Accurate parsimonious configural model

, 257

“Achievement Orientation”

, 91, 92

Advocacy hypothesis construction and testing

, 43–44

Algorithm models

, 48, 213, 224–226

Boolean algebraic model

, 153–154

calibration

, 154–157

consistency

, 157

coverage

, 157

stating and testing

, 153

visualizing findings for tests of algorithms

, 157–158

Analysis of variance (ANOVA)

, 7, 183–184, 249, 252, 265

Anecdotal data

, 124

ANOVA. See Analysis of variance (ANOVA)

“Anscombe’s quartet”

, 24, 25

Antecedent ingredients

, 8

A priori analysis

, 21, 26, 45

Asymmetric(al)

analysis

, 34–35

analytics

, 182

causality

, 177

configural models

, 184

empirical models

, 75

focus on building parsimonious patterns

, 233

fsQCA data analyses

, 199

insufficient but necessary

, 186

models

, 11, 12, 35

necessary-but-not-sufficient relationships

, 186

negation membership

, 137

outcomes

, 9, 232, 256

QCA

, 170

quintiles

, 60–61

relationships

, 6, 59, 69, 86–88, 151–153, 176, 183, 249, 252

screening tool

, 10

service research models

, 187

tests

, 12, 13, 24, 42, 61, 184

theory

, 14, 59, 68

B2B relationships. See Business–business (B2B) relationships

Bad practices, moving away from

advocacy hypothesis construction and testing

, 43–44

“Anscombe’s quartet”

, 24, 25

behavioral experiments

, 14

in business

, 4–5

cases with associations contrary to effects

, 22–24

causal path of article characteristics

, 10

CIT

, 9

complex antecedent conditions

, 16

complexity

, 15–18

configurational approaches

, 8

cross-sectional survey studies

, 29–30

dependent/outcome variable

, 42–43

failure to collecting/reporting real-life contextual data

, 40–42

fsQCA

, 14–15

hypothetical relationships

, 34

improving theory construction

, 2

interviewing one person per group

, 30–32

in journal manuscript submissions

, 6–7

laboratory experiments

, 40

measuring nonresponse bias

, 32–33

using median splits

, 47–48

mushy questions to measure thinking and behavior

, 40–42

net effects in regression models

, 24–26

non-significant terms in regression models

, 47

replicate findings

, 45–47

screening tool

, 10, 11

stepwise regression analysis

, 44–45

sub-disciplines of business/management

, 3

symmetric only modeling

, 34–35

symmetric statistical test

, 24

symmetric tests

, 12, 13

testing for fit validity

, 18–21

theory and analysis mismatch

, 7

useable response rates

, 32–33

verbal self-reports

, 26–29

void-treatment control group in experiments

, 35–39

Beauty salon/spa

, 184, 195, 196, 199

correlations of facets and intentions

, 204–205

expenditure levels

, 194–195, 223

facets

, 242

service provider and customer contexts in

, 188–189

Behavioral decision making

, 130, 152

Behavioral experiments

, 14

Behavioroid measures

, 41

BI indices. See Business International (BI) indices

Boolean-based asymmetric analytics

, 183

Boolean algebra

, 8, 12, 65, 67–69, 88, 135, 153, 154, 182, 185, 210, 256, 272

Boolean algebra-based software

, 152

Business–business (B2B) relationships

, 114

content analysis

, 130–133

CSR

, 116–123

DFA

, 133–134

direct research and observing processes

, 123–127

DSA

, 127, 128

EDTM

, 127–130

FS/QCA

, 134–138

measures of associations

, 139–142

Business International (BI) indices

, 91, 92

Butterfly effect

, 252–253

Calibration

, 12, 135, 154, 162–163, 210–211

field experiment

, 154

fuzzy-set

, 48, 210–211, 265, 266

index calculations

, 155–157

membership scores

, 171

statistical outliers

, 155

Case-based algorithms

, 151

Case-based modeling of B2B relationships

, 34, 114

content analysis

, 130–133

CSR

, 116–123

DFA

, 133–134

direct research and observing processes

, 123–127

DSA

, 127, 128

EDTM

, 127–130

FS/QCA

, 134–138

measures of associations

, 139–142

Case identification theory (CIT)

