CA2277261A1

CA2277261A1 - Method and system for matching one or more candidates with an employment position using qualitative and quantitative assessment parameters

Info

Publication number: CA2277261A1
Application number: CA002277261A
Authority: CA
Inventors: John E. Lertzman; Man Jit Singh; Yaron Hankin
Original assignee: Korn Ferry International Futurestep Inc
Current assignee: Korn Ferry International Futurestep Inc
Priority date: 1999-07-09
Filing date: 1999-07-09
Publication date: 2001-01-09
Also published as: AU3917199A

Abstract

A computer system and process for matching one or more candidates with an employment position of an employer is provided. The computer system includes a processor and a database accessible to the processor. The processor is programmed to:
retrieve employment parameters from the database;
retrieve candidate parameters from the database;
compare the candidate parameters with the employment parameters; and compute the degree of correspondence between the candidate parameters and the employment parameters.

Description

METHOD AND SYSTEM :FOR MATCHING ONE OR MORE
CANDIDATES WITH AN I~MPLOYMENT POSITION USING
QUALITATIVE AND QUANT'ITIVE ASSESSMENT PARAMETERS
FIELD OF T>EIE INVENTION
The present invention relates generally to candidate recruiting. More particularly, it concerns a computer system and method for matching candidates with employment positions using qualitative and quantitative assessment parameters.
BACKGROUND OF THE INVENTION
Traditionally, companies have located and hired employees using two methods:
recruiting firms and classified advertisements. Recruiting firms provide a popular and effective method for hiring employees because they are able to attract and have access to a much wider pool of candidates than the company would itself. Furthermore, by using a io recruiting firm, the company does not have to expend its own time and resources, which can be considerable, evaluating each candidate, Recruiting firms generally operate by collecting resumes from candidates.
While the universe of potential candidates is limitless, especially in the area of middle to upper level management, traditional methods (including networking and advertising) still only is provide the recruiting firm with a narrow cross-section of candidates who are qualified based only on quantifiable factors.
When a recruiting firm receives a candidate's resume, it is entered or scanned into the firm's database. Because a recruiting firm has no practical way of updating a candidate's personal information, a large percentage of a firm's database becomes 20 outdated and virtually useless within a short period of time. Therefore, the firm must constantly attract new candidates.
When a candidate search is initiated, the recruiter will search its database using single or multiple "keywords" in order to generate a group of candidates who are qualified based on quantitative factors such as experience or education. This method is is limited in that a recruiter can only pull up those resumes which contain the exact search word or words that are entered. For examplLe, a resume stating that the candidate has experience in "programming" and "desktop publishing" will not be pulled-up if the employer is searching for candidates with "C++" and "Quark" experience.
Furthermore,

2 once a qualified group of candidates is identified, recruiters use subjective criteria to assess the qualitative factors such as whether the candidate will fit with the company's organizational culture.
When a recruiter determines that a candidate is qualified, the recruiter will then s meet with the candidate, and if appropriate, will arrange for an interview with the employer. The recruiter will generally negotiate the terms of employment. A
typical recruiting firm may take up to six months to complete a candidate search, with the majority of time spent locating and assessing candidates. This can be a significant drain on a company if the position is vacant during that period.
~o Newspaper classifieds are another type of recruitment tool. An employer generally places an ad in the newspaper and. gives an address or number, to which a candidate can respond. While this method may be less expensive than using a recruiting firm, the employer must spend a great deal of its time and resources sorting through resumes and interviewing candidates who respond, many of whom will not be qualified i s or of equal importance, and/or fit within the. company's organizational culture.
Furthermore, the potential candidate pool is limited to the readers of the specific publication.
Over the past several years, Internet companies such as CareerMosaic.com, Monster.com, JobTrak.com, and HotJobs.com have utilized the World Wide Web to ao provide a new method of locating and hiring employees. These Internet career sites generally operate by providing an on-line se;archable database of resumes and job openings, which can be submitted and updated at-will. Many of these sites resemble little more than electronic versions of the classified ads: employers pay to list job openings while candidates search the openings for free. With the connectivity of the 2s Internet, finding resumes quickly and keeping information current has made the traditional databases in recruiting firms obsolete. The challenge has now become focused on accessing (mining) and assessing the data.
Similar to traditional firms, these Internet sites are generally only searchable using a keyword method, which can be highly ineffective with the millions of resumes 3o that need to be evaluated. Furthermore, once a person is identified as a qualified candidate based on quantitative factors, these Internet companies do not provide any way to evaluate the essential qualitative factors which are equally as important in ensuring a person is right for the job position. Much lili:e using classified ads, employers must use their own time and resources sorting through resumes and assessing candidates.
A
functional diagram of a typical prior art Internet career site system is illustrated in FIG. 1.
s Such a system generally includes a website display, a computer server connected to the Internet, and a database.
Therefore, there is a need for a system and method of using a wide area network, such as the Internet, to provide a recruiting service accessibility to candidates and employers around the world, in addition to the ability to update and provide information io at will. There is also a need for a recruiting service that can search a database using a plurality of parameters to better match qualified candidates with a particular employment position based on quantitative factors. There is also a need for a system and method that allows candidates to be assessed based qualitative factors. There is also a need for an automated system and method for screening; and matching qualified candidates with is employment positions based on quantitative: and qualitative parameters.
SUMMARY OF THE INVENTION
The present invention overcomes the challenges of candidate recruiting by providing a system and method for matching one or more candidates with an employment position of a company based on the assessment and evaluation of zo quantitative and qualitative parameters. Quantitative parameters can include such items of information as the salary, geographic location, and degree requirements associated with a given employment position. Likewise, quantitative parameters can also include a given candidate's job category, and employer. Qualitative parameters include job challenges, operating styles, role styles, leadership styles, motivations, business Zs environment experience, etc.
In one embodiment, the computer system includes a processor and a database accessible to the processor. The processor is programmed to:
~ Retrieve employment parameters from the database;
~ Retrieve candidate parameters from the database;
30 ~ Compare the candidate parameters with the employment parameters; and ~ Compute the degree of correspondence between the candidate parameters and the employment parameters.
According to one embodiment, a method and system employing a qualitative assessment tool is used to match candidates to an employment position taking into s account qualitative characteristics of importance to the employer. According to some embodiments, computer implemented versions of such a method and/or system are provided. According to some embodiments, such computer implemented methods and/or systems use the Internet.
According to one embodiment of thc: present invention, various parameters are ~o collected and stored in a database. For example, a record can be set up for each employment position. Each record can contain a set of employment position parameters defining certain required or desired characteristics for the employment position.
Employers can define these employment position pau-auneters based on desired or required characteristics and background. Likewise, candidates enter information and i s parameters, which are stored in a database. Parameters associated with a given candidate can be organized into a candidate record. The database may contain a number of candidate records associated with a number of candidates and a number of employment position records associated with a number o~f employment positions. Then a matching process may be performed to match one or more candidate records to a given 2o employment position record by comparing some or all of the parameters associated with each candidate record to some or all of the parameters associated with the given employment position. In this way, candidates can be matched to employment positions.
According to some embodiments, th:e employment position records and the candidate records include both "quantitativf;" parameters and "qualitative"
parameters.
zs According to some embodiments of the present invention, one or more candidate records can be matched to a given employrr~ent position record based on comparisons of associated quantitative and/or qualitative parameters.
BRIEF DESCRIPTION OF THE DRAWINGS
Other objects and advantages of the invention will become apparent upon reading 3o the following detailed description and upon reference to the accompanying drawings, in which:

FIG. 1 is a diagram illustrating a typical prior art Internet recruiting process;
FIG. 2 is a diagram of an overview of one embodiment of the present invention;
FIG. 3 is a flow diagram illustrating a process for setting up an employer and/or employment position;
s FIG. 4 is a flow diagram illustrating a process for specifying employment position parameters;
FIGs. 5a-Sg provide example screens used for specifying employer required and/or desired quantitative employment par;uneters;
FIGs. 6a-6p provide example screens used for specifying employer required io and/or desired qualitative employment parameters;
FIG. ? is a flow diagram related to ensuring that employment position parameters have been completely specified and preparing an employment position record for a matching process;
FIG. 8 is a flow diagram related to gathering candidate parameter information i s according to one embodiment of the present invention;
FIGs. 9a-9j provide example screens used for obtaining quantitative candidate parameters;
FIGS. l0a-lOx provide examples of screens for obtaining qualitative candidate parameters;
zo FIGs. 11 a-11 b illustrate a flow diagram related to determining whether a candidate record has been completed;
FIGS. 12a-12c illustrate examples of employer qualitative assessment feedback screens;
FIGS. 13a-13e illustrate examples of candidate qualitative assessment feedback 2s screens;
FIG. 14 is a flow diagram related to the identification of incomplete candidate records, the notification of candidates having incomplete records, and the completion of such records;
FIG. 15 is a flow diagram illustrating an example of a method of employing a 3o database containing employment position and candidate records to identify a desired number of qualified candidates for the position;

