10 Advertising

People don’t like intrusive ads, especially when they are vulnerable (Tybout and calkins 2005, 143)

Meta-analysis has inclusive evidence for the impact of advertising on firm performance (Sethuraman, Tellis, and Briesch 2011)

Advertising can increase firm value via consumer mindsets and behavior (Fang Wang, Zhang, and Ouyang 2008)

Advertising does not directly affect traditional media, online WOM, or consumer sentiment, but directly affects firm outcomes. (Hewett et al. 2016, 12)

10.1 Behavioral Approach

Visual Persuasion

  • (Messaris 1997) and (Scott and Batra 2003) are two books on this subject

  • Prior distribution for TV advertising elasticity for consumer packaged goods can be found in (Shapiro, Hitsch, and Tuchman 2018). A substantial portion of products has statistically insignificant or negative estimates for advertising elasticity. TV ads might not be the best vehicle to reach the customer now.

  • (Gordon, Jerath, et al. 2019) reframe our idea of advertising effect, which should be thought of in the sense of incremental effect above the baseline of an ad on consumer behavior.

  • (Gordon et al. 2017) and (Lewis and Rao 2015) provided evidence that observational data can hardly detect adverting effects from noise (i.e., we could either over or under-estimate).

  • The goal standard to measure advertising is a random experiment, but it is not possible. An improvement is to have panel data from different regions of your market to estimate the baseline for each market. Then, you can uncover the advertising effect.

(McGuire, 1978):

  • Information processing model of social influence (e.g., advertising effectiveness)

  • Behavioral chain of information profession model. (p. 158) (Markov process) - persuasion

  • P(presentation of message) x P(attention to message) x P(Comprehension of Conclusion) x P(Yielding to the Conclusion) x P (retention of the new belief) x P (Behaving on the basis of belief)

    • Presentation of message = # of reach (advertising investment)

    • attention to message = # of true reach or Recognition test

    • comprehension = recall test or semantic differential profiles or checklist tests

    • yielding = attitude change

    • retention = attitude change after time interval

    • behavior = actual observation

  • Independent variables (components of persuasive communication): source, message, channel, receiver, and destination (Table 1, p. 167)

  • Matrix of persuasion

  • Advertising appeals:

    • Positive (gain): reduce anxiety

    • Negative (loss): increase anxiety

  • Development in the field: this model assume 1 hierarchy, but later developments change the idea of hierarchy model.

Court et al. (2009)

Traditional funnel:

  1. Awareness

  2. Familiarity

  3. Consideration

  4. Purchase

  5. Loyalty

    1. Active loyalist

    2. Passive loyalist

Consumer decision journey McKinsey (2009):

  1. Initial consideration set
  2. Active evaluation
  3. Decision at moment of purchase
  4. Postpurchase experience
  • Customer Decision Journey is from the perspective of consumers

  • Customer Funnel is still important but it’s from the company’s perspective.

  • Analogy here is that you look from 3D (Customer Decision Journey) to 2D (Customer Funnel).

(Maclnnis and Jaworski 1989)

  1. Hierarchy of effects model (1960s)

  2. Multiattribute attitude model and cognitive response models (1970s)

Proposed model:

  • Antecedents:

    • Types of needs

      • Utilitarian needs

      • Expressive needs

        • Socially expressive needs: to express, or reflect self-image

        • Experiential needs: to satisfy one’s cognitive or sensory

    • Motivation (in CB involvement): “the desire to process brand information in the ad.” (p. 4)

      • situational

      • enduring

  • Processing

    • Attention:

      • higher processing motivation leads to higher attention

      • higher utilitarian needs, more focused on brand attributes

      • higher expressive needs, more focused on symbolic/ experiential value

    • Processing capacity:

      • greater processing motivation leads to greater processing capacity for analyzing the ad

      • greater the processing capacity results in less processing capacity for other task.

    • Level of brand processing matches processing operations

      1. feature analysis: salient properties/features

      2. basic categorization

      3. meaning analysis: basic understanding

      4. information integration: Is this brand association?

