Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Eng    2021, Vol. 8 Issue (3) : 356-369    https://doi.org/10.1007/s42524-021-0153-5
REVIEW ARTICLE
Do right PLS and do PLS right: A critical review of the application of PLS-SEM in construction management research
Ningshuang ZENG1, Yan LIU2(), Pan GONG3, Marcel HERTOGH2, Markus KÖNIG1
1. Civil and Environmental Engineering, Ruhr-Universität Bochum, Universitätsstraße 150, D-44780 Bochum, Germany
2. Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
3. School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China
 Download: PDF(268 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Partial least squares structural equation modeling (PLS-SEM) is a modern multivariate analysis technique with a demonstrated ability to estimate theoretically established cause-effect relationship models. This technique has been increasingly adopted in construction management research over the last two decades. Accordingly, a critical review of studies adopting PLS-SEM appears to be a timely and valuable endeavor. This paper offers a critical review of 139 articles that applied PLS-SEM from 2002 to 2019. Results show that the misuse of PLS-SEM can be avoided. Critical issues related to the application of PLS-SEM, research design, model development, and model evaluation are discussed in detail. This paper is the first to highlight the use and misuse of PLS-SEM in the construction management area and provides recommendations to facilitate the future application of PLS-SEM in this field.
Keywords PLS      SEM      construction management      literature review      misuse     
Corresponding Author(s): Yan LIU   
Just Accepted Date: 30 January 2021   Online First Date: 26 March 2021    Issue Date: 13 July 2021
 Cite this article:   
Ningshuang ZENG,Yan LIU,Pan GONG, et al. Do right PLS and do PLS right: A critical review of the application of PLS-SEM in construction management research[J]. Front. Eng, 2021, 8(3): 356-369.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-021-0153-5
https://academic.hep.com.cn/fem/EN/Y2021/V8/I3/356
Source title 2002–2003 2004–2005 2006–2007 2008–2009 2010–2011 2012–2013 2014–2015 2016–2017 2018–2019 Total
Accident Analysis and Prevention 1 1
Asian Social Science 1 1
Automation in Construction 1 2 1 4
Canadian Journal of Administrative Sciences 1 1
Computer-Aided Civil and Infrastructure Engineering 1 1
Construction Economics and Building 2 1 3
Construction Innovation 1 1
Construction Management and Economics 1 2 1 4
Engineering, Construction and Architectural Management 1 1 1 7 10
Global Business Review 1 1
Group Decision and Negotiation 1 1
Interdisciplinary Journal of Information, Knowledge and Management 1 1
International Journal of Civil Engineering and Technology 3 3
International Journal of Construction Education and Research 1 1
International Journal of Construction Management 3 3
International Journal of Enterprise Information Systems 1 1
International Journal of Innovation and Technology Management 1 1
International Journal of Innovative Technology and Exploring Engineering 1 1
International Journal of Managing Projects in Business 2 2
International Journal of Productivity and Quality Management 1 1
International Journal of Project Management 1 1 12 13 4 31
International Journal of Supply Chain Management 1 1
International Journal of Sustainable Construction Engineering and Technology 1 1
International Journal of Technology 1 1
Journal of Civil Engineering and Management 1 2 3
Journal of Cleaner Production 3 2 5
Journal of Construction Engineering and Management 1 1 1 2 7 6 5 23
Journal of Construction in Developing Countries 1 1
Journal of Financial Management of Property and Construction 2 2
Journal of Global Information Technology Management 1 1
Journal of Legal Affairs and Dispute Resolution in Engineering and Construction 1 1
Journal of Management in Engineering 1 6 5 12
Journal of Science and Technology Policy Management 1 1
KSCE Journal of Civil Engineering 1 1
Management Decision 1 1
Production Planning and Control 1 1
Research Journal of Applied Sciences, Engineering and Technology 1 1 2
Safety and Health at Work 1 1
Science and Engineering Ethics 1 1 2
Sustainability 4 4
Sustainable Cities and Society 1 1
Transport Policy 1 1
Total 1 0 0 3 5 7 30 43 50 139
Tab.1  Number of articles by journal/year
Fig.1  Number of PLS-SEM articles by year.
