Demad Forecastig ad the Determiatio of Employee Requiremets i Nigeria Public Orgaisatios Wurim, Be Pam Ph.D. (Assistat Chief Accoutat) Natioal Directorate of Employmet Plateau State, Nigeria. Abstract The right quality ad quatity of huma capital (employed) is a measure of a orgaisatio s stregth ad success. Where this optimum staff mi is ot maitaied, imbalaces i surpluses or deficits of employees may arise leadig to umaageable icreases i persoel costs, iefficiecy, abseteeism, turover ad productivity problems. The determiatio of this optimum staff mi is subject to the applicatio of certai methods. The pricipal objective of this paper is to assess the potecy of demad forecastig i the determiatio of employee requiremets. A hypothesis i lie with this objective is draw ad tested based o the data geerated through a questioaire. The survey ivestigatio method was used i collectig the primary data for the study. The sample cosisted of 349 top, middle ad low levels maagemet staff of five public sector orgaisatios i Nigeria. The result shows that demad forecastig is ot a potet tool i the estimatio of employee requiremets i Nigeria public orgaisatios. Based o the aforemetioed, the paper cocluded that although widely varyig approaches to forecastig the employees eeds of a orgaizatio eist, demad forecastig might ot predict with certaity the eact employee eeds of a orgaizatio. The paper recommeds that huma capital plaers should adopt a strategy of combiig both qualitative ad subjective methods i forecastig the employee eeds of a orgaizatio; Chief eecutive officers of orgaisatios should make it madatory for huma capital plaers to employ scietific methods i forecastig; ad the adoptio of a combiatio of the techical skills of eperts from various fields i the forecastig efforts. Itroductio The further ito the future it takes to pla huma capital, the greater will be the degree of certaity of the umber ad types of employees available for employmet both withi ad outside the orgaizatio. Effective workforce plaig for specific eterprises ivolves determiig which actios are eeded to achieve busiess objectives, idetifyig the types ad quatities of skills that are ecessary to accomplish those actios, determiig how those skills may vary from the skills that are curretly available, ad developig strategies for closig the gaps betwee today s workforce ad the workforce eeded to accomplish the busiess objectives (Ward, 1996:1). This brigs to the fore the import of demad forecastig. Armstrog (2003:371) defies demad forecastig as the process of estimatig the future umbers of people required ad the likely skills ad competeces they will eed. America Research Istitute for Policy Developmet 1 www.aripd.org/ppar
The ideal basis of the forecast is a aual budget ad loger term busiess pla, traslated ito activity levels for each fuctio ad departmet or decisios o dowsizig. The iformatio gathered from eteral evirometal scaig ad assessmet of iteral stregths ad weakesses is used to predict or forecast huma capital supply ad demad i the light of orgaizatioal objectives ad strategies. Forecastig uses iformatio from the past ad preset to idetify epected future coditios. Projectios for the future are, of course, subject to error. Chages i the coditios o which the projectios are based might eve completely ivalidate them, which is the chace forecasters take. Usually, though, eperieced people are able to forecast with eough accuracy to beefit orgaizatioal log-rage plaig (Gerhart et al, 2000:803). But what is the cost-beefit trade-off of the rigorous activities ivolved i demad forecastig? Why should orgaisatios cocer themselves with demad forecastig? There are several good reasos to coduct demad forecastig. It ca help: (i) quatify the jobs ecessary for producig a give umber of goods, or offerig a give amout of services, (ii) determie what staff-mi is desirable i the future, (iii) assess appropriate staffig levels i differet parts of the orgaisatio so as to avoid uecessary costs, (iv) prevet shortage of people where ad whe they are eeded most, ad (v) moitor compliace with legal requiremets with regard to reservatio of jobs (Aswathappa, 2005:74). I spite of the umerous fuctios ad advatages of demad forecastig, most orgaisatios are yet to take advatage of this scietific way of estimatig employee eeds. The problem The absece of effective ad scietific demad forecastig methods i most Nigeria public orgaizatios seems to be the mai bae of shortages ad ecesses i huma resources resultig to umaageable ad epesive imbalaces i the umber ad quality of employees eeded to optimally achieve orgaizatioal