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Index

Absolute zero point, 8 moderation analysis, 256,
Adanco, 86 269–270
Adjusted coefficient of
nonnormal data and, 61
determination (R2adj), 199–200 nonparametric multigroup
Algorithmic options and
analysis, 294
parameter settings, 89–91, nonparametric procedure, 149
93–94, 100, 168 percentile method, 155–156, 159
Alternating extreme pole p values, 177, 181
responses, 59 rules of thumb, 160
Archival data, 56 samples and cases
Average variance extracted (AVE),
111, 113–117, 126, 128, 171 terminology, 149
sample size considerations, 149
Bias-corrected and accelerated significance of outer
(BCa) bootstrap confidence
intervals, 156–157, 159, weights, 146
196–197 sign indeterminacy and sign

Bias in PLS-SEM, 21, 88–89 change options, 153–154
Binary coded data, 27 standard errors, 151, 155, 157,
Bivariate correlation, 144, 147
Blindfolding, 202–207, 217, 185, 195
structural measurement model
220, 222
Bonferroni correction, 288 evaluation, 196–197,
Bootstrap confidence intervals, 213–217
studentized method, 157
119–120, 130, 155–159, testing differences between
177–180, 183, 186, 196–197, adjusted R2 values, 200
217, 270 using SmartPLS software,
Bootstrapping, 87, 119, 138, 149, 130, 131, 157, 175–180,
152, 185–186 213–217
bias corrections, 156, 196 Box plots, 60, 68
confirmatory tetrad analysis,
Cascaded moderator analysis, 248
288 Casewise deletion, 57
double, 157–159, 196 Categorical moderating
formative measurement model
relationships, 41–43
evaluation, 146, 149–159, Categorical moderator variables,
175–180
heterotrait-monotrait (HTMT) 245–246
distribution, 119 Categorical scale, 7–8
mediation analysis, 235, Causal indicators, 47–48,
238–241
145–146. See also Formative
measurement models and
constructs

Causal links, 39 Competitive mediation, 232
Causal loops, 18, 27, 64 Complementary mediation,
Centroid weighting scheme,
232, 234
89, 285 Complex models
Circular relationships, 18, 27, 64
Classical test theory, 46 adjusted R2, 199–200
Cluster analysis, 295 hierarchical component models,
Coding, 9–10
Coefficient of determination (R2), 43–44, 110, 276, 281–285
PLS-SEM capabilities, 29
109, 198–200, 222 PLS-SEM versus CB-SEM
acceptable values, 199
adjusted R2, 199–200 performance, 88–89
convergent validity sample size considerations, 24
Composite-based SEM, 15. See
assessment, 140
f2 effect size, 201–202 also Partial least squares
maximizing explained variance, structural equation modeling
Composite indicators, 5,
92, 105 47–48, 138
PLS-SEM results, 92 assessing measurement
PLS-SEM weighting schemes, 89
sample size considerations, 25 invariance, 298–300
structural measurement model collinear indicator

evaluation, 198–200, combination, 144
210, 222 PLS regression and, 17
Collinearity, 27 See also Formative measurement
condition index, 144
formative indicators and, 49 models and constructs
formative measurement model Composite reliability, 111–112,
evaluation, 108,
140–144, 175 124, 125
hierarchical component Composite scores, 7
models, 281 Compositional invariance,
mediation analysis
considerations, 236 299–300
moderator analysis and, Conditional indirect effects,
249–250
multicollinearity, 142 259–261
Singular Data Matrix, 142 Condition index (CI), 144
standard error and, 142–143 Confidence interval, 155
structural measurement model
evaluation, 191–192, 194, bootstrap, 119–120, 130,
210, 212 155–159, 177–180, 183,
tolerance, 143 186, 196–197, 217, 270
variance inflation factor,
143–144, 145, 175, 194, coverage error, 156
210, 212 Configural invariance, 299–300
Common factor models, 15–16, Confirmatory statistical methods,
299, 303
Communality of items, 113, 115 3–4
Confirmatory tetrad analysis for

PLS-SEM (CTA-PLS), 51,
110, 276, 285–290, 306–307
Consistency at large, 88
Consistent PLS (PLSc), 21, 89,
276, 301–305, 308
Consistent reliability (rA), 301–302
Construct-level sign change
option, 154