      5. role-taking

      6. constructive processes

    • Ability and opportunity are moderators of processing

  • Consequences

    • Cognitive responses (thoughts)

    • Emotional responses (feelings)

    • Another classifications of responses:

      • message-related (brand-relevant)

      • execution-related (brand-irreverent)

      • viewing-context-related (environment-related)

Advantages of the proposed model: Incorporate

  • Elaboration Likelihood model: (Petty and Cacioppo 1986)

  • Mitchell’s brand processing model

  • Greenwald and Leavitt’s model

  • Lutz’s typology


  • \(A_{Ad}\) mediates \(A_B\) based on levels of processing (specifically, meaning analysis, information integration, and role taking - high levels).
  • Tradeoff between brand/message-related elements and ad-execution elements
  • Cohen and chestnut 1990, behavioral loyalty and attitudinal loyalty stem did not stem from the gap between attitude and actual behavioral.

(Vakratsas and Ambler 1999)

  • Advertising input -> Filters -> Consumer (Cognition, Affect, Experience) -> Behavior

  • Market Response Models:

    • Aggregate level (Bass’ model): market data

      • 3 exposures per purchase cycle is optimal (p. 29)
    • Individual level: individual brand choice

  • Cognitive Information Models (C)

    • There is a differential effect between price (increase) and non-price (decrease) advertising on price sensitivity
  • Pure Affect Models (A)

    • Mere exposure theories

      • Response competition and Optimal arousal theories: “wear-in” effect which means it takes time to getting familiar to advertising messages to reach optimal effectiveness.

      • Two-factor theory: wear-out effect: after optimal exposures, the effect of advertising starts to decrease

      • Hence, advertising response has an inverted-U shape

    • Affective responses to advertising:

      • Attitude towards the brand

      • Attitude towards the ad

  • Persuasive Hierarchy Models (CA)

    • Cognition -> Affect -> behavior. (CA)

    • Elaboration Likelihood Model (ELM): (Petty and Cacioppo 1986)

    • Another model is (Maclnnis and Jaworski 1989)

    • Fishbein-Ajzen (1975) involvement model

    • Batra and Ray (1985): Utilitarian and hedonic effect on attitudes towards the brand

    • Involvement moderates the effect of ad evaluation (persuasion is found in low-involvement consumers with attitude towards the ad).

    • Little support for CA model, but the authors still believe in it.

  • Low-involvement (motivation) Hierarchy Models (CEA)

    • Cognition -> Experience -> Affect
  • Integrative Models (CAE)

    • Information Integration Response Model (IIRM)

    • Deighton’s (1984, 1986) two-stage model:

      • first experience = expectations

      • second stage = product trail/experience

  • Hierarchy-Free Models (NH)

  • Generalizations:

    1. Experience, affect, cognition are mediators of advertising effects.

    2. “Short-term ad elasticities are small and decrease during the product life cycle (p. 35).

(Wind and Sharp 2009)

  • 23 Empirical Generalizations

  • Gaps:

    • Boundary conditions

    • Advertising properties

    • Measurement issues

(Batra and Keller 2016)

  • Considerations for a well-integrated marketing communications program

    • Consistency

    • Complementarity

    • Cross-effects

  • Dynamic Expanded Customer Decision Journey

    • traditional media

    • newer media

      • Search ads

      • display ads

      • websites

      • email

      • social media

      • Mobile

  • Interaction and Cross-effects

    • Traditional media synergies

    • sales force and personal selling interactions

    • Online and offline synergies

  • Drawbacks

    • Limited outcome variables

    • Limited longitudinal studies

    • Did not account for consumer decision stage

  • Media type

    • Paid (TV, print, direct)

    • owned (websites, blogs, apps, social media)

    • earned (WOM, press coverage)

  • Factors that affect consumer communication processing (model) (p. 130)

  • Communication Matching Model: “matches the expected main and interactive effects of different media options with the communications objectives for a brand”

  • Communications Optimization Model

    • Coverage

    • Cost

    • Contribution

    • Commonality: different communications share the same meaning

    • Complementarity

    • Cross-Effects

    • Conformability

  • IMC Conceptual Framework

10.1.1 Cognitive and Affective

Cognitive and Affective mediators of Advertising Effects

Evaluative responses = attitude

Cognitive approach -> Affective approach (not only comes from cognitive) -> Behavioral approach (Fishbein & Ajzen, 1975 - theory of planned behavior)

System 2: is kinda of independent of Cognitive, but Affective is system 1.

  • Expanded model added subjective norm

  • (Zajonc 1980)

  • Evaluation from cognitive approach is multi-attribute model

  • Anthony Greenwald: Cognitive Response Theory: what important is what is in the consumer mind when they see the ad.


  • Classical (Pavlovian) conditioning: physiological automatic reaction occurred after being exposed to an unconditioned stimulus.

