Branding and rebranding a city: from city logo design to city brand communications; the case of Helsinki, Finland

In the current global urban context, city branding has become a mainstream topic in the urban management process because of urban homogenization. As a result, more and more relevant research is conducted. However, although there are many studies on how to build or design a city brand, and what is the meaning and value of it, there is less research about city brand communication, much less quantitative research about that. This study, as research on city rebranding and communication, will take the city logo as the starting point, in order to build strategies related to the city brand communication quantitative data collection and analysis. Then, explore how the strategies could make city brand communication more effective and better combine with new communication technologies. In this research process, the research of the city rebranding process in Helsinki, Finland, is the first step, which based on qualitative research, such as case study and interviews. In this stage, the meaning of city brand, city logo redesign principles, and the whole city rebranding process is found and collected, and the lack of city brand communication strategy is also proved as well. Then, by means of a questionnaire survey to collect and analyze quantitative data based on the AISAS model and Likert Scale, the classification of city brand information and the correlation between city brand awareness are discovered, and possible strategies related to city brand communication are built

Enabling explainable artificial intelligence capabilities in supply chain decision support making

Explainable artificial intelligence (XAI) has been instrumental in enabling the process of making informed decisions. The emergence of various supply chain (SC) platforms in modern times has altered the nature of SC interactions, resulting in a notable degree of uncertainty. This study aims to conduct a thorough analysis of the existing literature on decision support systems (DSSs) and their incorporation of XAI functionalities within the domain of SC. Our analysis has revealed the influence of XAI on the decision-making process in the field of SC. This study utilizes the SHapley Additive exPlanations (SHAP) technique to analysis the online data using Python machine learning (ML) process. Explanatory algorithms are specifically crafted to augment the lucidity of ML models by furnishing rationales for the prognostications they produce. The present study aims to establish measurable standards for identifying the constituents of XAI and DSSs that augment decision-making in the context of SC. This study assessed prior research with regards to their ability to make predictions, utilization of online dataset, number of variables examined, development of learning capability, and validation within the context of decision-making, emphasizes the research domains that necessitate additional exploration concerning intelligent decision-making under conditions of uncertainty