THE EFFECT OF BIG DATA ANALYTICS ON PREDICTIVE POLICING:THE MEDIATION ROLE OF CRISIS MANAGEMENT

Objective: The objective of this study is to evaluate the impact of big data analytics (BDA) on predictive policing, particularly examining the mediating role of crisis management in this relationship. Theoretical Framework: The research is anchored in the domain of big data analytics, focusing on its application within law enforcement for enhancing predictive policing capabilities. The study explores how crisis management serves as a linkage between data analytics and predictive policing practices. Method: The study gathered data from 450 individuals working across various police departments in Dubai, utilizing a questionnaire to collect responses. The analytical approach was based on Structural Equation Modeling, conducted using AMOS software. Results and Discussion: Findings from the research indicate that big data analytics significantly boosts predictive policing and crisis management. Importantly, crisis management was identified as a mediating factor between big data analytics and its efficacy in predictive policing. These results suggest that big data analytics not only directly enhances predictive policing but also improves it indirectly through effective crisis management. Research Implications: This study underscores the importance of integrating big data analytics into police operations to advance predictive policing capabilities. It highlights the dual benefits of big data analytics in both direct application and enhancement through crisis management processes. Originality/Value: This research contributes to the limited but growing body of literature on the application of big data analytics in predictive policing. It offers practical guidelines for police forces, especially within the UAE, to better harness big data for improving their operational effectiveness and crisis management strategies. The study also discusses broader implications for both practice and ongoing research in this evolving field.


INTRODUCTION
The age of big data is evident in every aspect of our modern life.Today, large and government organizations rely on big data to support their work and mission.But there are also significant drawbacks linked to the usage of big data analytics.The fact that big data are typically biased, overly complex (Kaffash et al., 2021).Despite big data include certain crucial information; there are lots of biased information that produced misleading in some circumstances which affect the fairness and impartiality in the decision-making process (McFarland and McFarland, 2015).In addition, the traditional data analytics may not be able to handle such large quantities of data and distinguish between biased and non-biased information (Alshamsi et al., 2024).In fact, the problems of analyzing the large-scale data did not suddenly occurred but have been there for several years because the creation of data is usually much easier than finding useful things from the data (Tsai et al., 2015).The most basic rationale is that traditional prediction techniques are incapable of dealing with the magnitude, speed, and complexity inherent with big data (Kaffash et al., 2021).Concerns have also been expressed about the quality and integrity of data used for policing application, which may not be usable in doing algorithmic analysis (Alexander and Marion, 2020).
Despite the vast development of technology in policing, the adoption of information technology in law enforcement is not always successful and free from errors or biased results.
One of the problems noted by Giest (2017) and Ingrams (2019) is the government agencies lack of institutional support for big data management and shortage of sufficient capabilities to conduct accurate analysis on huge volume of data.Some scholars suggest that the potential of big data is evident in raising policing effectiveness in term of precision of crime prediction.
However, there are some concerns regarding the complexity of big data and the lack of expertise to select the useful data from useless data (Alexander and Marion, 2020).This can be accomplished through training government employees to understand the concept of big data and how to identify the useful information from a large volume of unstructured data (OECD, 2018).In this context, Günther et al. (2017) addressed some of the problems involved with implementing big data, one of them is that public organizations and associated agencies must establish sufficient capacity for an appropriate application of big data analytics.According to MAMPU (2016), when the government lacks analytical capacity, it must seek out new stakeholders with the necessary skill sets.Moreover, big data extends to wide range of fields, it would be a difficult task to benefit from this technology without the right application for big data analytics because the process of crime prediction is complicated and linked to several 4 factors, some of them associated with human errors shortage of necessary resources.In other words, the adoption of big data for the sake of predictive policing can only by trustworthy if the police provide the necessary resources to make this plan successful (Alzahrani, 2023).As this technology is relatively new, whereas more research yet to report more details.Thereby, it is important to identify how big data analytics contribute to predictive policing, and what other factors (e.g., crisis management) may contribute to predictive policing.
The UAE is one of the leading countries in the world that produce and receive a large amount of data every day.However, to identify useful information from the big data remains a challenge (Alkatheeri et al., 2019).It is not clear to the present how big data analytics promote predictive policing in UAE because this technology is still developing and new.As predictive policing utilize two techniques; the first is geographic which uses geospatial data to identify hotspots where crime is likely to occur in the future, allowing police departments to beef up surveillance in certain regions.The second is social which examines social networks and human behaviors in order to detect potential offenders or, conversely, individuals who are more likely to become victims (Jens, 2021).Unfortunately, there is an absence of empirical reports to know how well Dubai police using these two techniques in predicting criminal activities.
In other sense, the vast development in the adoption of technology in law enforcement, and the predictive policing reliance on algorithms for crime anticipation, has increased the pressure on police to deal with the large amount of sophisticated data (Mugari andEmeka, 2021).This scenario might affect the performance of police, e.g.despite the predictive policing helps to control criminal activities, it can also preserve criminal activities.Whereas criminals could use data analytics to strengthen their operations by reverse engineering predictive policing or counter predictive police (Gstrein et al. 2020).Thereby, predictive policing should be implemented by professionals and experts in this field in order fight crimes.Yet, there is a lack of empirical results on the role of computational methods based on big data in the context of predictive policing (Jens, 2021).While the relationship between big data and predictive policing must be evaluated, taking into account all significant and small issues connected to recently acquired technical breakthroughs in order to understand the basic foundation of applying big data analytics in policing (Gourisha and Abdul, 2020).
The previous arguments reveal that the literature have already reported the potential of big data technologies to increase police capabilities to predict crimes, but concerns have been expressed about the lake of expertise in using big data appropriately to promote predictive policing.In brief, the key challenges to successful implementation of predictive policing in the UAE police departments is the absence of empirical data on the role of big data to foster 5 predictive policing and crises management.The insufficient skills in handling big data and lack of resources in this technology has been attributed to weak performance in law enforcement (Alexander and Marion, 2020).Hence, an in-depth discussions and further investigations to provide clear understanding on these challenges will provide a bigger picture to the people in charge of big data analytics in Dubai police departments.

