Main Criteria for Job Selection - Essay Example

?Job selection The following report helps the client (Claire Dale) decide upon the best job option to choose from the five offers that she has in hand. Claire is an IT expert with an ambition of pursuing an MBA degree. MCDA techniques were discussed to see which will be useful in this situation (MAUT in our case). This was followed by zeroing in on the main criteria that would be the major deciding factors of her choice. Weights were assigned to these in order to prioritize one from the others. Salary, cost of living, travel and educational opportunities formed 67% of the weight in importance. A research on the job location revealed the most expensive and least expensive cities. Probability scores in the range of 1-10 were used to rank the various alternatives on criteria. Final scores of the product of weights and probability scores helped to reach the conclusion that American Systems Developers provides the best opportunity to Claire. Table of Contents S.No. Topic Page No. 1 Introduction 3 2 Problem statement 3 3 Goal statement 3 4 Background 3 5 Main criteria for job selection 4 6 Various alternatives available 5 7 Linking interests and alternatives 6 8 Methodology 7 9 Assumptions 8 10 Results 9 11 Conclusion 10 Introduction Claire Dale has job offers from five different companies. Each company has different salary structure to offer and a different job profile. Some are offering a job requiring a lot of travel while others are offering single location jobs. Claire also needs to look at the location where she will be working from so that she is able to balance her professional and personal life. She has a number of criteria which she has to look at before deciding which job has the potential to offer her maximum job satisfaction as well as compensate her financially and allow her to pursue her personal interests. Problem statement The candidate has been presented with five job offers. She is unable to decide which is best for her. Goal statement Selecting the right job offer which helps to satisfy the financial, professional and personal expectations of Claire. Background In real life situations, it sometimes become very difficult to decide which options to choose from when there are many criteria for deciding upon an option. MCDA techniques help us in deciding the best possible option in a scientific and methodological way. MCDA has a number of techniques to help in zeroing on an alternative and they all follow “similar steps of organization and decision matrix construction” (Linkov et al. 2006). However, each differs in the way it analyses the data. Some of the commonly used approaches are MAUT (Multi-Attribute Utility Theory), MAVT (Multi-Attribute Value Theory, AHP (Analytical Hierarchy Process) and Outranking. The first three approaches are grouped under optimization approaches. For example, MAUT tries to provide a numerical criterion to evaluate the various alternatives. It gives scores to the various criteria to show the merits of each. The final scores can be seen by summing up the individual scores. Similarly, AHP tries to judge alternatives based on their scores and chooses the one with the highest score. It compares pairs. Outranking is a technique assumes that one alternative has more dominance over the other (Linkov et al. 2006). This technique does not assume that one alternative can be identified. It compares two or more alternatives at a time and tries to find out the “extent to which one can be preferred over the other” (Linkov et al. 2006). This method looks at favoring an alternative that performs best on maximum number of criteria. Thus, the weaknesses that one criterion has are compensated by the advantages that are there in the other criteria. Main criteria for selecting the job After a discussion with Claire and the project team, following criteria were identified which would impact her, the most (in order of importance, first one being the most important) once she decides on a particular offer: 1. Financial – The net inflow she is going to get. This depends on a. Her in-hand salary b. Cost of living in a particular location which includes housing cost, cost of food and transportation which are the basic living expenses. 2. Job profile 3. Amount of travel required for the job 4. Degree of development of the city with respect to entertainment and cultural opportunities 5. Educational opportunities available in the city for Claire to pursue MBA on a part-time basis 6. Crime rate of the city is very important factor to decide how secure the place is for Claire. 7. Proximity to family and friends. This covers two criteria. One is that the city has already some friends and relatives of Claire who can help her in initial setup and can provide support even later. The second is the proximity of the city to Claire’s native place which has all the friends and relatives. 