Topic: BusinessManagement

Last updated: May 1, 2019

Factor rotationSubsequent to the factor extraction, Varimax rotation (a type of Orthogonal rotation) was executed. It minimised the cross-products loadings.

This minimisation yielded the simplified the factors which improved the interpretability of the retained components. The results, obtained after rotation, exhibited that the 33 variables were loaded on eight factors (Table 6.23). Rotated Component Matrix Component Matrix Com. 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 Cont2 .714 .026 .

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039 .027 .089 .061 .044 .070 .493 .

143 -.061 -.047 -.

304 -.104 .382 .

115 .531Over1 .676 .143 .007 .179 -.020 .161 .

043 .020 .549 .205 -.091 -.166 -.

269 .017 .235 .177 .538Cont1 .674 .079 .

179 .150 .094 -.012 .018 .018 .580 .061 -.

089 -.100 -.291 -.098 .

269 -.016 .525Over2 .

665 .213 -.051 .170 .106 .004 .087 .

039 .571 .213 -.177 -.089 -.189 -.157 .214 .

154 .540Cont3 .595 .123 .235 .184 -.

006 -.006 -.065 -.033 .

533 .044 -.123 -.193 -.272 .000 .

206 -.092 .464KSk9 .451 .168 .216 .

173 .136 -.104 .248 -.

028 .581 -.113 -.089 -.043 -.043 -.156 .

115 .031 .400I5S2 .078 .722 .023 .098 .

129 -.017 .019 .050 .

412 .294 -.306 -.039 .437 .031 -.066 -.102 .

558I5S4 -.012 .720 .074 .095 -.037 .065 .052 .

060 .358 .219 -.304 -.

074 .463 .211 -.098 -.055 .

545I5S3 .138 .671 .

063 .026 .107 -.064 .

026 .019 .401 .226 -.313 -.064 .

403 .002 .030 -.

115 .491I5S1 .171 .669 .068 -.053 -.

004 .067 -.021 -.007 .358 .264 -.

277 -.145 .390 .137 .

125 -.081 .489I5S5 .

189 .639 -.110 .

155 -.031 .002 .076 -.013 .390 .279 -.

352 -.160 .306 .034 -.062 .096 .487KSk2 -.074 .

005 .708 .111 .096 .068 .

144 .043 .372 -.408 .

230 .110 .084 .239 -.042 -.346 .556KSk1 .

201 .079 .702 .

125 .043 -.024 .139 .048 .535 -.

382 .062 .034 -.017 .184 .092 -.316 .

580KSk4 .165 .002 .

700 .079 -.022 .

049 .117 -.120 .437 -.

420 .169 -.122 -.020 .205 .113 -.296 .

554KSk3 .100 .044 .666 -.

004 -.039 -.022 .198 .010 .389 -.457 .075 .

020 .065 .212 .138 -.

250 .497Sp2 .274 .027 .018 .775 .006 .

050 .150 .041 .563 .030 -.

050 -.076 -.345 -.009 -.478 .174 .704Sp3 .172 .

133 .086 .747 .107 .

031 -.053 .016 .518 .136 -.

047 -.091 -.261 -.

007 -.508 -.066 .

628Sp1 .190 .158 .126 .675 .122 .012 .

007 .050 .546 .094 -.060 -.

041 -.208 -.010 -.

437 -.061 .551KSk8 .185 -.001 .135 .

446 -.007 .051 .

219 -.031 .501 -.234 .075 -.068 -.

156 .085 -.221 .008 .

396VSI2 -.014 .033 .019 .129 .724 .

094 .017 .046 .

275 .252 .367 .292 .

116 -.361 -.123 -.187 .553VSI1 .056 .097 -.048 .

107 .720 .048 .102 -.

097 .313 .238 .334 .167 .187 -.469 -.

086 -.102 .567VSI4 .122 -.049 .148 .018 .

685 .189 .009 .095 .328 .207 .440 .310 .

028 -.256 .071 -.205 .553VSI3 .110 .

062 -.024 -.040 .642 .070 .069 .029 .258 .

236 .290 .248 .144 -.370 .077 -.

099 .441SDO3 .019 .019 .061 .019 .

