# data[].attributes.body
Semiotics

Interpretation

Production

Use and Strategy of Messages

# data[].attributes.attachment_link.title
Theories of Human Communication : Stephen W. Littlejohn : Free Download, Borrow, and Streaming : Internet Archive

# data[].attributes.attachment_link.readable_text
Journal Articles selected by the author, to help you achieve high grades in your assessments.

Weblinks expand on what you learn in the textbook, with videos and websites provided to support each chapter.

Definition Flashcards let you revise key terms for your assignments and exams.

Just click on the links to the left.

, prepared by the author for each chapter, saves you time and supports your teaching each week.

encourage student engagement and provides answers for use in your teaching.

LMS / VLE Upload allows you to easily upload all the lecturer and student resources into your institution's learning management system (i.e. your Blackboard or Moodle), and customise the content to suit your teaching needs.

Log in or create an instructor account by clicking on the tab at the top.

For advice to support your studies visit the SAGE Study Skills website for videos, quizzes and tips to help with your essay and dissertation writing, presentations, literature reviews and more.

To learn more about the book, visit the SAGE Website.

# data[].attributes.attachment_link.title
Corporate Communication: A Guide to Theory and Practice

# data[].attributes.attachment_link.description
Journal Articles selected by the author, to help you achieve high grades in your assessments. Weblinks expand on what you learn in the textbook, with videos and websites provided to support each chapter. Definition Flashcards let you revise key terms for your assignments and exams. Just click on the links to the left.

# data[].attributes.headline
How to improve teamwork through communication?

# data[].attributes.subject
How to improve teamwork through communication?

# data[].attributes.headline
How to improve health through communication?

# data[].attributes.subject
How to improve health through communication?

# data[].attributes.body
Computational linguistics: Do we own the algorithms or do they own us?

Language and Gender: Are there differences in the styles of communication between males, females and transgender? And if es, how could these differences become productive for society?

Intercultural Challenges in Communication: How to make cultural differences productive through communication?

Political theory: How to talk to populists without losing your mind?

Effectiveness in virtual team communication

Effectiveness in crisis communication

Effectiveness in health communcation

Communication with Nature

# data[].attributes.body
Messages and Behavior Change

Relationships and Health

Managing Information and Risk

Health Disparities

# data[].attributes.body
•Social Bots

•Identity theft

•Fake News

•Conspiracy Theories

•Social Media Bubbles

•Online Mobbing

•Populist Rhetoric
•Marxism

•Critical Theory

•The Frankfurt School and the Theory of Communicative Action

•Cultural Studies

•Post-Structuralism

•Post-Colonialism

•Critical Rhetoric

# data[].attributes.body
What are the specifics of the German Memorial Culture compared to other nations?

How do you perceive the German Memorial Culture?

How would you counter arguments that criticize the German Memorial Culture?

# data[].attributes.headline
Topic 4 How to foster political awareness through communication and how to talk to populists (without loosing your mind)?

# data[].attributes.subject
Topic 4 How to foster political awareness through communication and how to talk to populists (without loosing your mind)?

# data[].attributes.attachment_link.title
A course in Cognitive Linguistics: Frame Semantics

# data[].attributes.body
•Cultural Hermeneutics

•Ethnograpy of Communication

•Performance Ethnography
•Anxiety-Uncertainty Management

•Model of Intercultural Sensibility

•Communication Accommodation
•Cross Cultural Adaption

•Differential Adaption

•Diffusion of Innovations
•Face Negotiation Theory

•Co-cultural Theory

•Cultural Contracts Theory

•Critical Race Theory

# data[].attributes.body
Communication between Humans and Nature

Naturalizing Culture

The Materiality of Nature

The Othering of Nature

Animate Rhetoric

# data[].attributes.attachment_link.title
Political Morality in Your Brain | Elisabeth Wehling | TEDxBerlin

# data[].attributes.body
The Medium/Production

Content and Effects

Motivations, Use and Consumption

Communication between Humans and Technology

# data[].attributes.body
Towards a new economic model?

# data[].attributes.attachment_link.title
Are women leaders really doing better on coronavirus? The data backs it up

# data[].attributes.attachment_link.description
Fewer cases, fewer deaths and earlier lockdowns were in evidence across nations with female figureheads.

# data[].attributes.headline
Topic 4 Group Input: Language and Gender: Are there differences in modes and styles of communication between males, females and transgender?  

# data[].attributes.subject
Topic 4 Group Input: Language and Gender: Are there differences in modes and styles of communication between males, females and transgender?  

# data[].attributes.headline
How to improve organizations through communication?

# data[].attributes.subject
How to improve organizations through communication?

# data[].attributes.body
Organizing and Structuring
Control and Resistance
Stakeholder Communication
Corporate Communication
CSR-Communication

# data[].attributes.body
Interaction Process Analysis

and Decision Emergence

Input-Process-Output Model

Symbolic Convergence Theory

Effective Intercultural Workgroups

Concertive Control Theory

Bona Fide Groups

Adaptive Structuration Theory

Model of Social Influence

Socio-Egocentric and Group –Centric Model

Transactive Memory Theory

Groupthink and Vigilant Interaction Theory

# data[].attributes.attachment_link.title
Idea Framing, Metaphors, and Your Brain - George Lakoff

# data[].attributes.body
Please read before you start writing

# data[].attributes.headline
Two social lives: How differences between online and offline interaction influence social outcomes

# data[].attributes.subject
Two social lives: How differences between online and offline interaction influence social outcomes

# data[].attributes.attachment_link.title
Donal Carbaugh 3 Dimensions of reflective dialogue

# data[].attributes.body
Please read or watch the Speech "This is water" by David Foster Wallace

Commencement Speech

[Withsubtitles: https://www.youtube.com/watch?v=ms2BvRbjOYo]

(About 20 min)

# data[].attributes.body
watch and discuss this speech

# data[].attributes.attachment_link.title
Learn English | David Foster Wallace "This is Water." (with BIG Subtitles)

# data[].attributes.attachment_link.readable_text
From writing a personal narrative to composing a response to an AP essay question, developing a well-organized essay is a vital skill for both high school and college students. Supporting a central claim with strong reasoning and evidence through writing can help you think more critically, use language creatively and communicate better in academic and professional environments. Awareness of purpose, solid supporting examples and strategic paragraph construction can help you write an essay that has an impact on readers and boosts your grade.

Understand the Prompt

Instructors grade essays according to how well you respond to the task given in the assignment prompt. Before you start writing, read the question several times to make sure you know what it's asking you to do. Key words can often provide clues to the essay's purpose; for example, "analyze" means you'll be investigating the causes and reasons for the topic; "compare" points to finding similarities between two topics; and "discuss" entails examining and evaluating both sides of an issue. Try underlining the key words in the prompt as a reminder of your essay's purpose.

Craft a Specific Thesis Statement

The keystone of your essay, your thesis statement, gives an overview of the paper's main ideas and introduces the question it seeks to answer. This statement of the essay's central focus needs to be clear, detailed and specific. For example, "People should adopt rescue animals" might be a good place to start, but it's too vague to carry audiences' interests through the rest of the paper. A better thesis might give specific examples of how this problem will be addressed: "Adopting a rescue animal is more cost effective, supports the protection of abandoned pets and saves the life of an animal in need."

Give Readers a Road Map

Most essay assignments for college or standardized tests require at least three paragraphs of discussion that build on each other as you respond to the prompt. The Harvard College Writing Center suggests structuring your body paragraphs by stating the main point of each; providing solid, credible evidence for your position; and discussing why this idea is significant to your thesis. As you write, try using transitions to link your points together. Words and phrases like "similarly," "because of this," "nevertheless" and "meanwhile" not only signal to readers that you're moving to another idea but also create a relationship to what you've already discussed.

Revisit Your Central Claim

The easiest way to conclude an essay is by simply reiterating your thesis statement and main points. In some cases, this approach is appropriate, such as when you're dealing with a complex subject or are pressed for time in an AP exam. Still, your instructor is likely to want you to dig deeper and find a way to illustrate the relevance of your topic. Try crafting your conclusion by answering the question "So what?" Detail the significance of the topic and why audiences need to know about it. If you're writing an essay for AP History, for example, you might relate the events you've described to a similar modern-day event or historical trend.

# data[].attributes.attachment_link.description
From writing a personal narrative to composing a response to an AP essay question, developing a well-organized essay is a vital skill for both high school and college students. Supporting a central claim with strong reasoning and evidence through writing can help you think more critically, use language creatively and ...

# data[].attributes.headline
Topic 3 Group Input: How do improve cultural awareness through communication?

# data[].attributes.subject
Topic 3 Group Input: How do improve cultural awareness through communication?

# data[].attributes.attachment_link.title
The World After: Bruno Latour and Hartmut Rosa on the consequences of the coronavirus crisis

# data[].attributes.body
Are women leaders really doing better on coronavirus?

# data[].attributes.attachment_link.readable_text
Your access to the NCBI website at www.ncbi.nlm.nih.gov has been temporarily blocked due to a possible misuse/abuse situation involving your site. This is not an indication of a security issue such as a virus or attack. It could be something as simple as a run away script or learning how to better use E-utilities, http://www.ncbi.nlm.nih.gov/books/NBK25497/, for more efficient work such that your work does not impact the ability of other researchers to also use our site. To restore access and understand how to better interact with our site to avoid this in the future, please have your system administrator contact info@ncbi.nlm.nih.gov.

# data[].attributes.attachment_link.description
Your access to the NCBI website at www.ncbi.nlm.nih.gov has been temporarily blocked due to a possible misuse/abuse situation involving your site. This is not an indication of a security issue such as a virus or attack.

