Learning Agenda Emotions & Sales Article

Sutton & Rafaeli

• Understanding the phenomenon

• Conducting an observational study– qualitative & quantitative info

• Regression Analyses

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

Case Study Stores Observed

Clerks Typically Display Positive

Emotion

Clerks Typically do not Display

Positive Emotion

High Sales 1 1

Low Sales 1 1

– Observed 1 busy & 1 slow hour – Took notes on structured topics– Talked informally with clerks

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

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

Visits to stores• Visited 40 Stores

• Collected qualitative measures of store pace

• Not much detail provided

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

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

Stratified sample of stores

2 Countries

1st Division

576 stores in total

18th Division10th Division........... ...........

1st District 72nd District50th District........... ...........

1st Store 8th Store4th Store .............. ...............

Who was observed in each store

• 1319 clerks– Mostly urban stores– 44% male clerks

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

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

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

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

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

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)

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

Measurement of Line Length

• Largest number of customers in line at primary cash register during each visit

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

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?

Measurement of Store Stock Level

• Rated on 5-point Likert scales

• Extent to which shelves, snack stands & refrigerators were fully stocked

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

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

How are store sales, positive emotions and line length

related?

Simple Correlations

Positive Emotions Line Length Sales

Positive Emotions

1

Line Length

-.18 1

Sales -.06 .18 1

What variables predict sales of a store

• 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)

Analysis used to answer the research question

What variables predict Sales?Variables Beta

Display of Positive Emotions -.10*

Line Length .14**

Clerk gender Composition .11**

Supervision Costs .13**

Interpretation of table: • Sales are

– negatively related display of positive emotions

– positively related to average line length supervision costs, clerk gender composition

What variables do not predict Sales?

Variables Beta

Clerk Image -.07

Level of Stock .06

Customer gender composition .07

Franchise store vs. not -.10

Region 1 vs. all other regions -.07

Region 2 vs. all other regions .11

Region 1 vs. all other regions .08

Do Positive Emotions significantly predict sales?

Type of Model Multiple R Adjusted R2

Without Positive Emotions .28 .06

With Positive Emotions .30 .07

• 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

What variables predict positive emotions within a store

• 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

Analysis used to answer the research question

What variables predict positive emotions?

Variables Beta

Total Store Sales -.09*

Line Length -.20**

Stock Level .23**

Clerk gender composition .15**

Region 2 .25*

Region 3 .12*

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

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

Does pace predict positive emotions?

Type of Model Multiple R Adjusted R2

Without Pace .40 .15

With Pace .44 .18

• Pace predicts 3% additional variance in positive emotions

Does line length predict the positive emotions of a clerk?

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

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

Type of Model Multiple R Adjusted R2

Without Line Length .27 .07

With Line Length .32 .10

Does line length predict the positive emotions of a clerk

in a busy vs. slow store?

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

• 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

Line length predicts the positive emotions of a clerk

only in a slow store

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

Type of Model Multiple R Adjusted R2

Only Control variables .27 .07

Line Length x Sales .29 .08

•Interaction b/w line length and total sales negatively predicted (β =-.07) the amount of positive emotion

Does line length predict the positive emotions of a clerk in a

busy vs. slow store?

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