, 9, 10

Case study research (CSR)

, 114–116, 141

core assumptions serving as rationales for

, 117

DFA in

, 133–134

exemplar methods in B2B contexts

, 122–123

key success factors

, 121

multiple mental processes

, 120

“nonconscious” decision-making

, 119

triangulation

, 118

Causal asymmetry

, 34, 62, 151, 161, 170, 181, 192, 196, 214, 217–220, 228, 233, 256

demographics-only causal asymmetry configurations

, 283

principle

, 16–17, 72, 253, 279

Causality

, 151, 177, 283

Causal recipes

, 31, 87, 92, 98, 103, 129, 130, 135, 136, 138, 139, 155, 160–161, 269

CCT. See Consumer Culture Theory (CCT)

CDERP. See Customer-directed extra role performance (CDERP)

Central Intelligence Agency (CIA)

, 95

CHAID. See Chi-squared automatic interaction detection (CHAID)

Chi-squared automatic interaction detection (CHAID)

, 15

CIA. See Central Intelligence Agency (CIA)

CIT. See Case identification theory (CIT)

Classic linear regression model

, 8

Cognitive consistency

, 119

Collaborative research support program (CRSP)

, 128

Comparative theory test

, 44

Competency-qualification algorithms

, 250

Complex antecedent

configuration

, 57, 185, 192, 196, 220–221, 223, 232

demographic models

, 229, 269

Complex antecedent condition

, 12–13, 15–16, 18, 23–24, 34, 42, 45, 61, 98, 100, 186, 214, 220–223, 232, 233, 235, 249, 251, 266, 268

computing scores

, 291–292

configural theory

, 253, 266

consistency and coverage

, 70, 157

modeling

, 66

models with

, 212

primary configural nature

, 189

reality and

, 176

of two or more simple conditions

, 67–71

Complex demographic configurations

, 191, 192

affecting customer evaluations of service facets

, 191–192

indicating customer evaluations of service facets

, 212–214

Complexity theory

age groups data

, 268–269

architecture of complexity

, 63–64

causal asymmetry

, 72, 192

complex antecedent conditions

, 67–71, 282

complex demographic configurations

, 191–192

complexity and customer evaluations

, 182–186

configurational theory of antecedents

, 249–262

configurations of customer service-facet evaluations

, 195–197

contrarian case analysis

, 74–75

contributions and service management practice

, 229–233

customer evaluating service facets

, 194

data analysis

, 265–268

data collection procedure

, 264–265

demographics-only causal asymmetry configurations

, 283

developing potential

, 65

discussion, limitations, and contributions

, 279

employee work-domain happiness

, 248–249, 249–262

equifinality principle

, 71–72

expenditure levels

, 194–195

facets

, 242

“facet-specific” happiness

, 246–247

happiness, IRP, AND CDERP

, 285

high employee work-domain happiness

, 247–248

individual feature in recipe

, 73

limitations and suggestions for future research

, 233–235, 285

managers assessment of employee performance

, 248–249, 249–262

methods

, 197, 262

modeling multiple realities

, 75–77

models for very high happiness and performance

, 269–272, 276

on-job antecedent configurations

, 280–281

qualitative comparative analysis

, 286

respondents

, 198–199

rote applications of regression analysis

, 58–62

scores for recipe

, 74

service outcomes

, 243

in service research

, 186–187

simple antecedent conditions

, 66–67, 192–194

survey instruments

, 197–198, 262–264

survey items

, 240–241

unique complex antecedent configurations

, 192

Venn diagram

, 189–191

work facet-specific scales

, 264

Complexity theory perspective (CTP)

, 61–62

Complexity theory tenets

, 8, 62, 63, 182, 185–186

in beauty and spa industry

, 189–191

thinking and testing

, 183

Complex service-facet configurations

, 196, 223

Complex service-outcome configurations

, 196, 223, 228

“Condition”

