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Emotions & Sales Sutton & Rafaeli How to conduct an observational study Deductive Study (Study 1) Inductive Study (Study 2) –Re-analysis of Study 1 data.

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Presentation on theme: "Emotions & Sales Sutton & Rafaeli How to conduct an observational study Deductive Study (Study 1) Inductive Study (Study 2) –Re-analysis of Study 1 data."— Presentation transcript:

1 Emotions & Sales Sutton & 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 R 2 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 R 2 and adjusted R 2 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 Sales11 Low Sales11 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


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