197 Chapter VI Conclusion VI.1 Introduction In the final chapter, a summary and conclusion are provided to reiterate key points from previous discussions and to highlight the practical implications of the findings for relevant stakeholders. This chapter is divided into three main sections: summarizing the research findings in answering research questions, recognizing the study's limitations, and suggesting directions for future research. The goal is to encapsulate the significance of the research, acknowledge its boundaries, and offer guidance for future scholars looking to build on this foundational work. VI.2 Conclusion This study specifically addresses the research questions concerning the role of organizational culture in digital transformation and how general culture, digital culture, and digital literacy influence employees' attitudes toward transformation and perceived performance. The study finds that a digital culture, as a form of organizational culture shifting, supported by digital literacy, is crucial for successful digital transformation. This culture integrates technology, mindsets, and attitudes to change into daily work practices, encouraging employees to adapt and embrace digital changes. The combination of digital culture and digital literacy within the organizational framework is essential for fostering positive attitudes to change and enhancing performance. This combination not only supports the adoption of new technologies but also shapes a conducive environment for innovation and adaptability, key factors in the success of digital transformation initiatives. General culture and digital culture significantly influence employees' attitudes to change. The study reveals that when employees are digitally literate, they are more likely to have positive attitudes towards digital changes. This positive attitude, in turn, accelerates cultural adaptation and improves overall organizational performance. Digital literacy emerges as a key mediator between digital culture and 198 employees' attitudes toward transformation. Employees with higher digital literacy are more adaptable and receptive to digital changes, thereby enhancing the impact of digital culture on their attitudes and performance. The study also demonstrates that an organization's performance in digital transformation is closely linked to how well its culture and digital capabilities align with employees' attitudes and behaviors towards change. Furthermore, the study reveals that TAT in measuring unconscious behavior provide a deeper insights than traditional methods in measuring conscious behavior. It avoids social desirability bias, often seen in questionnaire responses influenced by intellectual, emotional, and social factors. In contrast, TAT responses, rooted in the unconscious, are more reliable and unbiased. Organizational psychology theories, such as the theory of resistance to change (Piderit, 2000) and the Theory of Planned Behavior (Ajzen, 1991) , suggest that employees may consciously accept changes like digital transformation, but unconscious ly resist or feel uncertain. Another possibility is that employees may mistakenly perceive that they have already embraced a digital culture. Their experience of digital culture may be another manifestation of the general corporate culture, characterized by similar concepts expressed in different terms. This finding arises because the lower TAT with machine learning analytics scores than traditional questionnaire emphasizing the need to address unconscious aspects in change management. Utilizing Artificial Intelligence (AI) in this study proved highly effective, especially in analyzing unconscious patterns, offering deeper and faster insights into employee attitudes and behaviors that traditional methods might overlook. The study also concludes that using artificial intelligence to process and predict unconscious behavior measurement scores is more insightful and relatively unbiased by social factors. Additionally, machine learning can be employed to compare behavior scores between both conscious and unconscious measurements, leading to deeper and more comprehensive insights. The use of machine learning provides faster insights due to its ability to handle complex data and patterns, enhancing efficiency and enabling instant and personalized feedback for employees.