1. Appendix
(https://doi.org/10.6084/m9.figshare.5782152)
Achieving plant responsiveness from reconfigurable technology:
intervening role of SCM
Cesar H. Ortega Jimenez
Facultad de IngenierĂa-Universidad Nacional Autonoma de Honduras (UNAH);
Facultad de Posgrado-Universidad TecnolĂłgica Centroamericana (UNITEC); and
Universidad de Sevilla; Email: cortega@unah.edu.hn; cortegaj@unitec.edu
Pedro Garrido-Vega
Universidad de Sevilla
pgarrido@us.es
Cristian Andres Cruz Torres
Escuela de MatemĂĄtica- Universidad Nacional AutĂłnoma de Honduras (UNAH);
and Facultad de IngenierĂa y Arquitectura - Universidad TecnolĂłgica Centroamericana
(UNITEC), Email: cristian.cruz@unah.edu.hn
2. Contents
Table A1. Glossary of Terms, Abbreviations, and Acronyms .................................................. 3
Table A2. Measurement model: first order CFA ...................................................................... 4
Table A3. Measurement Model: Second order CFA................................................................. 5
Table A4. Construct descriptive statistics and correlations ...................................................... 5
References................................................................................................................................. 5
3. 3
Table A1. Glossary of Terms, Abbreviations, and Acronyms
BK Baron and Kenny
MS Manufacturing strategy
RT Reconfiguration technology
SC Supply chain
SCM Supply chain management
SRS Strategic reconfigurable system
TM Technology management
PR Plant responsiveness
SCM-I Supply chain management integration
SCM-H Supply chain management human support
SCM-Q Supply chain management quality
4. 4
Table A2. Measurement model: first order CFA
Item Factor
loading
Construct AVE ĎC DESCRIPTION
(References)
Item1 0.806 Modularization of Products 0.501 0.745
TM practices
(JimĂŠnez et al.,
2011; Machuca et
al., 2011)
Item2 0.519
Item3 0.764
Item2 0.658 Manufacturing involvement in product design 0.544 0.781
Item4 0.777
Item5 0.773
Item1 0.610 Reconfigurability 0.462 0.837
RT capabilities
(Koren, 2013, 2010)
Item2 0.663
Item3 0.775
Item4 0.724
Item5 0.686
Item6 0.606
Item1 0.825 Formulation of manufacturing strategy 0.591 0.812
MS practices
(Ortega-Jimenez et
al., 2012)
Item2 0.791
Item3 0.683
Item1 0.623 Manufacturing-business strategy linkage 0.462 0.720
Item3 0.702
Item4 0.711
Item5 0.824 Supply Chain Information Sharing with Suppliers 0.639 0.897
SCM-I practices
(Min et al., 2007)
Item6 0.691
Item7 0.921
Item8 0.843
Item9 0.692
Item6 0.835 Supply Chain Information Sharing by Suppliers 0.651 0.882
Item7 0.848
Item8 0.783
Item9 0.758
Item2 0.641 Supply Chain Information Sharing with Customers 0.720 0.882
Item3 0.992
Item4 0.874
Item3 0.864 Supply Chain Information Sharing by Customers 0.638 0.839
Item4 0.677
Item5 0.842
Item1 0.733 Interorganizational Coordination 0.654 0.881
SCM-H practice
(Min et al., 2007)
Item2 0.948
Item3 0.866
Item4 0.657
Item1 0.505 Supply chain quality focus 0.444 0.700
SCM-Q practice
(Min et al., 2007)
Item2 0.716
Item3 0.751
Item3 0.621 Plant responsiveness
(Time, Dependability, Flexibility)
0.422 0.813 PR
(Garrido-Vega et
al., 2015; Ortega-
JimĂŠnez et al., 2014;
Ortega-Jimenez et
al., 2015)
Item5 0.652
Item6 0.683
Item8 0.732
Item9 0.560
Item11 0.636
N= 330; all factor loadings at p<0.001; Fit Indexes: Ď2=3989.004 (p<0.01), df=1081; RMSEA= 0.046, SRMR= 0.067. Items not valid were deleted
from all scales.
NOTE: We may use only two of four indices (Hu and Bentler, 1998): (1) SRMR and (2) 0ne of the following: CFI, TLI or RMSEA. Cuando el modelo
contiene un gran nĂşmero de variables, si TLI y CFI estĂĄn ligeramente por debajo de lo esperado, pero el RMSEA es un poco mejor, puede no haber razĂłn
para preocuparse (Kenny and McCoach, 2003). Dado que este es nuestro caso (gran nĂşmero de variables observadas), hemos optado por el RMSEA
junto con el SRMR como Ăndices de bondad. Further, if the null model RMSEA < 0.158 (RMSEA=0.05 & TLI=0.90, means RMSEA null model is
0.158), an incremental measure of fit such as TLI or CFI may not be that informative (Kenny, 2015). Since this paper has a null model RMSEA of 0.140,
we should consider SRMR and RMSEA (Kenny et al., 2015; Kenny and McCoach, 2003; Lionetti et al., 2018; Navrady et al., 2018). If AVE<0.5 and
ĎC>0.6), convergent validity and reliability is acceptable (Fornell and Larcker, 1981; Huang et al., 2013; Stanton et al., 2014)
5. 5
Table A3. Measurement Model: Second order CFA
Construct Factor loading AVE ĎC
TM 0.542 0.700
Modularization of Products 0.637
Manufacturing Involvement in Product Design 0.824
MS 0.809 0.894
Manufacturing-business strategy linkage 0.957
Formulation of Manufacturing Strategy 0.838
SCM-I 0.461 0.770
Supply Chain Information Sharing with Suppliers 0.761
Supply Chain Information Sharing by Suppliers 0.780
Supply Chain Information Sharing with Customers 0.534
Supply Chain Information Sharing by Customers 0.609
N= 330; all factor loadings at p<0.001; Fit Indexes: = Ď2=3905.175 (p<0.01), df=1081; RMSEA= 0.046, SRMR= 0.067.
