On the relation between felt trust and actual trust: Examining pathways to and implications of leader trust meta-accuracy

Campagna, R. L., Dirks, K. T., Knight, A. P., Crossley, C., & Robinson, S. L. (2020). On the relation between felt trust and actual trust: Examining pathways to and implications of leader trust meta-accuracy. Journal of Applied Psychology, 105, 994-1012.

Abstract. Research has long emphasized that being trusted is a central concern for leaders (Dirks & Ferrin, 2002), but an interesting and important question left unexplored is whether leaders feel trusted by each employee, and whether their felt trust is accurate. Across two field studies, we examined the factors that shape the accuracy of leaders’ felt trust—or, their trust meta-accuracy—and the implications of trust meta- accuracy for the degree of relationship conflict between leaders and their employees. By integrating research on trust and interpersonal perception, we developed and tested hypotheses based on two theoretical mechanisms—an external signaling mechanism and an internal presumed reciprocity mechanism—that theory suggests shape leaders’ trust meta-accuracy. In contrast to the existing literature on felt trust, our results reveal that leader trust meta-accuracy is shaped by an internal mechanism and the presumed reciprocity of trust relationships. We further find that whether trust meta-accuracy is associated with positive relational outcomes for leaders depends upon the level of an employee’s actual trust in the leader. Our research contributes to burgeoning interest in felt trust by elucidating the mechanisms underlying trust meta-accuracy and suggesting practical directions for leaders who seek to accurately understand how much their employees trust them.

Spring 2020 Courses

People Metrics
Open to BSBA, MBA, and Specialized Masters students

Metrics are at the core of people analytics. The purpose of this course is to introduce you to the foundations of assessing behavior in organizations using novel measurement approaches and large datasets. Through classroom discussions and real-world applications, this course will enable you to add value to organizations through the development, use, and interpretation of innovative people metrics. Specifically, after taking this course, you will be able to:

  • Develop a clear and logical conceptual measurement model. A conceptual measurement model is the foundation of creating novel and useful new approaches for assessing intrapersonal characteristics (e.g., personality) and interpersonal behavior (e.g., knowledge sharing, teamwork).
  • Identify novel sources of data for innovative people metrics. Organizations are awash in the traces of individual behavior and social interactions. Decoding how data that already exist in an organization can be used to understand behavior is an essential skill for adding value in the field of people analytics.
  • Apply a rigorous process for validating new people metrics. Developing a measurement model and finding sources of data are necessary, but insufficient for adding value through people metrics. New measures must be validated.

Fall 2019 Courses

Leadership Development
2nd Year Full-Time MBA OB Core Course

This course builds upon the material from the 1st Year OB Core (OB 5620, Foundations for Leadership Effectiveness) and, importantly, from your time so far at Olin and during your summer work experiences. The focus of the course is on the attributes, behaviors, and tendencies of effective leadership. There are two primary objectives:

  • Gain new insights into your own beliefs and expectations regarding what constitutes effective leadership in organizations. You will accomplish this through a mixture of classroom discussion, case analysis, and self-assessment.
  • Learn about your own strengths and weaknesses in leading others. You will accomplish this in the classroom through controlled experiential exercises, which will be the basis for feedback from your peers. You will also reflect in depth on your strengths using feedback provided by people you have encountered in your life and career through a structured exercise.

Summer 2019 Courses

Foundations of Impactful Teamwork
Required Course for 1st Year MBA Students

Working effectively in and leading teams are essential competencies in modern organizations, both large and small. The purpose of this course is to lay a foundation of knowledge and skills that will enable you to differentiate yourself as an effective leader and member of impactful teams. The specific learning objectives for this course include:

  • Be able to skillfully launch and lead goal-directed project teams that meet or exceed stakeholders’ expectations for task performance, provide a positive working experience for team members, and enable team members to grow as a unit and as individuals.
  • Be able to diagnose common interpersonal challenges that arise in teams composed of diverse individuals who are working under pressure in unfamiliar environments; and, apply evidence-based practices for leading teams through these challenges.
  • Refine your awareness of your personal strengths and weaknesses as a leader and develop a plan for honing your leadership identity and interpersonal skills during your MBA program.
  • Augment your resourcefulness when navigating diverse local environments and unfamiliar cultures and societies.

On the emergence of collective psychological ownership in new creative teams

Gray, S. M., Knight, A. P., & Baer, M. (2020). On the emergence of collective psychological ownership in new creative teams. Organization Science, 31, 141-164.

