Activation functions

, 246

Advertising

, 89–90

context of

, 96

Agent-based simulation model

, 183

Airbnb

context

, 117

Smart Pricing algorithm

, 231

smart pricing tool

, 107

Airlines

, 105, 107

Alexa

, 289

Algorithmic bias

, 117

Algorithmic collusion

, 33, 118–119

Algorithmic sellers

, 109–110

Amazon Mechanical Turk

, 163

Amazon’s current business model

, 32–33

Anthropomorphism

, 185, 274–275

in AI

, 277–286

beneficial and harmful effects

, 287–290

conceptual framework

, 286–298

conditions

, 290–293

cumulative distribution of articles

, 275

future research directions

, 298–302

individual characteristics of AI users

, 293–296

insights emerging from literature

, 284–286

journals included in literature search

, 279

limitations

, 302

literature review procedures

, 278, 280, 283–284

related to context of employing AI anthropomorphism

, 299–300

related to effects of AI anthropomorphism

, 298–299

related to individual characteristics of AI users

, 300–302

relationship perspective

, 297–298

Apple (technology company)

, 13–14

Application programming interfaces (APIs)

, 159–160

Area Under the Curve (AUC)

, 87–88

Artificial intelligence (AI)

, 1–2, 13–14, 104–105, 125–126, 147–148, 170, 218, 274

advertising, persuasion, and communication

, 153

agenda for future work

, 34

AI-based algorithm

, 29

AI-based innovation

, 1–2

AI-based model selection tools

, 28–29

AI-based queries

, 154

AI-based solutions

, 133

AI-supported content generation

, 139–140

aiding marketing decisions

, 4–6

algorithmic collusion

, 118–119

anthropomorphism in

, 277–286

applications of AI-powered VOC

, 150–153

challenges in use of UGC

, 149

consequences for pricing

, 115–119

considerations for use of

, 139–140

consumer reactions

, 139

data available for AI and VOC

, 154–156

decisions

, 30–32

dynamic pricing

, 115–116

economic framework of

, 14–27

firms implementing AI for pricing

, 104–115

identifying and organizing customer needs

, 150

impact on consumers and society

, 8–9

level of impact of

, 27–28, 34

market research

, 6

marketing purpose of

, 4–6

opportunity identification for AI research

, 10

personalized pricing

, 117

potential abuse and need for regulation

, 139–140

prediction

, 28–30

price algorithms

, 111

promise of

, 149–150

promise of AI and Machine Learning

, 149–150

promise of user-generated content

, 149

reflection of branding through users

, 151–153

research in marketing on

, 16, 26, 40, 76

society

, 33–34

strategy

, 32–33

tools

, 32, 136, 140

understanding and forecasting demand

, 150–151

and VOC

, 148–150

VOC practice before

, 148–149

workforce implications

, 140

Artificial Intelligence Assistants (AIAs)

, 289

Artificial neural networks (ANNs)

, 227, 240–241

Attenuation bias

, 170, 175

Augmented reality (AR)

, 7, 228–229

Autocompletion for email and text messaging

, 139

Autoencoders

, 257

Autoencoding models

, 203

Automated content generation

, 129

Automation

, 104–105

Autoregressive models

, 203

Average treatment effect (ATE)

, 84

Azure’s Face API

, 222–223

“Bag-of-words”–based methods

, 180

Behavioral experiments

, 229–230

Berry–Levinsohn–Pakes–type random coefficient choice model

, 178

Bias mitigation

, 230–231

Bibliometric network

, 173

Bidirectional Encoder Representations from Transformers (BERT)

, 125–126, 157, 159, 180–181, 198–199, 202–203

Big data (see also Data)

, 104

VOC practice before

, 148–149

Big GAN (BigGAN)