8. The overall economic development of the city in terms of type of industry it supports - Since Claire is an IT professional it would be good for her to be located in a city which has more IT company’s so that she has better opportunities of growth in the future. Various alternatives available To understand the various criteria of each alternative, they have been tabulated in the following way. Firms Criteria American Systems Developers (ASD) Anderssun Consulting (AC) NCSS Gulf-South Company (GSC) Electronic village (EV) Type of firm Large – National level Large – National level Small – campus like single location office Medium – serving banks mostly around Southeastern states Large - National Area of operation National – many cities National – many cities Regional Regional National Job profile Work in project teams to develop decision support and information systems at national level Work in project teams to develop decision support and information systems at national level Software and computer systems development Develop information and support systems for bank operations Develop and maintain computer systems for inventory control at many stores nationally Work location Atlanta Washington DC Chicago Tampa Nashville – Tennessee Job related travel High - 6 to 9 months at a stretch High - 6 to 9 months at a stretch Low – not more than several weeks Minimum Minimum Salary ($) 38,000 41,000 46,000 35,000 32,000 Linking the interests and alternatives The next step is to link the various interest criteria with the alternatives available and understand the relationship between them (appendix). Each interest criteria needs to be mapped with the alternatives and then the best alternative needs to be chosen. Methodology We first developed a criteria on which the various alternatives (here jobs) will be evaluated). We also looked at the various sub-criteria within these criteria. For example the financial criteria was further divided into inputs (salary) received and money going out in terms of cost of living. This was followed by assigning weights to the various criteria and sub-criteria using the MAUT approach. We will then choose the highest scoring alternative. We have given rankings or points to the criteria which are based on the probability of satisfying Claire. This means that if the particular criterion has a high chance of satisfying Claire’s requirement it gets a high score between 7-10. If it has a very low chance of satisfying her needs it gets a low score ranging from 1-3. The ranges also help in ranking the criteria. For example, if a criterion like low crime rate for two of the alternatives has a low chance of satisfying Claire in ASD and AC, then one gets a score of 1 while the other gets 3. This further helps in identifying the finer difference between the two and reduces the chances of two criteria getting similar scores at the end of the exercise. The scores have been explained in a tabular form in the Appendix. Most of the data used is based on assumptions and credible internet sources. For weight, assumptions have been used while for creating the probability scores information on US cities has been obtained from credible internet sources. Journals on internet have been used to understand the techniques applied for the analysis. Weights have been assigned to the various criteria based on the importance they would have for Claire. Salary and cost of living together form 40% weight. This is because both these determine the net in-hand savings of Claire. What a person gets in hand at the end of the day plays an important motivating factor for him or her. Provide apt salary and appropriate financial incentives, and even non-financial praise for the employees’ motivation. (Armstrong 2004). If the cost of living is high, then no matter what one earns, he will not be able to save much for fulfilling his desires. This is followed by job profile because after financial satisfaction, comes job satisfaction. If a person is not enjoying the work, he can’t go on in a firm for a very long time. On the job travel is perceived in different ways by people. Some consider it a nuisance while others take it as an opportunity for exposure and growth. Educational opportunity is important for Claire as she wants to pursue her MBA on the job. So, it has been given a 10% weight. Entertainment opportunities and low crime rates are both given equal weights as both are in some way interdependent. To avail entertainment opportunities, especially by working women after work hours, they need to feel safe. These two have been given slightly lower weight than education as latter is needed for personal and professional growth. Once weights have been assigned to the criteria, alternatives are given probability scores as explained earlier. A product of probability scores of each alternative and weight of each criterion is calculated (Appendix – Table 2). This is the weighted average probability score of each criterion per alternative. A sum of scores of all criteria under each alternative is calculated. The highest scoring alternative has the highest satisfaction score based on the importance that has been attached to criterion. Assumptions Since Claire is working in an IT firm we can assume that her current location is in the northern California in the Silicon Valley. This implies that her immediate friends circle is in that location. This directly impacts the proximity to native place criteria probability score. Claire is not averse to travelling as she is young and likes to meet new people and visit new places. Since Claire is a purely technical expert right now, so we have assumed that she is looking for a profile which helps her improve her public relations and leadership skills. Hence, a profile that helps her to work on