PERSONALIZED INCENTIVE PLANS THROUGH EMPLOYEE

PROFILING

Silverio Petruzzellis

ezanne Software S.p.A. - Via Amendola, 172/C - I70126, Bari, Italy

Oriana Licchelli, Ignazio Palmisano, Giovanni Semeraro

Dipartimento di Informatica - Universit

a di Bari - Via E. Orabona, 4 - I70126, Bari, Italy

Valeria Bavaro, Cosimo Palmisano

DIMeG, Politecnico di Bari - Viale Japigia 182, I70100 Bari, Italy

Keywords:

Decision Support Systems, Knowledge Management, Machine Learning, HRM Decentralization.

Abstract:

Total reward management (TRM) is a holistic practice that interprets the growing need in organizations for

involvement and motivation of the workers. It is oriented towards pushing the use of Information Technol-

ogy in supporting the improvement of both organization and people performances, by understanding em-

ployee needs and by designing customized incentives and rewards. Customization is very common in the area

of e-commerce, where application of proﬁling and recommendation techniques makes it possible to deliver

personalized recommendations for users that explicitly accept the site to store personal information such as

preferences or demographic data. Our work is focused on the application of User Proﬁling techniques in

the Total Reward Management context. In the Team Advisor project we experimented the analogies Cus-

tomer/Employee, Product, Portfolio/Reward Library and Shop/Employer, in order to provide personalized

reward recommendations to line managers. We found that the adoption of a collaborative software platform

delivering a preliminary reward plan to the managers fosters collaboration and actively supports the delegation

of decision-making.

1 INTRODUCTION

Competitiveness is a key issue for knowledge-

intensive organisations in today’s turbulent global

marketplace. The most industrialised countries can

no longer expect to compete only on efﬁciency. The

labour cost gap is for the time being unbridgeable, at

least until the market’s natural economic forces in-

crease the labour cost in developing countries such as

India and China. It is a well-documented fact that to

be competitive now depends more than ever on a com-

pany’s capacity for innovation and on the level of its

customer services, both of which attributes depend on

the level of involvement and motivation of the work-

ers. Total reward management (TRM) is a holistic

practice that interprets these growing needs in organi-

sations and offers a new action mode oriented towards

pushing the use of Information Technology in sup-

porting the improvement of people performances, by

understanding employee needs and by designing cus-

tomized incentives and rewards. In a recent study car-

ried out by Watson Wyatt (Watson Wyatt Worldwide,

2000), interesting statistics emerged about the factors

that most commonly inﬂuence people’s performance

within a company. According to the study, there are

three factors that stand out from the rest, namely,

trust in senior leadership, chance to use skills on the

job, and competitiveness of rewards. The Human Re-

source (HR) departments that historically looked af-

ter all the personnel development issues are now fac-

ing a new challenge: to reinterpret their role and be-

come a more strategic component for the company, by

contributing in the deﬁnition of incentive plans and

professional development. To achieve this goal, they

strongly need the participation of those who better

know the single individuals, working side by side and

coordinating their activities: the line managers. Geo-

graphical dislocation, complexity of roles and the es-

calating average size of the companies, force the HR

departments to involve middle management in provid-

ing the right answers to the pressing demands of the

employees. The line managers on the other hand do

not possess the right background to cope with internal

conﬂicts, to make objective evaluations and to act as

compensation experts towards their staff. They need

the right support and a good mix of up-to-date infor-

107

Petruzzellis S., Licchelli O., Palmisano I., Semeraro G., Bavaro V. and Palmisano C. (2006).

PERSONALIZED INCENTIVE PLANS THROUGH EMPLOYEE PROFILING.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - AIDSS, pages 107-114

DOI: 10.5220/0002493401070114

 SciTePress

mation in order to apply their judgement in a timely

and effective manner.

2 THE ROLE OF INFORMATION

TECHNOLOGY IN TRM

IT support in this area could be very valuable if

designed in a way that foster collaboration and fa-

cilitate the devolution of some HRM responsibili-

ties to the line managers. Many authoritative man-

agement theories, like Drucker’s (Drucker, 1955),

Mintzberg’s (Mintzberg, 1973), include responsibil-

ities such as ”hiring”, ”motivating” and ”people de-

velopment” as well as other more operational activ-

ities. As Mintzberg sums up; ”He [the manager] de-

ﬁnes the milieu in which they [his subordinates] work,

motivates them, probes into their activity to keep them

alert and takes responsibility for hiring, training and

promoting them” ( (Mintzberg, 1973), p96).

