Learning Agenda Emotions

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

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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
• Managers 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

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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

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Visits to stores

• Visited 40 Stores
• Collected qualitative measures of store pace
• Not much detail provided

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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

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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

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Stratified sample of stores
2 Countries
1st Division
18th Division
10th Division
...........
...........
1st District
72nd District
50th District
...........
...........
1st Store
8th Store
4th Store
..............
...............
576 stores in total
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Who was observed in each store

• 1319 clerks
• Mostly urban stores
• 44 male clerks

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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

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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

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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

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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

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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

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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)

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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

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Measurement of Line Length

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

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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

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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?

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Measurement of Store Stock Level

• Rated on 5-point Likert scales
• Extent to which shelves, snack stands
  refrigerators were fully stocked

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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

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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

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How are store sales, positive emotions and line
length related?
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Simple Correlations
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What variables predict sales of a store
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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)

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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

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What variables do not predict Sales?
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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

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What variables predict positive emotions within a
store
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Analysis used to answer the research question

• Store as unit of analyses (n576)
• 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

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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
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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

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Does pace predict positive emotions?

• Pace predicts 3 additional variance in positive
  emotions

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Does line length predict the positive emotions of
a clerk?
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Description of Analysis used to answer the
research question

• Clerk as unit of analysis (n1319)
• Hierarchical multiple regression
• Dependent variablepositive emotion
• Cannot use sales bec. we do not have such
  information at the clerk level

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Does line length predict a clerks positive
emotions?

• Yes, line length adds 3 of variance
• Line length negatively predicted display of
  positive emotion ß.-14 plt.001

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Does line length predict the positive emotions of
a clerk in a busy vs. slow store?
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Description of Analysis used to answer the
research question

• Stores classified as busy vs. slow based on sales
  
• Above meanbusy (n250)
• Below meanslow (n326)
• Clerk as unit of analysis (n1319)
• Dependent variablepositive emotion
• Separate regressions for clerks at slow busy
  stores

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Line length predicts the positive emotions of a
clerk only in a slow store

• Line length was
• Negatively (ß -19) related to display of
  positive emotions in slow stores
• Marginally (ß 06) related to display of positive
  emotions in busy stores

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Another way of analyzing the data to answer the
same research question

• Hierarchical Regression analyses
• Clerk as unit of analysis (n1319)
• Dependent variablepositive 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

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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 (ß -.07) the amount of
  positive emotion

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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