Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making the SC happen but also, the open data movement has encouraged more research works using machine learning. Based on this line of reasoning, the aim of this paper is to conduct a systematic literature review to investigate open data-based machine learning applications in the six different areas of smart cities. The results of this research reveal that: (a) machine learning applications using open data came out in all the SC areas and specific ML techniques are discovered for each area, with deep learning and supervised learning being the first choices. (b) Open data platforms represent the most frequently used source of data. (c) The challenges associated with open data utilization vary from quality of data, to frequency of data collection, to consistency of data, and data format. Overall, the data synopsis as well as the in-depth analysis may be a valuable support and inspiration for the future smart city projects.

Business Intelligence and Analytics systems have the capability to enable organizations to better comprehend their business and to increase the quality of managerial decisions, and consequently improve their performance. Recently, organizations have embraced the idea that data becomes a core asset, and this belief also changes the culture of the organization; data and analytics now determine a data-driven culture, which makes way for more effective data-driven decisions. To the best of our knowledge, there are few studies that investigate the effects of BI&A adoption on individual decision-making effectiveness and managerial work performance. This paper aims to contribute to bridging this gap by providing a research model that examines the relationship between BI&A adoption and manager’s decision-making effectiveness and then his individual work performance. The research model also theorizes that a data-driven culture promotes the BI&A adoption in the organization. Using specific control variables, we also expect to observe differences between different departments and managerial positions, which will provide practical implications for companies that work on BI&A adoption.

We propose a methodological framework based on design science research for the design and development of data and information artifacts in data analysis projects, particularly managerial performance analysis. Design science research methodology is an artifact-centric creation and evaluation approach. Artifacts are used to solve real-life business problems. These are key elements of the proposed approach. Starting from the main current approaches of design science research, we propose a framework that contains artifact engineering aspects for a class of problems, namely data analysis using machine learning techniques. Several classification algorithms were applied to previously labelled datasets through clustering. The datasets contain values for eight competencies that define a manager’s profile. These values were obtained through a 360 feedback evaluation. A set of metrics for evaluating the performance of the classifiers was introduced, and a general algorithm was described. Our initiative has a predominant practical relevance but also ensures a theoretical contribution to the domain of study. The proposed framework can be applied to any problem involving data analysis using machine learning techniques.

Business intelligence and analytics (BI&A) have recently emerged as a strategic approach to managerial tasks, providing opportunities to improve work performance. Despite the growing interest in evaluating cases of BI&A adoption, to the best of our knowledge, few studies have addressed the influence of data-driven culture and the effects of BI&A adoption specifically on the work performance of managers. The aim of this study is to assess whether a data-driven culture predicts the adoption of BI&A in companies and its impact on decision-making effectiveness and managerial performance. This novel research model was tested with 180 managers from Romanian companies that work with BI&A tools. Based on PLS-SEM data analysis, our findings suggest that a data-oriented culture is a strong predictor of BI&A adoption and decision-making effectiveness. The results also confirm that BI&A utilization positively impacts decision-making effectiveness and individual work performance. The primary implication drawn from empirical evidence is that executives should prioritize the cultivation of a data-driven culture within their organizations, as this is essential for enhancing managerial performance through the adoption of business intelligence and analytics.