Academic research

, 101

Acceptance of chatbots, factors affecting

, 5–7

Adoption issues, implications for

, 175–176

Algorithmic biases

, 11

Algorithmic pricing solutions

, 87

Amazon

, 88

Analytical CRM solutions

, 134

Antecedents and consequences

, 150–151

Anthropomorphism

, 5–6

Application programming interface (API)

, 13

Artificial intelligence (AI)

, 2, 81, 164, 221

AI-based intelligent service design systems

, 165

AIaaS

, 165–166

context

, 177

ecosystems

, 176–177

findings of SLR

, 169–171

limitations

, 177

managerial implications

, 176–177

rule-based chatbots to

, 3

SLR

, 166–169

strategic implications of AI on marketing field

, 172–176

Artificial intelligence as a Service (AIaaS)

, 165–166

for value creation

, 166

Artificial Linguistic Internet Computer Entity (ALICE)

, 2–3

Attention, Interest, Desire, and Action (AIDA)

, 58

Augmented reality (AR)

, 25, 219

Autoencoders

, 41

Automation of pricing, from price model to

, 85–87

Autoregressive integrated moving average (ARIMA)

, 137–138

Bibliography

, 61–62

Big data

impact of big data on market environment

, 148

exploring influencing factors and ethical considerations in age of

, 149

“Black box” models

, 43

Brand

, 203

credibility

, 197–198

crisis

, 198

Business capabilities

, 104–106

and competencies

, 100–101

Business Model Canvas

, 98–100, 103–104

Businesses

, 87

business model–based AI revolution

, 169

models

, 80

new opportunities and strategic implications for

, 12–14

Catalog

, 64

Channel mix

, 213–216

research

, 217–218

Channel scope

, 218–219

Chatbots

, 165–166

algorithmic biases

, 11

CAI and

, 2

context-related factors

, 6–7

data privacy and cybersecurity

, 10–11

ethical issues in Chatbot implementation and usage

, 10–12

experiencing

, 8–10

factors affecting acceptance of

, 5–7

generative AI and revolution of NLP technologies

, 12–14

human replacement

, 11–12

market

, 3–5

technology-related factors

, 5–6

types

, 3

users-related factors

, 7

ChatGPT

, 12–14

Cognition

, 27–28

Collaborative CRM applications

, 134

Competitiveness through responsible personal data practices, achieving

, 156

Congruence

, 197

Construct satisfaction

, 67

Consumer behavior

, 58, 60, 212–213

Consumer customization

, 46

Consumer privacy

impact of big data on market environment

, 148

falsification of data

, 149

impact on consumer vulnerability and falsification of data in data-driven marketing

, 148–149

managing consumer privacy and ethics

, 149–150

need for organizations to manage

, 148–150

Consumer vulnerability

associated with personal data, reducing

, 150–154

customer data vulnerability and impact of data breaches

, 152–153

impact on consumer vulnerability and falsification of data in data-driven marketing

, 148–149

leveraging data analytics for reduced consumer vulnerability

, 150

mitigating effects of customer data breach vulnerability

, 153

overcoming complexity

, 151–152

perceived vulnerability of personal data

, 150–151

PMT in understanding privacy controls, vulnerability, and falsification of information

, 153–154

Consumers

, 87

Convenience sampling

, 136

Conventional product design

, 44–45

Conversational artificial intelligence (CAI)

, 2

Coping strategies

, 153–154

CorrGAN

, 41–42

Credibility crisis

, 198–199

Credibility source model

, 197–198

Cross-modal correspondences

, 26–27

Cross–channel concepts

, 216

Customer data

achieving competitiveness through responsible personal data practices

, 156

creating value through customer data

, 155

harnessing value of

, 154–156

managing customer vulnerability

, 154–155

mitigating effects of customer data breach vulnerability

, 153

proposal for ethical framework for data privacy marketing audits

, 155–156

vulnerability and impact of data breaches

, 152–153

Customer e-satisfaction

, 67

Customer experience (CX)

