aggregated based on mathematical model, delphi method, fuzzy
AHP, simple additing weighting method and TOPSIS.
Min-peng et al. (2012) use fuzzy comprehensive evaluation and
AHP to model the R&D staff performance appraisal.
Meng and Pei (2013) propose the weighted unbalanced
linguistic aggregation operators to synthesize linguistic
evaluation value, belief degree and experts’ weights. Espinilla
et al. (2013) present an integrated model for 360 degree
performance appraisal that can manage heterogeneous
information and compute a final linguistic evaluation for each
employee, applying an effective aggregation that considers the
interaction among criteria and reviewers relevance by
means of weights. Gürbüz and Albayrak (2014) add an
engineering point of view to this process by giving a hybrid
MCDM approach to evaluate employees’ performances working
for a same task and explain an efficient way of
handling the qualitative and quantitative data simultaneously.
Although, all the performance appraisal methods evaluated
the employees differently, the employees get scores for
each evaluation criteria and then their performances are
evaluated according to total scores. Alternatively, our proposed
method evaluates the employees by each evaluation criteria
separately.
Recently, researchers have been developing decision support
systems and expert systems to improve the outcomes of
human resource management. How the proposed method in this

study contributes to the literature is summarized as
follows:
1. When the literature is examined in depth, it is confirmed that
the ranking methods and 360 degree method are
used most commonly for performance appraisal problems. In
these methods, employees are sorted in a
descending order according to their total scores based on the
evaluation criteria and the appropriate
employee(s) is determined by this ordering. In conventional
methods; however, all the evaluation criteria are
rated separately, sorting of employees is done according to the
total score of each employee. However, the total
score can cause the loss of separated effects of all criteria.
Divergently in FCM, employees having the same
total score can be in different clusters. It means that different
employees having the same total score can be
dissimilar. Additionally, in this paper, the employees are
categorized in four classes instead of sorting. Thus, a
performance improvement can be applied when necessary.
2. To the best of our knowledge, there is not any performance
classification study published in the literature. In
this paper, each employee is assigned to a cluster and the
membership degrees of employees to all the clusters
are determined by FCM method. In this way, it is possible to
know the membership degree of each employee
to each cluster.
3. FCM does not do hard clustering, which is one of its major
advantages. Consequently, the final decision
belongs to the decision maker. In case membership degrees of
the employee to a couple of clusters are close to
each other, the decision maker is able to make further
qualitative analysis and assign this employee to another
cluster.

4. Finally, the proposed method is quite flexible and adaptive
and has a quite fast computation time.
3. FUZZY C- MEANS CLUSTERING
Clustering plays an important role in many engineering fields
such as pattern recognition, system modeling, image
processing, communication systems, data mining, taxonomy,
medicine, geology, and business. Clustering methods
divide a set of N input vectors into c groups so that the
members of the same group are more similar to one another than
to the members of other groups. The number of clusters may be
predefined or it may be determined by the method
(Tushir and Srivastava, 2010). Unlike traditional hard clustering
schemes, such as k-means, which assign each data
point to a specific cluster, fuzzy c – means (FCM) algorithm
employs fuzzy partitioning such that each data point
belongs to a cluster to some degree specified by a membership
grade (Chen and Wang, 2009). Dunn (1974) is the first
to construct a fuzzy clustering method based on the objective
function minimization. Bezdek (1981) generalizes the
171
objective function minimization to FCM algorithm by using
weighted exponent on the fuzzy memberships (Tushir and
Srivastava, 2010; Hung et al., 2008). Teppola et al. (1998) use a
combined approach of partial least squares and FCM
clustering for the monitoring of an activated-sludge waste-water
treatment plant. Liao et al. (2003) develop a modified

FCM clustering and apply it to generate fuzzy membership
functions for a data set obtained from an industrial
application. D’Urso and Giordani (2006) propose a fuzzy
clustering model for fuzzy dataset. De Carvalho (2007)
introduces adaptive and non-adaptive FCM clustering for
symbolic interval data partitioning. Pedrycz and Rai (2008)
introduce the concept of collaborative fuzzy clustering. Chen
and Wang (2009) use FCM clustering to create the
relationship between image blocks.
If data groups are well-separated, hard clustering approach
can be a natural solution. However, if the clusters are
overlapped and some of data belong partially to several clusters,
then fuzzy clustering is a natural way to deal with this
situation. In this case, the membership degree of a data object to
a cluster is a value from the interval [0,1]. The
illustration of fuzzy clustering is seen at Figure 1.
FCM is an unsupervised clustering algorithm that has a wide
domain of applications such as agricultural engineering,
astronomy, chemistry, geology, medical diagnosis, pattern
recognition and image processing (Ayvaz et al., 2007;
Rezaee et al., 1998).
In FCM, the clusters are identified based on a known number
of clusters (c), level of fuzziness (q); and initial
membership values for the input vector. The memberships of the
clusters are defined with corresponding membership
values, and clusters are described by prototypes that represent
the cluster centers.
Figure 1. Illustration of fuzzy clustering (Mingoti and Lima,
2006)
FCM is an iteratively optimal algorithm based on the iterative
minimization of the objective function in eq. (1).

( ) ( )∑∑
= =
−=
c
k
n
j
kj
q
kjq vxXVJ
1 1
2
,, µµ (1)
s.t. ∑
=
=
c
k
kju
1
1, [ ]1,0∈ kju , ∑
=

≤≤
n
j
kj nu
1
0
(2)
where n denotes the number of data objects and c denotes the
number of clusters. Paracompanymeter kjµ is the
membership degree of thj data object to cluster k set which is
defined as in eq (3). jx represents the
thj data object,
kv represents the center of cluster k which is defined in eq (4).
kj vx − denotes the Euclidean distance between data
object jx and cluster k . Parameter q is the membership
function weighting exponent that determines the amount of
fuzziness of the resulting partition (e.g., m = 1 means hard
clustering, m = ∞ means completely fuzzy). This parameter
can influence the performance of FCM and it is generally
suggested to choose a value between1.5 and 2.5 (Wu, 2012).
Therefore, in this study, q is set at 2.
By minimizing eq. (1) using the Lagrange multiplier method,
the updated equations of membership function and cluster
center are presented in eq. (3) and eq. (4) respectively.
∑
=
−

⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
−
−
=
C
i
q
ij
kj
kj
xx
vx

1
1/2
1
µ (3)
Ozkan et al.
172
∑
∑
=
=
=
n
j
q
kj
j
n
j

q
kj
k
x
v
1
1
)(
)(
µ
µ
(4)
3.1. Fuzzy C-Means (FCM) Algorithm
Inputs: Data objects to be clustered, number of clusters (c),
threshold value, clustering error (error_ rate) and the
maximum number of iteration (max_iteration).
1. Initialize kjµ (k=1, 2,…,c ; j=1,2,…,n)
2. FOR t = 1, 2, 3, …,max_iteration
2.1. Update cluster centers using eq. (4).
2.2. Update membership values )(newkjµ using eq. (3)
2.3. Update objective function using eq. (1).
2.4. IF ( ( ) ( ) rateerrorXVJXVJ oldq

new
q _,,,,
)(
≤− µµ ) then stop.
Output: Final data clusters.
MATLAB 2008b is used to perform FCM performance
appraisal method that mentioned above. In the next section,
we introduce a case study and explain how FCM algorithm for a
performance appraisal problem is implemented.
4. CASE STUDY
The company under consideration is a part of a conglomerate
and its business scope covers the production of fiberglass
for composites industry. Since the start of production in 1976,
the company has continually increased its capacity and
grown to one of the valuable European glass fiber
manufacturers. The company’s specialized glass fiber
reinforcements
find applications in advanced moulding and compounding
techniques throughout the world.
Market requirement continuously leads to the adaptation of
existing reinforcement products and to the development
activities of new products. Through application research, the
performance and properties of the fiber glass and polyester
products are tested and evaluated in order to guarantee quality,
efficient application on optimal price/performance
relationship and the required properties for end products.
The company evaluates the performance of its employees
conventionally using classical rating method. A decision
team is organized in order to evaluate the performance of the
employees. Evaluation team is composed of three decision
makers (DMs) namely, the human resource manager, the
supervisor and the department manager. These experts

primarily prepared an evaluation form as seen in Table 1
including performance criteria. Three DMs selected fifteen
criteria from the literature for performance appraisal (Golec and
Kahya, 2007; Jereb et al., 2005; Heath and Mills, 2000;
Gungor et al., 2009).
The DMs rate all criteria using a scale from 1 (low
performance) to 5 (high performance). Average ratings are
calculated for every criterion. To get the Total Performance
Point (TPP) for each personnel, the formula below is used:
TPP = (∑Average Rate)*100/75
The existing method uses TPP values for distinguishing
performance levels. The current rating method classifies
employees into four groups. Hence, TPP for each employee is
calculated with respect to the performance evaluation
criteria. These employees are grouped according to the score
intervals that defined by the company as shown in Table2.
We propose an alternative performance appraisal system
based on FCM and explain how it is applied for this
company. In the proposed method, employee data are clustered
by FCM algorithm. Thus, employees are separated into
different clusters according to their performances. Different
performance levels of employees are represented by the
formed clusters which are also represented by their centers. The
major advantage of FCM algorithm is not only
attaching data objects to precisely one cluster, but also defining
the membership degrees to all clusters. An employee
with higher membership degree (the closest member to cluster
center) indicates the characteristic of cluster better than
the other members. The cluster members are arranged based on
this consideration.