Constructs. See Latent variables consistent PLS (PLSc), 276,
(or constructs) 301–305, 308

Construct scores computation, 82, distributional assumptions,
87–88 11, 18

Content specification, 139 goodness-of-fit measures, 87,
Content validity, 108, 113–114, 105, 192, 221

138–139 measurement model problems,
Continuous moderating 27–28

relationships, 41–43 model evaluation metrics, 105
Continuous moderator variables, PLS-SEM bias, 21, 88–89
sample size considerations, 24,
246
Convergence of PLS-SEM 88–89
unsuitability for prediction, 17
algorithm, 91, 123, 167 when to use, 15, 22, 23
Convergent validity, 108, 112– Coverage error, 156
Criterion validity, 109
115, 126, 140–141, 172–175 Critical t values, 153, 195–196
Corporate reputation case Cronbach’s alpha, 53, 111–112,

study, xv 126, 127, 301
data collection and Cross-loadings, 111, 115–118,

examination, 66–68 126, 129
estimation results, 95–99 Cross-validated communality
extended model, 159–169, 240
formative measurement model approach, 207
Cross-validated redundancy
evaluation, 159–169,
172–184 approach, 207
mediation analysis, 238–243
model estimation, 92–99 Data collection and examination,
model specification, 62–76 56–62, 77–78
moderator analysis, 262–271
path model creation using case study illustration, 66–68
SmartPLS software, 68–76 data sources, 56
reflective measurement model guidelines for path model
evaluation, 122–132,
169–172 preparation, 62
structural measurement model missing data, 56–58, 67–68
evaluation, 209–221 outliers, 59–60, 68
Covariance-based structural suspicious response patterns,
equation modeling (CB-SEM),
xiii, 4 58–59
assessing measurement Data distributions. See Normal
invariance, 299
causal indicators and, 48, distributions
145–146 Data heterogeneity, dealing with.
common factor assumption,
15–16 See Heterogeneity in data
comparing PLS-SEM and Data matrix, 5, 82
CB-SEM, xiii–xiv, 14–18,
21–22, 32–33, 88–89, Singular Data Matrix,
300–301 95, 142

Dependent variables, 14
sequence in path model, 14,
37–39
See also Endogenous latent
variables

Diagonal lining, 59 modeling moderator effects,
Direct effects, 39, 228 247–248. See also
Moderation
moderation analysis results
interpretation, 256–257 sequence in structural model,
14, 38–39
testing significance of,
241, 242 See also Latent variables
Explained variance, maximizing, 82,
total effect interpretation,
197–198. See also Total 86, 92, 105, 192. See also R2
effects Exploratory statistical methods,

types of mediation effects, 3–4, 89
232–234 Extreme responses, 59–60

Direct-only nonmediation, 232 f2 effect size, 109, 201–202, 222,
Disattenuated correlation, 118 255–256, 270–271
Discriminant validity, 111,
Factor analysis, 3
115–122, 126–132, 171, 235 Factor (score) indeterminacy,
Distributional considerations. See
17, 87
Normal distributions Factor weighting scheme, 89
Double bootstrapping, Finite mixture PLS (FIMIX-PLS),

157–159, 196 295–296, 298
Dummy-coded indicators, 27 First-generation statistical
Dummy variables, 245
techniques, 3
Effect indicators, 46 Fit measures. See Goodness-of-fit
Effect size f2, 109, 201–202, 210,
measures
222, 255–256, 270–271 Formative-formative HCM, 282
Effect size q2, 109, 207–208, Formative index, 47
Formative measurement model
220, 221
Endogenous latent variables, 12, evaluation, 108, 137–140, 185
bootstrapping, 146, 149–159,
14, 40
maximizing explained variance, 160, 175–180
case study illustration, 159–169,
92, 105
modeling moderator effects, 172–184
collinearity assessment, 108,
247. See also Moderation
PLS-SEM construct scores, 140–144, 145, 175
content validity, 138–139
87–88 convergent validity, 140–141,
PLS-SEM R2 output, 92
sequence in structural model, 172–175
indicator significance and
14, 38–39
See also Latent variables relevance, 144–149
Equidistant scales, 8 numbers of indicators used on
Error-free assumption for
weights, 146–147
formative measures, procedure overview, 139
13, 49, 138 rules of thumb, 151
Error terms, 12–13, 32 significant and nonsignificant
causal indicators and, 48
Exact fit test, 194 weights, 147–149, 150
Exogenous latent variables, 12, using reflective measurement
14, 40
model evaluation
criteria, 138

