Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.

Purpose: This empirical research draws on the existing theory of transformational leadership, adaptive culture and organizational resilience, and investigates the effect of the elected TQM leadership style “transformational leadership” through the mediating effect of adaptive culture on organizational resilience, that is the key of survival during crises like the recent COVID-19 pandemic, which has severely impacted the business globally.Design/methodology/approach: This study exploited a cross-sectional online questionnaire of a random sample of Dubai service firms, with the unit of analysis being at the firm level. In total, 379 usable responses were received. Regression analysis was conducted to test hypotheses.Findings: The overall findings of this study supported that transformational leadership is positively associated with both adaptive culture and firm's resilience and significantly impacts them. Adaptive culture was found partially mediating the effect of transformational leadership on organizational resilience.Practical implications: The research findings provide important insights to practitioners (managers and leaders) to better improve their transformational qualities, as these qualities are expected to improve the organizational adaptive cultures and capacity of resilience.Originality/value: To the best of the authors' knowledge, this is one of the first studies to examine the transformational leadership effect on organizational adaptive culture and firm's resilience. This investigation expands the boundaries of leadership style theory into new arenas, attempting to partially address the identified knowledge gap in this vein.

Despite the plethora of m-learning acceptance studies, few have tackled the importance of examining the actual use of m-learning systems from the lenses of social influence, expectation-confirmation, and satisfaction. Additionally, most of the prior technology adoption literature tends to use the structural equation modeling (SEM) technique in analyzing the structural models. To address these limitations, this study extends the technology acceptance model (TAM) with the expectation-confirmation model (ECM) and social influence to predict the actual use of m-learning systems. A comparative approach using the partial least squares-structural equation modeling (PLS-SEM) and machine learning algorithms was employed to test the proposed model with data collected from 448 students. The results revealed that both techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases. The employment of a comparative analytical approach is believed to add a significant contribution to the information systems (IS) literature in general, and the m-learning domain in specific.