, 154

Configural analysis

, 12, 15, 61, 62, 150, 183, 246, 252, 284

beauty salon/spa facets

, 242

complexity and customer evaluations

, 182–186

service outcomes

, 243

service provider and customer contexts

, 188–189

survey items

, 240–241

See also Fuzzy-set qualitative comparative analysis (fsQCA); Multiple regression analysis (MRA)

Configural statement

, 98, 184–185, 251

Configural theory

, 247, 249, 253, 285

asymmetrical relationships

, 252

butterfly effect

, 252–253

competency-qualification algorithms

, 250

confirming four principles

, 283–284

equifinality

, 251–252

happiness–performance

, 253–254

independent variable

, 250–251

Configurational theory of antecedents

, 249–262

demographics to happiness-at-work, relevancy of

, 255–257

ERP

, 261–262

happiness-at-work and job performance

, 260

IRP

, 261–262

PWE

, 258

qip

, 258–259, 264, 271–272, 276–277, 279

work facet-specifics

, 257–258

Conjunctive models

, 157

Consistency

, 12, 18, 45, 69, 88, 155–156, 157, 170, 187, 223, 266

calculation

, 70, 139

cognitive

, 119

in fuzzy-set qualitative comparative analysis

, 107

index metrics

, 211–212

metrics

, 191

recipe

, 67

rule

, 193

for service facets

, 228

signals

, 139

for very high IPR

, 276

Consistency index

, 87, 155, 211, 212, 266

Consumer Culture Theory (CCT)

, 28, 41, 49

Contemporary marketing practices

, 114

Content analysis

, 122, 130, 142

emic interpretations

, 131

hermeneutic interpretation

, 132

sensemaking views

, 133

Context(s)

, 27, 59–60, 64, 88, 89, 158, 159, 173

B2B

, 116–119, 121–123, 125–126, 127–131

business

, 185

customer

, 188–189

hospitality-service employee work

, 277

multi-step

, 188

real-life

, 40, 116, 142

real-world

, 41, 49

research

, 116

Continuum

, 158

Contrarian case(s)

, 18, 22, 23, 47, 59, 61, 62, 74, 75, 183, 187, 203, 208, 222

analysis

, 62, 74–75, 183

negative and positive

, 232

tenet

, 17

Correlation and cross-tabulation findings

, 199

antecedent conditions

, 208, 210

beauty salon/spa facets and intentions

, 204–205

contrarian cases

, 203

correlations among service facets

, 203

correlations for service facet evaluations

, 203

customers’ evaluations and intentions

, 206–207

demographics with experience assessments

, 202

demographics with service facet evaluations

, 200–201

effective treatment and service quality segments

, 208

highly significant correlations

, 203

service quality and return intention segments

, 209

Coverage

, 18, 29, 69, 74, 88, 98, 139, 155, 157, 266, 268

calculation

, 70, 139

complex configuration

, 212

in fuzzy-set qualitative comparative analysis

, 107

index

, 87, 211, 212, 266

Creative leap

, 124

Critical test

, 44, 134

Cross-sectional research findings

, 116

Cross-sectional survey studies

, 29–30

CRSP. See Collaborative research support program (CRSP)

CSR. See Case study research (CSR)

CTP. See Complexity theory perspective (CTP)

Customer-directed extra role performance (CDERP)

, 75, 247, 253, 261–262, 264, 268, 275–276, 285

Customer contexts

, 188–189

Customer evaluates service facets

, 194

configurations

, 195–197

Customer evaluates service facets

, 222–223

Customer membership

, 137

Customer service-facet

assessments

, 199

configurations

, 223–228

evaluations

, 195–197

Data analysis

, 7, 14, 15, 24, 32, 42, 46, 178, 265

calibrated happiness scales

, 267–268

CIT to

, 9

fsQCA

, 265–266

Data collection

, 27, 49

PO

, 125

procedure

, 1, 264–265

Decision making

, 59, 121, 125, 259

B2B

, 131

behavioral

, 130, 152

naturalistic

, 119

non-programmed or semi-programmed

, 126

nonconscious

, 119

Decision systems analysis (DSA)

