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AbstractTraditional approaches to monitoring road safety have primarily focused on measuring outcomes such as the number of fatalities and injuries. While effective in capturing overall trends, this macroscopic approach often overlooks the underlying causes of unsafe conditions. Recognizing these limitations, many countries now embrace a safe system-based approach, which emphasizes a holistic view of road safety, considering various elements and their interactions. In response to this shift, this study introduces a five-step framework designed to provide comprehensive coverage and tailored assistance in selecting and utilizing appropriate Road Safety Indicators (RSIs) for more effective performance monitoring. The framework integrates a novel RSIs taxonomy aligned with critical elements of the safe system. It also incorporates an MCDA-based approach to account for decision-makers’ preferences when selecting suitable RSIs. A case study demonstrates the practical application of the proposed steps, including the identification, classification, selection, and development of descriptive sheets for each selected RSI, as well as the continuous updating of the RSIs set. The findings offered valuable insights into the commonly used indicators in international road safety reports, while also revealing the limitations of currents metrics and data in fully capturing critical elements and hierarchical level within the road safety management system. [ABSTRACT FROM AUTHOR]

This article discusses the use of Sankey diagrams in curriculum planning in higher education. Sankey diagrams are flow diagrams that visualize student progress and educational pathways. By analyzing student data, institutions can identify common prerequisites, evaluate course sequences, and tailor academic advising to individual student needs. Sankey diagrams also help optimize resource allocation and improve the quality of study programs. The article emphasizes the potential of data-driven decision-making in higher education and highlights the benefits of using Sankey diagrams for curriculum planning, student advising, and institutional excellence. [Extracted from the article]

In the competitive field of business intelligence, optimizing talent recruitment through data-driven methodologies is crucial for better decision-making. This study compares the effectiveness of various machine learning models to improve recruitment accuracy and efficiency. Using the recruitment data from a major Yemeni organization (2019–2022), we evaluated models including K-Nearest Neighbors, Logistic Regression, Support Vector Machine, Naive Bayes, Decision Trees, Random Forest, Gradient Boosting Classifier, AdaBoost Classifier, and Neural Networks. Hyperparameter tuning and cross-validation were used for optimization. The Random Forest model achieved the highest accuracy (92.8%), followed by Neural Networks (92.6%) and Gradient Boosting Classifier (92.5%). These results suggest that advanced machine learning models, particularly Random Forest and Neural Networks, can significantly enhance the recruitment processes in business intelligence systems. This study provides valuable insights for recruiters, advocating for the integration of sophisticated machine learning techniques in talent acquisition strategies. [ABSTRACT FROM AUTHOR]

The manufacturing industry is skill-intensive and plays a pivotal role in South Africa's economy, reflecting the nation's progress and development. The advent of technology has initiated a transformative era within the manufacturing sector. Workforce skills are at the heart of ensuring the sustained growth of the industry. This study delves into the skill-related aspects of the occupational landscape of the South African manufacturing sector, with a particular focus on two important manufacturing sectors: the food and beverage manufacturing (FoodBev) sector and the chemical manufacturing (CHIETA) sector. Leveraging the forecasting prowess of Autoregressive Integrated Moving Average (ARIMA), this paper outlines a sectorial occupational forecasting modeling exercise to reveal which job roles are poised for expansion and which are expected to decline. The approach predicted future skills' demand 80% accuracy for 473 out of 713 (66%) occupations for FoodBev and 474 out of 522 (91%) for CHIETA. These insights are invaluable for industry stakeholders and educational institutions, providing guidance to support the sector's growth in an era marked by technological advancement. [ABSTRACT FROM AUTHOR]