# data[].attributes.attachment_link.readable_text
When we buy something on Amazon or watch something on Netflix, we think it's our own choice. Well, it turns out that algorithms influence one-third of our decisions on Amazon and more than 80% on Netflix. What's more, algorithms have their own biases. They can even go rogue.
In his recent book titled, A Human's Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control, Kartik Hosanagar, a professor of operations, information and decisions at Wharton, focuses on these issues and more. He discusses how algorithmic decisions can go wrong and how we can control the way technology impacts decisions that are made for us or about us.
In a conversation with Knowledge@Wharton, Hosanagar notes that a solution to this complex problem is that we must "engage more actively and more deliberately and be part of the process of influencing how these technologies develop." An edited transcript of the conversation follows.
Knowledge@Wharton: There's a growing buzz about artificial intelligence (AI) and machine learning. In all the conversations that are going on, what are some points that are being overlooked? How does your book seek to fill that gap?

Kartik Hosanagar: Yes, there's a lot of buzz around AI and machine learning, which is a sub-field of AI. The conversation tends to either glorify the technology or, in many instances, create fear mongering around it. I don't think the conversation has focused on the solution, i.e. how are we going to work with AI, especially in the context of making decisions. My book is focused on making decisions through intelligent algorithms.

One of the core questions when it comes to AI is: Are we going to use AI to make decisions? If so, are we going to use it to support [human] decision-making? Are we going to have the AI make decisions autonomously? If so, what can go wrong? What can go well? And how do we manage this? We know AI has a lot of potential, but I think there will be some growing pains on our way there. The growing pains are what I focus on. How can algorithmic decisions go wrong? How do we make sure that we have control over the narrative of how technology impacts the decisions that are made for us or about us?

Knowledge@Wharton: The book begins with some striking examples about chatbots and how they interact with humans. Could you use those illustrations to talk about how human beings interact with algorithms and what are some of the implications?

Hosanagar: I began the book with a description of Microsoft's experience with a chatbot called "Xiaobing." In China, it's called "Xiaobing." Elsewhere in the world, it's called "Xiaoice." This was a chatbot created in the avatar of a teenage girl. It's meant to engage in fun, playful conversations with young adults and teenagers. This chatbot has about 40 million followers in China. Reports say that roughly a quarter of those followers have said, "I love you" to Xiaoice. That's the kind of affection and following Xiaoice has.

Inspired by the success of Xiaoice in China, Microsoft decided to test a similar chatbot in the U.S. They created a chatbot in English, which would engage in fun, playful conversations. It was targeted once again at young adults and teenagers. They launched it on Twitter under the name "Tay." But this chatbot's experience was very different and short-lived. Within an hour of launching, the chatbot turned sexist, racist and fascist. It tweeted very offensively. It said things like: "Hitler was right." Microsoft shut it down within 24 hours. Later that year, MIT's Technology Review rated Microsoft's Tay as the "Worst Technology of the Year."

That incident made me question how two similar chatbots or pieces of AI built by the same company could produce such different results. What does that mean for us in terms of using these systems, these algorithms, for a lot of our decisions in our personal and professional lives?

Knowledge@Wharton: Why did the experiences differ so dramatically? Is there anything that can be done about that?

Hosanagar: One of the insights that I got as I was writing this book, trying to explain the differences in behavior of these two chatbots, was from human psychology. Psychologists describe human behavior in terms of nature and nurture. Our nature is our genetic cord, and nurture is our environment. Psychologists attribute problematic issues like alcoholism, for instance, partly to nature and partly to nurture. I realized algorithms, too, have nature and nurture. Nature, for algorithms, is not a genetic cord, but the code that the engineer actually writes. That's the logic of the algorithm. Nurture is the data from which the algorithm learns.

Increasingly, as we move towards machine learning, we're heading away from a world where engineers used to program the end-to-end logic of an algorithm, where they would actually specify what happens in any given situation." If this happens, you respond this way. If that happens, you respond a different way." Earlier, it used to be all about nature, because the programmer gave very minute specifications telling the algorithm how to work. But as we moved towards machine learning, we're telling algorithms: "Here's data. Learn from it." So nature starts to become less important, and nurture starts to dominate.

If you look at what happened between Tay and Xiaoice, in some ways the difference is in terms of their training data. In the case of Xiaoice, in particular, it was created to mimic how people converse. In the case of Tay, it picked up how people were talking to it, and it reflected that. There were many intentional efforts to trip Tay - that's the nurture aspect. Part of it was nature, as well. The code could have specified certain rules like: "Do not say the following kinds of things," or "Do not get into discussions of these topics," and so on. So it's a bit of both nature and nurture, and I think that's what, in general, rogue algorithmic behavior comes down to.

Knowledge@Wharton: There was a time when algorithmic decision-making seemed to be about "Amazon will suggest what books to read," or "Netflix will recommend which movies you should watch."

But because of AI, algorithmic decision-making has become a lot more complex. Could you give some examples of this? Also, what are some of the implications for the choices that we make or don't make as a result?

"Psychologists describe human behavior in terms of nature and nurture ....When I was looking at algorithms, I realized algorithms, too, have nature and nurture."

Hosanagar: Yes, algorithms pervade our lives. Sometimes we see it - like Amazon's recommendations - and sometimes we don't. But they have a huge impact on decisions we make. On Amazon, for example, more than a third of the choices that we make are influenced by algorithmic recommendations like: "People who bought this also bought this. People who viewed this eventually bought that." On Netflix, they drive more than 80% of the viewing activity. Algorithmic recommendations also influence decisions such as whom we date and marry. In apps like Tinder, algorithms create most of the matches.

Algorithms also drive decisions at the workplace. For example, when you apply for a loan, algorithms increasingly make mortgage approval decisions. If you apply for a job, resume-screening algorithms decide whom to invite for an interview. They make life-and-death decisions, as well. In courtrooms in the U.S., there are algorithms that predict the likelihood that the defendant will re-offend, so that judges can make sentencing decisions. In medicine, we're moving towards personalized medicine. Two people with the same symptoms might not get the same treatment. It might be customized based on their DNA profile. Algorithms guide doctors on those decisions.

We're moving to a point where the algorithms don't merely offer decision support. They can function autonomously, as well. Driverless cars are a great example of that.

Knowledge@Wharton: With algorithms making more and more decisions, is there anything like free will in the world anymore?

Hosanagar: Free will is an interesting concept. For the most part, I used to think of free will in a philosophical sense. Philosophers have argued we don't have free will. But I think we have a literal interpretation of free will now in the context of algorithms, which is: Are you making the final choice?

As I said, a third of your choices on Amazon are driven by recommendations. Eighty percent of viewing activities on Netflix are driven by algorithmic recommendations. Seventy percent of the time people spend on YouTube is driven by algorithmic recommendations. So it doesn't feel like algorithms are merely recommending to us what we want. Think about a search on Google. We might see less than 0.01% of any search results, because rarely do we even cross page one. The algorithm has decided which pages we look at. So yes, they're making a lot of choices for us.

Do we have free will? At some level, yes - we do. But we don't have the level of independent decision-making we think we have. We think we see the recommendations and then we do what we want, but algorithms are actually nudging us in interesting ways. Mostly that's a good thing, because they're saving us time. But sometimes we become passive about how we use algorithms, and that can have consequences.

Knowledge@Wharton: You write in your book that design choices can have unintended consequences. Could you explain that?

Hosanagar: By unintended consequences, I'm referring to situations where you're trying to optimize some aspect of a decision. Perhaps you manage to improve that really well, but then something else goes wrong. For example, when Facebook was manually curating its trending stories through human editors, it was accused of having a left-leaning bias. These editors supposedly were choosing left-leaning stories and curating those more often. So Facebook used an algorithm for this curation and then tested it for political bias. It did not have any political bias, but there was something else it had which they hadn't explicitly tested for, which is fake news. The algorithm curated fake news stories and circulated them. That's an example of unintended consequences. Algorithm design can drive that in many ways.

I've done a lot of work on recommendation systems and how they influence the kinds of products we consume, the kinds of media we consume. I've specifically studied two kinds of recommendation algorithms. One kind is like what Amazon does: "People who bought this also bought this." It's based on social curation. The other kind of algorithm attempts to understand at a deeper level. It tries to find items that are similar to the user's interests. An example of that would be Pandora. Its music recommendations are not [based on social curation]. Pandora has very detailed information - more than 150 musical attributes for each song. For instance, how rhythmic is the song? How much instrumentation is there in the music? And every time you say you like a song or you don't it, they look at the musical qualities of the song, and then they adjust their recommendations based on other songs which have attributes similar to what you have liked or not liked.

"We think we see the recommendations and then we do what we want, but the algorithms are actually nudging us in interesting ways."

I looked at both these designs, and I looked at which design is more helpful in finding, let's say, indie songs or very novel and niche books or movies. At the time we did the study - this was some time back - the conventional wisdom was that all these algorithms help in pushing the long tail, meaning niche, novel items or indie songs that nobody has heard of. What I found was that these designs were very different. The algorithm that looks at what others are consuming has a popularity bias. It's trying to recommend stuff that others are consuming, and so it tends to lean towards popular items. It cannot truly recommend the hidden gems.

But an algorithm like Pandora's doesn't have popularity as a basis for recommendation, so it tends to do better. That's why companies like Spotify and Netflix and many others have changed the design of their algorithms. They've combined the two approaches. They've combined the social appeal of a system that looks at what others are consuming, and the ability of the other design to bring hidden gems to the surface.

Knowledge@Wharton: Let's go back to the point you brought up earlier about algorithms going rogue. Why does that happen and what can be done about it?

Hosanagar: Let me point to a couple of examples of algorithms going rogue, and then we'll talk about why this happens. I mentioned algorithms are used in courtrooms in the U.S., in the criminal justice system. In 2016, there was a report or study done by ProPublica, which is a non-profit organization. They looked at algorithms used in courtrooms and found that these algorithms have a race bias. Specifically, they found that these algorithms were twice as likely to falsely predict future criminality in a black defendant than a white defendant. Late last year, Reuters carried a story about Amazon trying to use algorithms to screen job applications. Amazon gets a million-plus job applications; they hire hundreds of thousands of people. It's hard to do that manually, and so you need algorithms to help automate some of this. But they found that the algorithms tended to have a gender bias. They tended to reject female applicants more often, even when the qualifications were similar. Amazon ran the test and realized this - they are a savvy company, so they decided not to roll this out. But there are probably many other companies that are using algorithms to screen resumes, and they might be prone to race bias, gender bias, and so on.