, 132

Binary Robust Independent Elementary Features

, 221

Brand logos

, 220

Brand perception

, 162

Brand selfies concept

, 153

Brand-related social tags

, 30

Branding

, 148

brand perception

, 151–152

brand positioning

, 152–153

reflection of branding through users

, 151–153

user–brand interaction

, 152–153

Brands marketing strategies

, 154

Business leaders

, 13–14

Business-to-consumer (B2C)

, 275

Canny edge detector

, 221

Causal inference

, 268

Causality

, 194–195

Charge supracompetitive prices

, 118

Classical ML models

, 242

Click-through rate (CTR)

, 89–90

Clustering

, 158

algorithms

, 150

Co-citation analysis

, 173

Collusive algorithm

, 118–119

Color histogram descriptor

, 221

Colors

, 221

Common method bias

, 170

Company-level topics

, 223–224

Computer vision

, 7–8

application domain

, 223–224

data format

, 219–221

future research

, 228–231

in marketing research

, 219–224

model structure

, 221–223

techniques

, 218

Conditional average treatment effect (CATE)

, 82

Conditional GAN (CGAN)

, 131–132

Conjoint analysis

, 149

Construct validity

, 164

Consumer reactions

, 139

Consumer silence

, 170–171

Consumer-centric perspective

, 276

Consumer-level topics

, 223

Content generation

considerations for use of AI-supported content generation

, 139–140

generating synthetic images

, 131–133

generating textual content with language models

, 129–131

potential for

, 129–133

potential for AI throughout customer journey

, 126–129

potential for content generation

, 129–133

supporting customer equity management with content generation

, 133–139

Content selection method

, 130

Content-related marketing tasks

, 140

Contextual bandit

, 82–83

Contour

, 221

Convolutional neural networks (CNNs)

, 28, 129, 157, 163, 202, 220–221, 246, 249, 254

Convolutional-LSTM

, 157

Counterfactual explanations

, 227–228

Counterfactual policy evaluation

, 84–85

Counterfactual validity

, 81–82

Criticisms

, 227–228

Cross-entropy

, 247

CTR prediction problems

, 81

Customer acquisition

, 134–136

Customer equity framework

, 126–127

Customer equity management with content generation

customer acquisition

, 134–136

customer retention

, 138–139

relationship development

, 136–138

supporting

, 133–139

Customer feedback

, 170

future of customer feedback research

, 183–185

online customer feedback

, 175–183

publication count by journal

, 172

publication count by year

, 172

review methodology

, 171–174

from user-generated content

, 176

Customer relationship management (CRM)

, 8

Customer retention

, 138–139

Customer reviews

, 154

Customer satisfaction research

, 174–175

DALL-E

, 137

Data

, 14, 154

available for AI AND VOC

, 154–156

customer reviews

, 154

data-generating process

, 87–88

direct queries to customers

, 154

images

, 155

preprocessing

, 156–157

social media

, 154

sources

, 154

text

, 155

trading

, 1

transformation

, 160

types

, 155–156

user engagement

, 155–156

De-bias pricing algorithms

, 117

Decision trees

, 82

Decision types

, 15–27

Decision-makers in marketing

, 218

Deep learning (DL)

, 8, 82, 192, 230, 239–240

algorithms

, 248, 250, 265

architectures for NLP

, 200–202

causal inference

, 268

combine unstructured data with structured data

, 265–266

common testbeds

, 268–269

customized algorithm development

, 265–266

customized constraint

, 266

deep learning–based language model

, 174

future directions

, 266–269

in marketing

, 239–240, 243–244

model efficiency improvement

, 267

models

, 126, 220–221, 224

multimodal, five senses, and networks

, 267

neural networks

, 242–248

plug and play

, 265

problems

, 265–266

properties

, 241–242

reinforcement learning

, 267–268

theory-driven architecture design

, 266

theory-driven initialization

, 266

Deep neural networks (DNN)

, 81, 242

Deep Q-Network (DQN)

, 248–249

Deep reinforcement learning (DRL)