objectives ad plas. As a result, public orgaizatios scorecard has remaied ecessive costs associated with ecessive turover, abseteeism, stress, low morale, shift work, healthcare services, low productivity ad iteral market iefficiecy. Several demad forecastig techiques curretly eist. They vary from fairly simple qualitative methods based o idividual or group judgemets to highly complicated methods ivolvig sophisticated statistical computerizatio. What is ot yet very clear is whether or ot these forecastig methods are used ad further still which particular techiques are used ad what is the result of such efforts? Are there beefits derivable from such eercises? Objectives of the study The pricipal objective of the paper therefore, is to assess the potecy of demad forecastig i the determiatio of employee requiremets. Specifically the paper seeks to assess the etet to which demad forecastig techiques are used i the determiatio of employee requiremets ad to fid out the degree to which demad forecastig leads to the determiatio of employee requiremets. Methodology The research desig used for the study is the survey research method. America Research Istitute for Policy Developmet 2 www.aripd.org/ppar
Primary data for the study were sourced from five public sector orgaisatios amely: Natioal Directorate of Employmet (NDE), Power Holdig Compay of Nigeria (PHCN), Plateau State Water Board (PSWB), Federal Miistry of Fiace (FMF) ad Nigeria Natioal Petroleum Corporatio (NNPC). The populatio of the study icludes all the 10,127 top, middle ad lower maagemet staff of the five orgaisatios. Give that the populatio of the study is fiite, the Taro Yamae (1964) statistical formula for selectig a sample was applied. The formula is give as: = N 1 + N (e) 2 Where: = Sample size; N = Populatio; e = level of sigificace (or limit of tolerace error) i this case 0.05; 1 = Costat value This gives a sample size of 385. For its data collectio, a suitable Likert Scale (5 Poits) questioaire was desiged ad developed. The data so collected was the aalyzed usig the chi-square ( 2 ) test statistic. Theoretical cosideratios Methods for forecastig huma resources rage from a maager s best guess to a rigorous ad comple computer simulatio. Simple assumptios may be sufficiet i certai istaces, but comple models may be ecessary for others. However, it has bee observed that despite the availability of sophisticated mathematical models ad techiques, forecastig is still a combiatio of quatitative methods ad subjective judgemet. I theory or i practice, the most commoly used techiques i forecastig the demad for huma resources iclude the followig: Maagemet / Eecutive Judgemet The simplest approach to mapower forecastig is to prepare estimates of future eeds based o the idividual opiios of departmetal or lie maagers. The techique may ivolve a bottomup approach by askig juior maagers to sit dow, thik about their future workloads ad decide how may people they eed. Alteratively, a top dowward approach ca be used, i which compay ad departmetal forecasts are prepared by top maagemet, possibly based o the advice/iformatio available from the persoel, ad orgaisatio ad methods departmets. The suggested forecasts are circulated dowwards for discussios ad therefore reviewed ad agreed with departmetal maagers (Se, 2007:135). Aswathappa (2005:74) idicates that i bottom-up ad top-dow approaches, departmetal heads are provided with broad guidelies. Armed with such guidelies, ad i cosultatio with the huma resource sectio i the huma resource maagemet departmet, departmetal maagers ca prepare forecasts for their respective departmets. Simultaeously, top huma resource maagers prepare compay forecasts. A committee comprisig departmetal maagers ad huma resource maagers will review the two sets of forecasts; arrive at a uaimity, which is the preseted to top maagers for their approval. Needless to say, this techique is used i smaller orgaisatios or i those compaies where sufficiet data-base is ot available. America Research Istitute for Policy Developmet 3 www.aripd.org/ppar