The Team Advisor project addressed those issues

by designing and implementing a platform for collab-

oration in the complex area of personnel development

and reward planning. Its goal was to enable the line

managers to be in charge of their own development

plans by providing them with a personalized and con-

textualised set of information about their teams, to-

gether with some policy guidelines to follow in the

implementation phase of those plans.

The use of proﬁling methodologies appeared very

promising in this area. Personalization is very com-

mon in the area of ecommerce, where a user explicitly

accepts the site to store information on her or himself

such as her or his preferences in order to be provided

of personalized recommendations. The more such

a system knows about users the better it can serve

them effectively. There are different styles, and even

philosophies, regarding how to teach computers about

user habits, interests, patterns and preferences. User

modelling simply means ascertaining a few bits of in-

formation about each user, processing that informa-

tion quickly, and providing the results to applications,

all without intruding upon the user’s consciousness.

The ﬁnal result is the construction of a user model or

a user proﬁle (Kobsa, 1993). The application of pro-

ﬁling methodologies was experimented then to create

personalized incentive plans that could reﬂect the his-

tory and the habits of the single individuals as well as

balancing the fulﬁlment of her or his needs with the

company’s global perspective.

3 MARKET ANALYSIS

Our analysis of the actual context of Human Resource

Management (HRM) applications has shown that

most of organisations that already use software so-

lutions for employee administration are now search-

ing for applications which can strengthen their HRM

systems with decision support functionalities that can

simplify their management processes. We did a mar-

ket research that involved the following software so-

lutions: Authoria Advisor

, Disc Proﬁler

, Incen-

tive Suite

, Etweb Enterprise

, Onesis IAS 19

,H1

HRMS

, IDP-IDipendenti

, Minosse

The market segmentation was analyzed focusing

on those products that appeared to be more compet-

itive in terms of proﬁling technologies and rewards

management system, which are essential requisites to

create customized reward recommendations (Spitzer,

1996; Busacca, 1998). The obtained results are shown

in a segmentation matrix (Figure 1) based on the two

variables ”variety of rewards offer” (rewards library

on axes Y) and ”focus on proﬁling techniques” (pro-

ﬁling technology on axes X).

None of the assessed information systems presented

a good mix of both required features. Thus they

are mainly positioned in the lower part of the ma-

trix, manifesting an insufﬁcient level of detail in the

rewards range deﬁnition. The analyzed softwares

have in fact been developed essentially for supporting

personnel administration, with low attention to more

complex processes, such as customized reward plan-

ning and assignment. On the other hand, the upper

right part of the matrix represents the ideal place-

ment of the envisioned software application, since

it requires not only a high-level quality in proﬁling

technologies and in reward library deﬁnition, but also

a strong interconnection between the two aspects.

More accurately, this application has been designed

for merging contents from proﬁling system and el-

ements of reward library and create suggestions for

customized incentives: the purpose is to create a kind

Authoria Inc., http://www.authoria.com/

AdvisorSeries.204.aspx

Axiom Software Ltd., http://www.

axiomsoftware.com/discprofile.asp

Nexcentec Software Inc., http://www.

nexcentec.com

ExecuTRACK Software AG, http://www.

executrack.com/en/solutions.html

Onesis S.p.A, http://www.onesis.it

EBC Consulting Srl, http://www.

ebcconsulting.com/cgi-bin/private/hrms\

area.cgi

TPC&Join s.r.l., http://www.idipendenti.

it/demo/demo.asp

Mizar Informatica s.r.l., http://www.mizar.it/

prodotti/index.php\#minosse

ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

108

of ”virtual manager” supporting enterprise compensa-

tion and reward policies.

Figure 1: Segmentation Matrix.

4 THE TEAM ADVISOR SYSTEM

Within the Team Advisor project, our work was

focused on the application of the User Proﬁling

techniques in the Total Reward Management con-

text, where we experimented the following analo-

gies with respect to the on-line market: Cus-

tomer/Employee, Product Portfolio/Reward Library

and Shop/Employer.