, 122, 127, 128, 133

Degrees-of-freedom analysis (DFA)

, 122, 133–134, 141

Degrees of freedom test

, 44

Demographic configurations

, 189, 192, 194, 196, 214, 220, 253, 282

complex

, 191–192, 212

Dependent variable (DV)

, 3, 7, 47, 85–86, 88, 89, 92, 175, 176, 235, 249

“net effects” of variables

, 58, 151

DFA. See Degrees-of-freedom analysis (DFA)

Direct research and observing B2B processes

, 123

creative leap

, 124

PO data collection

, 125

principles

, 126

sensemaking

, 127

DOFA. See Degrees-of-freedom analysis (DFA)

Dominant hypothesis approach

, 43, 44

“Double-blind” procedure

, 35–36

DSA. See Decision systems analysis (DSA)

DV. See Dependent variable (DV)

EDTM. See Ethnographic decision tree modeling (EDTM)

Electronic data-processing equipment

, 125

Emergence

, 252

Emic interpretations

, 131

Empirical positivism statistical testing

, 159–160

Empirical positivistic analysis, findings for

, 164

statistical analysis, findings from

, 164–170

Empirical positivistic tests. See Statistical tests

Employee happiness-at-work

CDERP

, 261–262

configural theory

, 249–253

configurational theory of antecedents and outcomes

, 249

demographics to happiness-at-work

, 255–257

happiness-at-work and job performance

, 260–261

happiness–performance configural theory

, 253–254

IRP

, 261–262

quality of employee performance relationship

, 284–285

work facet-specifics

, 257–260

Employee happiness-at-work, relevancy of demographics to

, 255–257

Employee work-domain happiness and managers’ assessment

, 247, 248–249

Equifinality

, 8, 12, 71, 151, 187, 233, 246, 252

case-based algorithms and

, 151

principle

, 16, 34, 71–72, 251, 266, 283

tenet of complexity theory

, 12

ERP. See Extra-role performance (ERP)

Ethnographic decision process model

, 66, 129

Ethnographic decision tree modeling (EDTM)

, 122, 127, 131, 141

behavioral decision making

, 130

binary flow models

, 128

cognitive science reporting

, 129

Etic interpretations

, 131, 133

Extra-role performance (ERP)

, 261

“Facet-specific” happiness

, 246

Female headed households (FHHs)

, 128

Field experiments, triple sensemaking in

, 149

comparing benefits and limitations of methods

, 175–177

empirical positivistic analysis

, 164–170

fsQCA

, 170–175

MRA

, 150

SAIM

, 151

stating and testing algorithm models

, 153–158

statistical modeling

, 151–152

unobtrusive field experimentation

, 158–164

Fit validity

, 62, 65, 94, 175, 235

fit and predictive validity

, 20

illusion of control

, 21

MRA models

, 19

“take-the-best” algorithm

, 19–20

testing for

, 18, 59

“Folk theory of mind”

, 29, 49

fsQCA. See Fuzzy-set qualitative comparative analysis (fsQCA)

Fuzzy-set calibration

, 210–211, 265

Fuzzy-set calibration

, 48, 210–211, 265, 266

Fuzzy-set qualitative comparative analysis (fsQCA)

, 14–15, 45, 61, 62, 83, 98, 113, 122, 134, 150, 152, 170, 231–232, 235, 265

asymmetric algorithm construction and testing

, 265

Boolean algebra-based software

, 152

calibration

, 154–155

calibration membership scores

, 171–173

causal recipe

, 138

computing consistency and coverage in

, 107

configural statements

, 98

consistency and coverage indexes

, 266

coverage index in

, 211–212

for efficiency, corruption, red tape, and GDP growth

, 103

fuzzy set scaling

, 135

fuzzy set scores for customer SOB

, 137

fuzzy truth table algorithms

, 170

isomorphic-management model

, 175

KSP

, 173

Mauro’s mechanisms

, 98

models

, 208

MRA vs.