, 262–265

Deepfakes

, 139–140

Demand, real-time swings in

, 105–110

Diachronic word embeddings

, 199–200

Dictionary and word frequency–based text mining

, 179–180

Difference-in-difference estimation approach

, 194–195

Digital cameras

, 150

Digital exhaust of individual behavior

, 1

Digital footprints

, 147–148

Digital voice assistants

, 139

Direct marketing context

, 138

Direct method

, 85

Direct queries to customers

, 154

Discriminative deep learning models

, 249–258

CNNs

, 249–254

RNN

, 254–255

transformers

, 255–258

Discriminative models

, 248–249

Discriminator network

, 131

Disney

, 88

Distributional hypothesis

, 199

doc2vec

, 200

Dominant color descriptor

, 221

Double machine learning (DML)

, 178

Doubly Robust estimator (DR estimator)

, 86

Doubly robust method

, 86

Dropout method

, 248

Dynamic methods

, 83–84

Dynamic models update customers

, 138

Dynamic pricing

, 105, 110, 115–116

E-commerce

, 104

Emails

, 132–133

Embedded Topic Model (ETM)

, 204

Embeddings

, 157–158, 198–200, 204

Entropy

, 87–88

Equilibrium analysis, strategic behavior and

, 95–96

ERNIE 3.0

, 203–204

European Union (EU)

, 33

Evaluation of AI methods

, 161–162

Evaluative Lexicon 2.0

, 197

Evidence lower bound (ELBO)

, 259

Example-based explanation techniques

, 227–228

eXplainable Artificial Intelligence (XAI)

, 219

External validity

, 164

Facebook (technology company)

, 13–14

engagement data

, 156

news feed algorithm

, 93

user-engagement data

, 155–156

Fairness in marketing

, 224

Fake reviews

, 183

fastText

, 198–199

Feature-level models

, 221

Feedback data

, 14

Field experimentation

, 84

Field experiments

, 229–230

Financial Times Top 50 journals (FT50 journals)

, 278–279, 284

Fine-tuning

, 130

Firms

, 3–4, 131

First-order methods

, 247

Flexible supervised learning algorithms

, 81

Frames

, 219

Fuzzy SVM

, 159

Gated recurrent unit (GRU)

, 255

GauGAN

, 137

Gender differences

, 294

General Data Protection Regulation (GDPR)

, 33

Generalizability

, 81–82

Generative adversarial networks (GAN)

, 8, 131, 248–249, 260, 262

Generative deep learning models

, 258–262

GAN

, 260–262

VAE

, 258–260

Generative models

, 140, 248–249

Generative Pre-trained Transformer 3 (GPT-3)

, 125–126, 180–181, 198–199

Generative video models

, 139

Generator network

, 131

Global interpretability

, 225

GLOVE

, 180, 198–199

Google (technology company)

, 13–14

search engine algorithm

, 134

search personalization

, 93

Gradient-weighted class activation mapping (Grad-CAM)

, 161, 226–227

Heatmap method

, 228

Hidden Markov Model (HMM)

, 165

Hulu

, 88

Human–machine collaboration

, 230

ImageNet

, 268

Images

, 155, 218

data

, 220

image-based social media

, 218

image/post clusters

, 153

tagging

, 157

Incentive-aware personalization

, 96

Individual-level personalization

, 77–78

InferNER approach

, 195

Influence methods

, 227

Input data

, 14

Insight generation

, 170–171

Instagram

, 171

Instrumental variable approach (IV approach)

, 175

Interactive methods

, 82–83

Internet

, 151

Interpretability

, 219, 228

issues

, 224–228

Inverse Propensity Score estimator (IPS estimator)

, 85–86

ISI Web of Science

, 171–172

Judgment

, 14–15

Knowledge extraction

, 227

LambdaMART ranking algorithm

, 29

Language models

, 131, 198, 204

generating textual content with

, 129–131

marketing applications of

, 204–205

Language structure and deep learning–based text mining

, 180

Large language models

, 140

Large-scale pretrained language models

, 129–130

LDA

, 195

Learning

from audio visual data

, 184

from interactive two-sided feedback

, 185

Lexicons

, 197–198

and word frequency–based methods

, 180

Linguistic Inquiry and Word Count (LIWC)

