faculty of economics
and business
department of marketing
| 1
What you do and how you
tell it: it matters!
Insights on the impact of service quality and message
content on firms’ success
KUMPEM Forum Retail Conference
Istanbul, May 14-15, 2015
Maarten J. Gijsenberg University of Groningen
faculty of economics
and business
department of marketing
| 2
The Impact of Service Crises on Perceived Service Quality over Time
Losses loom longer than
gains
Maarten J. Gijsenberg – University of Groningen
Harald J. van Heerde – Massey University
Peter C. Verhoef – University of Groningen
faculty of economics
and business
department of marketing
|
Mass service crises
faculty of economics
and business
department of marketing
| 4
Mass service crises
› Characteristics of mass service crises


Strong and sustained drops in operational service performance
Affecting many customers at the same time
- Production and consumption: same time
- All consumers using the service are affected
› Similar to, but different from, product-harm crises

Products are defective, causing harm to users, often leading to costly
product recalls
(e.g. Van Heerde, Helsen, and Dekimpe 2007)


Negative impact often limited to subset of customers
- Production and consumption: different times
Defective products can be recalled before consumption
faculty of economics
and business
department of marketing
| 5
Services performance & satisfaction
› Service performance is important driver of customer satisfaction

Satisfaction formation according to Expectancy-(dis)confirmation
paradigm
(Bolton and Drew 1991; Oliver 1977; 1980; Szymanski and Henard 2001)

Negative experiences have strong effect on satisfaction
(Anderson and Sullivan 1993)
› Service failures

Limited attention in literature, mainly in service recovery literature
(e.g., Smith, Bolton and Wagner 1999)

Focus on individual-customer level service failure
› Mainly short-term focus

Limited longitudinal research on customer satisfaction
(e.g., Mittal, Kumar, and Tsiros 1999; van Doorn and Verhoef 2008)
faculty of economics
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| 6
Objectives
› What are the short- and long-term effects of objective service
performance changes on perceived service quality?
› Do losses in objective service performance not only loom larger
than gains, but do they also loom longer?
› Do these effects depend on the trend in objective service
performance?
faculty of economics
and business
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| 7
Dynamic effects
› Service restoration

Excellent recovery can lead to higher satisfaction than before crisis
(Smith and Bolton 1998)

Negative experiences have stronger effects than positive experiences
(e.g., Antonides, Verhoef and Van Aalst 2002; Inman, Dyer and Jia, 1997)

Service restoration may not be strong enough to attain pre-crisis
levels of satisfaction
- Losses may loom longer than gains
› Trend in service performance may affect customers’ mindsets

“What have you done for me lately” heuristics
(Smith and Bolton 1998)
- Recent performance affects expectations
- Contrast and assimilation effects (Bolton 1998)

Prior beliefs also directly impact expectations
(Boulding, Kalra and Staelin 1999)
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| 8
Data
› Large European logistics service company
› Monthly data for seven years

January 2006-October 2012

Objective Service Performance
- % successful connections

Perceiced Service Quality
- Answer scale: 10 = excellent, 9 = very good, 8 = good, 7 = more than
sufficient/satisfactory, 6 = sufficient/satisfactory, 5 = inadequate, 4 = very
inadequate, 3 = bad, 2 = very bad, 1 = could not be worse
faculty of economics
and business
department of marketing
| 9
OSP & PSQ
faculty of economics
and business
department of marketing
| 10
OSP & PSQ
faculty of economics
and business
department of marketing
| 11
Model results
Parameter estimate
Standard error
p-value
-.060
.011*
-.027**
.002
-.000
.023**
-.308**
-.010*
-.019**
.412
.006
.011
.110
.007
.010
.110
.005
.008
.884
.091
.016
.983
.951
.033
.007
.058
.020
8.475
3.219
.092
.000
.006
.000
PSQ equation
Constant
𝑂𝑆𝑃𝑡
∆− 𝑂𝑆𝑃𝑡
∆𝑃𝑆𝑄𝑡−1
𝑂𝑆𝑃𝑡−1
∆− 𝑂𝑆𝑃𝑡−1
∆𝑃𝑆𝑄𝑡−2
𝑂𝑆𝑃𝑡−2
∆− 𝑂𝑆𝑃𝑡−2
R²
AIC
BIC
.568
-3.474
-3.203
OSP equation
Constant
∆𝑃𝑆𝑄𝑡−1
𝑂𝑆𝑃𝑡−1
R²
AIC
BIC
44.487**
9.063**
.515**
.423
3.755
3.845
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| 12
Long-term effects
› IRF over all possible histories

