Motivasi dan Pengalaman Kerja dalam Produktivitas Karyawan Astra Lampung: Work Motivation and Experience in Astra Lampung Employee Productivity - Mia Apriyanti; Hendri Dunan (Universitas Muhammadiyah Sidoarjo, 2026)
General Background: The automotive industry plays a significant role in Indonesia’s economic growth, requiring high employee productivity to sustain organizational performance. Specific Background: At PT. Astra International Tbk Bandar Lampung Branch, issues such as low employee motivation and varying levels of work experience were observed to affect productivity across divisions. Knowledge Gap: Previous studies report inconsistent findings regarding the relationship between work motivation, work experience, and employee productivity, particularly within regional automotive dealership contexts. Aims: This study aims to examine the relationship between work motivation and work experience on employee productivity using a quantitative associative approach. Results: Based on data from 55 employees analyzed using multiple linear regression, both work motivation and work experience show positive and significant relationships with employee productivity, supported by t-test and F-test results, although the adjusted R-squared value of 13.6% indicates that these variables explain only a small portion of productivity variation. Novelty: This study provides empirical evidence from an automotive dealership in Lampung, a context that remains underexplored in prior research. Implications: The findings suggest that while motivation and experience contribute to productivity, other factors such as work environment, leadership, and organizational culture should be considered in future research and managerial strategies. Highlights• Positive relationships identified between intrinsic drive and output levels• Professional tenure associated with improved task completion and understanding• Majority of productivity variance explained by external organizational factors KeywordsWork Motivation; Work Experience; Employee Productivity; Automotive Industry; Multiple Linear Regression