FIG. 16 is a flow diagram illustrating; the matching function according to one embodiment of the present invention;
FIG. 17 is a flow diagram of an embodiment of the responsibilities matching subroutine of FIG. 16;
s FIG. 18 is a flow diagram of an embodiment of the challenges matching subroutine of FIG. 16;
FIGS. 19a and 19b illustrate a flow diagram of an embodiment of the industry matching subroutine of FIG. 16;
FIG. 20 is a flow diagram of an embodiment of the company matching subroutine ~o of FIG. 16;
FIG. 21 is a flow diagram of an embodiment of the company size matching subroutine of FIG. 16;
FIG. 22 is a flow diagram of an embodiment of the company classification matching subroutine of FIG. 16;
~s FIG. 23 is a flow diagram of an embodiment of the experience matching subroutine of FIG. 16;
FIGS. 24a-24b illustrate a flow diagram of an embodiment of the degree matching subroutine of FIG. 16;
FIG. 25 is a flow diagram of an embodiment of the certification matching zo subroutine of FIG. 16;
FIG. 26 is a flow diagram of an embodiment of the qualitative assessment matching subroutine of FIG. 16;
FIGS. 27a-27c illustrate a table shovving an embodiment of the scoring subroutine of FIG. 16; and zs FIGs. 28a-28b are flow diagrams of an embodiment of the matching subroutine of FIG. 16.
DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
The present invention combines the best attributes of traditional recruiting firms 3o and Internet career sites to produce a new system and method for mining and assessing candidate data. This invention uses the speed and connectivity of the Internet, which provides accessibility to anyone in the world and allows candidates to update their information at will. It also allows a recruiter to search a database using a complex series of algorithms to mine qualified candidates based on multiple parameters and to match candidate profiles with a company's needs. In addition, the invention provides an s assessment tool to evaluate qualitative factors such as whether the candidate will fit the specific organizational culture of the company. Since one of the most important factors in successfully placing a candidate with a company is ensuring that the company's corporate culture is matched with an individual's management style and personality, this invention can dramatically reduce turnover. This invention also provides an essential Io recruiting tool for today's information based society. Further, this invention significantly reduces the amount of time spent locating a qualified candidate and therefore drastically reduces the amount of overall time spent on. a candidate search by, for example, 50 to 60%.
The present invention provides a system and method that automatically screens ~s candidates based on quantitative and qualitative characteristics so that employer resources are not wasted interviewing candidates who do not fit the organizational culture of the company.
FIG. 2 provides a functional overview of one embodiment of the present invention. There, a system 10 for matching candidates with available employment zo positions is shown. Information from employers regarding available employment positions and information from candidates is gathered and stored in a database such as database 500. The gathering of information from an employer about an employment position is represented by block 100. The information gathered during the employer input phase can include employer position parameters such as the education, experience, zs compensation, employment position location, and industry experience required and/or desired. In addition, the specific skills required, the employer's qualitative assessment, and the employer's weightings for each par,~meter are entered. Some embodiments for the collecting of employment position parameters are described in more detail below in connection with FIGS. 4-7.
3o Likewise, information from a number of candidates is gathered and stored in a database such as database 500. The database 500 may comprise one or more databases.

For example, one database may be used to store the candidate data, while another database stores the employer data, etc. The gathering of information from a candidate is represented by block 300. The information gathered during the candidate input phase can include candidate parameters such as the candidate's education, employment history, s prior experience, present employer, current position, desired position, desired geographic location, and qualitative assessment parameters. Some embodiments for collecting candidate parameters are described in more detail below in connection with FIGS. 8-1 I.
Then, for a particular employment position, a matching process is employed whereby the candidate information stored in the database 500 is searched in an effort to io locate one or more candidates whose associated information matches the employment parameters for the particular employment position at issue. The searching and matching process is represented by block 700. During the matching process, candidate information is compared with the employment parameters entered by the employer for the available position. According to some embodiments, a degree of correspondence between the ~ s information provided by a particular candidate and the employment parameters is then computed. The system 10 may then provide information regarding the results of the matching process by, for example, generating a list including a number of candidates and the degree of correspondence between each candidate and the employment parameters associated with the employment position at issue. The degree of correspondence Zo between the employment parameters and the parameters associated with each candidate may then be reviewed, for example, by the recruiter or the search consultant who initiated the particular matching process. Depending on the results of the search, one or more modified searches may be additionally performed.
Additional post-matching tasks may be performed to further assess the zs appropriateness of the candidates found suil:able for the particular employment position.
This post-matching activity is represented b~y block 900. This post-matching activity may include manual post-matching tasks performed by the employer and/or search consultant such as telephone interviews, video conferences, personal interviews, negotiations, and eventually placement of a qualified candidate in the employment 3o position.

y According to some embodiments of t:he present invention, an Internet based recruiting system is provided. According to such embodiments, the entity operating a recruiting server sets up the system so that an employer can enter on-line employment position parameters associated with an employment position which the employer would s like to have filled. The recruiting server ma:y comprise one or more interconnected servers. For example, one server may be used to run the website, while another server performs the processing functions described herein. The entity operating the recruiting server can be, for example, a recruiting or search firm. Likewise, the server can be set up so that candidates can enter detailed information about themselves on-line.
The server io can also be set up so that individual websites are established for each employer and/or candidate. Likewise, the server may provide for individual websites for each employment position to be filled. Accordingly, a given employer may have a number of websites residing on the server. Access to these individual websites may be secured to prevent unauthorized persons from accessing them. Security may be provided, for ~ s example, by requiring a password or authorization code to be entered before a given individual website can be accessed.
FIG. 3 illustrates a process for setting up an individual employment position website so that an employer may enter information associated with a new employment position to be added to the system. In the embodiment shown in FIG. 3, the server is 2o maintained by a recruiting firm. According to this embodiment, a search consultant enters the recruiting website, as represented by block 108. The search consultant then selects an existing client (an employer) from a menu of existing clients or enters a new employer (block 110). Each employer can have an associated identification number. For new employers, either the system generates or the search consultant designates such an is employer identification code or number when a new employer is specified.
Next, an engagement is selected or created, i.e., the available employment position is entered by the search consultant, as shown in block l la?. Each employment position can also have an associated engagement code or number that is generated, for example, by the system or designated by the search consultant. Accordingly, a specific employment position 3o website may have both a client code and an engagement code associated with it.
Alternatively, a specific employment position website may be identified solely by a single code, such as an engagement code, without the need for a client code.
In block 114, the search consultant enters the name of each user at the employer that ~.vill be permitted to enter information regarding the selected engagement or employment position. In embodiments where individual websites are secured via a password, the s search consultant can specify the password or passwords to be used. In block 116, the search consultant provides the employer wil:h the user identification code or codes and the associated passwords for entering a particular employment position website.
FIG. 4 illustrates an embodiment for inputting employment position parameters via an individual employment position website such as one created in the manner io described above in connection with FIG. 3. A registered employer logs onto the recruiting server by entering, for example, the company name at the employer website (block 118). Where the employer website requires a passwords) to gain access to the recruiting server, the employer first enters the appropriate password(s). The employer then enters the desired engagement number., i.e., the desired employment position to be is filled. Next, the employer enters various desired and/or required employment position parameters for the employment position (block 120). The employment parameters may include candidate education, certification, vrork experience, present employer classification, etc., along with the employer's position opening, employment position location, and employer qualitative assessment parameters. The employment parameters 2o are stored in a database such as database 500.
As used herein, the term "qualitative assessment" includes organizational cultural assessment, job profile assessment and conjoint analysis. The term "qualitative assessment" refers to how a candidate fits in with a company's organizational culture.
Such an assessment includes personality type matching between candidates and is companies. One such assessment is The Myers Briggs Personality Type system, which has sixteen different personality types. In one embodiment, the qualitative assessment tool of the present invention assesses parameters such as a candidate's job challenges, operating styles, role styles, leadership stylca, motivations, business environment experience, etc. Similarly, the employer's organization and operating style are evaluated 3o with respect to parameters such as company and position challenges, company operating style, leadership style, company motivations, fast growth business culture, rapidly ll changing business environment, etc. The qualitative assessment tool of the present invention assesses candidates and job positions and matches candidates to a job position where the company culture matches the candidate's personal traits. The qualitative assessment tool measures candidates' and employers' decision making styles, thinking s styles, cognitive motives, achievement motives, emotional styles, career concepts, and career motives. Therefore, according to one embodiment, the qualitative assessment tool seeks to match the career challenges and responsibilities of potential candidates with the expectations and organization of a particular company.
In one embodiment, the qualitative assessment uses questionnaires derived from to the book by MICHAEL 1. DRIVER, KENNETH lE~. BROUSSEAU & PHILLIP L.
HUNSAKER, THE
DYNAMIC DECISION MAKER: FIVE DECISION STYLES FOR EXECUTIVE AND BUSINESS
SUCCESS, (Self Discovery Press 1998). In another embodiment, the qualitative assessment uses conjoint analysis questionnaires derived from tools such as ACQNET
adaptive conjoint analysis provided by dataDirect, which is a subsidiary of Kingsley i s Research, Inc. of New York, NY, in cooperation with Sawtooth Software, Inc. of Sequim, WA.
FIGS. Sa-Sg provide example employer screens that are used in connection with specifying required and/or desired quantitative employment parameters such as the employer's position description, the position's scope of activities, the industry 2o experience required, the industry experience desired, etc. The employer selects the parameters he/she thinks are important for the available position and assigns a weight to each parameter, as shown in FIG. Sg.
FIGS. 6a-6p provide example employer screens that are used in connection with specifying the employer's required and/or desired qualitative assessment parameters.
2s These parameters include job challenges, operating styles, role styles, leadership styles, motivations, business environment experience, etc. The employer selects the qualitative parameters he/she thinks are important for the available position and assigns a weight to each parameter, as shown in FIGS. 6b-p. FI~Gs. 6c-6p illustrate example employer screens that are used in connection with obtaining qualitative information from an 3o employer that enables the system to perforrr~ an organizational cultural assessment of the employer and job position.

