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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.

How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.

This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.

Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.

Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.

Many countries have a renewed focus on health inequalities since COVID-19. In England, integrated care systems (ICSs), formed in 2022 to promote integration, are required to reduce health inequalities. Integration is supported by population health management (PHM) which links data across health and care organisations to inform service delivery. It is not well-understood how PHM can help ICSs reduce health inequalities. This paper describes development of a programme theory to advance this understanding.

This study was conducted as a mixed-methods process evaluation in a local ICS using PHM. The study used Framework to analyse interviews with health and care professionals about a PHM tool, the COVID-19 vaccination uptake Dashboard. Quantitative data on staff Dashboard usage were analysed descriptively. To develop a wider programme theory, local findings were discussed with national PHM stakeholders.

ICS staff used PHM in heterogeneous ways to influence programme delivery and reduce inequalities in vaccine uptake. PHM data was most influential where it highlighted action was needed for “targetable” populations. PHM is more likely to influence decisions on reducing inequalities where data are trusted and valued, data platforms are underpinned by positive inter-organisational relationships and where the health inequality is a shared priority.

The COVID-19 pandemic accelerated a shift toward use of digital health platforms and integrated working across ICSs. This paper used an evaluation of integrated data to reduce inequalities in COVID-19 vaccine delivery to propose a novel programme theory for how integrated data can support ICS staff to tackle health inequalities.

Digital transformations of business processes are on the rise and the result is a need for a better understanding of how the elements of intellectual capital (IC) play a role in achieving successful digital project outcomes. New structural capital in the form of digital technologies must be identified and understood. Evolving skills of human capital in assimilating digital elements must also be considered, while collaboration within the development process involving relational capital provides a critical integration among these IC elements. This study illustrates the importance of identifying and managing the integration of IC components within an agile project management framework that are essential to achieving success for a digital initiative. More specifically, this study describes the process by which a multinational technology-based products company successfully developed a dynamic decision support platform utilizing an agile approach to guide a project management team to better manage the company's operations.

This study focuses on a case analysis approach of a multinational commercial and consumer products company. The paper presents existing research on the evolving state of project management for digital initiatives and focuses on agile methods. This study then delves into the case analysis that illustrates how IC played an integral role in the company successfully developing effective decision support involving an interactive dashboard using agile Project Management (PM), which enabled the project management team to better manage resources.

An examination at the case level illustrates that effective management and integration of IC has positive effects on project outcomes. While a balanced approach is evident as a requirement, the unique characteristics of the agile project management approach entails greater emphasis on select elements to adapt to a more dynamic development process.

This work depicts the complexities in providing analytic-based decision support in an agile/flexible project management scenario. This work adds to existing research by illustrating elements within IC categories and the elements' interdependencies that play an essential role in achieving success in this more flexible project environment.

Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations’ (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.

A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.

In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research’s stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.

To the authors’ knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

This paper aims to present a literature review of remote monitoring systems for water infrastructure in the Global South.

Following initial scoping searches, further examination was made of key remote monitoring technologies for water infrastructure in the Global South. A standard literature search methodology was adopted to examine these monitoring technologies and their respective deployments. This hierarchical approach prioritised “peer-reviewed” articles, followed by “scholarly” publications, then “credible” information sources and, finally, “other” relevant materials. The first two search phases were conducted using academic search services (e.g. Scopus and Google Scholar). In the third and fourth phases, Web searches were carried out on various stakeholders, including manufacturers, governmental agencies and non-governmental organisations/charities associated with Water, Sanitation and Hygiene (WASH) in the Global South.

This exercise expands the number of monitoring technologies considered in comparison to earlier review publications. Similarly, preceding reviews have largely focused upon monitoring applications in sub-Saharan Africa (SSA). This paper explores opportunities in other geographical regions and highlights India as a significant potential market for these tools.

This review predominantly focuses upon information/data currently available in the public domain.

Remote monitoring technologies enable the rapid detection of broken water pumps. Broken water infrastructure significantly impacts many vulnerable communities, often leading to the use of less protected water sources and increased exposure to water-related diseases. Further to these public health impacts, there are additional economic disadvantages for these user communities.

This literature review has sought to address some key technological omissions and to widen the geographical scope associated with previous investigations.

