Human Resource Management DSS Peter Keenan Séan McGarraghy Conor McNamara Michael Phelan Department of Management Information Systems UCD Business Schools University College Dublin Dublin 4, Ireland Email:
[email protected]
Abstract Decision support systems (DSS) have achieved considerable success in many areas of business activity. The type of semi-structured problem where DSS is successful frequently involves the management or configuration of vehicles or machines. While DSS has been used for human resource problems such as personnel scheduling, there have been fewer DSS applications in the area of personnel placement. This paper describes a DSS to assist in the matching of the skills of the prospective employees with the needs of the employer. Keywords Decision Support System, Recruitment, Skill Matching, Personnel selection
1. INTRODUCTION 1.1 Project background In recent years there has been exponential growth in the use of personnel placement agencies. From the beginning of the last decade, job placement agencies have become more and more prominent in the personnel placement market. As the Irish economy moved from having an unemployment problem to a labour shortage, management began to realise that people are an organisation’s most important asset. This realisation among top management and the onset of the “Celtic Tiger” fuelled the growth in the job placement industry in Ireland in the late 1990s. To ensure the sustained success of personnel placement it is imperative that both the prospective candidate and employer receive a high quality of service; we felt that Decision Support System (DSS) techniques could be usefully applied to this problem. The nucleus of this project focused on matching the skills of the candidate with the needs of the employer while also incorporating auxiliary matching features such as location, salary and grade of degree. This paper summarises the design and implementation of an online personnel placement DSS, initially built by Master of Management Science students at University College Dublin (McNamara and Phelan, 2003). This system is directed at recent graduates and was built with the support of the Students Union placement service at the University of Limerick. The system allows prospective jobseekers to log on and enter all their details on a relational database, including personal, education, extra curricular, skills and work experience data. Jobseekers also specify their preferred salary and work location. The system then allows recognised employers to log on and search the database of jobseekers under a number of different criteria including skills, location, salary and degree grade. The system then returns the similarity matching for each jobseeker in the database and displays them on a results page. This matching offers employers a list of potential candidates; the final choice is left to the decision-maker who can consider factors not easily incorporated in a DSS. This system is therefore a DSS built around a web interface, relational database technology and a similarity model to associate applicants with potential employers. The DSS field originated in the 1970’s (McCosh and Scott Morton, 1978) and DSS has achieved considerable success in many areas of business activity since then. The type of semi-structured problem where DSS is successful frequently arises in logistics and operations management problems and many early DSS applications were in fields such as vehicle routing and production scheduling (Eom and Lee, 1990). While DSS has been used for human resource management (HRM) problems, these have generally involved fields such as personnel scheduling (Ernst et al., 2004). These problems are characterised by a limited supply of people to meet manpower needs and concern the distribution of groups of people to facilities (Bhargava and Snoap, 2003). In 525
Human Resource Management DSS many ways these systems addressing people scheduling problems are not so different from the scheduling of machines. However other aspects of HRM are perceived to require ‘soft’ skills and are therefore less suitable for the application of information technology: the use of IT in HRM has been seen to lag behind other business applications (DeSanctis, 1986). Expert systems have previously been used to address this problem, for example in the UK example of personnel selection for Marks and Spencer (GB Department of Trade & Industry, 1990). E Expert systems are seen as having a role to play in HRM applications (Martinsons, 1997). Wyatt and Jamieson (1996) indicate that expert systems can successfully capture expert knowledge in the HRM field as well as well as supporting and educating the managers engaged in personnel selection. However there have been few DSS applications in the area of personnel placement for individual jobs. This paper describes a DSS to assist in the matching of the skills of the prospective employees with the needs of the employer; this is a relatively uncommon form of HRM DSS application.