Abstract. We develop and test a theoretical model that explains how collective psychological ownership—shared feelings of joint possession over something—emerges within new creative teams that were launched to advance one person’s (i.e., a creative lead’s) preconceived idea. Our model proposes that such teams face a unique challenge—an initial asymmetry in feelings of psychological ownership for the idea between the creative lead who conceived the idea and new team members who are beginning to work on the idea. We suggest that the creative lead can resolve this asymmetry and foster the emergence of collective psychological ownership by enacting two interpersonal behaviors—help seeking and territorial marking. These behaviors build collective ownership by facilitating the unifying, centripetal force of team identification and preventing the divisive, centrifugal force of team ownership conflict. Our model also proposes that collective ownership positively relates to the early success of new creative teams. The results of a quantitative study of 79 creative teams participating in an entrepreneurship competition provided general support for our predictions, but also suggested refinements as to how a creative lead’s behavior influences team dynamics. The findings of a subsequent qualitative investigation of 27 teams participating in a university startup launch course shed additional light on how collective ownership emerges in new creative teams launched to advance one person’s idea.

Using R to Analyze Twitter

The code below will give you a start on processing text data from Twitter. There are some basic examples of how to pull down tweets for selected users and compare/contrast the sentiment of their posts.

#####################
# This script illustrates how to pull data from
# twitter and use default settings for English
# language sentiment analysis
#####################
library(twitteR)
library(rtweet)
library(syuzhet)
library(ngram)
library(reshape2)
require(dplyr)
library(timeDate)
library(ggplot2)

#####################
# This is just a crude string cleaning function for the purposes
# of illustration.
#####################

clean.string <- function(string){
    # Lowercase
    temp <- tolower(string)
    # Remove everything that is not a number or letter (may want to keep more
    # stuff in your actual analyses).
    temp <- stringr::str_replace_all(temp,"[^a-zA-Z\\s]", " ")
    # Shrink down to just one white space
    temp <- stringr::str_replace_all(temp,"[\\s]+", " ")
    return(temp)
}

#####################
# this function returns a crude sentiment analysis of the tweets from a set of
# users' timelines. You must provide a vector of users.
#####################

twit.sentiment <- function(users, n.tweets=200, include.retweet=FALSE) {
    sent.vars = c("anger", "anticipation", "disgust", "fear", "joy", "sadness", "surprise", "trust", "negative", "positive")   
    d.vars = c("user_id", "screen_name", "created_at", "retweet_count", "favorite_count", "followers_count", "friends_count", "text")
    d = data.frame(get_timelines(users, n=n.tweets, parse=TRUE))

    # do a very light text cleaning
    d$text_clean = unlist(lapply(d$text, clean.string))

    # count the clean words
    d$n_words = unlist(lapply(d$text_clean, wordcount))

    # Do the sentiment analysis using nrc. In a real production sentiment analysis, you would want
    # to consider several different dictionaries. Check out the following page for a walkthrough of
    # some of the different lexicons you might consider:
    # https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html
    d[,sent.vars] = bind_rows(lapply(d$text_clean, get_nrc_sentiment))
    head(d)

    # Get a percentage of pos/neg by number of words in the email
    d$neg_pct = d$negative/d$n_words
    d$pos_pct = d$positive/d$n_words

    if(include.retweet) {
        d.sub = d[,c(d.vars, sent.vars)]       
    } else {
        d.sub = d[!(d$is_retweet),c(d.vars, sent.vars)]    
    }
    return(d.sub)
}

#####################
# Explore the dictionaries, showing how different
# words are coded
#####################

nrc = get_sentiment_dictionary(dictionary = "nrc", language = "english")
syuzhet = get_sentiment_dictionary(dictionary = "syuzhet", language = "english")

nrc[nrc$word == "horrible", ]
syuzhet[syuzhet$word == "horrible", ]

nrc[nrc$word == "disastrous", ]
syuzhet[syuzhet$word == "disastrous", ]

#####################
# Exploring sentiment analysis
#####################

v1 = "Man, I am having the best day today. The sun is out and it is a beautiful day."
v2 = "So grateful to be part of this supportive community. This is an amazing place to work."
v3 = "What a horrible day. Not only is it storming, but I fell in the mud and broke my phone."
v4 = "Awful bosses and terrible co-workers. This is a ridiculously bad place to work."