In this context, a user proﬁle is meant as the set

of information collected about a user, in order to take

into account her or his needs, wishes, and interests

in granting those beneﬁts that are part of the organ-

isation’s incentive programmes, thus a set of recom-

mendations reﬂecting those preferences. Rather than

addressing the recommendations to the customer, as

it generally happens in the standard e-commerce ap-

plications, the Team Advisor project experimented a

new form of advice addressed to the line manager,

suggesting the best reward policy for a speciﬁc staff

member, depending on a proﬁle built upon both de-

mographic data and behavioural preferences collected

through a commercial Human Resources Manage-

ment portal, without asking to the employee to com-

pile a periodical questionnaire to provide her or his

preferences for potential rewards. As well as in the

e-commerce area, the privacy problem is also to be

considered in the TRM context: many users are dis-

trustful to give private data because they wish to have

their privacy guaranteed and they may also be afraid

of being controlled. One possibility is to handle per-

sonal data for the customer so that she can control/edit

her own user model. In addition, the company must

guarantee not to give personal data to a third party and

in addition should provide suitable technical means

for users to rectify user model items (Kobsa, 2001).

Actually, the core issues are trust and transparency,

because the user wants the companies to respect their

privacy by clearly declaring when they are collecting

the personal data, offering opt-in to data collection

programs rather than opt-out only choices. In fact,

the EU Privacy Directive (European Parliament and

Council of the European Union, 1995) gives users the

right to have access to ”the knowledge of the logic in-

volved in any automatic processing of data concern-

ing the user”. Taking into the right consideration pri-

vacy issues, the suggested metaphor allowed for a

new HRM paradigm in which the line manager acts

as a partner of the HR department. Provided with a

personalized recommendation system, he could eval-

uate individual needs and expectations and try to ﬁnd

the right balance between them and the organisation’s

rewarding strategy received by the HR department.

5 EMPLOYEE PROFILING

In the Team Advisor project, the Employee Proﬁl-

ing system creates detailed proﬁles of the co-workers.

The Employee Proﬁling system is built upon a Per-

sonalization Engine (PE) (Semeraro et al., 2003; Lic-

chelli, 2005) that takes advantage from information

stored in a commercial Human Capital Management

application. The PE component is a Java applica-

tion deployed in Apache Tomcat. The Human Capital

Management application, provided by a global soft-

ware player in the area of Human Resources Man-

agement Systems, is a web application written in Vi-

sual Basic for the Microsoft COM+ architecture. The

integration of the two systems was carried out by a

C# module run in a Microsoft .NET environment,

and was based on RDF/XML-driven interoperability.

The whole integration architecture schema is shown

in Figure 2.

Before starting with the proﬁle creation process, a

preliminary work is needed to establish a formal de-

scription of the features necessary to accomplish the

given task. Starting from these features it is possi-

ble to deﬁne the representation language of the entire

learning system. In parallel it is necessary to deﬁne a

number of classes that characterize the corresponding

kind of reward to be associated to the various con-

ﬁgurations of features. Then, it is possible to create

the employee proﬁles in two steps: during the ﬁrst

one a number of sample classiﬁcation are provided to

the Personalization Engine in order to create a set of

PERSONALIZED INCENTIVE PLANS THROUGH EMPLOYEE PROFILING

109

Figure 2: Employee Proﬁling System.

classiﬁcation rules. In the second phase, those rules

are applied, employee by employee, to the whole set

of identifying features. The application then gener-

ates a set of recommendations composed of a list of

rewards the system deemed appropriate for each indi-

vidual, together with a ranking useful to prioritize the

decisions.

5.1 Personalization Engine

The PE component is the part of the system that uses

supervised learning techniques to automatically dis-

cover users’ preferences. It interacts with the WEKA

library (Witten and Frank, 2000) in order to induce

rule sets for classiﬁcation. Both the input and the out-

put of the PE component is composed of RDF models.

These models contain:

• the input to the learning process (the set of posi-

tive and negative examples, as well as the deﬁnition

of the learning problem, i.e. the features together

with their possible values, and the name and type

of classiﬁcation to be learned), the main class for

this being pe:InstanceBag;

• the results of the learning process (a set of classiﬁ-

cation rules), represented by the pe:Classiﬁer class;

• the input to the classiﬁcation phase, in which the

learned rules are used to categorize previously un-

seen instances, which consists of a pe:Classiﬁer in-

stance and one or more pe:InstanceBag instance,

together with the related output, which is instance

of pe:ClassiﬁcationResult.

