A/B testing

, 71–72

Ability

, 150

Academic marketers

, 92–93

Academic researchers

, 128

Adverse technology–consumer interaction (ATCI)

, 123–126

examples

, 126

pathways of technology optimism and

, 124

response rates declining to customer surveys

, 128–132

significance

, 127–128

Agency

, 92

AirBnB

, 88–89

Alexa

, 86–87

Alibaba

, 88–89

Alphabet (Google)

, 51

AlphaGo Zero program

, 22–23

Amazon

, 2–3, 36, 51, 88–89, 93

current business model

, 3–4

reviews

, 47

Amazon Alexa app

, 108

Amazon Echo

, 86, 92–93, 108

Amazon Go

, 6–7

Amazon.com

, 182

American Association for Public Opinion Research (AAPOR)

, 129

Antecedents of perceived deception

, 148–154

elaboration ability

, 151–154

elaboration motivation

, 150–151

initial expectations

, 148–150

Anthropomorphism

, 93

AOL

, 3–4

Apple

, 51, 88–89, 106–107

Apple Watch

, 17

Apps

, 107–108

age

, 105–106

App Store

, 108

AR in

, 105

monetization

, 105–107

retailers

, 106–107

Argument quality

, 154–155

ARPANET

, 36

Artificial intelligence (AI)

, 4, 15, 92, 109–110, 113

competency

, 123

programs

, 22–23

Augmented reality (AR)

, 107, 112

in apps

, 105

AR View

, 107

Autonomous cars

, 108

Autonomy software

, 91–92

“Autopilot” technologies

, 6

Aversion to cashless payment transactions

, 126

B2B platform

, 51

Banner advertising

, 57

Biased search & analysis

, 68–69

Big Data

, 8–9, 38–41, 63–64, 70

A/B testing

, 71–72

benefits in marketing management

, 65–66

blindness to and falsification of hypotheses

, 72–73

combining Big Data with lean start-up methodology

, 77–79

communication

, 70

confidence

, 70–71

confirmation

, 68–69

control

, 69–70

deceived by Big Data

, 66–71

hubris

, 67–68

mitigating learning challenges of

, 71–76

and privacy

, 45–46

regularly updating analyses to reveal shifts and trends

, 73

statistical literacy and risk intelligence

, 76

3Vs of

, 69

transparent presentation of statistical insights

, 74–76

Bionic eye

, 25

Bitcoin

, 91–92

Black Swans event

, 69

Blind spots

, 127

Blockchains

, 91–92

Blurring (see Line tightening)

Brick and mortar

, 52–56

Build-measure-learn loop

, 64, 78

Business model evolution

, 64

Buyer inputs, Internet on

, 44–45

Buyer search

, 44–45

California consumer privacy act (2018)

, 107–108

Central routes of persuasion

, 154–158

Channel contracting

, 180

Chi-square analysis

, 176

Cloud-based services

, 22–23

CMO Survey

, 65

Co-locational targeting

, 101

Code

, 86

Cognitive dissonance theory (CDT)

, 146

Commercial 3D printers

, 171

Commercialization

, 36

Communication

, 16–17

of Big Data

, 74

networks

, 36–37

Commuters

, 101

Company strategy, research on

, 92–93

Competitive locational targeting

, 101

Complexity of market

, 88–90

Computer-aided design program (CAD program)

, 170

Computer-mediated environments

, 2

Computer numeric control (CNC) machines

, 179

Computers

, 18

Confirmation, control, communication, and confidence (4Cs)

, 67

lean start-up methodology and

, 77–79

Connected knowledge economy

, 3–4

“Connecting” concept

, 25

Consumer reactance to frequent price changes

, 126, 132–136

ATCI

, 133–135

reasons for

, 133

research opportunities

, 135–136

Consumer Reports

, 47

Consumer-to-consumer interactions

, 4

Consumer-to-firm interactions (C2B interactions)

, 4

Consumer(s)

, 1–2, 8

data

, 123

decision process

, 37

perception of non-deception

, 144

research on consumer behavior

, 93

role of

, 179–180

Consumers’ Perceptions Ethics of Online Retailers (CPEOR)