In today's business environments, data-driven decision-making has played a crucial role in bringing real-time sustainability to organizational behavior. Furthermore, the Internet of Things (IoT) has been widely adopted recently, but studies investigating its impact on organizational dynamics and sustainability are limited. This study explores the IoT's potential to improve the real-time decision-making and sustainability of organizations. A cross-sectional study was conducted on 250 respondents belonging to a diverse range of industries. Five variables were analyzed in the study—IoT implementation, real-time data analytics, decision-making, organizational behavior, and organizational performance. These variables were scored on a 5-point Likert-type scale, with responses ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The data were collected with the help of a structured questionnaire. The data were analyzed using descriptive statistics, Pearson correlations, and structural equation modeling (SEM) to test the relationships among the study variables. The findings indicate that enhanced organizational behavior (r = 0.1101), decision-making (r = 0.269), and real-time data analytics (r = 0.1888) are all strongly associated with the IoT. Applying structural equation modeling further reveals a direct connection between IoT adoption and company performance. Moreover, it is observed that the coefficients for organizational behavior (β = 0.0707, p < 0.01) and real-time data analytics (β = 0.0851, p < 0.001) are statistically significant. This study demonstrates how the IoT can bring real-time sustainability to organizational behavior by improving decision-making and business operations. Although decision-making is enhanced by the IoT, organizations still need to optimize their resource usage and reduce waste to enhance sustainability. This study bridges a substantial gap in the existing literature by carefully investigating the relationship between IoT technology, organizational behavior, and decision-making techniques. Our findings conclude that leveraging the IoT changes the nature of digital innovation and brings real-time sustainability to organizational behavior. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) originates from the practical experience of human beings' constant struggle with nature. In five thousand years, TCM has gradually risen from empirical medicine to modern evidence-based medicine with complete scientific principles such as fundamental systematic theories, treatment principles and methods, classic prescriptions, famous medicines. The development of information science, data science, and computer technology has provided effective models, methods, and technologies for modern TCM's quantitative and intelligent diagnosis and treatment decision-making. And it also has promoted the development of TCM from evidence-based medicine to intelligent TCM. Starting from the development of TCM, we introduce the rise and connotation of ancient, modern, and intelligent TCM. Moreover, we emphatically analyze the research status of quantification and intelligent decisions for the whole disease cycle, including data-driven modern TCM diagnosis, program optimization, and treatment program evaluation. In addition, we discuss the critical issues of data-driven TCM quantification and intelligent decision research and briefly elaborate on the new ideas of data-driven intelligent TCM research. In conclusion, compared with traditional research paradigms, the advantages of data-driven medical decision research paradigms are as follows: (1) From the perspective of decision-making subjects, the data-driven research paradigm describes the clinical decision-making mechanism in real scenarios with rigorous mathematical theories, which will break through the difference between the conclusions drawn by clinical design research methods and clinical practice. (2) By applying the results of basic theoretical research to clinical decision-making practice in real scenarios, the data-driven medical decision-making research paradigm will contribute to getting out of the dilemma that the conclusions drawn by traditional AI models are difficult to explain in clinical practical decision-making. [ABSTRACT FROM AUTHOR]

The study explores how organizational structure and culture in the South African property sector influence decision-making on Environmental, Social, and Governance (ESG) challenges and Green Policy development. It hypothesized that traditional hierarchical structures and non-participative cultures hinder digitalization, impairing data collection essential for informed ESG decisions. In contrast, participative cultures and modern or matrix structures facilitate data-driven insights, expediting ESG and Green Policy advancements. Qualitative methods, including unstructured in-depth interviews with key decision-makers across various sectors (listed companies, commercial developers, financiers, real estate agencies), alongside interviews with digital application developers, provided perspectives on organizational impacts. A systematic literature review supplemented the data. Thematic content analysis of the data revealed traditional structures and cultures complicate decision-making, impede digital transformation, and delay ESG and Green Policy implementation. Conversely, participative cultures and modern structures streamline data-driven decision-making processes, promoting ESG and Green Policy progress. The findings highlight opportunities for traditional organizations to enhance structures and cultures, removing barriers to digitalization and accelerating ESG and Green Policy initiatives. This study underscores the critical role of organization dynamics in fostering sustainability within the built environment, emphasizing digital transformation's importance in driving positive environmental and social outcomes. [ABSTRACT FROM AUTHOR]

With the rapid developments in ICTs in recent years, destination management organizations (DMOs) have been increasingly expected to adopt data-driven decision-making practices towards fulfilling their role as destination managers. While data-driven decision-making offers a smarter approach to building more sustainable and competitive destinations, there remains a limited understanding surrounding its adoption in practice. Therefore, this study applied a mixed methods approach in efforts to identify the existing practices and barriers facing DMOs at each phase of Athamena and Houhamdi's (2018) model of the data-driven decision-making process. The findings suggest that Canadian DMOs have been slow to engage in data-driven decision-making practices. Specifically, there remains a need to address the lack of data related to sustainability indicators, the quality of data sources, and the resource limitations faced by DMOs. Theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