2. MODEL AND DSS 2.1 Modelling recruitment skills The initial planning phase of the project involved extensive research through several different media. We interviewed numerous professionals in the HR and personnel placement industries to establish their expectations of such a system and what features they felt should be incorporated into the proposed system. As we had identified skills matching as the core of the system, it was imperative that we should establish a comprehensive list of skills that would cover the vast majority of disciplines available. To achieve this we interviewed 40 academics from a wide variety of subject areas. This process provided 17 distinct employment areas with 240 distinct skills. Another critical part of the data collection phase was the interviewing of proposed end users of the system to identify their requirements. The final part of the data collection phase involved the analysis of numerous existing online recruitment sites and tried to establish the features we felt should be incorporated into the design of this system. Having identified this set of employment areas, we now needed to get a set of specific skills for each employment area. This was achieved by asking academics to give us the skills for their area of expertise and to place each skill under one of 17 headings. Following initial meetings it became apparent that the academics were having trouble enumerating a complete list of skills: to overcome this problem we decided to come up with a preliminary set of skills for each of the employment areas. We reviewed numerous different recruitment websites, together with relevant publications on different courses (university promotional material, etc.) and extracted the skills that we felt might be relevant. It was hoped these preliminary lists would trigger other skills and give a more accurate refection of the subject area. It was decided that these skill lists would not be shown to the interviewee immediately. Instead, we decided to first give the interviewee the opportunity to identify skills without any prompts. Then when we felt this avenue had been exhausted, the interviewee would be shown the preliminary skill lists, in the hope that the interviewee would recall further skills. Having obtained a list of skills and their corresponding degree from the academic, we then needed to identify any relationships or correlations among these skills. In order to achieve this we approached Sean Reidy, the Marketing Director at the University of Limerick. With his help, we developed a set of relationship parameters by which two skills could be related (Table 1). This scoring system was used by the academics consulted to provide a correlation among all skills in their discipline. Initially skills located in different disciplines were assumed to be totally unrelated. Table 2 shows the information technology related skills including in the system. Figure 1 shows the screen used to capture this information.
Skill Relationship
Score
Totally unrelated
0
Slightly related
1
Moderately related
2
Strongly related
3
Very strongly related
4
Table 1 : Skill relationship scoring
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Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004
.NET
GUI Design
Project Management
Active X
HTML
RDBMS
ASP
HTTP
Shell Scripting
Assembly
IIS
SQL
ATM
IMAP
SQL Server
C
Java
TCP/IP
C#
JAVASCRIPT
Technical Support
C++
Linux
Technical Writing
CMM
Lotus Notes
UML
Cobol
MFC
UNIX
ColdFusion
ODBC
VB Script
COMM
OLE
Visual Basic
Crystal Reports
OO
White Box Testing
DCOM
PERL
Win 2000
DNS
Photoshop
Win 95/98
Flash
PHP
XDSL
Fortran
Process Improvement
XML
Table 2 : Information technology skills : proficiency list
Figure 1 : Employer screen to identify skill importance
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Human Resource Management DSS 2.2 Comparative evaluation strategies Having agreed on the matching criteria, we then had to decide what matching rules we were going to use. Two general strategies can be used here, compensatory and non-compensatory evaluation techniques. Noncompensatory techniques set cut-off values and select entries above strictly above these values. The noncompensatory strategies can then be split into candidate and attribute evaluation strategies. A conjunctive candidate strategy involves selecting all candidates above a certain level, for example selecting every candidate that has an honours degree AND who lives in Limerick. The disjunctive approach involves selecting all or any candidates that surpass a satisfactory level on any relevant criterion. For example, select everyone who has an honours degree OR is living in Limerick. Other approaches rely on elimination, excluding candidates who fall below a minimum value for several criteria. If the employer thinks that degree level is most important and they are looking for an honours degree, then anyone with a degree award less than this will be eliminated. The remaining candidates will then proceed to the criterion valued as second most important by the employer and again if they do not meet the desired level they will be eliminated. This process continues until only one candidate remains or all the searchable criteria have been used. The non-compensatory matching strategies can be readily carried out using database techniques, but have limitations in they can exclude candidates just below the cut-off point. These strict limits could be adjusted by using techniques such as fuzzy matching, this approach has been proposed for recruitment problems (Ruskova, 2002). Compensatory approaches to matching solve this problem by using a weighted scoring technique. The employer evaluates the candidates on all relevant attributes and then selects the candidate with the highest summed score of all the attributes, i.e., selects the candidate that provides the highest overall score for a combination of the performance ratings. Having reviewed the various comparative evaluation strategies, we decided to use the compensatory approach as we felt it best suited our needs. We felt that the non-compensatory approaches, although valid, may not always give the truest reflection of the jobseekers in the database. We feel that the best approach for this application is to use a compensatory approach coupled with the incorporation of a weighting system to allow employers weight the different criteria according to importance. 