, 144

Content

, 48–49

Contextual targeting

, 101

Conventional econometric model

, 39–40

Coping with digital

, 5–8

Copy machine

, 17

Creations

, 175, 176, 181–182

Criticality

, 161

Customers

, 101

facing technology

, 86–87

relationship management

, 180

Cybersecurity

, 1–2, 5, 6

Data

data-sensitive age

, 115

privacy

, 107–108

Data-based learning

benefiting from Big Data in marketing management

, 65–66

combining Big Data with lean start-up methodology

, 77–79

mitigating learning challenges of Big Data

, 71–76

Decentralized autonomous organizations (DAOs)

, 93

Deception

, 143–144

online

, 144

perception of

, 146

Decision-making

, 63–64, 77

Deep learning

, 23–24

Desktop 3D printers

, 168, 170, 172

Digital

attributes

, 44, 52–53

cameras

, 5

connectivity capabilities

, 17

innovations

, 1–2, 4

machine communication

, 22

manufacturing tools

, 168

marketplace

, 19

revolution

, 2

technologies

, 1, 5

transformation

, 1–2, 4–5

world

, 1

Digital, social media and mobile (DSMM)

, 4

Digital Age

, 14

digital dyads in marketplace

, 20–29

information in

, 19–20

Digital dyads in marketplace of Digital Age

, 20–29

human–human digital dyad

, 26–29

human–machine digital dyad

, 24–26

machine–machine digital dyad

, 21–24

Digital innovations on marketing and consumers

Big Data and privacy

, 45–46

buyer search

, 44–45

impact on seller inputs

, 38–41

impact on seller outputs

, 41–43

innovations relating to interactive marketing

, 36

Internet on retail markets

, 52–56

platforms

, 51–52

structure of online interactions between seller, platform and buyer

, 38

topics for further research

, 39

use of online devices

, 37

user generated content

, 46–51

Digitalization

, 1–2

blindsided by digital

, 3–5

in blink of eye

, 2–9

coping with digital

, 5–8

marketing in digital world

, 8–9

Digitization

, 5

Direct-to-customer model

, 115

Disconfirming analysis

, 72–73

Disembodied software

, 93

“Driver assist”

, 6

DuploBrio adapter

, 183–184

E-commerce

, 2

sales

, 52–53

e-Trade

, 3–4

East Asian strategy game “Go”

, 22–23

eBay

, 38, 51, 88, 104

Economic institutions, nature of

, 180–181

Education

, 1–2

Elaboration

ability

, 151–154

motivation

, 150–151

Elaboration Likelihood Model (ELM)

, 145–146

Electronic word-of-mouth (eWOM)

, 27–28, 164

Employment

, 1–2

Enlightenment

, 14–15

Enterprising retailers

, 184

Entrepreneurs

, 1, 78

Environmental context

, 41–42

Esthetic idealism

, 14–15

Ethical ideology

, 146, 148

Ethical orientations

, 149

European Union (EU)

, 45

Executive leadership

, 26

Expertise

, 151

Experts

, 152

Exploitation

, 23–24

Exploration

, 23–24

exploration-based algorithms

, 23–24

Eye-tracking technology

, 102

FabStores

, 174

Facebook

, 2–3, 18–19, 23, 28, 51, 88–89

Fair value line

, 73

Fake or cursory responses to online surveys

, 126

Familiarity

, 151

Firm-to-consumer interactions (B2C interactions)

, 4

Firm-to-firm (B2B interactions)

, 4

Fitbit (smart watches)

, 108

Fitness trackers

, 99

Flattening (see Line steepening)

Follow-up actions

, 164

Forecasting model

, 39–40

Fourth Industrial Revolution

, 15–16

Fraudulent negative review

, 126

Fused-deposition modeling

, 170

General Data Protection Regulation (GDPR)

, 46, 107–108

General skepticism

, 146, 148, 149–150

Geoconquesting

, 101

Ghost Ads

, 71–72

Google

, 2–3, 15–16, 36, 51, 88–89, 106–107

search engine

, 51–52

Google Assistant

, 86–87

Google Play

, 108

Google Sketchup

, 170

Gothic cathedrals

, 14–15

GPS navigation

, 2–3

Hash

, 91–92

Hedonic purchase

, 161

Hilton hotels

, 93, 115

Hosts value exchange

, 88–89

Human behavior

, 8

Human computers

, 22–23

Human–human connectivity

, 14

Human–human digital dyad

, 26–29

Human–human social communication

, 27

Human–machine communication

, 25

Human–machine connectivity

, 14

Human–machine digital dyad

, 24–26

Idealism

, 148–149

IKEA

, 107

Illinois MakerLab

, 184

In-app advertisements (IAA)