FIG. 7 is a flow diagram related to ensuring that employment position parameters have been completely specified and preparir;g an employment position record for a matching process. In the embodiment shown in FIG. 7, a search consultant enters the recruiting website and selects the proper employer and the engagement number s corresponding to the position to be filled, as shown in block 122. The search consultant determines whether the employment position record is complete, as shown in block 124.
If it is not complete, the employer is contacted to complete the employment questionnaire, as shown in block 126. If the: employment questionnaire is complete, the search consultant reviews the employer's responses to the employment questionnaire, as io shown in block 128.
In block 132, the search consultant determines whether there are any employer dispersion problems, i.e., differences in what various users feel are important parameters for the particular position. For example, if the human resource manager indicated that communication was an important parameter (e.g., gave this parameter 7/7) and the ~s manager over the position indicated that communication was of low importance (e.g., gave this parameter 1/7), the search consultant would talk with each manager to understand why this parameter was weighed differently by each user and, based on these conversations, overwrite the entry such that the appropriate weight is given this parameter. Thus, if dispersion problems are' discovered, the dispersion problems are 2o resolved by adjusting the input parameters, such as the qualitative assessment parameters for the organization, as shown in block 130.
If there are no dispersion problems, or once these problems are resolved, the system 10 generates a preliminary report detailing the job profile generated for the employment position, as shown in block 136. This report is reviewed with the employer is to evaluate whether there are any discrepancies or problems with the profile, i.e., whether the job profile is an accurate representation of the qualities the employer desires in a candidate (block 140). In other words, doe~~ the candidate profile include what the pertinent managers in the company regard as desirable or required candidate traits such as the proper amount of work experience, from a company of sufficient size or prestige, 3o from the proper position within that company, along with the proper education, certification, compensation, career motivations, company organizational culture fit, and 1. 3 decision making style. If there are such discrepancies or problems with the report, then blocks 130, 136, 140 and 142 may be repeated until all such problems are resolved.
Otherwise, the employment position record is made available for matching, as shown in block 144.
Turning now to FIG. 8, a flow diagram related to gathering candidate parameter information according to one embodiment of the present invention is illustrated. In the embodiment shown in FIG. 8, the candidate enters a recruiting website to register with a recruiting firm on-line, as illustrated in block 204. A questionnaire comprising a series of registration screens is provided to obtain information from the candidate such as the io candidate's name, e-mail address, current home address, current phone number, education, employment history, prior experience, present employer, current position, desired position, desired geographic location, etc. According to some embodiments, the questionnaire also includes a candidate qualitative assessment, which he/she answers on-line. Likewise, according to some embodirr~ents, the qualitative assessment includes a is conjoint analysis tool, which the candidate also takes on-line. The results of these assessments and/or analyses, along with the other registration information, are stored to the database 500 as candidate parameters.
FIGS. 9a-9j and FIGS. l0a-l Ox provide example candidate screens that are used in connection with obtaining information from a candidate regarding his/her quantitative 2o and qualitative parameters, respectively. Such quantitative parameters, illustrated in FIGS. 9a-9j, include the candidate's name, address, telephone number, desired position, current level, academic background and cerl:ifications, employment history, geographic preferences, compensation, etc. Such qualitative parameters include the candidate's skills, business environment experience, and answers to a qualitative assessment tool, as is illustrated in FIGs. l0a-10x. FIGS. l Oc-l0o provide example candidate screens that are used in connection with obtaining qualitative information from a candidate that enables the system to perform a organizational cultural assessment of the candidate.
FIGs. 1 Op-l Ox provide example candidate screens that are used in connection with obtaining qualitative information from a candidate that enables the system to perform a conjoint 3o analysis of the candidate.

l4 Referring again to FIG. 8, upon entering information into the database 500, the submitted candidate information may be organized into a candidate record.
After a candidate record has been created, the system can periodically determine whether the record is complete, i.e., whether the candidate completed all or at least the minimum s required information (see e.g., FIGs. l la-b). For example, after a candidate exits the recruiting website, the system can determinf: whether the candidate provided all requested information such as by, for example, answering all questions posed in the questionnaires. For example, the candidate information in-take process represented by block 204 may permit the user to complete all or only portions of the questionnaires io during any given visit. In this manner, information can be stored to the database as it is provided by a candidate without requiring the candidate to complete the entire in-take process during a single visit. Accordingly, a particular candidate can visit the recruiting website and begin entering information, exin. the website, and then return one or more times to complete the in-take process.
Is According to some embodiments, when a candidate initially enters some information into the system 10, the system c;an be designed to create an individual website for that individual candidate. Such individual candidate websites can be provided with security features such as associated passwords. When an individual candidate website is created, an associated website identification codes) and password zo can be assigned to the website and provided to the candidate. For example, the identification code could comprise a candidate's name. Accordingly, after exiting the recruiting website, a candidate may return to his or her individual website by entering his or her name and the appropriate password at the recruiting website.
If the candidate did not complete all the required in-take information, the zs candidate can be prompted to complete the registration process, as illustrated in block 210 (see e.g., FIG. 14). Methods of prompting the candidate include posting notices to the candidate on the candidate's individual candidate website, e-mailing the candidate, etc. Once the candidate completes the entire registration process, all the information is stored to the database 500 and made available for processing by the system 10, as shown 3o in block 212. For example, the complete candidate record may be made available for matching searches, as discussed below. According to some embodiments, the system permits even incomplete candidate records to be made available for limited processing by the system 10. For example, if the candidate; completed all the questionnaires relating to the candidate's quantitative parameters, the system 10 could determine whether the candidate at least had the required degree and experience for the employment position. If s the quantitative parameters were met, the employer or search consultant could then determine whether it would be beneficial to contact the candidate to obtain further information related to the candidate's qualitative parameters.
Upon completing the in-take process, the responses to the questionnaires are analyzed and the results of such analysis are stored in the database 500. For example, io based on a candidate's responses to questions posed by a qualitative assessment tool, qualitative candidate parameters can be determined and stored to be database.
The qualitative assessment tool is discussed in more detail in connection with FIGs. l0a-l Ox above and 13a-13f below. According to some embodiments, the qualitative candidate parameters are made available to the candidate, as represented by the candidate feedback is block 213 of FIG. 8. For example, the results can be e-mailed to the candidate or the results can be made available on-line at the candidate's individual candidate website for subsequent retrieval by the candidate. In one embodiment, the results are communicated to the candidate in about 24 hours.
FIGS. 11 a and 11 b illustrates a flow diagram related to determining whether a zo candidate record has been completed and determining various qualitative candidate parameters. In the embodiment shown in FI Gs. l l a,b, the system 10 determines whether the candidate qualitative assessment questionnaires are complete, as shown in block 220.
If they are not complete, in block 211, the c~~ndidate is prompted to complete the questionnaires. In one embodiment, the candidate is prompted to complete the zs qualitative assessment questionnaires by the methods mentioned above (i.e., posting notices to the candidate on the recruiting website, e-mailing the candidate, etc.). In another embodiment, an appropriate incomplete flag is set. In either embodiment, the system next moves to block 226. Otherwise;, when the assessment questionnaires are complete, the system 10 determines the candidate's qualitative assessment from the 3o responses to the questionnaires, as shown in block 222.