Improved service performance
- No long-term effect

Decrease in service performance
- Negative long-term effect
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and business
department of marketing
| 13
Impact of performance history
› 3 scenarios

Business as usual
- Relatively constant (up-down or down-up)

Sustained gains
- Upward trend in performance

Sustained losses
- Downward trend in performance
faculty of economics
and business
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| 14
Business as usual

Improved service performance
- Positive short-term effect, no
long-term effect

Decrease in service performance
- Negative short- & long-term effects
Conclusion: losses loom longer than gains, as before
faculty of economics
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| 15
Sustained gains

Improved service performance
- Positive short- & long-term
effects
- Explanation: customer delight

Decrease in service performance
- Negative short-& long-term effects
- Explanation: Extreme negative
expectancy disconfirmation
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and business
department of marketing
| 16
Sustained losses

Improved service performance
- Positive short-term effect, but

Decrease in service performance
- Negative short-term effect, but no
negative long-term effect
long-term effect
- Explanation: less predictability,
more risk, stronger effect of
negative experiences
- Explanation: confirming expectations
of bad and even ever worse service
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department of marketing
| 17
Implications
› Service recovery needs to more than overcome the service
failure to keep long-term customer satisfaction constant

The bar for future performance is raised
› Be mindful about the trend in performance



Upward shocks only have favorable long-term consequences during
upward trends
Downward shocks have strong negative long-term consequences
during upward trends and stable situations
Steady as it goes (up or down) is better for long-term satisfaction
than up-down or down-up scenarios as the latter create more “risk”
for consumers
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| 18
The impact of consistency and overlap in advertising content
on brands’ success
Probably the best
message in the world
Mike Friedman - UC Louvain
Maarten J. Gijsenberg - University of Groningen
Nicolas Kervyn - UC Louvain
faculty of economics
and business
department of marketing
| 19
faculty of economics
and business
department of marketing
| 20
faculty of economics
and business
department of marketing
| 21
Background
› Huge amounts of money invested in advertising
› Good insights about returns to adspend, but what about content?


Much anecdotal evidence
Much experimental evidence on “soft” outcomes
- Mainly on overlap
- Some on variation

No longitudinal evidence, no evidence on “hard” outcomes
› Should brands try to be consistent in their message over time?
› Should brands try to be different from competitors?
faculty of economics
and business
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| 22
Consistency
› Strong brands are built by consistent long-term communication
support (Keller 2008)
› Consistency in advertising content expected to have positive effect


Mere exposure effects (Zajonc 1968)
Prior exposure to same stimuli elicits positive affect towards the stimuli
(Janiszewski and Meyvis 2001)

Creating and reinforcing nodes and associations in consumers memory
(associative memory models: Anderson 1983; Wyer and Srull 1986; Keller 1993)
- More easily retreived and activated (e.g. Albrecht and Myers 1995; 1998; Wyer 2004; Luna
2005)
faculty of economics
and business
department of marketing
| 23
Overlap
› The extent to which the content of the advertising message is
similar to messages by other brands
› Successful brands take unique position in consumers’ minds


Clear positioning (e.g. Aaker 1996; Keller 2008)
Clear communication of unique benefits
(e.g. Aaker 1996; Keller 2008)
› Overlap in advertising content expected to have negative effect


Distinctive information is easier to retrieve (e.g. Craik 1979; Eysenck 1979)
Competitive interference and brand confusion
- Unconnected memory traces that resemble each other will get activated
simultaneously
(e.g. Keller 1987; 1991; Poiesz and Verhallen, 1989)
faculty of economics
and business
department of marketing
| 24
Overlap
› Is overlap always bad?

