Academic marketing researchers, implications for

, 43–45

Accessibility

, 288–289

Accountability, marketing

impact of advertising intensity

, 224

classification of selected articles

, 202

contributions

, 220–225

correlations and descriptive statistics

, 221

data and sample

, 217

environmental performance

, 197–198

limitations and future research directions

, 225–226

marketing actions

, 200

marketing actions impact on environmental and social performance

, 216–220

impact of marketing intensity

, 222

measures and models

, 217–220

measuring environmental and social performance

, 202–204

method

, 201–204

related frameworks

, 200–201

results

, 204, 216, 220

impact of selling, general, and administrative expenses intensity

, 223

social performance

, 198–200

Accountable

, 49–50

Accountants

, 16

Accounting

, 16, 19

equation

, 27–29

performance model

, 142–144

rules

, 21–25

Activism

, 4

Actors

, 235, 247

actor-level analysis

, 256–257

Adobe Advertising Cloud TV

, 156–160

Adobe Analytics. See Omniture SiteCatalyst

Advertising Intensity

, 217–218

Affective commitment

, 271–272

Affiliation network

, 235

Age

, 83–84

Aggregate data approaches to attribution

, 161–162

advantages and disadvantages of aggregate data

, 163–164

Aggregate store and service portfolios

, 297–301

Akaike information criterion (AIC)

, 218–220

Algorithmic approach

, 170

Algorithmic attribution models

, 171–173

Ambience

, 287–288

American Customer Satisfaction Index (ACSI)

, 51–52, 283–284

ANOVA

, 122

Antecedents of brand equity

, 77, 79–80

Artificial intelligence

, 5

Asset

, 19–20

Attention, interest, desire, and action model (AIDA model)

, 51

Attitudinal drivers of WOM

, 264

data

, 264

drivers of word of mouth

, 266–267

nature of word of mouth

, 265

simultaneous impact of

, 267–273

Attractiveness

, 286

Attribution modeling

channel analysis breadth in

, 165–169

data handling approaches in

, 156–165

experimentation and optimization of

, 174–178

optimizing

, 176–178

process for successful adoption of

, 178–180

testing efficacy of attribution models

, 174–176

Backfire effects

, 269

Backward-looking

, 5

Balance sheet

, 17

Baseline market-based assets creation

, 33

Bidirectional network ties

, 235

Big data

, 4

handling

, 182–183

Bimodal network

, 235

Binary framework

, 116

Blame

, 112

Brand deal

depth

, 87

level

, 87

Brand equity

, 6, 64–65, 75–77

data and measures

, 84–85

determinants of price and volume premium

, 81–84

distribution of revenue premium

, 89–90

and effect on price, volume, and revenue premiums

, 91–99

extended framework on brand equity and antecedents

, 77, 79–80

large number of manufacturer brands endangered

, 99–102

model fitting

, 87–89

model formulation

, 85

price vs. volume premium

, 90–91

relative importance of brand equity antecedents on price and volume premiums

, 102–105

revenue premium

, 81

Brand value chain (BVC)

, 78

Brand value creation

, 78

Brand(s)

, 75–76

awareness

, 52

consideration

, 52

continuity

, 168–169

display sales

, 87

evaluation, CFMs for

, 64–66

feature sales

, 87

liking

, 52

management

, 77

positioning

, 77, 79–80, 83, 86, 93, 96

price deal sales

, 87

structure

, 79–80, 82–83, 93

valuation

, 30, 66

Branding

, 75–76

strategy

, 79–82, 85, 91, 93

structure

, 86

Brick-and-mortar stores

conceptual framework

, 286–289

data and methodology

, 289–295

future research implications

, 310–311

managerial and societal implications

, 309–310

related literature

, 283–285

results

, 296–306

Budget allocation

, 184

Buzz

, 9

Calculative commitment

, 272–273

California Consumer Privacy Act (CCPA)

, 162

Cash flow statement

, 17

Category assortment

, 82–83

Category characteristics

, 84, 87, 96, 99

Category-level measure

, 87

Cause-related marketing

, 4

Centrality

, 255

Channel analysis breadth in attribution modeling

, 165–169

Citizen Satisfaction Index (CSI)

, 283–284

City centers

, 280

Click

, 169

Clickstream

, 161–162

advantages and disadvantages of Clickstream data

, 162–163

dealing with challenges of data

, 164–165

Coca-Cola advertising

, 23

Coca-Cola Company

, 26

Codifiability

, 135–136

Commitment

, 271

Common alternative

, 6

Common attribution models

, 169–173

differences in marketing attribution models

, 169–171

heuristic and algorithmic attribution models

, 171–173

Community living

, 4

Complexity

, 135–136

Congruity theory

, 116

Consensus strategy

, 134

Consumer behavior

, 10

Consumer services

, 303, 305

Consumer-packaged goods (CPG)