  • Evaluative conditioning: direct transfer of affect from one stimulus to another via a conditioning paradigm.

Affective route:

Moods = diffuse, hard to pin down the source, more long-lasting

Emotion = specific, discrete

(Wright 1973)

  • Three modes of spontaneous cognitive responses to advertising stimulus:

    • Counterargument

    • Source Derogation

    • Support Argument

\[ Accecptance = w_{SA} \sum_{i} SA_i - w_{CA} \sum_j CA_j - w_{SD} \sum_k SD_k \]

Situational Factors

  • Content-processing involvement: “stemming from receiver’s perception of the relevancy”

  • Message Modality: audio, print

(Batra and Ray 1986a)

  • Advertising repetition increases brand attitude and purchase intention when support and counter argument production are low; while under high level of such production, brand attitude and purchase intention level off.

  • What happen to make the downturn of advertising repetition earlier or later?

  • Appropriate interval: purchase cycle (number of exposure per purchase cycle). 3 exposures per purchase cycle is the optimal number

  • Wear-in: how many times it takes for the ad to take effect?

  • Wear-out: how many times it takes for the ad to bore you?

    • If you change the ad execution, the wear-out is pushed back.
  • Traditional thoughts advertising repetition would always wear out (inverted-U curve between repetition and impact on customer’s attitude) because of wearout and mere exposure

  • Ability, motivation and opportunity are antecedents of cognitive processing

(Batra and Ray 1986b)

  • Antecedents of attitude towards the ad:

  • Attitude toward the ad leads changes in brand attitudes (MacKenzie, Lutz, and Belch 1986; A. A. Mitchell and Olson 1981)

  • In low involvement context, execution cues and source likeability (message-oriented and communicator-oriented) have greater impact on persuasion

  • Affect typologies (p. 237)

(Holbrook and Batra 1987)

  • emotional reactions mediate the effect of advertising on attitudes toward ad or brand.

  • Why divided two articles? the second study claimed that the last paper’s list of positive affective mediators was limited, the second one expands to range of emotions.

  • Is there a difference between affect and emotions?

(R. R. Burke and Srull 1988) Competitive interference and consumer memory for advertising

  • Experiment 1: Retroactive Interference:

    • Objective: Analyze the impact of subsequent ads on memory for an initial ad.

    • Finding: Memory for a brand’s ad was hampered by:

      1. Later ads for other products within the same manufacturer’s line.

      2. Ads from competing brands in the same product class.

  • Experiment 2: Proactive Interference:

    • Objective: Examine how prior ads impact memory for subsequent ads.

    • Finding: Analogous interference effects observed, meaning prior ads can disrupt recall of later ads.

  • Experiment 3: Ad Repetition & Competition’s Influence:

    • Objective: Study the link between ad repetition and consumer memory in the face of competition.

    • Finding:

      1. Ad repetition positively affected recall when there was minimal or no advertising for analogous products.

      2. Presence of competitive ads altered the positive memory effect of repeated advertising.

(Batra and Ahtola 1991; Voss, Spangenberg, and Grohmann 2003) offer scale to measure the hedonic and utilitarian dimensions of consumer attitude

(Gibson 2008): Affective Responses Mediating Acceptance of Advertising

  • Using Implicit Association Test (???)

  • Evaluative conditioning only influences explicit attitudes when there is no previous strong preference or priori

(Pham, Geuens, and De Pelsmacker 2013)

  • ad-evoked feelings positively influence brand attitudes both directly and indirectly (via changes in attitude toward the ad), regardless of involvement with the product category, products types (e.g., durables, nondurables, services, search or experience goods).

  • This effect is greater among hedonic products than utilitarian ones.

(Kupor and Tormala 2015)

  • Momentary interruptions can promote persuasion

    • higher for low need for cognitive individuals (motivation to engage in thoughtful processing) than high ones
  • In other words, interruptions can increase consumers’ processing of a message.