Specific reason Number of articles Percentage (of 139)
Small sample size 81 58.27%
Non-normal data 56 40.29%
Exploratory research 44 31.65%
Formative measures 23 16.55%
Focus on prediction 20 14.39%
Model complexity 20 14.39%
Theory development 10 7.19%
Theory validation 9 6.47%
Categorical variables 7 5.04%
Mediation effect 6 4.32%
Not specified 11 7.91%
Tab.2  Reasons for using PLS-SEM
Sampling size Number of articles Sampling characteristics Number of articles Percentage (of 139)
Mean 165.14 Less than 100 observations 41 29.50%
Median 122 Non-response bias reported 120 86.33%
Range (25, 1387) Content validity reported 74 53.24%
Tab.3  Sampling size and characteristics
Number of articles Percentage (of 139)
Software
SmartPLS 94 67.63%
PLS Graph 10 7.19%
Warp PLS 3 2.16%
PLS-PM package in R 1 0.72%
Not reported 31 22.30%
Subsamples by bootstrapping
Under 500 6 4.32%
500–999 12 8.63%
1000–4999 10 7.19%
5000 and over 49 35.25%
Not reported 62 44.60%
Tab.4  Technical reporting
Model type Number of articles Percentage (of 141) Model feature Number of articles
Mode of measurement model Total number of latent variables
Reflective (only) 107 75.89% Mean 7.20
Formative (only) 13 9.22% Median 6
Reflective and formative 16 11.35% Range (3, 41)
Not specified (as reflective evaluated) 5 3.55% Total number of indicators
Number of models with mediator/moderator variables Mean 33.45
Mediator (only) 49 34.75% Median 27.50
Moderator (only) 8 5.67% Range (8, 274)
Mediator and moderator 13 9.22% Number of indicators per reflective construct a
Construct structure Mean 4.57
Single-item constructs 116 82.27% Median 4.50
Higher-order constructs (i.e., hierarchical component analysis) 22 15.60% Range (1, 98)
Nonlinear relationships 1 0.71% Number of indicators per formative construct b
Not specified 2 1.42% Mean 4.94
Model modified in the course of the analysis 7 4.96% Median 5.58
Range (2, 12)
Tab.5  Model descriptive statistics
Criterion Empirical test criterion in PLS-SEM Number of models Percentage (of 128)
Reliability
Indicator reliability Indicator loading 101 78.91%
Not reported 27 21.09%
Internal consistency reliability Cronbach’s α (only) 9 7.03%
CR (only) 36 28.13%
Cronbach’s α and CR 79 61.72%
Not reported 4 3.13%
Convergent validity AVE 122 95.31%
Not reported 6 4.69%
Discriminant validity Fornell-Larcker criterion (only) 41 32.03%
Cross-loadings (only) 2 1.56%
Fornell-Larcker criterion and Cross-loadings 66 51.56%
Not reported 19 14.84%
Evaluation overview All reflective criteria evaluated 68 53.13%
Partial reflective criteria evaluated 58 45.31%
No evaluation reported 2 1.56%
Tab.6  Reflective measurement model statistics
Criterion ?Empirical test criterion in PLS-SEM Number of models Percentage (of 29)
?Absolute indicator contribution to the construct ?Indicator weight 21 72.41%
Not reported 8 27.59%
?Significance of weights ?t-value/p-value, 90% ?significance level (α = 0.10) 18 62.07%
Not reported 11 37.93%
?Multicollinearity VIF/tolerance 18 62.07%
Not reported 11 37.93%
Evaluation overview All formative ?criteria evaluated 15 51.72%
Partial formative ?criteria evaluated 8 27.59%
Reflective evaluation ?criteria applied 4 13.79%
No evaluation reported 2 6.90%
Tab.7  Formative measurement model statistics
Criterion ?Empirical test criterion in PLS-SEM Number of models Percentage (of 141)
?Path coefficient b absolute value 141 100%
Not reported 0 0%
Significance of path coefficient ?t-value/p-value, 90% ?significance level (α = 0.10) 30 21.28%
?t-value/p-value, 95% ?significance level (α = 0.05) 69 48.94%
?t-value/p-value, 99% ?significance level (α = 0.01) 25 17.73%
?t-value/p-value, ?99.9% significance level (α = 0.001) 10 7.09%
Not reported 7 4.96%
??Coefficient of determination R2 (only) 81 57.45%
R2 and effect size f2 37 26.24%
Not reported 23 16.31%
??Predictive relevance Cross-validated redundancy Q2 (only) 29 20.57%
Cross-validated redundancy Q2 and effect size q2 2 1.42%
Not reported 110 78.01%
?Bootstrap confidence intervals (CI) 95% CI 4 2.84%
97.5% CI 1 0.71%
None 136 96.45%
?Total effect Total effect 8 5.67%
None 133 94.33%
Tab.8  Structural model statistics
1 M Ashraf (2004). A critical look at the use of group projects as a pedagogical tool. Journal of Education for Business, 79(4): 213–216
