1. Emotions & Sales Sutton & Rafaeli
How to conduct an observational study
Deductive Study (Study 1)
Inductive Study (Study 2)
Re-analysis of Study 1 store & individual level
Lessons learned

2. Theoretical Explanations
Emotions can be used as a “control move” to influence behavior
Positive, neutral vs. negative emotions
Some can be reinforcing
Positive emotions it may encourage customers to buy more, or to re-patronize store

3. Preliminary Hypothesis
Amount of positive emotion displayed leads to increased store sales
What is the predictor and criterion variable?

4. Study 1 Context Friendly behavior during transactions encouraged by
Training & incentives for clerks
Incentives for franchise store owners
25% Bonus over base salary for regional managers of corporate-owned stores

5. Participants 1319 clerks in 576 Convenience stores
8 stores from each of 72 districts that make up 18 divisions in 2 countries
Primarily urban sample of stores
44% male clerks
Does not state if the same clerk could have been observed multiple times (implications?)

6. Method Time of measurement 3 month period
Does not specify how long after training
Each store observed during one day & one swing shift
25% of stores observed during night shift
1-20 transactions per visit
Up to 60 transactions per store
11805 clerk-customer transactions
75% male customers

7. Procedure “Mystery shopper” observers
Observed clerks at pre-test stores w/research director before actual data collection period
Compared & clarified behavior coding differences
Corporate HR staff volunteers dressed according to the profile of a typical customer
May not be adequately matched for SES of working class male customers yrs bec. observers had a wide range of jobs

8. Procedure
Observers
Only coded clerk at primary cash register from magazine rack/coffee pots
Visited store in pairs
Selected small item, stood in line, paid for item
Spent 4-12 min per store depending on number of customers in store
3% of observations excluded due to clerks’ suspicions

9. Procedure Reliability of mystery shoppers’ codings
Director of field research
Sample of 274 stores
Accompanied by second original observer
Allowed for computing inter-rater correlations w/ratings of first original observer (mean=.82)

10. Predictor Variable Positive emotion display
Rated each transaction on 4 features
Greeting, thanking, smiling, eye-contact
Coded as 1 or 0 depending on display
Transactions aggregated at store level
Score for each of 4 features calculated as proportion of transactions in which behavior was displayed over total number of transactions
Overall store index of emotion composed of mean of 4 aspects (reliability=.76)

11. Criterion Variable Sales
Total store sales during the year of the observation obtained from company records
Standardized across stores included in sample to preserve confidentiality

12. Control Variables Store gender composition Customer gender composition
Proportion of women clerks observed over total number of store clerks observed at each store
Customer gender composition
Proportion of female customers over all customers present during all observations in that store

13. Control Variables Clerk image Store stock level
3 items rated on a yes/no scale
Was clerk wearing a smock?
Was smock clean?
Was clerk wearing name tag?
Store stock level
Rated on 5-point Likert scales as to whether shelves, snack stands & refrigerators were fully stocked

14. Control Variables Average Line length Store ownership
Largest number of customers in line at primary cash register during each visit
Store ownership
Franchise vs. corporation owned
Store supervision costs
Amount (in dollars) spent on each store
Region
Location of store in one of four geographical region (NOTE Coding method for regression)

15. Regression Analyses Hierarchical method using sales as dv
Step 1 = 8 control variables
Note: Adjusted R2 accounts for the increased likelihood of finding a large and significant R with a small sample, and/or with a several predictors (I.e., differences between R2 and adjusted R2 are greater in such cases)
Step 2 = Predictor variable i.e., Display of positive emotions

16. Regression Results Sales are positively related to
Average line length (store pace)
Supervision costs
Clerk gender composition
Sales are negatively related to
Display of positive emotions
contrary to hypothesis

17. Study 2
Explain the negative relationship between store sales and display of positive emotion

18. Data Collection Methods
Case studies of 4 stores
Researcher worked for a day as store clerk
Conversations with store managers
Customer service workshop
40 visits to different stores
Paper Organizational Issue: Ordering of descriptions (p. 472)