In terms of why algorithms go rogue, there are a couple of reasons I can share. One is, we have moved away from the old, traditional algorithms where the programmer wrote up the algorithm end-to-end, and we have moved towards machine learning. In this process, we have created algorithms that are more resilient and perform much better but they're prone to biases that exist in the data. For example, you tell a resume-screening algorithm: "Here's data on all those people who applied to our job, and here are the people we actually hired, and here are the people whom we promoted. Now figure out whom to invite for job interviews based on this data." The algorithm will observe that in the past you were rejecting more female applications, or you were not promoting women in the workplace, and it will tend to pick up that behavior.

The other piece is that engineers in general tend to focus narrowly on one or two metrics. With a resume-screening application, you will tend to measure the accuracy of your model, and if it's highly accurate, you'll rule it out. But you don't necessarily look at fairness and bias.

Knowledge@Wharton: What are some of the challenges involved in autonomous algorithms making decisions on our behalf?

Hosanagar: One of the big challenges is there is usually no human in the loop, so we lose control. Many studies show that when we have limited control, we are less likely to trust algorithms. If there is a human in the loop, there's a greater chance that the user can detect certain problems. And the likelihood that problems get detected is therefore greater.

Knowledge@Wharton: You tell a fascinating story in the book about a patient who gets diagnosed with tapanuli fever. Could you share that story with our audience? What implications does it have for how far algorithms can be trusted?

"Companies should formally audit algorithms before they deploy them, especially in socially consequential settings like recruiting."

Hosanagar: The story is that of a patient walking into a doctor's office feeling fine and healthy. The patient and doctor joke around for a while. The doctor eventually picks up the pathology report and suddenly looks very serious. He informs the patient: "I'm sorry to let you know that you have tapanuli fever." The patient hasn't heard of tapanuli fever, so he asks what exactly it is. The doctor says it's a very rare disease, and it's known to be fatal. He suggests that if the patient has a particular tablet, it will reduce the chance that he will have any problems. The doctor says: "Here, you take this tablet three times a day, and then you go about your life."

I asked my readers if they were the patient, would they feel comfortable in that situation? Here's a disease you know nothing about and a solution you know nothing about. The doctor has given you a choice and told you to go ahead, but he has not given you many details. And with that, I posed the question: If an algorithm were to make this recommendation - that you have this rare disease, and we want you to take this medication - without any information, would you?

Tapanuli fever is not a real disease. It's a disease in one of the Sherlock Holmes stories, and even in the original Sherlock Holmes story, it turns out that the person who is supposed to have tapanuli fever doesn't actually have it. But setting that aside, it brings up the question of transparency. Are we willing to trust decisions when we don't have information about why a certain decision was made the way it was?

What I highlight is that sometimes we seek more transparency from algorithms than humans. But in practice, lots of companies are imposing algorithmic decisions on us without any information about why these decisions are being made. Research shows that we're not fine with that. For example, a PhD student at Stanford looked at an algorithm that would compute grades for students and how they did when they just got their score, versus when they got their score with an explanation. As expected, when the students had an explanation, they trusted it more.

Then why is it that in the real world there are a lot of algorithms making decisions for us - or about us - and we have no transparency about those decisions? I advocate that we need a certain level of transparency with regard to say what kinds of data were used to make the decision. For example, if you applied for a loan, and the loan was rejected, we would like to know why that was the case. If you applied for a job, and you were rejected, it would be helpful to know that the algorithm not only evaluated what you submitted as part of your job application, but also looked at your social media posts. Transparency regarding what data was considered, what were the key factors that drove a decision, is important.

Knowledge@Wharton: At the end of the book, you recommend an Algorithmic Bill of Rights. What exactly is that, and why is it necessary?

Hosanagar: The Algorithmic Bill of Rights is a concept that I borrowed from the Bill of Rights in the U.S. Constitution. The history of the Bill of Rights is that when the Founding Fathers were drafting the Constitution, some people were worried that we were creating a powerful government here in the U.S. The Bill of Rights was created as a way to protect citizens.

Today, we are in a situation where there is a lot of talk about powerful tech companies. There's a feeling that consumers need certain protections. The Algorithmic Bill of Rights is targeted at that. A lot of consumers feel that they're helpless against big tech and against algorithms deployed by big tech. I feel that consumers do have some power, and that power is in terms of our knowledge, our votes, and our dollars.

Knowledge implies that we shouldn't be passive users of technology. We should be active and deliberate about it. We should know how it's changing decisions we are making or others are making about us. Look at how Facebook is changing its product design today. That change - support for encryption and so on - is because of a push from users. It shows that when users complain, changes do happen.

Votes are another aspect of that. They involve our being aware of which elected representatives understand the nuances of algorithms and the challenges and how to regulate them. It's about voting for them. The question is: How are these regulators going to protect us?

That's where the Bill of Rights comes in. The Bill of Rights I propose has a few key pillars. One pillar is transparency - transparency with regard to the data used to make decisions and with regard to the underlying decision itself. What were the most important factors that lead to a certain decision? Europe's GDPR [General Data Protection Regulation] has certain provisions, like right to explanations and information on the data that companies are using. I think some of that transparency is needed, and companies should provide that.

Another pillar in my Bill of Rights is the idea of some user control, that we cannot be in an environment where we have no control over the technology. We should, for example, be able to - with a simple instruction - tell Alexa: "You're not listening to any conversation in the house until I instruct you that it's allowed." There's no such provision at present. We are told that the system is not listening, but then we're also hearing from others that there are instances where it listens, even when you're not actually saying "Alexa," and giving it instructions.

This control is very important. If you look at Facebook and the issue of false news, two years ago there was no way for users to alert Facebook's algorithm and say: "This post in my newsfeed is false news." Today, with just two clicks, you can let Facebook know that a certain news post in your feed is either offensive or it is false. That feedback is very important for the algorithm to correct itself.

Lastly, I have been advocating the idea that companies should formally audit algorithms before they deploy them, especially in socially consequential settings like recruiting. The audit process must to be done by a team that is independent of the one that developed the algorithm. The audit process is important because it will help ensure that somebody has looked at things beyond, say, the prediction accuracy of the model. They have looked at things like privacy. They have looked at things like bias and fairness. That will help curb some of these problems with algorithmic decisions.

"[We need to] engage more actively and more deliberately and be part of the process of influencing how these technologies develop."

Knowledge@Wharton: Any final points that you would like to emphasize?

Hosanagar: Even though I talk about many of the challenges with algorithms in my book, I'm not an algorithm skeptic. I'm actually a believer in algorithms. The message I want to share is not "be wary," but "engage more actively and more deliberately and be part of the process of influencing how these technologies develop." The reason I say that is that studies show that algorithms, on average, are less biased than human beings. Furthermore, my contention is that it is easier in the long run to fix algorithmic bias than it is to fix human bias.

The challenge with algorithm bias is in the way it scales. A prejudiced judge can impact the lives of maybe 200 or 300 people, but an algorithm used in all the courtrooms in a country or across the world can influence the lives of hundreds of thousands, or even millions, of people. Similarly, a biased recruiter can affect the lives of hundreds of people, but a biased recruiting algorithm can affect the lives of millions of people. It's the scale that we have to worry about. That's why we need to take the issue seriously.

The key message is that we are going into a world where these algorithms will help us make better decisions. We'll have growing pains along the way. The few examples I mentioned are only the beginning. We'll hear many more. We should engage actively now to minimize those incidences.

# data[].attributes.attachment_link.description
When we buy something on Amazon or watch something on Netflix, we think it's our own choice. Well, it turns out that algorithms influence one-third of our decisions on Amazon and more than 80% on Netflix. What's more, algorithms have their own biases. They can even go rogue.

# data[].attributes.headline
Group Input Topic 5: What are the differences between online- and face-to-face-teamwork?

# data[].attributes.subject
Group Input Topic 5: What are the differences between online- and face-to-face-teamwork?

# data[].attributes.headline
Topic 7 Group Input Communicating with Nature

# data[].attributes.subject
Topic 7 Group Input Communicating with Nature

# data[].attributes.body
How do you interpret what the author calls the "default set of worship" in this text?

What philosophical assumptions are made by this text concering "reality" and our perception of it?

What does this text tell you about communication?

# data[].attributes.headline
Intercultural Challenges in Communication: How to talk to other people with a different background?

# data[].attributes.subject
Intercultural Challenges in Communication: How to talk to other people with a different background?

# data[].attributes.attachment_link.title
The rise of populism: from Le Pen to Trump with Cas Mudde

# data[].attributes.attachment_link.title
Charles Taylor über die Krise der Demokratie | Im Gespräch | Sternstunde Philosophie | SRF Kultur

# data[].attributes.attachment_link.title
Talk like Trump - understanding populist speech and how to overcome it| Christopher Kabakis |TEDxWHU

# data[].attributes.attachment_link.title
The possibility of political pleasure: David Graeber at TEDxWhitechapel

# data[].attributes.headline
Reece Peck: The History of Fox Populism, 2019

# data[].attributes.subject
Reece Peck: The History of Fox Populism, 2019

# data[].attributes.body
This presentation offers insights into why the Fox News Channel has been both commercially successful and politically effective. Where existing explanations of Fox’s appeal have stressed the network’s conservative editorial slant, Professor Reece Peck sheds light on the importance of its style as a generative mode of ideology.