  • Interruption amplified arousal (need for completion/ goal pursuit and curiosity)

(Dall’Olio and Vakratsas 2022) Effect of Advertising Creative strategy on Advertising Elasticity

  • offer composite metrics that measure aspects of creative strategy

  • Content affect advertising elasticity in the following descending order

    • Experiential content

    • Cognitive content

    • Affective content

10.1.2 Involvement

Two overarching frameworks in this stream of research are:

  1. Elaboration LIkelihood Model (Petty and Cacioppo 1979)
  2. Heuristic-Systematic Model H(Chaiken 1980)

Involvement as a key moderator in advertising effectiveness

From the ELM by (Petty and Cacioppo 1986) by use the word “motivation” in place of involvement. And if you use the term “motivation”, reviewers are less likely to fight with you since involvement is so fragmented

For review and operationalization, check (Muehling, Laczniak, and Andrews 1993) (preferable) or (Andrews, Durvasula, and Akhter 1990) and famous scale is (Zaichkowsky 1985)

Possible manipulation of involvement:

  • ego-involvement: how a product is relevant to you (e.g., pick a free product for you, or for others)

Involvement roughly means “How deeply you are as a consumer wants to think about a product,”

Motivation (2) Opportunity (kinda under ability in the original ELM, we as marketers separate this factor) (3) Ability are necessary for elaboration likelihood model (Petty, Cacioppo, and Schumann 1983)

Defensive processing is not fully captured under the ELM model: Motivation: not the desire to think, but also the desire to find out the truth, assuming that consumers want to find out the truth.

Involvement vs. Engagement:

  • According to (Greenwald and Leavitt 1984) (p. 583), define audience involvement and actor involvement (should be called engagement).

(Greenwald and Leavitt 1984)

  • derived from Sherif & Hovland (1961) Social Judgment (ego-involvement)

  • Enduring involvement vs. situational involvement (Houston & Rothschild, 1977) (A Paradigm for Research on Consumer Involvement)

  • Four levels of involvement:

    1. Preattention: little capacity

    2. Focal attention: modest capacity to decipher the message

    3. Comprehension: more capacity to analyze the message

    4. Elaboration: most capacity to integrate the message into the audience’s knowledge.

  • Antecedents: Situational involvement

  • Consequences:

    • Under high involvement: communication can modify beliefs

    • Under low involvement: communication affect perceptions, and can gradually be persuasive after repeated exposure.

    • Under ego-involvement: high involvement is more resistance to persuasion.

  • Processes of involvement:

    • High involvement creates link between new info to previous experience or attitude

    • differentiate high vs. involvement by central vs. peripheral routs to persuasion (Petty and Cacioppo 1986)

    • Mitchell (1979) equates high involvement to arousal/drive

  • Involvement stems from

    • Actor (participant) or audience (observer)

    • Distinction: Attentional capacity and attentional arousal

      • Arousal = “a state of wakefulness, general preparation, or excitement that facilitates the performance of well-leaned response.” (p. 583)

      • Capacity (also known as effort by Kahneman (1973)) = ” a limited resource that must be used to focus on a specific task and that is needed in increasing amounts as the cognitive complexity of a task increases.” (p. 583)

    • Levels of processing: influences long-term memories

    • Principles for the control of involvement:

      • Bottom-up (data-driven) processing

      • Top-down (concept-driven) processing

      • Competence (data) limitation

      • Capacity (resource) limitation

    • Effects of involvement

      • Immediate Effects: “analyze codes produced by prior processing”

      • Enduring Effects

        • Preattention: no definitive conclusion

        • Focal attention: (1) Familiar stimuli could be identified as separated objects and (2) Unfamiliar stimuli primes sensory memory traces

        • Comprehension: create traces at the propositional level of representation

        • Elaboration: “substantial freedom of memory and attitude from the specific details of th original message or its setting.”

      • Principle of higher-level dominance: the effect of the highest level of involvement is dominant in cases where the effects of different levels oppose one another.

        • Both routes can happen at the same time

        • deeper thinking, play judgment will dominate the net results (weights on whatever route is higher)

(Petty, Cacioppo, and Schumann 1983)

  • provides evidence for the two routes to persuasion

    • Central route: long-lasting and predictive of behavior

    • Peripheral route: associated with positive or negative cues , can be temporary and unpredictive of behavior.

  • Argument quality influences attitudes more under high than low involvement

  • Product endorsers (celebrities vs. joe) influences attitudes more under low than high involvement

  • Can use this as an example of (1) message content, and (2) executional cues (e.g., endorsers) can influence persuasiveness.

(Batra and Stayman 1990)

  • Mood affects cognitive elaboration, bias the argument quality, peripherally affect brand attitudes.

    • Positive moods reduces elaboration

(Macinnis, Rao, and Weiss 2002)

  • Under ELM, for the endorsers to have an effect, customers have to have some motivation (require some levels of cognition), while affective processing does not require any motivation. Hence, for consumers have higher ability (know about products because it’s mature).

