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Io3116021612
Io3116021612
Io3116021612
Io3116021612
Io3116021612
Io3116021612
Io3116021612
Io3116021612
Io3116021612
Io3116021612
Io3116021612
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  • 1. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-1612 Investigation into the Effects of Education, Training And Business Location on Product Quality *C.M.M. Ondieki, Bisanda, and W.O.Ogola The Case Study of Arc Welding in Small Scale Metalworking Enterprises in KenyaAbstract The quality of products from the micro work experienceand membership to businessand small enterprise sector is affected by both support and business’s attributes - its age, location,the entrepreneur’s and enterprise’s attributes. ownership structure, and formal status and businessThis paper presents and discusses findings of a activity. All these attributes determine thestudy that was designed to investigate performance and productivity of the enterpriseexperimentally the relationship between the (Kimuyu, 2001).quality of arc welding in the Small ScaleMetalwork sub-sector and the artisan’s This paper discusses the findings of a studyeducation and training levels and business that was designed to investigate experimentally thelocation. Four pairs of groups with a total of 36 relationships between the quality of arc welding inwith secondary education and 36 with primary the Small Scale Metalwork sub-sector and theeducation consisting of formally and informally artisan’s education level and mode of training,andtrained artisans from urban and rural areas business location. The understanding and validationparticipated in the evaluation. A mild steel of these relationships is important for the effectiveproduct was fabricated by each participating marketing of the MSE products.artisan, assessed and scores awarded based onthe quality of arc welding. The data collected was While it is accepted that higher educationanalyzed using the Statistical Analysis System and training is necessary for faster individual(SAS) and excel spreadsheet. The analysis of development, the same may not necessarily be truevariance (ANOVA) was used to show any for product quality. There is, therefore, a need tovariation in the quality of arc welding; find out how the artisan’s education level, mode ofcomparisons of means using the Least Significant training, business location and other attributesDifference (LSD) at the alpha level of 5% were influence the quality of products.Most of the studiesdone to determine which pairs of artisans on MSE activities have been carried out mainly inaffected quality significantly. The study found urban areas using either qualitative or surveyout that informally trained artisans with methods. The researchers obtained their datasecondary education working in urban areas through the use of one or more of the followingexhibited the highest quality of arc welding. The instruments: questionnaires, desk reviews,informally trained artisans with primary observations, interviews, focus group discussions,education working in rural areas exhibited the and content analysis. This study was mainlylowest quality of arc welding. Generally the experimental (with a bit of qualitative usingproduct quality from artisans with secondary observation as far as the use of welding equipmenteducation was higher than that from artisans and welding techniques are concerned to find outwith primary education. Formal training can which groups – secondary/primary or urban/rural orimprove the quality of products from rural trained/untrained - were proficient or understood theartisans. Business location does only affect the welding process).quality of welding from informally trainedartisans at any education level.It is recommended In arc welding processes the most commonthat the quality of products from artisans with defects are either surface defects (cracks, distortion,lower education and especially those working in overlaps and rolls, undercuts, excessive spatter, andrural areas can be improved by raising their bad weld surface appearance) or subsurface weldstandard of education and/or by formal training. defects. These defects come as a result of improper selection of processes, incorrect current setting,Key words: Modes of Training, Product Quality, prior defects (laminations or impurities), undesirableMSE, Metalworking sub-sector, Arc welding inclusion (e.g. Hydrogen), poor profile, incorrect joint preparations and poor fit-up, stray arcing, tool1.0 Introduction marks, undercuts, etc. (Parmar, 1997). The quality of products from the MSEsector is affected by both the artisan’s attributes-age, gender, educational level, mode of training, 1602 | P a g e