173
Table 1. Existing performance evaluation form
Criteria
Nr. Criteria Definition
DM1
Rate
DM2
Rate
DM3
Rate
Average
Rate
Cr1 Written and unwritten communication skills, non-verbal
communication
Cr2 Administrative orientation
Cr3 Tolerance for stress
Cr4 Leadership
Cr5 Negotiation
Cr6 Ability to work as part of a team
Cr7 Reliability and punctuality
Cr8 Appearance of self confidence
Cr9 Technical/ professional proficiency
Cr10 Ability to analyze a situation or problem logically

Cr11 Planning and organizing
Cr12 Delegation and control
Cr13 Work experience
Cr14 Foreign language
Cr15 Decision making
∑Average Rate
Table 2. Company’s existing performance levels
TPP Performance Class Performance Level
80 ≤ TPP Group 1 Excellent
65 ≤ TPP ≤ 79 Group 2 High
50 ≤ TPP ≤ 64 Group 3 Sufficient
TPP ≤ 49 Group 4 Low
This study consists of two parts. In the first part, data are
clustered by FCM algorithm to find out performance levels.
Membership degrees of each employee regarding all clusters are
calculated. Each cluster represents a different
performance level. In the second part, the clusters are labeled
according to the cluster centers in a descending order.
Regarding the fifteen evaluation criteria in the existing
evaluation form, DMs rated company's 43 white collar
employees according the seven-point-Likert scale as shown in
Table 3.
Table 3. Seven-point Likert scale

POINT STATUE
1 Strongly disagree
2 Disagree
3 Disagree somewhat
4 Undecided
5 Agree somewhat
6 Agree
7 Strongly agree
The DMs unanimously assigned only one score to each
employee for each criterion. Table 4 shows the assigned values
for each evaluation criteria.
The above-described FCM algorithm is executed using the
data given in Table 4. The necessary inputs of the FCM
algorithm for this case are determined:
• data objects to be clustered: they are given in Table 4,
Ozkan et al.
174
• number of clusters (c) = 4, (In this case, number of clusters is
selected as 4 to make a comparison between
FCM and present method in the company)
• threshold value = 2,
• clustering error (error_ rate) is assigned as 0.01 and,
• the maximum number of iteration (max_iteration) = 100

Table 4. Forty three employees and their degrees according to
fifteen criteria
Cr1 Cr2 Cr3 Cr4 Cr5 … Cr11 Cr12 Cr13 Cr14 Cr15
Emp1 3 5 2 3 3 … 5 5 6 7 7
Emp2 5 5 5 5 6 … 6 5 7 3 1
Emp3 1 1 2 5 1 … 3 1 4 1 3
Emp4 4 4 3 4 5 … 4 1 3 3 3
Emp5 6 7 7 1 7 … 5 4 6 6 6
Emp6 6 2 6 5 7 … 3 6 7 6 5
Emp7 7 7 7 7 7 … 5 4 5 5 6
Emp8 7 6 7 7 6 … 6 7 7 7 7
…
…
…
…
…
…
…

…
…
…
…
…
Emp38 7 6 7 7 5 … 5 7 7 7 7
Emp39 5 2 3 4 2 … 2 4 2 1 4
Emp40 7 4 7 5 7 … 1 1 7 6 5
Emp41 2 7 6 6 2 … 3 4 5 6 5
Emp42 5 6 6 7 7 … 3 2 6 6 5
Emp43 5 5 5 5 7 … 1 1 3 5 5
Provided performance data are evaluated through FCM
algorithm as defined in Section 3.1. The algorithm stops when
it reaches to error_ rate or max_iteration. FCM algorithm
builds four performance clusters as prescribed and,
calculates membership degrees for each employee to these
clusters. Calculation results are given in Table 5.
Table 5. Membership degrees of employees to clusters.
C1 C2 C3 C4

Emp1 0.21443633 0.333054928 0.331642207 0.120866535
Emp2 0.171293877 0.332097333 0.33287785 0.16373094
Emp3 0.044678208 0.102174467 0.102756383 0.750390942
Emp4 0.079157642 0.359219516 0.363810579 0.197812262
Emp5 0.281263461 0.307020511 0.306057201 0.105658827
Emp6 0.37790524 0.27944337 0.277199301 0.065452089
Emp7 0.282133918 0.310063914 0.308234455 0.099567713
Emp8 0.816997851 0.079740624 0.079139493 0.024122033
…
…
…
…
…
Emp38 0.851186915 0.065566013 0.064997284 0.018249788
Emp39 0.049265973 0.122513799 0.123239926 0.704980302
Emp40 0.211488565 0.317279163 0.317811551 0.153420721
Emp41 0.182358008 0.359145491 0.357400589 0.101095913
Emp42 0.333832814 0.309370753 0.30306031 0.053736123
Emp43 0.123176258 0.392829103 0.394525496 0.089469143

175
FCM algorithm determines for each employee the cluster
membership, considering the highest membership degree. As
seen in Table 5, each employee is attached to the most
appropriate cluster. For instance, Emp1 is attached to the cluster
2 with 0.333054928 and Emp3 is attached to the cluster 4 with
0.750390942 membership degree.
In machine learning approach, the clusters are represented by
their centroids effectively. In Table 6 the “cluster
center” column is another output of the FCM algorithm. After
the algorithm is completed, the clusters are constituted
and the cluster centers are calculated and, clusters are labeled.
Table 6. The classification of the employees by FCM algorithm
into the four performance level
CLUSTER
NUMBER CLUSTER MEMBERS
CLUSTER
CENTER CLUSTER LABEL
1 6, 8, 16, 22, 28, 38, 42
6.103838
Outstanding employee
2 1, 5, 7, 9, 10, 11, 12, 14, 17, 18, 19, 20, 23, 24, 26, 27, 31, 41
4.473125
Successful employee

3 2, 4, 13, 21, 29, 30, 32, 33, 34, 35, 40, 43
4.454098
Successful but development required in
certain criteria
4 3, 15, 25, 36, 37, 39 2.521343 Development required in many
criteria
As seen in Table 6, the proposed method clustered the
employees 6, 8, 16, 22, 28, 38 and 42 as cluster 1 that takes the
highest performance measure (6.103838) and, is labeled as
“outstanding employee”. An outstanding employee will be
awarded by the top management. This cluster involves the
highest performance employees according to FCM. The
employees included in cluster 2 will be encouraged and
motivated for the award in the future. At cluster 3, the
deficiencies of the employees should be eliminated by training.
3, 15, 25, 36, 37 and 39th employees are clustered as
cluster 4. They belong to the class of “development required in
many criteria”. This cluster with the smallest center
value (2.521343) includes the most inappropriate employees for
awarding within 43 employees.
While reviewing these clusters detailed, the membership
degrees should be examined. For instance in cluster 4, 3rd
employee’s membership degree is 0.75039; the 36th one’s is
0.6180; the 39th one’s is 0.7049. Between these three
employees, the 36th one has a better performance level then the
other two. The 3rd employee has the worst performance
level then 36th and 39th one. If the company wants to dismiss
an employee, in this situation the 3rd employee must be
chosen among these three employees. Likewise, if the company
wants to award only one employee, the 16th employee

with the 0.88948 membership degree to cluster 1 should be
chosen.
The classification of 43 based on TPP and FCM are shown
comparatively in Table 7.
Table 7. Comparison the results of TPP (existing performance
appraisal system) and FCM (proposed method)
EMPLOYEE
NUMBER
TPP FCM
Point Group Cluster Number
1 70 2 2
2 66 2 3
3 31 4 4
4 55 3 3
5 79 2 2
6 82 1 1
7 80 1 2
8 100 1 1
…
…
…
…