Formative measurement models Heterotrait-monotrait (HTMT)
and constructs, 13, 46, ratio of correlations, 111,
137–186 118–120, 129–132

assessment of results. See Hierarchical component models
Formative measurement (HCMs), 43–44, 110, 276,
model evaluation 281–285, 306

collinearity issues, 49 Higher-order components
content specification, 139 (HOCs), 281–283
error-free assumption,
Higher order models, 43–44
13, 49, 138
formative/reflective mode IBM SPSS Statistics, 60, 68
Importance-performance map
specification, 50–51, 52,
285–286 analysis (IPMA), 109,
measurement specification, 276–280, 306
46–51 Inconsistent mediation, 234
moderator analysis and, 253 Independent variables, 14
outer weights. See Outer path model sequence, 14,
weights
PLS path model estimation, 83 37–39
PLS-SEM capabilities, 28–29 See also Exogenous latent
Formative-reflective HCM, 282
Fornell-Larcker criterion, 111, variables
115–118, 126, 129, 173 Index of moderated mediation, 260
Full measurement Indicators, 6
invariance, 300
Full mediation, 233 as approximations in
PLS-SEM, 16
Genetic algorithm segmentation in
PLS-SEM (PLS-GAS), 297 causal, 47–48, 145–146
communality of, 113, 115
Goodness-of-fit index deletion and content validity,
(GoF), 193
113–114
Goodness-of-fit measures, formative, 47–48. See also
18, 21, 86–87, 105, 192,
193–194, 221 Composite indicators;
Formative measurement
G*Power, 25, 83 models and constructs
importance-performance map
Heterogeneity in data, 228, 243, analysis, 280
290–291 measurement model
specification, 45–46
multigroup analysis, 42, 244, metric scale requirements, 27
276, 291–295 nonsignificance and deletion of,
148–149, 150
threats to validity, 295 numbers of indicators used on
unobserved heterogeneity, 276, weights, 146–147
partial least squares path
291, 307 modeling, 11
unobserved heterogeneity, latent raw data input, 82
redundant, 112, 140
class techniques, 291, reflective, 46–47. See also
295–298 Reflective measurement
Heterotrait-heteromethod models and constructs
correlations, 118, 120

reliability of, 113. See also Latent class techniques, 60, 291,
Reliability 295–298

significant and nonsignificant Latent variables (or constructs), 6
weights, 147–149 comparing PLS-SEM and
CB-SEM, 15–17
validity of. See Validity data matrix, 82
See also Latent variables; importance-performance map
analysis, 276–280
specific types initial values for PLS-SEM
Indirect effects, 39–41, algorithm, 89–91
measurement theory, 13
228–229 mediating/moderating relations
conditional (moderated between. See Mediation;
Moderation
mediation), 259–261 partial least squares path
multiple mediation analysis, modeling, 11–13
rescaling, 277
236–238 sequence in path model, 14,
testing significance of, 238–241, 37–39
sign indeterminacy of scores,
242. See also Mediation 153–154
analysis standardized scores
total effect, 197–198, 236–238. requirement, 90
See also Total effects structural theory, 14
types of mediation effects, See also Endogenous latent
232–234 variables; Exogenous latent
See also Mediation variables
Indirect-only mediation, 232
Individual-level sign change Likert scales, 9–10
option, 154 Listwise deletion, 57
Initial values for PLS-SM Lower-order components (LOCs),
algorithm, 89–91, 154
Inner model, 11–12, 37. See also 281–283
Structural models LVPLS, 86, 90
In-sample predictive power, 198
Interaction effects, 244, 246 Main effect, 256
model evaluation criteria and, Mean value replacement, 57
255–256 Measurement, 5–7
See also Moderation Measurement equivalence, 298–300
Interaction terms, 246, 248–256, Measurement error, 7
261–263
Internal consistency reliability, measurement model evaluation,
108, 111–112, 126 106–107
Interpretational
confounding, 146 measurement model
Interval scale, 8, 27 invariance, 298
Iterative reweighted regressions
segmentation method PLS-SEM advantages in
(PLS-IRRS), 297–298 accounting for, 15

k-means clustering, 295 PLS-SEM bias, 88
Kolmogorov-Smirnov tests, second-generation statistical

