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2019 | OriginalPaper | Buchkapitel

9. Methodology

verfasst von : Liliana Avelar-Sosa, Jorge Luis García-Alcaraz, Aidé Aracely Maldonado-Macías

Erschienen in: Evaluation of Supply Chain Performance

Verlag: Springer International Publishing

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Abstract

The goal of this book is to identify risk perception levels, regional impact factors, and manufacturing practices in the manufacturing industry and then to assess the impact of these variables on supply chain performance. The information will be used to construct a series of latent variables via statistical analyses. Then, these variables will be tested and validated according to some criteria. Once validated, the variables will be integrated into different structural equation models to find the relationships among them. Later on, these models will be interpreted with respect to their research hypotheses. Then, industrial implications and conclusions will be provided with regards the impact of the latent variables on supply chain performance (SCP). The following sections describe the process followed to reach our goal.

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Vorheriges Kapitel Supply Chain Evaluation and Methodologies

Nächstes Kapitel Exploratory Analysis of the Data

Aboalkhair AM, Coolen FPA, MacPhee IM (2013) Nonparametric predictive reliability of series of voting systems. Eur J Oper Res 226(1):77–84. https://doi.org/10.1016/j.ejor.2012.11.001MathSciNetCrossRefMATH

Adamson KA, Prion S (2013) Reliability: measuring internal consistency using Cronbach’s α. Clin Simul Nurs 9(5):e179–e180. https://doi.org/10.1016/j.ecns.2012.12.001CrossRef

Akintoye A, McIntosh G, Fitzgerald E (2000) A survey of supply chain collaboration and management in the UK construction industry. Eur J Purchasing Supply Manag 6(3–4):159–168. https://doi.org/10.1016/S0969-7012(00)00012-5CrossRef

Al-Tahat MD, Bataineh KM (2012) Statistical analyses and modeling of the implementation of agile manufacturing tactics in industrial firms. Math Probl Eng. https://doi.org/10.1155/2012/731092CrossRef

Ala-Harja H, Helo P (2014) Green supply chain decisions—case-based performance analysis from the food industry. Transp Res Part E: Logistics Transp Rev 69:97–107. https://doi.org/10.1016/j.tre.2014.05.015CrossRef

Alcaraz JLG, Maldonado AA, Iniesta AA, Robles GC, Hernández GA (2014) A systematic review/survey for JIT implementation: Mexican maquiladoras as case study. Comput Ind 65(4):761–773. https://doi.org/10.1016/j.compind.2014.02.013CrossRef

Alfalla-Luque R, Marin-Garcia JA, Medina-Lopez C (2015) An analysis of the direct and mediated effects of employee commitment and supply chain integration on organisational performance. Int J Prod Econ 162:242–257. https://doi.org/10.1016/j.ijpe.2014.07.004CrossRef

Ambulkar S, Blackhurst J, Grawe S (2015) Firm’s resilience to supply chain disruptions: scale development and empirical examination. J Oper Manag 33–34:111–122. https://doi.org/10.1016/j.jom.2014.11.002CrossRef

Avelar-Sosa L, García-Alcaraz J, Vergara-Villegas O, Maldonado-Macías A, Alor-Hernández G (2015) Impact of traditional and international logistic policies in supply chain performance. Int J Adv Manuf Technol 76(5–8):913–925. https://doi.org/10.1007/s00170-014-6308-3CrossRef

Avelar-Sosa L, García-Alcaraz JL, Castrellón-Torres JP (2014) The effects of some risk factors in the supply chains performance: a case of study. J Appl Res Technol 12(5):958–968. https://doi.org/10.1016/S1665-6423(14)70602-9CrossRef

Ay Y, Karadağ E, Acat MB (2015) The technological pedagogical content knowledge-practical (TPACK-Practical) model: examination of its validity in the Turkish culture via structural equation modeling. Comput Edu 88:97–108. https://doi.org/10.1016/j.compedu.2015.04.017CrossRef