Work Study Techiques Work Study is as old as idustry itself. I the opiio of Currie (1972:22), work study is the study of huma work i the deepest sese ad digity of the word, ad ot merely i the special ad more restricted meaig used i the physical scieces. Eve today it is ot limited to the shop floor, or eve to maufacturig idustry. I oe or aother form it ca be used i ay situatio wherei huma work is performed. I the book Itroductio to work study (1983:29), the Iteratioal Labour Orgaisatio (ILO) defies work study as a geeric term for such techiques, particularly, method study ad work measuremet, as are used i the eamiatio of huma work i all its cotets ad which leads systematically to the ivestigatio of all the factors that affect the efficiecy ad ecoomy of the situatio beig reviewed i order to effect improvemet. Work study, therefore, has a direct relatioship with productivity. It is most frequetly used to icrease the amout produced from a give quatity of resources with little or o further capital ivestmet. Work study techique ca be used whe it is possible to apply work measuremet to calculate how log operatios should take ad the umber of people required. Work study techiques for direct workers ca be combied with ratio tred aalysis to calculate the umber of idirect workers eeded (Armstrog, 2003). The startig poit i a maufacturig compay is the productio budget, prepared i terms of volumes of saleable products of the compay as a whole, or volumes of output for idividual departmets. The budgets of productive hours are the compiled usig stadard hours for direct labour. The stadard hours per uit of output are the multiplied by the plaed volume of uits to give the total umber of plaed hours for the period. This is the divided by the umber of actual workig hours for a idividual operator to show the umber of operators required. Allowace will have to be made for abseteeism ad idle time. As already stated, work study techiques for direct workers ca be combied with ratio-tred aalysis to forecast for idirect workers, establishig the ratios betwee the two categories. The same logic ca be eteded to ay other category of employees. Ratio Tred Aalysis This is the quickest forecastig techique. The techique ivolves studyig past ratios, like the umber of workers ivolved i direct productio (direct hours) ad sales i a orgaisatio ad forecastig future ratios, makig some allowace for chages i the orgaisatio or its methods. Table1 shows how a aalysis of actual ad forecast ratios, betwee the umber of routie proposals to be processed by a isurace compay uderwritig departmet ad the umber of uderwriters employed could be used to forecast future requiremet (Armstrog, 1988:209). America Research Istitute for Policy Developmet 4 www.aripd.org/ppar
Table 1: Demad Forecast Ispectors Year No. of Employees Ratio Productio Ispectors : Ispectors Productio Actual - 3 1500 150 1 : 10-2 1800 180 1 : 10 Last year 2000 180 1 : 11 Net year 2200* 200** 1 : 11 Forecast + 2 2500* 210** 1 : 12 + 3 2750 230** 1 : 12 * Calculated by referece to forecast activity levels **Calculated by applyig forecast ratio to forecast activity levels. Source: Armstrog (1988:209), A Had book of Persoel Maagemet, New Delhi: Koga Page. Delphi Techique Epert Forecast Named after the aciet Greek Oracle at the city of Delphi, the Delphi techique is a subjective method used to predict future persoel eeds of a orgaisatio by itegratig the idepedet opiios of eperts. Schuler (1983:48) eplais that the techique ivolves a large umber of eperts who take turs at presetig their forecasts ad assumptios. A itermediary passes each epert s forecast ad assumptios to the others, who the make revisios i their forecasts. This process cotiues util a fial forecast emerges. The fial forecast may represet specific projectios or a rage of projectios depedig o the positio of the eperts. Origially developed as a method to facilitate group decisio makig, it has also bee used i workforce forecastig. Eperts are chose o the basis of their kowledge of iteral factors that might affect a busiess (e.g. projected retiremets), their kowledge of the geeral busiess plas of the orgaisatio, or their kowledge of eteral factors that might affect demad for the firm s product or service ad hece its iteral demad for labour. Eperts may rage from first-lie supervisors to top-level maagers. Sometimes eperts iteral to the firm are used, but if the required epertise is ot available iterally, the oe or more outside eperts may be brought i to cotribute their opiios. To estimate the level of future demad for labour, a orgaisatio might select as eperts, for eample, maagers from corporate plaig, huma resources, marketig, productio ad sales departmet. Face-to-face group discussio is avoided sice differeces i job status amog group members may lead some idividuals to avoid criticizig others ad to compromise o their good ideas. To avoid these problems, a itermediary is used. The itermediary s job is to pool, summarize, ad the feed back to the eperts the iformatio geerated idepedetly by all the other eperts durig the first roud of forecastig. The cycle is the repeated, so that the eperts are give the opportuity to revise their forecasts ad the reasos behid their revised forecasts. Successive rouds usually lead to a covergece of epert opiio withi three to five rouds. America Research Istitute for Policy Developmet 5 www.aripd.org/ppar