188 .743 -.121 -.

041 .150 .388 .565 -.056 .

037 .331 .042 .

023 .609SDO1 -.082 .

100 .049 .174 -.037 .721 .060 -.

066 .180 .242 .444 -.133 .106 .451 -.

145 .191 .578SDO4 .105 .060 -.

054 -.055 .131 .714 .

053 .037 .179 .370 .464 .011 .

080 .304 .133 .227 .

552SDO2 .075 -.111 .018 -.

014 .107 .643 -.064 -.028 .087 .281 .

508 -.043 -.069 .276 .096 .107 .448KSk6 .

011 .071 .185 .047 .094 .021 .787 .040 .406 -.438 .126 .212 .315 -.058 -.027 .389 .672KSk5 .114 .029 .237 .044 .089 -.007 .776 -.025 .455 -.482 .135 .142 .247 -.097 .037 .365 .683KSk7 .056 .032 .194 .065 .027 -.086 .775 -.002 .391 -.512 .048 .152 .248 -.101 -.020 .378 .655WSR3 -.059 .080 -.044 -.010 .009 -.012 .007 .822 .033 .157 -.279 .712 -.067 .268 -.006 .032 .688WSR1 .097 .094 .014 -.037 .054 -.016 -.024 .812 .138 .181 -.281 .686 -.119 .235 .109 -.015 .683WSR2 .053 -.065 .007 .120 .004 -.060 .027 .770 .112 .062 -.243 .660 -.250 .202 -.042 .040 .618Table 6.23 The factor loadings of measures for improvement outcomes from SPSSThe items to measure the use of shop floor management tools were loaded on to the four factors, which were developed originally, namely, Implementation of 5S practices (I5S), Use of the standard operations (SDO), Implementation of waste removal (WSR), and Use of visual management (VSI).Nevertheless, the items to measure the improvement implementation were loaded differently. Improvement of Knowledge and Skills (KSk) were loaded separately onto two different factors. As articulated by Doolen et al. (2003), the items from KSK1 to KSK4 were originally developed to measure knowledge of improvement. These 4 items were renamed improvement knowledge (IpKn). The items from KSK5 to KSK7 wereoriginally developed to evaluate shop floor skills. Hence, were named shop floor skills(SFK). The 3 Improvement Contribution (Cont) items and the 2 Overall Improvement Perceptions (Over) items were loaded together. A further item from the measure of Improvement of Knowledge and Skills (KSk9) was also loaded onto this component. These 5 items were grouped together and given a new name: shop floor performance (SFP).All 3 Sense of participation (Sp) items were loaded together into a single component. A further item from the measure of Improvement of Knowledge and Skills (KSk8) was loaded onto this component. These 4 items were clubbed together and given a new name: sense of participation (Sens).The items to measure Improvement of Knowledge and Skills (KSk) were loaded separately onto two different components. As proposed by Doolen et al. (2003), the items from KSK1 to KSK4 were originally developed to measure knowledge of improvement. These 4 items were renamed improvement knowledge (IpKn). The items from KSK5 to KSK7 were originally developed to evaluate shop floor skills. They were named shop floor skills (SFK).All 33 items were retained with high convergent validity (the items within the same scale are correlated, cross-loadings > 0.4) and discriminant validity (the items between different scales are distinct, cross-loading < 0.3) as defined by Hair et al.,(2010), Gaskin (2011) and Stangor (2011). In addition, the revised scales were rational and in line with previouse research (Doolen et al., 2003). The revised scales were listed as above (Table 6.24): shop floor performance (SFP); Implementation of 5S practices (I5S); improvement knowledge (IpKn); sense of participation (Sens); Use of visual management (VSI); Use of the standard operations (SDO); shop floor skills (SFK) and Implementation of waste removal (WSR). Rotated loadings Eigenvalues % Of Variance Cumulative %Shop floor performance (SFP) 5.469 16.572 16.572Cont2 .714 Over1 .676 Cont1 .674 Over2 .665 Cont3 .595 KSk9 .451 Implementation of 5S practice (I5S) 2.632 7.977 24.549I5S2 .722 I5S4 .720 I5S3 .671 I5S1 .669 I5S5 .639 Improvement knowledge (IpKn) 2.430 7.364 31.912KSk2 .708 KSk1 .702 KSk4 .700 KSk3 .666 Sense of participation (Sens) 1.989 6.027 37.939Sp2 .775 Sp3 .747 Sp1 .675 KSk8 .446 Use of visual management (VSI) 1.837 5.567 43.506VSI2 .724 VSI1 .720 VSI3 .685 VSI4 .642 Use of the standard operations (SDO) 1.545 4.683 