, 103, 106–107

In-app purchases (IAP)

, 106–107

In-vitro fertilization

, 72

Independent companies

, 163

Industrial Revolution

, 14–15

Information (see also Artificial intelligence (AI))

in Digital Age

, 19–20

evolution

, 18

in marketplace

, 17–19

products

, 87

technology

, 16–17, 86–87

Informative advertising

, 42

Initial expectations

, 146, 148–150

Innovative firms

, 72–73

Instagram

, 18–19

Intelligence

, 90–91

Internet

, 2, 17, 36

advertising

, 41

algorithms

, 23

expertise

, 153, 154

on retail markets

, 52–56

Internet of Services (IoS)

, 109

Internet of Things (IoT)

, 2–3, 98, 109, 113

Internet on buyer inputs

, 44–45

Big Data and privacy

, 45–46

buyer search

, 44–45

user generated content

, 46–51

Involvement

, 151

iPhone (Apple)

, 2–3, 15–16, 17

Islamic Golden Age

, 14–15

“Jailbreak”

, 25

Journal of Marketing (JM)

, 3

Kiosks

, 105

Knowledge

, 151, 153

KONE

, 109

Laser cutters

, 179

Lathing

, 170–171

Lean start-up methodology, combining Big Data with

, 77–79

and communication

, 79

and confidence

, 79

and confirmations

, 77–78

and control

, 78

Learning setting

, 66

Legacy firms

, 64

Leverage-salience theory

, 131

Line steepening

, 73

Line tightening

, 73

Location targeting leverages

, 101

Logistic regression

, 40

Low attention spans and distraction of Smartphone use

, 126

Machine learning (see also Artificial intelligence (AI))

, 23–24

algorithms

, 8–9

methods

, 39–40

models

, 109–110

Machine–machine communication

, 24

Machine–machine connectivity

, 14

Machine–machine digital dyad

, 21–24

Machine–machine dyad

, 20–21

MakerBot

, 170, 171, 174–175

Manufacturer Recommended Price Range (MSRP)

, 135

Market

, 18, 88

complexity

, 90

disruptions

, 1

Marketers

, 1, 105, 122, 127, 135

Marketing (see also Mobile marketing 2.0)

analytics

, 64, 65

applications

, 38–39

and consumer behavior

, 4

digital dyads in marketplace of digital age

, 20–29

in digital world

, 8–9

evolution of information in marketplace

, 17–19

information in Digital Age

, 19–20

and Internet

, 3

literature

, 169–170

manager

, 66–67

professionals

, 76

scholars

, 8, 179

scholarship

, 2, 167–168

spotlight on information

, 16–20

stages of ages

, 14–16

Marketing management

benefiting from Big Data in

, 65–66

combining Big Data with lean start-up methodology in

, 77–79

Marketing Science Institute (MSI)

, 3

Marketplace

information in

, 17–19

phenomena

, 14

revolution

, 18

Marketplace of Digital Age, digital dyads in

, 20–29

MasterCard

, 89

Materialism theory

, 178

Medical Expenditure Panel Survey

, 129

Menu costs

, 133

Mere-measurement effects

, 131

mHealth platform

, 106–107

Microsoft

, 51, 88–89

Milling

, 170–171

Minecraft

, 184

Minimum viable product (MVP)

, 78

Miskol (see Missed Call Marketing (MCM))

Missed Call Marketing (MCM)

, 103–104

Mobile

, 113–115

AR

, 107, 112

attributes and related marketing advantages

, 100

devices

, 99, 100–101, 103

marketers

, 100–101

marketing

, 98, 105–106

promotions

, 98, 99

retailing

, 99

technologies

, 105, 107

user experience

, 107–108

Mobile advertising

, 102–103

revenues

, 41–42

strategies

, 103–104

Mobile apps

, 97–98, 99, 104, 105

monetization

, 105–107, 112

Mobile marketing 2.0 (see also Marketing)