Baxter K, Courage C, Caine K (2015) Chapter 10—surveys. In: Caine KBC (ed) Understanding your users, 2nd edn. Morgan Kaufmann, Boston, pp 264–301. https://doi.org/10.1016/B978-0-12-800232-2.00010-9CrossRef

Bera AK, Jarque CM (1981) Efficient tests for normality, homoscedasticity and serial independence of regression residuals: Monte Carlo Evidence. Econ Lett 7(4):313–318. https://doi.org/10.1016/0165-1765(81)90035-5CrossRef

Boon Sin A, Zailani S, Iranmanesh M, Ramayah T (2015) Structural equation modelling on knowledge creation in Six Sigma DMAIC project and its impact on organizational performance. Int J Prod Econ 168:105–117. https://doi.org/10.1016/j.ijpe.2015.06.007CrossRef

Cohen J (1988) Statistical power analysis for the behavioral sciences. Lawrence Erlbaum, Hillsdale, NJMATH

Crofton C, Darby C, Farquhar M, Clancy C (2005) The CAHPS^® hospital survey: development, testing, and use. Joint Comm J Qual Patient Saf 31(11):655–659. https://doi.org/10.1016/S1553-7250(05)31084-1CrossRef

Cronbach L (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16(3):297–334. https://doi.org/10.1007/BF02310555CrossRefMATH

Chang C-J, Li D-C, Huang Y-H, Chen C-C (2015) A novel gray forecasting model based on the box plot for small manufacturing data sets. Appl Math Comput 265:400–408. https://doi.org/10.1016/j.amc.2015.05.006MathSciNetCrossRef

Chatelin YM, Vinzi VE, Tenenhaus M (2002) State-of-art on PLS path modeling through the available software. University of Connecticut, Connecticut

Chin TA, Hamid ABA, Raslic A, Heng LH (2014) The impact of supply chain integration on operational capability in Malaysian manufacturers. Procedia—Social Behav Sci 130:257–265. https://doi.org/10.1016/j.sbspro.2014.04.030CrossRef

Cho W-J, Chin W-K, Kuo C-T (2004) Effects of alcoholic moderators on anisotropic etching of silicon in aqueous potassium hydroxide solutions. Sensors Actuators, A: Phys 116(2):357–368. https://doi.org/10.1016/j.sna.2004.04.033CrossRef

Christmann A, Van Aelst S (2006) Robust estimation of Cronbach’s alpha. J Multivar Anal 97(7):1660–1674. https://doi.org/10.1016/j.jmva.2005.05.012MathSciNetCrossRefMATH

Desai DA, Kotadiya P, Makwana N, Patel S (2015) Curbing variations in packaging process through Six Sigma way in a large-scale food-processing industry. J Ind Eng Int 11(1):119–129. https://doi.org/10.1007/s40092-014-0082-6CrossRef

Fornell C, Larcker D (1981) Evaluating structural equation models with unobservable variables and measurement error. J Market Res 18(1):39–50CrossRef

Frémont H, Duchamp G, Gracia A, Verdier F (2012) A methodological approach for predictive reliability: practical case studies. Microelectr Reliab 52(12):3035–3042. https://doi.org/10.1016/j.microrel.2012.07.016CrossRef

Galvao AF, Montes-Rojas G, Sosa-Escudero W, Wang L (2013) Tests for skewness and kurtosis in the one-way error component model. J Multivar Anal 122:35–52. https://doi.org/10.1016/j.jmva.2013.07.002MathSciNetCrossRefMATH

García-Alcaraz J, Prieto-Luevano D, Maldonado-Macías A, Blanco-Fernández J, Jiménez-Macías E, Moreno-Jiménez J (2015a) Structural equation modeling to identify the human resource value in the JIT implementation: case maquiladora sector. Int J Adv Manuf Technol 77(5–8):1483–1497. https://doi.org/10.1007/s00170-014-6561-5CrossRef