THEORETICAL LITERATURE AND HYPOTHESIS DEVELOPMENT
The previous debates and arguments reveal many models and theory introduced in the literature to manage crises, but the questions arise, what is the most appropriate crisis management theory for law enforcement organization such as police departments.Coombs (2012) claimed that the two most influential models and theories in the discipline of crisis management in the public sector are Fink's (1986) four-stage model of a crisis lifecycle and Mitroff's (1994) five-stage model.As this study focus on the mediating influence of crises management, the five steps of stages of Mitroff's model explain how big data contribute to this process in the signal detection, whereas the large volume of data available from lots of resources could be highly useful for the team who do manage and analyze possible crises, e.g., the role of artificial intelligence in social media big data analytics has been proven to promote disaster management-initial results (Nunavath and Goodwin, 2018).Big data technology enables the early detection of crises through utilizing the predictive analytics capability in crisis management (AL- Ma'aitah, 2020).Because big data analytics is the best tool for disaster response and recovery, for example big data from social media can be used in crisis response for various purposes like, communicating with public during disaster response and recovery, detect early warning messages (Ragini et al., 2018).Hence, a framework based on big data analytics and crisis management could precisely explain how and predictive policing can be improved .(Brian, 2005).Hence, the Mitroff (1994) is more appropriate for the mission of law enforcement and in particular predictive policing so that security crisis can be predicted based on big data analytics in the early stage of crisis management (i.e.signal detection) of Mitroff's crisis management theory.Mitroff (1994) developed a model that divides crisis management into five stages as shown in the Figure 1: Five-stages model of crisis management (Mitroff, 1994) As illustrated in the diagram above, Mitroff's crisis management paradigm is divided into five stages: (1) signal detection; (2) probing and prevention; (3) damage containment; (4) recovery, (5) learning.Mitroff's crisis has an edge significance over another crisis management model.The first two stages (e.g., signal detection, probing and prevention), cover the proactive efforts that an organization can take prior to a crisis event.

PREDICTIVE POLICE IN UAE
The predictive policing is a tool, not an aim in itself.As a result, law enforcement organizations should use other tools in parallel to big data analytics like crisis management.To that end, this study suggests that predictive policing has a bright future in UAE, but that it must continue to overcome the challenges that impede its success.
Predictive policing employs data-driven analytical tools backed by algorithms to assist cops in determining where and when a crime is most likely to occur.Database sets of rules will make decisions in predictive policing using everyday computing (Zwitter, 2015).Dubai has demonstrated its desire to become the world's smartest police force, which will be aided by the most advanced Artificial Intelligence (AI) technologies and apps.In recent years, the Dubai Police stated that their AI-powered smart system as the first country in the Arab world to implement predictive policing based on AI (Alosani et al., 2020).
In 2016, the chief of Dubai Police declared that its latest predictive policing program, has been successfully installed in the headquarter office.This program, the first of its type in the area, was created in support of the UAE's Smart Governance Initiative and was specifically built to supplement the Dubai Police Department's modernized approach to crime prevention and better public safety.Crime Prediction examines existing intelligence and crime patterns from police databases and generates highly accurate statistics about when and where crime is likely to occur next using advanced algorithms.This intelligence is then used to notify patrol predictive technologies and data analysis to foresee offenders and the rates of crimes have witness a significant decrease in recent years.In 2021 the level of crimes marked a significant decrease of 19% from the 54 cases reported in 2020 (Salam, 2021).Furthermore, Dubai records 99.5% drop in unresolved, as well as disturbing crimes, thanks to the latest predictive technologies used by Dubai police department (Amira, 2019).