One method of determining the candidate's qualitative assessment, including an organizational cultural assessment, is detailed in MICHAEL J. DRIVER, KENNETH
R.
BROUSSEAU & PHILLIP L. HUNSAKER, THE DYNAMIC DECISION MAKER: FIVE DECISION
STYLES FOR EXECUTIVE AND BUSINESS SUCCESS, (Self Discovery Press 1998).
Several s companies offer qualitative assessment tools, including: Hogan Assessment System, Inc.
of Tulsa, Oklahoma; TTI Performance Systems, Ltd. of Scottsdale, Arizona;
Waldroop Butler Associates of Brookline, Massachusfas; and Winslow Research Institute, of San Mateo, California (The Winslow Behavioral Assessment System). The following factors have been found to be relevant in selecting .an appropriate qualitative assessment tool Io from the many available:
~ Generation of scores that are consistent both over time and interrata ~ Scoring based on input from the individual to be evaluated without the need for additional input from others (e.g., without input from others evaluating the candidate) is ~ The focus of the assessment tool., with a preference for those with a primary focus on the selection and matching of candidates to jobs ~ Availability of the test in foreign languages ~ Validity of the results ~ The clarity of the feedback, with a preference for tools providing numerical Zo results over those in which the fi~edback is entirely textual ~ The length of time needed to complete the test, with a preference for tests requiring less time ~ The extent to which the test is perceived to be valid by those taking it (facial validity).
Zs For example, the system 10 determines various qualitative candidate parameters, such as candidate problem solving skills, decision making skills, task performance proficiency, interpersonal skills, leadership skills, motives, operating styles, cultural fit, etc., on a scale indicating the candidate's disposition for each parameter. In one embodiment, the system 10 represents the candidate's proficiency for each candidate qualitative assessment parameter graphically en a scale indicating where the candidate falls on the scale for each parameter. Example candidate and/or employer qualitative assessment feedback screens illustrating candidate proficiency at several evaluated s parameters are shown in FIGS. 13a-13e.
For example, the candidate's proficiency for problem solving is rated from very action-oriented, to moderately action oriented and analytic, to very analytic.
Similarly, the candidate's proficiency for task performance is rated from very persistent, to moderately persistent and flexible, to very flexible. Likewise, the candidate's io proficiency at interpersonal skills is evaluated from very directive, to moderately directive and collaborative, to very collaborative. Other parameters are evaluated similarly by the system 10.
Then, the candidate's profile, comprised of candidate parameters, is stored to the database 500, as shown in block 224 of FIG. 11 a. In block 225, the system 10 provides is the candidate with feedback by, for example, updating the candidate's homepage on the recruiting website with the qualitative assessment information calculated in block 222.
In block 226 of FIG. 11 b, the system 10 determines whether the candidate conjoint analysis questionnaires are complete. If it is not complete, in block 211, the candidate is prompted to complete the questionnaires by, e.g., posting notices to the zo candidate on the recruiting website, e-mailing the candidate, etc. or setting an appropriate incomplete flag and moving back to block 220. Otherwise, when the conjoint analysis questionnaire is complete, the system 10 processes the candidate's conjoint analysis based on the questionnaire responses, as shown in block 228. A conjoint analysis forces a candidate to choose between two different: options. For example, the candidate is first zs asked to choose between options A and B. Next, the candidate is asked to choose between options B and C, and so on. Example conjoint analysis questions are shown in FIG. l Op-x. The outcome of this analysis helps to determine whether the candidate shares the same professional motivating factors with a certain employer and a certain position by evaluating the candidate's behavioral traits. An example of a commercially 3o available conjoint analysis tool is the ACQNET adaptive conjoint analysis provided by dataDirect, which is a subsidiary of Kingsle~y Research, Inc. of New York, NY, in cooperation with Sawtooth Software, Inc. of Sequim, WA. Conjoint analysis is described in several publications, including: SUSAN M. SHERIDAN, THOMAS R.
KRATOCHWILL, & ,10HN R. BERGAN, CONJOINT BEHAVIORAL CONSULTATION: A
PROCEDURAL MANUAL (APPLIED CLINICAL PSYCHOLOGY); .TORDAN ,T. LOUVIERE, ANALYZING DECISION MAKING: METRIC CONJOINT ANALYSIS (QUANTITATIVE
APPLICATIONS IN THE SOCIAL SCIENCES, NO 67) (1988); CONJOINT ANALYSIS: A GUIDE
FOR DESIGNING AND INTERPRETING CONJOINT STUDIES/O44 (1992); DAVID B.
MONTGOMERY, CONJOINT CALIBRATION OF 'fHE CUSTOMER/COMPETITOR INTERFACE IN
INDUSTRIAL MARKETS (REPORT NO 85 112) (1985); SAS~ TECHNICAL REPORT R-109, t0 CONJOINT ANALYSIS EXAMPLES (1993); RICHARD P. BAGOZZI, ADVANCED METHODS OF
MARKETING RESEARCH (Blackwell Publishing); and 10E CURRY, UNDERSTANDING
CONJOINT ANALYSIS IN I S MINUTES (Quirk's Marketing Research Review), all of which are incorporated herein by reference in their entirety. See Appendix A and Appendix B
for conjoint analysis examples.
is The candidate's conjoint analysis results are then stored to the database 500, as shown in block 230 of FIG. 1 lb. In block 234, the system 10 provides candidate feedback by, for example, updating the candidate's homepage on the recruiting website with the conjoint analysis information calculated in block 228.
In the embodiment where an incomplete flag is set, once the process of FIGS.
20 11 a,b is completed, an appropriate reminder is sent to the candidate by posting notices to the candidate on the candidate's homepage on the recruiting website, e-mailing the candidate, etc. If no incomplete flags are set, then a complete record flag is set indicating that the candidate's record is available for fizll matching.
FIG. 14 is a flow diagram related to the identification of incomplete candidate zs records, the notification of candidates having incomplete records, and the completion of such records. In the embodiment shown in FIG. 14, the system 10 generates reports on incomplete candidate profiles, as shown in block 236. The system 10 can then generate a reminder e-mail to all candidates having an incomplete profile, as shown in block 238.
Additionally or alternatively, the system 10 generates an individual reminder e-mail to 3o each candidate having an incomplete profile, as shown in block 240.
Additionally or alternatively, the system 10 generates a promotional offer on the recruiting website to each candidate having an incomplete profile to return to the website and complete his/her registration profile, as shown in block 242. :For example, one promotional offer is an on-line subscription to a new service. The candidate may re-enter the recruiting website to compete his/her incomplete profile, as shown in block 244. The candidate then s completes the candidate questionnaire screens and the system 10 accepts the candidate's responses to the candidate questionnaire, as shown in block 248. According to some embodiments, the candidate's responses are stored to the database 500 after each screen is completed.
FIG. 15 is a flow diagram illustrating an example of a method of employing a io database containing employment position and candidate records to identify a desired number of qualified candidates for the position. In the embodiment shown in FIG. 15, a search consultant enters the recruiting website and enters the employer identification number and the desired engagement number, as illustrated in block 302. The search consultant then selects the matching function from the menu on the website, as shown in is block 304. The search consultant then reviews the weights of the employment parameters, as determined previously by employer, as shown in block 306. These weights were assigned according to their importance to the position being filled. The employment parameters include education, ~iesired/required certifications, desired/required degrees, experience (by function, position, and/or number of years of zo experience), position opening, compensation, employer location, desired company size, desired company, desired classification of company, desired/required industry, employer qualitative assessment (job profile and cultural fit), career challenges, and responsibilities (skill match) parameters.
The system 10 then prompts the employer to select a subset of the search zs parameters identified above, if desired, as shown in block 307. In this way, the search consultant can focus the search to emphasize certain of the parameters. The system 10 then performs a matching function in block 308, which is described in more detail in relation to FIGS. 16-28b below. Next, the search consultant reviews the output from the matching function (e.g., the list of the top 50 candidates), as shown in block 310. He/she 3o can then review, print-out or bookmark the matching candidates, as shown in block 312.
FIGs. 12a-12c and 13a-13d illustrates example screens showing how one example L:O
candidate compares to the employment position parameters. This report may be reviewed with the employer to evaluate whether the candidate's qualities match what the employer requires and/or desires in a candidate.
In block 314, the search consultant decides whether enough qualified candidates s were found by the matching function, block 308. If there were, the matching candidates and their corresponding parameters are noted, printed-out or bookmarked, as shown in block 316. If not enough qualified candidates were found, the search consultant decides what parameters to manipulate to net more candidates, as shown in block 318.
As shown in block 320, the number of candidates retrieved from the database 500 can be adjusted, io e.g., from 50 to 500 candidates. Additionally or alternatively, the search consultant may redefine the position restrictions and executf: another database search, as shown in block 322. Additionally or alternatively, the search consultant may re-search the database 500 by targeting the candidate's desired position, or the candidate's actual work experience, or both, as shown in block 324. Additionally or alternatively, the search consultant may ~ s review and/or override the employer's original employment parameter weighting, as shown in blocks 326 and 306. Then, the search can be re-run (block 308). The loop comprising blocks 308, 310, 312, 314, 318, 320, 322, 324, and/or 326 may be repeated until enough qualified candidates are found.
The matching function of block 308 of FIG. 15 is shown in more detail in FIG.
20 16. In the embodiment shown in FIG. 16, the system 10, in block 330, compares the employment responsibilities with the responsibilities of each candidate in the database 500 (see FIG. 17). In block 332, the system 10 compares the employment challenges (as identified by the employer) with the challenges identified by each candidate in the database 500 (see FIG. 18). Next, the required/desired industry to be targeted by the Zs employer is compared with the industry in which each candidate in the database 500 works, as shown in block 334 (see FIGS. 19a and 19b).
The system 10 then compares the specific company from which to hire the candidate (as identified by the employer) with the company in which each candidate in the database 500 works, as shown in block 336 (see FIG. 20). In block 338, the system 30 10 compares the required/desired company aize with the size of the company in which each candidate in the database 500 works (see FIG. 21 ). The system 10 next compares 2,1 the required/desired company classification with the company classification of the company where each candidate in the database 500 works, as shown in block 340 (see FIG. 22). The system 10, in block 342 and ..43, compares the required/desired work experience with the work experience of each candidate in the database 500 (see FIG. 23).
The system 10 then compares the rea~uired/desired degree with the degree of each candidate in the database 500, as shown in block 344 (see FIGS. 24a and 24b).
Next, the system 10 compares the required/desired professional certification with the professional certification, if any, of each candidate in the database 500, as shown in block 346 (see FIG. 25). The system 10 compares the emplloyer's qualitative assessment parameters ~o with the qualitative assessment results of each candidate in the database 500, as shown in block 348 (see FIG. 26).
The system 10 then applies, in block 350, the employment parameter weights and computes the degree of correspondence between the candidate information for each candidate and the employment parameters, as shown in the scoring table illustrated in ~s FIGs. 27a-c. In one embodiment, the systenn 10 computes the degree of correspondence between each employment parameter and each corresponding candidate parameter.
A
parameter comparison value is calculated for each parameter. In one embodiment, the degree of correspondence for each parameter is represented by a number between 0 and 100, 100 being a perfect match. The systelr~ 10 then calculates, for each candidate, a 2o candidate matching value based on the parameter comparison values. In one embodiment, the parameter comparison values for each parameter are summed and adjusted according to the parameter weights. assigned by the employer. For example, if the employer weighed ten parameters equally (i. e. , 10 points each), a candidate with 80 points for a particular parameter would havf; an adjusted score of 8 points.
All the zs adjusted point totals for the ten parameters would then be summed and the resulting total would be the candidate's score (matching value). The degree of correspondence (matching value) for each candidate is stored in the database 500.
All of the matching steps need not be completed for each candidate. Rather, candidate records can be eliminated as inadequate matches are found. For example, if 3o the employment position is a marketing job at a pharmaceutical company and several candidates listed their desired position as an associate attorney in a private law firm, :?2 those candidates are eliminated after evaluating the desired position parameter match.
Similarly, if the employment position required a college degree and several candidates had no college degree, those candidates are eliminated after evaluating the education parameter match.
s FIG. 17 illustrates an embodiment of a responsibilities matching process of block 330 of FIG. 16 in more detail. There, skill parameters refer to employee skills such as working in a fast growth business, in a rapidly changing environment, etc. In the embodiment shown in FIG. 17, the system ll0 first obtains the employer's skill requirements, as shown in block 362. The system 10 next obtains a candidate's skill ~o information from the database 500, as shown in block 364. In block 365, the system 10 determines whether the candidate is seeking; the identical position that the employer is seeking to fill. If the candidate is not seeking the identical position, the system moves to block 370. Otherwise, the system moves to block 366 where the system 10 computes the correspondence between the employer's skill requirements and the candidate's skills ~ s information. In one embodiment, the system 10 determines the correspondence by computing the least squares value between the employer's skill requirements and the candidate's skills information. This curve fitting approach gives a good estimate of the correspondence between the employer's paa~ameters and the candidate's parameters.
Other known techniques for determining the correspondence between two parameters ao may be used, as will be appreciated by those skilled in the art. In block 368, the system calculates the points for the skills match.. The system then moves to the next functional block 332 to calculate the challenges match. Where the candidate is not seeking the identical position that the employer is seeking to fill, the system 10 will determine whether the candidate's skills are in the same functional area as specified by is the employer, as shown in block 370. If they are not, the system moves to the challenges match, block 332. Otherwise, if the candid~~te's skills are in the same functional area as specified by the employer, the system sets default points corresponding to the functional area of the candidate's skills, as shown in block 371. Then, the system stores the applicable matching points corresponding to the skills match and/or the functional area 3o match, as shown in block 372. The system 10 then moves on to the next functional block 332 to calculate the challenges match.

2:3 FIG. 18 illustrates an embodiment of a challenges matching process of block of FIG. 16 in more detail. In the embodiment shown in FIG. 18, the system 10 first obtains the employment challenges identified by the employer, as shown in block 374.
The system 10 then obtains the candidate challenges identified by the candidate, as s shown in block 376. These challenges may include working in a rapidly changing environment, working with major new systems initiatives, being number one or two in an industry, working at a fast growth company, with major new systems, focusing on market share increases, etc. The system 10 l:hen matches the employment challenges with the challenges identified by each candidate in the database 500, as shown in block io 378. Candidates with at least three matches are then identified, as shown in block 380.
Candidates with at two matches are then identified, as shown in block 382.
Next, the system 10 identifies candidates with one challenges match, as shown in block 384. The system 10 calculates the corresponding points for each candidate for the challenges match in block 386. The points are then stored to the database 500, as shown in block is 388.
FIGS. 19a and 19b illustrate an embodiment of the industry matching process of block 334 of FIG. 16 in more detail. In the embodiment shown in FIG. 19a, the system retrieves from the database 500 the employer's required industry classification, i.e., the industry from which the candidate must be obtained (as specified by the employer), 2o as shown in block 390. In one embodiment., the U.S. government's standard industry classification (SIC) is used to determine whether the employer's required industry experience matches the candidate's industry experience. SIC codes are classified such that each category has a two number code, and each subcategory thereunder has from a three to five digit code depending on the spf:cificity of the subcategory. An example SIC
is code for engineering is as follows:
87 Engineering & Management Services 871 Engineering & Architectural Services 8711 Engineering services 8712 Architectural services 30 8713 Surveying Services 872 Accounting, Auditing; & Bookkeeping.