, 76

Consumers failure perceptions and attributions

, 112–113

Coordination

, 131–132

Core set

, 133

Corporate social responsibility (CSR)

, 58, 61, 194–195, 199

composite measure of

, 218

Cost per acquisition (CPA)

, 177–178

COVID-19

, 4

Critical brand management decisions

, 79–80

Cross-network interactions

, 255–256

Custom-weighted attribution (CWA)

, 171

Customer behavior, CFMs with

, 56–61

Customer characteristics

, 83–84, 86, 96

Customer effort score (CES)

, 51–52

Customer equity (CE)

, 6, 50

Customer experience (CX)

, 274

Customer feedback metrics (CFMs)

, 50–51

impact of

, 60

for brand evaluation

, 64–66

challenges of

, 66–68

classification

, 52–54

dashboard creation, validation, and usage

, 62–64

deep dive into customer-focused feedback metrics

, 54–56

drivers of

, 59

future

, 68–70

history of

, 51–52

to make marketing more accountable

, 61–66

in McKinsey’s customer decision journey model

, 53

relation CFMs with customer behavior and firm performance

, 56–61

relation to retention

, 64

selection to include in dashboard

, 61–62

Customer influence value (CIV)

, 194–195

Customer knowledge value (CKV)

, 194–195

Customer lifetime values (CLVs)

, 6, 50, 194–195

Customer referral value (CRV)

, 194–195

Customer satisfaction

, 54–55, 270

Customer valuation

, 30

Customer-focused feedback metrics, deep dive into

, 54–56

customer satisfaction

, 54–55

net promoter score

, 55–56

other CFMs

, 56

Customer-focused metrics and analytics

, 5

Dashboard creation

, 62–64

Data handling approaches in attribution modeling

, 156–165

Density

, 255

Dependent variable

, 85

Descriptives

, 296–297

Direct traffic reduced approach

, 170

Distribution

, 296

Diversity

, 283–284

Dollar Index

, 86

Dynamic capabilities of market orientation

, 130–131

Econometrical model

, 55

Economic balance sheet of business look like

, 31–32

challenge of value arising

, 31–32

market-based assets

, 31

Economic commitment

, 272–273

Education

, 83–84

Efficacious attribution models

, 174

Ego network

, 235

Emotions

, 271

Environmental disclosure score

, 218

Environmental performance (EP)

, 8, 194, 197–198

marketing actions impact on

, 216–220

measuring

, 202–204

Extended framework on brand equity and antecedents

, 77–80

External attribution

, 113

External reporting

, 16

Facebook

, 156–160

spend

, 161

Failure and multiple loci causal agents

, 114–115

Financial accountants

, 23

Financial accounting

, 16–18

accounting conventions fail to represent economic reality

, 21–30

accounting created to solve

, 18–21

actions to measure and report intangible value

, 29–30

concerns about accounting rules

, 21–25

economic balance sheet of business look like

, 31–32

financial statements

, 32–43

IAS

, 25–26

material complaint

, 26–29

Financial performance (FP)

, 194

Financial reporting

, 5–6

Financial statements

, 32–43

adjustment for market-based assets approach

, 33–34

example of adjusting

, 34–41

implications for academic marketing researchers

, 43–45

implications for senior marketers

, 42–43

information needed to adjust

, 32–33

limitations of adjusting for market-based approach

, 40–42

Firm performance

, 8–9

CFMs with

, 56–61

intrafirm networks and

, 245

measures of

, 4–7

Firms

, 49–50, 67

balance economic profits

, 194

marketing

, 153–154

Forced commitment

, 272–273

Forward-looking

, 5

Fractional attribution. See Shapley value attribution (SVA)

FTA

, 170

General Data Protection Regulation (GDPR)

, 162

Generally accepted accounting principles (GAAP)

, 17–18

German Federal Statistical Office

, 289

Global Financial Crisis

, 21–22

Google

, 156–160

Google Ads

, 161

Google Analytics

, 156–160

Granularity

, 5

Greenhouse gas (GHG)

, 8, 218

Handelsverband Deutschland (HDE)

, 281

Heterogeneity among store and service provider categories

, 302–305

Heuristic approach

, 170

Heuristic attribution models

, 171–173

Hierarchical Bayes model

, 275

Hierarchical interaction

, 170

Hierarchical interaction attribution (HIA)

, 171, 173

Household

affluence

, 83–84

size

, 83–84

HR professionals

, 24

Hypothesized model

, 139

Identifiable asset

, 19

Income statement

, 17

Independent variables

, 85–87

Indirect economic value

, 194–195

Industry-level CFMs

, 53–54

Inner city

, 280, 284

attractiveness

, 283–284

Instagram

, 156–160

Intangible assets

, 22, 27

Inter-organizational networks

, 8–9

Internal attribution

, 113

Internal reporting

, 16

International Accounting Standards (IAS)