The main functionalities are then:

Phase name Input Output

Rule building pe:InstanceBag pe:Classiﬁer

instance instance

Classiﬁcation pe:InstanceBag pe:ClassiﬁcationResult

(unlabelled)

pe:Classiﬁer

5.2 User Proﬁle Generation Process

In the context of TRM, the problem of learning em-

ployee’s preferences can be cast to the problem of in-

ducing general concepts from examples labelled as

members (or non-members) of the concepts them-

selves. Given a ﬁnite set of categories of rewards

C = {c

, ···c

}, the task consists in learning the

target concept T

”employees interested in the cate-

gory c

”. In the training phase, each individual set of

features represents a positive example regarding the

categories the employee is interested in and a negative

example regarding those she or he dislikes. To this

goal we chose an operational description of the tar-

get concept T

, using a collection of rules that match

ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

110

Figure 3: Personalization Engine learning process.

against the features describing a user in order to de-

cide if she or he is a member of T

. The subset of

the instances chosen to train the learning system has

then to be labelled by a domain expert, that classi-

ﬁes each instance as member or non-member of each

category. Stated that, in the context of TRM, the do-

main expert belongs to the HR department, in order

to facilitate the deﬁnition of the training sets we intro-

duced a pre-classiﬁcation process interesting popula-

tion samples instead of single individuals. The expert

was then asked to classify groups of employees shar-

ing the same subset of characteristics, and to decide

weather they belong to a certain category or not.

There are 4 rules extracted for class Company Car:

if Seniority <= 7.0

and Job Class = Employee-CONS then

Class: yes

else if Seniority > 7.0 then

Class: Estate

else if Job Class = White Collar-SALES then

Class: no

Otherwise Class: Saloon

Figure 4: An example of classiﬁcation rules for the reward

category “Company Car”.

This approach allowed the generation of training

sets without the direct knowledge of the candidates,

thus taking advantage of a natural clustering habit of

the expert user. The training instances were processed

by the Personalization Engine, which induced a clas-

siﬁcation rule set for each reward category by tak-

ing into account the whole set of features at a higher

Figure 5: Employee Proﬁle.

level of detail than the expert user can afford (Fig-

ure 3). Each rule is composed of an antecedent, or

precondition, and a consequent, or conclusion. The

antecedent is a series of tests, like the tests at nodes

in decision trees, while the consequent gives the cat-

egory that applies to instances covered by that rule

(Figure 4). The learning algorithm adopted in the pro-

ﬁle generation process is based on PART (Frank and

Witten, 1998), a rule-based learner that produces rules

from pruned partial decision trees, built using C4.5’s

heuristics (Quinlan, 1993). The main advantage of

this method is its simplicity: it produces good rule

sets without any need for global optimizations.

The actual employee proﬁle generation process is

performed by applying this set of rules to the em-

ployee data. For each reward category, the system is

PERSONALIZED INCENTIVE PLANS THROUGH EMPLOYEE PROFILING

111

Figure 6: A list of the most representative data.

able to predict whether the employee is interested in

it or not, specifying the hypothetical satisfaction level

for each. An employee proﬁle (Figure 5), as intended

within the Team Advisor project, is then composed by

two main frames: the frame of employee data, and the

frame of the rewards ”learned” by the system.

6 MANAGER ADVISOR

TeamAdvisor has been designed to provide person-

alized reward recommendations (Kerr, 1995) through

the analysis of user proﬁles stored in a support data-

base. This allows for a rationalization of the process

of incentives planning, with consistent time savings

in the implementation of decisions. However, we had

to cope with the problem of identifying who is the

best candidate to examine the recommendations pro-

vided and to exploit them. This requires on the one

hand some degree of familiarity with compensation

and rewarding practices, and on the other hand a good

acquaintance with the employee they are addressed

to (Wanless, 2003). An innovative solution could be

to integrate the traditional competencies of the HR de-

partment by delegating to the line managers some of

their activities. Legge (Legge, 1995) underlines that

HRM is ”vested in line management as business man-

agers responsible for coordinating and directing all

resources in the business unit in pursuit of bottom-

line proﬁts”. Therefore evaluation process, employee

development, performance management and rewards

assignation can be considered the best areas for an

effective intervention of the line on HRM. In order

to simplify organizational structure and to human-

ize personnel administration procedures, they seem to

be the most appropriate ﬁgures to accomplish these

changes (Renwick, 2003). Moreover, they are sup-

posed to know personally the staff they manage, also

thanks to informal conversations in the workspace, so

that they can add a personal value in examining rec-

ommendations and in choosing which reward to as-

sign. Two factors play against a partial devolution of

HR strategic management to the line manager (Whit-

taker and Marchington, 2003):

• They generally have a heavy load of work respon-

sibilities, which should not be increased with tasks

they have no great competence about.

• Because of their dimensions, many large-sized en-

terprises make it very difﬁcult to build human re-

lations that go beyond formal employer-employee

relationships.

The answer to these objections can be found in the

philosophy of the proposed IT solution: the adoption

of a collaborative software platform delivering a pre-

liminary reward plan to the managers, makes the de-

volution process a real thing, and actively supports

decision-making. Line managers can then add their

personal judgement coming from personal acquain-

tance storing from the information provided by sys-

tem as a result of the analysis of objective data stored

in the company database. To substantiate these reﬂec-

tions with a use case, we created a demo data set rep-

resenting a US ﬁrm working in the banking/ﬁnancial

industry, and did a full run of the Team Advisor sys-

ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

112

Figure 7: Selected rewards into the organizational incentive

plan.

tem within the company reward assignment process.

An HR domain expert sets up the employee pro-

ﬁle generation process in two phases. First, she se-

lects a list of the most representative data about iden-

tity, professional path and socio-demographical back-

ground of the employees. Anagraphical and socio-

demographical data closely pertain to human dimen-

sion of the employee and are the same for every com-

pany. They can be collected and updated through peri-

odical on line surveys. On the contrary, data about job

conditions, although generally available in all com-

panies’ databases, are context-sensitive, and may be

more or less relevant according to the external envi-

ronment and to the company policy. In ﬁgure 6 it is

shown, for a demonstrative purpose, a partial list of

the attributes enclosed in the employee-proﬁle. This

can be properly customised according to the needs of

each company.

Second, she selects the rewards listed into the orga-

nizational incentive plan, shown in Figure 7, to build

the Team Advisor Reward Library. For each item a

domain is also speciﬁed, made up either of yes/no la-

bels or of multivalued labels (i.e. saloon, estate, city

car, as possible values for the item ”company car”).

The reward library provided in the example is created

following a recent study by OD&M (OD&M Consult-

ing, 2005), and is given as a basic customizable set.

A user proﬁle generation process is then started, and

it eventually produces the employee proﬁles as de-

scribed above (see Section 5.2).

Take now a manager of our sample enterprise, Fred

Sidel from the Atlanta Branch, receiving recommen-

dations about potential rewards to be assigned to his

team, through a dedicated section of his digital dash-

board. The system provides him with a detailed view

of the recommendations for each of his thirteen co-

workers, as shown in Figure 8.

For each recommendation one single label is speci-

ﬁed, that is the domain value for which the recom-

mender has computed the highest popularity rating;

the goal of recommendations is to inform the line

manager about the preferred rewards and their adher-

ence with the related employee proﬁle. It’s up to the

manager then to decide whether to use such informa-

tion, including the reward recommendation into the

incentive plan for each employee, or ignoring it and

adopting an autonomous evaluation method.

7 CONCLUSION AND FUTURE

WORKS

The work carried out in the Team Advisor project sug-

gested that the application of proﬁling and recommen-

dation techniques in the context of the incentive plan-

ning processes it is not only possible but also effective

in providing support to the decision-making. We ob-

served how the recommendations could act as a valu-

able starting point for the managers who need support

in an activity they are not used to perform, by provid-

ing a means for unleashing unexpressed and undis-

covered connections among the various elements that

govern the reward assignation process. In addition,

the recommendation system allows for the distribu-

tion of the knowledge of the HR domain expert by

providing timely and accurate information about the

preferences of the employees, not forgetting the over-

all company perspective. In this context, the judgment

of the managers can be applied to reﬁne the sugges-

tions and to convert them into an effective develop-

ment plan.