García-Alcaraz JL, Alvarado-Iniesta A, Blanco-Fernández J, Maldonado-Macías AA, Jiménez-Macías E, Saenz-Díez Muro JC (2015b) The impact of demand and supplier on wine’s supply chain performance. J Food Process Eng:n/a-n/a. https://doi.org/10.1111/jfpe.12257CrossRef

García-Alcaraz JL, Prieto-Luevano DJ, Maldonado-Macías AA, Blanco-Fernández J, Jiménez-Macías E, Moreno-Jiménez JM (2015c) Structural equation modeling to identify the human resource value in the JIT implementation: case maquiladora sector. Int J Adv Manuf Technol 77(5):1483–1497. https://doi.org/10.1007/s00170-014-6561-5CrossRef

García J, Maldonado A, Alvarado A, Rivera D (2014a) Human critical success factors for kaizen and its impacts in industrial performance. Int J Adv Manuf Technol 70(9–12):2187–2198. https://doi.org/10.1007/s00170-013-5445-4CrossRef

García J, Rivera D, Iniesta A (2013a) Critical success factors for Kaizen implementation in manufacturing industries in Mexico. Int J Adv Manuf Technol 68(1–4):537–545. https://doi.org/10.1007/s00170-013-4750-2CrossRef

García JL, Rivera DG, Iniesta AA (2013b) Critical success factors for Kaizen implementation in manufacturing industries in Mexico. Int J Adv Manuf Technol 68(1–4):537–545CrossRef

García JL, Rivera L, Blanco J, Jiménez E, Martínez E (2014b) Structural equations modelling for relational analysis of JIT performance in maquiladora sector. Int J Prod Res 52(17):4931–4949. https://doi.org/10.1080/00207543.2014.885143CrossRef

Giaquinta M (2009) Mathematical analysis: an introduction to functions of several variables. Springer, New York, NY, USACrossRef

Giles JA, Giles DEA (1996) Risk of a homoscedasticity pre-test estimator of the regression scale under LINEX loss. J Stat Plann Infer 50(1):21–35. https://doi.org/10.1016/0378-3758(95)00043-7MathSciNetCrossRefMATH

Giménez E, Crespi M, Garrido MS, Gil AJ (2012) Multivariate outlier detection based on robust computation of Mahalanobis distances. Application to positioning assisted by RTK GNSS Networks. Int J Appl Earth Obs Geoinf 16:94–100. https://doi.org/10.1016/j.jag.2011.11.011CrossRef

Gligor DM, Esmark CL, Holcomb MC (2015) Performance outcomes of supply chain agility: when should you be agile? J Oper Manag 33–34:71–82. https://doi.org/10.1016/j.jom.2014.10.008CrossRef

Godfrey LG, Orme CD (1991) Testing for skewness of regression disturbances. Econ Lett 37(1):31–34. https://doi.org/10.1016/0165-1765(91)90238-GCrossRefMATH

Gonzalez AA, Girotti M, Wakefield TW, Dimick JB (2013) Predictive reliability of hospital readmission rates in vascular surgery. J Vasc Surg 57(5):52S. https://doi.org/10.1016/j.jvs.2013.02.186CrossRef

Ha J, Seok S, Lee J-S (2015) A precise ranking method for outlier detection. Inf Sci 324:88–107. https://doi.org/10.1016/j.ins.2015.06.030MathSciNetCrossRefMATH

Hair J, Anderson R, Tatham R (1987) Multivariate data analysis. Macmillan, New York, NY

Hair J, Black W, Babin B, Anderson R (2009) Multivariate data analysis. Prentice Hall, Upper Saddle River, NJ

Hansson G, Stewart GAB, Sharp D, Lee A, Cotton DWK, Rogers S, Wilson APR (1993) DATA PRESENTATION: box-plots for microbiologists? The Lancet 341(8840):282. https://doi.org/10.1016/0140-6736(93)92626-5CrossRef