  • 2. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-16121.1 Objectives of the Study artisans who had completed form four of the1.1.1 Overall objective Kenyan secondary education. The artisans were The main objective of this study was to selected both from rural and urban areas with twoinvestigate the factors affecting the quality of arc modes of training (on-the-job training and formalwelding in small scale Metalworking sub-sector in technical training).Kenya. The Kenyan MSE sector engages about1.1.2 Specific Objectives 8.33 million operators (Government of Kenya,To assess the relationship between the quality of arc 2010). Out of this the Jua Kali sector (the MSEs thatwelding and: are engaged in technical work) is about 18% Education level; according to the National MSE baseline Survey Modes of Training; and conducted in 1999. The most widely used welding Business location. method is arc welding for mild steel products, and according to the survey the number of artisans1.2 Hypotheses engaged in welding and fabrication is about 37,485To achieve the objectives of this research study the (Government of Kenya, 1999). About 60% and 40%following hypotheses were postulated: of this number comprise primary education class There is no significant difference between eight graduates and secondary education form fourthe quality of arc welding from trained artisans with graduates respectively (Government of Kenya,primary education and that from trained artisans 2004).with secondary education. There is no significant difference between Based on these figures the total populationthe quality of arc welding from formally trained for primary class eight artisans was taken to beartisans and the quality of arc welding from artisans 22,491 and for secondary form four was taken to bethat are trained-on-the-jobs. 14,994. A total of 36 artisans with primary There is no significant difference between education class eight and a total of 36 artisans withthe quality of arc welding from artisans working in secondary education form four were selected forthe rural areas and the quality of arc welding from assessment. The sample size determination wasartisans working in the urban areas. based on the relation: Nc 2The independent variables of the study are the n= 2 ; where n = sample size, N =artisan’s attributes (education level and mode of c  N 1e 2training) and the business characteristics (business population size,location), while the dependent variable are the c = coefficient of variation (≤ 30%), and e = errorscores awarded to indicate the quality of the product margin (≤ 5%).fabricated by the artisan by using arc welding This formula enabled the researchers toprocesses. minimize the error and enhance stability of the estimates (Nassiuma, 2000). In this study c was2. Material and Methods taken to be 30% and e to be 5% (using the2.1 Sampling maximum percentage in each case). Table 1 showThe target population of the study consisted of the number and category of artisans whoexperienced artisans who had completed class eight participated in this study.of the Kenyan primary education and experiencedTable 1: Number and category of artisans who participated Education Level Attributes Urban Area Rural Area Total Formally trained 5 14 19 Secondary Informally trained 10 7 17 Education Total 15 21 36 Primary Formally trained 6 10 16 Education Informally trained 8 12 20 Total 14 22 36 Total 29 43 72The Directorate of Industrial Training (DIT) testing to minimize the effect on the quality of thecenters were used for this research. This was meant fabricated products due to the condition of the 1603 | P a g e
  • 3. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-1612welding equipment; (the welding equipments used  Formally and informally trained artisans within all DIT testing centers are more else of the same secondaryeducation in urban areas.working condition).  Formally and informally trained artisans with secondaryeducation in rural areas.The selected DIT testing centers were those with  Formally and informally trained artisans withhigh concentrations of welders, and easily primary education in urban areas.accessible by the researchers. A total of ten (10)  Formally and informally trained artisans withDIT testing centers were used as shown in Table 2. primary education in rural areas.Work started at the same time in all testing centers.Research assistants (who had been selected from Besides the above primary groups, the effect ofamong the DIT trained examiners) were used to mode of training was also evaluated by comparingsupervise the participating artisans. the mean scores of the following combined groups of artisans with different other attributes:2.2 Evaluation  Formally and informally trained artisans with secondaryeducation.a) The effect of education level was evaluated by  Formally and informally trained artisans withcomparing the mean scores of the following primaryeducation.groups:  Formally and informally trained artisans Formally trained artisans with secondary working in urban areas. education and those with primaryeducation  Formally and informally trained artisans working in urban areas. working in rural areas. Formally trained artisans with secondary  Formally and informally trained artisans. education and those with primaryeducation working in rural areas. c) The effect of business location was evaluated by Informally trained artisans with comparing the mean scores of the following secondaryeducation and those with groups: primaryeducation working in urban areas.  