, 98

AI

, 109–110

data privacy and mobile user experience

, 107–108

emerging trends

, 105–110

IoT

, 109

key issues in mobile marketing

, 106

mobile app monetization

, 105–107

mobile AR

, 107

mobile marketing practice

, 113–116

mobile-led cross-channel effects

, 104–105

personalized mobile advertising

, 102–104

research agenda based on emerging trends

, 111–113

research agenda based on gaps in extant literature

, 110–111

review of mobile marketing research

, 99–105

self-driving vehicles

, 108–109

targeted mobile promotions

, 100–102

wearables and other smart devices

, 108

Mobile-led cross-channel effects

, 99, 104–105, 111

Moderators

, 162

variables

, 159–161

Money-back guarantees

, 163

Motivation

, 48–49, 150, 151

Motorola Moto X cellphone

, 25

Moveable Feast

, 14, 19–20, 22

Multi-purpose AI platforms

, 123

Multiplayer online battle arenas (MOBA)

, 28–29

National Health Interview Survey

, 129

Nervous System

, 182

Netflix

, 2–3, 23

Network structure

, 48–49

Neural Network Processor (NNP)

, 22–23

Non-deception, consumers’ perception of

, 144

Non-proximal mobile users

, 101–102

Non-retail settings

, 104

Not-yet-imagined digital technology

, 1

NVivo11 qualitative software program

, 148

Object

archetypes

, 175

notion of

, 178–179

Office Max

, 182

Offline retailers

, 57

Omnichannel retailing

, 104, 122

Online

advertising

, 41–43

deception

, 144

expertise

, 153

interactions

, 38

purchasing

, 142

retailers

, 182

reviews

, 46–48

search

, 45

services

, 142

sources

, 40–41

Online consumer reviews (OCR)

, 142, 143, 147

Online word-of-mouth (eWOM)

, 46

Opportunity network

, 3–4

Opt-in, get customers to

, 115

“Optimal experience” of psychological flow

, 2

Optimism

, 122

Outsourcing

, 135

Paid apps

, 106–107

Paperclip AI

, 92

Parrot Pot

, 25

Pay Pal

, 51

Perceived deception

antecedents of

, 148–154

consequences

, 158–159

Perceived Deception in Online Consumer Reviews (PDOCR)

, 142, 143, 162, 163

data analysis

, 147–148

data collection and sample

, 147

findings and conceptual model

, 148–161

literature review

, 143–146

method

, 147–148

theoretical and managerial implications

, 162–164

Perceived homophily

, 155, 156, 163–164

Perceived riskiness of purchase

, 159–160

Peripheral cues

, 157

Peripheral routes of persuasion

, 154–158

Personalization

, 103, 115

Personalized mobile advertising

, 99, 102–104, 110–111

Persuasion

central and peripheral routes of

, 154–158

persuasive advertising

, 42

Pet grooming

, 133–134

Pew Research Center

, 48

“Plant Sitter” mode

, 25

Platforms

, 51–52

Positive-NPV marketing program

, 131

Potential negativity

, 161

Practitioners

, 8

Pre-Internet

, 88

Price

, 92

fluctuation

, 133

volatility

, 134

Privacy

, 45–46

Product

knowledge

, 151

and services

, 159–160

Public policy

, 92–93

Purchase involvement

, 150–151

Rapid prototyping

, 170

Re-evaluate targeting strategy

, 115

Reformation

, 14–15, 18–19

Renaissance

, 18–19

Era

, 14–15

Replacements

, 175, 176, 183

Replicator Rapid Prototype project (RepRap Prototype project)

, 171, 176

Research questions (RQ)