11, 61 techniques and, 4
Kurtosis assessment, 11 single-item measures and, 51

Measurement invariance of Measurement model specification,
composite models (MICOM) 44–55, 76–77
procedure, 299–300
case study illustration, 64–66
Measurement model evaluation, confirmatory tetrad analysis,
104–110, 133
51, 110, 276–280,
case study illustration, 122–132, 285–290
159–184 formative and reflective models,
46–51
content specification, 139 formative/reflective mode
formative measurement models, specification, 50–51, 52,
285–286
137–186 guidelines for path model
important metrics, 105, 111 preparation, 55
measurement error, 106–107 indicator selection, 45–48
moderation effects single-item measures, 51–53, 54
sum scores, 53–55
considerations, 255–256 systematic procedure, 106
reflective and formative Measurement scales, 7–9
coding considerations, 9–10
constructs, 107–108 general PLS-SM
reflective measurement models, requirements, 27
moderator variables and, 245
111–134 Measurement theory, 13–14,
rules of thumb, 110, 122 44–46
single-item measures and, Mediated moderation, 259–261
Mediation, 39–41, 227–232, 271
108–109 discriminant validity and, 235
See also Formative measurement moderated, 259–261
suppressor variables, 234
model evaluation; total effect interpretation,
Reflective measurement 197–198
model evaluation types of mediation effects,
Measurement model invariance, 232–234
276, 298–300, 307–308 Mediation analysis, 233, 271
Measurement model bootstrapping, 235, 238–241
misspecification, 285 case study illustration, 238–243
Measurement models, 12, 37, measurement model evaluation,
44–45 235–236
CB-SEM problems, 27–28 multiple mediation, 236–238
formative and reflective, 13, rules of thumb, 239
46–51. See also Formative testing mediating effects, 235
measurement models and Mediator variables, 227–228
constructs; Reflective validity and reliability,
measurement models and 235–236
constructs See also Mediation
formative/reflective mode Metric data requirements, 27
selection, 50–51, 52 MICOM procedure, 299–300
important metrics, 105. See Microsoft Excel, 238
also Measurement model
evaluation
multivariate measurement, 106
PLS path model estimation,
83–84
PLS-SEM bias, 21, 88
PLS-SEM capabilities, 28–29

Missing data treatment, 25, Multigroup analysis, 42, 110, 244,
56–58, 70 276, 291–295, 307

blindfolding procedure, 202 assessing measurement
case study model, 67–68, invariance, 298–300

93–94, 167 Multiple mediation analysis,
Mode A measurement, 46. See 236–238

also Reflective measurement Multivariate analysis, 2
models and constructs first- and second-generation
Mode B measurement, 47. See methods, 3–4
also Formative measurement PLS regression, 17
models and constructs See also Structural equation
Moderated mediation, 259–261 modeling
Moderation, 41–43, 228,
243–245, 271 Multivariate analysis,
interaction terms, 246, considerations in method
selection, 4–11, 31
248–256, 261–263
mediated, 259–261 coding, 9–10
modeling moderator effects, composite variables, 5
data distributions, 10–11.
246–248
single-item measures and, 246 See also Normal
types of moderator variables, distributions
measurement, 5–7
244–246 PLS-SEM application issues,
Moderator analysis, 271–272 17–20
sample size, 22–25
bootstrapping, 256, 269–270
cascaded moderator No-effect nonmediation, 232
Nominal (or categorical) scale,
analysis, 248
case study illustration, 7–8
Nonparametric multigroup
262–271
model evaluation criteria and, analysis, 294
Normal distributions
255–256
multigroup analysis, 42, 244 bootstrapping and nonnormal
orthogonalizing approach, data, 61

250–251, 252, 255, 258 mediation analysis assumptions,
product indicator approach, 235

249, 255 SEM assumptions, 11, 18, 25,
results interpretation, 256–259 27, 60–61, 87, 149
rules of thumb, 263
slope plots, 258, 259, 268, 269 tests for normality, 11, 61
two-stage approach, No sign change option, 154