Trained artisans with secondary education Informally trained artisans with working in urban areas with those in rural areas. secondaryeducation and those with primary  Artisans that are trained-on-the-job with education working in rural areas. secondary education working in urban areas with those in rural areas.Besides the above primary groups the effect of  Trained artisans with primary educationeducation level was also evaluated by comparing working in urban areas with those in rural areas.the mean scores of the following combined groups  Artisans that are trained-on-the-job withof artisans with different other attributes: primary education working in urban areas with Formally trained artisans with secondary those in rural areas. education and those with primaryeducation. Informally trained artisans with Besides the above primary groups, the effect of secondaryeducation and those with business location was also evaluated by comparing primaryeducation. the mean scores of the following combined groups Artisans with secondaryeducation and those composed of artisans with different other attributes: with primaryeducation working in urban areas.  Artisans with secondary education working in Artisans with secondaryeducation and those urban areas with those in rural areas. with primaryeducation working in rural areas.  Artisans with primary education working in Artisans with secondaryeducation and those urban areas with those in rural areas. with primary education.  Trained artisans working in urban areas with those in rural areas.b) The effect of mode of training was evaluated  Artisans trained-on-the-job working in urbanby comparing the mean scores of the following areas with those in rural areas.groups:  Artisans working in urban areas with those in rural areas. 1604 | P a g e
  • 4. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-1612Table 2: DIT Testing Centres and Number of Participating Artisans DIT Centre Education Urban Rural Total (Province) Level F I F I 1. NIVTC Primary 2 2 0 0 4 (Nairobi) Secondary 2 3 0 1 6 2. Ruaraka Primary 1 0 0 0 1 (Nairobi) Secondary 1 2 0 0 3 3. Kakamega Primary 0 0 1 1 2 (Western) Secondary 0 0 1 0 1 4. Turbo Primary 0 0 4 0 4 (Western) Secondary 0 0 11 1 12 5. Kiambu Primary 0 0 2 2 4 (Central) Secondary 0 0 0 1 1 6. Machakos Primary 0 0 3 7 10 (Eastern) Secondary 0 0 0 2 2 7.Mombasa Primary 0 5 0 0 5 (Coast) Secondary 1 2 0 1 4 8. Eldoret Primary 0 0 0 0 0 (Rift Valley) Secondary 0 1 0 0 1 9. Nakuru Primary 0 0 0 1 1 (Rift Valley) Secondary 1 0 1 0 2 10.Kisumu Primary 3 1 0 1 5 (Nyanza) Secondary 0 2 1 1 4 Total 11 18 24 19 72F – Formally Trained; I – Informally Trained (i.e. trained-on-the-job)2.3 Data Generation Tools in such a way that most of the welding techniquesTwo instruments were used to collect the required were to be used in fabricating it. In this study,data. These were: manual welding was employed; the artisans werei) Structured questionnaires, and given materials in the form of sheets and they wereii) Assessment of fabricated product. supposed to measure and cut the parts to the sizes shown. The parts were joined together using arc The questionnaire was used mainly to get welding processes. The assessment was carried outinformation regarding the artisan’s attributes and by checking for the correct part sizes (by usingbusiness characteristics. The participating artisans verniercalipers), and examining for the correct partwere generally observed to find out how proficient alignment, correct welding and product finish. Thethey were in using the welding equipment and quality of welded joints depends upon the design ofmethods/techniques as outlined in the introduction. the product, the performance of welding equipment, the welding procedures followed, and2.4 Assessment of Product Design the skill of the operator. In this study any A drawing of the product shown in figure deficiency in the design and equipment affected all1 was used in the research. The welding project artisans equally. Therefore, the skill of the welderwas marked out 100%. The product was designed was to determine the scores obtained. 1605 | P a g e