  • For mature brands, affectively based executional cues can induce sales

  • Advertisement with positive feelings induces sales

(Schivinski, Christodoulides, and Dabrowski 2016)

  • Propose consumers’ engagement scales (in the context of social media)

  • Three dimensions of consumer’s engagement based on previous research Muntinga, Moorman, and Smit (2011)

    • Consumption (e.g., using)

    • Contribution: (e.g., liking or sharing, participating)

    • Creation: (e.g., posting, producing contents)

McQuarrie (1998): Meta analysis

  • Lab experiments (in advertising context) are different from real-world phenomenon because:

    • Forcing exposure

    • Failing to measure choice

    • does not consider competitive ads, decay, repeated exposures or mature/familiar brands.

(Muehling, Laczniak, and Andrews 1993)

  • A review on involvement in advertising research

  • See figure 1 (p. 43) for involvement conceptualization

10.1.3 Visual Cues

X.-Y. (Marcos). Chu, Chang, and Lee (2021)

  • Prestigious brands whose brand image is associated with status and luxury, consumers’ attitude toward the product becomes more favorable and their willingness to pay a premium for the product grows as the distance between the visual representations of the product and the consumer increases.

  • Popular brands whose brand image is associated with broad appeal and social connectedness, the closer the distance, the more favorable is consumers’ attitude and the higher their willingness to pay a premium.

10.2 Econometric Approach

10.2.1 Product Placements

(Fossen and Schweidel 2019a) Measuring the Impact of Product Placement with Brand-Related Social Media Conversations and Website Traffic

  • Investigation of relationship between product placement in TV programs and social media activity/website traffic for featured brand

  • Study uses data on nearly 3,000 product placements for 99 brands from fall 2015 television season

  • Results show that prominent product placements, especially verbal placements, lead to increases in online conversations and web traffic

  • Decreasing returns observed at high levels of prominence

  • Most placement modalities not enhanced by TV advertising in close proximity to placement activities.

10.2.2 Deceptive Advertising

Regulatory reports of deceptive advertising have negative impact on stock market performance, but brand reputation can mitigate this negative effect (Wiles et al. 2010)

(A. Rao 2022)

  • Fake news advertising are those that “mimics the format of its surrounding news content in both digital and print media, promotional segments aired on news programs without disclousre to the public.” (p. 534)

  • Research question: What happens when the fake news ad path is shutdown?

  • Data:

    • Browsing data: comScore

    • Purchase data: Ripoff Report, Complaints Board

    • Ad spend: Neilsen AdSpend data

    • keep only first visits.

  • Total site visits = organic visits + fake news ad referrals + regular ad referrals.

\[ \begin{aligned} Q_{merchant} &= Q_{org} + Q_{fn} + Q_{reg} \\ Q_{org} &= \alpha_{org} + \gamma_1^{fn} Ad_{fn} + \gamma_1^{reg} Ad_{reg} \\ Q_{fn} &= \alpha_{fn} Ad_{fn} + \gamma_2^{reg} Ad_{reg} I(Ad_{fn} > 0) \\ Q_{reg} &= \alpha_{reg} Ad_{reg} + \gamma_3^{fn} Ad_{fn} I(Ad_{reg} > 0) \end{aligned} \]


  • \(fn\) = fake news

  • \(reg\) = regular advertising

  • \(Q\) = demand

  • \(\alpha\) = direct effect

  • \(\gamma\) = spillover effect

  • Two effects of interest of fake news

    • Treatment Effect: directly to \(Q_{fn}\), spillover to organic searchers \(\gamma_1^{fn}\) or regular ad referrals \(\gamma_3^{fn}\)

    • Selection Effect: In the absence of fake news, use other channels (substitution effect), 2 segments

      • Those who will use other channels (e.g., regular)

      • Those who will stop

      • In the presence of fake news,

  • Results

    • Fake news ads and regular ads are substitutes (i.e., increase in regular ad referrals after FTC shutdown)

    • Probability of a merchant receiving a complaint declines by 8% after the FTC shutdown (somewhat mechanistically)

    • The selection effect is small (i.e., substitution to the regard ad channel is dominated by the decline in organic demand + fake news ad referrals)

    • Supply side: no change in spend, duration and impressions from the customers, or new fake news ad campaigns from merchants.

  • Robustness

    • Negative publicity (from the press) does not explain visits reduction.

