Acculturation

, 155

Acquisition

, 154, 158–159

“Active” emotions

, 196

Adult Decision-Making Competence (A-DMC)

, 197–198

Adult learning theory/transformational learning

, 73

Affect-based attachment

, 178–179

Affective commitment

, 175–176

Affective events theory (AET)

, 158, 178–179, 199

Affective organizational commitment

, 174–181

ANOVA

, 146

Anxiety

, 78–79

Approach motivation

, 35–36

Artificial intelligence (AI)

, 14–15, 28

AI-driven innovations

, 15–16

algorithms

, 15

Assisted intelligence

, 15

Augmented intelligence

, 15

Augmented reality (AR)

, 20, 70

examples

, 20–21

measuring and changing emotion-related phenomenon

, 21–24

ways emotions researchers and practitioners

, 24–25

Autonomous intelligence

, 15

Average variance’s square root (AVE)

, 58–59

Avoidance motivation

, 35–36

Bartlett’s test of sphericity

, 58, 65

Beck Depression Inventory

, 79–80

Belief assessment

, 197–198

Bem Sex Role Inventory (BSRI)

, 143–145

BetterUp

, 52

Beyond Blue study

, 81–82

Biographical approach

, 219–220

Boredom

, 203

boredom–emotion

, 204–205

continuum

, 202–203

at work

, 196–197

Burnout

, 81–83

Business family

, 218, 231

Business-to-business transactions (B2B transactions)

, 11

Business-to-consumer transactions (B2C transactions)

, 11

Case-based learning (CBL)

, 86

Chat-based service conversations

, 111–112

Chatbots

, 15

Chi-square analysis

, 146

CoachBots

, 13

for teams

, 52

CoachHub

, 52

Coaching

, 73–76

intervention

, 3

relationships

, 2–3

Cognition

, 196

Cognitive appraisal theory

, 157–158

Cognitive biases

, 37–39

Communication

, 106–107

Comparative fit index (CFI)

, 182–183

Compassion

, 55, 62

Competencies

, 60

Composite reliability (CR)

, 58–59

Computational social science

, 108

Confidence interval (C.I)

, 182–183

Configural invariance

, 182–183

Confirmatory factor analysis (CFA)

, 58–59, 182–183

Connor-Davidson Resilient Beliefs Scale

, 75

Consistency in risk perception

, 206

Convergent validity

, 58–59

Coping strategies

, 83–84

Corporate social responsibility (CSR)

, 5, 218, 229

to FBSR

, 229–233

Cronbach’s alpha

, 58, 205–206

Crowdsourcing

, 14

Customer assessment of employee performance

, 113

Customer emotions

, 118–121

in different industries

, 125–126

Customer satisfaction variables

, 113

Customer service

, 12

customer emotions

, 118–121

digital analyses of emotion in

, 109–110

magnitude of data

, 111

nature of variables available in digital traces data

, 111–112

new insights from analyses of digital traces of emotions

, 115–127

relating customer expression of emotion to service agents’ behaviors

, 126–127

studying emotion in

, 111–112

Cybersickness

, 21

Cynicism

, 81–82

Data analysis

, 57

Data-driven themes

, 159–160

Decision science

, 197–198

Decision-making

, 197–198

process model

, 199–200

Decision-making competence (DMC)

, 195, 201, 213

augmenting cognitive calculation

, 201

Deichmann cases

, 222–223

development over time

, 223–229

Depression

, 79–80

Depression Anxiety and Stress Scales 21 (DASS-21)

, 85, 87

Descriptive accounts

, 197–198

Deterioration emotional climate

, 165–166

Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5)

, 78

Digital age

, 1–2, 11

adapting to

, 2–4

approach

, 130

conduct and application of emotions research

, 13–14

impact of digital age on services

, 12

emotion regulation in

, 13

emotions and care in

, 4–5

emotions and service in

, 1

emotions in

, 12–13

future of work

, 14–16

service delivery

, 107

Digital analyses of emotion in customer service

, 109–110

Digital communication

, 3, 70

effects of leaders’ emotion display and social presence of

, 72–73

leaders’ emotion display in

, 71–72

Digital economy

, 11

Digital emotional expression

, 13

Digital literacy

, 10

Digital technology

, 11, 13–14

Digital trace(s)