The system 10 retrieves from the database 500 the SIC code for each candidate, as shown in block 392. Next, the system 10 matches the employer SIC code with each candidate's SIC code, as shown in block 394. If the SIC codes match, the system 10 s calculates the points corresponding to the match, as shown in block 400.
Otherwise, if the SIC codes do not match, the system 10 drops from the employer's required classification the last SIC digit and then compares the resulting SIC code with each candidate's SIC code to determine whether there is a match, as shown in block 396.
Because of the SIC code classification scheme, dropping the last SIC digit from the ~o employer's required classification and then re-comparing the resulting SIC
code with each candidate's SIC code determines whether there is a more general industry classification match. That is what is done in block 396. It will be appreciated, however, that other industry classification systems may be used instead, such as, for example, the North American Industry Classification Sysi:em (NAILS) which is a six digit ~s classification code, as opposed to the five digit SIC code. Similar to the SIC code, the first two digits of the NAILS code designate major economic sectors.
In the described embodiment, if the '.iIC classifications match, the system 10 moves on to block 400. Otherwise, if the SIC classifications do not match, the system 10 then determines whether the employer SIC code resulting from block 396 is less than 2 zo digits, as shown in block 398. If it is not, blocks 396 and 398 are repeated. Otherwise, there is no classification match and the system 10 moves on to block 400.
There, the system 10 calculates the points for the industry classiflcation(s) match.
Where only a partial industry match is found (e.g., a two digit SIC code match), less points are awarded. The total points awarded are then stored to the database 500, as shown in block zs 402.
In the embodiment shown in FIG. 1 Sib, the system 10 retrieves from the database 500, in block 404, the employer's "desired" SIC industry classification, as opposed to the "required" SIC classification, as detailed above in connection with FIG. 19a.
The system then retrieves from the database 500 the SIC industry classification for each 3o candidate, as shown in block 406. Next, the system 10 matches the employer's desired SIC industry classification with each candidate's SIC code, as shown in block 408. If the f,5 SIC codes match, the system 10 calculates the points corresponding to the match, as shown in block 416. Otherwise, if the SIC codes do not match, the system 10 drops from the employer's desired industry classification the last SIC digit and then compares the resulting SIC code with each candidate's SI(: code to determine whether there is a match, s as shown in block 410. If the SIC codes match, the system 10 moves on to block 416.
Otherwise, if the SIC codes do not match, the system 10 then determines whether the employer SIC code resulting from block 41 CI is less than 2 digits, as shown in block 412.
If it is not, blocks 410 and 412 are repeated. Otherwise, there is no industry classification match and the system 10 moves on to block 414. Block 414 checks io whether the system 10 has reached the end of the candidate's SIC codes, i.e., where the candidate has worked in more than one industry, the system 10 compares each of the candidate's classification codes for each industry. If the system has not reached the end of the candidate's SIC list, the candidate's next SIC code is retrieved (block 406) and the process is repeated. Otherwise, the system ll0 moves on to block 416. There, the system Is 10 calculates the points for the industry classifications) match. The points are then stored to the database 500, as shown in block 418.
FIG. 20 illustrates an embodiment oiF a company matching process of block 336 of FIG. 16 in more detail. In the embodiment shown in FIG. 20, the system 10 first retrieves from the database 500, in block 420, the employer's company preference, i.e., 2o the company from whick the employer desires to hire the candidate. The system 10 then retrieves from the database 500 the current or last company where each candidate works (or worked), as shown in block 422. In block 424, the system 10 then looks-up the company identification number for each candidate's current or last company in the database 500. The company identification numbers are obtained from lists such as is Fortune 500, America's Most Admired Companies, The Global 500, etc. Next, the system 10 determines whether the employer's desired company matches each candidate's company, as shown in block 426. If the connpanies match, the system 10 calculates the points corresponding to the match, as shown in block 432. Otherwise, if the companies does not match, the system 10 determines whether the system 10 has reached the end of so the list of companies where each candidate worked, as shown in block 428.
If the system has not reached the end of the candidate's company list, the next listed company is 2,6 retrieved (block 422) and processing continues using the candidate's next prior employer.
Otherwise, the system 10 moves on to block 430 where the system 10 determines whether it has reached the end of the employer's list of preferred companies.
If the system 10 has not reached the end of the em:ployer's list, the next listed preferred s company is retrieved at block 420 and processing continues using the employer's next preferred company. Otherwise, the system 10 moves on to block 432 where the system calculates the points for the company(s) match. The points are then stored to the database 500, as shown in block 434.
FIG. 21 illustrates an embodiment of a company size matching process of block ~0 338 of FIG. 16 in more detail. In the embodiment shown in FIG. 21, in block 600, the system 10 retrieves from the database 500 the company size desired by the employer. In other words, the size of the company from v~rhich the employer desires to hire the candidate. Next, the system retrieves the candidate's company size experience data, as shown in block 602. In one embodiment, the size of each prior company where the ~s candidate has worked is retrieved along with the candidate's position in each company (e.g., if the candidate was an executive at a company warranting a size adjustment, then a multiplier is multiplied to the company size adjustment). The system 10 then computes each candidate's current company size as a percentage of the company size desired by the employer and applies a corresponding number of matching points, as shown in block 604. In block 606, the system determines whether the candidate has any additional experience. If he/she does not, the system moves to block 614. If the candidate does have additional experience, the system computes each candidate's next prior company size as a percentage of the company size desired by the employer and applies a corresponding number of matching points, as shown in block 608. In block 610, the 2s system again determines whether the candidate has any additional experience. If he/she does not, the system moves to block 614. If the candidate does have additional experience, the system computes each candidate's second prior company size as a percentage of the company size desired by the employer and applies a corresponding number of matching points, as shown in block 612. Then, in block 614, the system 3o awards each candidate with the points corresponding to the largest of each candidate's current, or prior, company. The points are then stored to the database 500, as shown in block 616. Next, the system 10 moves on to the next functional block 340 to perform the company classification match.
FIG. 22 illustrates an embodiment of the company classification matching process of block 340 of FIG. 16 in more detail. In the embodiment shown in FIG. 22, the s system 10 retrieves from the database 500, in block 436, the employer's preferred company classification, i.e., the type of company from which the employer desires to hire the candidate. Examples of preferred company classification lists include: The Fortune 100, The Fortune 500, The Fortune 1,000, The Global 500, America's Fastest Growing Companies, America's Most Admired Companies, etc. The system 10 then retrieves io from the database 500 the current or last connpany where each candidate works (or worked), as shown in block 438. In block 440, the system 10 then looks-up the company identification number for each candidate's company in the database 500. Next, the system 10 matches the employer's desired company classification with each candidate's company classification, as shown in block 442. If the company classifications match, the is system 10 calculates the points corresponding to the match, as shown in block 448.
Otherwise, if the company classifications dc. not match, the system I 0 determines whether it has reached the end of the list of companies where the candidate has worked, as shown in block 444. If the system has not reached the end of the candidate's list, the next company on the list is retrieved (block 438) and the process is continued using the 2o candidate's next prior employer. Otherwise, the system 10 moves on to block 446 where the system 10 determines whether it has reached the end of the employer's list of preferred company classifications. If the system 10 has not reached the end of the employer's list, the next preferred company classification on the list is retrieved (block 436) and processing continues using the employer's next preferred company Zs classification. Otherwise, the system 10 moves on to block 448 where the system 10 calculates the points for the company classiiFication(s) match. The points are then stored to the database 500, as shown in block 450.
FIG. 23 illustrates an embodiment of an experience matching process of blocks 342 and 343 of FIG. 16 in more detail. In the embodiment shown in FIG. 23, the system 30 10 retrieves from the database 500, in block 452, the employer's required experience.
The system 10 then retrieves from the database 500 each candidate's number of years of ~.8 experience by position and by functional area, and each candidate's total number of years of experience, as shown in block 454. In block 456, the system 10 determines each candidate's experience as a percentage of the employer's required experience by position (e.g., CFO), as shown in block 456. For example, a candidate may have 60% of the s number of years of experience required by the employer for the employment position.
The system 10 calculates, in block 458, the points corresponding to the degree of match found in block 456. The system 10 then determines each candidate's experience as a percentage of the employer's required experience by functional area (e.g., accounting), as shown in block 459. The system 10 then determines, in block 460, each candidate's io experience as a percentage of the employer's required total number of years of experience. Next, the system 10 calculates the combined points corresponding to the degree of match between each candidate's experience by functional area and each candidate's total years of experience, in block 461. Each candidate's points are then stored to the database 500, as shown in block 462. Next, the system 10 moves on to the is next functional block 344 to perform degree matching.
FIGS. 24a and 24b illustrate an embodiment of a degree matching process of block 344 of FIG. 16 in more detail. In the embodiment shown in FIG. 24a, the system retrieves from the database 500, in block 463, the employer's required degree.
The system 10 then retrieves from the database '.>00 each candidate's education data (including degree(s)), as shown in block 464. In block 466, the system 10 determines whether the employer's required degree matches each candidate's degree. If the degrees match, the system 10 calculates the points corresponding to the match, as shown in block 472. Otherwise, if the degrees do not match, the system 10 determines whether it has reached the end of each candidate's list of degrees, as shown in block 468. If the system is has not reached the end of the candidate's list, the candidate's next degree is retrieved (block 464) and processing continues using that degree. Otherwise, the system 10 moves on to block 470 where the system 10 determines whether it has reached the end of the employer's list of required degrees. If the system 10 has not reached the end of the employer's list, the next required degree is :retrieved (block 463) and processing 3o continues using the employer's next required degree. Otherwise, the system 10 moves on to block 472 where the system 10 calculates the points for the degrees) match. The points are then stored to the database 500, as shown in block 474 In the embodiment shown in FIG. 24b, the system 10 retrieves from the database 500, in block 476, the employer's desired education data such as degree, degree type s and/or major, as opposed to the required degree, as detailed above in connection with FIG. 24a. As used herein, the term "degree" refers to whether a degree is a bachelor of arts or science degree or an associate degree. the term "degree type" refers to whether a candidate's degree is an undergraduate and graduate degree, and the term "major" refers to a candidate's college major, such as, for example, accounting, finance, sales, ~o marketing, engineering, etc. The system 10 retrieves from the database 500, in block 478, each candidate's education data (including degree(s), degree types) and/or major(s)).
In block 480, the system 10 determines whether the employer's desired degree matches each candidate's degree. If the degrees match, the system 10 moves on to block is 486 where the system 10 determines whether it has reached the end of the candidate's education data. If the system 10 has not reached the end of the candidate's data, the candidate's next degree is retrieved (block 478) and processing continues using the candidate's next degree. Otherwise, if the degrees do not match, the system 10 determines whether the desired degree type (e.g., a masters degree) matches the Zo candidate's degree type, as shown in block X182. If there is a degree type match, the system 10 moves on to block 486 where the system 10 determines whether it has reached the end of the candidate's education data. If the system 10 has not reached the end of the candidate's data, the candidate's next degree and/or degree type is retrieved (block 478) and processing continues using the candidate's next degree and/or degree type.
is Otherwise, the system 10 determines whether the employer's desired major matches the candidate's major, as shown in block 484.
Whether or not the majors match, the system 10 moves on to block 486 where the system 10 determines whether it has reached the end of the candidate's education data. If the system 10 has not reached the end of the; candidate's data, the candidate's next 3o degree, degree type and/or major is retrieved (block 478) and processing continues using the candidate's next degree, degree type and/or major. Otherwise, the system 10 moves on to block 488 where the system 10 determines whether it has reached the end of the employer's list of desired education data. If the system 10 has not reached the end of the employer's list, the next desired degree, degree type and/or major is retrieved (block 476) and the process is continued using the employer's next desired degree, degree type and/or s major. Otherwise, the system 10 moves on to block 490 where the system 10 calculates the points corresponding to the education data match. The points are then stored to the database 500, as shown in block 492.
FIG. 25 illustrates an embodiment of a certification matching process of block 346 of FIG. 16 in more detail. In the embodiment shown in FIG. 25, the system ~o retrieves from the database 500, in block 620, the professional certifications required or desired by the employer. The system then rfarieves a candidate's experience data including professional certifications, if any, in block 622. Next, the system determines, in block 624, whether the candidate has the employer's desired certification.
If there is no match, the system moves to block 628. Otherwise, if there is a desired certification ~ s match, the system moves to block 626 where the system awards the total points available for a match. In block 628, the system determines whether the candidate has any other certifications. If the system has not reached the end of the candidate's certifications, the candidate's next certification is retrieved (block622) and processing continues using the candidate's next certification. Blocks 622, fi24, 626, and 628 are repeated until all of the 2o candidate's certifications are evaluated. Ne:~ct, the points are stored to the database 500, as shown in block 630. The system 10 then moves on to the next functional block 348 to perform assessment matching.
FIG. 26 illustrates an embodiment of a qualitative matching process of block of FIG. 16 in more detail. In the embodiment shown in FIG. 26, the system 10 retrieves zs from the database 500, in block 632, the qualitative assessment results for the employer.
The system 10 then retrieves the qualitative assessment results for a candidate, in block 634. Next, the system determines, in block 636, the degree of correspondence between the employer and candidate qualitative assessment results. In one embodiment, the system 10 determines the correspondence by computing the least squares value between 3o the employer's and the candidate's results. This is one of a number of known curve fitting techniques that can be used. In another embodiment, a matching program developed by Decision Dynamics Group of Thousand Oaks, California is used.
This embodiment is explained in more detail below in relation tc FIGs. 28a-28b. The system then stores the average degree of match percentage to the database 500, as shown in block 644. The system 10 then moves on to the next functional block 350, the scoring s table.
In the matching program developed by Decision Dynamics Group, the system 10 retrieves, in block 650, the responses from the candidate questionnaires (representative candidate questions are shown in FIGS. l Oc-x). The candidate responds to each question by selecting a qualitative parameter value, e. g., between one to seven. The system 10 io then retrieves, in block 652, the responses from the employer questionnaires (representative employer questions are shown in FIGS. 6c-p). The employer responds to each question by selecting a qualitative parameter value, e.g., between one to seven.
Next, the system matches the candidate and employer responses, as shown in block 658.
The matching block 658 is shown in more detail in FIG. 28b. There, each qualitative ~s candidate parameter is matched with each corresponding qualitative employer parameter and the degree of correspondence between them is calculated as a percentage (block 660). For example, if the candidate assigned the first parameter a value of six and the employer assigned the first parameter a value of six, there would be a 100%
match. The percentage match for each group of parameters is then determined (block 662).
For 2o example, if the qualitative parameters are grouped into three groups, the average degree of match for each group is determined (see ~~.g., FIG. 12a for an example employer screen showing the percentage match for tlwee groups of qualitative parameters, "overall style fit", "role style fit", and "operating style fit"). If there are, e.g., three parameters per group, and the correspondence between the employer and candidate parameters is 100%, 2s 80% and 60%, respectively, then the average degree of match for that group is 80%.
Next, the percentage match for all the qualitative parameters is totaled and averaged (block 664). The average degree of match between all the qualitative parameters is determined by totaling the percentage match between each employer and candidate parameter and dividing by the total number of parameters.
so FIGs. 27a-c illustrate a table showing an embodiment of the scoring process of block 350 of FIG. 16 in more detail. In one embodiment, the system 10 first applies the