Process Aalysis The widespread iterest i reegieerig activities has produced a hypothetical approach to workforce demad forecastig based o process aalysis. Some articles o the topic suggest busiesses should develop a detailed aalysis of process compoets of work activities ad that predictive ratios could the be desiged to forecast the associated workload for each uit level of process output (Ward, 1996:2). Data collectio ad aalysis phase of a process aalysis approach accordig to Ward is similar to the traditioal historical ratio approach. Process steps are substituted for work activity steps, so that the aalysis is doe at a orgaizatioal level rather tha a work group. Ward goes further to observe that the bech mark aalysis showed some reegieered compaies have developed the traditioal historical ratio aalysis described i sectio 3, ad have the adjusted those ratios for their assumed productivity gais to be achieved via process improvemets. I theory, the positive ad egative aspects of this process would mirror those described for historical ratios. The cocept seems fudametally soud, but the bechmarkig efforts do ot seem to fid a sigle case where this cocept has bee traslated ito a operatioal model. I order for the process to work as hypothesized, the work load aalysis should be icorporated withi a reegieerig study. It might fairly be questioed whether the etesive level of aalysis should become part of a aual plaig cycle or should oly be doe i cojuctio with a major reegieerig effort. Flow Model Flow models are very frequetly associated with forecastig persoel eeds. The simplest oe is called the Markov model. I this techique, Rothwell (1988:175) outlies the activities to be carried out by the forecasters as follows: 1. Determie the time that should be covered. Shorter legths of time are geerally more accurate tha loger oes. However, the time horizo depeds o the legth of the huma resource pla which, i tur, is determied by the strategic pla of the orgaisatio. 2. Establish categories, also called states to which employees ca be assiged. These categories must ot overlap ad must take ito accout every possible category to which a idividual ca be assiged. The umber of states ca either be too large or too small. 3. Cout aual movemets (also called flows ) amog states for several time periods. These states are defied as absorbig (gais or losses to the compay) or o-absorbig (chage i positio levels or employmet status). Losses iclude death or disability, abseces, resigatios ad retiremets. Gais iclude hirig, retiremets, trasfer ad movemet by positio level. 4. Estimate the probability of trasitios from oe state to aother based o past treds. Demad is a fuctio of replacig those who make a trasitio. There are alteratives to the simple Markov model. Oe, called Semi-Markov, takes ito accout ot just the category but also the teure of idividuals i each category. After all, likelihood of movemet icreases with teure. Aother method is called the Vacacy model which predicts probabilities of movemet ad umber of vacacies. While the Semi-Markov model helps estimate movemet amog those whose situatios ad teure are similar, the vacacy model produces the best results for a orgaisatio. Markov aalysis is advatageous because it makes sese to decisio makers. They ca easily uderstad its uderlyig assumptios. They are therefore, likely to accept results. America Research Istitute for Policy Developmet 6 www.aripd.org/ppar
The disadvatages iclude: (i) heavy reliace o past orieted data, which may ot be accurate i periods of turbulet chage, ad (ii) accuracy i forecasts about idividuals is sacrificed to achieve accuracy across groups. Statistical techiques The most commoly used statistical approaches to huma capital forecastig rage from methods of simple scatter diagram through regressio or correlatio aalysis, to ecoomic models. All of these methods deped, for their validity, o the assumptio that developmets i the future will ehibit some cotiuity with the past. Simple etrapolatio assumes that past treds will cotiue, regressio aalysis assumes that particular relatioships will hold firm ad ecoometric models assume that the basic iter-relatioship betwee a whole rage of variables will be carried o ito the future. Regressio ad Correlatio This method seeks to provide a measure of the etet to which movemets i the values of two or