The new era of hyper-communication has led organizations to increasingly adopt communications technologies such as instant messaging (IM) for better connections and improved work efficiency. The ongoing COVID-19 pandemic has exerted further pressure and many organizations increasingly depend on IM tools to manage remote workers. In this new context, negative workplace impacts of IM seem more plausible, because technology use causes technostress. In this paper, we propose a research model to analyze instant messaging usage effects on individual work performance and well-being by the means of relevant techno-stressors. The model was tested on a sample of 372 employees from Romanian organizations that use instant messaging at work. Econometric analysis relies on the Structural Equation Modelling methodology and uses SPSS AMOS. Our findings suggest that the usage of instant messaging significantly influences on perceived techno-complexity, techno-overload and techno-invasion, which in turn influences, to some extent, both individual work performance and well-being. Dividing the sample according to the type of work (teleworking and office work), we compared the results between these two groups and encountered some significant differences.

Energy is the sector most strongly connected with climate change moderation, and this correlation and interdependency is largely investigated, in particular as regards renewable energy and sustainability issues. The United Nations, European Union, and all countries around the world declare their support for sustainable development, materialized in agreements, strategies, and action plans. This diversity, combined with significant interdependencies between indicators, brings up challenges for data analysis, which we have tackled in order to decide on relevant indicators. We have built a research framework based on Business Intelligence & Analytics for monitoring the SDG7 indicators that aim at “Ensuring access to affordable, reliable, sustainable, and modern energy for all”, in relation with SDG13 indicators targeting the sustainable aspect of energy. In developing the Business Intelligence & Analytics framework, we have considered Design Science Research in information systems guidelines. We have designed a process for carrying out Design Science Research by describing the demarche to develop information artifacts, which are the essence of a Business Intelligence & Analytics system. The information artifacts, such as data source, preprocessed data, initial and final data model, as well as data visualizations, are designed and implemented in order to support clean and affordable energy data analysis. The proposed research model, applied for Romania in this paper, serves as a point of departure for investigating data in a more integrated way, and can be easily applied to another country case study.

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.

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.