, 197

Local interpretability

, 226

Local Interpretable Model-Agnostic Explanations (LIME)

, 226

Long short-term memory (LSTM)

, 129, 157, 201–202, 255

Low response rates

, 170

Lyft

, 107

Machine learning (ML) (see also Deep learning (DL))

, 14–15, 29–30, 32, 147–148, 170, 219, 240

algorithms

, 150

methods

, 82

promise of AI and

, 149–150

Manual encoding

, 219–220

Manual inspection

, 160

Mapping methods to research questions

, 162–165

posteriori–identified phenomena and constructs

, 162–163

priori–defined constructs

, 163–164

validation

, 164–165

Market

fairness

, 224

research

, 6

Marketers

, 1–2

Marketing

, 274

AI’s impact on consumers and society and vice versa

, 8–9

algorithms and methods

, 7–8

applications of language models

, 204–206

communications

, 132–133

data

, 6–7

marketing-AI ecosystem

, 2–4

modelers

, 242–246

novel approaches for established tasks

, 204

novel approaches for novel tasks

, 204–205

opportunity identification for AI research

, 10

purpose of AI

, 4–6

research in marketing on Artificial Intelligence

, 40–76

scholars

, 1–2, 14, 28–30, 34, 193

Markov decision process (MDP)

, 263

Matrix factorization approaches

, 81

Maximum likelihood estimation (MLE)

, 247

Mean Average Error (MAE)

, 87–88

Measurement error

, 180

Megatron-Turing NLG

, 125–126

Menu costs

, 103

Metaphor elicitation technique

, 151–152

Methodological approaches to personalization

, 79–84

dynamic methods

, 83–84

generalizability and counterfactual validity

, 81–82

online and interactive methods

, 82–83

scalability

, 80–81

Metric-based evaluation

, 87–88

Mind perception

, 299

mini-Xception

, 222–223

Model interpretability

, 219

Model interpretation

, 160–161

with manual inspection and data transformation

, 160

post hoc model explanation

, 161

Model-agnostic interpretability

, 227

Model-agnostic techniques

, 226

Model-specific interpretability

, 226–227

Multi-armed bandit (MAB)

, 113

Multiarmed bandit models (MAB models)

, 5

Multihead attention

, 202

Naive Bayes classifier

, 178

Named entity extraction (NER)

, 195

Natural language generation models (NLG models)

, 125–126, 129–130

Natural language inference task (NLI task)

, 205

Natural language processing (NLP)

, 3, 7, 150, 172–173, 192

applications

, 192

challenges, biases, and potential harms

, 208–209

concept and topic extraction

, 195–197

current state of NLP in marketing

, 195–198

embeddings, language models, transfer learning

, 198–204

established and novel tools for diverse text-based marketing applications

, 196

marketing applications of language models

, 204–205

relationship extraction

, 197

roadmap and future trends

, 206–207

sentiment and writing style extraction

, 197–198

text in marketing

, 193–195

Netflix

, 88

Network embeddings

, 163

Neural networks (NN)