more variables as for eample, labour iput ad sales are related (or correlated) with each other. The aim is to predict chages i oe variable by referece to chages i the other or others, where the future value of these other (or eplaatory) variables are already postulated. Regressio therefore, is a techique used to describe a relatioship betwee two or more variables, i mathematical terms. Fracis (2004:173) asserts that regressio is cocered with obtaiig a mathematical equatio which describes the relatioship betwee two variables. The equatio ca be used therefore for compariso or estimatio purposes. The process of obtaiig a liear regressio relatioship for a give set of (bivariate) data is ofte referred to as fittig a regressio lie. Fracis (2004:174) asserts that there are three methods commoly used to fit a regressio lie to a give set of bivariate data. (a) Ispectio This method is the simplest ad cosists of plottig a scatter diagram for the relevat data ad the drawig i the lie that most suitably fits the data. The mai disadvatage of this method is that differet people would probably draw differet lies usig the same data. It sometimes helps to plot the mea poit of the data (that is, the mea of the s ad y s respectively) ad esure the regressio lie passes through this. I Figure 1, possible relatioships are eamied to see whether they might prove useful for forecastig. Fracis goes further to eplai that for ay set of bivariate data, there are two regressio lies which ca be obtaied viz: i) The y o regressio lie that regressio lie which is used for estimatig y give a value of ad ii) the o y regressio lie that regressio lie which is used for estimatig give a value of y. The two regressio lies are quite distict. (b) Semi averages The method of semi-averages accordig to Fracis is for obtaiig the y o regressio lie usig the followig steps: STEP 1 Sort the (bivariate) data ito size order by -value. STEP 2 Split the data up ito equal groups, a lower half ad a upper half (if there is a odd umber of items, igore the cetral oe). STEP 3 Calculate the mea poit for each group. STEP 4 Plot the above mea poit o a graph withi suitably scaled. This method is cosidered superior to the method of ispectio. However, a major drawback of the semi average techique for obtaiig a regressio lie is the fact that it relies o oly two poits, both meas of the two respective data groups. If there are etreme values preset either or both of the meas are easily distorted, thus so is the regressio lie. America Research Istitute for Policy Developmet 7 www.aripd.org/ppar
y 200 - Employee size - 180 - - 160 - - 140 - - 120 - - Mid Poit 5 52 Regressio lie (by ispectio) 100 - - 80-18 20 22 24 26 28 Sales ( 000 N) Figure 2.8- Regressio lie relatioships betwee sales ad employmet size. Source: Fracis, A (2004:176), Busiess Mathematics ad Statistics, Lodo Bedford Row: Thomso Learig. As already metioed, a importat use of regressio lies is for estimatig the value of oe variable give a value of the other. (c) Covetioal Statistical Techique Simple Liear Regressio ad Multiple Regressio (Least Square Method). The least squares regressio method ca be used to forecast direct labour employmet eeds of a orgaisatio. I simple liear regressio (least square method), a forecast of future huma capital demad is based o past relatioship betwee employmet level ad a variable related to employmet. For eample, the umber of beeficiaries supervised () determies the umber of persos eeded for employmet (i.e. iteral demad), y. The least square method is cosidered to be the stadard method of obtaiig a regressio lie. The derivatio of the techique has mathematical base which ivolves all values ad is thus cosidered to be superior. (Fracis, 2004:173-174). Bereso et al (1985:587) assert that the computatio is represeted by two simultaeously solved equatios give as: America Research Istitute for Policy Developmet 8 www.aripd.org/ppar
Xi Yi - ( Xi) ( Yi) b i = i 1 i =1 i = 1 2 X i - ( X i ) 2 i=1 i = 1....................................... 