# data[].attributes.attachment_link.title
The History of Fox Populism: How Rupert Murdoch, Roger Ailes, & Bill O’Reilly Remade Television News

# data[].attributes.attachment_link.title
Yuval Noah Harari in conversation with Audrey Tang

# data[].attributes.body
The social dilemma. Dokudrama, Neflix, 2020
The grand hack of Cambridge Analytics, Neflix, 2020

# data[].attributes.attachment_link.title
Netflix’s The Social Dilemma: Filmmaker and Tech Experts in Conversation with Katie Couric

# data[].attributes.attachment_link.title
The Great Hack's David Carroll finally sees his Cambridge Analytica data

# data[].attributes.body
Jim VandeHeit, founder of "Axios" and "Politico" discusses our media landscape.

# data[].attributes.attachment_link.title
Jim VandeHei: The Transformation of our Media Landscape and Its Political Implications

# data[].attributes.body
Identity Theories

Agency

Biophysiological Theories

Cognitive and Information Processing Theories

Communication Competence

# data[].attributes.body
Bruno Latour: Where to land after the pandemic?

What are the activities suspended in the crisis you would like to see not resumed or minimized in the future?

Which of the suspended activities and behavioral rules would you like to resume and develop or even create from scratch?

# data[].attributes.attachment_link.readable_text
This is an exercice extracted from a paper in French ( translated in English by Stephen Muecke) in AOC-Media
A little exercise to make sure things don't restart after the lock out just as they were before*
If you wish to share your auto description: here is a platform:
Proposed by @BrunoLatourAIME following arguments proposed in Down to Earth Politics in the New Climatic Regime (Polity, 2018).
Let us take advantage of the forced suspension of most activities to take stock of those we would like to see discontinued and those, on the contrary, that we would like to see developed.
I suggest that readers try to answer this short questionnaire for themselves. It will be especially useful as it will be based on a personal experience that has been directly lived. This exercise is not a question of expressing an opinion but of describing your situation and may be investigating. It is only later, if one were to give oneself the means of compiling the answers of many respondents and then composing the landscape created by their intersections, that one could find a form of political expression - but this time embodied and situated in a concrete world.
Answer the following questions first individually and then if possible with others:
Question 1: What are the activities now suspended that you would like to see not resumed?
Question 2: Describe why you think this activity is harmful/ superfluous/ dangerous/inconsistent and how its disappearance/suspension/substitution would make the activities you favor easier/ more consistent. (Make a separate paragraph for each of the activities listed in question 1).
Question 3: What measures do you recommend to ensure that the workers/employees/agents/entrepreneurs who will no longer be able to continue in the activities you are removing are helped in their transition toward other activities.
Question 4: Which of the now suspended activities would you like to develop/resume or even create from scratch?
Question 5: Describe why this activity seems positive to you and how it makes it easier/ more harmonious/ consistent with other activities that you favor and helps to combat those that you consider unfavorable. (Make a separate paragraph for each of the activities listed in question 4).
Question 6: What measures do you recommend to help workers/ employees/ agents/ entrepreneurs acquire the capacities/ means/ income/ instruments to take over/ develop/ create this favored activity.
Now, find a way to compare your descriptions with those of other participants. Compiling and then superimposing the answers should gradually produce a landscape made of lines of conflict, alliances, controversy and opposition. This terrain may provide a concrete opportunity for creating the forms of political expression these activities require.

# data[].attributes.attachment_link.title
A little exercise to make sure that, after the virus crisis, things don't start again as they were before

# data[].attributes.attachment_link.description
This is an exercice extracted from a paper in French ( translated in English by Stephen Muecke) in AOC-Media A little exercise to make sure things don't restart after the lock out just as they were before* If you wish to share your auto description: here is a platform: Proposed by @BrunoLatourAIME following arguments proposed in Down to Earth Politics in the New Climatic Regime (Polity, 2018).

# data[].attributes.attachment_link.title
Is feminism incompatible with free markets?

# data[].attributes.attachment_link.title
Donal Carbaugh 3 Dimensions of reflective dialogue

# data[].attributes.attachment_link.readable_text
Abstract

We investigate the relationship between personal and professional familiarly, team effectiveness, and viability, and how these relationships are mediated by information elaboration in global virtual teams. We further assess whether virtuality moderates the relationships between both types of familiarity and information elaboration. Based on data collected from 63 global virtual supply chain teams, our results suggest that professional familiarity is positively associated with team information elaboration, which in turn relates positively to both manager-rated team effectiveness and team leader-rated viability. Furthermore, team virtuality enhances the influence of personal familiarity on information elaboration, but dampens the relationship between professional familiarity and information elaboration. Our results suggest that professional familiarity is a more salient antecedent of information elaboration in global virtual teams. We discuss the implications of our results for both theory and practice.

The globalization of work along with an increased reliance on information and communication technologies (ICT) has resulted in many organizations turning to global virtual teams (GVTs) to help solve complex problems ( Zakaria, Amelinckx, & Wilemon, 2004). An advantage of GVTs is that they allow organizations to assemble teams of the most qualified individuals without being constrained by members having to work together in a physical location or even within the same time zone (e.g., Bell & Kozlowski, 2002). While there are a multitude of factors that shape GVT success, we examine three primary areas-team member familiarity, information elaboration, and virtuality.

Our focus on team familiarity is based on calls to better understand the role of team member relationships in distributed settings (e.g., Hinds & Cramton, 2013). In face-to-face (FtF) teams, member familiarity has been proposed and found to be a salient factor shaping team processes and performance (e.g., Dalal, Nolan, & Gannon, 2017; Huckman, Staats, & Upton, 2009; Kurmann et al., 2014). Team members who possess interpersonal knowledge of one another are more likely to trust each other, know what to expect from one another, coordinate actions, and communicate more clearly and effectively (e.g., Espinosa, Slaughter, Kraut, & Herbsleb, 2007; Granovetter, 1985; McEvily, Perrone, & Zaheer, 2003). However, within the GVT literature, familiarity has not been considered extensively, in part, because it is often assumed that people are placed on a team based on what they can contribute to the task. Yet, this does not rule out that GVT members may have prior knowledge about one another and this familiarity may shape the team's ability to exchange and integrate information that is communicated among members.

Early work on familiarity proposed that the construct is best considered as multidimensional consisting of professional or work-related familiarity, as well as familiarity with others as individuals or familiarity at a more personal level ( Espinosa et al., 2007; Hanft, 2002). In teasing apart professional and personal familiarity, we argue that the extent to which team members possess knowledge about one another's strengths and weaknesses as they pertain to their work (i.e., professional familiarity) is qualitatively different from knowing something about a team member personally (e.g., Do they have children? What are their hobbies?). Furthermore, we contend that these differences are particularly salient when teams work in virtual contexts.

Communication and information sharing has been shown to be a key driver of team performance (e.g., Mesmer-Magnus & DeChurch, 2009) as well as playing a salient role in the success of GVTs (e.g., Rafaeli & Ravid, 2003). However, prior research also suggests that teams often fail to fully utilize all the information that resides within the team (e.g., Mesmer-Magnus & DeChurch, 2009; Stasser & Stewart, 1992) because team members tend to only share and discuss that which is common and neglect unique information (e.g., Gigone & Hastie, 1993; Stasser & Stewart, 1992; van Knippenberg, De Dreu, & Homan, 2004). Van Knippenberg and colleagues (2004) introduced the concept of information elaboration and emphasized the importance of not only sharing but also processing and integrating information within teams. Information elaboration has been shown to be particularly salient in complex and dynamic situations (e.g., Homan et al., 2008; Homan, van Knippenberg, Van Kleef, & De Dreu, 2007) which often applies to the work of GVTs.

In the current study, we examine the effects of professional and personal familiarity on information elaboration and, in turn, the resulting effect that information elaboration has on team effectiveness and viability. For teams to be successful, members need to exchange diverse information and integrate this information into their decision making ( Hertel, Geister, & Konradt, 2005). The relationships that exist between team members are apt to influence their ability to integrate unique and relevant information ( Mesmer-Magnus & DeChurch, 2009), and team communication research finds that familiarity has a positive effect on communication and performance (e.g., Marlow, Lacerenza, Paoletti, Burke, & Salas, 2018). Accordingly, we argue that when GVT members are more familiar with one another, they will be more likely to share, elaborate upon, and integrate diverse information that will lead to improved outcomes.

Recently it was noted that electronic communication is the predominate means of communication for most teams ( Krumm, Terwiel, & Hertel, 2013). As a result, research has begun to adopt the view that all teams are to some extent virtual (i.e., Gibson & Cohen, 2003; Griffith, Sawyer, & Neale, 2003) and has moved to greater consideration of the concept of team virtuality ( Kirkman & Mathieu, 2005). Accordingly, the final contribution of this research is to examine the effects of virtuality on the both the professional and personal familiarity-information elaboration relationships. Martins, Gilson, and Maynard (2004) suggested that team virtuality may be best viewed as a "double-edged sword" because in some situations virtuality can be detrimental, while at other times, it can be advantageous to team functioning (e.g., Barnwell, Nedrick, Rudolph, Sesay, & Wellen, 2014; Germain & McGuire, 2014). In our hypothesized model, we echo this sentiment and suggest that virtuality will dampen the relationship between professional familiarity and a team's ability to share, process, and integrate information (i.e., information elaboration). Conversely, we argue that team virtuality will enhance the relationship between information elaboration and team personal familiarity. Accordingly, our view of virtuality is in line with Johns's (2006) contention that context can be both an opportunity as well as a constraint on other organizational behavior constructs. In the sections below, we propose specific hypotheses and test our model using data from 63 GVTs.

Literature Review and Hypotheses

Information sharing has been shown to be a key driver of team performance (e.g., Mesmer-Magnus & DeChurch, 2009) and to play a role in the success of GVTs (e.g., Rafaeli & Ravid, 2003). However, developing a shared understanding can be challenging in a virtual context where there are fewer non-verbal cues, members may experience communication delays, and messages are often perceived as less clear (e.g., Cramton, 2001; Hinds & Weisband, 2003; Nurmi, 2011). In addition, van Knippenberg and colleagues (2004) describe information sharing as only one part of effective communication stating that for information to be effectively used in decision making, it needs to be constructively discussed and integrated ( Hoever, van Knippenberg, van Ginkel, & Barkema, 2012). As such, information elaboration captures the full process of exchanging, integrating, and building upon information within the team. An important component of information elaboration is that team members must incorporate their own knowledge with the information that is being shared by others. We propose that in GVTs, information elaboration will be influenced by the relationships that exist between team members. In particular, we propose that team member familiarity will positively affect information elaboration and thereby influence subsequent team effectiveness and viability.

