A Rejoinder to Rigdon et al. (2014)

Information Systems Research

Volumn 25, Issue 4, 2014, Pages 785-788

  Aguirre Urreta, Miguel I ^a   Marakas, George M ^b  

^a TEXAS TECH UNIVERSITY   (United States)

^b FLORIDA INTERNATIONAL UNIVERSITY   (United States)

Author keywords

Data generation; Endogenous constructs; Formative specification; Partial least squares; Structural equation model

Indexed keywords

HARDWARE; INFORMATION SYSTEMS;

DATA GENERATION; DIJKSTRA; ENDOGENOUS CONSTRUCTS; PARTIAL LEAST SQUARE (PLS); STRUCTURAL EQUATION MODELING;

LEAST SQUARES APPROXIMATIONS;

EID: 84923293654     PISSN: 10477047     EISSN: 15265536     Source Type: Journal    
DOI: 10.1287/isre.2014.0545     Document Type: Article

References (15)

1. Aguirre-Urreta MI, Marakas GM (2014) Partial least squares and models with formatively specified endogenous constructs: A cautionary note. Inform. Systems Res. 25(4):761-778.
2. Bollen K (2011) Evaluating effect, composite, and causal indicators in structural equation models. MIS Quart. 35(2):359-372.
3. Chin W (1998) The partial least squares approach for structural equation modeling. Marcoulides G, ed. Modern Methods for Business Research (Lawrence Erlbaum Associates, Mahwah, NJ), 295-336.
4. Chin W, Newsted P (1999) Structural equation modeling analysis with small samples using partial least squares. Hoyle R, ed. Statistical Strategies for Small Sample Research (Sage Publications, Thousand Oaks, CA), 307-341.
5. Diamantopoulos A, Riefler P, Roth K (2008) Advancing formative measurement models. J. Bus. Res. 61(12):1203-1218.
6. Goodhue D, Lewis W, Thompson R (2007) Statistical power in analyzing interaction effects: Questioning the advantage of PLS with product indicators. Inform. Systems Res. 18(2):211-227.
7. Goodhue D, Lewis W, Thompson R (2012) Does PLS have advantages for small sample size or non-normal data? MIS Quart. 36(3):981-1001.
8. Hui B, Wold H (1982) Consistency and consistency at large of partial least square estimates. Jöreskorg K, Wold H, eds. Systems Under Indirect Observation-Part II (North-Holland Publishing, New York), 119-130.
9. Jarvis C, MacKenzie S, Podsakoff P (2003) A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Consumer Res. 30(2): 199-218.
10. MacKenzie S, Podsakoff P, Jarvis C (2005) The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. J. Appl. Psych. 90(4):710-730.
11. McIntosh C, Edwards J, Antonakis J (2014) Reflections on partial least squares path modeling. Organ. Res. Methods 17(2):210-251.
12. Petter S, Straub D, Rai A (2007) Specifying formative constructs in information systems research. MIS Quart. 31(4):623-656.
13. Rigdon EE, Becker J-M, Rai A, Ringle CM, Diamantopoulos A, Karahanna E, Straub DW, Dijkstra TK (2014) Conflating antecedents and formative indicators: A comment on Aguirre-Urreta and Marakas. Inform. Systems Res. 25(4):780-784.
14. Ringle C, Sarstedt M, Straub D (2012) Editor's comments: A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quart. 36(1):iii-xiv.
15. Wold H (1982) Soft modeling-the basic design and some extensions. Jöreskorg K, Wold H, eds. Systems Under Indirect Observation-Part II (North-Holland Publishing, New York), 1-54.