, 3–4

analyses of over million service chat conversations

, 112–127

data

, 13

emotions in customer service conversations

, 115–127

nature of variables in

, 111–112

organic data

, 106–107

Digitalization

, 4, 12, 194

Doctor–patient interaction, psychological androgyny and

, 140–141

Dropbox

, 61

Effective coaching

, 52–53

constructs and measures

, 57–59

data collection

, 56

future research

, 61

hypothesized model

, 54

limitations

, 62

practical implications

, 61–62

results

, 59

sample

, 57

survey instruments

, 56

survey process

, 56

theory development and hypotheses

, 54–56

Emoticons

, 12, 13

Emotional carrying capacity (ECC)

, 174, 181

Emotional climate

, 155–159

analytical framework

, 157–158

in Gamma–Delta acquisition

, 161–165

managerial implications

, 167–168

organizational culture to

, 156

from organizational culture to

, 156

positive emotional climate

, 156–157

postacquisition integration

, 155–156

qualitative case study design

, 158–161

Emotional Competence Scale

, 144–145

Emotional Intelligence Appraisal Questionnaires (EIA)

, 143

trait scale

, 144

Emotional/emotions

, 31, 72, 139, 154, 196

assessments

, 129–130

bored

, 197, 200–201

and care in digital age

, 4–5

contagion

, 74–75

in digital age

, 12–13

digital analyses of

, 109–110

digital traces organic data

, 106–107

displaying expectations

, 139–140

expression

, 179

fascinated

, 197

future research

, 128–130

intelligence

, 53–54, 62, 141–142

management

, 148

and moods

, 196–197

regulation in digital age

, 13

regulation theory

, 70

relating customer expression of

, 122–125

research

, 20

in robotics and AI studies

, 32–33

in service

, 106, 127

and service in digital age

, 1

studying in customer service by analyzing chat-based service conversations

, 111–112

terms, resources, and tools for digital service research about

, 107–109

Empathy

, 82–83

Employee(s)

, 157, 176

commitment

, 176

employee–organization commitment

, 187–188

reactions to changes at Delta

, 162–163

reactions to Gamma

, 161–162

turnover rate

, 164

Executive coaching

, 52, 73

Expanded decision-making process model

, 210

Expedia

, 12

Exploratory factor analysis (EFA)

, 58

Eye tracking

, 21, 24

Face-to-face communication

, 74

Family

, 218, 236

business research

, 218, 229–230

family-owned business

, 218, 227

firms

, 219–220

Family business social responsibilities (FBSR)

, 5, 219

cases

, 222–238

CSR to

, 229–232

data analysis

, 221

data collection

, 221

Deichmann, Henkel, and Vorwerk

, 222–223

Deichmann–development over time

, 223–229

idiosyncratic and genuine SR vs. strategic and reputation-driven activities

, 232–235

methodology

, 219–221

transcending generations

, 235–238

Feeling bored

, 195, 209–211

Feeling robots

, 35

Feminine

, 140

Femininity

, 140

Financial capital

, 229–230, 231

First Call Resolution (FCR)

, 124–125

First Contact Resolution (FCR)

, 113

Followers

emotions in

, 70, 74–75

leaders and

, 71–72

Functional theories of emotions

, 72

Future of work in digital age

, 14–16

Gamma–Delta acquisition, emotional climate in

employee reactions to changes at Delta

, 162–163

employee reactions to Gamma

, 161–162

evolving emotional climate at Delta

, 164–165

Gender

behavior

, 140

socialization

, 140

stereotypes

, 139–140

General educational stressors

, 77

Gig economy

, 14

Gig workers

, 14

Google Daydream View

, 21

Google docs

, 61

Google Glasses

, 20

Gross domestic product (GDP)

, 11

Group emotions

, 157

Healthcare

, 138–139

Heinz-Horst Deichmann foundation

, 231

Henkel cases

, 222–223

Heritage

, 237–238

HoloLens

, 20

Human capital

, 229–231

Human integration

, 155

Humanistic psychology

, 73

Human–robot interaction (HRI)

, 31, 34

Hybrid process decision-making model (HPDMM)

, 199

Idiosyncratic

and genuine SR

, 232–235

nature of FBSR

, 229

Implicit Association Test (IAT)