Information elaboration requires team members to share, freely exchange, and integrate information which is an inherently social phenomenon. Thus, in formation sharing is more likely to occur when team members have relatively higher levels of comfort-that is, psychological safety-with one another ( Edmondson, 1999). When members are more familiar with one another, they are more likely to suggest and use the ideas of others based on their levels of interpersonal comfort, which reduces fear of ridicule or exclusion. In support of this, research finds team member familiarity has a positive effect on communication ( Marlow et al., 2018) and productivity (e.g., Goodman & Leyden, 1991), enhances decision making quality ( Jehn & Shah, 1997), reduces accident levels ( Goodman & Garber, 1988), and improves individual and team performance ( Dalal et al., 2017). Conversely, the amount of information disclosure within a team can be hampered by inexperience with teammates and the lack of a shared history ( Gibson & Cohen, 2003). In addition, team member familiarity has been associated with an increased willingness to listen, which can affect how information is both exchanged and perceived (e.g., Vitharana & Ramamurthy, 2003).

Seminal work on familiarity by Hanft (2002) argued that the construct is best considered as multidimensional. For instance, Espinosa and colleagues (2007) adopted a multidimensional approach and examined the effects of team (extent of working with team members on past projects) and task familiarity (extent of prior history working on this task) on performance within geographically dispersed software development teams. Although Espinosa and colleagues' (2007) work creates the precedence for considering different forms of familiarity, we contend that both task and team are actually different forms of professional familiarity.

While the concepts of professional and personal familiarity were introduced in the literature almost two decades ago, there has been very little research that has investigated their unique effects. Instead, most research considers only the overarching level of familiarity possessed by team members (e.g., Smith-Jentsch, Kraiger, Cannon-Bowers, & Salas, 2009). The only study that we could find that discussed different types of familiarity within an organizational setting was conducted by Killumets, D'Innocenzo, Maynard, and Mathieu (2015). However, the empirical part of that work only measured professional familiarity and found that it was positively related to team performance as mediated by team processes. Accordingly, we build upon the work of Espinosa and colleagues (2007), Hanft (2002), and Killumets and colleagues (2015) by considering both professional and personal familiarity.

We propose that when team members are familiar with the work and expertise of others (i.e., professional familiarity), they should be more likely to listen to, and consider their input, even when it is substantively different from their own. In part, this is because members are more likely to be accepting of ideas and suggestions, consider them more deeply, and incorporate them into the team's task when they know the person proposing them is speaking from their area of expertise. Familiarity in the form of prior work experience has been found to predict individual and overall performance because of increased coordination ( Dalal et al., 2017). Similarly, research that has examined the link between shared team experience and transactive memory systems (TMS) finds that knowing the expertise of one's teammates is enhanced to the extent individuals have worked together previously (e.g., Akgun, Byrne, & Keskin, 2005; He, Butler, & King, 2007). Similar to TMS, professional familiarity is valuable in large part because it helps team members know who knows what on the team, and therefore, whose expertise can be called upon when needed to complete the task. In contrast, information shared by someone with whom one is not professionally familiar may not be fully considered given the lack of connection with that information provider (e.g., Granovetter, 1973).

While prior working experience certainly can lead to professional familiarity, work experience and familiarity are not synonymous. For instance, even though two individuals may have worked together on prior projects, this does not guarantee they know any more about one another, other than their respective areas of expertise and how the work was completed (e.g., Schippers, Den Hartog, Koopman, & Wienk, 2003; Tjosvold, Tang, & West, 2004). Research indicates online communications differ between dyads that are friends versus those who are strangers and that individuals who are personally familiar with one another are less likely to take offense if a particular communication comes across as curt or antagonistic (e.g., Riordan, Markman, & Stewart, 2013). Personal familiarity can mean that team members know what is being said before any words are uttered, and can assist with team coordination ( O'Neill & Rothbard, 2017). In addition, friendship between team members can increase cooperation and coordination ( Jehn & Shah, 1997). Taken together, within GVTs, members should be better able to interpret and integrate what is being said, or not said, when they personally know the person conveying the information. Therefore, we hypothesize that both professional and personal familiarity will be advantageous for information elaboration in GVTs.

Hypothesis 1 (H1): Team member professional familiarity will be positively associated with information elaboration.

Hypothesis 2 (H2): Team member personal familiarity will be positively associated with information elaboration.

While the hypotheses above suggest that both forms of familiarity will have positive effects on information elaboration in GVTs, we further envision that they will not have the same level of impact. Given the limited amount of work that has examined professional and personal familiarity within a single study (see Fuchs, Fuchs, Dailey, & Power, 1985, for an exception), it remains an unanswered question as to which form of familiarity will have a greater impact on information elaboration. Our supposition is given that virtual teams (VTs) on the whole tend to be more task focused ( Martins et al., 2004), GVTs are comprised of individuals who might have different norms with regard to how much personal information is acceptable to be share ( Chen, 1995), and GVTs often work together for short periods of time on a particular task or project ( Zakaria et al., 2004), members will be more likely to have professional rather than personal familiarity. Furthermore, because information elaboration focuses on sharing and expanding upon information (e.g., Homan et al., 2007), being able to leverage member expertise is critical. In support of this, early work on TMS found that members of long-tenured groups (i.e., more professional familiarity) relied more on each other to acquire, process, and communicate distinct information ( Lewis, 2003; Wegner, 1987). Therefore, we posit that for GVTs, professional rather than personal familiarity will have a more pronounced impact on information elaboration. Accordingly, we posit the following:

Electronic communication is now considered the predominate means of communication for most teams ( Krumm et al., 2013). As a result, many have adopted the view that all teams are, to some extent, virtual (i.e., Gibson & Cohen, 2003; Griffith et al., 2003). Accordingly, the concept of team virtuality ( Kirkman & Mathieu, 2005) has been widely adopted in the extant literature ( Gilson, Maynard, Jones Young, Vartiainen, & Hakonen, 2015). However, it has also long been argued that virtuality is a "double-edged sword" ( Martins et al., 2004) and that while it can beneficial in some instances, it can also be detrimental (e.g., Barnwell et al., 2014; Germain & McGuire, 2014). In keeping with such sentiments, we propose that virtuality will dampen the relationship between professional familiarity and information elaboration while enhancing the relationship between information elaboration and team personal familiarity.

In teams that are less virtual, meaning they either frequently meet FtF or rely on richer technologies ( Daft & Lengel, 1984), members who are professionally familiar with one another should find it relatively easy to share and integrate information. This line of thinking is in keeping with the logic introduced for H1 stating that when team members are professionally familiar, they will be more apt to accept and integrate ideas from others, because they have an understanding of the expertise and knowledge the other members bring to the task, thus enhancing team information elaboration. Accordingly, for low virtuality teams, the relationship between professional familiarity and information elaboration will be relatively strong. However, as virtuality increases, we contend that the relationship between professional familiarity and information elaboration will be dampened (i.e., become weaker).

There are numerous studies that have highlighted the challenges inherent in communicating through ICT (e.g., Pauleen & Yoong, 2001; Shachaf, 2008). Namely, there appears to be agreement in the literature that virtual communication lowers the capacity to transfer nonverbal cues (e.g., Cramton, 2001; Walther, Loh, & Granka, 2005). Likewise, technology-enabled communication can inhibit a team's ability to share and integrate members' diverse knowledge and information ( Cramton, 2001). In support of this, Gibson and Gibbs (2006) note that virtuality can have an adverse impact on the quality of team communication and information exchange.

In the face of the challenges that emerge as team virtuality increases, professional familiarity may not provide a sufficient amount of interpersonal strength (e.g., Granovetter, 1973) to enable team members to adequately share and integrate divergent information. Accordingly, although the effect of professional familiarity on information elaboration is likely positive for teams across the entire virtuality continuum, as virtuality increases, the value of professional familiarity on subsequent information elaboration is minimized. Stated formally, we propose the following:

Hypothesis 4 (H4): Team virtuality will moderate the relationship between professional familiarity and information elaboration, such that as virtuality increases, the relationship between professional familiarity and information elaboration will be dampened.

Conversely, we propose that as virtuality increases, the relationship between personal familiarity and information elaboration will be enhanced. Another way of looking at this hypothesized moderation is that as teams rely more on technology to communicate, it becomes more important that members possess personal familiarity to overcome the challenges inherent in communicating primarily via ICT. This thinking is in keeping with prior research that has emphasized the importance of FtF interactions early in the life cycle of a VT (e.g., Kirkman, Rosen, Tesluk, & Gibson, 2004) or that in FtF teams, jovial relationships among members increase team coordination ( O'Neill & Rothbard, 2017). In effect, a theorized benefit of these early FtF interactions is that they provide team members with the opportunity to develop a level of personal familiarity that will be helpful later in a project when they are no longer able to see each other, hear tone of voice, or pick up on nonverbal cues.

In our model, we propose that as virtuality increases, personal familiarity will pay greater dividends in terms of information elaboration. As such, while personal familiarity should be valuable in all team contexts, we believe that its greatest value will be reaped in more virtual contexts. In contrast, in teams that are less reliant on technology (i.e., lower levels of virtuality), the effects of personal familiarity may be dampened. In fact, there could be an argument for high levels of personal familiarity being a disadvantage for the team in terms of information elaboration. For instance, if personal familiarity is high and members are very comfortable with one another, their interactions may become too social (e.g., Tse & Dasborough, 2008), too automatic, or too reliant on perceived cues (e.g., Cheshin, Rafaeli, & Bos, 2011) rather than what is actually said. Work by Souder (1988) on new product development teams suggests that excessive social interaction can impede the effective integration of knowledge. Research also finds that friendships among coworkers can result in subgroup formation and silos that ultimately reduces overall group communication (e.g., Argote, Ingram, Levine, & Moreland, 2000; Carton & Cummings, 2012).

