How Election Cycles Influence Automation Adoption: A Comparative Study of Public and Private Sector Policies in the U.S
Description
This dataset examines how U.S. electoral cycles influence automation adoption trends in the public and private sectors. Drawing on a mixed-method approach, the data combine longitudinal quantitative analysis of automation rates over the period 2000–2020 with qualitative insights from semi-structured interviews with 30 decision-makers. Significant increases in public sector automation are seen after the election, given the policy for modernization by an incoming administration, while private sector investments in automation are much lower during the pre-election period due to uncertainty over future regulations. The current study fills an important gap between political economy and technological adoption by providing empirical evidence and practical recommendations for policymakers and business executives. These drivers include regulatory uncertainty and an electoral mandate; a dataset on the trends, regression analyses, and thematic insights was drawn upon.
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Steps to reproduce
1. Quantitative data collection: Source automation adoption data from industry reports, government publications, and proprietary databases. On the public sector, focus on governmental technology reports and public investment records. For the private sector, make use of industry analytics reports, besides corporate filings. 2. Quantitative Analysis: Use of IBM SPSS Statistics: Data analysis will be done using descriptive statistics and multiple regression analysis, controlling economic and industry-wide variables. Ensure this dataset covers 20 years, from 2000 to 2020, to understand the trends around the U.S. presidential election cycles. 3. Qualitative data collection: Semi-structured interviews will be performed with 30 decision-makers, including 15 in the public sector-for example, city planners and heads of government agencies-and 15 in the private sector-for example, CTOs and operations managers. Data collection depended on a semistructured interview guide predeveloped based on how election cycles affect automation decisions. Interview them with their consent, and then transcribe the recordings for analysis. 4. Qualitative Analysis: Use thematic analysis to identify the recurring themes related to election-cycle influences on automation adoption. Transcribe and code the interview using NVivo software. 5. Integration of Data: Integrate quantitative and qualitative findings that allow detailed insight into the pattern of automation adoptions. Use qualitative insights to frame the quantitative trends of sector-specific behaviors. 6. Ethical Issues: Approvals shall be taken from an Institutional Review Board. Get written informed consent from the participants. Anonymize all data to protect confidentiality.