48.189SDO3 .743 SDO1 .721 SDO4 .714 SDO2 .643 Shop floor skills (SFK) 1.277 3.870 52.059KSk6 .787 KSk5 .776 KSk7 .775 Implementation of waste removal (WSR) 1.156 3.503 55.562WSR3 .822 WSR1 .812 WSR2 .770 Table 6.24 The revised scales with factor loadings Reliability of the Revised ScalesHaving completed the factor analysis, it is pertinent to check the scales’ internal consistency to make certain that all the designed and developed questions ‘hang together’ and measure the underlying construct (Field, 2005). Cronbach’s (1951) alpha was calculated to ascertain the reliability of the factors. The following Table 6.25 presents the resulting values and the associated minimum inter-item correlation values generated by SPSS.Nunnally and Bernstein (1994) suggested that Cronbach’s alpha coefficient as a scale should reach 0.7 or above to corroborate the internal consistency of the containing items. Nevertheless, if numbers of items in the scale are less than 10, then optimal mean inter-item correlation values that range from 0.2-0.4 are acceptable (Pallant, 2007). In this way, the scales to measure the use of shop floor management tools remained the same.Factors Cronbach’s AlphaSFP 0.768I5S 0.745IpKn 0.716Sens 0.713VSI 0.679 (* 0.287)SDO 0.686 (* 0.291)SFK 0.764WSR 0.735Table 6.25 Cronbach’s Alpha Values for Revised Survey Scales (* minimum inter-item correlation value)The revised scales to measure improvement implementation are depicted in Table 6.26. Based on the factor analysis results, these possess high construct validity which implies that the questions actually measure what they are designed to measure,( Hair et al., 2010; Stangor, 2011).Revised Scales Item ListShop floor performance (SFP) • Over1: Overall, the performance of my improvement activities was a success in my company• Over2: Overall, my improvement activities were vital in my company• Cont1: My improvement activities have a positive effect on the shop floor area• Cont2: This shop floor area improved measurably as a result of my improvement activities• Cont3: My improvement activities have improved the performance of this shop floor area• KSk9: Overall, the improvement activities helped me and my colleagues work together to improve performanceShop floor skills(SFK) • KSk5: I can communicate new ideas as a result of participation in improvement activities• KSk6: I gained new production skills as a result of participation in improvement activities• KSk7: In general, the participation in improvement activities motivated me to perform betterSense of participation(Sens) • Sp1: I like taking part in the current improvement activities• Sp2: I would like to take part in the improvement activities in the future• Sp3: In general, I am comfortable working with others to identify improvements on my shop floor area• KSk8: Overall, the improvement activities increased my work interestsImprovement Knowledge (IpKn) • KSk1: Overall, the improvement activities increased my knowledge of what CI is• KSk2: In general, the improvement activities increased my knowledge of how CI should be applied• KSk3: Overall, the improvement activities increased my knowledge of the need for CI• KSk4: In general, the improvement activities increased my knowledge of my role in CITable 6.26 The revised measures for improvement outcomes SummaryThis chapter elaborated the data collection and screening procedures. The data were collected from 10 Indo-Japanese automotive joint ventures. A questionnaire was derived from pretested and validated questions. It was distributed using the self-administered method. 1000 questionnaires were distributed of which 527 were returned. However, 25 (6.8%) contained missing values, so 502 were valid samples, giving a response rate of 50.0.2%. SPSS was used to assess the construct validity and summarise the patterns of the collected samples.In the subsequent chapter, the theoretical model will be developed. Thereafter, structural equation modelling with path analysis will be used to analyse the data, shape the proposed theoretical model, and test the hypotheses.

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