, 148, 149

Resistance to using self-service technologies

, 126

Response rates declining to customer surveys

, 126, 128–132

consequences

, 131

evidence for

, 129

research opportunities

, 131–132

technology-based reasons for

, 129–131

Retail markets, Internet on

, 52–56

Retailing research

, 167–168

Retailing thought and practice, implications for

, 177–184

nature of economic institutions

, 180–181

notion of objects

, 178–179

role of consumers

, 179–180

Retailing-related issues

, 180

Review

consistency

, 155, 156

number of

, 154–155

platform

, 160–161

quantity

, 155

Revolutionary technologies

, 15–16

Risk intelligence

, 76

Robotics

, 4, 91

Savvy marketers

, 122

Selective laser sintering

, 170

Self-driving

technologies

, 6

vehicles

, 108–109

Self-esteem

, 28

Self-manufactured objects, typology of

, 174–177

Self-manufacturers

, 169–170, 180

Self-manufacturing

, 169–174

3D printing

, 170–171

3D printing literature

, 171–174

Self-protecting behaviors

, 158, 162

Self-service technology

, 86–87, 93

by consumers

, 122

Seller inputs, impact on

, 38–41

research needs

, 40–41

Seller outputs, impact on

, 41–43

shares of U.S. advertising spending by medium

, 41

U.S. online advertising revenues

, 42

Service Dominant Logic theory (SDL theory)

, 178

Shapeways.com

, 170, 182

Shopping

AR View

, 107

IoT and

, 109

journey archetypes

, 133–134

mobile apps offer

, 105–106

mobile influences

, 102

Short messaging service (SMS)

, 103

Showrooming

, 57

practices

, 126

Silicon Valley

, 15–16

Simultaneous platform response functions

, 89

Siri (Apple)

, 86–87

Smart devices

, 98, 108, 112

Smart speakers

, 109–110

Smarter mobile technologies

, 105

Smartphone

, 13–14, 98, 103

virtual assistants

, 109–110

Social

manufacturing ecosystem

, 174

networking sites

, 27–28

networks

, 48–49

production

, 180–181

roles and interactions

, 48–49

Social media

, 2–3, 28, 37, 48–50

advertising

, 57

Social Science Citation Index

, 46–47

Software

, 3–4, 85–87

agency

, 92

autonomy

, 91–92

complexity

, 88–90

intelligence

, 90–91

product

, 87–92

regulation and public policy

, 93

research directions

, 92–93

research on company strategy

, 92–93

research on consumer behavior

, 93

Solidworks

, 170

Solutions

, 175, 176, 183–184

Staples

, 182

Statistical analyses

, 68–69

Statistical literacy

, 76

Stereo-lithography

, 170

Substitutes

, 175, 176, 182–183

Super Bowl (2019)

, 6–7

Survey-based research

, 129

Tablets

, 103

Tangible goods

, 86

Target setting

, 66

Targeted mobile promotions

, 99–102, 110

Tech-expert

, 152

Technology

, 122

orientation

, 123

technology-enabled dynamic pricing methods

, 134

technology-enabled retail fraud by consumers

, 126

Technology Acceptance Model

, 7

Teenage Engineering

, 183

Telecom

, 3–4

Telecommunicators

, 18

Temporal targeting

, 101–102

Tencent

, 88–89

Text mining methods

, 40

Thank God It’s Friday (TGIF)

, 110

Thingiverse.com

, 168, 174–175

3D printers

, 168, 170, 179

3D printing

, 170–171, 183

of existing objects and new creations

, 172–173

literature

, 169, 171–174

technology

, 4

threat and opportunity for retailers

, 173–174

Time series analysis

, 50

Tinkercad

, 170

Traditional non-platform firms

, 89

Transaction cost theory

, 180

Transformation

, 76

Transparent presentation of statistical insights

, 74–76

Travelocity

, 3–4, 38

TripAdvisor

, 142

Trust

, 93

TrustedShops.com

, 163

Turing Machine

, 86

Twitter

, 18–19, 39–40

U.S. adults on “Automation in Everyday Life”

, 6

Uber

, 93

Ultimaker

, 170, 171

University of Illinois

, 184

US Ninth Circuit

, 93

User generated content

, 46–51

online reviews

, 46–48

research

, 48, 50–51

social media

, 48–50

Utilitarian purchase

, 161

Utility function

, 92

Value exchange

, 86, 88

Virtual reality (VR)

, 99

Virtual social interaction

, 18–19

Visualizations

, 74, 76

Voice-based devices

, 108

Volume, velocity, and variety (3Vs)

, 64, 65, 67–68

of Big Data

, 69

Volume, velocity, variety and veracity (4Vs)

, 63–64, 65, 67, 68

W-Fi

, 2–3, 36

Wearable devices

, 99, 108, 112

Web browser

, 36

Webrooming practices

, 126

Windows

, 51

Word of mouth (WOM)

, 27–28

Yahoo

, 3–4

Zero marginal costs of software businesses

, 92

ZocDoc app

, 108