https://doi.org/10.3200/JOEB.79.4.213-216
2 R P Bagozzi (2011). Measurement and meaning in information systems and organizational research: Methodological and philosophical foundations. Management Information Systems Quarterly, 35(2): 261–292
https://doi.org/10.2307/23044044
3 R P Bagozzi, Y Yi (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1): 74–94
https://doi.org/10.1007/BF02723327
4 J M Becker, A Rai, C M Ringle, F Völckner (2013). Discovering unobserved heterogeneity in structural equation models to avert validity threats. Management Information Systems Quarterly, 37(3): 665–694
https://doi.org/10.25300/MISQ/2013/37.3.01
5 M Bentler (1980). Multivariate analysis with latent variables: Causal modeling. Annual Review of Psychology, 31(1): 419–456
https://doi.org/10.1146/annurev.ps.31.020180.002223
6 T Bjorvatn, A Wald (2018). Project complexity and team-level absorptive capacity as drivers of project management performance. International Journal of Project Management, 36(6): 876–888
https://doi.org/10.1016/j.ijproman.2018.05.003
7 T Blomquist, M Hällgren, A Nilsson, A Söderholm (2010). Project-as-practice: In search of project management research that matters. Project Management Journal, 41(1): 5–16
https://doi.org/10.1002/pmj.20141
8 K Bollen, R Lennox (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2): 305–314
https://doi.org/10.1037/0033-2909.110.2.305
9 J Bröchner, B C Björk (2008). Where to submit? Journal choice by construction management authors. Construction Management and Economics, 26(7): 739–749
https://doi.org/10.1080/01446190802017698
10 D Cao, H Li, G Wang (2014). Impacts of isomorphic pressures on BIM adoption in construction projects. Journal of Construction Engineering and Management, 140(12): 04014056
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000903
11 W W Chin (1998). The partial least squares approach to structural equation modelling. In: Marcoulides G A, ed. Modern Methods for Business Research. London: Lawrence Erlbaum Associates, 295–336
12 W W Chin (2010). How to write up and report PLS analyses. In: Esposito Vinzi V, Chin W W, Henseler J, Wang H, eds. Handbook of Partial Least Squares. Berlin: Springer, 655–690
13 W W Chin, R A Peterson, S P Brown (2008). Structural equation modeling in marketing: Some practical reminders. Journal of Marketing Theory and Practice, 16(4): 287–298
https://doi.org/10.2753/MTP1069-6679160402
14 A L Comrey (1993). A First Course in Factor Analysis. London: Psychology Press
15 L J Cronbach (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3): 297–334
https://doi.org/10.1007/BF02310555
16 A Diamantopoulos, P Riefler, K P Roth (2008). Advancing formative measurement models. Journal of Business Research, 61(12): 1203–1218
https://doi.org/10.1016/j.jbusres.2008.01.009
17 A Diamantopoulos, H M Winklhofer (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2): 269–277
https://doi.org/10.1509/jmkr.38.2.269.18845
18 T K Dijkstra (2010). Latent variables and indices: Herman Wold’s basic design and partial least squares. In: Esposito Vinzi V, Chin W W, Henseler J, Wang H, eds. Handbook of Partial Least Squares. Berlin: Springer, 23–46
19 R F Fellows, A M M Liu (2015). Research Methods for Construction. 4th ed. Hoboken, NJ: John Wiley & Sons
20 C Fornell, F L Bookstein (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4): 440–452
https://doi.org/10.1177/002224378201900406
21 C Fornell, D F Larcker (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1): 39–50
https://doi.org/10.1177/002224378101800104
22 G Goertz, J Mahoney (2012). A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences. Princeton: Princeton University Press
23 M J Grant, A Booth (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2): 91–108
https://doi.org/10.1111/j.1471-1842.2009.00848.x pmid: 19490148
24 S Gregor, G Klein (2014). Eight obstacles to overcome in the theory testing genre. Journal of the Association for Information Systems, 15(11): 5
https://doi.org/10.17705/1jais.00382
25 S P Gudergan, C M Ringle, S Wende, A Will (2008). Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, 61(12): 1238–1249
https://doi.org/10.1016/j.jbusres.2008.01.012
26 C Hahn, M D Johnson, A Herrmann, F Huber (2002). Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Business Review, 54(3): 243–269