  • 5. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-1612Figure 1:Mild Steel Welding Project2.5 Weld Inspection and Testing was used to detect surface defects; careful visual Weld inspection and testing was carried inspection of welds can detect about 80% to 90%out to determine the quality of the welded product of the defects and flaws (Parmar, 1997); this wasfrom each artisan by way of awarding scores. This carried out at the testing centers.Radiographic andwas also to ensure the artisans prepared and fitted hardness testing was carried out by the Ministry ofup the work properly with particular reference to Roads, department of material testing.whether: The parts are formed and dimensioned 3. Results and Discussionwithin the tolerance limits in accordance with the The data (scores) collected were analyzeddrawing specifications; using the Statistical Analysis System (SAS) and The parts to be welded are spaced properly excel spreadsheet. The means and standardat the joint; deviations were generated to describe the quality of The fusion faces are free from dirt, rust, arc welding with regard to the education level /grease, etc. mode of training/business location. The scores were matched with the artisans’ attributes and Welding inspection and testing are also to business characteristics to find their relationships.confirm whether the artisans used the correct The analysis of variance (ANOVA) was used towelding procedures, including welding current, show any variation in the quality of arc welding inweld angles, electrode size, welding sequence and each of the eight groups of artisans due to thenumber of weld runs, arc length, welding speed, different treatments, that is, education level, modeinter-run deslagging, chipping back when a sealing of training and business location. Comparisons ofrun is applied, etc. ; the use of correct welding all possible pairs of means using the Leastprocedures and parameters produce quality Significant Difference (LSD) method with alpha setproducts and earn more marks.Visual inspection at 5% were done to determine which pairs of 1606 | P a g e
  • 6. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-1612artisans with quality performances that was analysis of variance was carried out and the resultssignificantly different. are presented in Tables 3 and 4. Table 3 shows mean scores of product quality for education level3.1 Effect of education level on product for primary groups of artisans with the samequality attributes, and Table 4 showsmean scores of The artisans’ marks awarded for quality of product quality for education levels for combinedarc welding provided the data for determining the groups of artisans with different other attributes.impact of education level on product quality. TheTable 3: Mean scores of product quality for education levels from primary groups of artisans with sameattributesEducation Urban (Mean Score =70.33) Rural (Mean Score =61.57) OverallLevel Formal Informal Formal Informal Mean Training Training Training TrainingSecondary 68.700a 73.450a 69.857a 65.071b 69.96a a a b cPrimary 66.583 70.250 60.350 50.875 60.43bThe means followed by the same letter in the same scores. This means that education level does notcolumn are not significantly different at  = 5% have a significant impact on product quality fromusing LSD. artisans working in urban areas. However, it is notedThe results in Table 3 for both formally and that the mean scores from artisans formally trainedinformally trained artisans with secondary and in the rural are higher in comparison to those fromprimary education working in urban areas show no artisans trained-on-the-job in the rural. The overallsignificant differences, while the results for the rural mean scores are significantly different, with that forartisans in both cases differ significantly with the artisans with secondary education being higher thansecondary education holders having higher mean the mean score for artisans with primary education.Table 4: Mean scores of product quality of education levels for combined groups of artisans with differentother attributes Education Formal Informal Urban Rural Overall Level Training Training Areas Areas Mean Secondary 69.55a 70.00a 71.83a 68.26a 69.76a b c a bc Primary 62.69 58.63 68.68 58.18 60.43bThe means followed by the same letter in the same column are not significantly different at  = 5% using LSDTable 4 shows that there is a significant difference The study found out that for an enterprise toin mean scores of both formally and informally graduate from, say micro to small enterprise, thetrained artisans, with secondary education graduates education level and training of the manager/ownerperforming better than primary education graduates. and the sector to which the enterprise belonged to,This analysis shows that higher level of education had a significant influence on enterprise graduation.has a higher positive impact on product quality. Mullei (2003) also found out that more Overall artisans with secondary education than half of small producers were primary schoolperform better than artisans with primary education. graduates whose ability to assimilate newThese results are consistent with the findings of technologies, innovate and imitate perfectly isSonobeet al, (2003) who found out that primary limited. The study, therefore, recommends thegraduates took 12.6 years to produce quality raising of managerial, vocational and technical skillsmachine tools while high school and college of small entrepreneurs for long-term industrialgraduates took 0.7 of a year to produce quality development. This shows the importance of highermachine tools. This shows that higher education is level of education and formal training.essential in learning to produce high qualityproducts. 