| faculty of economics and business department of marketing What you do and how you tell it: it...

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faculty of economicsand business

department of marketing

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 ConferenceIstanbul, May 14-15, 2015

Maarten J. Gijsenberg University of Groningen

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Losses loom longer than gains

The Impact of Service Crises on Perceived Service Quality over Time

Maarten J. Gijsenberg – University of Groningen

Harald J. van Heerde – Massey University

Peter C. Verhoef – University of Groningen

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Mass service crises



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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

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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)

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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?

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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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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

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OSP & PSQ

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OSP & PSQ

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Parameter estimate Standard error p-value

PSQ equation

Constant -.060 .412 .884 .011* .006 .091

-.027** .011 .016.002 .110 .983-.000 .007 .951.023** .010 .033

-.308** .110 .007-.010* .005 .058-.019** .008 .020

R² .568 AIC -3.474 BIC -3.203

OSP equation

Constant 44.487** 8.475 .000

9.063** 3.219 .006.515** .092 .000

R² .423 AIC 3.755 BIC 3.845

Model results

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› IRF over all possible histories

Improved service performance- No long-term effect

Long-term effects

Decrease in service performance- Negative long-term effect

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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

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Business as usual

Improved service performance- Positive short-term effect, no

long-term effect

Decrease in service performance- Negative short- & long-term effects

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Conclusion: losses loom longer than gains, as before



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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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Sustained losses

Improved service performance- Positive short-term effect, but

negative long-term effect- Explanation: less predictability,

more risk, stronger effect of negative experiences

Decrease in service performance- Negative short-term effect, but no

long-term effect- Explanation: confirming expectations

of bad and even ever worse service

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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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Probably the best message in the world

The impact of consistency and overlap in advertising content on brands’ success

Mike Friedman - UC Louvain

Maarten J. Gijsenberg - University of Groningen

Nicolas Kervyn - UC Louvain

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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?

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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)

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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)

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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

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Message Consistency

Category factors+

Product factors+

Brand factors+

Message Overlap

Category factors+

Product factors-

Brand factors-

Marketing controls

Relative adspend+

Relative price-

Time controls

Seasonality

Trend

Brand market share



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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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General descriptives

Market Share # Messages Yearly Adspend(*1000)

Sample 7 brands 30.8% 336 £26,986Mean 4.4% 48 £3,855

Stdev 4.3% 26.5 £2,977 Max 13.3% 88 £10,410

Min .1% 18 £1,407 Category 66 brands 100% 839

Mean 1.5% 12.7 Stdev 2.9% 18.6 Max 17.9% 88

Min .0% 0

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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

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Category: consistency & overlap

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Category factors Hyperparameter Z-score

Consistency Short run 0.480 *** 2.330

Long run 0.645 *** 2.735

Overlap 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



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Product: avoid overlap

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Product factors Hyperparameter Z-score

Consistency Short run -0.100 -0.949

Long run -0.059 -0.449

Overlap Short run -0.199 *** -4.184

Long run -0.191 *** -3.220




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Brand: consistency

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Brand-related factors Hyperparameter Z-score

Consistency Short run 0.147 * 1.631

Long run 0.240 ** 2.149

Overlap Short run 0.058 1.444

Long run 0.092 1.476



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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?

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› 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 [email protected]

www.rug.nl/staff/m.j.gijsenberg

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mailto:[email protected]

http://www.rug.nl/staff/m.j.gijsenberg



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Appendix 1

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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

Steepness asymmetry (-.049; p<.05)- Perceived Service Quality shows faster decreases than recovery

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time

PSQ

time

PSQ



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Econometric model› Starting point: Bivariate Structural Var model (Pauwels 2004)

› Allowing for asymmetric effects

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Econometric model› Final model: Double-Asymmetric Structural Vector AutoRegressive

(DASVAR) model

Asymmetric effects of decline vs improvement in service performance Asymmetric number of lags L1 and L2 across equations

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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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Model fit

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MAPE Asymmetric Lags Symmetric Lags

Asymmetric Losses-Gains

Focal Model:DASVAR

Benchmark 2:Asymmetric Effect SVAR

In-sampleOut-of-sample

.454%

.489%In-sampleOut-of-sample

.472%

.543%

Symmetric Losses-Gains

Benchmark 1:Asymmetric Lag SVAR

Benchmark 3:Symmetric SVAR

In-sampleOut-of-sample

.473%

.552%In-sampleOut-of-sample

.474%

.553%



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Predictive performance

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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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Appendix 2

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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 long-

term effects

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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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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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Consistency and overlap

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Basic model› Error correction model at the brand level

Estimate per brand, separately for each factor

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Endogeneity› Control for possible endogeneity of relative adstock and relative

price

Simultaneous estimation with full variance-covariance matrix

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∆𝑅𝑒𝑙𝐴𝑑𝑠𝑡𝑡=𝛾𝑎𝑑𝑣 ,0+𝛾𝑎𝑑𝑣 ,1∗∆𝑅𝑒𝑙𝐴𝑑𝑠𝑡𝑡 −1+𝛾𝑎𝑑𝑣 ,2∗∆ h𝑀𝑆 𝑎𝑟𝑒𝑡−1+𝜈𝑎𝑑𝑣 ,𝑡

∆𝑅𝑒𝑙𝑃𝑟𝑖𝑐𝑒𝑡=𝛾𝑝𝑟𝑖 ,0+𝛾𝑝𝑟𝑖 ,1∗∆𝑅𝑒𝑙𝑃𝑅𝑖𝑐𝑒𝑡−1+𝛾𝑝𝑟𝑖 , 2∗∆ h𝑀𝑆 𝑎𝑟𝑒𝑡 −1+𝜈𝑝𝑟𝑖 , 𝑡



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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)

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Main model: control variables

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Main equation Hyperparameter Z-score

Intercept 10.555 °°° 5.596

x Sinus 0.075 °°° 3.651

x Cosinus -0.227 °°° -8.109

x Trend -0.007 °°° -4.648

x NoAdv -1.099 * -1.641

∆RelPrice -2.033 *** -6.979

∆RelAdstock -0.041 -1.029

LagRelPrice -1.793 *** -4.722

LagRelAdstock 0.039 0.949

Adjustment -0.986 *** -12.082


β0β1β2β3β4

𝛼1𝑠𝑟

𝛼2𝑠𝑟

𝛼1𝑙𝑟

𝛼2𝑙𝑟

Π



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Side equations

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Advertising equation Hyperparameter Z-score

Intercept 0.001 0.046

Lag∆RelAdstock -0.071 -1.355

Lag∆MShare 0.180 °°° 2.032


Price equation Hyperparameter Z-score

Intercept 0.000 0.063

Lag∆RelPrice -0.359 °°° -5.467

Lag∆MShare 0.009 0.613

𝛾𝑎𝑑𝑣 , 0



𝛾𝑝𝑟𝑖 ,0

𝛾𝑝𝑟𝑖 ,1

𝛾𝑝𝑟𝑖 ,2



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