v5 = "I am not having the best day today. The sun is not out and it is not a beautiful day."
v6 = "Some days are better than others. This is the latter."
v7 = "So, got my final back. Um, yeah. The professor sure knows how to give the gift of a great day."
v8 = "Great idea Olin...Make all the students swipe their cards just to get onto the 4th floor. Beautiful building that we can't access."

get_nrc_sentiment(clean.string(v1))
get_nrc_sentiment(clean.string(v2))
get_nrc_sentiment(clean.string(v3))
get_nrc_sentiment(clean.string(v4))
get_nrc_sentiment(clean.string(v5))
get_nrc_sentiment(clean.string(v6))
get_nrc_sentiment(clean.string(v7))
get_nrc_sentiment(clean.string(v8))

#####################
# The first thing you need to do is create an app for your twitter account
# you can find instructions here:
# https://developer.twitter.com/en/docs/basics/apps/overview.html

# Once you've created an app, then add the following information to this script
#####################
# twitter_consumer_key = "YOUR INFO HERE"
# twitter_consumer_secret = "YOUR INFO HERE"
# twitter_access_token = "YOUR INFO HERE"
# twitter_access_secret = "YOUR INFO HERE"

setup_twitter_oauth(twitter_consumer_key, twitter_consumer_secret, twitter_access_token, twitter_access_secret)

#####################
# Sample sentiment analysis on accounts where
# we have strong priors about their sentiment
#####################

sad_happy = c("sosadtoday", "angrymemorys", "gohappiest", "kindnessgirl")
d.sh = twit.sentiment(users=sad_happy, n.tweets=200, include.retweet=F)
boxplot(positive~screen_name, data=d.sh, cex.axis=.7, las=2, main="positive")
boxplot(negative~screen_name, data=d.sh, cex.axis=.7, las=2, main="negative")

#####################
# Illustrating the potential for looking at specific users and
# comparing / contrasting individual employees' sentiment
#####################

OlinPeeps = c("DeanTaylorWashU", "sjmalter", "LamarPierce1", "OrgStratProf")
BSchoolDeans = c("DeanTaylorWashU", "scottderue")
BSchools = c("OlinBusiness", "Wharton")

d.olin = twit.sentiment(users=OlinPeeps, n.tweets=300, include.retweet=F)
d.deans = twit.sentiment(users=BSchoolDeans, n.tweets=300, include.retweet=F)
d.schools = twit.sentiment(users=BSchools, n.tweets=300, include.retweet=F)

boxplot(positive~screen_name, data=d.olin, cex.axis=.7, las=2, main="positive")
boxplot(negative~screen_name, data=d.olin, cex.axis=.7, las=2, main="negative")

boxplot(positive~screen_name, data=d.deans, cex.axis=.7, las=2, main="positive")
boxplot(negative~screen_name, data=d.deans, cex.axis=.7, las=2, main="negative")

boxplot(positive~screen_name, data=d.schools, cex.axis=.7, las=2, main="positive")
boxplot(negative~screen_name, data=d.schools, cex.axis=.7, las=2, main="negative")

#####################
# Illustrating the potential for looking at trends over time
#####################
olin.all = c("DeanTaylorWashU", "sjmalter", "LamarPierce1", "OrgStratProf", "sethcarnahan", "peterboumgarden",
    "jrobmartin", "milbourn_todd", "danbentle", "wustlbusiness", "drpatsportsbiz", "analisaortiz", "krwools")

d.lrg = twit.sentiment(users=olin.all, n.tweets=300, include.retweet=F)

d.lrg$date = as.Date(d.lrg$created_at)
d.lrg$year = as.numeric(strftime(d.lrg$date, format="%Y"))
d.lrg$month = as.numeric(strftime(d.lrg$date, format="%m"))
d.lrg$woy = as.numeric(strftime(d.lrg$date, format="%V"))

o = aggregate(d.lrg[,c("positive", "negative")], by=list(d.lrg$year, d.lrg$month), mean)
names(o)[1:2] = c("year", "month")

plot(o[o$year == 2018, "month"], o[o$year == 2018, "positive"], type="l", ylim=c(0,3), col="dark green", lwd=3, ylab="sentiment", xlab="month")
lines(o[o$year == 2017, "month"], o[o$year == 2017, "positive"], type="l", col="dark green", lwd=3, lty=2)

lines(o[o$year == 2018, "month"], o[o$year == 2018, "negative"], type="l", col="dark red", lwd=3)
lines(o[o$year == 2017, "month"], o[o$year == 2017, "negative"], type="l", col="dark red", lwd=3, lty=2)

boxplot(positive~screen_name, data=d.lrg, cex.axis=.7, las=2, main="positive")
boxplot(negative~screen_name, data=d.lrg, cex.axis=.7, las=2, main="negative")

d.lrg$name = as.factor(d.lrg$screen_name)

p <- ggplot(d.lrg, aes(x=name, y=positive)) + geom_violin()
p <- ggplot(d.lrg, aes(x=name, y=negative)) + geom_violin()

d.lrg[d.lrg$negative > 7, ]