1 b o = Y - bi X................................................ 2 Where: b o = Coefficiet of y itercept, b i = the slope used i predictig Y, X= umber of beeficiaries selected, traied, placed ad moitored/ Supervised ( i.e. the work load). Y = Mapower demad (umber of persos eeded for employmet), y i = Actual value of y for observatio, i = Actual value of, Y = yi; ad X = i i = 1 i = 1 Eamiig the above equatios, it is observed that there are five quatities that must be calculated i order to determie bo ad bi. These are, the sample size; Xi, the sum of the X values; i = 1 2 yi, sum of the Y values, X i, the sum of the square of X values, i = 1 i = 1 ad Xi Yi, the sum of the cross product of X ad Y. i = 1 Where there are more tha oe idepedet variables to be used for eample, umber of beeficiaries, productivity, turover, abseteeism, etc, this method becomes ieffective ad gives room to MULIPLE REGRESSION MODEL Oe which could utilize several eplaatory variables ( i, y i = bo + b 1, i + b 2 2 i..3 i = 1 i = 1 i = 1 1i y i = bo 1 i + b 1 2 i + b 2 1 i 2 i...4 i = 1 i = 1 i = 1 i = 1 2 i y i = bo 2 i + b 1 1 i 2 i 2 i 5 i = 1 i = 1 i = 1 i = 1 2,., ) to predict a quatitative depedet variable (y). If the least squares method is utilized to compute the sample regressio coefficiet (bo, b 1 ad b 2 ) we will have the followig three ormal equatios (Bereso, et al, 1985:650): America Research Istitute for Policy Developmet 9 www.aripd.org/ppar
Stadard Error of the Estimate Although the least squares method results i the lie that fits the data with the miimum amout of variatio, the regressio equatio is ot a perfect predictor, especially whe samples are take from a populatio, uless all the observed data poits fall withi the predicted regressio lie. Thus, the regressio lie serves oly as a approimate predictor of a y curve, for a give value of. Therefore, the measure, of variability aroud the lie of regressio is called the stadard error of the estimate ad is give by the symbol Sy ad defied as: ^ ( Y i - Y i ) 2 Sy = i = 1 2 Where: Yi = Actual value of Y for a give Xi, ^ Y = Predicted value of Y for a give Xi. I the fial aalysis, the stadard error of the estimate Sy ca thus be obtaied usig the followig computatioal formula: Y 2 i - bo Y i - b 1 X i Y i Sy i = 1 i = 1 i = 1 2 Computer Simulatios ad Modelig The most commo packages available for use whe developig regressio models for busiess applicatio are the statistical aalysis system (SAS) (Referece11 ad 18), the statistical package for the social scieces (SPSS) (Referece 20), ad Miitab (refereces 12 ad 17) (Bereso, et al, 1985:711). Usig ay of the three packages, the values of the three sample regressio coefficiets i equatios 3, 4 ad 5 may be obtaied. That is to say that the computer ca be used to effectively forecast iteral mapower demad eve whe there are may depedet variables. To improve huma capital decisio makig, a orgaisatio must also be cocered with its eteral demad coditios especially withi the idustry ad i the ecoomy as a whole. This ca also be predicted by the use of the Delphi ad covetioal statistical techiques, ad estimatig the eeds of other orgaisatios i the same idustry, ad the mass media. Time Series Aalysis It is ecessary to aalyse past treds i huma capital activities ad sift the sigificat poits while preparig a forecast. This requires a uderstadig of the cocept of the time series. Fracis (2004:214) asserts that a time series is the ame give to the values of some statistical variables measured over a uiform set of time poits. A time series therefore, is a ame give to umerous data that is described over a uiform set of time poits- data classified chroologically - for eample, mothly abseteeism rates. America Research Istitute for Policy Developmet 10 www.aripd.org/ppar
The recordig of such casual relatioships betwee differet variables for eample, is there a positive correlatio betwee absece ad age or legth of service, or with predictio of future? Depedig o the ature, compleity ad etet of the aalysis required, there are various types of models that ca be used to describe time series data. They iclude two models called simple additive ad multiplicative models. The compoets that go to make up each value of a time series are described i the followig defiitios: The time series additive model y = t + s + r Where: y = a give time series value, t = the tred compoet, s = the seasoal compoet ad r = the residual compoet. The time series multiplicative model y = t s R Where: y = a give time series value, t = the tred compoet s is the seasoal compoet, R is the residual compoet (Fracis, 2004:215). Tred (t) is the uderlyig, log-term tedecy of the data. Seasoal variatios are short-term cyclic fluctuatios i the data about the tred which take their ame from stadard busiess quarters of the year. Seaso, however, ca have may differet meaigs, for eample, daily, mothly or quarterly seasos. Residual variatios iclude other factors ot icluded i tred or seasoal factors. Time series therefore, is a alterative method that ca be used to aalyse employmet levels over a time ad used as a basis for forecastig huma capital levels. This meas projectig the past ito the future ad the allowig for ay