3.2 parameter weights assigned by the employer and then computes the total degree of correspondence score for each candidate. Referring to the scoring table ef FIG. 27a, the responsibilities match of block 330 is determined, in one embodiment, by computing the least squares value between the employer's responsibilities requirements and the s candidate's responsibilities information. Initially, the employer distributes 100 points between 10 required responsibilities (see e.g., FIG. 6b). Likewise, the candidate distributes 100 points between 10 responsibilities in which the candidate has experience (see e.g., FIG. l0a). The calculations procec;d by subtracting the points assigned by the employer from the points assigned by the candidate and squaring that difference for each ~o responsibility. Then, the sum of the squares is added to yield a gross value. Depending on where the gross value falls on the match Criteria Scale, the system 10 awards the appropriate points. For example, if the gross value is 350, then the candidate has responsibilities that are Related to the employer's requirements. Accordingly, the system awards 70 points for the Related match.
is The challenges match of block 332 i;s determined, in one embodiment, by determining whether the candidate identified any of the challenges listed by the employer as being important. In one embodiment, the employer selects three challenges out of a list of, for example, 15 challenges as being important to the employment position (see e.g., FIG. 6a). Then, if the candidate selects. the same three challenges (see e.g., FIG.
zo 1 Ob), the system awards 100 points. Similarly, if the candidate selects two of the same challenges, the system awards 80 points, and if the candidate selects one of the same challenges, the system awards 60 points.
The required industry match of block 334 is determined, in one embodiment, by comparing the industry classification identidied by the candidate with the required is industry identified by the employer. For example, the system will provide a list of industries from which the candidate can chose (see e.g., FIG. 9b). Each industry has a corresponding 2-5 digit SIC code. Thus, the SIC code corresponding to the industry identified by the candidate is compared with the SIC code of the required industry identified by the employer (see e.g., FIG. Sb). If there is a five-digit match, 100 points 3o are awarded. Likewise, if there is a four-digit match, 80 points are awarded, if there is a three-digit match, 60 points are awarded, andl if there is a two-digit match, 40 points are awarded. Otherwise, no points are awarded.
The desired industry match of block 334 is determined in a similar manner;
however, a maximum of 80 points are awarded to differentiate a desired industry match s from a required industry match. Thus, if there is a five-digit match, 80 points are awarded, if there is a four-digit match, 60 points are awarded, if there is a three-digit match, 40 points are awarded, and if there is a two-digit match, 20 points are awarded.
Otherwise, no points are awarded. If the candidate has both the required and the desired industry experience sought by the employer, the higher of the two scores is awarded to ~ o the candidate.
The determination of whether a candidate works at a desired company, as specified by the employer, (block 336) either produces a match or it does not produce a match; the candidate either works for the desired company or he/she does not.
Thus, the system awards 100 points for a match, and no points otherwise.
is Likewise, the determination of whether a candidate works for a company in a desired classification, as specified by the employer, (block 340) either produces a match or it does not produce a match; the candidate either works for a company in the desired classification or he/she does not. Thus, the system awards 100 points for a match, and no points otherwise.
Zo Referring to FIG. 27b, the company size match of block 338 is determined, in one embodiment, by comparing the annual sales of the candidate's current employer with the annual sales range identified by the employer as desirable. For example, if an employer desires a candidate from a company having annual sales between $101-$300 million, and a candidate works at, for example, a manufa~:.turing company with annual sales of $150 is million, the system will award 100 points. However, if the candidate works at, for example, a manufacturing company with amoral sales of $25 million, the system will award 60 points. In another embodiment, the number of staff at the candidate's company (for example a law firm or accounting firm) is compared with the number of staff range identified by the employer as desirable. For example, if an employer desires a candidate so from a law firm having between 75-99 attorneys, and a candidate works at a law firm with 80 staff attorneys, the system will award 100 points. However, if the candidate works at a law firm with 10 staff attorneys, the system will award 40 points.
In still another embodiment, the total assets of the candidate's company (for example a bank or brokerage firm) are compared with the assets range identified by the employer as desirable. For example, if an employer desires a candidate from a brokerage firm having s between $251-$500 billion in assets, and a candidate works at a brokerage firm with $270 billion in assets, the system will award 100 points. However, if the candidate works at a brokerage firm with $15 billion in assets, the system will award 60 points.
The experience match of block 342 is determined, in one embodiment, by comparing the candidate's number of years of experience (measured in months) with the ~o years of experience required by the employer. If the candidate has less than 50% of the required experience, no points are awarded. However, if the candidate has at least 50%
of the required experience, the system will award between 50 and 100 points.
This is a linear function. Thus, if the candidate has 52% of the required experience then, 4 points are awarded. Likewise, if the candidate has 99% of the required experience, then 98 ~ s points are awarded.
The candidate's experience by functt.on (block 342) is computed by comparing the candidate's number of years of experience (measured in months) at that function (e.g., accounting) with the years of experience by function required by the employer.
This is computed as a linear function. Thus, if the candidate has 52% of the required zo experience by function, then 52 points are awarded. Likewise, if the candidate has 99%
of the required experience by function, then 99 points are awarded.
The total points awarded for the candidate's years of experience and years of experience by function are then averaged. 7~hus, in the above example, if the candidate was awarded 4 points and 52 points for the candidate's years of experience and years of zs experience by function, respectively, then the system would award a total of 28 points for the candidate's experience.
The candidate's experience by position (block 343) is computed by comparing the candidate's number of years of experience (measured in months) at that position (e.g., CFO) with the years of experience by position required by the employer. This is 3o computed as a linear function. The candidate's current and last three jobs are evaluated, with each prior job point total being discounted by an appropriate multiplier.
Thus, if the ?~ 5 candidate's current job provides 52% of the required experience by position, then 52 points are awarded. Likewise, if the candidate's first prior job provides 40%
of the required experience by position, then 40 points times 80% or 32 points are awarded. If the candidate's second prior job provides 75% of the required experience by position, s then 75 points times 60% or 45 points are awarded. And if the candidate's third prior job provides 30% of the required experience by position, then 30 points times 40%
or 12 points are awarded. The total of all the points awarded for the candidate's current and last three jobs are totaled, with a maximum of 100 points awarded.
Referring to FIG. 27c, the degree match of block 344 is determined, in one ~o embodiment, by comparing the candidate's degree with the employer's required degree.
Here, either the degrees match or they do not match; thus, the system awards 100 points for a match, and no points otherwise.
The desired degree match of block 344 is computed by comparing the candidate's degree with the employer's desired degree. If, for example, the desired degree is a Ph.D.
~s in finance, and the candidate has a Ph.D. in finance, 80 points are awarded (not 100 so that the required and desired degree scores c;an be differentiated). If the candidate has a Ph.D. in accounting, 60 points are awarded for this equivalent degree (i.e., a doctorate degree). If the candidate has an MBA in management, 40 points are awarded for this lower level degree (i.e., a masters degree). l(f the candidate has a BS in finance, 0 points zo are awarded.
The certification match of block 346 is determined, in one embodiment, by comparing the candidate's certification with the employer's required certification. Here, either the certifications (e.g., CPA) match or they do not match; thus, the system awards 100 points for a match, and no points other,vise.
Zs Likewise, the employer's desired certification (block 346) either matches or it does not match the candidate's certification; thus, the system awards 100 points for a match, and no points otherwise.
The quantitative parameter weights .are then applied to the ten point totals from blocks 330, 332, 334, 336, 338, 340, 342, 343, 344 and 346 found in FIGS. 27a-c such so that each candidate has between 0 and 100 points. For example, if the employer weighed all the first ten parameters equally (i.e., 10 points each) when entering the information on FIG. Sg, a candidate with 80 points for the challenges match would have 8 points. All the adjusted point totals for the ten parameters would be summed, yielding (y).
However, if the employer weighed four of the parameters equally (i.e., 25 points each for four of the ten parameters), a candidate with 80 points for the challenges match would s have 20 points. The adjusted point total for vthe four weighted parameters would be summed, yielding (y). The candidate's quantitative parameter subtotal is determined by totaling the score for each quantitative parameter (x) and dividing by the total of the adjusted points (y), yielding (z).
The qualitative assessment match of block 348 is determined by comparing the io candidate and employer responses for each group of parameters. In the illustrated embodiment, there are three groups of qualitative parameters, (a), (b), and (c). The percentage match for each group is then totaled and averaged, yielding (d).
The candidate's final score is determined by taking the qualitative match percentage (d) (multiplying by 100) and adding that result to the quantitative parameter is subtotal (z) and dividing by two (the candidate's final score will be a number between one and 100).
In one embodiment, the qualitative assessment result (d) is 70% of the candidate's score and the ten quantitative parameter subtotal (z) is 30% of the candidate's score. However, the search consultant can adjust these default settings to Zo vary the weight given the qualitative assessment result, or any of the quantitative parameters, to prioritize the candidates found by the system.
In an alternative embodiment, candidates for a unique employment position are recruited by advertising in a publication, such as the Wall Street Journal. In this embodiment of the system 10, an advertisement is placed in a publication advertising an Zs employer's position and targeting specific candidates. The ad directs candidates to the recruiting website and includes a code that c;an be entered at the site allowing such candidates to access modified registration screens specifically targeted to obtaining information relevant to filling the advertised employment position. The employment parameters are matched with the candidate parameters, as detailed above in relation to so FIGS. 16-28b. The candidates are provided feedback, as detailed above in relation to FIG. 8.