Iacobucci D, Posavac SS, Kardes FR, Schneider MJ, Popovich DL (2015) Toward a more nuanced understanding of the statistical properties of a median split. J Consum Psychol 25(4):652–665. https://doi.org/10.1016/j.jcps.2014.12.002CrossRef

Intakhan P (2014) Direct and indirect effects of top management support on ABC implementation success: evidence from ISO 9000 certified companies in Thailand. Procedia—Social Behav Sci 164:458–470. https://doi.org/10.1016/j.sbspro.2014.11.103CrossRef

Jackson LA, Singh D (2015) Environmental rankings and financial performance: an analysis of firms in the US food and beverage supply chain. Tour Manag Perspect 14:25–33. https://doi.org/10.1016/j.tmp.2015.02.002CrossRef

Jadhav NH, Kashid DN, Kulkarni SR (2014) Subset selection in multiple linear regression in the presence of outlier and multicollinearity. Stat Methodol 19:44–59. https://doi.org/10.1016/j.stamet.2014.02.002MathSciNetCrossRef

Jakhar SK (2015) Performance evaluation and a flow allocation decision model for a sustainable supply chain of an apparel industry. J Clean Prod 87:391–413. https://doi.org/10.1016/j.jclepro.2014.09.089CrossRef

Jarque CM, Bera AK (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ Lett 6(3):255–259. https://doi.org/10.1016/0165-1765(80)90024-5MathSciNetCrossRef

Katrutsa A, Strijov V (2017) Comprehensive study of feature selection methods to solve multicollinearity problem according to evaluation criteria. Expert Syst Appl 76:1–11. https://doi.org/10.1016/j.eswa.2017.01.048CrossRef

Kaynak R, Sert T, Sert G, Akyuz B (2015) Supply chain unethical behaviors and continuity of relationship: using the PLS approach for testing moderation effects of inter-organizational justice. Int J Prod Econ 162:83–91. https://doi.org/10.1016/j.ijpe.2015.01.010CrossRef

Kerman SC, McDonald JB (2013) Skewness–kurtosis bounds for the skewed generalized and related distributions. Stat Probab Lett 83(9):2129–2134. https://doi.org/10.1016/j.spl.2013.05.028MathSciNetCrossRefMATH

Ketikidis PH, Koh SCL, Dimitriadis N, Gunasekaran A, Kehajova M (2008) The use of information systems for logistics and supply chain management in South East Europe: current status and future direction. Omega 36(4):592–599. https://doi.org/10.1016/j.omega.2006.11.010CrossRef

Kim T-H, White H (2004) On more robust estimation of skewness and kurtosis. Finan Res Lett 1(1):56–73. https://doi.org/10.1016/S1544-6123(03)00003-5CrossRef

Kock N (2011) A mathematical analysis of the evolution of human mate choice traits: implications for evolutionary psychologists. J Evol Psychol 9(3):219–247CrossRef

Kock N (2013) Using WarpPLS in e-collaboration studies: what if I have only one group and one condition? Int J e-Collab 9(3):1–12MathSciNet

Kock N (2015) WarpPLS 5.0 user manual. scriptwarp systems, Laredo, TX, USA

Kock N, Lynn GS (2012) Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. J Assoc Inf Syst 13(7):546–580

Kock N, Verville J, Danesh-Pajou A, DeLuca D (2009) Communication flow orientation in business process modeling and its effect on redesign success: results from a field study. Decision Support Syst 46(2):562–575. https://doi.org/10.1016/j.dss.2008.10.002CrossRef

Kopalle PK, Lehmann DR (1997) Alpha inflation? The impact of eliminating scale items on Cronbach’s alpha. Organ Behav Human Decis Process 70(3):189–197. https://doi.org/10.1006/obhd.1997.2702CrossRef

Kottner J, Streiner DL (2010) Internal consistency and Cronbach’s α: a comment on Beeckman et al. (2010). Int J Nurs Stud 47(7):926–928. https://doi.org/10.1016/j.ijnurstu.2009.12.018CrossRef