https://doi.org/10.1007/BF03396655
27 J F Hair Jr, M C Howard, C Nitzl (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109: 101–110
https://doi.org/10.1016/j.jbusres.2019.11.069
28 J F Hair Jr, C M Ringle, M Sarstedt (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2): 139–152
https://doi.org/10.2753/MTP1069-6679190202
29 J F Hair Jr, M Sarstedt, L Hopkins, V G Kuppelwieser (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2): 106–121
https://doi.org/10.1108/EBR-10-2013-0128
30 J F Hair Jr, M Sarstedt, C M Ringle, J A Mena (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3): 414–433
https://doi.org/10.1007/s11747-011-0261-6
31 A Hartmann, M Hietbrink (2013). An exploratory study on the relationship between stakeholder expectations, experiences and satisfaction in road maintenance. Construction Management and Economics, 31(4): 345–358
https://doi.org/10.1080/01446193.2013.768772
32 T G Harwood, T Garry (2003). An overview of content analysis. Marketing Review, 3(4): 479–498
https://doi.org/10.1362/146934703771910080
33 J Henseler, W W Chin (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17(1): 82–109
https://doi.org/10.1080/10705510903439003
34 J Hulland (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2): 195–204
https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7
35 C B Jarvis, S B MacKenzie, M Podsakoff (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2): 199–218
https://doi.org/10.1086/376806
36 M Kante, C Chepken, R Oboko (2018). Partial least square structural equation modelling’s use in information systems: An updated guideline of practices in exploratory settings. Journal of Research & Innovation, 6(1): 49–67
37 G Klein, R Müller (2019). Quantitative research submissions to project management. Project Management Journal, 50(3): 263–265
https://doi.org/10.1177/8756972819840141
38 L Koskela (2017). Why is management research irrelevant? Construction Management and Economics, 35(1–2): 4–23
https://doi.org/10.1080/01446193.2016.1272759
39 A Lachenbruch, J Cohen (1989). Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates
40 S Mohamed (2002). Safety climate in construction site environments. Journal of Construction Engineering and Management, 128(5): 375–384
https://doi.org/10.1061/(ASCE)0733-9364(2002)128:5(375)
41 E A Nadhim, C K H Hon, B Xia, I Stewart, D Fang (2018). Investigating the relationships between safety climate and safety performance indicators in retrofitting works. Construction Economics and Building, 18(2): 110–129
https://doi.org/10.5130/AJCEB.v18i2.5994
42 Y Ning, F Y Y Ling (2013). Reducing hindrances to adoption of relational behaviors in public construction projects. Journal of Construction Engineering and Management, 139(11): 04013017
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000745
43 C Nitzl, W W Chin (2017). The case of partial least squares (PLS) path modeling in managerial accounting research. Journal of Management Control, 28(2): 137–156
https://doi.org/10.1007/s00187-017-0249-6
44 J Nunnally (1978). Psychometric Methods. New York: McGraw-Hill Book Co.
45 J P Peter (1981). Construct validity: A review of basic issues and marketing practices. Journal of Marketing Research, 18(2): 133–145
https://doi.org/10.1177/002224378101800201
46 S Petter, D Straub, A Rai (2007). Specifying formative constructs in information systems research. Management Information Systems Quarterly, 31(4): 623–656
https://doi.org/10.2307/25148814
47 C M Ringle, M Sarstedt, R Mitchell, S P Gudergan (2018). Partial least squares structural equation modeling in HRM research. International Journal of Human Resource Management, 31(12): 1617–1643
https://doi.org/10.1080/09585192.2017.1416655
48 C M Ringle, M Sarstedt, D W Straub (2012). A critical look at the use of PLS-SEM in MIS quarterly. Management Information Systems Quarterly, 36(1): iii–xiv
https://doi.org/10.2307/41410402
49 J R Rossiter (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19(4): 305–335
https://doi.org/10.1016/S0167-8116(02)00097-6
50 P N Sharma, G Shmueli, M Sarstedt, N Danks, S Ray (2019). Prediction–oriented model selection in partial least squares path modeling. Decision Sciences, on line, doi: 10.1111/deci.12329