2.3 Case Based Reasoning A number of different techniques came to mind when we first investigated the possibility of matching the skills of a job seeker with those sought by an employer. The first technique we investigated was that of Case Based Reasoning (CBR). This approach is based on the principle that situations often recur and that using the knowledge gained from solving similar problems in the past provides a good basis for solving a similar one today. With CBR the task of implementation is reduced to deciding which features are needed to describe a case and should therefore be stored. Another trait of CBR is that routine everyday cases are merged into generalised or composite cases while unusual cases that deviate from the norm are remembered as specific composite cases. Although CBR is undoubtedly a useful approach and there may be circumstances where it could be applied to the area of personnel placement, it did not seem to be applicable to the DSS described in this paper. If we look at the ‘help desk’ scenario where CBR has been this widely used, the development of a large case base has been fundamental to the systems success. In our proposed system a case base will not exist initially, therefore the placement of users initially will be unable to rely on the CBR approach. Recruitment agencies do not usually receive feedback on the long-term performance of graduate jobseekers placed by the agency. However the Irish CASPER system used a CBR inspired approach to identify patterns in the interaction of the user and exploited this interaction history to improve searching (Smyth et al., 2002). We felt that there was insufficient interaction to make this approach truly useful and we sought to devise an alternative approach. We decided to approach each searching criteria individually and develop some sort of matching technique for each. Four major factors were of interest; location, experience and education could be reasonably easily modelled using a numeric scoring system. Comparability of skills is a more complex matter. 2.4 Suitability Scoring With location being one of the four searchable criteria of interest to an employer, it was imperative that we came up with an accurate way of calculating the similarity between the employer’s location and the candidate’s preferred location. A simple measure of location suitability is to identify the co-ordinate locations involved (for example using the Irish National Grid) and to use a simple Euclidean co-ordinate distance calculation to identify the approximate distance between the two points. We incorporated this simple measure in the system. The location data can then be included in the matching process by scaling the distance to provide a suitability measure. A prospective applicant living within 20km of the employer might be regarded as being 100% suitable on location grounds; someone living 50km away would have a lower score. A more sophisticated location analysis would take into account travel time and might be quite non-linear, as major highways or fast rail links might allow convenient access from comparatively distant places. 528
Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004 Any modelling of the acquisition of proficiency in a certain skill is based on an understanding of the learning process. From a very early stage in the research into this area it became obvious to us that a linear approach was overly simplified. A learning process involves two conflicting processes: needing to have knowledge in order to acquire more, and slowing down in the acquisition as there is less and less left to acquire. This can be approximated as a systemic process in which the rate of growth is related to the amount already accumulated and the amount remaining to accumulate. Our research did not reveal a clear consensus on how individual learning curves were shaped. As we were initially looking at software skills, we adopted an ‘S’ shaped learning curve. Such curves have been proposed by Kanter and Muscarello (2001). We approximated the differential equation for the learning curve for recent graduates by a simple experience score (Table 3). Experience Score
Level of Experience
0.1
Graduate
0.25
One Years Experience
0.5
Two years Experience
0.75
Three Years Experience
0.9
Four Years Experience
1
Five Years Experience
Table 3 : Experience scoring A simple calculation was used to score the similarity of jobseeker salary expectations and employer salary levels. If the required salary of the jobseeker was less than the amount the employer was willing to pay, he or she automatically got a similarity value of one 1. However, if the salary desired by the jobseeker was greater than the salary offered by the employer, then a linear scale was adopted with the salary range being broken into intervals of €2000 and then calculating the interval difference between the two salaries. 2.5 Skills An effective skill matching is central to the operation of this system and is the most difficult aspect of the DSS to model. Initially we looked at the use of a topological tree structure together with subsumption matching to develop a matching technique for the skills feature. This would involve setting up skills according to different levels of abstraction. For example, programming skills could be grouping into imperative programming (IMP) or Object Oriented Programming (OOP). If an employer is looking for someone proficient in OOP then anyone proficient in a skill that is a direct descendant of OOP in the topological tree structure is seen to be an exact match. For example anyone with Java or C++ experience would be an exact match for employers looking for OOP experience (Figure 2) though this approach is definitely applicable in certain fields where a hierarchical structure of skills and proficiencies can be developed, its application in the personnel placement industry in general would be fairly limited. For instance, there is not a close connection between a skill in C and one in C++ in this example, although they are closely related in reality. Consequently, we felt that the tree structure was too limited to model the personnel problem.