251–255, 258 Omission distance (D), 202–204
Moderator variables, 243 Omnibus test of group differences

model evaluation criteria and, (OTG), 294
255–256 One-tailed test critical

types of, 244–246 values, 195
See also Moderation Online data collection, 57
Monotrait-heteromethod Ordinal scale, 8

correlations, 118, 120 PLS-SEM application issues, 27
Multicollinearity, 142

Ordinary least squares (OLS) Partial least squares (PLS)
regression regression, 17

collinearity influences, 27, Partial least squares structural
191–192 equation modeling
(PLS-SEM), xi–xii, 4, 14,
PLS-SEM predictive advantages, 21, 32
17
circular relationships and, 18,
PLS-SEM results, 91–92 27, 64
PLS-SEM statistical properties,
comparing PLS-SEM and
86–89 CB-SEM, xiii–xiv, 14–18,
Orthogonalizing approach, 21–22, 32–33, 88–89,
300–301
250–251, 252, 255, 258
Outer loadings composite-based approach, 15
critical considerations in
indicator reliability and,
113–114 application, 17–20
data considerations, 22–27, 28,
PLS path model estimation,
84, 86 56–62, 77–78
fit measures, 18, 21,
PLS-SEM results, 91–92, 124
relevance testing, 114 86–87, 105, 192,
Outer model, 12, 44. See also 193–194, 221
key characteristics, 19–20
Measurement models limitations of, 18, 20
Outer weights maximization of explained
variance, 82, 86, 92, 105,
bootstrapping confidence 192. See also Coefficient of
intervals, 183 determination
measurement model metrics,
bootstrapping results, 182 105. See also Measurement
collinearity issues, 144 model evaluation
implications of numbers of modeling capabilities, 27–29
nonparametric distributional
indicators used, 146–147 assumptions, 11, 18, 27,
importance-performance map 60–61, 87, 149
path modeling with latent
analysis and, 280 variables, 11–13
indicator significance and PLS regression and, 17
PLS-SEM bias, 21, 88–89
relevance, 144–149 predictive capabilities, 17,
initial values, 154 86–87
PLS path model estimation, sample size considerations, 18,
22–25, 26, 83
83–84, 86 statistical properties, 86–89,
PLS-SEM results, 91–92 100–101
significant and nonsignificant structural model metrics,
105. See also Structural
weights, 147–149 measurement model
See also Weights evaluation
Outliers, 27, 59–60, 68
Out-of-sample predictive power

(predictive relevance ),
202–207

Pair-wise deletion, 57–58
Parametric approach, multigroup

analysis, 293–294
Parsimonious models, 199

studies in management and Path model specification, 36–37
marketing, xivf case study illustration, 62–76
content specification, 139
systematic application data collection and
procedure, 30 examination, 56–62,
66–68, 77–78
variance-based approach, 17 guidelines, 55
when to use, 15, 22, 23 indicator selection, 45–46
See also Path model estimation; measurement models, 44–55,
64–66, 76–77. See also
Path model specification; Measurement model
PLS-SEM algorithm specification
Partial measurement mediating effects, 39–41.
invariance, 300 See also Mediation
Partial mediation, 233 moderator effects, 41–43.
Path coefficients See also Moderation
calculation, 86 sequence of constructs, 14,
standardized values, 90, 195 37–39
structural measurement model structural models, 37–44,
evaluation, 195–198, 63–64, 76. See also
212–217, 221–222 Structural model specification
Path model estimation, 81, using SmartPLS software, 68–76
92–99
advanced analyses, 109–110 Path weighting scheme, 89
case study illustration, Percentile method, 155–156, 159
92–99 Permutation test, 294
convergence and stopping PLSe2, 301, 305
criterion, 91, 123 PLS-Graph, 86, 90
measurement model PLS-GUI, 86
calculations, 83 PLS-IRRS, 297–298
minimum sample size, 83 PLS-MGA, 294
model assessment. See PLSPM, 86
Measurement model PLS-POS, 297–298
evaluation; Structural PLS-SEM algorithm, 82–86,
measurement model
evaluation 99–100
options and parameter settings, construct scores computation,
89–91, 93–94
PLS-SEM algorithm, 82–89 82, 87–88
results, 91–92, 95–99, 101 convergence and stopping
Path models, 11–13, 37
assessment of results. See criterion, 91, 123, 167
Measurement model initial values, 89–91, 154
evaluation; Structural options and parameter settings,
measurement model
evaluation 89–91, 93–94, 100, 168
causal links, 39 raw data input, 82
dependent and independent software programs, 86. See also
variable sequence, 14
theory-based development, 13 SmartPLS 3
statistical properties, 86–89,