BIG DATA ANALYTICS
Big data analytics is powering everything people do online today in every industry.Data is also available in a variety of formats, including structured data, semi-structured data, and unstructured data.In a standard Excel sheet, for example, data is classed as structured data with a certain format.Emails, on the other hand, are classified as semi-structured data, while photos and videos are classified as unstructured data.Big data group all these types to form a large volume of data.Sun (2015) suggested that big data analytics is a process of collecting, aggregating, examining, and exploiting massive volumes of data from disparate and autonomous resources in order to identify patterns and other useful information for better managerial decisions.8 In other words, big data analytics is a developing field of study related to huge amount of data, and with technological advancements and the ever-increasing volume of data produced every day, various tools and methods are still being developed by various innovation agents, such as Microsoft, IBM, Tableau, and Palantir, all these companies utilize big data analytics to gain unlimited advantages (SharesPost, Inc., 2017).Big data analytics enable the implementation of new approaches for data collection and analysis, allowing for new ways to ask and answer certain questions that help stakeholders in various industries, such as health, manufacturing, education, and law enforcement.Rather of attempting to extract insights from datasets that are limited in scope, temporality, and quantity, big data analytics solves the problem of handling and investigating massive, dynamic, and diverse datasets.The solutions of big data analytics are based on machine learning and complex algorithms (Kitchin, 2014).Kude et al. (2017) claimed that big data analytics refers to the collection of a vast amount of data and technology from various sources that allows an organization to achieve a competitive advantage through improved organizational performance.It is important to note that the analytical abilities and tools are critical mechanisms for big data analytics (De Mauro et al., 2016).Big data analytics includes a variety of techniques for inspecting data that businesses obtain from various sources in order to uncover significant trends.According to McAfee and Brynjolfsson (2012), big data analytics is a potential value creator that many businesses are implementing to help them make decisions.Analytical methods for evaluating huge data were necessary for effective execution of big data analytics (Cao et al., 2015).Hence, to discover implications and build intuitions, big data analysts must have certain skills (Stubbs, 2014).

CRISIS MANAGEMENT
A crisis as an abnormal circumstance that poses major risks to an organization or the country, and has the potential to become a disaster if ignored or mismanaged (Abdul and Tayyibah, 2010), a crisis is an unprecedented situation that does not allow for much planning.
A crisis as an occurrence contains elements such as surprise, threat, and a limited response time (Tarawneh, 2011).A crisis is an unanticipated event.As a threat, the crisis must be addressed immediately so that the organization can resume normal operations (Fajri and Mawadati, 2018).
When organizations attempt to cope with a crisis, this is referred to as crisis management.When there is a shortage and the organization is in crisis, it must deal with it as soon as viable.9 Tarawneh (2011) claimed that if the crisis worsens, it could lead to catastrophe situation which could not be controlled or avoided.In other words, a crisis is characterized as occurrences that exceed the strength of society, businesses, and systems, necessitating tremendous efforts to recover and restore in order to return to normal situation (Sa'diyah, 2013).Others stated that a crisis is an incident or a series of circumstances that jeopardizes an individual's or organization's safety, reputation, or even survival (Junhong and Vanhala, 2010).The number of potential crises is huge, but crises can be concentrated in a specific period of time (Coombs, 1999) The term 'crisis management' refers to the comprehensive handling of a crisis.Smith (2005) defined crisis management as a treatment by a specialist team according to circumstances outside the organization's control (Smith, 2005).Crisis management is divided into three stages: (1) pre-crisis, (2) crisis, and (3) post-crisis (Sa'diyah, 2013).Pre-crisis can be viewed as the earliest endeavor to collect data linked to the crisis issue and detect difficulties an organization might face when a crisis happens.The crisis management takes the shape of implementing a program strategy to deal with the crisis based on large scale of data.This method is related to identifying problems as the foundation for developing a crisis plan.Postcrisis management is the following stage of crisis management.This step tries to assess the process of putting a crisis plan into action.In brief, the previous arguments indicate crisis management encompasses a variety of issues, including strategies for responding to both the reality and perception of a crisis, as well as defining metrics to determine what scenarios constitute a crisis and, as a result, should trigger the necessary reaction mechanisms.