Therefore, in situations where team members interact more via FtF or through richer technologies, and members are personally familiar within one another, their communication may be more social in nature or may focus more on shared personal information rather than integrating new task relevant information. Similarly, research notes that friendship may result in detrimental effects for group communication as a whole ( Pillemer & Rothbard, 2018) and we contend this effect is more likely within less virtual teams. In contrast, as virtuality increases, personal familiarity may become more salient for information elaboration. Taken together, we suggest that team virtuality will enhance the effect of personal familiarity on information elaboration and virtuality may reduce process losses ( Steiner, 1972) that can accrue from personal familiarity. As such, we hypothesize the following:

Hypothesis 5 (H5): Team virtuality will moderate the relationship between team personal familiarity and information elaboration, such that as virtuality increases, the relationship between personal familiarity and information elaboration will be enhanced.

While our first five hypotheses focus on the primary contribution our research makes to the GVT literature by examining the relationships between professional and personal familiarity, information elaboration, and virtuality, we next turn our attention to two critical outcomes-team effectiveness and viability. Information elaboration has consistently demonstrated a positive relationship with team decision quality ( van Ginkel, Tindale, & van Knippenberg, 2009), overall team performance ( Homan et al., 2008; Kearney & Gebert, 2009), and a reduction in negative outcomes associated with member faultlines (e.g., Chiu & Staples, 2013).

While there is much to be learned from this prior research, most of it has sampled students performing classroom assignments or has been conducted in laboratory settings. Consequently, we believe that the impact of information elaboration may be more pronounced in organizational settings and in GVTs in particular. Prior research has evidenced the magnified importance of information elaboration in complex and turbulent situations (e.g., Homan et al., 2008; Homan et al., 2007). Given that GVTs typically work on complex tasks, use multiple different types of technologies to communicate, and need to leverage the diverse information that is possessed by their members, we hypothesize that in GVTs, information elaboration will be positively associated with team effectiveness.

Although effectiveness is a central outcome variable examined in much of the organizational team literature, not all outcomes are task-driven (e.g., McGrath, Arrow, Gruenfeld, Hollingshead, & O'Connor, 1993). In fact, Hackman (1987) advocated that team effectiveness measures should include assessments of current as well as future team functioning which may be tied to affective reactions. Mathieu and Gilson (2012) noted specifically that attention needs to be given to members' affective reactions. However, while there are several calls to consider both task-related and affective outcomes, research examining team information elaboration has primarily considered task-related outcomes such as the quality of decisions or overall team performance (e.g., Homan et al., 2008; Kearney & Gebert, 2009).

Team viability captures "a team's potential to retain its members through their attachment to the team, and their willingness to stay together as a team" ( Balkundi & Harrison, 2006, p. 51). As such, viability includes both satisfaction with fellow teammates as well as the more behavioral component of their desire to remain in the team ( Barrick, Stewart, Neubert, & Mount, 1998). Work by Zellmer-Bruhn, Maloney, Bhappu, and Salvador (2008) argues that when teams share information, members perceive their work styles to be more similar. Taking this a step further would suggest that teams that integrate information should have a greater desire to continue working together. In part, this may be attributable to members feeling that they are being heard within teams that practice team information elaboration in keeping with the interactional justice literature (e.g., Cropanzano, Prehar, & Chen, 2002). Therefore, we suggest that team viability will be higher when member information is leveraged and integrated into the team's task (i.e., higher levels of team information elaboration).

Method

We tested our hypothesized model using a sample of GVTs from an international information technology company. These teams are responsible for the supply chain for all software, hardware (e.g., servers, mainframe computers, and printers), and retail store solutions (RSS) produced by the company. In particular, teams are responsible for product introduction as well as the manufacturing and distribution of software and RSS products-primarily self-checkout equipment or cash registers. Software is delivered both physically and electronically, and the team has the lead role in electronic delivery strategy and initiatives. The teams worked closely with all software development labs and with the RSS personnel, and were highly involved in the company's supply chain business transformation strategy and initiatives.

Individual team members ( N = 363) from 68 teams were asked to complete online surveys on three occasions staggered about a month apart. As detailed in Figure 1, the Time 1 survey included the team familiarity and virtuality constructs and was completed by 220 team members for a 61% response rate. The Time 2 survey included the information elaboration items and was completed by 200 members (56% response rate). Finally, at Time 3, we collected team leads' ratings of team viability and external managers' assessment of team's effectiveness. Collecting viability measures from team leads was appropriate given that they are responsible for the day-to-day operations of the team and therefore have greater interaction with all team members which allows them to have a better feel for the team's desire to continue to remain as a team (i.e., viability). Similarly, given that the team manager is responsible for the team's overall performance and holds responsibility over the team's results, expenses, and human resource (HR) functions, they are better equipped to provide an overall assessment of the team's effectiveness. We retained 63 teams for which we received two or more responses on both occasions so that we could calculate agreement and intraclass correlation (ICC) statistics (see Wolfson, Tannenbaum, Mathieu & Maynard, 2018).

The 63 teams represented 354 individuals who were 54% male, the average age was 42.4 years ( SD = 10.6). Members had worked with the participating company for an average of 15.6 years ( SD = 10.7) and in their current position for an average of 4.6 years ( SD = 4.1). The average team size was 5.6 members ( SD = 7.1), and teams had been together for an average of 2.7 years ( SD = 0.90). In addition, as this is an international company, team members were drawn from 23 different locations from 10 different countries including China, Ireland, Japan, Mexico, Taiwan, and the United States.

All multi-item measures used 7-point Likert-type response scales with higher values representing greater levels of each variable. Although measures were collected from individual team members, our model and hypotheses are at the team-level of analysis. We calculated two types of ICCs. ICC(1) indexes the reliability of individual ratings, whereas ICC(2) represents the reliability of the group average rating. ICCs inform us as the relative within-team versus between-team variance in respondents' ratings, but are not equivalent to, and should not be confused with, measures of internal consistency ( LeBreton & Senter, 2008). Indeed, when it comes to justifying the aggregation of individuals' responses, James (1982) submitted,

Accordingly, we calculated and reported team members' interrater agreement using a rwg index developed by James, Demaree, and Wolf (1984). Median multi-item rwg values ≥.70 are generally considered sufficient agreement to warrant aggregation. Accordingly, we also report aggregate scale internal consistencies for the multi-item measures. This strategy aligns the measurement reliability information with the level of analysis of our structural model ( Chen, Mathieu, & Bliese, 2004). For multi-item measures, we averaged the corresponding items per construct to form scale scores.

We first specified the relationships between the latent variables and their corresponding observed scores (i.e., lambdas) as equal to the square root of their respective reliabilities. Similarly, the measurement error (i.e., thetas) were set equal to (1 − rxx) times the variance of the observed scores. We set the reliability of the composite team virtuality measurement to a relatively conservative (i.e., high) value of .90, following the recommendations of Anderson and Gerbing (1988). We next fit a series of nested structural equation models using MPlus 7.2 ( Muthén & Muthén, 2014) to test our hypotheses. The three covariates were included as predictors of all three endogenous variables and allowed to freely covary with exogenous variables. First, we fit a linear effects structural model to the data, which amounts to a test of the hypothesized model depicted in Figure 1, excluding the two interactions involving team virtuality.

The hypothesized model, in effect, implies that team information elaboration fully mediates the influences of the two forms of familiarity and team virtuality on the outcomes. Therefore, we also tested alternative partial-mediation models which included direct effects of the exogenous variables on the outcomes (see Mathieu & Taylor, 2006). Specifically, in one model, we added direct effects from both familiarities and virtuality to team effectiveness, whereas in another, we added directs effects to team viability. In both cases, we supplemented the analyses by considering each potential direct effect individually. This analytic approach provides for a detailed investigation of not only the hypothesized model but also viable alternative models and has been advocated by several authors (e.g., Anderson & Gerbing, 1988; Mathieu & Taylor, 2006). Finally, we introduced the two interaction terms to test their incremental validity on the hypothesized model. MPlus employs a maximum likelihood estimation approach for testing interactions (cf. Klein & Moosbrugger, 2000).

Results

Table 1 contains correlations and descriptive statistics for all study variables. Of note, although professional and personal familiarity were significantly correlated ( r = .55, p < .001), neither exhibited significant zero order correlations with team effectiveness, and only professional familiarity correlated significantly with viability ( r = .27, p < .05).

Study Variable Correlations ( N = 63 Teams).

The linear effects structural model exhibited acceptable fit indices, χ ²(7) = 9.39, ns; CFI = .966, SRMR = .048, and the parameter estimates are shown in Figure 2. Notably, as for the covariates, neither group size (βs = −.10, .07, −.05) nor geographical diversity (βs = .19, −.16, −.09) exhibited any significant linear effects with information elaboration, viability, or team effectiveness, respectively. Average member team tenure evidenced a significant linear effect with information elaboration (β = −.26, p < .05), but not with viability or effectiveness (βs = .09, −.14), respectively. Consistent with H1, professional familiarity related positively and significantly to information elaboration (β = .78, p < .001), although inconsistent with H2, personal familiarity (β = −.19, ns) did not. Using a model equality constraint, we determined that the path from professional familiarity to information elaboration was significantly, χ ²(1) = 27.50, p < .001, greater than the one from personal familiarity supporting H3. In addition, consistent with H6 and H7, information elaboration related significantly to both team effectiveness (β = .36, p < .01) and viability (β = .41, p < .01). The linear relationship between team virtuality and information elaboration was significant and positive (β = .26, p < .05).