, 20, 59–60, 63

challenges of triggering sensorial experiences in online touchpoints

, 22–23

in online touchpoints

, 20–22

process of triggering sensorial experiences in online touchpoints

, 28–30

trigger sensations in online touchpoints

, 25–26

trigger sensorial experiences in online touchpoints

, 23–28

trigger sensorial perceptions in online touchpoints

, 26–28

Customer journey

, 20, 213–214

Customer relationship cell

, 106

Customer relationship management systems (CRM systems)

, 132

applications

, 133

data analysis

, 137–138

democratization and proliferation of

, 135–136

experiment

, 136–138

experiment findings

, 138

exploring types of

, 133–135

marketing strategies and

, 132–136

selection of sampling elements

, 136–137

solutions

, 132–133, 140

variables definition and data extraction

, 137

Customer satisfaction

, 67

Customer service experience

, 106–107

Customer support

, 64–65

Customer vulnerability, managing

, 154–155

Cybersecurity

, 10–11

Data, falsification of

, 149

Data analysis

, 137–138

Data analytics for reduced consumer vulnerability, leveraging

, 150

Data breaches, customer data vulnerability and impact of

, 152–153

Data colonialism

, 164

Data extraction, variables definition and

, 137

Data privacy

, 10–11

Data privacy marketing audits

, 155–156

proposal for ethical framework for

, 155–156

Data–driven marketing, impact on consumer vulnerability and falsification of data in

, 148–149

Decision-making process

, 213–214

Deep learning (DL)

, 2–3, 51

Deep neural networks

, 2–3

Democratization and proliferation of CRM systems

, 135–136

Digital business model

, 83–84

to price model

, 84–85

Digital economy

future pricing challenges

, 88–89

paradigm shift in pricing in digital environment

, 82–88

price communication in

, 87–88

pricing challenges

, 88–89

Digital environment

digital business model to price model

, 84–85

paradigm shift in pricing in

, 82–88

price communication in digital economy

, 87–88

from price model to automation of pricing

, 85–87

from value drivers to new business models

, 82–84

Digital era

channel mix

, 213–216

channel scope

, 218–219

channel scope opportunities

, 220–221

future research opportunities

, 220–221

limitations

, 222–223

literature review

, 216–220

main topics

, 216–218

theoretical approach

, 219–220

theoretical approach opportunities

, 221

topic opportunities

, 220

Digital influencers

, 196, 201–203

Digital marketing

, 126

Digital platforms

, 148–149

Digital pricing

, 80, 82, 84

Digital resignation

, 148–149

Digitalization

, 81, 88–89

Domain-based analysis

, 100

Dynamic adjustment

, 86

Dynamic pricing

, 86

E-loyalty

, 68

Electronic commerce (e–commerce)

, 50, 57–58, 85, 88, 212

ELIZA

, 2

Emerging markets, implications for

, 172–174

Emotional Design

, 5–6

Emotional preference-based design

, 44–45

Endorsements

, 198–199

Engagement

, 8–10

Enterprise-wide risk assessment (EWRA)

, 184

Ethical framework for data privacy marketing audits, proposal for

, 155–156

Expectation confirmation theory (ECT)

, 60

Facebook

, 123–124

Facebook Messenger (Online messaging platforms)

, 3–4

Fakes

, 49

Falsification of data

, 149

in data-driven marketing

, 148–149

Falsification of information, PMT role in understanding

, 153–154

Faves

, 49

Firms

, 155

Force feedback

, 25

Fourth Industrial Revolution (IR 4.0)

, 164

Freemium strategy

, 84

Gender

, 5–6

General Data Protection Regulation (GDPR)

, 150

Generative adversarial networks (GANs)