foresee chages resultig i a chage i use of capital ad machiery, chage i eteral ecoomic climate, iteral problems withi the orgaisatio ad emergece of competitors. Cotrary to the three factors metioed by Fracis, Lych (1982:72) metios four factors or movemets to be revealed by a aalysis of a time series as: (a) a log-term (basic) tred; (b) seasoal fluctuatios; (c) catastrophic (abormal) movemet; ad (d) residual (chace movemet). Results The questioaire was distributed to 385 top, middle ad lower levels staff of the five selected orgaisatios but oly 349 completed ad retured the questioaire yieldig a overall respose rate of 92%. We set out to provide the ecessary lead for empirical eamiatio of the degree to which demad forecastig leads to the determiatio of employee requiremets i orgaisatios. For this reaso, hypothesis oe was formulated thus: H 1 : Demad forecastig positively affect the determiatio of employee requiremets. Table 1 shows that 57.54% of the respodets agreed that scietific calculatio of the quality ad quatity of staff before recruitmet leads to proper estimatios while 42.46% respoded to the cotrary; 62% of the respodets agreed that job aalysis is made before a fit perso is employed ad that it leads to accurate estimatio of employee eeds but 38% disagreed. America Research Istitute for Policy Developmet 11 www.aripd.org/ppar
Also, 66% of the respodets affirmed that the total umber ad quality of workers i their orgaisatios is estimated based o their orgaisatio s policies ad objectives which leads to accurate mapower eeds determiatio, while 34% disagreed; 64% affirmed that the total umber ad quality of workers is estimated based o their orgaisatio s workload/sales or productio targets Table 1: Opiio of Respodets o the Impact of Demad Forecastig o the Estimatio of Employee Requiremets S/o Descriptio Respose Frequecy % 1 Scietific calculatio ad Agreemet category 145 57.54% evaluatio of staff before Disagreemet category 107 42.46 recruitmet leads to proper estimatio 2 A clear aalysis of the eeds, Agreemet category 166 62.41 eperiece ad epectatios of a particular job before recruitmet leads to proper estimatio Disagreemet category 100 37.59 3 Number ad quality of workers is estimated based o my orgaizatio s policies ad objectives 4 Total umber ad quality of workers is estimated based o my orgaizatio s workload/ sales or productio targets a 5 Subvetio from Govermet ad /or iterally geerated icome determies the right umber ad quality of workers employed Agreemet category 177 Disagreemet category 91 Agreemet category 185 Disagreemet category 106 Agreemet category 204 Disagreemet category 95 Source: Field Survey, 2012 66.05 33.95 63.57 36.43 68.23 31.77 Which leads to the accurate estimatio of persoel eeds while 36% disagreed. Lastly, 68% of the respodets agreed that subvetio from govermet ad iterally geerated reveue determies the right umber ad quality of workers employed but 34% of the respodets disagreed. The Chi-square ( 2 ) test statistic was used to test the hypothesis (H 1 ). The theoretical frequecy for each cell i Table 1 was computed usig the formula: R c / as show i Table 2. The X 2 t 4 uder 0.05 = 9.49 while the calculated X 2 c = 7.29. America Research Istitute for Policy Developmet 12 www.aripd.org/ppar
Table 2 - Chi-square (X 2 ) Table for Testig Hypothesis H 1 Cell f o f t f o f t (f o f t ) 2 f t 1 145 161 16 1.60 2 107 91 16 2.40 3 166 170 4 0.09 4 100 96 4 0.17 5 177 171 6 0.20 6 91 97 6 0.37 7 185 185 0 0 8 106 106 0 0 9 204 191 13 0.89 10 95 108 13 1.57 Total 1376 1376 0 7.29 Source: Field survey, 2012 d. f. = (r 1)(c 1) = (5 1)(2 1) = (4)(1) = 4 X 2 t 4 uder 0.05 = 9.49. But calculated Chi-square (X 2 c) = 7.29 Statistical Decisio Level of sigificace = 0.05, Sample size () = 349; Test statistic = 2. Decisio rule: Accept H o if calculated value (X 2 c) Chi-square (X 2 t), if otherwise, reject the H o ad accept H 1. Sice the calculated Chi-square ( 2 c) value falls withi the acceptace regio (i.e. 2 c = 7.29 < 2 t = 9.49), we accepted the ull hypothesis ad rejected the alterate ad we thus cocluded that demad forecastig is ot a potet tool i the estimatio of employee requiremets i Nigeria public orgaisatios. Discussio ad Implicatios of Fidigs Result of the test of the hypothesis idicate that demad forecastig does ot sigificatly affect the estimatio of employee eeds i Nigeria public orgaisatios (α = 0.05, 2 c = 7.29 < 2 t = 9.49), we thus coclude that the two variables are ot associated: The result is cotrary to Kare Legge theory which states that demad forecastig is a very potet tool i huma capital forecastig that