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This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

We utilise the actor-network theory (ANT) – based especially on Latour (2005) – to examine how management accounting tools affect physicians’ representations and new managerial practices in French public hospitals currently undergoing reform.

We conducted a longitudinal case study – based on interviews and observations – in a large French public hospital in which dashboards are diffused to physicians and nurses dealing with both medical and managerial activities.

The case shows that head physicians and nurses are implicated in their new managerial tasks and spend time analysing dashboards. Management accounting tools thus play a role, as mediators, in organising new managerial practices, and dashboards are a means of materialising and giving structure to new managerial practices and enabling discussions and exchanges to take place between actors who were previously separated.

The case shows that management accounting tools are not necessarily useful because they help in decision-making or control – as in the dominant paradigm; rather, they are beneficial because they may help in changing representations and building a new collective organisation. Future research should therefore expand on the organisational and social roles of management accounting tools, especially in the healthcare field.

Most ANT-inspired studies in management accounting focus on explaining changes in accounting practices, which are perceived as a consequence of an ANT process. This chapter, however, analyses the practices by which management accounting tools act as a vehicle to organisational change.

Urban logistics pooling is seen as a serious alternative to imposed urban consolidation centers. However, such strategies are quite new in urban distribution and merit to be evaluated using adapted methods that take into account the group decision nature of resource pooling. This chapter aims to propose, via an experimental collaborative decision support method, to define a grid of indicators and a reference situation database to measure the sustainable performance of urban logistics pooling systems.

The proposed methodology combines a systematic literature analysis of Key Performance Indicators and a group decision support method to choose a suitable set to define a dashboard. First, we identify the main sustainability indicators from an overview of the literature, and class them into the categories of the 4As Sustainable Transport vision (i.e., Awareness, Act and shift, Avoidance, and Anticipation). Then, a group of 20 experts is solicited for an iterative experimental group decision-making method to converge to the concordance of a set of indicators.

The method allowed us to define a hierarchic dashboard agreed by all experts with seven main indicators and nine secondary indicators. Moreover, the experts signaled the need of defining a unified basis of comparison to estimate initial situations. To do this, we proposed a database of urban routes from the French Surveys on Urban Goods Transport.

The proposed dashboard is an example, and to provide a more unified one, the experience has to be iterated using different groups of decision-makers.

This method has the advantage of proposing a dashboard agreed by all involved stakeholders. Therefore, this chapter shows the patterns to reproduce it since the method is able to be replicated in any context of group decision in urban logistics.

The originality of the chapter arises on the use of an experimental group decision method using a group with a majority of practitioners, and to validate it by consensus.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Analytics is the science of examining raw data with the purpose of drawing conclusions about that information and using it for decision-making. Before the formal written language, there were pictures which shared ideas, plans, and history. Most of the knowledge that we have of our ancestors is from these pictures drawn on caves or monuments. In today’s world, visualizations in the form of bar charts, scatter plots, or dashboards are essential tools in business intelligence as they help managers to absorb information and take apt decisions quickly. Dashboards in particular are very helpful for managers as multiple charts and graphs giving the latest information about sales, returns, market share, etc. keep them up to date on the latest developments in the company. There are a number of visualization software in the market which are easy to learn and communicate the analyzed data in an easily understood form; the leading ones being Tableau, QlikView, etc. with each one having its positives. This chapter also looks at the pairing of visualization tools with different measurements of data.

Attitudes, perceptions, and intentions of a firm's customers, which can be captured via customer feedback metrics (CFMs), provide valuable information about the state of a firm's customer base. CFMs can help capture the impact of marketing actions on future customer behavior and future firm performance, and thus can help make marketing become more accountable. CFMs have received much attention in marketing research and business practice since the 1970s. In this chapter, we provide a short historical overview of the development of, and research about, CFMs, we classify the different types of CFMs, we highlight the empirical findings of the drivers and consequences of CFMs, and we explore how CFMs can be integrated in a firm's customer dashboard in order to make marketing more accountable. We furthermore explore some of the challenges in accurately measuring CFMs, and in the end of this chapter, we provide information on how to capture CFMs in the age of social listening via modern tools involving text-, voice-, and video-mining.

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.