100–101
weighting schemes, 89–90

PLS-SEM bias, 21 Reflective measurement model
PLS-SEM model assessment. evaluation, 108, 111,
133–134
See Measurement model
evaluation; Structural bootstrapping, 119–120, 130
measurement model case study illustration, 122–132,
evaluation
PLS-TPM, 296–297 169–172
PLS typological path modeling composite reliability, 111–112
(PLS-TPM), 296–297 convergent validity, 112–115,
Postcoding, 9
Precoding, 9 126, 140–141
Prediction, PLS-SEM capabilities, discriminant validity, 115–122,
17, 86–87
Prediction-oriented segmentation 126–132, 171
in PLS-SEM (PLS-POS), formative measure assessment
297–298
Predictive power, 198–200 and, 138
moderator variables and, 246 important metrics, 111
See also Coefficient of internal consistency reliability,

determination; Predictive 108, 111–112, 126
relevance rules of thumb, 122
Predictive relevance (Q2), 109, Reflective measurement models
202–207, 217, 220, 222
Primary data sources, 56 and constructs, 13, 46,
Principal component analysis, 17 107–108
Product indicator approach, confirmatory tetrad analysis,
249, 255
p values, 153 285–290
bootstrapping, 177, 181, 196 formative/reflective mode

Q2 (predictive relevance), specification, 50–51, 52,
109, 202–207, 217, 285–286
220, 222 measurement specification,
46–51
q2 effect size, 109, 207–208, moderator analysis and, 253
220, 221 PLS path model estimation, 83
PLS-SEM capabilities, 28
Quasi-metric scales, 27 selecting suitable measures,
140–141
R2. See Coefficient of Reflective-reflective HCM, 282
determination Regression, partial least squares
(PLS), 17
Ratio scales, 9, 27 Regressions based on sum scores,
Raw data input, 82 14–16, 18, 53–55
Raw data transformation, 90 Relevance of significant
REBUS-PLS, 296–297 relationships, 197
Redundancy analysis, 140–141, Reliability, 107, 108
case study illustration,
172–175 124–126
Redundant indicators, 112, 140 composite, 111–112,
Reflective-formative HCM, 282 124, 125
consistent PLS (PLSc), 301–302
Cronbach’s alpha, 53, 111–112,
126, 127, 301

evaluating measurement models, Significance testing
106. See also Measurement bootstrapping, 138, 149–159,
model evaluation 160, 175–180, 185–186,
239–241
exact (or consistent) rA, critical values, 153
301–302 formative indicators and,
144–149, 175–184
internal consistency, 108, p values, 153
111–112, 126 relevance of relationships, 197
significant and nonsignificant
measurement error and, 107 weights, 147–149
mediator constructs, 236 structural model path coefficients,
outer loadings and, 113–114 195–198, 213–217
single-item measures and,
Sign indeterminacy, 153–154
51, 109 Simple effect, 256–258
Repeated indicators Single-item constructs, 7, 51–53,

approach, 283 54, 77, 106, 108–109
Rescaling latent variable evaluating reliability and

scores, 277 validity, 109
Response-based procedure for moderator variables and, 246
redundancy analysis and, 140–141
detecting unit segments in Singular Data Matrix, 95, 142
PLS path modeling Skewness assessment, 11
(REBUS-PLS), 296–297 Slope plots, 258, 259, 268, 269
Response-based segmentation SmartPLS 3, xii, xv, 68–69, 86
techniques, 295–298 bootstrapping, 130, 131, 157,
Root mean square residual
covariance (RMStheta), 175–180, 213–217
193–194 confirmatory tetrad analysis,
R program, 86
289–290
Sample characteristics, 22–23 estimation results, 95–99
Sample size extended model in, 164, 240
formative measurement model
bootstrapping procedure, 149
collinearity issues, 142 evaluation, 163–169,
PLS-SEM application 173–184, 186
handling missing data, 57, 70
considerations, 18, 22–25, initialization setup, 90–91
26, 83 latent class techniques, 298
PLS-SEM versus CB-SEM mediation, 238–243
performance and, 88–89 moderator analysis, 262–271
rules of thumb, 24–25, 83 path model estimation, 92–99
single-item measures path model specification, 68–76
and, 53 reflective measurement model
Scales of reflective measures, 47 evaluation, 122–132,
Screening questions, 59 169–172
Secondary data sources, 56 structural measurement model
Second-generation statistical evaluation, 209–221
techniques, 3–4 website, 69
semPLS, 86
Shapiro-Wilk test, 11, 61
Sign change options, 154