Skills
OOP
IMP
C
Pascal
C++
Java
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Human Resource Management DSS Figure 2 : Topological tree structure for skills
Java 2
2
C
C++
3 1 1
1
Delphi
1
2 4
1
Pascal
Figure 3 : Network representation of skill relationships To overcome this limitation we decided to investigate the use of a weighted interconnected graph to represent our skills (Figure 3). Each skill would be represented by a node on the graph with the weight on the interconnected edges representing the relationships between the skills. Initially we felt that there might only be an indirect relationship between skills and that to determine the correlation between two skills would require the application of a shortest path algorithm. However, we realised after collecting all of the correlation data that we could use a complete graph structure for all the skills and the relationship between two skills could be calculated directly. We initially felt that grouping or clustering skills that were closely related may be an option in solving the skills dilemma. However, further examination suggested that this approach was not useful and we did not use this approach in the system. 2.6 Multi Criteria Decision Making (MCDM) in this system In order to add another level of functionality to this system, we decided to investigate the idea of incorporating a weighting structure into the employer search page. As the system offers 4 different criteria by which an employer can search the database, it would be very unrealistic to assume that each employer would weight these criteria equally. Consequently, we felt that for an accurate matching process it would be necessary to allow the employer weight the different criteria in order of importance. We therefore looked at the area of Multi Criteria Decision Making (MCDM) for suitable approaches, Brugha (2004) identifies two options suitable for a weighting system suitable for our needs. The first approach is the Direct Interactive Structured Criteria Utility Scoring (DISCUS) technique, this is usually termed as weighting the criteria on its ‘own merits’. Here the user is asked to rate each criterion out of a set score (usually 100), if the user feels that this criterion is vitally important then they can assign that criterion a maximum score. The user can potentially assign the maximum weight to all of the criteria. The second option is the Direct Interactive Structured Criteria Relative Scoring (DISCRIM) technique. This approach is slightly different in that all the criteria weights must add up to a set score (usually 100). This requires the user to weight the criteria relative to each other. Whereas, with the DISCUS technique it is possible to give all of the criteria the maximum weighting, the DISCRIM technique necessitates the user to come up with some sort of compromise. A number of studies undertaken by Brugha (2004) indicated that the DISCRIM approach is most effective with three or less criteria. Having conducted some end user prototype testing, it became apparent that the DISCRIM approach was the preferred option for this problem also.
3. IMPLEMENTATION 3.1 Prototype Initially implementation of the system was started in JavaServer Pages Technology (JSP), but the system was eventually implemented in Active Server Pages (ASP) with the backend being a Microsoft Access database running on a Windows 2003 Server over Internet Information Server (IIS).
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Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004 •
As there were major time constraints, the developers used the platforms with which the developers were most familiar, in this case the IIS server, ASP and the Access database.
•
It was felt that an Access database was more than adequate for the system in its initial phases, due to relatively low volumes of data.
•
At the initial stage IIS 5 security was a major concern, and the option of using an Apache server was examined. However, the emergence of IIS 6 in Server 2003, and its low-price availability due to a Microsoft Site Licence agreement in UL, alleviated these concerns.