19. Case studies Clerks Typically Display Positive Emotion
Clerks Typically do not Display Positive Emotion
High Sales 1
Low Sales
Two 1-hour observations in each case study store
Clerk consented to observer, had informal conversations re: customer service

20. Case studies
Semi-structured interviews with store managers of case study store
30-60 mins long
17 questions re:
Manager’s prior experience
Selection, socialization, reward systems used in store
Employee courtesy and its influence on store sales
Info on how responses were coded not provided

21. Data Collection Methods
Researcher works as clerk for a day
In store with low sales but frequent display of positive emotions
Viewed 30 min training video on employee courtesy before working
Conversations w/store managers
150 hours of informal conversations re: negative relationship b/w positive emotions & sales

22. Data Collection Methods
Customer service workshop attendance
2 hour prg. focusing on methods for coaching and rewarding clerks for courteous behavior
Discussion on the role of expressed emotions in the store
40 visits to different stores
Qualitative measures of store pace
Not much detail provided

23. Theoretical Explanations
Store pace determined norms re: emotional expression that affected emotions displayed
Busy time evoked norms for fewer positive emotions
Slow times evoked norms for more positive emotions

24. Norms for Busy Stores
Fewer positive emotions helped maintain store efficiency
Discourage customers from prolonging transactions
Were perceived as more efficient by other customers waiting in line
Evoked feelings of tension among clerks leading to fewer positive emotions

25. Norms for Slow Stores More positive emotions displayed by clerks
Low pressure for speed/efficiency on clerks
Customers have different scripts for slow stores
Clerks regarded customers as a source of entertainment

26. Revised Hypothesis
Expression of positive emotion is negatively related to store pace (as measured by store sales & line length)

27. Regression Analyses
Hierarchical method with display of positive emotions as dv
Step 1 = 7 of 8 control variables (as in Study 1)
Step 2 = line length & total store sales

28. Regression Results
Display of positive emotion is negatively related to
Store sales
Average line length (store pace)
Control variables
Store ownership
Stock level
Display of positive emotions is positively related to store clerk gender composition

29. Individual-Level Data Analyses
N=1319 (clerks)
Hierarchical multiple regression
Step 1=Control variables
Step 2= Line length negatively predicted display of positive emotion
Did not use store sales as predictor bec analyses is at individual level, whereas store sales info is at store level

30. Typically Busy Stores
Clerks show fewer positive emotions during slow times
Slow times provide ‘opportunities’ to catch up on other tasks, customers are not perceived as source of job variety or entertainment
Measured as large amount of store sales

31. Typically Slow Stores
Clerks show fewer positive emotions during busy times
Less experience in coping with pressure of busy times and feel tense
Therefore…
Stronger negative relationship between line length & display of positive emotion for slow stores
Measured as small amount of store sales

32. Individual-Level Data Analyses
Hierarchical multiple regression
Step 1 & 2 as previous analyses
Step 3= Interaction b/w line length and total sales negatively predicted amount of positive emotion

33. Individual-Level Data Analyses
Classified stores as busy/slow based on store sales being above/below mean
Separate hierarchical multiple regressions for clerks at slow & busy stores
Line length was
Negatively (-19) related to display of positive emotions (for slow stores)
Marginally (06) related to display of positive emotions (for busy stores)

34. Discussion
Found negative relation b/w positive emotions and store sales
Why?
Stores sales reflect store pace which causes emotions
Could be different
In diff org’n with different ‘service ideal’ (e.g., Mcdonalds)
For longer transactions (e.g., restos)

35. Discussion Emotions as control moves affect things other than sales
Negative/neutral emotions as control moves to increase efficiency
Positive emotions used to achieve individual rather than org’n goals

36. Discussion
Relative strength of corporate norms vs. store norms & inner feelings in determining display of emotions
Reduce stress to encourage display of positive emotions

37. Discussion Observational methods
Ethics of secret/unobtrusive observation
Benefits of non-reactive vs. contrived observations
Clerks informed about mystery shoppers
Anonymity of clerks observed
But each store had only 8-10 clerks!

38. Discussion Presenting the research process
Acceptability of inductive & deductive process in
Organizational behavior research publication process
Corporate environments
Media presentations
Reader friendliness
Student learning