, 242, 248

activation functions

, 246

architecture

, 246

objective function

, 247

optimizer

, 247–248

regularization

, 248

News personalization

, 83

Nonconvergence

, 262

Nonparametric approach

, 115

Nontech firms

, 267

Nontextual data

, 194

Objective function

, 247

Offline beacons

, 1

Online customer feedback

, 175–183

AI and machine learning in analyzing unstructured review data

, 178–181

challenges in learning from

, 181–183

economic impact of online reviews

, 177–178

Online forum discussions

, 30

Online methods

, 82–83

Online platforms

, 147–148

Online reviews

, 30

OpenCV

, 222–223

Optimal algorithm

, 115

Optimizer

, 247–248

Overlap assumption

, 82

Peer influence

, 182–183

Personality

, 295

Personalization

algorithms

, 82–83, 91

alternative approaches

, 87–88

direct method

, 85

doubly robust method

, 86

evaluation

, 84–88

extensions to special settings

, 86–87

IPS estimator

, 85–86

methodological approaches to personalization

, 79–84

models

, 94–95

multiple objectives and long-term outcomes

, 94–95

problem definition

, 78–79

returns to personalization

, 88–90

signal-to-noise ratio

, 94

strategic behavior and equilibrium analysis

, 95–96

time drifts

, 95

and welfare

, 90–93

Personalized policy design

, 78–79

Personalized pricing

, 89–90, 110, 113, 117

Personification

, 278

Photorealistic images

, 131

Pix2pix approach

, 132

Pixel-level models

, 222

Plug and play language models (PPLM)

, 130

Poisson factorization

, 197

Polarization

, 93

Position encoding

, 255, 257

Post hoc model explanation

, 161

Posteriori–identified phenomena and constructs

, 162–163

Prediction Machines

, 14, 32–33

Predictions

, 4, 28, 30

prediction-based algorithms

, 158–159

process

, 14

Predictive ML algorithms

, 163

Preprocessing images

, 157

Price discrimination

, 110–113

Price experimentation

, 113–115

Pricing

automation

, 105–107

consequences of AI for pricing

, 115–119

dynamic pricing

, 105–110

firms implementing AI for pricing

, 104–115

personalized pricing

, 110–113

price experimentation

, 113–115

Primary data

, 149

Prime Video

, 88

Principal component analysis (PCA)

, 258

Priori–defined constructs

, 163–164

Privacy, personalization and welfare

, 91–92

Probabilistic content generation process

, 130

Product development

, 150

Propensity-based approaches

, 87

Prospective customers

, 126

Prototypes

, 227–228

Q-learning

algorithm

, 115

models

, 118

Q-value function approximator

, 264–265

Quantitative marketers

, 1

Racist language

, 130

Random Forests

, 81–82

Recency Frequency Monetary value (RFM value)

, 115

Rectified linear units (ReLu)

, 246

Recurrent neural networks (RNNs)

, 129, 159, 200–201, 248–249, 254–255

Recursive neural networks

, 129

Regression models

, 225

Regularization

, 248

Regulators

, 4

Reinforcement learning (RL)

, 248–249, 267–268

Relationship development

, 136–138

Relationship extraction

, 197

Relative Information Gain (RIG)

, 87–88

Representation learning

, 240–241

Reputation

platforms

, 171

systems

, 175

ResNet-50

, 222

Restricted Boltzmann machine (RBM)

, 242

Ride-hailing platforms

, 107

RoBERTa

, 157, 198–199

Robots

, 289–290

Rule-based learners

, 226–227

Scalability, methodological approaches to personalization

, 80–81

Scale-Invariant Feature Transform (SIFT)

, 221

SCImago Journal & Country Rank

, 278–279

SE-ResNet-50

, 222

Search engine optimization (SEO)

, 30, 133, 205

Second-order methods

, 247

Seeded LDA

, 195–196

Selection bias

, 183

Self-attention

, 255

Self-selection

, 182

Self-supervised representation learning

, 200

Semantic network analysis

, 179–180

Sentence-based LDA

, 195–196

SentenceBERT

, 200

Sentiment analysis

, 151, 198

Sentiment and writing style extraction

, 197–198

Sequence-to-sequence models

, 203

SHapley Additive exPlanations (SHAP)

, 161, 226

algorithm

, 226

values

, 163

“Shipping then shopping” strategy

, 32–33

“Shop, then ship” model

, 4

Short-term rental market

, 107

Signal-to-noise ratio

, 94

Small-and medium-sized enterprises (SMEs)

