**3.1 Data abstraction and analysis**

Assessment and analysis were done on the remaining publications. Focused efforts were made on research that provided answers to the posed questions. To extract the data, the abstracts and entire papers were thoroughly examined before identifying the pertinent topics and sub-themes. Whittemore and Knafl [34] claim that adopting qualitative or mixed-method techniques that allow the researcher to make ongoing comparisons across primary data sources is the best way to synthesize or evaluate integrative data. To find themes relating to leaders' innovation, qualitative analysis utilizing content analysis was conducted. All 80 publications have undergone indepth analysis, especially in the abstract, results, and discussion parts. The research questions served as the basis for the data abstraction, which means that all information from the examined studies that could help answer the research questions was taken out and put into a table. The researcher then carried out a thematic analysis to find themes and sub-themes based on observations of patterns and themes, creativity, innovativeness, creative work behavior, and connections that existed within the abstracted data [35].

Generating themes is the first stage in a thematic analysis. The patterns that developed from the abstracted data of all reviewed publications must be recognized during this procedure. The authors reexamined all the main and sub-themes created during this approach to guarantee their utility and accurate representations of the data. The correctness of these themes was then reviewed. The writers then moved on to the next step by identifying the themes for each group and its subgroup. Prior to naming the themes for the sub-group, the authors began by naming the themes for the main group (see **Table 1**).