    • Google might change algorithm because of the FTC shutdown. Hard to know. Hence, claim that the measured decline is the upper bound of the true impact of the FTC regulations.

    • Might consider using logistic regression in rare events data here (G. King and Zeng 2001) (but this is a borderline case since they have 95% 0, whereas rare events are usually 99% 0).

10.2.3 Advertising Effects

A. Mitra and Lynch, Jr. (1995)

  • Advertising affect price elasticity via:

    • the size of consideration set

    • the relative strength of preference

Anand and Shachar (2011)

  • Horizontal differentiation: “advertisement (informational role) decreases the consumer’s probability of not choosing her best alternative by approximately 10%.”

(Gopinath, Chintagunta, and Venkataraman 2013) Blogs and Advertising on Movie Performance

  • Objective:

    • Examine how pre- and postrelease blog activity and advertising affect the box office performance of 75 movies across 208 U.S. geographic markets.
  • Factors in Consideration:

    • Blog Volume: Quantity of blog discussions.

    • Blog Valence: Sentiment orientation of blogs.

    • Advertising: Promotional efforts by studios.

  • Demographics:

    • Market differences are attributed to variations in demographic characteristics which influence access to, exposure to, and response to blogs.
  • Time Phases:

    1. Prerelease Factors: Influence on opening day box office performance.

    2. Postrelease Factors: Impact on box office performance one month after release.

  • Methodology:

    • Use of instrumental variables to account for potential confounders in measurement.
  • Key Findings:

    1. Heterogeneity in Effects: Differences are found across consumer and firm-generated media and across various markets. Variations are influenced by demographics like gender, income, race, and age.

    2. Release Day Impact: Prerelease blog volume and advertising have the most pronounced effect on opening day performance.

    3. Postrelease Impact: Performance a month post-release is influenced by postrelease blog sentiment and advertising.

    4. Variation in Elasticities: Greater variance exists in advertising and postrelease blog valence than in prerelease blog volume across markets.

    5. Most Responsive Markets: 20 markets are identified as having the highest sensitivities to advertising, prerelease blog volume, and postrelease blog valence.

    6. Market Classification: Markets are classified based on their responsiveness to these factors, aiding studios in targeting for limited release strategies.

    7. Overlap Analysis: Studios, at first release, only cater to 53% of the most responsive advertising markets and 44% of the most responsive prerelease blog volume markets, suggesting room for optimization in their limited release strategies.

  • Implications:

    • Movie studios can greatly benefit from understanding market sensitivities to blogs and advertising.

    • Given the clear impact of both pre- and postrelease blog activity, studios should consider integrating them into their promotional strategies.

    • A more nuanced approach to market targeting can help studios maximize their box office returns, especially in limited release scenarios.

Lewis and Reiley (2014)

(Gopinath, Thomas, and Krishnamurthi 2014) Impact of Online Word of Mouth vs. Advertising on Firm Performance

  • Objective:

    • Analyze the comparative significance of consumer-generated online word of mouth (OWOM) and advertising on firm performance over time post-product launch.
  • OWOM Distinctions:

    • Volume: Total quantity of conversations.

    • Valence: Specific sentiment orientation.

      • Attribute-oriented: Focus on product features.

      • Emotion-oriented: Relates to feelings or experiences.

      • Recommendation-oriented: Direct suggestions or endorsements.

  • Advertising Content Classifications:

    • Attribute Advertising: Rational, feature-based messages.

    • Emotion Advertising: Affective, sentiment-focused messages.

  • Analytical Approach:

    • Utilized a Dynamic Hierarchical Linear Model (DHLM).

    • Benchmarked DHLM against dynamic linear model, vector autoregressive model, and a generalized Bass model.

    • Addressed potential endogeneity concerns in key metrics.

  • Key Insights:

    1. OWOM’s Direct Impact: Only the valence of recommendation-based OWOM directly affects sales, emphasizing that not all OWOM types are equally influential.

    2. Time Factor: The influence of recommendation OWOM increases with time, while the impact of both attribute and emotion advertising wanes.

    3. Ad Message Wear-out: Rational messages (attribute-oriented) exhibit faster wear-out than emotion-oriented advertisements.

    4. Volume vs. Content: The overall volume of OWOM doesn’t significantly impact sales. Instead, the sentiment or “what people say” is more crucial than sheer quantity or “how much people say”.

    5. OWOM Valence Dynamics: Initially, recommendation OWOM is influenced by attribute valence. As the product ages, emotion valence becomes dominant.