The existing body of literature indicates a growing interest in research pertaining to the influence of artificial intelligence (AI) on marketing strategies, processes, and practices. However, further studies are required to fully unravel its complete potential and the implications it holds for practical application. The aim of this special issue on "The Next 'Deep' Thing in X to Z Marketing: An Artificial Intelligence-Driven Approach" is to explore the next frontiers and delve into the various facets of AI-driven marketing, shedding light on cutting-edge research and practical insights that can shape the future of the field. It also focuses on novel ways of using AI techniques to derive innovative insights that can streamline marketing processes and make businesses more effective. The papers herein contribute not only to the advancement of knowledge and understanding surrounding the utilisation of AI in marketing but also play a crucial role in establishing a renewed and revitalised research agenda. [ABSTRACT FROM AUTHOR]

Through analysis and discussion of basic systemic properties of a rural municipality, this paper explores how aggregating data in planning and land use modeling can potentially obscure intricate real-world behavior. Complexity theory is applied as a theoretical framework for explaining this hypothesis. Thus, the aim of this study is to address the author's desire to understand systemic complexity when designing a data-driven decision-making model for rural planning. The novelty of this approach is two-fold: one, most studies on scalability issues in planning addresses spatial complexity, more so than systemic complexity within the complex system that the very act of planning strives to dictate. Two, although delimited to the scope of the study, the accessibility to and use of complete and valid socio-demographic data enables a rarely demonstrated accurate representation of an entire population. It is ultimately observed that on the disaggregated municipal level, systemic dispersion increases parallelly with population size, a correlation that is significantly influenced by gender ratio in any given parish – a characteristic that was not visible at the aggregated municipal level. In addition to advancing the understanding and placement of complexity science within spatial data science, these insights will make it easier to assess the generalizability of any given administrative unit by quantifying basic complexity attributes; in this case based on the correlation dispersion caused by the fragmentation of a municipality into its comprising parishes. [ABSTRACT FROM AUTHOR]

Introduction The community-based health information system (CBHIS) is a vital component of the community health system, as it assesses community-level healthcare service delivery and generates data for community health programme planning, monitoring, and evaluation. CBHIS promotes data-driven decision-making, by identifying priority interventions and programs, guiding resource allocation, and contributing to evidence-based policy development. Objective This scoping review aims to comprehensively examine the use of CBHIS in African countries, focusing on data generation, pathways, utilisation of CBHIS data, community accessibility to the data and use of the data to empower communities. Methods We utilised Arksey and O'Malley's scoping review methodology. We searched eight databases: PubMed, EMBASE, HINARI, Cochrane Library, Web of Science, Scopus, Google Scholar, and grey literature databases (Open Grey and OAIster). We synthesised findings using a thematic approach. Results Our review included 55 articles from 27 African countries, primarily in Eastern and Southern Africa, followed by West Africa. Most of the studies were either quantitative (42%) or qualitative (33%). Paper-based systems are primarily used for data collection in most countries, but some have adopted electronic/mobile-based systems or both. The data flow for CBHIS varies by country and the tools used for data collection. CBHIS data informs policies, resource allocation, staffing, community health dialogues, and commodity supplies for community health programmes. Community dialogue is the most common approach for community engagement, empowerment, and sharing of CBHIS data with communities. Community empowerment tends towards health promotion activities and health provider-led approaches. Conclusion CBHIS utilises both paper-based and electronic-based systems to collect and process data. Nevertheless, most countries rely on paper-based systems. Most of the CBHIS investments have focused on digitisation and enhancing data collection, process, and quality. However, there is a need to shift the emphasis towards enabling data utilisation at the community level and community empowerment.

Introduction The community-based health information system (CBHIS) is a vital component of the community health system, as it assesses community-level healthcare service delivery and generates data for community health programme planning, monitoring, and evaluation. CBHIS promotes data-driven decision-making, by identifying priority interventions and programs, guiding resource allocation, and contributing to evidence-based policy development. Objective This scoping review aims to comprehensively examine the use of CBHIS in African countries, focusing on data generation, pathways, utilization of CBHIS data, community accessibility to the data and use of the data to empower communities. Methods We utilised Arksey and O'Malley's scoping review methodology. We searched eight databases: PubMed, EMBASE, HINARI, Cochrane Library, Web of Science, Scopus, Google Scholar, and grey literature databases (Open Grey and OAIster). We synthesized findings using a thematic approach. Results Our review included 55 articles from 27 African countries, primarily in Eastern and Southern Africa, followed by West Africa. Most of the studies were either quantitative (42%) or qualitative (33%). Paper-based systems are primarily used for data collection in most countries, but some have adopted electronic/mobile-based systems or both. The data flow for CBHIS varies by country and the tools used for data collection. CBHIS data informs policies, resource allocation, staffing, community health dialogues, and commodity supplies for community health programmes. Community dialogue is the most common approach for community engagement, empowerment, and sharing of CBHIS data with communities. Community empowerment tends towards health promotion activities and health provider-led approaches. Conclusion CBHIS utilizes both paper-based and electronic-based systems to collect and process data. Nevertheless, most countries rely on paper-based systems. Most of the CBHIS investments have focused on its digitization and enhancing data collection, process, and quality. However, there is a need to shift the emphasis towards enabling data utilisation at the community level and community empowerment.

