Learning Agenda Emotions & Sales Article Sutton & Rafaeli

1. Learning Agenda Emotions & Sales Article Sutton & Rafaeli • Understanding the phenomenon • Conducting an observational study • qualitative & quantitative info • Regression Analyses

2. Sources of Data that help understand the phenomenon • Observations of 4 case study stores • Interviews with case study store manager • Content Analysis of customer service workshop • 40 visits to different stores • Observations while working for a day as store clerk • Conversations with all levels of employees • Stratified sample of Store level variables

3. Case Study Stores Observed • Observed 1 busy & 1 slow hour • Took notes on structured topics • Talked informally with clerks

4. Structured Interviews with Case Study Store Managers • 30-60 mins • 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

5. Content of Customer Service workshop Attended • 2 hour prg. focusing on methods for coaching and rewarding clerks for courteous behavior • Discussed role of expressed emotions in the store

6. Visits to stores • Visited 40 Stores • Collected qualitative measures of store pace • Not much detail provided

7. Working in store for a day • Viewed 30 min training video on employee courtesy before working • Store with low sales but frequent display of positive emotions

8. Conversations with employees at all levels of the organization • 150 hours of informal conversations with corporate executives, customer service representatives, field supervisors, store managers re: negative relationship b/w positive emotions & sales

9. Stratified sample of stores 2 Countries 1st Division 10th Division ........... 18th Division ........... 1st District ........... 50th District ........... 72nd District 1st Store .............. 4th Store ............... 8th Store 576 stores in total

10. Who was observed in each store • 1319 clerks • Mostly urban stores • 44% male clerks

11. What was observed in each store • 11805 transactions • 3 month observation period • For each of the 576 stores • 1 day + 1 swing shift • 25% of stores observed during night shift • 1-20 transactions/visit • Up to 60 transactions/store • 75% male customers

12. Who were the observers for each store • Corporate HR staff volunteers dressed according to the profile of a typical customer • May not be adequately matched for SES of customers who were working class male customers b/w 18-34 yrs • Visited store in pairs

13. Training of store observers • “Mystery shoppers” observed clerks at pre-test stores w/research director before actual data collection period • Compared & clarified coding differences in behavior with the director

14. How transactions were observed • Only observed behavior of clerk at primary cash register from magazine rack/coffee pots • 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

15. Reliability of mystery shoppers’ coding • Compared to firms’ director of field research coding of • 274 stores • Observed with second original observer • Mean correlation was .82

16. Measurement of Positive Emotions • Each transaction rated 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 • Store index of emotion was mean of 4 aspects (reliability=.76)

17. Measurement of Sales • Total store sales during the year of the observation • Obtained from company records • Standardized across stores included in sample to preserve confidentiality

18. Measurement of Line Length • Largest number of customers in line at primary cash register during each visit

19. Measurement of Clerk gender & Customer Gender • Clerk gender • 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

20. Measurement of Clerk Image • 3 items rated by observers on a yes/no scale • Was clerk wearing a smock? • Was smock clean? • Was clerk wearing name tag?

21. Measurement of Store Stock Level • Rated on 5-point Likert scales • Extent to which shelves, snack stands & refrigerators were fully stocked

22. Measurement of ownership, supervision & region • 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

23. Research Questions • How are store sales, positive emotions and line length related? • What predicts store sales? • What predicts positive emotions • at store level • at clerk level • for clerks at different types of stores

24. How are store sales, positive emotions and line length related?

25. Simple Correlations

26. What variables predict sales of a store

27. Analysis used to answer the research question • Hierarchical regression analysis • Dependent Variable= Sales • Predictor variables • only 8 control variables (aka Model without positive emotions ) • 8 control variables + positive emotions (aka Model with positive emotions)

28. What variables predict Sales? Interpretation of table: • Sales are • negatively related display of positive emotions • positively related to average line length supervision costs, clerk gender composition

29. What variables do not predict Sales?

30. Do Positive Emotions significantly predict sales? • Positive emotions predicts 1% additional variance in sales • Adjusted R2 accounts for increased likelihood of finding a large & significant R with a small sample, and/or with several predictors • Diffs between R2 & adjusted R2 are greater in such cases

31. What variables predict positive emotions within a store

32. Analysis used to answer the research question • Store as unit of analyses (n=576) • Hierarchical regression analysis • Dependent variable = Display of positive emotions • Predictor variables • 7 control variables (one less than Study 1) • Line length & total store sales plus 7 control variables

33. What variables predict positive emotions? Note: Region Betas imply that stores in the west were more likely to express positive emotions but stores in the Northeast were the least likely to do so

34. Description of previous slide • Display of Positive emotion is • Negatively related to • Store sales • Average line length (store pace) • Stock level • Positively related to store clerk gender composition

35. Does pace predict positive emotions? • Pace predicts 3% additional variance in positive emotions

36. Does line length predict the positive emotions of a clerk?

37. Description of Analysis used to answer the research question • Clerk as unit of analysis (n=1319) • Hierarchical multiple regression • Dependent variable=positive emotion • Cannot use sales bec. we do not have such information at the clerk level

38. Does line length predict a clerk’s positive emotions? • Yes, line length adds 3% of variance • Line length negatively predicted display of positive emotion β=.-14 p<.001

39. Does line length predict the positive emotions of a clerk in a busy vs. slow store?

40. Description of Analysis used to answer the research question • Stores classified as busy vs. slow based on sales • Above mean=busy (n=250) • Below mean=slow (n=326) • Clerk as unit of analysis (n=1319) • Dependent variable=positive emotion • Separate regressions for clerks at slow & busy stores

41. Line length predicts the positive emotions of a clerk only in a slow store • Line length was • Negatively (β =-19) related to display of positive emotions in slow stores • Marginally (β =06) related to display of positive emotions in busy stores

42. Another way of analyzing the data to answer the same research question • Hierarchical Regression analyses • Clerk as unit of analysis (n=1319) • Dependent variable=positive emotion • Enter the combined effect of sales and line length as a term by multiplying the two variables in a separate step • First standardize the variables, then multiply them

43. Does line length predict the positive emotions of a clerk in a busy vs. slow store? • Interaction b/w line length and total sales negatively predicted (β=-.07) the amount of positive emotion

44. What we learned today • Can be rigorous in collecting qualitative data • Understand a phenomenon by collecting qualitative data • Explain the quantitative data with qualitative data • Conduct regression analyses based on potential explanations