Emotions & Sales Sutton & Rafaeli

1. Emotions & SalesSutton & Rafaeli • How to conduct an observational study • Deductive Study (Study 1) • Inductive Study (Study 2) • Re-analysis of Study 1 data @ 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 18-34 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 • 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 • 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 • 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 • 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