, 23–24

Industry 4.0

, 11, 52, 54

Information age. See Digital age

Integration

, 197–198

management

, 155

process

, 167

research

, 154–155

International Coaching Federation (ICF)

, 60

International Foundation of Robotics Research

, 29

International Journal of Robotics Research

, 29

International Journal of Social Robotics

, 30

International Symposium of Robotics Research

, 29

Internet of Things (IoT)

, 11–12

Interpersonal behavior

, 139

Interpersonal competence

in Indian doctors

, 138–139

psychological androgyny and

, 141–142

Interpersonal competency Scores (IEA Scores)

, 146–148

limitations and suggestions for future studies

, 148–149

Job satisfaction

, 187–188

Kaiser–Meyer–Olkin measure of sampling adequacy (KMO of sampling adequacy)

, 65

Knowledge-creation capability

, 177–178

Labor-intensive process

, 106–107

Latent-difference-score approach (LDS approach)

, 173, 182

final structural model

, 183

limitations and future research directions

, 187–188

means, SD, and correlations

, 184

measures

, 181–182

method

, 180–182

negative emotions in work relationships

, 174–177

participants and data collection

, 180–181

positive work relationships and affective organizational commitment

, 177–180

results

, 182–184

Leaders’ emotion expressions

, 3, 70

display and social presence of digital communication

, 72–73

display in digital communication

, 71–72

effects on social distance

, 70

leaders’ emotion management with digital technologies

, 72–73

Leadership development

, 61

Linguistic Inquiry and Word Count tool (LIWC)

, 109–110

LivePerson

, 109

Macro-organizational environment

, 199

Masculine traits

, 140, 142

Masculinity

, 140

Mechanic robots

, 35

Medicine

, 141

Mental health of medical students and doctors

, 71–72

Mental model

, 75–76

Metacognition

, 197–198

Metasystem approach

, 5

Metric invariance

, 182–183

Mixed emotions

, 36

Mixed reality (MR)

, 20

examples

, 20–21

measuring and changing emotion-related phenomenon

, 21–24

ways emotions researchers and practitioners

, 24–25

Mood(s)

, 197, 210–211

bored

, 197

excited

, 197

MSCEIT

, 144

Multiple emotions

, 115–116

National Mental Health Survey of Doctors and Medical Students (NMHSD)

, 71–72

Natural language processing (NLP)

, 13–14, 111

Negative coping strategies

, 83

Negative emotions

, 115–116, 174–177

in work relationships

, 174–177

Net Promoter Score (NPS)

, 113, 124

Normative analysis

, 197–198

Oculus Go

, 21

Online coaching model

prior approaches to supporting well-being of medical staff

, 72–73

coaching

, 73–76

development and evaluation of

, 70

implementation and evaluation of online coaching

, 85–86

mental health and well-being of medical students and doctors

, 71–72

method

, 86–87

proposed intervention

, 84–85

theoretical positioning

, 72–73

unpacking well-being issues for medical students and doctors

, 76–84

Online panel data

, 108

Openness as moderator between feeling bored and managers’ DMC

, 198–199, 212

augmenting cognitive calculation

, 201

effect of boredom

, 208

boredom at work

, 196–197

contextual relevance of DMC

, 201

correlations between emotions/moods

, 206

decision-making

, 197–198, 199–200

descriptive statistics for study variables

, 206

discussion

, 209–213

emotion bored

, 200–201

effect of excited on risk perceptions

, 209

limitations

, 213

literature review

, 196–202

methods

, 202–206, 203, 204

moderation model of confidence

, 207

moderation models of risk perception

, 208

personality

, 198–199

personality trait openness

, 201–202

practical implications for retail organizations

, 212–213

recommendations for future research

, 211–212

results

, 206–209

Organic data

, 107–108

Organizational

climate

, 156

commitment

, 178

culture to emotional climate

, 156

socialization

, 155–156

Pain©

, 85

Parasympathetic nervous system (PSNS)

, 55

“Passive” emotion

, 196

Perceived quality of employee coaching relationship scale (PQECR)

, 54–55, 57, 60

Personality

, 198–199, 207, 210–211

trait openness

, 201–202, 205

Perspective©

, 85

Physical service robots

, 28–29

Physician health

, 72

Physiology©

, 85

Pick-A-Mood instrument (PAM instrument)