Larson NI, Neumark-Sztainer D, Story M, Hannan PJ, van den Berg P (2009) Survey development for assessing correlates of young adults’ weight-related behaviors. J Am Diet Assoc 109(9):A72. https://doi.org/10.1016/j.jada.2009.06.237CrossRef

Lecchi M (2011) Evaluation of predictive assessment reliability on corroded transmission pipelines. J Nat Gas Sci Eng 3(5):633–641. https://doi.org/10.1016/j.jngse.2011.07.005CrossRef

Lem S, Onghena P, Verschaffel L, Van Dooren W (2013) The heuristic interpretation of box plots. Learn Instr 26:22–35. https://doi.org/10.1016/j.learninstruc.2013.01.001CrossRef

Li D-C, Huang W-T, Chen C-C, Chang C-J (2014) Employing box plots to build high-dimensional manufacturing models for new products in TFT-LCD plants. Neurocomputing 142:73–85. https://doi.org/10.1016/j.neucom.2014.03.043CrossRef

Loperfido N (2013) Skewness and the linear discriminant function. Stat Probab Lett 83(1):93–99. https://doi.org/10.1016/j.spl.2012.08.032MathSciNetCrossRefMATH

Lourenço VM, Pires AM (2014) M-regression, false discovery rates and outlier detection with application to genetic association studies. Comput Stat Data Anal 78:33–42. https://doi.org/10.1016/j.csda.2014.03.019MathSciNetCrossRef

MacKinnon DP, Krull JL, Lockwood CM (2000) Equivalence of the mediation, confounding and suppression effect. Prev Sci 1(4):173–181CrossRef

Manenti F, Buzzi-Ferraris G (2009) Criteria for outliers detection in nonlinear regression problems. In: Jacek J, Jan T (eds) Computer aided chemical engineering, vol 26. Elsevier, pp 913–917. http://dx.doi.org/10.1016/S1570-7946(09)70152-X

Mason R, Brown WG (1975) Multicollinearity problems and ridge regression in sociological models. Soc Sci Res 4(2):135–149. https://doi.org/10.1016/0049-089X(75)90008-3CrossRef

Midiala Oropesa V, Jorge Luis García A, Aidé Aracely Maldonado M, Valeria Martínez L (2016) The impact of managerial commitment and Kaizen benefits on companies. J Manuf Technol Manag 27(5):692–712. https://doi.org/10.1108/JMTM-02-2016-0021CrossRef

Moon KK-L, Yi CY, Ngai EWT (2012) An instrument for measuring supply chain flexibility for the textile and clothing companies. Eur J Oper Res 222(2):191–203. https://doi.org/10.1016/j.ejor.2012.04.027CrossRef

Nunnally J, Bernstein I (1994) Psychometric theory. McGraw-Hill, New York, NY, USA

Ohtani K, Toyoda T (1980) Estimation of regression coefficients after a preliminary test for homoscedasticity. J Econ 12(2):151–159. https://doi.org/10.1016/0304-4076(80)90003-2MathSciNetCrossRefMATH

Oropesa-Vento M, García-Alcaraz JL, Rivera L, Manotas DF (2015) Effects of management commitment and organization of work teams on the benefits of Kaizen: planning stage. Efectos del compromiso gerencial y organización de equipos de trabajo en los beneficios del Kaizen: Etapa de planeación 82(191):76

Park J (2017) Tolerance intervals from ridge regression in the presence of multicollinearity and high dimension. Stat ProbabLett 121:128–135. https://doi.org/10.1016/j.spl.2016.10.016MathSciNetCrossRefMATH

Patil N, Das D, Pecht M (2015) Anomaly detection for IGBTs using Mahalanobis distance. Microelectr Reliab 55(7):1054–1059. https://doi.org/10.1016/j.microrel.2015.04.001CrossRef

Pearl J (2009) Causality: models, reasoning, and inference. Cambridge University Press, Cambridge, EnglandCrossRef