The aim of this paper is to analyse how technological innovations in performance measurement systems make it possible to overcome some of the challenges that public healthcare organizations face where management and control are concerned. The changes that could be applied to the performance measurement system of healthcare organisations were analysed together with an evaluation of the responses developed in order to achieve these changes.

The paper contains an in-depth case-study of a public university hospital which utilises an innovative information system.

The case-study highlights how technological innovations in performance measurement systems impact the management and monitoring information system in a public university hospital, through the implementation of a multidimensional management dashboard.

The limitation of this paper is that only one case-study is analysed, albeit in depth, while it would be interesting to consider more public university hospitals.

The paper highlights the fundamental role of middle management in change processes in the healthcare sector.

The case-study highlights how critical the active involvement of middle management is in performance measurement and management, and how this is achieved thanks to the adoption of a simple, clear method which ensures comprehensible communication of the objectives, as well as the measurement of performance by means of radar plots.

This paper aims to present a dynamic model for strategic and personalized decision-making in human resources (HR), using data mining techniques to enhance corporate social sustainability (CSS). The focus is on the interconnectedness of employee engagement (EE), enablement and the quality of work life.

The proposed model integrates various HR data, including demographic information, job specifications, payment and rewards, attendance and absence, alongside employees’ perceptions of their work-life quality, engagement and enablement. Data mining processes are applied to generate meaningful insights for senior and middle managers.

The study implemented the model within a production organization, revealing that factors influencing EE and enablement differ based on gender, marital status and occupational group. Performance-based rewards play a significant role in enhancing engagement, regardless of the reward amount. Factors such as “being recognized for competency” influence engagement for women, while payment has a greater impact on men. Engagement does not directly influence the quality of work life, but subcomponents like perceived transparency and the organization’s processes, particularly the “employee performance evaluation system,” improve work-life quality.

The findings are specific to the studied organization, limiting generalizability. Future research should explore the model’s effectiveness in different cultural and organizational settings.

The proposed model provides practical implications for organizations that enhance CSS. Organizations can gain insights into factors influencing EE and enablement by using data mining techniques, enabling informed decision-making and tailored human resource management practices.

This research addresses the societal concern regarding the impact of business activities on sustainability. Organizations can contribute to a more socially responsible and sustainable business environment by focusing on work-life quality and EE.

This paper offers a dynamic model using data mining and machine learning techniques for sustainable human resource management. It emphasizes the importance of customization to align practices with the unique needs of the workforce.

This compact case provides a relatively large data set that students explore using visualization and a Tableau dynamic dashboard that they create. Students were asked to describe what the data set contained in relation to employee attrition experience of Baca Beverage Distributors (BBD). The application and managerial questions are set in human resources and a company that is facing high attrition during the pandemic.

BBD shared their data and problem scenario for this compact case. The protagonist, Morgan Matthews, was the authors’ contact and provided significant clarification and guidance about the data. Both the company and the protagonist have been disguised. Some of the job positions have been rephrased. All names of employees, supervisors and managers have been replaced with codes.

During the 2020–2022 pandemic years, BBD experienced, like many companies, a higher than usual employee turnover rate and Morgan Matthews, Director of People, was concerned. Not only was it time-consuming, expensive and disruptive but the company had prided itself on being a good place to work. Were they hiring the right people, people that fit the company culture and people that fit the positions for which they were hired? The company had been using the Predictive Index [1] when on-boarding employees. In addition, there were results from self-reviews and manager reviews that could be used. Morgan wondered if data visualization and visual analytics would be useful in describing their employees and whether it would reveal any opportunities to improve the turnover rate. Before seeking a solution for the high turnover, it was important to step back and learn what the data said about who was leaving and the reasons they gave for leaving.

This compact case can be used in courses that include visualization using Tableau and dashboards. As it is a compact case, it requires less preparation time from the students and less class time for discussion. The case is for students who have been recently introduced to business analytics, specifically visualization and data storytelling with Tableau. For this reason, significant guidance has been provided in the case assignment. The level of the case can be adjusted by the amount of guidance provided in the case assignment. Courses include introduction to business analytics, descriptive analytics and visualization, communication through data storytelling. The case can be used for all modalities – in person, hybrid, online. The authors use it here for visualization and dynamic dashboards but using the same data set and compact case description, exploratory data analysis could be assigned.

Supplementary material for this article can be found online.