This research investigates age differences in technology adoption and sustained usage in the workplace, using the Theory of Planned Behavior (TPB). The study follows 118 workers over a 5-month period as they are introduced to a new software system. Key findings include: 1. **Short-Term Usage**: Younger workers' decisions to adopt new technology are more influenced by their attitude toward using the technology, while older workers are more influenced by subjective norms and perceived behavioral control. These effects diminish over time. 2. **Long-Term Usage**: After 3 months, older workers no longer place significant emphasis on subjective norms, aligning with younger workers. 3. **Confounding Variables**: Income, occupation, and education levels did not significantly confound the age effects. 4. **Theoretical and Practical Implications**: Training programs should consider the different needs of younger and older workers. Younger workers are more driven by instrumental factors, while older workers are more influenced by social and process factors. 5. **Future Research Directions**: Further studies should explore different conceptualizations of age, measure gender differences, and examine the generalizability of findings across organizations. The research highlights the importance of understanding age differences in technology adoption to improve organizational strategies and training programs.This research investigates age differences in technology adoption and sustained usage in the workplace, using the Theory of Planned Behavior (TPB). The study follows 118 workers over a 5-month period as they are introduced to a new software system. Key findings include: 1. **Short-Term Usage**: Younger workers' decisions to adopt new technology are more influenced by their attitude toward using the technology, while older workers are more influenced by subjective norms and perceived behavioral control. These effects diminish over time. 2. **Long-Term Usage**: After 3 months, older workers no longer place significant emphasis on subjective norms, aligning with younger workers. 3. **Confounding Variables**: Income, occupation, and education levels did not significantly confound the age effects. 4. **Theoretical and Practical Implications**: Training programs should consider the different needs of younger and older workers. Younger workers are more driven by instrumental factors, while older workers are more influenced by social and process factors. 5. **Future Research Directions**: Further studies should explore different conceptualizations of age, measure gender differences, and examine the generalizability of findings across organizations. The research highlights the importance of understanding age differences in technology adoption to improve organizational strategies and training programs.