3.2 Effect of mode of training on product quality These results are also consistent with the A total of 35 formally trained and 37findings of Mullei (2003) in his study on small informally trained artisans were selected tomanufacturing firms where he sought to identify participate in this study. The artisans’ marksfactors that determine firm growth and awarded for quality of arc welding provided the datatransformation among small firms in Kenya. The for determining the impact of training on productstudy covered food processing, woodworking, quality. The analysis of variance was carried outtextile and garments, and metal working sub-sectors. and the results are presented in Tables 5 and 6. 1607 | P a g e
  • 7. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-1612Table 5 shows mean scores of product quality for of product quality for mode of training for combinedmode of training for primary groups of artisans with groups of artisans with different other attributes.the same attributes, and Table 6 showsmean scoresTable 5: Mean scores of product quality for modes of training from primary groups of artisans with sameattributesMode Secondary Primary Cumulativeof Mean ScoresTraining Urban Rural Urban RuralFormal 68.700a 69.857a 66.583a 60.350b 66.41b a a a cInformal 73.450 65.071 70.250 50.875 63.85bThe means followed by the same letter in the same column are not significantly different at  = 5% using LSD.Table 5 shows that there are no significant artisans with secondary education irrespective ofdifferences in the mean scores of the first three their business locations. In the case of artisans withcolumns. Both formally and informally trained primary education the combined effect of businessartisans with secondary education perform equally location and training has very little effect on theirrespective of their business locations. The same performance of those in urban areas while having aalso applies to those artisans with primary significant effect on those artisans in the ruraleducation but located in urban areas. However, areas; those informally trained artisans in the ruralthere is a significant difference in mean scores of areas performed poorly. Although the analysis ofthe formally trained and informally trained artisans variance (ANOVA) showed that the combination oflocated in the rural areas, with the mean score of education level, mode of training and businessproduct quality from artisans formally trained being location does not have significant impact on thehigher. The implication from this analysis is that quality of arc welding, it does affect the informallythe combined effect of business location and trained artisans in the rural areas significantly.training has very little effect on the performance ofTable 6: Mean scores of product quality for modes of training from combined groups of artisans withdifferent other attributes Modes of Training Secondary Primary Urban Rural Overall Mean Formal 69.55a 62.69b 67.55a 65.90b 66.41a a b a c Informal 70.00 58.63 72.03 56.11 63.85aThe means followed by the same letter in the same column are not significantly different at  = 5% using LSD.Table 6 shows that there are no significant transformation among small firms in Kenya. Thedifferences in the mean scores in all columns study covered food processing, woodworking,except for the rural column. This means that the textile and garments, and metal working sub-performance of both formally and informally sectors. The study found out that for an enterprisetrained artisans with secondary and primary to graduate from, say micro to small enterprise, theeducation, or working in urban areas does not education of the manager/owner and the sector tosignificantly differ. This implies that the mode of which the enterprise belonged to, had a significanttraining does not have a significant impact on influence on enterprise graduation. Education wasproduct quality of products from artisans with found to have a marginal effect on graduation,secondary and primary education, or working in probably indicating the importance of vocationalurban areas. However, there is a significant training skills (formal training), which are lackingdifference in the mean scores in the rural column. among many small producers. Mullei (2003) alsoThis implies that the mode of training has found out that more than half of small producerssignificant impact on product quality of products were primary school graduates whose ability tofrom artisans working in rural areas. Overall there assimilate new technologies, innovate and imitateis no