Integrating R and PollEverywhere

I wrote a short function to extract poll results from PollEverywhere. I frequently use PollEverywhere in classes and executive education programs, but have always found pulling the results clunky.

PollEverywhere does have an api, but it doesn’t seem like something that (at least publicly available) is a big focus of attention. Nonetheless, it is possible to use basic http authentication to get poll results. Here’s the function that will do that.

## -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ##
## This function extracts responses to poll everywhere polls
## This is currently setup just to do a simple multiple choice
## poll. This could be extended to other types of polls and response sets
## More information here: https://api.polleverywhere.com/
## -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ##

pe.responses = function(ids, un, pw) {
    require(httr)
    require(jsonlite)
    ## The id of the poll is at the end of the URL if you open one of the poll
    # in your browser
    ## I could not find a way with the api to list all of the polls for
    ## a given account. This would be ideal, but it's not there as far as I can see

    url.root = "https://www.polleverywhere.com/multiple_choice_polls"

    for(id in ids) {
        # This will just pull the attributes for this particular poll
        url = paste(url.root,id,sep="/")
        # I am only interested in the option set right here, but you could
        # get more information about the poll from this
        ops = fromJSON(content(GET(url, authenticate(un,pw)), "text"))$options
        names(ops)[1] = "response_id"

        # This will pull the responses to this particular poll
        url = paste(url.root,id,"results",sep="/")
        responses = fromJSON(content(GET(url, authenticate(un,pw)), "text"))
        responses$poll_id = id

        # Add the keyword response in addition to the value
        mrg = merge(responses, ops[, c("response_id", "value", "keyword")], by="value", all.x=T)

        if(id == ids[1]) {
            res.out = mrg
        } else {
            res.out = rbind(res.out, mrg)
        }
    }
    res.out$response_num = as.numeric(res.out$keyword)

    # Format the date/time
    res.out$response_date = unlist(lapply(strsplit(res.out$created_at, "T"), function(x) x[1]))
    res.out$response_time = unlist(lapply(strsplit(res.out$created_at, "T"), function(x) x[2]))
    res.out$response_time = substr(res.out$response_time,1, 8)
    res.out$response_date_time = paste(res.out$response_date, res.out$response_time, sep=" ")
    res.out$response_date_time = as.POSIXlt(res.out$response_date_time)
    return(res.out)
}

## Enter your polleverywhere credentials here
# un = "{Your Username Here}"
# pw = "{Your Password Here}"

## Here's a set of poll ids that I was interested in getting the results for
# ids = vector of pollids that you want to extract the results for

o = pe.responses(ids, un, pw)

Sentiment analysis on Gmail with R: The gmailr package

For today’s exploration, I wanted to connect to my gmail account, pull messages, and do a quick sentiment analysis on the text. The focus of this code is pulling and transforming the data from gmail’s api–not doing a precise and polished sentiment analysis. I wanted to learn a bit about the gmail api the gmailr package (which right now is pretty thin on documentation).

There is much potential with this. The api would make everything from sentiment analysis to network analysis on your own gmail account possible.

##########################################
# This script gives an example of how to connect
# to a personal gmail account, extract a set of messages
# and do a quick-and-dirty sentiment analysis on the
# body of the messages.
# NOTE: This is not a pure or clean analysis of this text data.
# For production, you would want to make sure to clean up the
# body of the text data (e.g., ensuring that you don't have duplicate
# messages that are appended at the bottom of replies).
#
# However, this should give you a place to start for making sense of your email.
##########################################


#### -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ####
## Setup
#### -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ####
# Setup your environment, marking a particular working directory where you'd like
# to output files and loading libraries that you'll use
# syuzhet has a set of functions for doing sentiment analysis
library(syuzhet)
# ngram is useful for breaking up and parsing text data
library(ngram)
# reshape2 is also helpul for parsing text data
library(reshape2)
# use this to smash a list
require(dplyr)
# gmailr has a set of functions for connecting to gmail and parsing emails
library(gmailr)