Pinto FST, Fogliatto FS, Qannari EM (2014) A method for panelists’ consistency assessment in sensory evaluations based on the Cronbach’s alpha coefficient. Food Qual Prefer 32:41–47. https://doi.org/10.1016/j.foodqual.2013.06.006CrossRef

Preacher KJ, Hayes AF (2004) SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav Res Methods Instrum Comput 36(4):617–731CrossRef

Rasoolimanesh SM, Jaafar M, Kock N, Ramayah T (2015) A revised framework of social exchange theory to investigate the factors influencing residents’ perceptions. Tour Manag Perspect 16:335–345. https://doi.org/10.1016/j.tmp.2015.10.001CrossRef

Reinheimer DA (2007) Validating placement: local means, multiple measures. Assessing Writ 12(3):170–179. https://doi.org/10.1016/j.asw.2008.02.004CrossRef

Rimoldini L (2014) Weighted skewness and kurtosis unbiased by sample size and Gaussian uncertainties. Astron Comput 5:1–8. https://doi.org/10.1016/j.ascom.2014.02.001CrossRef

Rindskopf D (2015) Reliability: measurement. In: Wright JD (ed) International encyclopedia of the social and behavioral sciences, 2nd edn. Elsevier, Oxford, pp 248–252. doi:http://dx.doi.org/10.1016/B978-0-08-097086-8.44050-XCrossRef

Roni SM, Djajadikerta H, Ahmad MAN (2015) PLS-SEM approach to second-order factor of deviant behaviour: constructing perceived behavioural control. Procedia Econ Finan 28:249–253. https://doi.org/10.1016/S2212-5671(15)01107-7CrossRef

Rosenthal R, Rosnow R (1991) Essentials of behavioral research: methods and data analysis. Mc Graw Hill, Boston, MA

Rouquette A, Badley EM, Falissard B, Dub T, Leplege A, Coste J (2015) Moderators, mediators, and bidirectional relationships in the International Classification of Functioning, disability and health (ICF) framework: an empirical investigation using a longitudinal design and Structural Equation Modeling (SEM). Soc Sci Med 135:133–142. https://doi.org/10.1016/j.socscimed.2015.05.007CrossRef

Schubring S, Lorscheid I, Meyer M, Ringle CM (2016) The PLS agent: predictive modeling with PLS-SEM and agent-based simulation. J Bus Res 69(10):4604–4612. https://doi.org/10.1016/j.jbusres.2016.03.052CrossRef

Simpson RJ, Johnson TA, Amara IA (1988) The box-plot: an exploratory analysis graph for biomedical publications. Am Heart J 116(6):1663–1665. https://doi.org/10.1016/0002-8703(88)90791-0CrossRef

Spirtes P, Glymour C, Scheines R (1993) Causality, prediction and search. Springer-Verlag, Berlin, GermanyCrossRef

Swafford PM, Ghosh S, Murthy N (2006) The antecedents of supply chain agility of a firm: scale development and model testing. J Oper Manag 24(2):170–188. https://doi.org/10.1016/j.jom.2005.05.002CrossRef

Tastle WJ, Wierman MJ (2007) Consensus and dissention: a measure of ordinal dispersion. Int J Approximate Reason 45(3):531–545. https://doi.org/10.1016/j.ijar.2006.06.024MathSciNetCrossRefMATH

Temme D, Kreis H, Hildebrandt L (2006) PLS path modeling—a software review. Humboldt University Berlin, Berlin, Germany, Institute of Marketing

Tenenhaus M, Vinzi VE, Chatelin YM, Lauro C (2005) PLS path modeling. Comput Stat Data Anal 48(1):159–205MathSciNetCrossRef

Tlapa D, Limon J, García-Alcaraz JL, Baez Y, Sánchez C (2016) Six Sigma enablers in Mexican manufacturing companies: a proposed model. Ind Manag Data Syst 116(5):926–959. https://doi.org/10.1108/IMDS-06-2015-0265CrossRef