Notably, the model shown in Figure 1 implicitly suggests that information elaboration will mediate effects of both forms of familiarity and virtuality on the two outcomes. Accordingly, we performed an indirect effects analysis in MPlus and found significant relationships between professional familiarity and team viability (indirect = .28, p < .05) and with team effectiveness (indirect = .32, p < .01) via informational elaboration. Neither personal familiarity (indirects = −.07 and .08, ns) or virtuality (indirects = .09 and .10, ns) evidenced any significant indirect effects. Collectively, the linear relationships of the hypothesized model accounted for 48%, 16%, and 23% of variance in team information elaboration, effectiveness, and viability, respectively.

While the above results revealed support for three of the four linear hypotheses, we conducted additional analyses to check for the potential of significant omitted direct effects. Following the procedures outlined by Mathieu and Taylor (2006), we next fit alternative nested models that are summarized in Table 2. First, we added direct effects from the two familiarities and virtuality to team effectiveness, both collectively (Model 2) and individually (Models 2A-2C). Second, we added direct effects from the three antecedents to team viability, both collectively (Model 3) and individually (Models 3A-3C).

Summary of Alternative Structural Equation Models ( N = 63 Teams).

Model 2, containing the combined direct effects on team effectiveness did not evidence a significant improvement as compared with the fully mediated model, Δχ ²(3) = 6.57, ns. Testing the individual directs effects, however, did reveal a significant negative direct path from professional familiarity-Model 2B, Δχ ²(1) = 4.19, β = −.43, p < .05-and a significant positive direct path from virtuality-Model 2C, Δχ ²(1) = 3.98, β = .28, p < .05. In other words, adding either direct effect would significantly improve the model, but neither addition would change the significance of the other model parameters. If added together, neither path would exhibit a significant effect. Model 3, containing the combined direct paths on team viability did not differ significantly from the full mediated model, Δχ ²(3) = 2.37, ns, nor did any of the follow-up individual parameter models. In summary, these findings are supportive of the linear effects of the hypothesized model and suggest only one potential path, from professional familiarity or virtuality to effectiveness, would add to the overall model fit.

Although the linear effects model evidenced acceptable fit indices, that does not preclude the presence of moderated relations. Therefore, we introduced the two hypothesized interactions to the model which produced a significant model improvement, Δχ ²(2) = 7.86; p < .05, and accounted for 18% additional variance of information elaboration (see Figure 2). Notably, the interaction between virtuality and professional familiarity, as related to information elaboration, was significant (β = −.45, p < .01), as was the interaction between virtuality and personal familiarity (β = .25, p = .05). We plotted the interactions by applying traditional formulas for depicting lines for relationships at relatively "high" (+1 SD), "moderate" (mean), and "low" (−1 SD) levels of team virtuality ( Cohen, Cohen, West, & Aiken, 2003). As shown in Figure 3, the relationship between professional familiarity and information elaboration is positive throughout the range of virtuality, yet the slope dampened as the degree of virtuality increases. These results are consistent with H4. These findings also reveal that virtuality significantly moderates the relationship between professional familiarity and information elaboration, and thereby its indirect effects on both outcomes.

Figure 4 presents a plot of the personal familiarity by virtuality interaction. As shown, team virtuality exhibited a positive moderation effect on the relationship between personal familiarity and information elaboration. That said, personal familiarity did not exhibit a significant relationship with information elaboration at relatively high degrees of virtuality. In addition, contrary to our expectations, personal familiarity evidenced an increasingly negative relationship with information elaboration to the extent that virtuality became relatively lower. Thus, it appears that when team members possess relatively little personal knowledge of one another, it is best (in terms of information elaboration) that they interact through less virtual means. Again, this would be in line with those who have advocated that VTs try to have FtF interactions early in the team's existence (e.g., Kirkman et al., 2004) as it may help to overcome the lack of personal connections of team members and thereby assist in information elaboration. In contrast, when personal familiarity was relatively high, team information elaboration was highest when virtuality also was relatively high. While perhaps counter-intuitive, this result is in line with our supposition that if a team has high levels of personal familiarity, this may inhibit information elaboration in less virtual interactions because communication may become too social. Accordingly, these findings are overall consistent with H5. Supplemental analyses revealed that, at low levels of virtuality, personal familiarity evidenced significant negative indirect effects with both outcomes, as mediated by information elaboration.

In sum, the positive linear effect of professional familiarity on information elaboration was supported (H1) and significantly greater (H3) than a nonsignificant linear effect associated with personal familiarity (H2). Any interpretation of the linear effects, however, must be tempered by the fact that both were significantly moderated by virtuality. Specifically, virtuality negatively moderated the professional familiarity → information elaboration relationship (H4), while it simultaneously positively moderated the personal familiarity → information elaboration relationship (H5). Finally, the hypothesized positive linear effects of information elaboration on both team effectiveness (H6) and viability (H7) were supported.

Discussion

Organizations, practitioners, and scholars have to some extent, a love/hate relationship with GVTs. Organizations find that GVTs make sense as they allow the best individuals to be placed on a team regardless of physical location. In addition, GVTs can work on a task around the clock, and the necessary investment in ICT continues to become less expensive. Yet, time and time again, we read that the results do not live up to expectations (e.g., Gilson et al., 2015; Martins et al., 2004). Team members report being frustrated, isolated, and having their unique contribution to the team ignored or not integrated into team decision making. While research on GVTs has made progress over the last decade ( Gilson et al., 2015), there remain unanswered questions regarding how best to leverage their potential. As such, within the current study, we examine the role of professional and personal familiarity, information elaboration, and virtuality on team effectiveness and viability.

Our study extends prior work that has sought to better understand member relationships in GVTs. For instance, O'Leary, Wilson, and Metiu (2014) examined the impact of perceived proximity on relationship quality. We build upon this work within the context of GVTs, by investigating the relationship of GVT members, and find that the type of familiarity matters. Specifically, while personal familiarity does not appear to influence information elaboration, professional familiarity seems to play a significant role in shaping subsequent levels of information elaboration. In terms of personal familiarity, it may be that this type of familiarity sets the team up for enhanced process losses because conversations may become too informal or social, and assumptions may be made about what is really being said, and therefore, such familiarity may not actually influence information elaboration.

We further examine the impact of virtuality on the familiarity-information elaboration relationships. Today, GVTs have a range of ICT at their disposal and some teams are more reliant on it than others. Here, we find that higher levels of virtuality appear to dampen the positive relationship between professional familiarity and information elaboration. In part, this effect may occur because the challenges inherent in working in more virtual environments may constrain the benefits that professional familiarity can have on information elaboration. Meaning, even for teams whose members are professionally familiar with one another, if too much of the work is reliant on technology, the benefits of this form of familiarity on information elaboration will be diminished. Conversely, while personal familiarity did not exhibit a linear effect on information elaboration, the results become more compelling when looking at this relationship across the virtuality continuum. Namely, in relatively low virtuality contexts, personal familiarity was negatively related to information elaboration. However, this relationship turned slightly positive when teams exhibited relatively high degrees of virtuality. We attribute these findings to the fact that when GVTs interact via richer media or more FtF means (i.e., low virtuality), personal familiarity may result in high levels of socializing (e.g., O'Neill & Rothbard, 2017; Pillemer & Rothbard, 2018) which in turn may impair information elaboration. In contrast, when GVTs interact primarily through virtual means (i.e., high virtuality), the comfort associated with being personally familiar with one's teammates appears beneficial in terms of information elaboration. These findings are in keeping with those who have advocated for the need to consider temporal considerations because a team's past "can be expected to have an impact on what occurs in the present" ( Gully, 2000, p. 28). While team familiarity has been considered in studies assessing overall performance, conflict, and affective reactions (e.g., Bartone, Johnsen, Eid, Brun, & Laberg, 2002; Goodman & Leyden, 1991), our study is the first to propose and test information elaboration as a mediating mechanism linking familiarity to team outcomes.

Summing up, this work makes several contributions to the extant literature. First, we extend the familiarity literature by examining two distinct forms of familiarity (professional and personal). Here, we find that the two forms are distinguishable and, more importantly, have differential relationships with information elaboration, and ultimately team outcomes. Second, we extend the information elaboration nomological network by including member familiarity as an antecedent. Our findings show that knowing one's team members is important, but how you know them is the key. Third, we replicate the positive relationship between information elaboration and team outcomes within an organizational setting and with GVTs.

The current study has several practical implications. First, the findings suggest that team leaders need to identify ways to enhance information elaboration if a team is to serve its internal and external customers (effectiveness) and be able to work together in the future (viability). Second, this study highlights the value of professional familiarity in enhancing information elaboration within GVTs. Practically, this means that managers need to help ensure VT members build stronger relationships with one another (e.g., Pauleen & Yoong, 2001) with regard to areas such as skills, knowledge, and abilities (KSAs). While many have advocated for trust among VT members, and the importance of personal information being shared (e.g., Rusman, van Bruggen, Sloep, & Koper, 2010), our findings temper such admonishments. Specifically, our results suggest that professional familiarity, rather than personal familiarity, is salient in shaping GVT information elaboration and thereby shaping team effectiveness and viability.

As such, while we are not calling for a moratorium on conducting FtF meetings in GVTs, our results suggest that if such meetings are held, they should focus on providing members with knowledge about each other's work-related strengths, weaknesses, and past experiences. In fact, regardless of how virtual the team may be, as professional familiarity was enhanced, so too was the level of information elaboration. In contrast, personal familiarity did not exhibit a significant relationship with information elaboration, and its effect on information elaboration was actually negative in lower virtual teams. Accordingly, interventions designed to build up connections among members should focus on building professional relationships if information elaboration is to be enhanced.

Our findings align with suggestions put forth by Bierly, Stark, and Kessler (2009) who proposed that the impact of familiarity is in part attributable to the "fear of the unknown." When individuals are not familiar with their teammates' work, they may be hesitant to fully disclose information out of fear of not knowing how it will be received or used by others. Therefore, in addition to considering the KSAs of individuals when staffing a team (e.g., Stevens & Campion, 1999), managers also may benefit by considering the extent to which individuals know each other professionally. If managers are not able to build their GVTs with professional familiarity in mind, they may want to consider employing various techniques to enhance such familiarity. For instance, it may be beneficial for GVT members to leverage internal collaboration and social networking technologies as a way to enhance professional familiarity (e.g., Bughin & Chui, 2010). Likewise, GVT managers may investigate whether interventions such as team building or chartering style activities ( Mathieu & Rapp, 2009) increase levels of professional familiarity.