Initially each individual module was then tested independently: Salary; Degree; Location; Experience; Skill and Skill incorporating experience. Following the unit testing, the modules were integration tested. All modules were tested in combination with the MCDM weighting module and the results of these tests were verified against manual computations. The system has since gone live with a database of students from the University of Limerick. A Pentium 4 computer with Windows 2003 Server Web was used as a web server for the system. The developed system used ASP scripting and ActiveX Data Objects (ADO) on the server side and JavaScript on the web pages for client side data validation. The prototype system was designed to be easy to use, employing multiple choice entry using drop boxes where appropriate and with data validation for other fields (Figure 4). When the prototype system was built, we sent out questionnaires to prospective end users that had agreed to take part in a testing process and complete a questionnaire on the process. This proved very beneficial, as it not only highlighted any faults of the system, but also allowed users give their opinion on the overall functionality and any recommendations they had on future improvements to the site. It was more difficult to get recruitment consultants to review the site, however one review was obtained. The site was reviewed with respect to the usability of the help and the comprehensibility of the questions from an international standpoint.
Figure 4 : Entry screen for skill weights
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Human Resource Management DSS Currently, the system is being redesigned: the main algorithm for searching records is being converted from a procedural approach to one using more SQL; the SQL used is being tuned; and the system is being reimplemented using Apache/PHP and a MySQL database. This should increase performance, as the Jet engine employed by Access is not an industrial strength application. Also, as the Apache server will run the scripts and embedded SQL on the server side, this should further increase speed. The ability to implement the system under several choices of platform demonstrates the independence of the design from operating environment. 3.2 Example Finally we will give a worked example with two students of the search feature in operation. Students Information Student Number
Salary
Location
Degree
Skill
Experience
Student 1
€35,0000 – €38,000
Dublin
UG – 2.2
C++
1 Year
Cork
PC – 2.1
IC Design
Graduate
€26,0000 – €29,000
All Ireland
UG – 2.1
Calibration
5 Years
HACCP
3Years
Student 1 Student 2 Student 2
When an employer logs and searches the database, the following search criteria are specified. Employers Information Criteria
Employer Selection
Weight
Salary
€29,000 - €32,000
20
Location
Naas
20
Degree
2.1 Hons – Both UG Only
20
Skill 1
Java
40
Experience Skill 1
2 Years
Importance Skill 1
Important
Skill 2
HACCP
40
Experience Skill 2
5 Years
Importance Skill 2
Of slight Importance
Skill 3
IC Design
40
Experience Skill 3
Graduate
Importance Skill 3
Very Important
Similarity Calculations Location Student Number
Location Student
Location Employer
Similarity
1
Dublin
Naas
0.85
1
Cork
Naas
0.35
2
All Ireland
Naas
1
Salary Student Number
Salary Student
Salary Employer
Similarity
1
€35,0000 – €38,000
€29,000 - €32,000
0.972
2
€26,0000 – €29,000
€29,000 - €32,000
1
Degree Student
Degree Employer
Similarity
1
UG – 2.2
2.1 Hons UG
0.8
2
UG – 2.1
2.1 Hons UG
1
Degree Student Number
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Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004
Student
Skill Student
Skill Employer
Skill
1
C++
Java
1
IC Design
Java
Number
Experience Similarity
Importance
Total Skill
Factor
Similarity
0.25
0.5
0.66
0.0825
0
0
0.66
0
Similarity
Best Similarity
0.0825
1
C++
IC Design
0
0
1
0
1
IC Design
IC Design
1
1
1
1
Best Similarity 1 1
C++
HACCP
0
0
0.33
0
1
IC Design
HACCP
0
1
.33
0
Best Similarity Total Similarity Average Similarity
0 1.0825 0.360833
2
Calibration
Java
0
0
0.66
0
2
HACCP
Java
0
0
0.66
0
Best Similarity
0
2
Calibration
IC Design
0
0
1
0
2
HACCP
IC Design
0
0