_i 7 While the present invention has been. described with reference to one or more embodiments, those skilled in the art will recognize that many changes may be made thereto without departing from the spirit and. scope of the present invention which is set forth in the following claims.

Claims

THE EMBODIMENTS OF THE INVENTION IN WHICH AN EXCLUSIVE
PROPERTY OR PRIVILEGE IS CLAIMED ARE DEFINED AS FOLLOWS:

1. A computer system for matching one or more candidates with an employment position of an employer, the computer system comprising:
a processor; and a database accessible to the processor;
the processor being programmed to:
retrieve employment parameters from the database;
retrieve candidate parameters from the database;
compare the candidate parameters with the employment parameters; and compute the degree of correspondence between the candidate parameters and the employment parameters.

2. The computer system of claim 1, wherein the computer system is further programmed to assign points based on the degree of correspondence.

3. The computer system of claim 2, wherein the computer system is further programmed to accumulate for the candidates a point total corresponding to the degree of correspondence.

4. The computer system of claim 1, wherein the database has stored therein employment parameters associated with an employment position record and a plurality of candidate records, each candidate record having candidate parameters corresponding to the employment parameters;
wherein the candidate parameters are compared with the employment parameters by:
comparing individual ones of the employment parameters associated with the employment record with corresponding individual ones of the candidate parameters associated with each of the candidate records; and wherein the degree of correspondence is computed by:
calculating, for each comparison, a parameter comparison value; and calculating, for each candidate record, a candidate matching value based on the parameter comparison values.

5. The computer system of claim 1, wherein the computer system is further programmed to:

provide a candidate questionnaire to the candidate, the candidate questionnaire including candidate questions and qualitative assessment questions;
receive responses to the candidate questionnaire;
determine from the responses to the candidate questionnaire candidate parameters; and store the candidate parameters to the database.

6. The computer system of claim 1, wherein the computer system is further programmed to:
provide an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
receive responses to the employment questionnaire;
determine from the responses to the employment questionnaire employment parameters; and store the employment parameters to the database.

7. The computer system of claim 1, wherein the employment parameters include the education, experience, position opening, compensation, employer location, and employer qualitative assessment parameters.

8. The computer system of claim 7, wherein the candidate parameters include present employer, education, employment history, desired position, desired compensation, location preference, and candidate qualitative assessment parameters.

9. The computer system of claim 8, wherein the computer system is further programmed to provide the candidate qualitative assessment parameters to the candidate.

10. The computer system of claim 8, wherein the computer system is further programmed to compare the set of candidate parameters with the set of employment parameters by:
comparing each candidate's education with the position's education parameters;
comparing each candidate's employment history with the position experience parameters;
comparing each candidate's desired position with the position opening parameters;

comparing each candidate's desired compensation with the position compensation parameters;
comparing each candidate's location preference with the position location parameters; and comparing each candidate's qualitative assessment with the employer qualitative assessment parameters.

11. The computer system of claim 10, wherein the candidate parameters include the candidate's education information, current position information, location preference, and current salary.

12. A computer system for matching one or more candidates with an employment position of an employer, the computer system comprising:
a processor; and a database accessible to the processor;
the processor being programmed to:
retrieve employment parameters from the database;
retrieve responses to a candidate questionnaire, the candidate questionnaire including candidate questions and qualitative assessment questions; and determine from the responses to the candidate questionnaire candidate parameters.

13. The computer system of claim 12, wherein the computer system is further programmed to:
provide an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
receive responses to the employment questionnaire; and store the employment parameters to the database.

14. The computer system of claim 13, wherein the computer system is further programmed to determine from the responses to the employment questionnaire employment parameters.

15. The computer system of claim 14, wherein the computer system is further programmed to compare the candidate parameters with the employment parameters.

16. The computer system of claim 12, wherein the candidate parameters include present employer, education, employment history, desired position, desired compensation, location preference, and candidate qualitative assessment parameters.

17. The computer system of claim 16, wherein the computer system is further programmed to provide the candidate qualitative assessment parameters to the candidate.

18. A method for matching one or more candidates with an employment position of an employer by processing electronically captured information, the method comprising:
providing a candidate questionnaire to a candidate, the candidate questionnaire including candidate questions and qualitative assessment questions;
electronically capturing responses to the candidate questionnaire;
determining from the responses to the candidate questionnaire candidate parameters;
storing the candidate parameters to a database;
providing an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
electronically capturing responses to the employment questionnaire;
determining from the responses to the employment questionnaire employment parameters;
storing the employment parameters to the database;
retrieving the candidate parameters and the employment parameters from the database;
comparing the candidate parameters with the employment parameters; and computing the degree of correspondence between the candidate parameters and the employment parameters.

19. The method of claim 18, further including assigning points based on the degree of correspondence.

20. The method of claim 19, further including accumulating a point total for the candidate corresponding to the degree of correspondence.

21. The method of claim 20, further including selecting the candidate based at least partially on the point total.

22. The method of claim 18, wherein the candidate parameters include present employer, education, employment history, desired position, desired compensation, location preference, and candidate qualitative, assessment parameters.

23. The method of claim 18, wherein the employment parameters include education, experience, position opening, compensation, employer location, and employer qualitative assessment parameters.

24. The method of claim 23, wherein comparing the candidate parameters further includes:
comparing each candidate's education with the position's education parameters;
comparing each candidate's employment history with the position experience parameters;
comparing each candidate's desired position with the position opening parameters;
comparing each candidate's desired compensation with the position compensation parameters;
comparing each candidate's location preference with the position location parameters; and comparing each candidate's qualitative assessment with the employer qualitative assessment parameters.

25. A method of matching one or more candidates with an employment position of an employer using a computer server to process. data from an Internet web site, the computer server including a database, the method comprising:
receiving employment parameters;
storing the employment parameters to the database;
receiving candidate parameters from a plurality of candidates;
storing the candidate parameters to the database;
retrieving the candidate parameters fir each candidate and the employment parameters;
comparing the candidate parameters for each candidate with the employment parameters; and computing the degree of correspondence between the candidate parameters for each candidate and the employment parameters.