, 104

predictive validity of

, 173–175

set intersection

, 136

software for

, 98

testing configural models

, 174

See also Configural analysis

Fuzzy truth table algorithms

, 170

Gatekeeper analysis

, 129

GCT. See General complexity theory (GCT)

GDP. See Gross domestic product (GDP)

General complexity theory (GCT)

, 189, 191, 192, 193, 195

Global personal care industry

, 189

GNP. See Gross national product (GNP)

Good practice(s)

, 3, 7, 17, 25–26, 32, 50

See also Bad practice(s)

Gross domestic product (GDP)

, 91, 95–100, 103–104, 110

Gross national product (GNP)

, 210

Group conflict

, 259

Hair care services industry

, 189

Happiness–performance configural theory

, 253–254

Hawthorne effect. See Measurement reactivity effect

“HC-2001 Head and Capstan Cleaner kit”

, 161–162

Hermeneutic analysis framework

, 132

Hermeneutics interpretations

, 131, 132

HFSE. See Hospitality front-line service employees (HFSE)

Highly reliable organizations (HROs)

, 72, 177

Hospitality front-line service employees (HFSE)

, 246–247

HROs. See Highly reliable organizations (HROs)

Human rational behavior

, 59–60, 150, 158

Hypothetical bias

, 40

ICRAF. See International Centre for Research on Agroforestry (ICRAF)

In-role performance (IRP)

, 75, 247, 261–262, 285

and CDERP

, 275–276

configural modeling associations with HFSE

, 277

configurational models for demographics and happiness

, 274

high happiness and low performance

, 276, 278

model

, 6, 279

very low happiness and very high

, 272–273

work facet-specific and happiness configurational models relating to

, 276–279

Index metrics for measuring consistency and coverage of complex configuration

, 211–212

Inter-group conflict

, 259

Inter-organizational conflict. See Inter-group conflict

Interest and taxes by assets (ROA)

, 42

Interest and taxes by sales (ROS)

, 42

International Centre for Research on Agroforestry (ICRAF)

, 128

Interpersonal conflict

, 259

Interpersonal conflict

, 259, 260, 264

Interpersonal relationship, quality of

, 258–259, 272

Interpersonal relationships

, 258–259

IRP. See In-role performance (IRP)

Isomorphic-management models

, 149, 150, 175

Key failure paths (KFPs)

, 121, 130

Key success factors (KSFs)

, 31, 72, 88, 89, 121, 130, 173, 176, 283

Key success paths (KSPs)

, 89, 121, 130, 173, 176, 177

Laboratory experiments

, 40

“Main effects” hypotheses

, 48

cases with associations contrary to

, 22–24

Main independent variables (MDVs)

, 47

Marketing organization theory (MOR theory)

, 114

Mauro’s “mechanisms”

, 98

MDVs. See Main independent variables (MDVs)

Measurement reactivity effect

, 36

Measures of associations

, 139

EDTMs

, 141–142

strategy and theory implications

, 140

Median splits

, 47–48

Mental model(s)

, 131, 132, 133, 142

Mental processes

, 26

CCT research

, 28

data collection methods

, 27

five-, six-, or seven-point scales

, 27

“Folk theory of mind”

, 29

in research on industrial marketing-buying thinking

, 120

self-generated validity

, 28–29

MOR theory. See Marketing organization theory (MOR theory)

MRA. See Multiple regression analysis (MRA)

Multiple regression analysis (MRA)

, 7, 9, 18–19, 58, 59, 61, 83, 150, 175–176, 184, 196, 249

and algorithms

, 95

analysis of joint lagged impact

, 100

BI indices

, 91

complex antecedent conditions

, 98, 100

computing consistency and coverage in fsQCA

, 107

consistency index

, 87

correlations

, 88, 97

data for country efficiency

, 101

efficiency, corruption, red tape, and GDP growth data

, 108–111

estimating relationships variables

, 94

findings from fsQCA

, 102, 103

fit to prediction validity

, 93

fsQCA

, 98, 104

GDP growth

, 98, 100, 103

models

, 47, 235

multicollinearity

, 85

net effects symmetric tools

, 59

percentages of outstanding and typical executives

, 90

and QCA

, 178

for random samples

, 99

significant correlations

, 87

statistical tool

, 84

symmetrical and asymmetrical relationships

, 86

from symmetric tests

, 255

tenets support

, 85

testing

, 8, 95–96

tipping points

, 90

tools-to-theory perspective

, 84

See also Configural analysis

NA. See Negative affect (NA)