Sobel test, 235 Structural measurement model
Soft modeling, 25, 27 evaluation, 109, 190–192
Software programs for
blindfolding and predictive
PLS SEM, 86. See also relevance Q2, 202–207,
SmartPLS 3 217, 220, 222
SPAD-PLS, 86
Specific indirect effect, 236 bootstrapping, 196–197,
SPSS, 60, 68 213–217
Standard error
bootstrap, 151, 155, 157, case study illustration, 209–221
collinearity assessment,
185, 195
collinearity issues, 191–192, 194, 195, 210
f2 effect size, 201–202, 210,
142–143
Standardized moderator 222, 255–256
important metrics, 105,
indicators, 257
Standardized root mean square 109, 192
mediating effects, 198. See also
residual (SRMR), 18, 20,
Mediation
193–194 moderation effects
Standardized scores, 90
Standardized values for path considerations, 255–256
overview of procedure, 191
coefficients, 195 path coefficients, 195–198,
Statistical power, PLS-SEM
212–217, 221–222
advantages, 18, 89 q2 effect size, 207–208,
Stopping criterion for PLS-SEM
220, 221
algorithm, 91, 123, 167 R2 values, 198–200, 210, 222
Straight lining, 58–59 relevance of relationships, 197
Structural equation modeling rules of thumb, 110, 208–209
significance of relationships,
(SEM), xiii, 1, 31
comparing approaches, 14–18, 195–197
systematic procedure, 106
21–22 Structural models, 11–12, 37
considerations in method mediation effects. See Mediation
moderation effects. See
selection, 4–11. See also
Multivariate analysis, Moderation
considerations in method PLS path model estimation,
selection
distributional considerations, 84–85
11. See also Normal sample size considerations, 24
distributions Structural model specification, 76
second-generation statistical case study illustration, 63–64
techniques, 3–4 hierarchical component models,
variance-based approach, 17
See also Covariance-based 43–44
structural equation sequence of constructs, 14,
modeling; Partial least
squares structural equation 37–39
modeling Structural theory, 13–14
Studentized bootstrap

method, 157
Student’s t test, 151

Sum scores, 14–16, 18, 53–55 evaluating measurement models,
Suppressor variables, 234 106. See also Measurement
Survey response issues, 58–60 model evaluation
Suspicious response patterns,
measurement error and, 107
58–59 measurement invariance and,
Symmetric scaling, 9–10
298–299
t distribution, 153 measurement model
Technology acceptance model
misspecification and, 285
(TAM), 21 mediator constructs, 235–236
10 times rule, 24, 83 single-item measures and, 109
Tetrads, 286–288 unobserved heterogeneity and,
Theory-based path model
295
development, 13 Vanishing tetrad, 287–288
Three-way interaction, 248 Variance-based approach to SEM,
Tolerance (TOL), 143, 194
Total effects, 197–198, 212–213 17. See also Partial least
squares structural equation
importance-performance map modeling
analysis, 276–280 Variance inflation factor (VIF),
143–144, 145, 175, 194, 212
Total indirect effect, 236–238 VisualPLS, 86
Two-stage approach for
WarpPLS, 86
moderator analysis, Weights, 5
251–255, 258
Two-stage HCM analysis, algorithm weighting schemes,
283–284 89–90
Two-tailed test critical values, 195
Two-way interaction, 248 case study model, 93–94
collinearity and, 142
Unexplained variance, 12, 32 hierarchical component models,
Unobserved heterogeneity, 276,
284–285
291, 307 implications of numbers of
latent class techniques, 291,
indicators used, 146–147
295–298 initialization, 154
PLS path model estimation,
Validity, 107, 108
case study illustration, 126–132 83–84, 86
content, 108, 113–114, regression based on sum scores,
138–139
convergent, 108, 112–115, 126, 15–16
140–141, 172–175 significant and nonsignificant
criterion, 109
discriminant, 111, 115–122, weights, 147–149
126–132, 171, 235 See also Outer weights

XLSTAT, 86

z-standardization, 90