, 40–41, 44

advantages of

, 42

current applications of GANs in marketing

, 44–47

differentiating faves vs. fakes

, 49

disadvantages of

, 42–43

distribution

, 50

ethical considerations

, 44

implications in customer lifetime value

, 48

and marketing strategy

, 47–48

price perception management

, 49–50

product innovation and development

, 48–49

product promotion changes

, 49

research directions

, 50–51

strategic implications of

, 47–50

Generative AI

, 12–14

Geo-conquesting

, 148

Geofencing

, 148

Gross Domestic Product (GDP)

, 172–174

“Guilt-by-association” effect

, 153

Human replacement

, 11–12

Hybrid chatbots

, 3

Hybrid human–machine pricing approaches

, 87

Identity

, 5–6

Image generation

, 44–45

Industries, type of

, 169

Industry 4.0

, 80

Influence marketing on social media

brand

, 203

case studies

, 201–203

conceptual background

, 197–199

credibility crisis

, 198–199

credibility source model

, 197–198

digital influencer

, 201–203

methodology

, 200–201

research problem

, 199–200

results

, 203–206

Information

, 63–64

Information and Communication Technology

, 175

Instagram

, 204

Internal technological capabilities

, 104–106

International Finance Corporation

, 172–174

International Monetary Fund (IMF)

, 172–174

Internet

, 212

Interview-based approach

, 170–171

Large language models (LLMs)

, 14

Latent loyalty

, 67–68

Loyalty

, 8, 10, 67–68

Machine learning (ML)

, 2–3, 40, 49–50

Market environment, impact of big data on

, 148

Marketing

consumer customization

, 46

current applications of GANs in

, 44–47

image generation

, 44–45

product trail via visual try-ons

, 47

video enhancement

, 45–46

Marketing field

implications for adoption issues

, 175–176

implications for emerging markets

, 172–174

implications for SDGs

, 174–175

strategic implications of AI on

, 172–176

Marketing strategy

, 47–48, 97–98

and CRM systems

, 132–136

Marketplaces

, 88

Markov chains

, 2–3

Mental imagery

, 27–28

Messenger-based chatbots

, 3–4

Mobile channel case

, 219

Multi-channel concepts

, 216

Multi-Label AD-GAN

, 45

Natural language processing (NLP)

, 2–3, 164, 172, 174

revolution of NLP technologies

, 12–14

Net income

, 137

Netnography method

, 200

Networked branding

, 124–125

Neural networks

, 40–41

“Ninja” robots

, 101–102

Nonsensory cues

, 26

Omnichannel concepts

, 216

Online content and political marketing

, 123–124

Online customer experience

, 21

Online messaging platforms

, 3–4

Online store selection

catalog

, 64

consumer behavior

, 58–60

customer support

, 64–65

CX

, 60–63

literature review

, 58–68

loyalty

, 67–68

perceived value

, 65

satisfaction

, 67

security and privacy

, 66

terms and conditions

, 64

trust

, 65–66

web content

, 63–64

Operational CRM

, 134

Organizations

, 23–24, 26, 28, 149–150

p-value

, 138

Perceived Service Quality

, 8–10

Perceived value

, 65

Perceived vulnerability

of personal data

, 150–151

strategies for mitigate

, 151–152

Perception

, 24–25

Personal data, consumer vulnerability associated with

, 150–154

Personalization

, 8, 10, 118

Political branding

, 124–125

Political campaigning

, 118

Political candidates, social media marketing strategies of

, 121

Political marketing

, 123–124

on social media

, 119–120, 122–123

Politicians’ approach toward social media

, 122

Politics

, 117–118

Price communication in digital economy

, 87–88

Price model

to automation of pricing

, 85–87

digital business model to

, 84–85

Price perception management

, 49–50

Pricing process

, 87

Privacy concerns

, 148–149, 154–155

Privacy controls, PMT role in understanding

, 153–154

Privacy dashboards

, 151–152

Product aesthetics

, 44–45

Product customization

, 46

Product information

, 63–64

Product promotion changes

, 49

Productivity

, 8

Protection motivation theory (PMT)

, 153–154

in understanding privacy controls, vulnerability, and falsification of information