Starting from the results achieved in the prototyp-

ical phase, the Team Advisor working group is com-

mitted to perform both a technical and an organiza-

tional evaluation of a commercial version of the soft-

ware developed. For what concerns the technical val-

idation we are interested in checking how the system

is able to replicate the knowledge of the domain ex-

pert and thus to provide coherent recommendations.

On the organizational level it is instead important to

verify how the provided recommendations can con-

tribute to streamline the compensation and rewarding

process of the organisations. To this goal, we’re going

to submit an ad-hoc survey to real users of the system,

chosen among the managers implementing personal-

ized incentive plans. This survey will aim at evaluat-

ing the overall satisfaction as well as the acceptance

level of the recommendations as for their usefulness,

clarity and completeness.

For what concerns possible future software devel-

opments, the group is aiming at embedding new func-

tionalities capable of collecting live user feedbacks on

the recommendations, and use them in order to re-

PERSONALIZED INCENTIVE PLANS THROUGH EMPLOYEE PROFILING

113

Figure 8: Recommendations for an employee.

ﬁne the process of proﬁle generation. This will enable

the system to become a dynamic engine of employee

proﬁling, thus supporting the organisations in reﬁning

and sharing the knowledge about its human capital.

REFERENCES

Busacca, A. G. (1998). Il customer proﬁling. In Costruire

Fedelt

a. Il Sole 24ore.

Drucker, P. F. (1955). ”management science” and the man-

ager. Management Science, 1(2):115–126.

European Parliament and Council of the European Union

(1995). Directive 95/46/ec of 24 October 1995 on the

protection of individuals with regard to the process-

ing of personal data and on the free movement of such

data. In Ofﬁcial Journal of the European Communi-

ties, page 31.

Frank, E. and Witten, I. (1998). Generating accurate rule

sets without global optimization. In Proceedings of

the 15

International Conference on Machine Learn-

ing, pages 144–151. Morgan Kaufmann.

Kerr, S. (1995). An academy classic: On the folly of re-

warding A, while hoping for B. Academy of Manage-

ment Executive, 9(1):7–14.

Kobsa, A. (2001). Tailoring privacy to users’ needs. In Pro-

ceedings of the 8

International Conference on User

Modelling. LNAI 2109, pages 303–313. Springer.

Legge, K. (1995). Human Resource Management:

Rhetorics and Realities. Macmillan, Hampshire.

Licchelli, O. (2005). Personalization in Digital Libraries

for Education. Computer Science in the Graduate Di-

vision of the University of Bari, Italy.

Mintzberg, H. (1973). The Nature of Managerial Work.

Prentice-Hall, London.

OD&M Consulting (2005). Rapporto beneﬁts 2005. in col-

laborazione con Lavoro & Carriere - ed. Il Sole 24

Ore.

Quinlan, J. R. (1993). C4.5: Programs for Machine Learn-

ing. Morgan Kaufmann.

Renwick, D. (2003). Line manager involvement in HRM:

an inside view. Employee Relations, 25(3):262–280.

Semeraro, G., Abbattista, F., Degemmis, M., Licchelli, O.,

Lops, P., and Zambetta, F. (2003). Agents, personal-

isation and intelligent applications. In Corchuelo, R.,

Cort

es, A. R., and Wrembel, R., editors, Technologies

Supporting Business Solutions, Part IV: Data Analysis

and Knowledge Discovery, Chapter 7, pages 141–160.

Nova Sciences Publishers.

Spitzer, D. R. (1996). Power rewards: Rewards that really

motivate. Management Review, 85(5):45–50.

Wanless, C. (2003). Devolution of HR activity to

the line: does the reality lag behind the rethoric?

http://business.king.ac.uk/research/hrm/wanless.pdf.

Watson Wyatt Worldwide (2000). WorkUSA 2000

- Employee Commitment and the Bottom Line.

http://www.watsonwyatt.com/research/resrender.asp?

id=W-304&page=1.

Whittaker, S. and Marchington, M. (2003). Devolving

HR responsibilities to the line. Threat, Opportunity or

Partnership? Employee Relations, 25(3):245–261.

Witten, I. H. and Frank, E. (2000). Data Mining: Practi-

cal Machine Learning Tools and Techniques with Java

Implementations. Morgan Kaufmann Publishers, San

Francisco.

ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

114