Not necessarily!
- Brands should create “deep” awareness (Keller 2008)
- Strong links to product category
- High top-of-mind awareness
› Effects may consequently depend on type of content

Different types of nodes in the ad memory trace
(cfr. Hutchinson and Moore 1984)
- Category-related: e.g. how and when to use the product
- More overlap likely beneficial: clear category link
- Product-related: e.g. unique product features / benefits
- More overlap likely detrimental: no unique product features / benefits
- Brand-related: e.g. brand values
- More overlap likely detrimental: no unique brand positioning
faculty of economics
and business
department of marketing
| 25
Marketing
controls
Message
Consistency
Message
Overlap
Time
controls
Relative adspend
+
Category factors
+
Category factors
+
Seasonality
Relative price
-
Product factors
+
Product factors
-
Trend
Brand factors
+
Brand factors
-
Brand market share
faculty of economics
and business
department of marketing
| 26
Data
› United Kingdom

Chocolate
› 2008p2 – 2012p3: >4 years of data, 4-week periods


Transcripts of all print and tv advertising messages per brand
Volume sales, price and advertising spending per brand
› Focus on most active advertisers

66 brands in the category
- Many of them very infrequent advertisers and low-share brands


Initial choice: top-10 most active advertisers
Only advertising spending available for 7 of these top-10 brands
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| 27
General descriptives
Sample
Category
Yearly Adspend
Market Share
# Messages
7 brands
30.8%
336
£26,986
Mean
Stdev
4.4%
4.3%
48
26.5
£3,855
£2,977
Max
13.3%
88
£10,410
Min
.1%
18
£1,407
66 brands
100%
839
Mean
Stdev
1.5%
12.7
2.9%
18.6
Max
17.9%
88
Min
.0%
0
(*1000)
faculty of economics
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| 28
Text analytics
› Focus on the following factors

Category factors
- Usage context of the product category: social psychological processes

Product factors
- Biological processes

Brand factors
- Personal concerns
faculty of economics
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department of marketing
| 29
Category: consistency & overlap
Category factors
Consistency
Overlap
Hyperparameter
Z-score
Short run
0.480 ***
2.330
Long run
0.645 ***
2.735
Short run
0.220 ***
3.423
Long run
0.317 ***
3.236
* p < 0.10, one-sided; ** p < 0.05, one sided; *** p < 0.01, one-sided ; ° p < 0.10, two-sided; °° p < 0.05, two sided; °°° p < 0.01, two-sided
faculty of economics
and business
department of marketing
| 30
Product: avoid overlap
Product factors
Consistency
Overlap
Hyperparameter
Z-score
Short run
-0.100
-0.949
Long run
-0.059
-0.449
Short run
-0.199 ***
-4.184
Long run
-0.191 ***
-3.220
* p < 0.10, one-sided; ** p < 0.05, one sided; *** p < 0.01, one-sided ; ° p < 0.10, two-sided; °° p < 0.05, two sided; °°° p < 0.01, two-sided
faculty of economics
and business
department of marketing
| 31
Brand: consistency
Brand-related factors
Consistency
Overlap
Hyperparameter
Z-score
Short run
0.147 *
1.631
Long run
0.240 **
2.149
Short run
0.058
1.444
Long run
0.092
1.476
* p < 0.10, one-sided; ** p < 0.05, one sided; *** p < 0.01, one-sided ; ° p < 0.10, two-sided; °° p < 0.05, two sided; °°° p < 0.01, two-sided
faculty of economics
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| 32
Managerial implications
› It pays off to clearly link the product/service to the category, and
to resemble your competitors in that sense

Not just once, but in a sustained way
› When positioning the product/service, it is important to be clearly
different than competitors

What makes the product/service so unique?
- Look into those characteristics that do matter to customers, and stress
unique features
› When positioning the brand, it is important to be consistent over
time

What is the brand identity/image?
- What are the personal concerns of customers the brand appeals to?
faculty of economics
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department of marketing
| 33
faculty of economics
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department of marketing
| 34
› Contact Information

dr. ir. M.J. Gijsenberg
Assistant Professor of Marketing
Department of Marketing
Faculty of Economics and Business
University of Groningen
PO Box 800
9700 AV Groningen
The Netherlands
Tel +31 50 363 8249
E-mail m.j.gijsenberg@rug.nl
www.rug.nl/staff/m.j.gijsenberg
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| 35
Appendix 1
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department of marketing
| 36
Asymmetries in PSQ evolution
› Asymmetry Tests