38 98 1 1
39 39 4 4
40 69 2 3
41 69 2 2
42 82 1 1
43 66 2 3
Ozkan et al.
176
The results of TPP and FCM methods, which are shown in
Table 7, point out two occurring cases: the 2nd, 40th and
43rd employees are in the second group with the points 66, 69,
and 66, respectively, according to the TPP; however,
these employees are in the 3rd cluster according to FCM.
Similarly, according to TPP, the 7th employee is in the first
group, but FCM classifies the same employee as a “successful
employee.” In brief, the conventional method classifies
the 2nd, 7th, 40th, and 43rd employees different than FCM. In
another case, although the 40th and 41st employees have the
same TPP value (69), FCM assigns them to different clusters.
This is probably due to the fact that the accumulated TPP
value in the conventional method can cause the loss of the
separate effects of the fifteen criteria.
5. DISCUSSION AND CONCLUSION
Performance appraisal problems have been solved by several
methods. The most commonly used one in practice is
ranking and paired comparison ranking. In this process, as a

multi criteria decision making problem, the raters always
express their preferences on alternatives or on the attributes of
employees, which can be used to help rank and
categorize the employees or select the most appropriate one(s).
While the performance appraisal method covers an
important requirement, it is well known that traditional
techniques have several weaknesses. Therefore, in this study,
FCM algorithm is proposed as an alternative to conventional
methods for performance appraisal problem and successful
results are obtained. The proposed method makes some
significant and remarkable contributions to strengthen the
weaknesses of the conventional methods. Moreover, the
proposed method enables the managers to group their
employees with respect to several criteria. Thus, managers can
determine the most appropriate employee(s), in case of
promotion, salary determination, etc. In addition, in need of
personal education, they will know which employee(s)
requires training using the proposed method.
FCM evaluates the employees according to their criteria
values separately. This is the first contribution of the
algorithm. On the other hand, using the current practice, the
employees are sorted in a descending order with respect to
the total scores based on evaluation criteria and grouped
according to the predetermined thresholds. In addition,
although, all evaluation criteria are rated separately, the
classification of employees is done according to an aggregated
value. The aggregated TPP value can cause a loss of separated
effects of fifteen criteria. Divergently in FCM, the
employees having the same total score can be assigned to
different clusters. It means that different employees having
the same total score can be dissimilar. Furthermore, in this
paper, employees are categorized in four classes instead of
sorting. Thus, a performance improvement can be done if
necessary.
The next contribution is about the determination of the
clusters. Using the present practice of the company, the

clusters are predetermined based on some thresholds to form the
groups. However, only the cluster number and
clustering error are necessary for FCM algorithm and the
clusters and cluster bounds are formed automatically.
Another contribution of the algorithm is about the
membership degrees of the employees to the clusters. In the
conventional method, an employee is strictly a member of a
specific group or not. However, in FCM, each data point
belongs to a cluster to some degree specified by a membership
degree. The membership degrees of each employee to all
clusters are computed using FCM. In this manner, prioritization
can be done using these membership degrees in a
certain cluster.
The soft clustering ability of FCM is another advantage
benefited in this paper. This allows managers to make final
decision better. Namely, if the membership degrees of an
employee to more than one cluster are closer to each other, the
decision maker can assign this employee to the with higher
membership degree
In this paper, FCM is used for the first time as an effective
solution method for performance appraisal problem. FCM
algorithm not only selects the most appropriate employee(s), but
also clusters all of them according to their membership
degrees. Consequently, all sectors and all enterprises can use
FCM for performance appraisal easily and efficiently due
to its flexibility and fast computation time.
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Memory Access Impact on Performance:
Assume that main memory accesses take 80 ns and that memory
accesses are 42% of all instructions. The L1 cache has a miss
rate of 9% and a hit time of 0.58 ns.
1.Assume that the L1 hit time determines the cycle time for the
processor. What is the clock rate?
1._______
2.What is the Average Memory Access Time for the processor?
2.________
3.Assuming a base CPI of 1.0 without any memory stalls (once
the pipeline is loaded), what is the total average CPI for the
processor?
3. _______
We will consider the addition of an L2 cache to try to reduce
the average CPI; on a miss, P1 will now first check L2 cache,
and only if that is a miss, will then need a main memory access.
The L2 miss rate is 85%, and L2 hit time is 4.88ns

4.What is the AMAT for the processor, with the inclusion of the
L2 cache?
4._________
5. Assuming a base CPI of 1.0 without any memory stalls, and
using the same instruction miss as part 1 of this question, what
is the total CPI (for all instruction types) for P1 with the
addition of an L2 cache?
5._________
Question #1: The cache for this problem has 16 words. You are
going to evaluate the cache performance based on different
mapping schemes, and different size blocks, to try to come up
with the best mapping and block arrangement for this series of
memory access calls.
Your mappings will be:
16 one-word blocks, Direct Mapping
16 one-word blocks, Fully Associative Mapping
4 4-word blocks, Direct Mapping
4 4-word blocks, Fully Associative Mapping
4-way Set-Associative, 16 one-word blocks
For each of the schemes,
1. Fill out the “top” table of “tags”
2. Show the placement of the block in the “bottom” table
3. Count the “hits” and “misses”
4. Compare the hit/miss ratios for the different mappings and
block arrangements
If you were designing the cache, what do you think would have
to most impact on the performance: larger block sizes, or
different mapping schemes? Explain your reasoning.

Word Address
Word
Bit Address
Direct 16
Tag
Direct 4
Tag
Fully Assoc.
16 Tag
Fully Assoc.
4
2
00000010
3
00000011
11
00001011
16
00010000

21
00010101
13
00001101
64
01000000
48
00110000
19
00010011
11
00001011

3
00000011
22
00010110
4
00000100
27
00011011
6
00000110
11
00001011

# hits : # misses _________ ________
_________ _________
CAT size in bits:
16 Blocks
Direct Map
1word blocks
Associative
1word blocks
4 Blocks
Direct Map
4-word block
Associative
4-word block
0 (0000)
0 (00)
1 (0001)
2 (0010)

3 (0011)
4 (0100)
1 (01)
5 (0101)
6 (0110)
7 (0111)

8 (1000)
2 (10)
9 (1001)
10 (1010)
11 (1011)
12 (1100)
3 (11)
13 (1101)

14 (1110)
15 (1111)
4-way Set Associative with 1-word blocks
Word Address
Word
Bit Address
4 way set
Tag
2
00000010
3
00000011
11
00001011
16
00010000

21
00010101
13
00001101
64
01000000
48
00110000
19
00010011
11
00001011
3
00000011
22
00010110
4
00000100
27
00011011
6
00000110
11
00001011

# hits : # misses ____________
CAT size in bits: ____________
4 way sets
Slot 1
Slot 2
Slot 3
Slot 4
0 (00)
1 (01)
2 (10)
3 (11)

Research article
Looking for performance in personality inventories: The
primacy of evaluative
information over descriptive traits
SYLVAIN CARUANA1,2*, RÉGIS LEFEUVRE1 AND
PATRICK MOLLARET1
1Cognition, Health & Socialization Laboratory (C2S),
University of Reims Champagne-Ardenne, Reims, France;
2CDE Consultants, Reims, France
Abstract
Three experiments were designed to demonstrate that job
performance inferences from personality inventories rely more
on the agentic or communal value conveyed by the items
compared with the Big-Five traits they are supposed to describe.
In the first two experiments, the participants had to predict the
job performances of fictitious job applicants based on their
responses to a personality inventory. In Experiment 1, the
information on personality was held constant, such that the
applicants’ responses varied solely on their agentic, communal,
or purely descriptive orientation. In Experiment 2, the
social value of the responses again varied as well as the
information about the applicants’ personality (agreeable vs.
conscientious). The results showed that the agentic profiles
were the most predictive of the performance, regardless of

the personality factors. In Experiment 3, we reversed the
procedure. The participants filled out a personality inventory
in the place of a more or less successful employee. The results
here showed that the information about the
performance had the greatest impact on the agentic items,
independent of the personality factors measured. These results
confirm the relevance of social judgment models in personality
research. Copyright © 2014 John Wiley & Sons, Ltd.
Personality inventories are widely used in evaluative
settings to recruit individuals and to make predictions about
their performances (Kuncel, Ones, & Sackett, 2010;
Rothstein & Goffin, 2006). Individuals and professionals
are able to infer the performance of individuals from
personality profiles (Dunn, Mount, Barrick, & Ones,
1995). Nevertheless, because many items from personality
inventories display both evaluative and descriptive informa-
tion about individuals (Backström, 2007; Backström,
Björklund, & Larsson, 2009), the interpretation of the link
between personality scores and the prediction of perfor-
mance remains unclear. Broadly speaking, the evaluative
part of personality inventories relies on the positive or
negative connotation of the items, whereas their descriptive
part concerns the behavioral tendency evoked by the items.
For example, being efficient and productive at work
(a Revised NEO Personality Inventory (NEO PI-R) item)
conveys both evaluative information (the item is positive)
and descriptive information (the item describes a tendency
to behave in a conscientious way). The aim of the present
contribution was (1) to dissociate the descriptive informa-
tion from the evaluative information, which is confounded
in most items from personality inventories, and (2) to show
that the evaluative dimension prevails in the inference of
performance at work, which is in line with an evaluative
approach to social judgment.