51 P Spector (1992). Summated Rating Scale Construction. London: Sage Publications Inc.
52 S Streukens, S Leroi-Werelds (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34(6): 618–632
https://doi.org/10.1016/j.emj.2016.06.003
53 M Suprapto, H L M Bakker, H G Mooi, M J C M Hertogh (2016). How do contract types and incentives matter to project performance? International Journal of Project Management, 34(6): 1071–1087
https://doi.org/10.1016/j.ijproman.2015.08.003
54 G Themistocleous, S H Wearne (2000). Project management topic coverage in journals. International Journal of Project Management, 18(1): 7–11
https://doi.org/10.1016/S0263-7863(99)00030-7
55 J R Turner, R Müller (2003). On the nature of the project as a temporary organization. International Journal of Project Management, 21(1): 1–8
https://doi.org/10.1016/S0263-7863(02)00020-0
56 J P Weir (2005). Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. Journal of Strength and Conditioning Research, 19(1): 231–240
pmid: 15705040
57 Q Wen, M Qiang, N An (2017). Collaborating with construction management consultants in project execution: Responsibility delegation and capability integration. Journal of Construction Engineering and Management, 143(7): 04017021
https://doi.org/10.1061/(ASCE)CO.1943-7862.0001312
58 M Wetzels, G Odekerken-Schröder, C van Oppen (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. Management Information Systems Quarterly, 33(1): 177
https://doi.org/10.2307/20650284
59 C K Wing (1997). The ranking of construction management journals. Construction Management and Economics, 15(4): 387–398
https://doi.org/10.1080/014461997372953
60 S Wold, M Sjöström, L Eriksson (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2): 109–130
https://doi.org/10.1016/S0169-7439(01)00155-1
61 B Xiong, M Skitmore, B Xia (2015). A critical review of structural equation modeling applications in construction research. Automation in Construction, 49(Part A): 59–70
[1] Jun DU, Fangwen WENG. Construction management and technology innovation for main projects of Quanzhou Bay Bridge[J]. Front. Eng, 2021, 8(1): 151-155.
[2] Xiaoxiao XU, Patrick X. W. ZOU. System dynamics analytical modeling approach for construction project management research: A critical review and future directions[J]. Front. Eng, 2021, 8(1): 17-31.
[3] Huchang LIAO, Yinghan CHANG, Di WU, Xunjie GOU. Improved approach to quality function deployment based on Pythagorean fuzzy sets and application to assembly robot design evaluation[J]. Front. Eng, 2020, 7(2): 196-203.
[4] Elodie HOCHSCHEID, Gilles HALIN. Generic and SME-specific factors that influence the BIM adoption process: An overview that highlights gaps in the literature[J]. Front. Eng, 2020, 7(1): 119-130.
[5] Yang YU, Jiafu TANG. Review of seru production[J]. Front. Eng, 2019, 6(2): 183-192.
[6] Ying ZHOU, Wan WANG, Hanbin LUO, Yan ZHANG. Virtual pre-assembly for large steel structures based on BIM, PLP algorithm, and 3D measurement[J]. Front. Eng, 2019, 6(2): 207-220.
[7] Juan LIU, Fei QIAO, Yumin MA, Weichang KONG. Novel slack-based robust scheduling rule for a semiconductor manufacturing system with uncertain processing time[J]. Front. Eng, 2018, 5(4): 507-514.
[8] Hongtao ZHOU, Hongwei WANG, Wei ZENG. Smart construction site in mega construction projects: A case study on island tunneling project of Hong Kong-Zhuhai-Macao Bridge[J]. Front. Eng, 2018, 5(1): 78-87.
[9] SangHyun LEE. Applying system dynamics to strategic decision making in construction[J]. Front. Eng, 2017, 4(1): 35-40.
[10] Ting Gong,Jian Yang,Hao Hu,Feng Xu. Construction Technology of Off-Site Precast Concrete Buildings[J]. Front. Eng, 2015, 2(2): 122-124.
[11] Burcu Akinci. Situational Awareness in Construction and Facility Management[J]. Front. Eng, 2014, 1(3): 283-289.
[12] Mao-run Feng,Zheng-song Zhao. Rational Understanding about Renaissance Path of China's Road and Bridge Engineering[J]. Front. Eng, 2014, 1(1): 42-51.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed