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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 Ingeniería-Universidad Nacional Autonoma de Honduras (UNAH);
Facultad de Posgrado-Universidad Tecnológica 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 Matemática- Universidad Nacional Autónoma de Honduras (UNAH);
and Facultad de Ingeniería y Arquitectura - Universidad Tecnológica 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 ρC DESCRIPTION
(References)
Item1 0.806 Modularization of Products 0.501 0.745
TM practices
(Jiménez 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-
Jiménez 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: χ2=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 número de variables, si TLI y CFI están ligeramente por debajo de lo esperado, pero el RMSEA es un poco mejor, puede no haber razón
para preocuparse (Kenny and McCoach, 2003). Dado que este es nuestro caso (gran número de variables observadas), hemos optado por el RMSEA
junto con el SRMR como índices 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
ρC>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 ρC
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: = χ2=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)
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6. 6
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