significant difference in mean scores between perfectly is limited. The study, therefore,the formal training and the informal training; recommends the raising of managerial, vocationalhowever, the formally trained artisans mean score and technical skills of small entrepreneurs for long-is slightly better than that from artisans with term industrial development. This shows theinformal training. importance of higher level of education and formal These results are consistent with the training.findings of Mullei (2003). In his study of small In overall there is no significant differencemanufacturing firms Mullei (2003) sought to in mean scores from formally and informallyidentify factors that determine firm growth and trained artisans, as the last column shows. This 1608 | P a g e
  • 8. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-1612further confirms the ANOVA results using SAS evaluation in this study. The artisans’ scoreswhich showed that the mode of training has very awarded for quality of arc welding provided thelittle impact on product quality from artisans. This data for determining the impact of businessalso confirms that the combined effect of training location on product quality. The analysis ofand education does not have a significant impact on variance was carried out and the results areproduct quality. presented in Tables 7 and 8. Table 7 shows mean scores of product quality for business locations for3.3 Effect of business location on product primary groups of artisans with the same attributes,quality and Table 8 showsmean scores of product quality A total of 29 participating artisans was for business locations for combined groups ofselected from urban areas and 43 participating artisans with different other attributes.artisans were selected from rural areas forTable 7: Mean scores of product quality for business locations for primary groups of artisans with sameattributes Business Mean Scores Cumulative Location secondary Primary Mean Scores Formally Informally Formally Informally Trained Trained Trained Trained Urban 68.70a 73.45a 66.58b 70.25a 70.33a a b b c Rural 69.86 65.07 60.35 50.88 61.57bThe means followed by the same letter in the same column are not significantly different at  = 5% using LSDTable 7 shows that there is no significant difference business location has significant impact on productin the mean scores in the first and third columns. quality of these groups of artisans. The high qualityThus the performance of both formally trained was exhibited by the informally trained artisansartisans with secondary or primary education from with secondary education working in the urbanurban and rural areas does not significantly differ. areas, while the low quality was exhibited by theThis implies that the business location does not informally trained artisans with primary educationhave a significant impact on product quality of working in the rural areas. Overall artisans in urbanthese groups of artisans.However, there is a areas emerged with the best product quality assignificant difference in the mean scores in the compared with product quality from artisans insecond and fourth columns; this implies that the rural areas.Table 8: Mean scores of product quality for business locations for combined groups of artisans withdifferent other attributes Business Secondary Primary Formal Informal Overall Location Education Education Training Training Mean Urban 71.83a 68.68a 67.55b 72.03a 70.33a a c b c Rural 68.26 58.18 65.90 56.11 61.57bThe means followed by the same letter in the same column are not significantly different at  = 5% using LSD.Table 8 shows that there is no significant difference producing quality products between enterprises inin the mean scores in the first and third columns, urban and remote centres (rural areas) werethat is, the overall performance of artisans with different; urban enterprises performed better thansecondary education or with formal training is rural enterprises.more else the same irrespective of the businesslocation. However, in the second and third columns The mean score for the informally trainedthe differences in mean scores are significant; this artisans working in rural areas is significantlyimplies that in overall artisans from urban areas different from the other mean scores. The majoritywith a primary education or informally trained have in this group have primary education class eighttheir product quality higher than their counterparts artisans (12 out of 19 artisans). It is evident fromfrom rural areas. Tables 7 and 8 that the informally trained artisans working in urban areas perform better than their Overall artisans in urban areas emerged counterparts who are formally trained.with the best product quality as compared withproduct quality from artisans in rural areas. Theresults agree with Sonobeet al, (2002) who, when 3.4 The Level of Influence on Product Qualitystudying on the performance of garment enterprises by the three Factorsin Jili, China, found out that the performance in 1609 | P a g e