different platforms will be preferable. In fact, in the initial years of her career she is hoping to get the maximum exposure by working on different projects. Results Here we will briefly look at the logic behind giving a particular score to a particular alternative. Table 1 summarizes the various scores. NCSS is offering the highest salary, hence it has been scored 10 as it has the highest probability of satisfying the financial need of Claire. The others have been relatively scored with a difference factor of 2 as the average difference between nth and (n-1)th salary is 3500. This has been equated to a factor of 2 in probability score. Cost of living index shows that Washington has the highest cost and Nashville the lowest with 100 taken as the base (Kiplinger 2010). Hence, Nashville gets the highest score of 10 as compared to other. Tampa and Atlanta have a very little difference (with Atlanta being slightly better) hence they have a similar score of 9 and 8 respectively (Appendix – cost of living index). Job profile – Both ASD and AC have similar and highest scores on this criterion as they offer similar work area and are also national level firms. NCSS scores the lowest here as it is a small firm with regional reach and does not provide opportunities for great exposure. Traveling is highest in ASD and AC which is preferable by Claire (assumption). Rest of the criteria have been accordingly scored based on the probability of their need satisfaction. Information regarding educational avenues, industry type and entertainment opportunities was used to understand the city’s standing on each of the criterion (infoplease). The final results (Appendix – Table 2) show that ASD scores the highest among the five alternatives. This means that it scores high in majority of the criteria. The criteria on which it scores low are – type of industry, crime rate and proximity to native place (Appendix – Table 2). However, since these together form only 12% of the total weight, they do not impact the overall score much. The lowest score is that of GSC. EV is scoring better than GSC even though it has lower salary to offer. This is because since Claire will be stationed in Nashville, which has the lowest cost of living, her financial needs will still be satisfied. The job profile and travel exposure also compensate for the loss of salary. If we look at the closest contender, AC, we can see that it loses heavily on cost of living criterion as the position is based out of Washington which has one of the highest living costs. In fact, for 55% of the criteria weights, it scores better than ASD. If Claire could ignore the cost of living completely, AC performs better. Conclusions Looking at the weighted average score we can conclusively say that ASD offers her the best opportunity. Even if we were to ignore the last five criteria which have a total weight of 33%, the rankings would not change much. That means, if we give equal weights to first 4 criteria (25% each), even then ASD is the top scorer. We even tried to give higher weight to job profile (Appendix – Table 3). ASD still scored the best (Appendix – Table 4). References Armstrong, M 2004, How to Be an Even Better Manager: A Complete A-Z of Proven Techniques, Kogan Page, London. infoplease.com, Profiles of 50 largest cities of US, viewed on May 3, 2011 http://www.infoplease.com/ipa/A0108481.html Kiplinger.com 2010, How does your city stack up?, viewed on May 3, 2011 http://www.kiplinger.com/tools/bestcities_sort/ Linkov, I., Satterstrom, FK., Steeves, J., Ferguson, E and Pleus, RC 2006, Multi-criteria decision analysis and environmental risk assessment for nanomaterials, Journal of Nanoparticle Research, Vol. 9, pp 543 – 554, viewed on May 3, 2011 http://www.nanoarchive.org/774/1/opr000VH.pdf nscu.edu 2008-2011, Multi-criteria Decision Analysis, viewed on May 2, 2011 http://www.ncsu.edu/nrli/decision-making/MCDA.php Appendix Table 1 Criteria (0-100) Alternatives – points scored   weights ASD AC NCSS GSC EV Salary 20 6 8 10 4 2 Cost of living 20 9 3 5 8 10 Job profile 15 10 10 5 6 8 Travel 12 10 10 5 7 8 Educational opp. 10 10 8 4 5 3 Entertainment opp. 8 9 8 7 5 6 Crime rate 8 1 3 9 7 6 Proximity to native place 5 7 6 9 4 8 Type of industry 2 8 4 5 4 8 Total score 100           Table 2 Criteria Weighted Average Score of Alternatives   ASD AC NCSS GSC EV Salary 120 160 200 80 40 Cost of living 180 60 100 160 200 Job profile 150 150 75 90 120 Travel 120 120 60 84 96 Educational opp. 100 80 40 50 30 Entertainment opp. 72 64 56 40 48 Crime rate 8 24 72 56 48 Proximity to native place 35 30 45 20 40 Type of industry 16 8 10 8 16 Total score 801 696 658 588 638 Probability of satisfying Claire low 1 - 3 medium 4 - 6 high 7 - 10 Cost of living Index City Cost of living index Atlanta 94 Washington 138 Chicago 100 Tampa 99 Nahville 88 Source: Kiplinger.com Table 3 Criteria (0-100)   weights Salary 10 Cost of living 10 Job profile 40 Travel 15 Educational opp. 10 Entertainment opp. 5 Crime rate-low 5 Proximity to native place 3 Type of industry 2 Total score 100 Table 4 Weighted Average Score of Alternatives ASD AC NCSS GSC EV 60 80 100 40 20 90 30 50 80 100 400 400 200 240 320 150 150 75 105 120 100 80 40 50 30 45 40 35 25 30 5 15 45 35 30 21 18 27 12 24 16 8 10 8 16 887 821 582 595 690 Linking goals and interests Source: ncsu.edu Read More