, 25–26

International Accounting Standards Board (IASB)

, 17–18, 25

International Integrated Reporting Framework (IRF)

, 200–201

International Organization for Standardization (ISO)

, 66

Interorganizational networks

, 246–247

Intrafirm networks

, 8–9

and firm performance

, 245

Key performance indices (KPIs)

, 51–52

Last touch attribution (LTA)

, 155, 170

Latent Dirichlet allocation (LDA)

, 69

Lead

, 171

Learning

, 131–133

Level of analysis

, 249, 256–257

Linking marketing

, 5

Locus

, 114

Managerial accounting. See also Financial accounting

, 16–17

Manufacturer brand

joint effect of store image and manufacturer brand equity

, 118–123

in product failures

, 114–115

Market asset

, 6

Market exploration

, 133, 140

Market orientation

, 7, 49–50, 130, 132, 150–151

accounting performance model

, 142–144

capabilities

, 130–136

capability

, 140

data

, 139–140

exploration

, 133, 141

framework

, 136–139

fundamental market orientation capabilities

, 132–136

limitations and future research

, 147

measures

, 140–142

research method

, 139–142

results

, 142–146

self-reported performance model

, 145–146

tacitness

, 135, 141

tests of measure validity and reliability

, 142

theoretical contribution

, 146–147

Market-based assets approach

, 31

adjusting level of assets related to marketing

, 34

adjustment for

, 33–34

baseline market-based assets creation

, 33

estimating marketing investments and marketing expenses

, 33–34

limitations of adjusting for market-based approach

, 40–42

Marketers

, 5–6

Marketing

, 3, 10

academia

, 4

adjusting level of assets related to

, 34

analytics

, 4

expenses

, 33–34

intensity

, 217–218

investments

, 33–34

mix

, 84, 87, 99

SNR review in

, 235–247

Marketing accountability

, 194

marketing accountability for marketing and nonmarketing outcomes

, 3–4

measures of firm performance

, 4–7

measures of social interaction

, 7–9

measures related to societal outcomes

, 9–10

Marketing Accountability Standards Board (MASB)

, 30

brand investment

, 76

Marketing actions

, 200

impact on environmental and social performance

, 216–220

Marketing attribution

, 154–155, 157, 160

differences in

, 169–171

future research agenda for

, 180–185

Marketing–Performance Outcome Chain framework

, 130, 136–137, 139

hypothesized model of effects

, 138

Markov chain

, 170

Markov chain attribution (MCA)

, 171–172

Matched market testing

, 175

Matching

, 22–23

costs and revenues

, 23

Material complaint

, 26–29

Materiality

, 20

Multichannel attribution models

, 176

Multichannel data-driven attribution models

background literature

, 155–156

brand continuity

, 168–169

channel analysis breadth in attribution modeling

, 165–169

Clickstream and aggregate data approaches to Attribution

, 161–162

common attribution models

, 169–173

data handling approaches in attribution modeling

, 156–165

experimentation and optimization of attribution models

, 174–178

future research agenda for marketing attribution

, 180–185

offline-only attribution research

, 166

Omnichannel attribution research

, 167–168

online-only attribution research

, 166–167

process for successful adoption of attribution models

, 178–180

Multiloci attributions

, 113

cause of product failure

, 123

establishing multiple loci

, 117–118

failure and multiple loci causal agents

, 114–115

joint effect of store image and manufacturer brand equity

, 118–123

limitations

, 124–125

recommendations

, 125

role of manufacturer brand and store image in product failures

, 114–115

theoretical and managerial implications

, 124

theoretical background and hypotheses

, 114

Multitouch

, 169

National brands

, 90

National business systems (NBS)

, 199

Net promoter score (NPS)

, 50, 55–56

Networks

dynamics and evolution

, 256

mechanisms

, 251

mode

, 248

ties

, 235

and WOM

, 236–244

Node

, 235

Nonexhaustive list

, 52

Nonreduced traffic reduced approach

, 170

North American Industry Classification System (NAICS)

, 274–275

Offline-only attribution research

, 166

Omnichannel attribution research

, 167–168

Omnichannel frameworks

, 183

Omniture SiteCatalyst

, 167

Online-only attribution research

, 166–167

Open-ended questions

, 117

OpenStreetMap (OSM)

, 282, 289–290

Operationalization

, 290–291

Opportunity

, 171

Ordinary least squares (OLS)

, 291

Organizational theory

, 233–234

Overstating intangibles

, 18

Pixer

, 170

Place branding

, 283–284

Position-based attribution (PBA)

, 171

Price premium

, 6, 78, 85, 90–91

brand equity and effect on

, 91–99

determinants of

, 81–84

relative importance of brand equity antecedents on

, 102–105

Private label

, 76, 79–80, 82–85

Proactive market orientation

, 130

Probit

, 170

attribution

, 171

Probit model attribution (PMA)