NOTE: see note from previous table (A2).
Table A4. Construct descriptive statistics and correlations
Variable
Variable Mean SD SRS SCM EC
SRS 3.61 0.423
SCM 4.82 0.330 0.579
EC 2.41 0.073 0.058 0.204
PR 2.74 0.163 0.736 0.761 0.129
N=330; all at p<0.05; Estimation with robust standard errors and bootstrapping (1000 iterations)
References
Fornell, C., Larcker, D.F., 1981. Evaluating Structural Equation Models with Unobservable
Variables and Measurement Error. J. Mark. Res. 18, 39.
Garrido-Vega, P., Ortega Jimenez, C.H., de los RĂos, J.L.D.P., Morita, M., 2015.
Implementation of technology and production strategy practices: Relationship levels in
different industries. Int. J. Prod. Econ. 161, 201â216.
Hu, L., Bentler, P.M., 1998. Fit indices in covariance structure modeling: Sensitivity to
underparameterized model misspecification. Psychol. Methods 3, 424â453.
Huang, C.-C., Wang, Y.-M., Wu, T.-W., Wang, P.-A., 2013. An Empirical Analysis of the
Antecedents and Performance Consequences of Using the Moodle Platform. Int. J. Inf.
Educ. Technol. 3, 217â221.
Kenny, D.A., 2015. SEM: Fit [WWW Document]. Struct. Equ. Model. URL
http://davidakenny.net/cm/fit.htm (accessed 1.15.19).
Kenny, D.A., Kaniskan, B., McCoach, D.B., 2015. The Performance of RMSEA in Models
With Small Degrees of Freedom. Sociol. Methods Res. 44, 486â507.
Kenny, D.A., McCoach, D.B., 2003. Effect of the Number of Variables on Measures of Fit in
Structural Equation Modeling. Struct. Equ. Model. A Multidiscip. J. 10, 333â351.
Koren, Y., 2013. The rapid responsiveness of RMS. Int. J. Prod. Res. 51, 6817â6827.
Koren, Y., 2010. The Global Manufacturing Revolution. John Wiley & Sons, Ltd, Hoboken, NJ,
USA.
Lionetti, F., Aron, A., Aron, E.N., Burns, G.L., Jagiellowicz, J., Pluess, M., 2018. Dandelions,
tulips and orchids: evidence for the existence of low-sensitive, medium-sensitive and high-
sensitive individuals. Transl. Psychiatry 8, 24.
6. 6
MacHuca, J.A.D., Ortega JimĂŠnez, C.H., Garrido-Vega, P., De Los RĂos, J.L.P.D., 2011. Do
technology and manufacturing strategy links enhance operational performance? Empirical
research in the auto supplier sector. Int. J. Prod. Econ. 133.
Min, S., Mentzer, J.T.J.T., Ladd, R.T.R.T., 2007. A market orientation in supply chain
management. J. Acad. Mark. Sci. 35, 507â522.
Navrady, L.B., Adams, M.J., Chan, S.W.Y., Ritchie, S.J., McIntosh, A.M., McIntosh, A.M.,
2018. Genetic risk of major depressive disorder: the moderating and mediating effects of
neuroticism and psychological resilience on clinical and self-reported depression. Psychol.
Med. 48, 1890â1899.
Ortega-JimĂŠnez, C.H., DomĂnguez Machuca, J.A., GarridoâVega, P., 2014. From lean to
reconfigurability: systematic review of high performance manufacturing. Int. J. Manag.
Sci. Technol. Inf. 99â131.
Ortega-Jimenez, C.H., Garrido-Vega, P., Machuca, J.A.D., 2012. Analysis of interaction fit
between manufacturing strategy and technology management and its impact on
performance. Int. J. Oper. Prod. Manag. 32, 958â981.
Ortega-Jimenez, C.H., Garrido-Vega, P., PĂŠrez DĂez De Los RĂos, J.L., GarcĂa GonzĂĄlez, S.,
2011. Manufacturing strategyâtechnology relationship among auto suppliers. Int. J. Prod.
Econ. 133, 508â517.
Ortega-Jimenez, C.H.O., Machuca, J.A.D., Garrido-Vega, P., Filippini, R., 2015. The pursuit of
responsiveness in production environments: From flexibility to reconfigurability. Int. J.
Prod. Econ. 163, 157â172.
Stanton, N., Landry, Steven, Bucchianico, Giuseppe Di (Eds.), 2014. Advances in human
aspects of transportation, Part III. AHFE Conference.