, 205

Platform economy

, 14

Play©

, 85

Pokémon GO game

, 20

Positive Affect Negative Affect Schedule (PANAS)

, 85, 87

Positive customer emotions

, 129

Positive emotion

, 115–116, 174–175

Positive emotional attractor (PEA)

, 55

Positive emotional climate

, 156–157, 166

Positive organizational scholarship

, 174

Positive psychology

, 73

Positive work relationships

, 177–180

Positivity

, 154–155

Post-acquisition integration process

, 4–5

Postacquisition integration

, 154, 155–156, 158–159

PrEmo instrument

, 204–205

Psychological androgyny

, 4, 140

Bem Sex Role Inventory scores

, 145

demographic details

, 145

and doctor–patient interaction

, 140–141

in Indian doctors

, 138–139

and interpersonal competence

, 141–142

measures

, 143–145

method

, 142–145

participants

, 142–143

procedure

, 143

Psychological capital (PsyCap)

, 87

Psychological Capital Questionnaire (PCQ)

, 85, 87

Psychological stress

, 77

Purpose in life, physiological well-being, play activities, pain, perspective (5P Model of Wellbeing©)

, 86

Purpose©

, 85

Qualitative overload

, 196, 201

Qualitative underload

, 196

Quality of life (QOL)

, 71

Questionnaire-and-experiment study

, 12

“Recurring positive experiences”

, 179

Recursive Neural Tensor Network model (RNTN model)

, 109

Relational climate

, 53–55

Relational Climate Scale/Survey (RCS)

, 53–54, 57

Relational energy

, 56, 62

Reputation

, 234–235

Retail organizations, practical implications for

, 212–213

Robo-Sapiens

, 35

Robot Manipulators: Mathematics, Programming, and Control

, 29

Robotics

, 28

Robots. See also Service robots

, 28, 31

Root-mean-square error of approximation (RMSEA)

, 182–183

Samsung Gear VR

, 21

Self-determination theory

, 73

Self-service

, 12

Sentiment analysis

, 13–14, 109

tools

, 109–110

SentiStrength

, 110

Service robots

, 28–30

capabilities and design attributes

, 34–35

frontstage or backstage

, 31–34

importance

, 35–37

insights and directions for future research

, 40–44

in organizations

, 29–30

service settings and

, 30–35

trust

, 39–40

Servicescape model

, 34–35, 40–41

Shared vision

, 55

6-point frequency scale

, 144

Smartphone applications

, 13

Social capital

, 231

Social distance

effects of leaders’ emotion display and social presence

, 72–73

leaders’ emotion effects on

, 70

Social robots

, 13, 29–30

Socialization

, 155–156

Socially assistive robots (SAR)

, 13

Socially disengaging emotions

, 76

Socially engaging emotions

, 76

Soft skills

, 16

Standardized root-mean square residual (SRMR)

, 182–183

Stanford tool

, 110

State Rating©

, 84

Stress

, 77–78

Structural equation modeling (SEM)

, 59, 182

Succession

, 225–227

Suicidal ideation

, 80–81

Symbolic interactionist approach (Schein)

, 53–54

Technologically altered realities

, 20

Telephone conferences

, 70

Text emotion mining

, 14

Text Sentiment Analysis

, 14

Theory-based themes

, 159–160

Thinking robots

, 35

Touch integration

, 21–22

Traditional focus of therapy-based interventions

, 74

Travelocity

, 12

Trust

, 39–40

Tucker Lewis index (TLI)

, 182–183

Two-dimensional valence/activation continuum

, 211

Uncertainty-activated emotions

, 210–211

Under/overconfidence

, 206

Unstructured Data (UD)

, 108–109

Valence-activation approach

, 179

Validation research

, 129–130

Value assessment

, 197–198

Virtual headsets

, 21

Virtual reality (VR)

, 20, 70, 74

examples

, 20–21

measuring and changing emotion-related phenomenon

, 21–24

ways emotions researchers and practitioners

, 24–25

Virtual service robots

, 28–29

Vorwerk cases

, 222–223

Well-being

, 72

of medical students and doctors

, 71–72

mental model

, 75–76

unpacking well-being issues for medical students and doctors

, 76–84

Work relationships

, 5

negative emotions in

, 174–177

World business council for sustainable development (WBCSD)

, 218