THE TWO EVALUATIVE DIMENSIONS
Research has shown that everyday social judgments are
constructed around two fundamental evaluative dimensions
(Abele & Wojciszke, 2007, 2013; Bi, Ybarra, & Zhao, 2013;
Dubois & Beauvois, 2005; Fiske, Cuddy, & Glick, 2007;
Fiske, Cuddy, Glick, & Xu, 2002; Judd, James-Hawkins,
Yzerbyt, & Kashima, 2005; Woike, Lavezzary, & Barsky,
2001; Ybarra et al., 2008). Communion, the first dimension,
explains most of the variance in social judgments (Abele &
Wojciszke, 2007; Ybarra, Park, Stanik, & Lee, 2012) and
evaluates the degree to which individuals are oriented toward
others (Cislak, 2013). Communion is operationalized by
adjectives referring to warmth, morality, or sociability, which
can communicate a positive evaluation (e.g., warm, sociable,
and honest) or a negative one (e.g., cold, aggressive, and
dishonest). Agency, the second dimension, refers to the
promotion of the self and dominance. Agentic traits are
defined as self-profitable traits (Peeters, 1992), meaning that
they are more useful for the self than for others. Beyond the
usefulness for the self in daily interactions, it has been
proposed that agentic behaviors are the most adaptive for use in
professional life (Beauvois & Dubois, 2009; Dubois &
Beauvois,
2005). Indeed, as shown by Dubois (2010), individuals with the
most valued occupations are positively judged on agentic traits
(e.g., dynamic, active, and competent). Conversely, an
individual
*Correspondence to: Sylvain Caruana, Laboratoire C2S
(Cognition, Santé, Socialisation), Université de Reims
Champagne-Ardenne, UFR Lettres et Sciences
Humaines, 57, rue Pierre Taittinger, 51096 Reims Cedex,
France.
E-mail: [email protected]
European Journal of Social Psychology, Eur. J. Soc. Psychol.

44, 622–635 (2014)
Published online 26 June 2014 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/ejsp.2034
Copyright © 2014 John Wiley & Sons, Ltd. Received 6
December 2013, Accepted 28 April 2014
judged negatively on agency (e.g., defeatist, lazy, or naive) is
likely to be associated with the lowest social status. Judgments
on communion or agency are not deemed to describe how
individuals or groups really are or their genuine psychological
tendencies. Instead, they may reflect the system of intergroup
relations and rationalize stereotypes (Fiske et al., 2002), justify
the inequality of economic resources by ascribing the positive
agentic traits to affluent individuals (Oldmeadow & Fiske,
2007, 2010), and communicate the socio-economic status of
individuals (or even animals; see Dubois & Beauvois, 2011).
THE FIVE DESCRIPTIVE DIMENSIONS
Personality assessment is grounded on a different research
tradition, which identifies the major dimensions on which indi-
viduals differ from each other in terms of general personality
tendencies. The Five-Factor Model (FFM) currently dominates
the field of personality research. This model is considered to
be a predictive and universal summary of personality along
five dimensions (Conscientiousness, Extraversion, Agreeable-
ness, Openness, and Emotional stability) that are assumed to
be endogenous and stable across time (McCrae & Costa,
2006). These five dimensions are measured with several
focused behavioral descriptions and tendencies, which
operationalize each personality factor (e.g., “I am a very active
person” operationalizes Extraversion). The items can take the
form of an attitude (e.g., “I’m efficient and productive at

work”), a behavioral frequency (e.g., “I often try new and for-
eign foods”), or a propensity to act in a particular way (e.g., “I
laugh easily” and “being active”). Respondents are instructed
to sincerely indicate the degree to which each item is a good
descriptor of themselves. The responses are then computed
in a global score for each personality factor. Nevertheless, it
appears that the more valorized items offer the opportunity to
present oneself in a favorable way (Backström et al., 2009;
Backström, Björklund, & Larsson, 2011) and to make a good
impression regarding performance abilities.
DESCRIPTIVE VERSUS EVALUATIVE APPROACH
TO PERFORMANCE PREDICTION
Personality inventories that measure the FFM (e.g., Alter Ego,
Caprara, Barbaranelli, Borgogni, & Perugini, 1993; NEO PI-R,
Costa & McCrae, 1992) have been widely used to predict
professional performance (Barrick & Mount, 1991, 2005;
Barrick, Mount, & Judge, 2001; Hough, Eaton, Dunnette,
Kamp, & McCloy, 1990; Judge, Higgins, Thoresen, & Barrick,
1999). Conscientiousness and Emotional stability appear to
predict professional performance regardless of the nature of the
job (Barrick & Mount, 1991, 1993; Barrick et al., 2001;
Chiaburu, Oh, Berry, Li, & Gardner, 2011; Judge et al., 1999;
Kuncel et al., 2010; Salgado, 1997), whereas Extraversion and
Openness predict performance only for specific occupations,
such as sales representative performance or training
proficiency,
respectively (Barrick & Mount, 1991; Salgado, 1997). In con-
trast, Agreeableness is not directly linked to performance
(Barrick & Mount, 1993; Barrick et al., 2001; Judge et al.,
1999; Kuncel et al., 2010). It becomes predictive only in very
specific cases where interdependence among co-workers is
absolutely decisive (Mount, Barrick, & Stewart, 1998). This
descriptive approach to performance prediction dominates the

field of research.
Few studies have investigated the role of the two evaluative
dimensions in the inference of job performance. In a study by
Abele (2003), correlation and regression analyses showed a
reciprocal link between agency and career success. Individuals
reporting higher agency also reported higher career success;
the opposite was also true. In another experiment, Abele,
Rupprecht, and Wojciszke (2008) administered arbitrary
feedbacks of success versus failure to participants engaged in
performance tasks. Independent of their actual performance,
the participants who believed that they succeeded in the task
considered themselves to be more agentic than those who failed.
Importantly, the communal dimension was unaffected by the
feedback manipulation. In fact, agency self-evaluations are de-
termined by an individual’s perception of success (see also
Wojciszke, Baryla, Parzuchowski, Szymkow, & Abele, 2011;
Wojciszke & Sobiczewska, 2013). Agentism self-attributions
are also affected by status. For example, Moskowitz, Suh, and
Desaulniers (1994) showed that individuals who experienced a
supervisor role described themselves as more agentic than
individuals who experienced a supervisee role. Hence, self-
descriptions seem to be influenced by the social context and
the nature of the tasks. Additionally, it appears that the
evaluation of others on agency and communion is partly
determined by the social context and the nature of the task
and the relationships (Cottrell, Neuberg, & Li, 2007). While
everyday relationships are evaluated on the communal dimen-
sion (Abele & Bruckmüller, 2011), agency takes primacy in
more evaluative settings. Abele and Brack (2013) showed that
participants searched for agency in others only when they
shared goals or exchanged relationships with them, whereas
they searched for communion when they were committed to
independent goals. In a similar vein, Dubois and Aubert
(2010) showed that an agentic target was more chosen than
a communal one under professional settings, and conversely,

the communal target was more chosen under a friendly con-
text. Finally, Cislak (2013) found that having power or status
leads to a greater search for agency in subordinates.
These results confirm that agency emerges from performance
stakes because of its ability to distinguish productive
individuals
from unproductive ones in professional settings. In the
personal-
ity literature, performance and competence (i.e., productivity)
are recurrently assessed with scales completed by the manager
(e.g., Hogan, Rybicki, Motowidlo, & Borman, 1998; Tsui,
Pearce, Porter, & Tripoli, 1997). Because these scales aim to
quantify the success versus failure of employees, or at least
their
ability to succeed or fail in their jobs, only agentic items should
be related to performance inferences in recruitment settings.
Importantly, several studies have documented the structural
representation of the FFM across the two evaluative dimen-
sions. For some authors, agency and communion work as
two supra-ordinate evaluative factors of the Big Five
(Blackburn, Renwick, Donnelly, & Logan, 2004; Digman,
1997; McCrae et al., 2008) in which conscientiousness ap-
pears as the most agentic dimension and agreeableness is the
most communal one. However, from an item perspective
Personality, evaluative information, and performance inference
623
Copyright © 2014 John Wiley & Sons, Ltd. Eur. J. Soc.
Psychol. 44, 622–635 (2014)
analysis, most items from personality inventories may convey

