Is the organizational digital divide geographical?

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Title: Is the organizational digital divide geographical?

Mentor: Dr. Amrita George

Approach: Develop comparative models using national/state measures from US and India to understand how the organizational digital divide differs across the two regions. Students will gain experience with data analytics – data cleaning and management, mapping, conducting exploratory data analysis, and estimating regression models to predict organizational performance in a digital economy across the two regions. Summary: While digitally competent organizations are predicted to be ahead of their peers in the US/Europe, the impact of the organizational digital divide across the US/India has not been studied. India has a relatively younger population (the digital natives) and is likely to be less impacted by the organization's digital divide. On the contrary, the US launched many initiatives to improve technical know-how amongst its population (the digital migrants) older than their India peers. We don't know if launching these initiatives is enough to bridge the digital divide gap in organizations in US. This study aims to utilize industry-related data and FCC/NHFS data aggregated at national and state levels to create regression models to identify the impact of the digital divide on organizational performances in the two regions. These findings can inform theoretical explanations of the organizational digital divide when considering initiatives such as Build Back Better.

Student Research Activities: The REU fellows will perform the following major tasks: • Read theoretical and empirical literature on organizational digital divide.

• Identify appropriate measures for theoretical concepts (i.e., organizational performance).

• Develop testable hypotheses.

• Survey publicly available industry and FCC/NHFS data.

• Download, pre-process, and manage data; prepare datasets for analyses.

• Estimate exploratory data models.

• Estimate regression models.

Student Background: Students need to have basic computing and statistics knowledge, and introductory programming skills in Python or R.