(e.g. Deleersnyder et al. 2004; Lamey et al. 2007 Randles et al. 1980)
Deepness asymmetry
(-.036; p<.05)
- Perceived Service Quality shows stronger decreases than recovery
PSQ
time

Steepness asymmetry
(-.049; p<.05)
- Perceived Service Quality shows faster decreases than recovery
PSQ
time
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| 37
Econometric model
› Starting point: Bivariate Structural Var model

∆𝑆𝑎𝑡𝑖𝑠𝑡 = 𝛽1,0 +

𝑃𝑒𝑟𝑓𝑡 = 𝛽2,0 +
𝐿
𝑙=0 𝛽1,1,𝑙
𝐿
𝑙=1 𝛽2,1,𝑙
𝑃𝑒𝑟𝑓𝑡−𝑙 +
𝑃𝑒𝑟𝑓𝑡−𝑙 +
𝐿
𝑙=1 𝛽1,2,𝑙
𝐿
𝑙=1 𝛽2,2,𝑙
∆𝑆𝑎𝑡𝑖𝑠𝑡−𝑙 + 𝜀1,𝑡
∆𝑆𝑎𝑡𝑖𝑠𝑡−𝑙 + 𝜀2,𝑡
› Allowing for asymmetric effects

∆− 𝑃𝑒𝑟𝑓𝑡−𝑙 =
0
𝑃𝑒𝑟𝑓𝑡−𝑙 − 𝑃𝑒𝑟𝑓𝑡−𝑙−1
𝑖𝑓 𝑃𝑒𝑟𝑓𝑡−𝑙 − 𝑃𝑒𝑟𝑓𝑡−𝑙−1 ≥ 0
𝑖𝑓 𝑃𝑒𝑟𝑓𝑡−𝑙 − 𝑃𝑒𝑟𝑓𝑡−𝑙−1 < 0
(Pauwels 2004)
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| 38
Econometric model
› Final model: Double-Asymmetric Structural Vector AutoRegressive
(DASVAR) model

∆𝑆𝑎𝑡𝑖𝑠𝑡 = 𝛽1,0 +

𝑃𝑒𝑟𝑓𝑡 = 𝛽2,0 +


𝐿1
𝑙=0 𝛽1,1,𝑙
𝐿2
𝑙=1 𝛽2,1,𝑙
𝑃𝑒𝑟𝑓𝑡−𝑙 +
𝑃𝑒𝑟𝑓𝑡−𝑙 +
𝐿1
𝑙=1 𝛽1,2,𝑙
𝐿2
𝑙=1 𝛽2,2,𝑙
∆𝑆𝑎𝑡𝑖𝑠𝑡−𝑙 +
𝐿1
𝑙=0 𝛽1,3,𝑙
∆− 𝑃𝑒𝑟𝑓𝑡−𝑙 + 𝜀1,𝑡
∆𝑆𝑎𝑡𝑖𝑠𝑡−𝑙 + 𝜀2,𝑡
Asymmetric effects of decline vs improvement in service performance
Asymmetric number of lags L1 and L2 across equations
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| 39
Econometric model
› Long-term effects


Impulse-Response Functions
Importance of performance history
- Effect of one-time shock depends on history prior to the shock
- Role of asymmetric effects (cfr. Killian and Vigfusson 2011)
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| 40
Model fit
MAPE
Asymmetric Losses-Gains
Asymmetric Lags
Symmetric Lags
Focal Model:
DASVAR
Benchmark 2:
Asymmetric Effect SVAR
In-sample
Out-of-sample
.454%
.489%
Benchmark 1:
Asymmetric Lag SVAR
Symmetric Losses-Gains
In-sample
Out-of-sample
.473%
.552%
In-sample
Out-of-sample
.472%
.543%
Benchmark 3:
Symmetric SVAR
In-sample
Out-of-sample
.474%
.553%
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| 41
Predictive performance
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| 42
Conclusions
› Losses not only loom larger but also longer than gains

Deepening of existing knowledge on prospect theory
› Important “moderating” role of service performance history


Might occur due to different mindsets of customers
Reinforcement of prior beliefs
› DASVAR model