  • 9. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-1612 The degree to which the three factors (that The following results were found from theis, education levels, modes of training, and business marginal effects that were generated after logisticlocations) affect product quality were obtained regression of the equation:from the SAS results which indicated that: • Higher educational level affects positively the• Education level has the highest impact on probability of producing good quality welding; product quality (with a probability of 0.0005) Having a secondary educational level as followed by opposed to primary education increases the• Business location (with a probability of 0.0018), probability of having good quality welding by and 0.173 or 17.3%. This coefficient is statistically• The combinations of business location and significant at 10% confidence interval (with p- training (with a probability of 0.0382), followed value of 0.053). by • The probability of producing good quality• The combination effect of education and welding is 0.157 or 15.7% lower for those business location (with a probability of 0.0625) artisans who have been trained-on-the jobs as followed by compared to those artisans who have been• The impact of mode of training (with a trained formally. This coefficient is statistically probability of 0.3102) significant at 10% confidence interval (with p-• The combination effect of education and value of 0.065). training follows (with a probability of 0.4997) • The probability of producing good quality• The interaction of all the three factors together welding is 0.269 or 26.9% higher for a business has very little or no impact on product quality located in urban area as opposed to a business (with a probability of 0.7394). operating in rural areas. This coefficient is statistically significant at 5% confidence3.5 Test for Multicollinearity interval (with p-value of 0.002). Multicollinearity is a violation of theassumption that no independent variable is a linear 4 Conclusions and Recommendationsfunction of one or more other independentvariable.To test for multicollinearity a logic model 4.1 Conclusionswas estimated at: 4.1.1 Education Level: 𝑃 𝐿 𝑖 = Ln 1− 𝑖 𝑃 = 𝛽1 + 𝛽2 𝐸𝑑𝑢𝑐 + 𝛽3 𝐵𝑢𝑠𝐿𝑜𝑐 + • Higher level of education has a higher positive 𝑖 impact on product quality. Products made by 𝛽4 𝑀𝑜𝑇 + 𝑢 𝑖 and three regressions using STATA artisans with primary education level are ofsoftware, one for each explanatory variable on the lower quality than those products made by theirremaining others were run and the magnitude of the counterparts with secondary education level.resulting R-squared were examined.The R-squared • Artisans with higher levels of education in rural(education R2 = 0.0089, training R2 = 0.107 and areas perform better than those with lowerbusiness locationsR2 = 0.0046) of the three levels of education.regressions were relatively low, and therefore it • The quality of products made by artisanswas concluded that there is no multicollinearity (especially in the rural areas) can be improvedamong education level, mode of training and by raising the standard of education.business location variables. • Artisans with lower education and training are constrained by lack of adequate knowledge to enable them understand the welding theory on3.6 Heteroscedasticity their own. Heteroscedasticity is a violation of one ofthe classical linear regression model assumptions 4.1.2 Mode of Training:that requires that the disturbances have the same • Training alone has very little impact on productvariance. It is caused by several factors including quality; however, when combined with businessthe presence of outliers in the data, omitted location the impact on product quality is veryvariables, incorrect functional forms, and it is much significant.more present in cross-sectional data. The logit • Formal training can improve the product qualitymodel in the equation from artisans with lower education working in 𝑃𝑖 rural areas.(𝐿 𝑖 = Ln = 𝛽1 + 𝛽2 𝐸𝑑𝑢𝑐 + 𝛽3 𝐵𝑢𝑠𝐿𝑜𝑐 + 1− 𝑃 𝑖 • The product quality from informally trained 𝛽4 𝑀𝑜𝑇 + 𝑢 𝑖 ) Was regressed using STATA artisans with low education can be improved bysoftware, and to control for heteroscedasticity the either giving them formal training or raising theirrobust standard errors were used. Marginal education level.effects were generated and used in the • While formal training improves performance ofinterpretation. artisans working in the rural areas, it, however, 1610 | P a g e