Todeschini R, Ballabio D, Consonni V, Sahigara F, Filzmoser P (2013) Locally centred Mahalanobis distance: a new distance measure with salient features towards outlier detection. Analytica Chimica Acta 787:1–9. https://doi.org/10.1016/j.aca.2013.04.034CrossRef

Troskie CG, Conradie WJ (1986) The distribution of the ratios of characteristics roots (condition numbers) and their applications in principal component or ridge regression. Linear Algebra Appl 82:255–279. https://doi.org/10.1016/0024-3795(86)90156-4MathSciNetCrossRefMATH

Valaei N, Baroto MB (2017) Modelling continuance intention of citizens in government Facebook page: a complementary PLS approach. Comput Human Behav 73:224–237. https://doi.org/10.1016/j.chb.2017.03.047CrossRef

Villanueva-Ponce R, Garcia-Alcaraz J, Cortes-Robles G, Romero-Gonzalez J, Jiménez-Macías E, Blanco-Fernández J (2015) Impact of suppliers’ green attributes in corporate image and financial profit: case maquiladora industry. Int J Adv Manuf Technol 80:1–20. https://doi.org/10.1007/s00170-015-7082-6CrossRef

Wagner CH (1982) Simpson’s paradox in real life. Am Stati 36(1):46–48

Wang C, Li J, Guo P (2015) The normalized interval regression model with outlier detection and its real-world application to house pricing problems. Fuzzy Sets Syst 274:109–123. https://doi.org/10.1016/j.fss.2014.06.009MathSciNetCrossRefMATH

Wang S-G, Tse S-K, Chow S-C (1990) On the measures of multicollinearity in least squares regression. Stat Probab Lett 9(4):347–355. https://doi.org/10.1016/0167-7152(90)90145-WMathSciNetCrossRefMATH

Wetzels M, Odekerken-Schroder G, van Oppen C (2009) Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quart 33(1):177–196CrossRef

Willaby HW, Costa DSJ, Burns BD, MacCann C, Roberts RD (2015) Testing complex models with small sample sizes: a historical overview and empirical demonstration of what partial least squares (PLS) can offer differential psychology. Personal Ind Diff 84:73–78. https://doi.org/10.1016/j.paid.2014.09.008CrossRef

Withers BE, Ebrahimpour M, Hikmet N (1997) An exploration of the impact of TQM and JIT on ISO 9000 registered companies. Int J Prod Econ 53(2):209–216. https://doi.org/10.1016/S0925-5273(97)00118-7CrossRef

Withers CS (1987) The bias and skewness of L1-estimates in regression. Comput Stat Data Anal 5(4):301–303. https://doi.org/10.1016/0167-9473(87)90053-3CrossRefMATH

Wold S, Trygg J, Berglund A, Antti H (2001) Some recent developments in PLS modeling. Chemometr Intel Labor Syst 58(2):131–150. https://doi.org/10.1016/S0169-7439(01)00156-3CrossRef

Wooldridge JM (1991) A note on computing r-squared and adjusted r-squared for trending and seasonal data. Econ Lett 36(1):49–54CrossRef

Xiaojun L, Morris JM (1991) On measuring asymmetry and the reliability of the skewness measure. Stat Probab Lett 12(3):267–271. https://doi.org/10.1016/0167-7152(91)90089-AMathSciNetCrossRef

Zimmermann R (2015) On the condition number anomaly of Gaussian correlation matrices. Linear Algebra Appl 466:512–526. https://doi.org/10.1016/j.laa.2014.10.038MathSciNetCrossRefMATH

Titel: Methodology
verfasst von: Liliana Avelar-Sosa
Jorge Luis García-Alcaraz
Aidé Aracely Maldonado-Macías
Verlag: Springer International Publishing
Buch: Evaluation of Supply Chain Performance
Print ISBN: 978-3-319-93875-2

Electronic ISBN: 978-3-319-93876-9

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-93876-9_9

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