In the era of society’s ongoing digitization and the exponential growth in data volume, alongside a growing energy demand, energy management plays an integral role in data centers (DCs) and is a key factor in the quest for decarbonization. In light of the complex nature of DCs, traditional energy management strategies are inadequate. This research introduces a data-driven decision-making framework for DCs, grounded in the OODA (Observation, Orientation, Decision, and Action) loop and based on insights from an Ericsson-operated DC in Linköping, Sweden. The developed framework enables DCs to enhance energy efficiency effectively. Rooted in the OODA loop and leveraging extensive datasets from DCs’ building management systems, this framework aids in decreasing cooling energy usage through strategic, data-driven decision-making. By adopting AI methods, specifically K-means clustering in this research, for continuous monitoring and fine-tuning (Proportional, Integral, Derivative) PID parameters, the framework aids in improving operational efficiency.

Introduction The community-based health information system (CBHIS) is a vital component of the community health system, as it assesses community-level healthcare service delivery and generates data for community health programme planning, monitoring, and evaluation. CBHIS promotes data-driven decision-making, by identifying priority interventions and programs, guiding resource allocation, and contributing to evidence-based policy development. Objective This scoping review aims to comprehensively examine the use of CBHIS in African countries, focusing on data generation, pathways, utilization of CBHIS data, community accessibility to the data and use of the data to empower communities. Methods We utilised Arksey and O'Malley's scoping review methodology. We searched eight databases: PubMed, EMBASE, HINARI, Cochrane Library, Web of Science, Scopus, Google Scholar, and grey literature databases (Open Grey and OAIster). We synthesized findings using a thematic approach. Results Our review included 55 articles from 27 African countries, primarily in Eastern and Southern Africa, followed by West Africa. Most of the studies were either quantitative (42%) or qualitative (33%). Paper-based systems are primarily used for data collection in most countries, but some have adopted electronic/mobile-based systems or both. The data flow for CBHIS varies by country and the tools used for data collection. CBHIS data informs policies, resource allocation, staffing, community health dialogues, and commodity supplies for community health programmes. Community dialogue is the most common approach for community engagement, empowerment, and sharing of CBHIS data with communities. Community empowerment tends towards health promotion activities and health provider-led approaches. Conclusion CBHIS utilizes both paper-based and electronic-based systems to collect and process data. Nevertheless, most countries rely on paper-based systems. Most of the CBHIS investments have focused on its digitization and enhancing data collection, process, and quality. However, there is a need to shift the emphasis towards enabling data utilisation at the community level and community empowerment.

The study aims to investigate the influence of business intelligence on decision-making quality, considering organizational ambidexterity as a moderating factor. The sample included a broad group of professionals from the high-technology segment, surveying 450 respondents; 254 valid responses were obtained from senior executives, supervisors, and analysts. These respondents were selected as they had a clear ability for a deeper dive into strategic goals and international expansion strategies, effectiveness in capacities, and daily financial management improvement. The results showed that with the use of business intelligence tools, organizations were in a better position to provide a higher quality of strategic decision-making (p < 0.01) as it helped in the functioning of new opportunities to identify and capitalize, while keeping an eye on the current capabilities. The study underscores that organizational ambidexterity is one of the most dominant causes (p < 0.01) for the effective realization of the benefits of business intelligence. Thus, for effective decision-making, it is crucial for organizations to synergize business intelligence tools with their ambidextrous capabilities. This underscores the significant role of organizational ambidexterity as a moderator and further demonstrates its importance in optimizing the use of business intelligence to improve strategic decision-making processes.