In addition, while not as essential for information elaboration as professional familiarity, there does appear to be limited value in enhancing the personal familiarity that exists between team members. However, this appears to only be the case within high virtuality teams. In fact, creating personal familiarity appears detrimental to information elaboration in teams that interact through less virtual means. Accordingly, only within highly VTs should managers consider the need for members to get to know one another on a personal basis through interventions such as teambuilding activities (e.g., Kirkman, Rosen, Gibson, Tesluk, & McPherson, 2002).

While the contributions of this study have been discussed above, from a design perspective, our study has several strengths. We sampled teams that were dispersed across the globe and who worked in an information intensive environment and interacted, in part, through virtual means. Thus, our work answers calls for team virtuality research to move beyond student sample laboratory-based studies (e.g., Gilson et al., 2015). Furthermore, we were able to collect data from teams at three distinct time periods in such a way that strengthens the temporal precedence of the variables in our model. However, future research may want to connect data collection efforts to better sync up to the developmental stages of teams. This could not be performed here given the differing lengths of time over which these teams had worked together. Finally, we collected data from three distinct sources, team members, team leads, and team managers, which should minimize same source bias ( Podsakoff et al., 2003).

These strengths aside, there are some inherent limitations which need to be discussed. Based on our review of the literature, this is one of the first studies to examine different types of team familiarity (i.e., personal and professional) and actually measure these forms of familiarity rather than relying on experience working together as a proxy for familiarity. While the results of the current study emphasize the relative value of professional familiarity over personal familiarity, future research is needed to better understand whether personal familiarity has beneficial effects on constructs not considered here.

Although our familiarities, information elaboration, and virtuality measures all exhibited high agreement indices justifying aggregation of members' responses to the team level, our ICC were relatively small-average ICC(1) = .13, ICC(2) = .34-indicating a restricted range of team scores which may have attenuated our findings ( Chen et al., 2004). The magnitudes of our ICC(2)s were also influenced by the relatively small team sizes in our investigation, and would have averaged .42 and .59 with teams of five or 10 members, respectively.

Our familiarity measures asked respondents to consider their familiarity with all team members and then averaged across their ratings. Future research may want to leverage the techniques being developed within the social network literature (see Crawford & LePine, 2013) to understand whether it matters where the familiarity actually exists within the team. In particular, examining the effects of the differentiated familiarity relationships may be appropriate in teams where there is more status differences and hierarchical differences. These differences were not as pronounced within our sample and, thus, we relied on a team average familiarity measure. However, in other contexts, it may be beneficial to drill down into the specific relationships within the team and account for the fact that certain dyads within a team may be very familiar while other dyads within the same team may have very limited amounts of familiarity.

With regard to virtuality, our measure is consistent with prior operationalizations of the construct ( Kirkman et al., 2004, 2006) and includes the dimensions articulated by Kirkman and Mathieu (2005). However, others have considered subdimensions such as electronic dependence, geographic dispersion, percentage of time apart on task, and synchronicity of team members' interaction (e.g., Gibson & Cohen, 2003; Griffith et al., 2003). Accordingly, future research may want to leverage different considerations of virtuality. By doing so, future research could explore whether the moderations evidenced here were attributable to certain dimensions of virtuality. In addition, while our focus here is on virtuality moderating the relationships between familiarity and information elaboration, future research may want to examine whether team virtuality affects the actual development of professional and personal familiarity. In particular, this might allow researchers to see whether virtuality exhibits similar double-edge sword effects with these relationships as well.

Finally, as stated previously, we designed our study to collect data from GVTs at three distinct time periods. That said, true longitudinal designs that measure all variables repeatedly over time would further strengthen causal inferences. In particular, longitudinal research designs would allow future research in this area to explore possible reciprocal relationships between familiarity and information elaboration. This is salient given that it is feasible that not only does familiarity shape subsequent information elaboration, but the process of information elaboration is also apt to shape later levels of familiarity. Furthermore, given the distinction we are making between the two types of familiarity, such work could explore whether information elaboration has differential impacts on each form of familiarity.

Conclusion

In conclusion, this study extends the GVT literature by examining the effects of professional and personal familiarity on information elaboration, as moderated by virtuality, and the resulting relationship between information elaboration and both team effectiveness and viability. Our findings suggest that professional familiarity is the more salient of the two in terms of its impact on information elaboration within GVTs. In addition, our study demonstrates that team virtuality moderates the relationships between information elaboration and each form of team familiarity in different ways. In terms of information elaboration, we expanded its nomological network by considering its relationship (within a field-based setting involving GVTs) with both team effectiveness and viability. In conducting this study, we hope to have provided a springboard for future research examining not only the role of different forms of familiarity within teams but also the role of information elaboration in shaping GVT processes and critical team outcomes.

Associate Editor: William Gardener

References

Barnwell, D., Nedrick, S., Rudolph, E., Sesay, M., Wellen, W. (2014). Leadership of international and virtual project teams. International Journal of Global Business, 7(2), 1-8.
Google Scholar

Bughin, J., Chui, M. (2010). The rise of the networked enterprise: Web 2.0 finds its payday. Retrieved from https://www.mckinsey.com/industries/high-tech/our-insights/the-rise-of-the-networked-enterprise-web-20-finds-its-payday
Google Scholar

Homan, A. C., van Knippenberg, D. (2003, May). The beneficial effects of cross-categorizing informational and demographical diversity in groups. Paper presented at the 11th European Congress of Work and Organizational Psychology, Lisbon, Portugal.
Google Scholar

Kirkman, B. L., Rosen, B., Gibson, C. B., Tesluk, P. E., McPherson, S. O. (2002). Five challenges to virtual team success: Lessons from Sabre, Inc. The Academy of Management Executive, 16, 67-79.
Google Scholar

Pillemer, J., Rothbard, N. P. (2018). Friends without benefits: Understanding the dark sides of workplace friendship. Academy of Management Review, 43(4). |
Google Scholar Crossref

Zellmer-Bruhn, M. E., Maloney, M. M., Bhappu, A. D., Salvador, R. (2008). When and how do differences matter? An exploration of perceived similarity in teams. Organization Behavior and Human Decision Processes, 107, 41-59. |
Google Scholar Crossref | ISI

Author Biographies

M. Travis Maynard, PhD, is an associate professor within the Department of Management at Colorado State University. Travis has conducted extensive research in the area of organizational team effectiveness with a special emphasis on the role that team context has on team interactions and outcomes. In particular, Travis has become increasingly interested in the topics of team adaptation and resilience in a variety of contexts including healthcare, military, law enforcement, energy, expedition, and NASA.

John E. Mathieu is a board of trustees distinguished professor at the University of Connecticut, and holds the Friar Chair in Leadership and Teams. His interests include models of team and multi-team effectiveness, and cross-level models of organizational behavior. He has worked with several Fortune 500 companies, the armed services, federal, state, and public organizations.

Lucy L. Gilson, PhD, is a professor and Department head within the Management Department at University of Connecticut where she also serves as Faculty Director for the Geno Auriemma UConn Leadership Conference, and the faculty advisor to the UConn Women's MBA association. Her research focuses on creativity, virtual teams, and team effectiveness; in particular, she is interested in examining teams in different organizational settings performing a diverse range of jobs. Specifically, she studies how creativity, employee empowerment, diversity, fairness issues, leadership, and virtual communication influence team effectiveness.

Diana R. Sanchez, PhD, is an assistant professor in the Industrial/Organizational Psychology program in the College of Science and Engineering at San Francisco State University. Her research interests have focused on technology innovation and implementation, specifically looking at how organizations can integrate technological solutions to help manage personnel (e.g., virtual training, virtual teams, online assessment). Dr. Sanchez runs a workplace technology research lab where she works with graduate and undergraduate students examining the uses of various forms of technology in the workplace.

Matthew D. Dean, PhD, is a clinical associate professor in the area of Operations and Information Technology at the Mason School of Business at the College of William & Mary. Dr. Dean earned his Ph.D. in Operations and Information Management from the University of Connecticut School of Business and B.S. in Mathematics from William & Mary. The crux of his research involves developing modeling tools and methodologies for addressing complex management decision-making challenges.

# data[].attributes.attachment_link.title
Do I Really Know You and Does It Matter? Unpacking the Relationship Between Familiarity and Information Elaboration in Global Virtual Teams - M. Travis Maynard, John E. Mathieu, Lucy L. Gilson, Diana R. Sanchez, Matthew D. Dean, 2019

# data[].attributes.attachment_link.description
Abstract We investigate the relationship between personal and professional familiarly, team effectiveness, and viability, and how these relationships are mediated by information elaboration in global virtual teams. We further assess whether virtuality moderates the relationships between both types of familiarity and information elaboration.

# data[].attributes.headline
Comparing online and offline Self Disclosure. A Systematic Review

# data[].attributes.subject
Comparing online and offline Self Disclosure. A Systematic Review

# data[].attributes.attachment_link.title
Charles Taylor on Deep Diversity - James Tully

# data[].attributes.attachment_link.title
Case 40_Three Stages to a Virtual Workforce

# data[].attributes.attachment_link.title
Charles Taylor on "Democracy and its Crisis"  Walter-Benjamin-Lectures 2019 (Day 3)

# data[].attributes.attachment_link.title
HBS Working Paper 12-088 on SCR Strategy

# data[].attributes.attachment_link.title
Bruno Latour | On Not Joining the Dots || Radcliffe Institute

# data[].attributes.attachment_link.title
The end of endings - with Timothy Morton’s philosophy

# data[].attributes.attachment_link.readable_text
0:00 / 5:45•Watch full video

# data[].attributes.body
How to build a conspiracy theory. A historical example