X-Culture was originally envisioned purely as an experiential learning project. Accordingly, we were first collecting only data on student performance that we needed for grading/marking purposes. However, in an attempt to better understand processes and outcomes in global virtual teams to develop better pre-project training programs for our students, we gradually started collecting more data that are not directly used for student evaluation purposes.
Additionally, we started experimenting with different learning conditions, team composition, assignments and evaluation systems to see which teaching approach gives the best learning outcome and student satisfaction.
Our database contains over 1,300 individual-level, 500 team-level, 200 instructor-level and 100 country-level variables.
X-Culture data are multi-source, multi-level and longitudinal in nature:
- Individual and team-consensus responses (various self-report surveys)
- Instructor evaluations
- Peer evaluations
- Deadline and other objective performance records
- Administrative records on participant background (e.g., country of studies, gender, etc.), team composition and the the like
- Data from external sources (e.g., time zone information, characteristics of the economic, cultural and institutional environment of each country, etc).[/servicebox]
- 12 waves of survey
- Starting with the pre-project survey of the student and instructors
- Weekly surveys and data records during the project
- Post-project surveys and evaluations
In terms of the specific measures, we have information about:
- Pre-project training and test performance.
- Team member background (demographics, international background, international experience, etc.).
- Knowledge and skills (cultural intelligence, skills with international virtual collaboration tools, etc.).
- Attitudes (values, various attitudes, beliefs, preferences, perceptions and biases, most measured before and then again after the project).
- Team composition and characteristics, including size, national, demographic, and skill composition, various inter-member distances, including time-zone, cultural, economic, perceptual and the like.
- Expectations about project challenges, communication mode, group interactions, dynamics and performance, measured before project start, and after the project end.
- Observations of project challenges, communication mode, group interaction, dynamics and performance, measured after project finish.
- Status and leadership.
- Open-ended question comments, feedback, suggestions, and other qualitative data.
- Various measures of team dynamics, including satisfaction, commitment, conflict, self-efficacy, etc.
- Various measures of team processes, including communication frequency and mode, workload distribution, coordination, leadership and more.
- Various measures of individual and team performance and outcomes, including multi-dimensional multi-rater assessment of the team report quality, ability to meet deadlines, satisfaction, peer evaluations and the like.
- Original team reports and other records suitable for qualitative and content analysis.
- Characteristics of the experimental conditions, including the specific task the teams have to complete, allocated time, deadlines, etc.
- Information about the participating instructors, their courses and universities, as well as the information course delivery mode (online/face-to-face), level of studies (UG, MBA, EMBA, etc.).
- Information about various experimental conditions.
Most variables are deliberately manipulated (e.g., team size, cultural diversity, time allocated to complete each task, etc.) to create enough variation along each variable needed for a meaningful analysis. Many factors vary naturally (different teams choose different communication modes, leadership structures, etc.).
We are constantly searching for better ways to evaluate the effects and effectiveness of project, as well as to further explore what shapes cross-cultural interactions and group dynamics. If interesting research ideas are put forth, we would also be happy to consider incorporating new measures and experimental conditions into our project for studies that may not be directly related to global virtual teams.
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How to Request Data
Please first read our Collaboration Principles.
To request existing data or collection of new data, please email us your research proposal.
There are no submission deadlines and no fixed research proposal formatting requirements, but the proposal must contain the following information:
- Research question(s) of the proposed study, no problem if specific hypotheses have not been developed yet, just a general research question or purpose of the study is sufficient at this stage. If available, it would also help to briefly describe the theoretical foundation of the study.
- Constructs: A bullet-list of constructs/variables that you would need from our existing database or additional measures that we need to add to our research design to collect new data. Please see the X-Culture Data webpage for what’s already measured.
If requesting new data to be collected, please attach a complete list of items, as well as detailes on the timing (when need to be measured) and other information on how the data should be collected.
- Expected research outcomes: A brief review of the goals and results of the study, expected publications, etc.
- Expected timing: Expected research project schedule and other timing information that can give us information as to how long it is likely to take to complete the project – very general information would be sufficient at this stage.
- Preferred Collaboration Mode: A summary of the researcher’s preferences as to collaboration with the X-Culture team, what resources are at the researcher’s disposal, what tasks or functions the researcher prefers to complete on his/her own and what tasks or functions the researcher prefers to be completed by collaborators from the X-Culture team, any other preferences with respect to communication, workload distribution, timing, co-author team composition, etc.
- CV of the researcher(s) submitting the proposal.
- Other: Any other information you feel would be relevant.
Do NOT spend too much on this initial proposal. It only has to provide a general summary of the idea so we can determine if this is something that we already published or are working on, or if it is a new idea. As long as it is a new idea, we will be happy to share our data. If it is an “old/existing” idea, we will work with you to find a way to refine the angle of your study to make it significantly distinct from the idea already published or in development.
Once we establish that the study is new and we (should) have suitable data, we’ll have a longer meeting (or several) to figure out together which exact variables from our database would be best suited to capture/represent the constructs in your model. It often takes several meetings to get the dataset prepared and ready for analysis, answer all questions about how each variable was measured and whatever other questions the researcher may have about the data, as well as work out the authorship, copyright, and data use issues.