, 172

Procter and Gamble

, 84

Product attractiveness

, 52, 136–139

Product awareness

, 52

Product failures

, 112, 114

manufacturer brand and store image in

, 114–115

Product innovativeness

, 136–139, 142

Product superiority

, 141

Product variations

, 82

Product-as-a-Service

, 133

Profit maximization

, 184

Profitability

, 142–144

Proportional hazard

, 170

Proportional hazard attribution (PHA)

, 171, 173

Purchase price allocation (PPA)

, 22

Quality vs. quantity effects in cities’ store and service provider portfolios

, 305–306

Random coefficient simultaneous equation model (RCSEM)

, 87–89

Reactive market orientation

, 130

Retail agglomerations

, 281–282, 284–285

Retail brand equity

, 112–113

Return on assets (ROA)

, 218–220

Return on investment model (ROI model)

, 56–58, 156, 160

Return on sales

, 136–139

Revenue

, 81

Revenue premium. See also Price premium

, 6, 76, 78, 81

brand equity and effect on price, volume, and

, 91–99

distribution of

, 89–90

Root mean square error of approximation (RMSEA)

, 142

Sale

, 171

Sales growth

, 142

Salesperson performance

, 8–9

social networks and

, 244–245

Same-session vs. 7-day

, 170–171

Self-brand connection (SBC)

, 273

Self-reported performance model

, 145–146

Self-reported profitability

, 142

Selling, general, and administrative expenses Intensity (SG&A Intensity)

, 217–218

Senior marketers, implications for

, 42–43

SERVQUAL

, 53

Shapley value

, 170

Shapley value attribution (SVA)

, 169–171

Sharing economy platforms

, 133

Shopping streets

, 281–282

Single touch

, 169

Social capital theory

, 245

Social disclosure scores

, 218

Social function

, 9–10

Social interaction, measures of

, 7–9

Social listening, moving to

, 68–70

Social network research (SNR)

, 8–9, 233–235

actors

, 247

antecedents of network formation and position

, 257–258

classification schema

, 251–252

compositional performance

, 253

compositional process

, 254

configurational performance

, 252–253

configurational process

, 253–254

consequences of social networks

, 249

cross-network interactions

, 255–256

beyond density and centrality

, 255

direct measures of network processes

, 258

interorganizational networks

, 246–247

intrafirm networks and firm performance

, 245

level of analysis

, 249

level of analysis

, 256–257

network boundaries

, 248–249

network dynamics and evolution

, 256

network impact

, 249–251

network mechanisms

, 251

network mode

, 248

practical considerations of

, 247–251

research agenda

, 254

research agenda and classification schema

, 251–258

review in marketing

, 235–247

selective review of marketing subareas using

, 237–243

social networks and salesperson performance

, 244–245

Social networks

, 8–9, 235

Social performance (SP)

, 8, 194, 198, 200

marketing actions impact on

, 216–220

measuring

, 202–204

Societal well-being

, 4

Stakeholders

, 49–50

Standardize reporting

, 19

Stimulus–Organism–Response model (SOR model)

, 286

Store and service portfolio

, 287

Store image

joint effect of store image and manufacturer brand equity

, 118–123

in product failures

, 114–115

Strength of weak ties (SWT)

, 233–234

Structural equation model (SEM)

, 291–295

Structural VARA

, 171

Swedish Customer Satisfaction Barometer (SCSB)

, 51–52

System dependence

, 135–136

Tacit knowledge

, 135

Tangible assets

, 27

Teachability

, 135–136

Text-mining technique

, 68

Time decay attribution (TDA)

, 171

Touch

, 169

Town center management (TCM)

, 281

Twitter

, 156–160

TwitterScraper

, 69

Underreporting of intangibles

, 18

Unidirectional network tie

, 235

Uniform resource locator (URL)

, 167

Urbanity

, 283–284

US Financial Accounting Standards Board (FASB)

, 17–18

US Patent and Trademark Office (USPTO)

, 85

Validation

, 62–64

Valuation

, 21

model

, 76

Vector autoregression (VAR)

, 170

Vector autoregression attribution (VARA)

, 171–173

Vector of dummy variables

, 86

Video-mining technique

, 68

Virtual reality

, 5

Vis-à-vis manufacturer

, 116

Voice-mining technique

, 68

Volume premium. See also Revenue premium

, 6, 78, 90–91

brand equity and effect on

, 91–99

determinants of

, 81–84

model

, 86

relative importance of brand equity antecedents on

, 102–105

Web analytics tool

, 156–160

Word of mouth (WOM)

, 8–9, 235, 249–250, 264

drivers of

, 266–267

nature of

, 265

networks and

, 236–244

YouTube

, 156–160