Goes beyond traditional (S)VAR models by
- Including asymmetric number of lags across equations
- Including asymmetric effects of service performance losses vs gains
- Allows for IRFs conditioned on performance history

Is superior to models not allowing for these asymmetries
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| 43
Appendix 2
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and business
department of marketing
| 44
Issue
› To be consistent and/or to be different: that is the question
› This study:




Quantifies consistency and overlap in advertising messages
Quantifies the effect of consistency and overlap in advertising
messages on brands’ performance
Investigates whether effects are different for different types of
advertising content
Investigates whether we find differences between short- and longterm effects
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| 45
Methodology
› 5 steps





Extract information using text analytics
Define consistency and overlap
Basic model: error correction model
Endogeneity
Individual and overall insights
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| 46
Text analytics
› Linguistic Inquiry and Word Count (LIWC2007)


(Pennebaker et al. 2007)
Classifying text content of the messages according to predefined
libraries
Main focus:
- Linguistic processes
- Psychological processes
- Personal concerns

Result:
- Scores on 1-100 scale on different categories of words.
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| 47
Consistency and overlap
𝐶𝑜𝑛𝑠𝑖𝑠𝑡𝑏,𝑡,𝑓
𝑖𝑓 𝑁𝑐𝑎𝑚𝑝𝑏,𝑡 = 0
0
=
−1 ∗
𝑁𝑐𝑎𝑚𝑝𝑏,𝑡
𝑖=1
𝑁𝑐𝑎𝑚𝑝𝑏,𝑡−𝑗 𝐿𝑓
13
𝑗=1 𝑘=1
𝑙=1 𝑇𝑆𝑏,𝑡,𝑖,𝑙 − 𝑇𝑆𝑏,𝑡−𝑗,𝑘,𝑙
𝑁𝑐𝑎𝑚𝑝𝑏,𝑡−𝑖 𝐿𝑓
𝑁𝑐𝑎𝑚𝑝𝑏,𝑡 13
𝑗=1 𝑘=1
𝑙=1 1
𝑖=1
𝑖𝑓 𝑁𝑐𝑎𝑚𝑝𝑏,𝑡 > 0
𝑂𝑣𝑒𝑟𝑙𝑎𝑝𝑏,𝑡,𝑓
𝑖𝑓 𝑁𝑐𝑎𝑚𝑝𝑏,𝑡 = 0
0
=
−1 ∗
𝑁𝑐𝑎𝑚𝑝𝑏,𝑡 𝑁𝑐𝑜𝑚𝑝𝑐𝑎𝑚𝑝𝑏,𝑡 𝐿𝑓
𝑘=1
𝑙=1 𝑇𝑆𝑏,𝑡,𝑖,𝑙
𝑖=1
𝑁𝑐𝑎𝑚𝑝𝑏,𝑡 𝑁𝑐𝑜𝑚𝑝𝑐𝑎𝑚𝑝𝑏,𝑡 𝐿𝑓
𝑘=1
𝑙=1
𝑖=1
− 𝑇𝑆𝑏,𝑡,𝑘,𝑙
1
𝑖𝑓 𝑁𝑐𝑎𝑚𝑝𝑏,𝑡 > 0
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Basic model
› Error correction model at the brand level
∆𝑀𝑆ℎ𝑎𝑟𝑒t = β0 + β1 𝑆𝑖𝑛𝑡 + β2 𝐶𝑜𝑠𝑡 + β3 𝑇𝑟𝑒𝑛𝑑𝑡 + β4 𝑁𝑜𝐴𝑑𝑣𝑡
+𝛼1𝑠𝑟 ∆𝑅𝑒𝑙𝐴𝑑𝑠𝑡𝑡 + 𝛼2𝑠𝑟 ∆𝑅𝑒𝑙𝑃𝑟𝑖𝑐𝑒𝑡 + 𝛼3𝑠𝑟 ∆𝐶𝑜𝑛𝑠𝑖𝑠𝑡𝑡 + 𝛼4𝑠𝑟 ∆𝑂𝑣𝑒𝑟𝑙𝑎𝑝𝑡
𝛼1𝑙𝑟 𝑅𝑒𝑙𝐴𝑑𝑠𝑡𝑡−1 + 𝛼2𝑙𝑟 𝑅𝑒𝑙𝑃𝑟𝑖𝑐𝑒𝑡−1
+Π 𝑀𝑆ℎ𝑎𝑟𝑒t−1 −
+𝛼3𝑙𝑟 𝐶𝑜𝑛𝑠𝑖𝑠𝑡𝑡−1 + 𝛼4𝑙𝑟 𝑂𝑣𝑒𝑟𝑙𝑎𝑝𝑡−1
+εt