Data reporting in the Australian education system produces deficit-based comparisons of academic achievement between Indigenous and non-Indigenous students. Since 2009, data from the National Assessment Program – Literacy and Numeracy (NAPLAN) have been used by the Australian government to allow it to report on 'Closing the Gap' policy. These data are used to justify government-funded initiatives designed to improve educational outcomes for Indigenous students. While the logic of this approach has political cachet, the 'gaps' have persisted. Instead, we propose using 'within-cohort, peer matching' quantitative methods to achieve such aspirational goals. Using publicly available NAPLAN data, we examined patterns of relative performance within the cohort of Indigenous students. We assessed trends in Indigenous students' performance in NAPLAN relative to their Indigenous peers across grades (3, 5, 7, and 9), states and territories, remoteness categories, and calendar years from 2010 to 2019 inclusive. Key insights available through this approach include detailed patterns of higher and lower relative performance for Indigenous students within matched remoteness categories, across states and territories, and over time, laying the groundwork for further analyses of the local, systemic, and school-level factors associated with these differing outcomes. Our methods provide novel findings that justify changing the deficit assumption, supporting the use of within-cohort peer matching instead. [ABSTRACT FROM AUTHOR]

Research Summary: We evaluate a pilot in an Inspections Department to explore the returns to a pair of algorithms that varied in their sophistication. We find that both algorithms provided substantial prediction gains, suggesting that even simple data may be helpful. However, these gains did not result in improved decisions. Inspectors often used their decision authority to override algorithmic recommendations, partly to consider other organizational objectives without improving outcomes. Interviews with 55 departments find that while some ran pilots seeking to prioritize inspections using data, all provided considerable decision authority to inspectors. These findings suggest that for algorithms to improve managerial decisions, organizations must consider both the returns to algorithms in the context and how decision authority is managed. Managerial Summary: We evaluate a pilot in an Inspections Department to explore the returns to algorithms on decisions. We find that the greatest gains in this context come from integrating data into the decision process in the form of simple heuristics, rather than from increasing algorithmic sophistication or additional data. We also find that these improvements in prediction do not fully translate into improved decisions. Decision‐makers were less likely to follow data‐driven recommendations, partly in consideration of other organizational objectives, but without substantially improving on them overall. These findings suggest that organizations should consider the returns to technical sophistication in each context, and that the design and management of decision authority can be a key choice that impacts the value organizations can capture from using predictive analytics. [ABSTRACT FROM AUTHOR]

Public procurement is an essential government function representing a substantial part of a nation’s economy. How decisions are made in public procurement influences nations' economic health and citizens' daily lives. In this study, we employ the technology–organization–environment (TOE) framework to investigate public procurement officials' adoption of spend analytics in Norway. Based on an analysis of survey data from 529 Norwegian procurement entities collected by the Norwegian Agency for Public and Financial Management, we find that 61% do not utilize spend analytics, with adoption rates varying across different types of entities. A correlation analysis indicates that procurement analysis competencies are significantly associated with higher adoption rates, highlighting the critical role of analytical skills. Organizational factors such as procurement volume and a centralized purchasing unit are positively linked to the use of spend analytics. Environmental factors offer a contrasting picture: while specific factors seem to drive spending analytics adoption, a strong orientation towards sustainability and competency challenges may hinder it. These findings encourage a systemic look at how the public procurement system could be more data-driven. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Pennsylvania's Student Assistance Program (SAP) began in the mid‐1980s to address student barriers to academic success. SAP teams, groups of trained school and community professionals, review referrals, and connect students to services. State leadership conducts an annual SAP team survey, but capacity to evaluate data and affect change is limited. In 2020, leadership partnered with [institution name] to collaboratively review the survey data. METHODS: Frequencies and percentages were calculated. Open responses were coded. Logistic regression models evaluated the association between SAP team size, team meeting frequency, and team maintenance. RESULTS: The 2019 to 2020 survey had 1003 respondents. Median number of SAP team members was 8 (range 1‐21). The majority (54%) indicated their SAP team met once per week/cycle for 30 to 90+ minutes. Larger teams met more often. Annual team maintenance occurred for 38% of teams, and was more common for larger teams. SAP team members identified mental health (68%), trauma (44%), and parent engagement (36%) as top training needs. CONCLUSIONS: An academic partnership successfully provided the capacity to review SAP survey responses, and informed evidenced‐based discussion of best practice guidelines and realignment of staff professional development opportunities. [ABSTRACT FROM AUTHOR]