, 267

“Smart pricing” tool

, 117

Social media

, 147–148, 154, 218

messages

, 135–136

messaging

, 135

posts

, 132–133

Social Sciences Citation Index (SSCI)

, 171–172

Speeded-Up Robust Features

, 221

Standard reinforcement learning algorithm

, 118

Stanford Named Entity recognizer

, 195

“Stick-and carrot” strategies

, 118

Stochastic gradient descent (SGD)

, 247–248

Stochastic parrots

, 140

Stroop test performance

, 293

Structural models

, 225

Style-based GAN (StyleGAN)

, 131–132

Subnetworks

, 131

Subscription-based “shipping-then-shopping” business model

, 32–33

Supervised learning algorithms

, 81

Supervised ML models

, 151

Supply, real-time swings in

, 105–110

Support vector machines (SVM)

, 29, 159, 178

Surge pricing algorithms

, 107

Survey-based perceptual maps

, 152

Synthetic images, generating

, 131–133

Technology

, 274

companies

, 13–14

Text data

, 7, 155

Text in marketing

, 193–195

causality

, 194–195

dependent variable

, 194

dual role of language

, 193

independent variables

, 194

Text mining

, 192

algorithms

, 240

Textual analysis in marketing

, 192–193

Textual consumer feedback

, 179–181

Textual content with language models, generating

, 129–131

Textures

, 221

3D convolutional neural network

, 220–221

TikTok

, 218

Time drifts

, 95

Topic modeling

, 158, 192

Traditional LDA approach

, 195–196

Training data

, 14

Training process

, 131

Transaction data

, 149

Transfer learning

, 198, 202, 204, 222

Transform data

, 157

Transformer-based models

, 157, 202, 204

Transformers

, 202, 255, 258

Twitter

, 171

Uber

, 107

Unconditional counterfactual explanations

, 227–228

Unconfoundedness assumption

, 82

Underspecification

, 209

Uniform policy

, 79

Unstructured data

, 170, 192, 218

Unsupervised learning

, 157–158

clustering

, 158

embeddings

, 157–158

topic modeling

, 158

Upper confidence bound algorithm (UCB algorithm)

, 115

US Congress

, 116

User clusters

, 153

User engagement

, 155–156

User-generated content (UGC)

, 30, 147–149, 170–171

challenges in use of

, 149

customer feedback from

, 176

data preprocessing

, 156–157

evaluation

, 161–162

hybrid of unsupervised and supervised learning

, 159–160

model interpretation

, 160–161

prediction-based algorithms

, 158–159

promise of

, 149

tools and methods to understand

, 156–162

unsupervised learning

, 157–158

User-generated text

, 156

User–brand interaction

, 152–153

VADER

, 197

Validation

, 164–165

Value functions

, 263

Variational autoencoders (VAE)

, 8, 160, 248–249, 258, 260

Vector semantics

, 199–200

VGG-16 algorithm

, 159

Video analytics

, 7

Video content

, 137

Video data

, 220

Video platforms

, 218

Virtual reality (VR)

, 7, 228–229

Visual consumer feedback

, 181

Visual content

, 137

Visual data

, 7

Visualization techniques

, 227

Voice of the Customer (VOC)

, 6, 147–148, 150

data available for AI AND

, 154–156

importance of

, 148

practice before artificial intelligence and big data

, 148–149

Volume, velocity, variety (3Vs)

, 3

VOSviewer software

, 173

Welfare

fairness

, 92–93

personalization and

, 90–93

polarization

, 93

privacy

, 91–92

search cost

, 91

White House’s Council of Economic Advisors (White House’s CEA)

, 117

Word embeddings

, 198–199

Word-of-mouth (WOM)

, 172–173

Word2Vec (language embedding algorithm)

, 157, 180, 198–200

XAI methods

, 224–228

model specificity

, 226–228

model transparency

, 224–225

scope of explanation

, 225–226

XGBoost

, 81, 159

Yelp

, 171

YouTube

, 88, 93, 218

ZIP codes

, 111