National Football League (NFL)

, 66, 69

Negation membership

, 137

Negative affect (NA)

, 23

Negative contrarian case

, 182, 208, 232

“Net effect” approach

, 72, 85, 151

employee demographic variables on work performance

, 255

FS/QCA

, 134–135

MRA

, 175–176

in regression models

, 24–26

using symmetrical testing

, 196

theory from net effects perspective

, 159–160

Network theory

, 64

NFL. See National Football League (NFL)

Non-respondents

, 33

Nonresponse bias, measuring

, 32–33

Obtrusive field experiment

, 158–159

Organization conflict. See Group conflict

PA. See Positive affect (PA)

PANAS. See Positive and Negative Affect Schedule (PANAS)

Participant observation (PO)

, 125

Peer conflicts

, 259–260

Personal conflict

, 259

Physical work environment (PWE)

, 258, 263, 272, 276

Placebo

control group

, 35, 37, 47

treatment

, 35–39

PO. See Participant observation (PO)

Positive affect (PA)

, 23

Positive and Negative Affect Schedule (PANAS)

, 22, 23

Positive contrarian case

, 182, 208, 232

Property space analysis

, 124, 191–192

PWE. See Physical work environment (PWE)

Qualitative comparative analysis (QCA)

, 8, 15, 21, 88, 164, 170, 187

See also Fuzzy-set qualitative comparative analysis (fsQCA)

Quality of interpersonal relationship (qip)

, 258–259, 264, 271–272, 276–277

relationships

, 263, 272, 279

Ratio-scale indicators

, 210

Real-life decision making. See Behavioral decision making

Recipe principle

, 16, 67

using Boolean algebra

, 68, 69

calculating consistency and coverage

, 70–71

rule of thumb

, 69

Recognition-primed decision making model (RPD model)

, 119

Rectangular relationships

, 250

necessary-but-not-sufficient relationships

, 186

Regression analysis

rote applications of

, 58–62

statistical hypothesis testing in

, 185

Regression models

net effects in

, 24–26

non-significant terms in

, 47

R ² findings for

, 13

symmetric tests

, 42

See also Multiple regression analysis (MRA)

“Replication”

, 45–46

Return on investment (ROI)

, 198

ROA. See Interest and taxes by assets (ROA)

ROI. See Return on investment (ROI)

ROS. See Interest and taxes by sales (ROS)

RPD model. See Recognition-primed decision making model (RPD model)

Rule of thumb

, 21, 69, 74

SAIM. See Statistical, algorithm, isomorphic-management modeling (SAIM)

SBs. See Sentiments and beliefs (SBs)

SDL. See Service dominant logic (SDL)

SEM. See Structural equation modeling (SEM)

Senior decision-maker

, 116

Sensemaking

, 126–127, 129, 131, 132

hermeneutic interpretation

, 132

See also Field experiments, triple sensemaking in

Sentiments and beliefs (SBs)

, 121

Service dominant logic (SDL)

, 182

beauty salon/spa facets

, 242

complexity and customer evaluations

, 182–186

service outcomes

, 243

service provider and customer contexts

, 188–189

survey items

, 240–241

Service provider and customer contexts

, 188–189

Service research contexts

, 191, 233

Set-theoretic

analysis

, 34, 35

connection

, 139

consistency

, 155

coverage

, 157

Set intersection

, 136

Set theory

, 135, 153

7-point Likert scales

, 262

Simple antecedent

conditions

, 66–67, 157, 192–194, 221, 222, 266, 268, 292

service facets

, 196, 223

Simple service-facet antecedent conditions

, 223

Simple service-outcome antecedents

, 197

conditions

, 196–197

Society of Applied Anthropology

, 28

SPI. See Subjective personal introspections (SPI)