evaluative information (Backström et al., 2009, 2011;
Wojciszke, 1994), which can be encoded according to both
agency and communion (McAdams, Hoffman, Mansfield, &
Day, 1996; Woike, Gershkovich, Piorkowski, & Polo, 1999;
Woike et al., 2001; Woike & Polo, 2001; Wojciszke, 1994).
Our objective was to demonstrate that the descriptive infor-
mation by the FFM is orthogonal to the evaluative informa-
tion. We hypothesized that removing the agentic part of the
personality dimensions should also exclude performance from
personality scores. In other words, we aimed to show that
performance is inferred from the evaluative part of the
personality scores. As an implication, conscientiousness could
appear to be predictive of performance only because of its
over-representation of agentic items.
OVERVIEW OF THE PRESENT STUDY
We conducted three experiments designed to show that infer-
ences of professional performance rely more on the social
value of the items than on the personality factors these items
are supposed to operationalize. The experiments were pre-
ceded by a pilot study designed to measure the agentic or com-
munal value communicated by the items of two well-known
personality inventories based on the FFM. The objective was
to obtain a new inventory in which each of the five dimensions
was illustrated by the same proportion of agentic and commu-
nal items. This was, in fact, the only way to orthogonalize
evaluative and descriptive information conveyed by the items
of the personality inventories.
In Experiments 1 and 2, the participants had to predict task
performance and contextual performance (organizational
citizenship behavior (OCB)) of a fictitious applicant. The re-
sponses of the fictitious applicant to our personality inventory
were the only piece of information provided to the participants.

In Experiment 1, the responses varied only on agency or
communion, whereas the information about the five personality
factors was held constant. In Experiment 2, the applicant
profiles
varied both in social value (agency vs. communion) and in
personality (conscientiousness vs. agreeableness). Based on
research that showed that the agentic self-descriptions corre-
spond most closely to the expectations in an occupational
context, we postulated that inferences about task performance
would vary according to the level of agency conveyed by the
profiles (Experiments 1 and 2) and not according to the descrip-
tive personality factors (Experiment 2). Moreover, as the
contex-
tual performance (organizational citizenship) is associated with
altruistic motives and co-worker assistance (Borman &
Motowidlo, 1997; Tsui et al., 1997), we expected this variable
to be influenced by both the agentic and communal information
conveyed by the profiles.
In Experiment 3, the paradigm was reversed: the partici-
pants had to infer the personality of a target from information
about the target’s high versus low job performance. As
all the personality dimensions of our inventory had the
same evaluative implications, we expected that a high-
performance would convey a strong agentic profile within
all the personality factors.
PILOT STUDY
As the social value of the items in the personality inventories
had never been measured, the pilot study assessed the agentic
and communal dimensions of the NEO PI-R (sample 1) and
Alter Ego items (sample 2). Our main objective was to
construct a questionnaire that crossed the Big Five with the
two evaluative dimensions. To build this questionnaire, we
used two well-known personality inventories (the Alter Ego,

Caprara et al., 1993, French translation, Caprara, Barbaranelli,
& Borgogni, 1997; and the NEO PI-R, Costa & McCrae, 1992,
French translation, Costa, McCrae, & Rolland, 1998).
Participants
Sample 1
One hundred and fifty-three participants (85 men, 65 women,
3 non-specified; mean age: 34.8 years; SD: 12.5) were
interviewed at their workplace. Our sample was composed of
managers and supervisors (26.80%), employees and techni-
cians (46.40%), and students (21.57%). The remaining
5.23% were classified as “Other occupations.” Eighty-five
participants were asked about the items’ agentic content, and
68 participants were asked about the items’ communal content.
Sample 2
Fifty-two participants (21 men, 31 women; mean age:
33.21 years; SD: 11.1) were interviewed at their workplace.
The sample consisted of diversified occupational sectors
(sales, accountants, clerical, and industry production workers)
and education levels. Twenty-nine participants were asked
about the items’ agentic content, and 23 participants were
asked about the items’ communal content.
Procedure and Materials
The questionnaire was presented as a study investigating the
relevance of personal information that individuals communi-
cate in social or professional networks (e.g., Hall, Pennington,
& Lueders, in press; Marcus, Machilek, & Schütz, 2006; Utz,
2010). The participants were asked about the evaluative conse-
quences of describing themselves with items drawn from the
personality inventories, in either an agentic or a communal

context. They were instructed to rate a series of statements to
create a “personal profile” for either Facebook (a social
network that operationalizes the communal context) or Viadeo
(a professional network that operationalizes the agentic
context). Facebook was briefly described as a network that al-
lows users to develop friendly relations with other members.
The participants had to rate each item on a 7-point scale. For
each item, the question was “Does this particular item give
the impression of someone who has the sort of qualities
needed to attract a large number of friends on Facebook (+3)
or someone who is devoid of those qualities (�3)?” Viadeo
was described as a professional network designed to promote
professional contacts. In this context, the question was “Does
this particular item give the impression of someone who is
624 Sylvain Caruana et al.
Psychol. 44, 622–635 (2014)
likely to succeed in his/her professional life (+3) or someone
who is liable to fail (�3)?” All items were taken from the
French versions of the NEO PI-R (sample 1) and the Alter
Ego (sample 2). The participants in both samples only had to
rate the items for one of the two rating contexts (Facebook
vs. Viadeo). For the NEO PI-R, only 80 of the 240 items were
presented to each participant, to minimize the fatigue effect.
Thus, each NEO PI-R item was evaluated by approximately
25 participants. Because of its lower number of items (132),
the Alter Ego was integrally presented to each participant.
Result Analyses and Questionnaire Elaboration
The aim of this pilot study was to build a questionnaire that

measures the five factors via the same amount of agentic, com-
munal, and neutral items. The items were selected following
two principles. Using one-sample t-tests, we first compared
each item score with the central point of the scale (0). Two
thirds of these comparisons were statistically significant for
both the agentic and communal dimensions, indicating a posi-
tive bias for the major part of the items. Second, we performed
independent group t-tests to compare the agency and commu-
nion ratings. By crossing these two criteria, we obtained four
categories of items: neutral, agentic, communal, and both
agentic
and communal items. We should first note that for both samples,
these four categories of items were not equally represented
among the Big-Five factors (sample 1: χ2(12) =49.27, p< .001;
sample 2: χ2(12) = 22.92, p < .05). For both questionnaires,
conscientiousness was primarily an agentic factor, and
agreeableness was mainly a communal factor. To construct
a more balanced inventory, we proceeded as follows.
For agentic versus communal items, we selected the items with
a high rating on only one dimension (agency vs. communion)
and which were the most neutral on the other. We also verified
that the agentic or communal ratings were significantly (or at
least marginally) higher on the dimension of interest. Finally,
we selected only the items with the same valences on the
personality
dimension and the social value. For neutral items, we selected
the more neutral items on both agency and communion with
no significant difference between agentic and communal
ratings. A 30-item questionnaire was developed, which
measures each personality dimension with six items (two
agentic,
two communal, and two neutral; see Appendix 1). The mean
scores of agency and communion as a function of the item’s
type
are reported in Table 1.

This questionnaire allowed us to test the following
hypotheses:
(i) At equal scores on the five factors, agentic-oriented profiles
should lead to higher performance ratings compared with
communion-oriented or neutral profiles (Experiment 1).
(ii) Descriptive information variation should have less impact
on performance ratings compared with agentic informa-
tion variation (Experiment 2).
(iii) Information about job performance should have a higher
im-
pact on agentic items compared with communal or neutral
ones, independent of the Big-Five factors (Experiment 3).
EXPERIMENT 1
This study was designed to show that the item’s social value is
perceived before the Big-Five traits and is sufficient to make
job performance inferences via personality items. We elabo-
rated three strictly similar personality profiles that varied only
on the social value orientation (agentic, communal, or neutral).
In line with the social judgment models, we first postulated
that the task performance attribution would be higher for the
agentic profile compared with the communal or neutral ones.
Second, we hypothesized that the contextual performance pre-
diction (i.e., OCBs) would be influenced by both the agentic
and communal values, leading to a higher contextual perfor-
mance for the agentic and communal profiles compared with
the neutral one.
Participants
The participants included 74 third-year psychology
undergraduates