Estimate per brand, separately for each factor
faculty of economics
and business
department of marketing
| 49
Endogeneity
› Control for possible endogeneity of relative adstock and relative
price
∆𝑅𝑒𝑙𝐴𝑑𝑠𝑡𝑡 = 𝛾𝑎𝑑𝑣,0 + 𝛾𝑎𝑑𝑣,1 ∗ ∆𝑅𝑒𝑙𝐴𝑑𝑠𝑡𝑡−1 + 𝛾𝑎𝑑𝑣,2 ∗ ∆𝑀𝑆ℎ𝑎𝑟𝑒𝑡−1 + 𝜈𝑎𝑑𝑣,𝑡
∆𝑅𝑒𝑙𝑃𝑟𝑖𝑐𝑒𝑡 = 𝛾𝑝𝑟𝑖,0 + 𝛾𝑝𝑟𝑖,1 ∗ ∆𝑅𝑒𝑙𝑃𝑅𝑖𝑐𝑒𝑡−1 + 𝛾𝑝𝑟𝑖,2 ∗ ∆𝑀𝑆ℎ𝑎𝑟𝑒𝑡−1 + 𝜈𝑝𝑟𝑖,𝑡

Simultaneous estimation with full variance-covariance matrix
faculty of economics
and business
department of marketing
| 50
Individual & overall insights
› Basic estimation for individual brands



Allowing for differences among brands (heterogeneity)
Per brand, separate estimate for each factor
Per brand, combine estimates for control variables across estimations
› Combine individual-brand estimates into overall insights

Meta-analytic approach
- Uncertainty-weighted average parameter estimate
- Significance: Added-Z method
(e.g. Rosenthal, 1991; Van Heerde et al., 2013; Gijsenberg, 2014)
faculty of economics
and business
department of marketing
| 51
Main model: control variables
Main equation
Hyperparameter
Z-score
Intercept
β0
10.555 °°°
5.596
x Sinus
β1
0.075 °°°
3.651
x Cosinus
β2
-0.227 °°°
-8.109
x Trend
β3
-0.007 °°°
-4.648
x NoAdv
β4
-1.099 *
-1.641
∆RelPrice
𝛼1𝑠𝑟
-2.033 ***
-6.979
∆RelAdstock
𝛼2𝑠𝑟
-0.041
-1.029
LagRelPrice
𝛼1𝑙𝑟
-1.793 ***
-4.722
LagRelAdstock
𝛼2𝑙𝑟
Adjustment
Π
0.039
-0.986 ***
0.949
-12.082
* p < 0.10, one-sided; ** p < 0.05, one sided; *** p < 0.01, one-sided ; ° p < 0.10, two-sided; °° p < 0.05, two sided; °°° p < 0.01, two-sided
faculty of economics
and business
department of marketing
| 52
Side equations
Advertising equation
Hyperparameter
Z-score
Intercept
𝛾𝑎𝑑𝑣,0
0.001
0.046
Lag∆RelAdstock
𝛾𝑎𝑑𝑣,1
-0.071
-1.355
Lag∆MShare
𝛾𝑎𝑑𝑣,2
Price equation
0.180 °°°
Hyperparameter
Intercept
𝛾𝑝𝑟𝑖,0
Lag∆RelPrice
𝛾𝑝𝑟𝑖,1
Lag∆MShare
𝛾𝑝𝑟𝑖,2
0.000
-0.359 °°°
0.009
2.032
Z-score
0.063
-5.467
0.613
* p < 0.10, one-sided; ** p < 0.05, one sided; *** p < 0.01, one-sided ; ° p < 0.10, two-sided; °° p < 0.05, two sided; °°° p < 0.01, two-sided