## User-defined function for doing a quick-and-dirty clean-up on text
# You could add elements to this to create an even more precise set of
# text data to parse for your sentiment analysis. For a production
# text analysis, you would want to create a clean set of data.

clean.string <- function(string){
    # Lowercase
    temp <- tolower(string)
    # Remove everything that is not a number or letter (may want to keep more
    # stuff in your actual analyses).
    temp <- stringr::str_replace_all(temp,"[^a-zA-Z\\s]", " ")
    # Shrink down to just one white space
    temp <- stringr::str_replace_all(temp,"[\\s]+", " ")
    return(temp)
}

## User-defined function for pulling a set of messages from gmail
# and doing a sentiment analysis on those messages. This will also retain the actual
# body of the messages in case you want to do something further with it down
# the line. The only input into the function is a vector of message ids
# that you want to pull and process.


gmail.sentiment = function(ids) {

    # a vector of the sentiment variables
    sent.vars = c("anger", "anticipation", "disgust", "fear", "joy", "sadness", "surprise", "trust", "negative", "positive")
    # a vector of the email vars
    email.vars = c("id", "to", "from", "cc", "bcc", "date", "subject", "body") 
    # put together and also add the number of words in the body
    all.vars = c(email.vars, "n_words", sent.vars)

    null.to.na = function(x) {
        x = ifelse(is.null(x), NA, x)
        return(x)
    }

    # Loop through the vector of message ids and pull the info for that specific message
    # We're creating a data.frame here that contains the information for this query of messages
    for(i in 1:length(ids)) {

        # Get the header info for the message, replacing any null values with NA
        id = ids[i]
        msg = message(id)
        to = to(msg)
        to = null.to.na(to)
        from = from(msg)
        from = null.to.na(from)    
        cc = cc(msg)
        cc = null.to.na(cc)
        bcc = bcc(msg)
        bcc = null.to.na(bcc)      
        date = date(msg)
        date = null.to.na(date)
        subject = subject(msg)
        subject = null.to.na(subject)  
        body = unlist(body(msg))
        body = null.to.na(body)

        # Create a holding line
        res.line = data.frame(cbind(id, to, from, cc, bcc, date, subject, body), stringsAsFactors=F)

        # if this is the first pass through, then create an outset. Otherwise, append this line
        # to the existing outset
        if(i == 1) {
            res.out = res.line
        } else {
            res.out = rbind(res.out, res.line)
        }
    }

    # do a very light text cleaning
    res.out$body_clean = unlist(lapply(res.out$body, clean.string))

    # count the clean words
    res.out$n_words = unlist(lapply(res.out$body_clean, wordcount))
   
    # Do the sentiment analysis using nrc. In a real production sentiment analysis, you would want
    # to consider several different dictionaries. Check out the following page for a walkthrough of
    # some of the different lexicons you might consider:
    # https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html
    res.out[,sent.vars] = bind_rows(lapply(res.out$body_clean, get_nrc_sentiment))

    # Get a percentage of pos/neg by number of words in the email
    res.out$neg_pct = res.out$negative/res.out$n_words
    res.out$pos_pct = res.out$positive/res.out$n_words

    # parse the date information into some variables to use in graphing
    res.out$dow = substr(res.out$date, 1, 3)   

    res.out$date_time = substr(res.out$date, 6, nchar(res.out$date))
    o = colsplit(trimws(res.out$date_time), " ", names=c("day", "month", "year", "time", "offset"))
    d = cbind(res.out, o)
    d$date_time_format = as.Date(paste(d$month, " ", as.numeric(d$day), " ", as.numeric(d$year), sep=""), format="%b %d %Y")
    d$month_num = as.numeric(substr(d$date_time_format, 6,7))
    d$day_num = as.numeric(substr(d$date_time_format, 9,10))

    return(d)
}

#### -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ####
## Connect to gmail
#### -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ####

## Note, you will need to create your own application to connect to gmail
## Here are some steps for doing this:
## 1. Go to https://console.developers.google.com/
## 2. Create a new project
## 3. Copy-and-paste the Client ID and Client Secret into the fields below
## 4. Add an authorized redirect URI: http://localhost:1410/

client_id = "{INSERT YOUR ID HERE}"
client_secret = "{INSERT YOUR SECRET HERE}"