(64 women, 10 men, Mage = 21.96 years, SD = 3.83), who took
part in the experiment during a tutorial. This experiment was
presented as part of a study on how individuals form
impressions
about professional abilities. The participants were randomly
assigned to one of three different experimental conditions
(agency profile, communion profile, or neutral). They were
not familiar with the theoretical background and did not
receive any remuneration for participating in the study.
Materials and Procedure
The participants had to put themselves in the place of a
recruiter and read through a fictitious job applicant’s responses
to a personality inventory. They were given 5 minutes to con-
sult the response profile and form an overall impression of the
applicant. Then, the participants had to predict the applicant’s
task performance and contextual performance and indicate
how likely the applicant was to possess different agentic or
communal personality traits (manipulation check). The order
in which the manipulation check and the dependent variables
were performed was counterbalanced.
Independent Variable: Profile Type
The fictitious applicant was introduced to the participants via
his
responses to the personality inventory, which had been
constructed following the pilot study. This inventory allowed
us to construct three response profiles (agency-oriented,
commu-
nion-oriented, and neutral; see Appendix 2 for sample items).
The item ratings were calculated so that all the personality
factors were scored equally (8/18 for each factor) and were
held constant across the three profiles. In contrast, the agency,

Table 1. Agency and communal mean scores as a function of the
item’s value orientation (pilot study)
Item’s type
Agency-oriented Neutral Communion-oriented
Agency 1.47 0.16 0.43
Communion 0.36 0.20 1.38
Note: The mean scores can vary from 0 to 3.
625
Psychol. 44, 622–635 (2014)
communion, and neutral values of the personality profiles sys-
tematically varied. In the agency-oriented profile, the agency
items were given the most positive or negative ratings (+3 or
�3, depending on the positive or negative formulation of the
items), whereas the communion and neutral items were given
middle ratings (0, 1, �1). For the communion-oriented profile,
the communion items were given the most positive or negative
ratings (+3, �3), whereas the agency and neutral items were
given middle ratings (0, 1, �1). Finally, for the neutral profile,
the neutral items were given the highest positive ratings, and
the agency and communion items were given moderate ratings.
Accordingly, none of the profiles contained a particularly
salient personality characteristic, and there was no descriptive
difference between the three profiles. Each participant saw only
one of the three profiles.

Manipulation Check: Agency and Communion Ratings
To check that the experimental manipulations had the
expected effect, we asked the participants to assess the profiles
on the basis of 15 agency traits (nine positive: ambitious,
competent, motivated, successful, self-confident, hard working,
active, inventive, and ingenious; six negative: indecisive,
inefficient, slow, negligent, lazy, and modest) and 15 commu-
nion traits (nine positive: altruistic, friendly, warm, honest,
sensitive, sincere, considerate, indulgent, and temperate; six
negative: distant, selfish, uncommunicative, hypocritical,
heartless, and arrogant). All traits were also applicable to
the Big Five. The trait choice was based on the items
contained in the French version of the Big-Five Inventory
(Plaisant, Courtois, Réveillère, Mendelsohn, & John, 2010).
The participants rated each trait on a 7-point scale ranging
from 1 (Don’t agree) to 7 (Completely agree). We computed
a communion score (α = .70) and an agency score (α = .82).
Dependent Variables: Task Performance and Organizational
Citizenship
Two main types of indicators of job performance are reported
and have been linked with personality in the literature: task
and contextual performance.
Task performance (objective or other-assessed) is directly
linked with the job description. In our study, task performance
predictions took the form of ratings on six items taken from
the task performance scale developed by Tsui et al. (1997;
α = .89). This scale is commonly used to probe aspects, such
as the quality (e.g., “Employee’s standards of quality are
higher than formal standards on this job”), quantity (e.g.,
“Employee’s quantity of work is higher than average”), and
efficiency (e.g., “The employee’s efficiency is much higher

than average”) of the work completed by a co-worker from
the point of view of his or her superior.
Contextual performance (e.g., OCBs) measures the employee’s
broader behaviors toward the working team, management, and
organization. The predictions of the OCB took the form of
ratings on eight items (e.g., “The employee expresses opinions
honestly when others think differently,” “The employee
makes suggestions to improve work procedures,” and “The
employee informs management of potentially unproductive
policies and practices”), which were taken from the same
authors’ citizenship behavior scale (Tsui et al., 1997; α = .91).
For both scales, the participants rated items with the same
7-point scale.
Results
Manipulation Check: Agency or Communion Profile
We subjected the data to an analysis of variance (ANOVA)
with the profile type (agency, communion, or neutral) as the
independent variable and the agency and communion ratings
as the repeated-measures factors. The ANOVA revealed an
interaction between the profile type and the perceived value,
F(2, 71) = 64.96, p < .001, η2p = .65. Helmert-type contrasts
validated the experimental manipulations, showing that the
agentic ratings were higher for the agency-oriented profile
(M = 6.00, SD = 0.49) than they were for the other two profiles
(communion-oriented: M = 4.43, SD = 0.79; neutral: M = 4.37,
SD = 0.82), F(2, 71) = 48.89, p < .001, t(71) = 9.26, p < .001,
which did not differ from each other (t(71) = .34, ns). The
communal ratings also varied in the expected direction and
were significantly higher for the communion-oriented profile
(M = 5.16, SD = 0.74) compared with the neutral (M = 4.65,
SD = 0.92) and agency-oriented (M = 4.04, SD = 0.61) profiles,

F(2, 71) = 13.85, p < .001, t(71) = 4.35, p < .001. Finally, the
neutral profile had higher communal ratings compared with
the agency-oriented profile (t(71) = 2.86, p < .001).
Task Performance and Organizational Citizenship Behavior
Predictions
The task performance and OCB predictions were analyzed
separately (r = .42, p < .001). We ran two ANOVAs on the
data, with the profile type as the independent variable and
the task performance and organizational citizenship as the
dependent variables. Helmert-type contrasts confirmed our hy-
pothesis on task performance predictions and showed that the
agency-oriented profile was perceived as being more success-
ful (M = 5.27, SD = 0.76) compared with both the communion-
oriented (M = 3.65, SD = 1.12) and neutral (M = 3.70,
SD = 0.93, t(71) = 6.88, p < .001) profiles, F(2, 71) = 23.752,
p < .001, η2p = .40, which did not differ from each other
(t(71) = �0.181, ns).
Concerning organizational citizenship, as expected, the
agency-oriented profile elicited more favorable predictions
(M = 5.34, SD = 0.81) compared with both the communion-ori-
ented (M = 4.46, SD = 0.92) and neutral (M = 3.70, SD = 0.99,
t(71) = 5.70, p < .001) profiles, F(2, 70) = 19.725, p < .001,
η2p = .36. Furthermore, the communion-oriented profile
attracted more favorable OCB predictions compared with the
neutral profile (t(71) = 2.86, p < .001).
Discussion
The aim of this experiment was to show that the variation in
social value on a personality inventory is sufficient to develop
inferences about professional performance. The results con-
firmed this hypothesis. All personality factors being equal,
an agency-oriented profile elicited higher task performance

Psychol. 44, 622–635 (2014)
and OCB predictions compared with a communion-oriented or
a neutral profile. If reasoning about the performance was based
on the descriptive information (i.e., personality factors), the
participants would not have been able to distinguish between
the profiles. This suggests that the agency information is
linked to the performance inferences and is well perceived
by people. Communion-oriented profile also attracted higher
OCB predictions compared with the neutral profile. This
suggests that in addition to agency, information about
an individual’s degree of communion also helps to shape
impressions based on the responses to personality inventories.
A limitation of the experiment concerns the over-representa-
tion of the evaluative information. While the descriptive
informa-
tion is provided by six items per factor (two high-scored items
per
factor), the evaluative information comes from 10 high-scored
items. The imbalance between the two types of information may
explain the prevalence of the evaluative part of items.
Experiment
2 was designed to overcome this shortcoming.
EXPERIMENT 2
Experiment 2 was designed to test the hypothesis that evalua-
tive information takes primacy, even when the profiles differ
on personality factors that are theoretically more or less sought

after in an occupational setting: Agreeableness versus Consci-
entiousness (Dunn et al., 1995; see also Pauls & Crost, 2005).
Conscientiousness almost always predicts performance
(Barrick et al., 2001; Kuncel et al., 2010), whereas agreeable-
ness has far less predictive power (or only in specific cases;
Mount et al., 1998). The results of the previous experiment
and the unequal repartition of agency and communion among
the Big-Five factors showed by the pilot study led us to expect
this divergence to be due to the agentic part of conscientious-
ness. If evaluative information takes primacy, we can postulate
that the impact of agreeableness and conscientiousness on
performance inferences should be the same when both factors
are viewed as strongly agentic. Moreover, when the agentic
part of Conscientiousness is reduced, this factor should no
longer be linked to performance. To test this hypothesis, iden-
tical procedure as in Experiment 1 was used, except that the
descriptive information (agreeableness vs. conscientiousness)
and the evaluative information (agency vs. communion) were
simultaneously manipulated.
Participants
The participants included 133 students who were either in their
third year of a management science course or their first year of
a
Master’s degree in auditing or financial management at Reims
University (62 women, 66 men, 5 n/a, Mage =22.11, SD= 2.54).
They completed the experiment during a tutorial. The
participants
were randomly divided between six experimental conditions.
They were not familiar with the theoretical background and did
not receive any remuneration for participating in the study.
Materials and Procedure
As in Experiment 1, the participants had to put themselves in