Sponsor identity bias

, 33

SPSS. See Statistical Packages for Social Sciences (SPSS)

Statistical, algorithm, isomorphic-management modeling (SAIM)

, 151, 173–174

Statistical analysis

, 164, 166

effects of price and purchase pal

, 166

multiple regression models predicting purchase

, 167

predictive validity

, 166, 170

profit models

, 169

purchase models

, 168

unit sales

, 165

Statistical hypothesis testing

, 68, 155, 185, 191

Statistical modeling

, 149, 151, 160

Statistical Packages for Social Sciences (SPSS)

, 211

Statistical tests

, 33, 133, 134, 141, 149, 228–229, 252, 284

Stepwise regression analysis

, 44–45

Structural equation modeling (SEM)

, 6, 7, 21, 58, 59, 84, 249, 252

Subjective personal introspections (SPI)

, 30, 60

Survey instruments

, 197–198, 262–264

SVH. See Symmetrical variable hypotheses (SVH)

Symmetric

analysis

, 12

analytics

, 182

models

, 12

necessary-but-not-sufficient relationships

, 186

net effects

, 9

only modeling

, 34–35

outcome

, 9

quintiles

, 60–61

relationships

, 75, 86–88, 151–152, 186–187, 202, 250, 269

search algorithm

, 45

statistical test

, 24

theory

, 14

tools

, 59, 170

Symmetric-based theories

, 232

Symmetrical tests

, 2, 7–8, 12–14, 24, 58, 61, 199, 203, 232

beauty/spa expenditure levels

, 223

calibration

, 210–211

causal asymmetry occurs

, 214, 217–220

complex demographic configurations

, 212–214

configurations of customer service-facet

, 223–228

correlation and cross-tabulation findings

, 199–210

customer evaluates service facets

, 222–223

index metrics

, 211–212

simple antecedent conditions

, 221–222

support for demographic algorithms

, 229

unique complex antecedent configurations

, 220–221

XY plot for high consistency

, 228–229

Symmetrical variable hypotheses (SVH)

, 7

Symmetric only modeling

, 34–35

Symmetric quintiles vs. asymmetric quintiles

, 60–61

Symmetric statistical test

, 24

“Take-the-best” algorithm

, 19–20, 93

Teamwork

, 260, 263

Tipping points

, 15, 63, 89–90, 187, 250–251

Tools-to-theory perspective

, 84

“True experiment”

, 35, 36, 39, 41, 47, 49, 253

“Truth table”

, 191–192

Unique complex antecedent configurations

, 192, 220–221

Unique configurations of service facets

, 223

outcomes

, 196, 228

Unobtrusive field experimentation

, 152, 158, 161

antecedents

, 162–163

calibrations

, 163–164

“HC-2001 Head and Capstan Cleaner kit”

, 161–162

predictive validity of data

, 164

theory from causal recipe perspective

, 160–161

theory from net effects perspective

, 159–160

Useable response rates

, 32–33, 59

Variable-focused only modeling. See Symmetric only modeling

Variable-level analysis

, 18

Venn diagrams

, 76, 189, 195, 233, 253, 257

Verbal self-reports

, 26

CCT research

, 28

data collection methods

, 27

five-, six-, or seven-point scales

, 27

“Folk theory of mind”

, 29

self-generated validity

, 28–29

Void-treatment control group in experiments

, 35

“double-blind” procedure

, 35–36

in field and laboratory experiments

, 37

marketing field experiments

, 38

placebo treatment

, 37–38

random assignment

, 36

test treatment

, 35

true experiment

, 35, 39

Work facet-specific(s)

, 257

configurations for very high and very low happiness

, 279

and happiness configurational models relating to negation of IRP

, 276–279

peer conflicts

, 259–260

PWE

, 258

quality of interpersonal relationships

, 258–259

scales

, 264

teamwork

, 260

Work facet-specific antecedents

, 257–258, 260

Writing quality

, 9, 13–14