# Running this will open a web browser and ask you to authenticate
# If you are already authenticated into gmail, it will just give you a confirmation
# message, indicating that you are authenticated. You can close the browser and begin using gmail
# NOTE: After a period of time, your authentication will time-out. When you try to pass
# a request to gmail, you'll get an error. Just re-run the line below and you'll re-authenticate.
gmail_auth(scope="read_only", id=client_id, secret=client_secret)

#### -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ####
## Request a set of message ids that match a given query.
## There are many slick ways to search for messages (or threads) in gmail. Any of these methods can be used
## in the search=" " argument.
## For a full set of search options, check out this page:
## https://support.google.com/mail/answer/7190?hl=en
#### -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ####

## For this example, I'm going to pull all messages that I sent (i.e., those that gmail auto-labeled as SENT)
## I'm going to specify a particular time window and a maximum of 10k messages.
msgs = messages(search="before:2019/01/01 after:2005/12/01", num_results = 10000, label_ids="SENT")

# the messages function abovewill return an object with thread and message ids. The function below
# will return a vector of string ids that can be used in subsequent pulls.
# Note that because the function has to call each message, this can take sometime to process
# So, if you have something like 4000 messages, expect for it to take several minutes to finish running.
# Be patient! It's not efficient code.
ids = gmailr::id(msgs, what="message_id")
o = gmail.sentiment(ids)

# Because this took so long to do, I'm going to write out the results
write.table(o, "./gmail_text_analysis.csv", sep=",", row.names=F)

#### -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ####
# At this point, you can use your favorite graphing and analysis tools
# to analyze this dataset at different levels of analysis (e.g., time, day, day of week, month, year)
#### -- ## -- ## -- ## -- ## -- ## -- ## -- ## -- ####

SublimeText 3 and R: Setup and Usage

My favorite text editor for coding — TextWrangler — has been retired. I’m using this as a forced push (avoiding BBEdit) to make a full switch to SublimeText. Here’s the setup that I’m using on a Mac running macOS 10.14.

First, Install R and SublimeText 3. Next, within SublimeText, do the following:

  • Install Package Control
  • Then Install the following packages: R-Box, SendCode, SublimeREPL

Interestingly, it seems like a file has to be actually saved as a .R for SendCode to open up the multiple options for sending code to the R Gui (and other targets). Similarly, SublimeREPL R was not functioning until I saved the source file as a .R. The default key bindings for sending code then worked nicely.

When I tried SublimeText before, I almost exclusively used SublimeREPL with a 2-column layout (i.e., the source code on the left and REPL on the right). What’s frustrating about this is that sending code to REPL moves the cursor, which disrupts my workflow.

I like that SendCode makes it easy to direct my source code to a shell or to the R Gui. The latter best mimics my workflow with TextWrangler.

I’m going to force myself to use SublimeText for a bit–resisting the urge to drift back to the known.

Spring 2019 Courses

People Metrics
Open to BSBA, MBA, and Specialized Masters students

Metrics are at the core of people analytics. The purpose of this course is to introduce you to the foundations of assessing behavior in organizations using novel measurement approaches and large datasets. Through classroom discussions and real-world applications, this course will enable you to add value to organizations through the development, use, and interpretation of innovative people metrics. Specifically, after taking this course, you will be able to:

  • Develop a clear and logical conceptual measurement model. A conceptual measurement model is the foundation of creating novel and useful new approaches for assessing intrapersonal characteristics (e.g., personality) and interpersonal behavior (e.g., knowledge sharing, teamwork).
  • Identify novel sources of data for innovative people metrics. Organizations are awash in the traces of individual behavior and social interactions. Decoding how data that already exist in an organization can be used to understand behavior is an essential skill for adding value in the field of people analytics.
  • Apply a rigorous process for validating new people metrics. Developing a measurement model and finding sources of data are necessary, but insufficient for adding value through people metrics. New measures must be validated.

Organizational Research Methods
Doctoral Course

The purpose of this course is to expose you to a range of methods for conducting research on organizations. We will do this through readings, class discussions and exercises, as well as through writing and reviewing one another’s work. Because this is a survey course, we will cover a range of topics and specific research methods. The objectives of the course are:

  • Introduce you to general concepts of methodological rigor and the core foundations of measurement
  • Enhance your understanding of the suite of methods commonly used in organizational research
  • Improve your skill in critically consuming research from a variety of methodological approaches