Data-driven decision-making is a common approach for identifying child maltreatment. However, such strategies must be guided by ethical, equitable, and evaluative frameworks due to their potential for bias and error. This study analyzes the key features, potential challenges, and research evidence of a data-driven strategy known as birth match. Interviews with key informants across four states indicate that programs share features and objectives but that they differ regarding match criteria, data integrity processes, and responses to identified cases. Further, little outcome and equity evidence exists. Results emphasize the need for additional implementation and evaluation infrastructure to ensure transparency and effectiveness. [ABSTRACT FROM AUTHOR]

Project portfolio selection is a multi-criteria decision-making process that is always accompanied by important decisions with uncertainty. Considering the existence of different data in the field of various projects, the use of data-driven approaches to evaluate and select projects in previous studies has been neglected. To cover this gap, the purpose of this study is to provide a hybrid data-driven framework for selecting investment projects in the telecommunication industry. To do this, first by reviewing the theoretical literature and surveys of experts, project evaluation criteria through sustainability paradigm as well as strategic issues are identified, and then with the best–worst fuzzy approach (FBWM), the weight of each criterion is determined. According to the FBWM findings, the most important criteria identified include initial capital requirements, initial revenue, number of specialists employed, energy consumption reduction, and market share increase. After identifying the important criteria, using data envelopment analysis (DEA), the performance of each project was determined separately for each year. Then, based on project labels specified by DEA, the efficiency of projects is measured and evaluated with a forward-looking view through machine learning algorithms such as random forest and support vector regressors. Results show that both models can predict the project performance appropriately, while the random forest regressor has the highest evaluation metrics in both train and test sets. So, the managers could use these results for more efficient project selection. [ABSTRACT FROM AUTHOR]

The study explores how organizational structure and culture in the South African property sector influence decision-making on Environmental, Social, and Governance (ESG) challenges and Green Policy development. It hypothesized that traditional hierarchical structures and non-participative cultures hinder digitalization, impairing data collection essential for informed ESG decisions. In contrast, participative cultures and modern or matrix structures facilitate data-driven insights, expediting ESG and Green Policy advancements. Qualitative methods, including unstructured in-depth interviews with key decision-makers across various sectors (listed companies, commercial developers, financiers, real estate agencies), alongside interviews with digital application developers, provided perspectives on organizational impacts. A systematic literature review supplemented the data. Thematic content analysis of the data revealed traditional structures and cultures complicate decision-making, impede digital transformation, and delay ESG and Green Policy implementation. Conversely, participative cultures and modern structures streamline data-driven decision-making processes, promoting ESG and Green Policy progress. The findings highlight opportunities for traditional organizations to enhance structures and cultures, removing barriers to digitalization and accelerating ESG and Green Policy initiatives. This study underscores the critical role of organization dynamics in fostering sustainability within the built environment, emphasizing digital transformation's importance in driving positive environmental and social outcomes.

The Volt/Var optimization (VVO) enables advanced control strategy development for voltage regulation. With the recent advancement of data-driven approaches and communication infrastructure, realtime decision-making through VVO can effectively address distributed energy resources (DERs) uncertainties without relying on models and topologies of distribution networks. In this paper, a comprehensive review on data-driven VVO in distribution networks is presented, focusing on statistics and machine learning (supervised/unsupervised, ensemble, and reinforcement learning (RL)). State-of-the-art monitoring devices essential in data-driven VVO frameworks are firstly discussed. How data-driven structures serve as primary or supplementary tools in VVO frameworks is then detailed. Since RL is increasingly used, RL-based algorithms (value-based, policy-based, actor-critic-based, and graph-based algorithms) are reviewed. Decision-making processes for RL-based VVO frameworks, such as the Markov decision process (MDP), Markov game, constrained Markov decision process, constrained Marko game, and adversarial Markov decision process, are also surveyed. Future research directions in this area are recommended in the paper.

This opinion paper explores the burgeoning influence of Artificial Intelligence (AI) within the event management industry. AI has emerged as a powerful tool, offering innovative solutions for event planning, execution, and attendee engagement. This paper provides a comprehensive overview of AI's role in event management, discussing its applications, benefits, and potential drawbacks based on contemporary researches and publications. By analysing six (6) current trends and four (4) critical reception of AI in the event industry, we aim to shed light on its transformative potential and its implications for the future of event management. At the end of the paper, the researchers provide recommendations on how to navigate the obstacles in implementing AI technology in event management. This paper is critical in its role to ignite discussions regarding AI implementation in event industry as there are currently very few academic writings on this topic.