  • 10. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-1612 contributes very little to those artisans working in • It was observed that artisans working in urban urban areas. areas with the same education levels, those4.1.3 Business Location: without formal training performed better than• Business location has a significant impact on those artisans with formal training. However, it product quality; urban work experience has a was expected that the formally trained artisans higher positive impact on product quality as should perform better than those trained-on-the- compared to rural work experience. jobs as was the case with the artisans working in• Urban work experience when combined with rural areas. It is, therefore, recommended that higher education level, even without formal further research be conducted to investigate this training, has a higher positive impact on product phenomenon. quality than higher education level combined with formal training. REFERENCES• Business location does not affect the [1]. Baucus, A. and Human, S., (1994). performance of formally trained artisans at any Second career entrepreneurs: A multiple education level, but it affects the performance of case study analysis of entrepreneurial those artisans trained-on-the-jobs at any processes and antecedent variables. education level. Entrepreneurship Theory and Practice 19,• Urban work experience contributes more No. 2: pp. 41-71. significantly to product quality than education [2]. Bosire, J and Gamba, P. level alone and formal training alone. (2003).Measuring Business Skills• Overall, there is a significant difference in Cognition: The Case of Informal Sector product quality between artisans working in Entrepreneurs in Kenya.EASSRR, vol. urban areas and artisans working in rural areas . XIX, No. 2• This implies that business location alone has [3]. Ferej, A., (1994).The efficacy of significant impact on product quality. apprenticeship training as preparation for self-employment in Kenya.Doctoral All the three variables (education level, mode of Thesis.Illinois; University of Illinois attraining and business location) were shown to be Urbana-Champaign.independent of each other by using [4]. Government of Kenya,multicollinearity testing. (2010).Economic Survey 2010. Nairobi, Government Printer.4.2 Recommendations [5]. Government of Kenya (2005).SessionalThe following recommendations are suggested: Paper No.2 of 2005 on Development of• More resources should be directed towards Micro and Small Enterprises for Wealth training and raising education levels of artisans and Employment Creation for Poverty with low education and working in rural areas. Reduction.Government Printers, Nairobi. Adult education strategies should be used to [6]. Government of Kenya, create and stimulate interest among the trainees. (2004).Directorate of Industrial Training• MSE/Jua Kali artisans should be encouraged to Records on Trade Tests. Nairobi; DIT take the Government Trade Tests up to at least records Grade II so as to raise their competency level. [7]. Government of Kenya, (1999).National The Kenya Bureau of Standards should also be Micro and Small Enterprise Baseline asked to enforce quality standards in the Survey 1999. Nairobi: Government informal sector. Printer [8]. Kinyanjui, M., (1997).A study of trainingThe following research studies are recommended to needs and aspects of training in informalfurther augment the present achievements of this workshop clusters in Nairobi. Nairobi;study: Institute for Development Studies,• This study investigated the impact of education University of Nairobi. level, mode of training and business location in [9]. McGrath S. and King K. with Leach F. the metalwork sub-sector. Research studies and Roy C., in association with Boeh- should be designed to investigate how other Ocansey, O., DSouza, K., Messina, G. attributes affect product quality and/or the and Oketch, H., (1994). Education and performance of the MSE sub-sectors. training for the informal sector. Education• Research should also be conducted to Research Paper No. 11. London; ODA. investigate how education level, mode of [10]. Mullei, A. (ed) (2003).Growth and training and business location affect the quality Transformation of Small Manufacturing of product quality in other disciplines especially Firms in Africa: Insights from Ghana, those that are mostly dominated by women. Kenya, and Zimbabwe, Nairobi: African Centre for Economic Growth. 1611 | P a g e
  • 11. C.M.M. Ondieki, Bisanda, W.O.Ogola / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.1602-1612[11]. Nassiuma D. K., (2000). Survey sampling: Theory and methods. Nairobi, University of Nairobi press.[12]. Parmar R.S., (1997).Welding Engineering and Technology, First edition; Khanapubishers, New Delhi, India.[13]. Sonobe, T., Hu, D. and Otsuka, K. (2002). “Process of Cluster Formation in China: A Case Study of a Garment Town,” Journal of Comparative Economics, Vol. 32, No. 3, pp. 542-563.[14]. Sonobe, T., Kawakami, M. and Otsuka, K. (2003). “Changing Roles of Innovation and Imitation in Industrial Development: A Case Study of a Machine Tool Industry in Taiwan,” Economic Development and Cultural Change, Vol. 52, No. 1, pp. 103- 128 1612 | P a g e

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