1
0
Best Similarity
0
2
Calibration
HACCP
0.5
1
0.33
0.165
2
HACCP
HACCP
1
0.75
0.33
0.2475
Best Similarity
0.2475
Total Similarity
0.2475
Average Similarity
0. 0825
Skill Weighted Similarity Student 1
Student 2
Skill
0.360833
0. 0825
Weight
0.4
0.4
Total
0.1443332
0.033
Location
0.85
1.0
Weight
0.2
0.2
Total
0.17
0.2
Salary
0.972
1.0
Weight
0.2
0.2
Total
0.1944
0.2
Degree
0.8
1.0
Weight
0.2
0.2
Total
0.16
0.2
Total Similarity :
Student 1 Student 2
66.87% 63.3%
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Human Resource Management DSS
4. CONCLUSIONS This system addresses the recruitment problem, which is typical of the type of problem of practical importance where DSS techniques have yet to make a full contribution. The recruitment field provides a classic semistructured problem, where there are specific factors that can be modelled in a computer process, but the ultimate decision remains with the decision-maker. Our experience in the system suggests that it can make a useful contribution for skills associated with the more technical areas such as engineering and IT which give the student specific ‘hard’ skills. University humanities courses aim to furnish the student with a rounded education and advisors in these disciplines find it much more difficult to quantify their skill set. Consequently, these ‘soft’ skill sets are much more difficult to integrate into a DSS. However we feel that this system can have a valuable input into the recruitment process, while also acting as a building block for further DSS development in this area.
REFERENCES Bhargava, H. K. & Snoap, K. J. (2003) Improving recruit distribution decisions in the US marine corps, Decision Support Systems, 36, 1, 19-30. Brugha, C. M. (2004) Phased multicriteria preference finding, European Journal of Operational Research, forthcoming. DeSanctis, G. (1986) Human Resource Information Systems: A Current Assessment, MIS Quarterly, 10, 1, 217-234. Eom, H. & Lee, S. (1990) Decision support systems applications research: A bibliography (1971-1988), European Journal of Operational Research, 46, 3, 333-342. Ernst, A. T., Jiang, H., Krishnamoorthy, M. & Sier, D. (2004) Staff scheduling and rostering: A review of applications, methods and models, European Journal of Operational Research, 153, 1, 3-27. GB Department of Trade & Industry (1990) Personnel selection screening : graduates for management : Marks and Spencer, Expert system opportunities ; case study 6 HMSO, London, pp. 40p : ill ; 25cm, pbk. Kanter, H. A. & Muscarello, T. J. (2001) Learning (Experience) Curve Theory: A Tool for the Systems Development and Software Professional, URL www.cobolreport.com Accessed 31 March 2004. Martinsons, M. G. (1997) Human Resource Management Applications of Knowledge-based Systems, International Journal of Information Management, 17, 1, 35-53. McCosh, A. M. & Scott Morton, M. S. (1978) Management decision support systems, Macmillan, London. McNamara, C. & Phelan, M. (2003) Personnel Assignment Decision Support System, MIS Department University College Dublin, Dublin, pp. Ruskova, N. A. (2002) Decision support system for human resources appraisal and selection, Intelligent Systems 2002, Vol. 1 IEEE, Varna, Bulgaria, pp. 354-357. Smyth, B., Bradley, K. & Rafter, R. (2002) Personalization techniques for Online recruitment services, Communications of the ACM, 45, 5, 39-40. Wyatt, D. & Jamieson, R. (1996) Improving Recruitment And Selection Decision Processes With An Expert System, Second Americas Conference on Information Systems Association for Information Systems, Phoenix, Arizona, USA.
COPYRIGHT Keenan, P., McGarraghy, S., McNamara, C., Phelan, M. © 2004. The authors grant a non-exclusive licence to publish this document in full in the DSS2004 Conference Proceedings. This document may be published on the World Wide Web, CD-ROM, in printed form, and on mirror sites on the World Wide Web. The authors assign to educational institutions a non-exclusive licence to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced. Any other usage is prohibited without the express permission of the authors.
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