## 2 Background and Hypotheses  
### Technological-Oriented KM, Innovation, and Smart City Performance  
For numerous eras, the practice of knowledge management (KM) has attracted the attention of researchers and experts alike. Academics and professionals have focused their efforts on the discussion of how to effectively employ KM in contemporary organizations to achieve better outcomes [6; 8]. The fundamental KM approach and its application to accomplish benefits of performance and competitive advantages are critical success factors in this context [26; 27]. Considering the significance of technological innovation and knowledge sharing in our economic system, knowledge management will hold a significant role in the corporate in the future [28]. Consequently, the digital revolution and the increased high-tech innovations in various disciplines will absorb tedious tasks, abandoning only complicated operations for highly competent, primarily white-collar workers [29]. Concurrently, new forms of knowledge are developed because of such new technologies, leading to new prerequisites for administering knowledge [30]. Previous literature suggests one of the main approaches of KM, which is referred to as technology-oriented KM, and it follows a codified strategy to find explicit knowledge that is stashed in external databases [31]. Digitization can effectively process enormous amounts of heterogeneous data, knowledge, and information by employing AI and associated technologies. There are two aspects that distinguish AI applications, which determine our understanding of knowledge and how it is managed in institutions. First, AI algorithms can process data and discover trends autonomously, perhaps more effectively than people. As a corollary, these evolutionary computations can instantly develop important types of knowledge from data [30]. Smart city governments are constantly under pressure to enhance public service delivery with a citizen-friendly approach to digital transformation. Local governments in smart cities are constantly interested in improving the citizen-friendly delivery of public services in the age of technology revolution to enhance efficiency. Instead of focusing on a specific range of services for target markets, as is common in the private sector, municipal government services must manage a broad, diverse array of services that must be delivered to all inhabitants [32]. Even though distinctive clusters of residents will have unique attributes and expectations, access to public services and information must be guaranteed [33], while the cost efficiency of service delivery must be sustained. Knowledge sharing is critical to the principle of Knowledge Alignment because knowledge integration cannot be easily accomplished without sharing. Consequently, numerous previous researchers found no association between Knowledge Stock and Knowledge Integration [34], which is not surprising given that the level of expertise does not indicate proclivity to share. It is consistent with prior research, which discovered that knowledge had little or no direct impact on performance [35]. Subject Matter Experts may be reluctant to share their knowledge with non-domain professionals for various reasons, including power, language differences, and time constraints [36]. On the contrary, most organizations claim that an effective and efficient KM process will benefit organizational performance. As a result, knowledge management is widely accepted as an important predictor of organizational innovation or performance [37]. However, there are some differences in the outcomes of KM sub-processes or sub-dimensions and organizational performance. Performance is a common thread in most disciplines, such as social science and management, and it is significant to academics and practitioners. Although the relevance of the notion of performance is broadly accepted, the intervention of performance in study designs is perhaps one of the most difficult issues that is encountered by academic researchers today.