yields accurate or precise estimatio of employee requiremet i terms of umber ad quality (Legge 1989: 36). The result is also cotrary to the fidigs of a survey of 115 large orgaisatios coducted joitly by the America Maagemet Associatio ad Creasp, McCormick ad Paget which idicated that some firms particularly, i stable busiesses like utilities or isurace, simply perfected requiremets o the basis of past growth or sales forecasts or compay budgets. The result is precisio i the estimatio of employee requiremets (Se, 1987:20). ] The weakess i the relatioship betwee huma capital forecastig ad employee requiremets as witessed i the Nigeria Public Sector Orgaisatios could be as a result of the lack of proper kowledge ad epertise. Bartholomew (1976: 67) asserts that huma capital forecastig requires the combied techical skills of statisticias, ecoomists ad behavioral scietist, maagers ad plaers. It is also possible that the iability of public orgaisatios i Nigeria to forecast with precisio its employee requiremets could be as a result of forecastig i isolatio from other sectors or departmets of the orgaisatio. America Research Istitute for Policy Developmet 13 www.aripd.org/ppar
Bramham (1982: 22 23) strogly belief that huma capital forecastig caot be doe i isolatio from forecastig i other domais. To him, havig established a fud of kowledge o all aspects of the firm s busiess, it is possible to move at attempts to idicate i which directio huma capital is goig ad the directio it should take to meet orgaisatioal objectives. Also, the fidig is ulike results from a research coducted o some selected Idia Public Sector orgaisatios where huma capital forecastig gave precise estimatio of employee eed requiremets (Se, 2005: 129 163). However, Se goes o to coclude that forecastig could be right or wrog. Coclusio Widely varyig approaches to forecastig the employee eeds of a orgaizatio eist ad effective forecastig requires a combiatio of quatitative methods ad subjective judgemet. Where demad forecastig is coscietiously pursued, imbalaces i surpluses or deficits of employees ca be detected ad hadled before they become umaageable leadig to decrease i persoel costs. Also, demad forecastig is a iterdiscipliary activity which requires the combied techical skills of statisticias, ecoomists, behavioural scietists, together with the practical kowledge of huma capital maagers. Lastly, forecastig caot tell what will happe, but oly what might happe uder give coditios ad circumstaces. Recommedatio I view of the fidigs ad coclusio above, the followig recommedatios are hereby submitted: 1. Huma capital maagers should adopt a strategy of combiig both quatitative methods ad subjective judgemet i forecastig the employee eeds of orgaizatio. 2. Chief eecutive officers of public orgaisatios should make it madatory for huma capital plaers to employ scietific methods i forecastig. This is with a view to reducig persoel cost, accurate estimatio of employee requiremets ad the achievemet of orgaisatioal effectiveess ad employee productivity. 3. Sice forecastig is a iterdiscipliary activity, there should be a combiatio of the techical skills of statisticias, ecoomists, behavioural scietists ad huma capital maagers i plaig the huma capital i public orgaisatios. America Research Istitute for Policy Developmet 14 www.aripd.org/ppar
Refereces Armstrog, M (1988), A Hadbook of Persoel Maagemet Practice, Sterlig: Koga Page. Armstrog, M. (2003), A Hadbook of Huma Resource Maagemet Practice, Sterlig: Koga Page. Aswathappa, K. (2005), Huma Resource & Persoel Maagemet, Kuala Lumpur: Tata McGraw-Hill. Bereso, M. L. ad Levie, D. M. (1989), Basic Busiess Statistics: Cocepts ad Applicatios, New Jersey: Pretice-Hall. Currie R. M. (1972), Work Study, Revised by Joseph. E. Raraday, British Istitute of Maagemet, Sterlig: Pitma. Fracis, A. (2004), Busiess Mathematics ad Statistics. Bedford Row, Bedford Row: Thomso Learig. Gerhart, B. (2000), Measurig Error i Research o Huma Resources, Persoel Psychology Joural. Lych, J.G. (1982), Makig Mapower Effective, Sterlig: Pa Origial Macmilla. Rothwell, S. (1988), Strategic Huma Resource Plaig ad Maagemet, New Jersey: Pretice Hall. Schuler, R. S. (1983), Effective Persoel Maagemet, St. Paul: West Publishig Compay. Se, A. K. (2007), Huma Resource Developmet, Plaig ad Deploymet (Public Sector Perspective), Calcuta: Iteratioal Istitute of Maagemet Scieces. Ward, D. (1996), Huma Resource Plaig, AllBusiess D & B Compay. Available at: http:/www:allbusiess.com/humaresoru ces/568478-1.htm America Research Istitute for Policy Developmet 15 www.aripd.org/ppar