26. The method of claim 25, further including assigning points based on the degree of correspondence for each candidate.

27. The method of claim 26, further including accumulating for each candidate a point total corresponding to the degree of correspondence for each candidate.

28. The method of claim 27, further including selecting one or more candidates based at least partially on the point total for each candidate.

29. The method of claim 25, further including:
providing an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
receiving responses to the employment questionnaire; and determining from the responses to the employment questionnaire the employment parameters.

30. The method of claim 25, further including:
providing a candidate questionnaire to each candidate, the candidate questionnaire including candidate questions and qualitative assessment questions;
receiving responses to the candidate questionnaire from each candidate; and determining from the responses from each candidate to the candidate questionnaire the candidate parameters for each candidate.

31. The method of claim 25, wherein the candidate parameters include present employer, education, employment history, desired position, desired compensation, location preference, and candidate qualitative assessment parameters.

32. The method of claim 31, wherein the employment parameters include education, experience, position opening, compensation, employer location, and employer qualitative assessment parameters.

33. The method of claim 32, wherein comparing the candidate parameters further includes:
comparing each candidate's education with the position's education parameters;

comparing each candidate's employment history with the position experience parameters;
comparing each candidate's desired position with the position opening parameters;
comparing each candidate's desired compensation with the position compensation parameters;
comparing each candidate's location preference with the position location parameters; and comparing each candidate's qualitative assessment with the employer qualitative assessment parameters.

34. A computer server for matching one or more candidates with an employment position of an employer by processing data from an Internet web site, the server comprising:
a processor; and a database accessible to the processor;
the processor being programmed to:
provide a candidate questionnaire to a candidate, the candidate questionnaire including candidate questions and qualitative assessment questions;
receive responses to the candidate questionnaire;
determine from the responses to the candidate questionnaire candidate parameters;
store the candidate parameters to the database;
provide an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
receive responses to the employment questionnaire;
determine from the responses to the employment questionnaire employment parameters;
store the employment parameters to the database;
retrieve the candidate parameters and the employment parameters from the database;

compare the candidate parameters with the employment parameters; and compute the degree of correspondence between the candidate parameters and the employment parameters.

35. The computer server of claim 34, wherein the server is further programmed to assign points based on the degree of correspondence.

36. The computer server of claim 35, wherein the server is further programmed to accumulate for the candidate a point total corresponding to the degree of correspondence.

37. The computer server of claim 34, wherein the candidate parameters include present employer, education, employment history, desired position, desired compensation, location preference, and candidate qualitative assessment parameters.

38. The computer server of claim 37, wherein the employment parameters include education, experience, position opening, compensation, employer location, and employer qualitative assessment parameters.

39. The computer server of claim 38, wherein the server is further programmed to compare the candidate parameters with the employment parameters by:
comparing the candidate's education with the position's education parameters;
comparing the candidate's employment history with the position experience parameters;
comparing the candidate's desired position with the position opening parameters;
comparing the candidate's desired compensation with the position compensation parameters;
comparing the candidate's location preference with the position location parameters; and comparing the candidate's qualitative assessment with the employer qualitative assessment parameters.

40. A method of receiving employer information from an employer, the method comprising:
providing an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
receiving responses to the employment questionnaire;

determining from the responses to the employment questionnaire employment parameters; and storing the employment parameters to a database.

41. The method of claim 40, wherein the employment questionnaire seeks information relating to the qualities the employer desires in a candidate such as the candidate's work experience, company size, company position, education, certifications, and compensation.

42. A computer system for matching one or more candidates with an employment position of an employer, the computer system comprising:
a processor; and a database accessible to the processor;
the processor being programmed to:
provide an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
receive responses to the employment questionnaire;
determine from the responses. to the employment questionnaire employment parameters, the employment parameters including education, experience, position opening, compensation, employer location, and employer qualitative assessment parameters;
store the employment parameters to the database;
provide a candidate questionnaire to a candidate, the candidate questionnaire including candidate questions and qualitative assessment questions;
receive responses to the candidate questionnaire;
determine from the response; to the candidate questionnaire candidate parameters, the candidate parameters including present employer, education, employment history, desired position, desired compensation, location preference, candidate qualitative assessment parameters;
store the candidate parameters to the database;
provide the candidate qualitative assessment parameters to the candidate;

compare the candidate parameters with the employment parameters, including:
comparing the candidate's education with the position's education parameters;
comparing the candidate's employment history with the position experience parameters;
comparing the candidate's desired position with the position opening parameters;
comparing the candidate's desired compensation with the position compensation parameters;
comparing the candidate's location preference with the position location parameters; and comparing the candidate's qualitative assessment with the employer qualitative assessment parameters;
compute the degree of correspondence between the candidate parameters and the employment parameters;
assign points based on the degree of correspondence; and accumulate a point total for the candidate corresponding to the degree of correspondence.

43. A computer system for matching one or more candidates with an employment position of an employer, the computer system comprising:
a processor; and a database accessible to the processor;
the processor being programmed to:
provide an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
receive responses to the employment questionnaire;
determine from the responses to the employment questionnaire employment parameters, the employment parameters including education, experience, position opening, compensation, employer location, and employer qualitative assessment parameters;
store the employment parameters to the database;
provide a candidate questionnaire to a plurality of candidates, the candidate questionnaire including candidate questions and qualitative assessment questions;
receive responses to the candidate questionnaire from each candidate;
determine from the responses from each candidate to the candidate questionnaire candidate parameters for each candidate, the candidate parameters including present employer, education, employment history, desired position, desired compensation, location preference, and candidate qualitative assessment parameters;
store the candidate parameters for each candidate to the database;
provide the candidate qualitative assessment parameters for each candidate to each candidate;
compare the candidate parameters for each candidate with the employment parameters, including:
comparing each candidate's education with the position's education parameters;
comparing each candidate's employment history with the position experience parameters;
comparing each candidate's desired position with the position opening parameters;
comparing each candidate's desired compensation with the position compensation parameters;
comparing each candidate's location preference with the position location parameters; and comparing each candidate's qualitative assessment with the employer qualitative assessment parameters;
compute the degree of correspondence between the candidate parameters for each candidate and the employment parameters;
assign points based on the degree of correspondence for each candidate;
and accumulate a point total for each candidate corresponding to the degree of correspondence for each candidate.

44. A computer system for matching one or more candidates with a specific employment position of an employer by advertising in a publication, the computer system comprising:
a processor; and a database accessible to the processor;
the processor being programmed to:
receive a code from the advertisement;
receive responses to a candidate questionnaire, the candidate questionnaire including candidate questions and qualitative assessment questions relating to the advertised employment position;
determine from the responses from each candidate to the candidate questionnaire candidate parameters for each candidate;
compare the candidate parameters with the employment parameters, and compute the degree of correspondence between the candidate parameters and the employment parameters.

45. A computer implemented method for matching one or more candidates with an employment position of an employer, comprising:
retrieving employment parameters from a database;
retrieving candidate parameters from. the database;
comparing the candidate parameters with the employment parameters; and computing the degree of correspondence between the candidate parameters and the employment parameters.

46. The method of claim 45, wherein the database has stored therein employment parameters associated with an employment position record and a plurality of candidate records, each candidate record having candidate parameters corresponding to the employment parameters;
wherein comparing the candidate parameters with the employment parameters comprises comparing individual ones of the employment parameters associated with the employment record with corresponding individual ones of the candidate parameters associated with each of the candidate records; and wherein computing the degree of correspondence comprises:
calculating, for each comparison, a parameter comparison value; and calculating, for each candidate record, a candidate matching value based on the parameter comparison values.

47. An Internet based method of receiving information, the method comprising:
providing a website having a user interface including one or more user windows, the one or more windows providing an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
receiving at the website responses to the employment questionnaire;
determining from the responses to the employment questionnaire employment parameters; and storing the employment parameters to a database.

48. The method of claim 47, further comprising:
providing a website having a user interface including one or more user windows, the one or more windows providing a candidate questionnaire to a candidate, the candidate questionnaire including candidate quantitative questions and qualitative assessment questions;
receiving at the website responses to the candidate questionnaire;
determining from the responses to the candidate questionnaire candidate parameters; and storing the candidate parameters to the database.

49. A computer-readable storage medium containing computer executable code for instructing a computer to operate as follows:
provide an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
receive responses to the employment questionnaire;

determine from the responses to the employment questionnaire employment parameters; and store the employment parameters to a database.

50. A method for matching one or more candidates with an employment position of an employer by executing computer code contained on a computer-readable storage medium, the method comprising:
providing an employment questionnaire to an employer, the employment questionnaire including employment position questions and qualitative assessment questions;
receiving responses to the employment questionnaire;
determining from the responses to the employment questionnaire employment parameters; and storing the employment parameters to a database.

51. The method of claim 50, further comprising:
providing a candidate questionnaire to a candidate, the candidate questionnaire including candidate quantitative questions and qualitative assessment questions;
receiving responses to the candidate questionnaire;
determining from the responses to the candidate questionnaire candidate parameters; and storing the candidate parameters to a database.

52. The method of claim 51, wherein the candidate questionnaire includes qualitative assessment questions related to the candidate's job challenges, operating styles, role styles, leadership styles, motivations, and business environment experience.

53. The method of claim 52, wherein the qualitative assessment questions include conjoint analysis questions.

54. The method of claim 52, wherein the qualitative assessment questions include organizational cultural assessment, job profile assessment, and conjoint analysis questions.

55. The method of claim 52, wherein the qualitative assessment questions include organizational cultural assessment questions.

56. The method of claim 52, wherein the qualitative assessment questions include job profile assessment questions.

57. The method of claim 50, wherein the employment questionnaire includes questions related to the employer's organization, operating style, company challenges, position challenges, leadership style, and company motivations.

58. A method of receiving quantitative candidate information from a candidate, the method comprising:
providing a candidate questionnaire to the candidate, the candidate questionnaire including quantitative candidate questions;
electronically capturing responses to the candidate questionnaire;
determining from the responses to the candidate questionnaire candidate parameters;
storing the candidate parameters to a database;
retrieving employment parameters from the database;
comparing the candidate parameters with the employment parameters; and computing the degree of correspondence between the candidate parameters and the employment parameters.

59. The method of claim 58, wherein the; candidate parameters include present employer, education, employment history, desired position, desired compensation, and location preference parameters.

60. The method of claim 59, further including verifying the candidate's education.

61. The method of claim 58, further including determining a skills rating for the candidate based on the candidate parameters.