Literature Theory Key points
computational advertising = broad, data-driven, characteristics: personalised, data-driven, interactive,
algorythms to create/deliver message, monitor continuous, measurable, personalised marketing
individual behaviour, data-driven advertising = communication = target group definition, personalised
intersection of marketing, advertising, computer content, timing
science
personalisation paradox: consumers see both, EU GDPR (general data-protection regulation, 2016):
benefits (relevance, less information-overload) and protection, transparency, control, awareness, consent,
concerns (privacy) and evaluate which worth more remedies and sanctions
protection motivation theroy: people like to say no objective knowledge activates threat & coping appraisal,
Joanna Srrycharz, guest to personalised ads in settings but only legal knowledge increases coping appraisal, not
lecture technical (it lowers severity of threat appraisal)
digital literacy: having the knowledge (about cookies)
and skill (being able to do banking, search, etc.) to
operate within digitalised society
ethical side-effects: identifiability, inequality, vulnerability: children, elderly, having a bad day, lack of
chilling effect (don't want to be seen as Fox news personal control and limited ability to act rationally
viewer so will not watch it), objectification,
exploitation, manipulation, information-assymetry
COBRAs (Consumers' Online Brand Related Activities) consumption, contribution, creation
Uses & gratification theory (U&G): people use media does not fully explain people's motivation for online
to gratify specific wants and needs, unlike many brand-related activities
media theories that view media users as passive, U&G
sees users as active agents who have control over their
media consumption
6 motivations for COBRAs: empowerment (a strong profile- vs content-based platform (not important!)
effect on contribution weak effect on the creation of
Buzeta et al. (2020) brand-related content, also consumption)
remuneration (stronger effect on consumption & customised- vs broadcast messages
creation than on contribution, stronger with
customised messages), entertainment, information
(only influence consumption), personal identity (no
effect)
each motivation differ for consumption,
contribution, creation depending on platform- &
message-type, do not copy the same message to all
platforms!
consumer engagement practices
consumer-based taxonomy of digital engagement
practices, 5 types of practices: for fun, learning,
feedback, work for brand, talk about brand
3 types of motivations for engagement practices: all related differently to willingness to engage in each 5
cognitive, emotional, behavioural types of digital engagement practices
B2C: for fun (emotional), learning about brand I. consumer brand-engagement reflects their
(cognitive) motivational state that manifests itself in behaviour that
Eigenraam et al. (2018)
go beyond purchase (intention) (behaviour can also fuel
motivational state, leading to interactive engagement
processes)
C2B: work for brand (can be emotional), feedback (X) II. consumers distinguish hedonic practices (e.g. Playing a
game) from more utilitarian ones (e.g. Signing up for
updates)
C2C: talk about brand (X) III. consumers make distinction between brand- and
consumer- generated practices
AI influencers = non-human, virtual representation similar benefits to human-influencers, in the eyes of
AI building-blocks: natural language, processing, consumers less viewed as unique identity, so
image/speech recognition for data gathering, transgression by AI is more seen as interchangeable to all
problem-solving, machine learning Ais, than if it was a human
transgression by AI leads to same negative results as but human influencers are seen as unique entities, not
Thomas and Fowler (2021)
transgression by human interchangeable and applicable to all
2X2 between-subject deisng (study 1: effect of replacing an AI by celebrity is the best, no-replacement
transgression), 3X2 design (study 2: effect of different second, AI-replacement worst, because they are seen
replacements) interchangeable, but humans are not, so if AI-
transgression happens, repace it with a human!
computational advertising = broad, data-driven, characteristics: personalised, data-driven, interactive,
algorythms to create/deliver message, monitor continuous, measurable, personalised marketing
individual behaviour, data-driven advertising = communication = target group definition, personalised
intersection of marketing, advertising, computer content, timing
science
personalisation paradox: consumers see both, EU GDPR (general data-protection regulation, 2016):
benefits (relevance, less information-overload) and protection, transparency, control, awareness, consent,
concerns (privacy) and evaluate which worth more remedies and sanctions
protection motivation theroy: people like to say no objective knowledge activates threat & coping appraisal,
Joanna Srrycharz, guest to personalised ads in settings but only legal knowledge increases coping appraisal, not
lecture technical (it lowers severity of threat appraisal)
digital literacy: having the knowledge (about cookies)
and skill (being able to do banking, search, etc.) to
operate within digitalised society
ethical side-effects: identifiability, inequality, vulnerability: children, elderly, having a bad day, lack of
chilling effect (don't want to be seen as Fox news personal control and limited ability to act rationally
viewer so will not watch it), objectification,
exploitation, manipulation, information-assymetry
COBRAs (Consumers' Online Brand Related Activities) consumption, contribution, creation
Uses & gratification theory (U&G): people use media does not fully explain people's motivation for online
to gratify specific wants and needs, unlike many brand-related activities
media theories that view media users as passive, U&G
sees users as active agents who have control over their
media consumption
6 motivations for COBRAs: empowerment (a strong profile- vs content-based platform (not important!)
effect on contribution weak effect on the creation of
Buzeta et al. (2020) brand-related content, also consumption)
remuneration (stronger effect on consumption & customised- vs broadcast messages
creation than on contribution, stronger with
customised messages), entertainment, information
(only influence consumption), personal identity (no
effect)
each motivation differ for consumption,
contribution, creation depending on platform- &
message-type, do not copy the same message to all
platforms!
consumer engagement practices
consumer-based taxonomy of digital engagement
practices, 5 types of practices: for fun, learning,
feedback, work for brand, talk about brand
3 types of motivations for engagement practices: all related differently to willingness to engage in each 5
cognitive, emotional, behavioural types of digital engagement practices
B2C: for fun (emotional), learning about brand I. consumer brand-engagement reflects their
(cognitive) motivational state that manifests itself in behaviour that
Eigenraam et al. (2018)
go beyond purchase (intention) (behaviour can also fuel
motivational state, leading to interactive engagement
processes)
C2B: work for brand (can be emotional), feedback (X) II. consumers distinguish hedonic practices (e.g. Playing a
game) from more utilitarian ones (e.g. Signing up for
updates)
C2C: talk about brand (X) III. consumers make distinction between brand- and
consumer- generated practices
AI influencers = non-human, virtual representation similar benefits to human-influencers, in the eyes of
AI building-blocks: natural language, processing, consumers less viewed as unique identity, so
image/speech recognition for data gathering, transgression by AI is more seen as interchangeable to all
problem-solving, machine learning Ais, than if it was a human
transgression by AI leads to same negative results as but human influencers are seen as unique entities, not
Thomas and Fowler (2021)
transgression by human interchangeable and applicable to all
2X2 between-subject deisng (study 1: effect of replacing an AI by celebrity is the best, no-replacement
transgression), 3X2 design (study 2: effect of different second, AI-replacement worst, because they are seen
replacements) interchangeable, but humans are not, so if AI-
transgression happens, repace it with a human!