  • Brands Classification:

    • Based on the research outcomes, brands can be categorized as:

      • Consumer Driven: Where OWOM is the dominant influencer.

      • Firm Driven: Where advertising efforts are more impactful.

  • Implication:

    • Brands need to be aware of the evolving nature of OWOM and adjust their advertising strategies accordingly, emphasizing the kind of message being communicated over mere volume.

Draganska, Hartmann, and Stanglein (2013)

N. Sahni, Zou, and Chintagunta (2014)

Hoban and Bucklin (2015)

Stephens-Davidowitz, Varian, and Smith (2017)

W. R. Hartmann and Klapper (2018)

(Fossen and Schweidel 2017) Advertising Effect on WOM

  • Research focuses on the link between TV advertising and online word-of-mouth (WOM).

  • Concept introduced: “social TV” (joint TV program consumption and social media activity).

  • Goals:

    • Determine how TV ads affect online WOM about brands and programs.

    • Identify what boosts or hinders viewer engagement in social TV.

  • Data used:

    • TV advertising instances.

    • Minute-by-minute social media mentions.

  • Key findings:

    • TV ads influence online WOM for the brand and the program.

    • Most talked-about programs online aren’t always best for brand engagement.

    • Specific brand, ad, and program traits can promote or deter social TV engagement.

(Fossen and Schweidel 2019b) Social TV,Advertising, and Sales

  • Television viewers often engage in media-multitasking, especially using another screen.

  • “Social TV” is defined as simultaneous TV viewing and social media discussions about the program.

  • While social TV can increase audience engagement, it might also divert attention from ads.

  • Central research question: Are “social shows” (programs with high social TV activity) beneficial for advertisers?

  • Study examines:

    • Connection between TV advertising, social TV, online traffic, and online sales.

    • Impact of program-related online discussions on online shopping for advertisers during the show.

  • Key findings:

    • Ads in programs with higher social TV activity lead to greater online shopping engagement.

    • Ad responsiveness is influenced by the ad’s mood.

    • Affective ads, especially funny and emotional ones, drive the most online shopping activity.

(Lu et al. 2022) Frenemies: Corporate Advertising Under Common Ownership

  • Ownership structure impacts corporate advertising expenditures.

  • Mutual fund mergers serve as an exogenous shock to ownership structure.

  • Competing firms with common institutional blockholders experience reduced advertising spending.

  • Reduction is more likely in industries with higher competition and advertising intensity.

  • Greater common ownership and concentrated institutional ownership intensify the effect.

  • Firms located in the same state show a stronger impact on advertising strategy.

(Fossen et al. 2022) Political advertising effectiveness

  • Objective: Understand the connection between TV political ad content and ad effectiveness, focusing on message slant and alignment with primary campaign messaging.

  • Metrics Evaluated:

    1. Online word-of-mouth (WOM)

    2. Voter preference (via daily polls)

  • Dataset: 2016 presidential election.

  • Key Insights:

    1. Centrist Messages: Ads with a more moderate tone correlate with increased online WOM and voter preference for the respective candidate.

    2. Consistency: Ads mirroring the candidate's primary-election messages associate with gains in online WOM and voter preference.

    3. Temporal Significance of Consistency: Ad messaging consistency is crucial in the early campaign phase (before October).

    4. Post-Primary Messaging Strategy: While moderation post-primary might benefit candidates, abandoning primary messaging early in the general election can be detrimental.

  • Implication on Extreme Messaging:

    • Despite its growing popularity, extremist political advertising may harm candidate WOM and preference.

(Jindal and Slotegraaf 2023) Impact of Marketing Investments on Bankruptcy Spillovers:

  • Objective:

    • Examine how a firm’s marketing investments, specifically in advertising and R&D, influence the spillover effects when a competitor faces bankruptcy.
  • Context:

    • While the relationship between marketing investments and a firm’s own bankruptcy is well-studied, little is known about how these investments affect spillover outcomes from a rival’s bankruptcy.
  • Key Findings:

    1. Contrasting Effects: In the realm of bankruptcy spillovers, the typical positive impacts of advertising and R&D can manifest differently—both positively and negatively.

    2. Advertising:

      • Industry Growth as a Moderator:

        • In low-growth industries, advertising tends to decrease a firm’s stock return when a competitor files for bankruptcy.

        • Conversely, in high-growth industries, advertising can increase a firm’s stock return under similar circumstances.