the position of a recruiter and form an impression of a fictitious
job applicant on the basis of the individual’s responses to a per-
sonality inventory. This time, however, the fictitious question-
naire only concerned two personality factors (agreeableness
and conscientiousness). The personality profiles operationalized
the crossing of the personality (agreeableness, conscientious-
ness) and social value (agency, communion) dimensions.
The personality questionnaire presenting the target was
constructed along the same principle as the one in Experiment
1, except that it only contained the items that measured the
agreeableness and conscientiousness factors. The applicants
were therefore introduced via their responses to six agreeable-
ness items and six conscientiousness items (i.e., two agentic,
two communal, and two neutral items per personality dimen-
sion). As in the previous experiment, we used the responses
the applicant had supposedly provided to these items to vary
both the social value of their profiles (agency-oriented vs. com-
munion-oriented) and their personality scores (Agreeableness
vs. Conscientiousness vs. Standard). The agency and communal
mean scores by the type of items are shown in Table 2.
We began by constructing two standard profiles that had
equivalent scores (8/18) on the agreeableness and conscien-
tiousness factors, but conveyed different values: one profile
was agency-oriented and the other profile was communion-ori-
ented. With the exception that they were focalized on consci-
entiousness and agreeableness items, these two standard
profiles were the same as the agentic and communal profiles
of Experiment 1. To construct the agreeableness and conscien-
tiousness profiles without modifying the degree of agency or
com-
munion, we started from these standard profiles and gave the
neutral items (measuring either agreeableness or cons-
cientiousness) the most positive ratings. Thus, we increased the

agreeableness/conscientiousness score to 13/18. The
participants
encountered one of six profiles crossing descriptive (agreeable,
conscientious, or standard) and evaluative (agency-oriented or
communion-oriented) information (see Appendix 3 for more
details about the profiles).
The participants had 2 minutes to familiarize themselves
with the profile that they were given. Then, they had to predict
the applicant’s task performance and organizational citizen-
ship with the same material as Experiment 1. Finally, to check
that the personality characteristics had been correctly encoded,
the participants were instructed to recall the responses the
applicant had made.
Manipulation Check: Perception of Descriptive Information
After collecting the task performance and OCB predictions,
we checked the encoding and the perception of the descriptive
information. The participants had to recall the applicant’s
responses on three conscientiousness and three agreeableness
Table 2. Agency and communal mean scores as a function of the
item’s value orientation (Experiment 2)
Items’ type
Agency-oriented Neutral Communion-oriented
Agency 1.73 0.20 0.62
Communion 0.44 0.35 1.51
Note: The scores can vary from 0 to 3.
627

Psychol. 44, 622–635 (2014)
items. For both dimensions, one of the items was agency-
oriented, one item was communion-oriented, and one item
was neutral. Thus, the possible effects of the item’s value
on memorization were neutralized. The participants had to
recall the applicant’s responses on the same 7-point scales
as those presented in the profiles.
Results
Manipulation Check: Perception of Personality Information
We conducted two ANOVAs with the personality profile
(standard, agreeable, or conscientious) as the independent
variable and the conscientiousness and agreeableness scores
as the dependent variables. As expected, these ANOVAs
revealed a main effect of the personality profile, F(4,
254) = 3.51, p < .01, η2p = .05. In line with our expectations,
the
conscientious profiles were perceived as more conscientious
(M = 5.42, SD= 1.10) compared with the other two personality
profiles (Mstandard =4.86, SD= 0.97; MAgreeable =4.98, SD=
0.79;
F(2, 132) = 4.10, p < .05). Similarly, the agreeable profiles
were perceived as more agreeable (M = 4.02, SD = 0.86) com-
pared with the other two personality profiles (Mstandard = 3.56,
SD = 0.85; MConscientious = 3.82, SD = 1.15; F(2, 132) = 2.69,
p = .07). The perceptions of the profiles therefore matched
the experimental manipulations.
Task Performance and Organizational Citizenship Behavior

Predictions
The task performance and the OCB predictions were analyzed
separately (r = .29, p < .001). Gender effects were tested for
both dependent variables. No significant interaction with the
profile value or the descriptive information on the task
performance was observed, F(2, 133) = .08, ns, or on the
OCB prediction, F(2, 133) = .43, ns. Gender was not taken into
account in the final analyses.
Regarding the task performance (cf. Figure 1), the ANOVA
revealed a main effect of social value, F(1, 133) = 10.37, p <
.01,
η2p = .08. As expected, the agency-oriented profiles elicited
higher task performance predictions compared with the commu-
nion-oriented profiles. The analyses failed to reveal either a
main
effect of descriptive personality information, F(2, 133) = .73,
ns,
or an interaction effect, F(2, 133) = .77, ns. Moreover, in line
with our expectations, the performance predictions were higher
for the agentic–agreeable profile compared with the commu-
nal–conscientious profile, t(43) = 2.14, p < .05, when these two
experimental conditions were compared separately.
The ANOVAs also revealed a main effect of social value on
the OCB predictions, F(1, 133) = 7.44, p < .01, η2p = .06 (see
Figure 1). The agency-oriented profiles attracted higher OCB
predictions compared with the communion-oriented profiles,
independent of the variations in the descriptive personality
scores. There was no main effect of personality information,
F(2, 133) = .07, ns, and no interaction effect, F(2, 133) = .42,
ns. Moreover, as expected, the performance predictions were
higher for the agentic–agreeable profile compared with the
communal–conscientious profile, t(43) = 1.97, p = .05, when
these two experimental conditions were specifically compared.

Discussion
The aim of this experiment was to demonstrate that descriptive
information has less impact on performance predictions than
evaluative information, even though individuals are fully capa-
ble of perceiving differences in the personality between the
profiles. The results confirmed that impressions formed from
the responses to a personality questionnaire are predominantly
based on the agentic versus communal value of those
responses (see Wojciszke, Abele, & Baryla, 2009). Moreover,
although the participants correctly perceived and memorized
the descriptive personality information, an increased conscien-
tiousness score had no impact on the task performance
predictions if the responses were not agency-oriented. Further-
more, agreeableness personality profile is judged to be just as
effective as a conscientious (or standard) profile, provided that
it is agency-oriented. The third experiment further explored
the reasoning of the respondents (cf. Kuncel, Borneman, &
Kiger, 2011). We reversed the procedure and tested our
hypothesis from the respondent’s point of view.
EXPERIMENT 3
Hogan (1996; Hogan et al., 1998; Hogan & Shelton, 1998;
Johnson & Hogan, 2006) described the completion of an in-
ventory as a form of social situation in which an individual
Figure 1. The mean task performance and organizational
citizenship
behavior (OCB) predictions according to the personality profile
and
the social value (Experiment 2)

Psychol. 44, 622–635 (2014)
(actor) communicates something to someone (observer). The
author argued that personality measures elicit self-presenta-
tions in which individuals manage their reputation according
to the context. Therefore, our third experiment hypothesize
that when respondents seek to present themselves as effective
employees, they base their reasoning more on the value con-
veyed by the responses than on the personality information.
This time, the participants had to put themselves in the place
of a more or less successful fictitious employee. They were
given information about this employee’s task performance
and asked to complete a personality inventory in which each
personality factor was equilibrated in terms of its social value.
We expected that the information on the task performance
would lead to inferences in terms of social value but not
personality. We expected this pattern to be observed for each
of the personality factors.
Participants
The participants included 74 students (39 women, 35 men,
Mage = 21.29, SD = 2.23) recruited from a range of courses
(e.g., management, economics, finance and accounting,
law, and languages). They were approached either during
tutorials or in the university library to take part in a study on
personality and professional abilities. The participants were
randomly assigned to three different experimental conditions
and did not receive any remuneration for participating in
the study.
Procedure