, 153–154

ProteinGAN

, 41–42

PsyXpert

, 2

Qualitative approach

, 170–171

Recognition

, 24–25

Regulators

, 184

Repricing technology

, 86

Research agenda

, 98, 108

Research methodologies and approaches

, 169–171

Responsible personal data practices, achieving competitiveness through

, 156

Retrieval-based chatbots

, 3

Revenue streams

cell

, 107

service robots impact cost structures and

, 102–103

Review

, 213

Rule-based chatbots to AI-based chatbots

, 3

Safeguarding privacy

harnessing value of customer data

, 154–156

need for organizations to manage consumer privacy

, 148–150

reducing consumer vulnerability associated with personal data

, 150–154

Sampling elements, selection of

, 136–137

Satisfaction

, 8, 10, 67

Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR)

, 167

Segmentation

, 217

Sensation

, 24, 30

in online touchpoints

, 25–26

Sensorial customer experiences

, 30

Sensorial experiences in online touchpoints

, 23–28

challenges of triggering

, 22–23

process of triggering

, 28–30

Sensorial perceptions

, 24–25

in online touchpoints

, 26–28

Sensory cues

, 26

Sensory imagery

, 27

Sensory marketing

, 28, 30–31

Sensory overload

, 23

Sensory-enabling technologies

, 28

Sephora Virtual Artist chatbot

, 3–4

Service Robot Innovation Canvas

, 104, 107–108

business capabilities

, 104–106

customer service experience

, 106–107

findings

, 100–103

future research directions

, 108

limitations

, 110

methodology

, 98–100

profit formula

, 107

service robots demand new core business capabilities and competencies

, 100–101

service robots impact cost structures and revenue streams

, 102–103

service robots offer new value propositions and service experiences

, 101–102

Service robots

, 97–98

demand new core business capabilities and competencies

, 100–101

impact cost structures and revenue streams

, 102–103

new value propositions and service experiences

, 101–102

Small-and medium-sized enterprises (SMEs)

, 135

Social contract theory

, 156

Social CRM solutions

, 134

Social media

, 117–118, 219

influencers

, 196

platforms

, 117–118, 197–198

politicians’ approach toward

, 122

Social media marketing

adopting social media for political marketing

, 119–120

current state of research

, 118–119

future research

, 125–126

online content and political marketing

, 123–124

politicians’ approach toward social media

, 122

research themes

, 119–125

social media and political branding

, 124–125

social media marketing and voter behavior

, 122–123

social media marketing strategies of political candidates

, 121

Social networks

, 199

Social-oriented chatbots

, 6–7

Sociocultural measurement system

, 175–176

Spurious loyalty

, 67–68

Strengths, weaknesses, opportunities, and threats (SWOT)

, 156

Stress testing

, 184

Subscription models

, 85

Sustainable Development Goals (SDGs)

, 172

implications for

, 174–175

Synesthesia

, 27

Systematic literature review (SLR)

, 167

findings of

, 169–171

research focus

, 169

research methodologies and approaches

, 169–171

type of industries

, 169

Technologization

, 81

Technology acceptance model (TAM)

, 60

Technology-based e-commerce firms

, 50–51

TextureGAN

, 45

Theoretical construct (TC)

, 60

Theory of planned behavior (TPB)

, 60

Theory of reasoned action (TRA)

, 60

3D visualization

, 25

TikTok

, 204

Time series analysis

, 137

Transparency

, 153

True loyalty

, 67–68

Trust

, 8, 10, 65–66

United Nations (UN)

, 174

Value creation

, 82

AI important for

, 166

Variables definition and data extraction

, 137

Video customization

, 46

Video enhancement

, 45–46

Virtual “tryons”

, 47–48

Virtual reality (VR)

, 221

Voter behavior

, 122–123

Vulnerability, PMT role in understanding

, 153–154

Web content

, 63–64

Web-based chatbots

, 3–4

WhatsApp (Online messaging platforms)

, 3–4

World Wide Web

, 212