    3. R&D:

      • Industry Concentration as a Moderator:

        • In low-concentration industries, R&D investments decrease a firm’s stock return when a rival goes bankrupt.

        • However, in high-concentration industries, R&D can enhance the firm’s stock return.

    4. Advertising in High-Concentration Industries: Advertising exerts a more potent impact on stock returns in industries that have higher concentration.

10.2.4 Estimation of Advertising Effects

Shapiro (2016)

Blake and Coey (2014)

Lewis and Rao (2015)

Lewis, Rao, and Reiley (2011)

Guitart and Stremersch (2021)

10.2.5 Spillovers

Kitts et al. (2014)

Joo et al. (2014)

N. S. Sahni (2016)

S. Yang and Ghose (2010)

Ghose and Todri-Adamopoulos (2016)

Oliver J. Rutz and Bucklin (2011)

(Fossen, Mallapragada, and De 2021) Spillover Effects of Political Ads on TV

  • Context: Concerns about the influence of political TV ads on viewers’ responses to subsequent advertisements.

  • Objective: Examine how political TV ads impact:

    • Subsequent ad viewership.

    • Online conversations about subsequent ads.

  • Data Source:

    • 849 national political TV ads from the 2016 election.
  • Methodology:

    • Quasi-experimental design to assess the impact of a political ad on the following ad.
  • Core Findings:

    • Political ads lead to positive spillover effects.

    • Ads after a political ad see an 89% reduction in audience decline, reaching a larger audience.

    • A 3% increase in positive online chatter is observed for ads airing after political ads.

  • Contribution:

    • Fills the gap in advertising research regarding ad-to-ad spillover effects.

    • Offers insights into the influence of political messages on consumers.

(Ghosh Dastidar, Sunder, and Shah 2023) Societal Spillovers of TV Advertising

  • Research Question: Can TV advertising influence societal outcomes, especially beyond typical marketing results like sales and brand awareness?

  • Context: Analyzing the effect of TV advertising during the COVID-19 pandemic.

  • Data Source:

    • Daily advertising and mobility data.

    • 2,194 counties across 204 designated market areas in the U.S.

  • Methodology:

    • Used a border identification strategy to leverage discontinuities across TV markets.
  • Key Findings:

    • TV ads with COVID-19 narratives positively affect social distancing behaviors.

    • Effects are 11 times larger in counties without government interventions (e.g., mask mandates, shelter-in-place) compared to those with interventions.

    • Overall effect of government ads on social distancing is nonsignificant, but:

      • Effect is negative with policy interventions.

      • Effect is positive without policy interventions.

  • Robustness: Findings are consistent across different model specifications, variable definitions, and data considerations.

  • Implications:

    • TV ads, particularly from brands, can have spillover effects during significant public crises.

    • Brand-sponsored TV ads can compensate for the absence of local government policies.

    • Advertising can potentially enhance public health outcomes and promote societal well-being.

10.2.6 Attribution of Advertising Effects

Simonson et al. (2001)

Lambrecht and Tucker (2013)

H. (Alice). Li and Kannan (2014)

Blake, Nosko, and Tadelis (2015)

Zantedeschi, Feit, and Bradlow (2017)

(Fossen and Bleier 2021) Online Program Engagement and Audience Size During Television Ads

  • Research topic: Connection between television viewers’ online program engagement (OPE) and the audience size during ads of those programs.

  • Data source:

    • 8417 ad instances.

    • OPE activity metrics (Twitter mentions about the program).

    • Audience size during the advertisements.

  • Core findings:

    • Increase in OPE volume and positive deviations from an episode’s average OPE before an ad are associated with a larger ad audience size.

    • OPE is indicative of viewers’ program involvement, which reduces their likelihood to change channels during ads.

    • Positive OPE deviations result in larger ad audiences, especially for the initial ads in a commercial break.

  • Implication: TV networks and advertisers can enhance ad audience size by strategically placing ads during “social episodes” (high OPE volume) and “social moments” (positive OPE deviations).

10.2.7 Advertising Content

L. Xu et al. (2014)

T. Teixeira, Picard, and el Kaliouby (2014)

Tucker (2015)

Liaukonyte, Teixeira, and Wilbur (2015)

A. Rao and Wang (2015)

Sudhir, Roy, and Cherian (2016)

10.2.8 Consumer Demand for Ads

Goldstein et al. (2014)

Wilbur (2016)

Tuchman, Nair, and Gardete (2017)