The procedure was based on the identification paradigm
(Gilibert & Cambon, 2003). The participants had to identify
themselves with a low-performance, moderate-performance,
or high-performance employee (between-participants design)
and then respond to a personality questionnaire. They were
provided with one of three performance profiles created from
the responses that the employee’s manager had supposedly
given to the task performance questionnaire used in the
previous two experiments. After completing the personality
questionnaire, the participants rated the employee on two
single-item agency and communion scales.
Independent Variable: Profile Type
The applicants’ profiles were built from six items taken from
the task performance scale developed by Tsui et al. (1997;
see Experiments 1 and 2). The managers’ responses about their
employees were rated on 7-point scales, which provided an
opportunity to vary the performances according to the three
modalities (high, moderate, and low performance). For the
high-performance profile (ideal employee), the manager’s
ratings on the task performance items were extremely
favorable (6 or 7). The moderate-performance profile
(standard employee) was operationalized by central ratings
(3, 4, or 5), and the low-performance profile (unproductive
employee) consisted of negative ratings (1 or 2). Deviations
in task performance scores were standardized between the
three experimental conditions.
Dependent Variable: Personality Measure
The personality questionnaire was exactly the same as the one
used in Experiment 1, but it served as a dependent variable.
This inventory permitted us to compute three social value
scores (agency, communion, and neutral items scores), which
were crossed with the Big-Five factor scores (conscientious-

ness, agreeableness, neuroticism, extraversion, and openness
scores).
Manipulation Check
The participants had to rate the employee’s agency and com-
munion on a 7-point scale, which ranged from 1 (Don’t agree
at all) to 7 (Agree completely). Agency was defined as the
employee’s propensity to have “all the qualities required in
order to perform well in the company and have a successful ca-
reer.” Communion was defined as the propensity to have “the
qualities required to be on good terms with other people.” The
agency ratings allowed us to confirm that agency was induced
by our experimental manipulation of the task performance.
The communion ratings allowed us to identify halo effects
generated by the positive task performance assessments.
Results
Manipulation Check: Perceptions of Agency and Communion
We ran an ANOVA on the agency and communion ratings. In
line with expectations, the manipulation of the employee’s task
performance had a significant effect on the perceived agency,
F(2, 71) = 32.15, p < .001, η2p = .49. The high-performance
pro-
file scored significantly higher on agency (M = 5.71, SD = 1.00)
compared with the moderate-performance profile (M = 4.91,
SD= 0.95), which, in turn, scored significantly higher than the
low-performance profile (M = 3.33, SD = 1.17). The per-
ceived communion scores did not vary with the perfor-
mance level, F(2, 71) = 1.34, ns.
Impact of Performance Information on the Responses to the
Personality Inventory

Gender did not interact with the Profile Type, the Social
Value, or the Big-Five scores, F(20, 540) = 1.15, ns. Analyses
were then conducted independently of gender. We first ran a
multivariate ANOVA (MANOVA) with the performance
profile as the independent variable and the Big-Five and social
value scores as the measures factors. As expected, the
MANOVA revealed a main effect of performance on the social
value scores, F(4, 114) = 4.94, p < .01, η2p = .15. There was
also a main effect of performance on the Big-Five scores,
F(8, 110) = 2.103, p < .05, η2p = .13, as well as a Perfor-
mance Profile × Big Five × Social Value interaction, F(16,
102) = 1.78, p < .05, η2p = .22. We broke these effects down
into their component parts.
Analysis of personality scores. Looking more closely at
the effect of performance profiles on the Big-Five scores, we
629
Psychol. 44, 622–635 (2014)
found that the participants in the ideal employee condition
described themselves as more conscientious, F(2, 71)=16.40,
p < .001, η2p = .32, extraverted, F(2, 71) = 4.33, p < .05,
η2p = .11, and emotionally stable, F(2, 71) = 6.95, p < .01,
η2p = .16 (see Table 3 for detailed means) compared with the
participants in the other two experimental conditions (stan-
dard, unproductive). No effect of performance profile was
found on agreeableness, F(2, 71) = 1.43, ns, or openness,
F(2, 71) = .06, ns.
Analysis of social value scores. In line with our hypothe-

sis on the item’s social value, the ideal employee profiles
scored higher on the agentic items compared with the standard
employee profiles, which, in turn, scored higher on agency
than the unproductive employee profiles, F(2, 71) = 13.97,
p < .01 (see Figure 2). The communion scores did not vary
with the experimental condition, F(2, 71) = 1.41, ns. Contrary
to expectations, the profiles’ performance levels also had an
impact on the scores for the neutral items, F(2, 71) = 4.00,
p < .05. Bonferroni post-hoc comparisons showed that the
unproductive profile scored lower on the neutral items com-
pared with the standard and ideal profiles, which did not differ
from each other. Generally speaking, however, the difference
between the agentic and neutral items was greater for the ideal
profile than it was for the unproductive and standard profiles,
F(4, 142) = 5.76, p < .001.
We broke the interaction effect down into separate persona-
lity factors, crossing the performance profile and the item’s
value (see Table 4 for detailed means and results). The results
showed that the agentic items were more sensitive compared
with the communal or neutral items to information about per-
formance. Table 4 shows that with the exception of openness,
this expected increase in the scores was observed for the
agentic items of every personality factor (extraversion,
conscientiousness, agreeableness, and emotional stability). A
detailed analysis showed that the agreeableness–agency item
scores were higher in the ideal condition than in the standard
and unproductive conditions (communion scores followed
the opposite tendency). With regard to the emotional stability
and extraversion, the information about the performance
affected the agentic items but appeared to have no impact on
the communal and neutral ones. For conscientiousness, the
communal and neutral items followed the same trend as the
agentic items, with significantly higher scores for the standard
and ideal profiles compared with the unproductive profile.
However, this effect seemed stronger for the agentic items

compared with the communal or neutral ones. Finally, comple-
mentary analyses showed that the agentic openness items
attracted significantly higher scores compared with the other
Table 3. Mean Big-Five scores (SD) as a function of the profile
type
(Experiment 3)
Profile
Unproductive Standard Ideal
Conscientiousness 3.93 0.98 4.98 0.71 5.20 0.76
Extraversion 3.91 0.89 4.13 0.85 4.59 0.71
Emotional stability 3.41 0.75 4.08 0.72 4.20 0.89
Agreeableness 4.20 0.98 4.47 0.80 4.06 0.79
Openness 3.84 1.06 3.91 0.99 3.92 0.69
Figure 2. The mean scores as a function of the profile type and
the
item value (Experiment 3)
Table 4. The mean scores as a function of the item’s type
(Social Value × Big Five) and the performance profile type
(Experiment 3)
Big-Five factor Item type
Task performance profile
F(4, 142) p < ƞ2Unproductive Standard Ideal
Agreeableness Agency 4.21a 4.54a 5.02b 4.03 .005 .10
Communion 4.54a 4.79a 3.56b
Neutral 3.96 4.10 3.67

Conscientiousness Agency 4.71a 5.23a 6.15b 2.47 .05 .07
Communion 3.63a 4.96b 5.06b
Neutral 3.38a 4.73b 4.33b
Emotional stability Agency 3.58a 5.02b 5.15b 3.36 .05 .09
Communion 3.52 3.63 4.06
Neutral 3.06 3.58 3.42
Extraversion Agency 4.06a 4.62b 5.75c† 4.50 .005 .11
Communion 4.42 4.19 4.15
Neutral 3.21 3.54 3.88
Openness Agency 3.92 4.04 4.33 0.46 ns
Communion 3.71 3.63 3.52
Neutral 3.85 4.06 3.92
Note: Two different letters denote a significant difference (p <
.05; Helmert-type contrast).
†p = .05.
Psychol. 44, 622–635 (2014)
two types of items for the ideal profile (t(73) = 2.21, p < .05),
while this was not the case for the standard and unproductive
profiles.
Therefore, the Profile Type × Big Five × Social Value inter-
action mainly appeared to be due to the different ways in
which the scores changed according to the value of the items

and highlighted the expected pattern of the results for four of
the five personality factors.
Discussion
The aim of this third experiment was to show that information
about the task performance is coded in terms of the agency and
communion, rather than in terms of the personality dimen-
sions. The results mostly supported our hypothesis. Individuals
seeking to convey the image of an ideal employee primarily
use agentic items to describe themselves. Although the
increase in the scores for the conscientiousness, extraver-
sion, and emotional stability factors was predicted by the
Big-Five theory (Pauls & Crost, 2005), the application of the
two-dimensional model of social judgment allowed us to add
an important nuance: manipulating performance leads to a po-
tential increase in the scores on all factors—provided that they
are measured on agentic items.
GENERAL DISCUSSION
All three experiments explored the use of the descriptive ver-
sus evaluative properties of the personality inventory items.
The first experiment demonstrated that when participants are
exposed to strictly similar psychological profiles in terms of
the personality dimensions, they attribute higher performances
to the agency-oriented profiles than to the communion-ori-
ented or neutral ones. The second experiment replicated this
result with personality profiles that also varied on a descriptive
characteristic that is more (i.e., conscientiousness) or less (i.e.,
agreeableness) associated with job success. The results then
showed that conscientiousness and agreeableness led to the
same performance inferences when they were measured with
agency items. In a complementary fashion, the third experiment
showed that participants seeking to convey the image of an
ideal

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