PREDICTIVE POLICING
Predictive policing is the utilization of data, combined with using machine learning or mathematical algorithms to predict the threats of crime in precise locations and predicting the times for committing possible criminal activities.Predictive policing has raised confidences and expectation as well as strengthened the application of big data technology for crime control (Chan and Bennett, 2016).Predictive policing also defined as the use of analytical techniques 10 to identify promising targets for police intervention with the goal of preventing crime, solving past crimes, and identifying potential offenders and victims (Carrie and James, 2017).It has been reported that police officers had achieved a remarkable success in using predictive analysis to reduce crimes rates in some developed countries like the US and UK (Hayes, 2015).Big data is increasingly being employed in several sectors of human activities, with potentially important implications for our society in various way such as prediction of future crimes.The role of big data in predictive policing can't be denied (Mayer-Schönberger and Cukier, 2013).Nowadays, many police agencies utilize predictive analysis as part of a technique known as "predictive policing" to identify potential future criminal locations and then implement interventions to prevent or stop criminals in those locations.The analysis's accuracy appears to be depending on the quantity and quality of previous data available for examination (Hayes, 2015).
The term predictive policing refers to a variety of analytic techniques and law enforcement practices.It is also claimed as "ability of police to predict where and when the next crime or series of crimes will take place" (Uchida, 2013).Along with improvements in law enforcement decision making, notably officer deployment, based on big data forecasts, binds these together to reach to a specific conclusion.The analytic element, as practiced primarily consists of special software tool that analyses historical crime data (and sometimes other data such as social media, weather, and mortgage defaults) to predict where, but sometimes by whom or to whom, crime will occur in the future.The major claim of big data is that it will transform the work of police in big cities and that, because modern police rely on data-driven, evidenceled, and algorithmically analyzed data, it is the new era of policing (Carrie and James, 2017).
Big data analytical software providers, law enforcement officials all data they need in order to actively support the use of these types of data for crime prevention.However, some authors argue that people should be cautious in their expectations of what predictive analytics can do.While there appears to be a link between the installation of predictive analysis tools and a reduction in crime, there is no proof of a direct cause and effect relationship.Other elements, such as demographics, economics, and a variety of other societal issues, should be addressed when determining the cause of lower crime in any given area (Singh, and Reddy, 2015).
Predictive policing is a policing methodology that use sophisticated computer tools to forecast future crimes based on historical crime and socio-demographic data from different sources.The fundamental purpose of crime prediction is to assist law enforcement in combating crime, both strategically and tactically.Thus, crime prediction in and of itself is insufficient until the results of crime prediction are used for decision making, particularly those involving the deployment of persons and resources (Meijer and Wessels, 2021).11 Predictive policing is defined by Perry et al. (2013) as the use of analytical tools, particularly quantitative techniques, to identify likely targets for police involvement and to prevent or solve past crimes by making statistical forecasts (Perry et al. 2013).Similarly, Meijer and Wessels (2019) stated that "predictive policing is a concept that is predicated on the notion that it is possible to forecast when and where crimes will occur again in the future by employing advanced computer analysis of information about previously committed crimes".Moreover, Meijer and Wessels (2019) claimed that predictive policing is the collection and analysis of data about previous crimes for the identification and statistical prediction of individuals or geospatial areas with an increased probability of criminal activity to aid in the development of policing intervention and prevention strategies and tactics.While the concept appears to encompass the core principles of predictive policing, it may be a little convoluted.

Relevant studies to the research context
Author/Year Objectives Findings Singh, and Reddy, 2015 To go over an in-depth examination of hardware and software platforms for big data analytics.
The research solely looked at the hardware and software platforms utilized for big data analytics.The focus of the review is on the impact of criteria such as scalability, data volumes, and resource availability on big data analytics.However, the review did not cover the most recent big data analytics apps and technologies for successful business decision making.Hashem et al., 2015 To examine the relationship between big data and cloud computing Discuss the fundamentals of cloud computing and big data technology.Furthermore, the study presents fundamental definitions, characteristics, and problems for implementing big data analytics in a cloud computing environment.Tsai et al., 2015 To provide a high-level overview of big data analytics in terms of data mining and knowledge discovery approaches.
Discuss the classic data mining, knowledge discovery, and distributed computing approaches for big data analytics.Nonetheless, little mention was made of the problems, applications, current technologies, or data sources for big data analytics.Mohebi et al., 2016 To examine iterative clustering algorithms for huge data processing utilizing the MapReduce architecture.
The focus of the review is on the iterative clustering strategy for big data processing.

Mohamed et al., 2019
To give a literature review that assesses various tools and methodologies, applications, and trends in big data research.
This study is directly related to our review because it presents big data analytics techniques, trends, and applications.Nonetheless, the study fails to present the many analytics kinds that serve as the foundation of big data analytics.Furthermore, the study did not go into detail on the measures that are essential for success in big data and business analytics.Furthermore, the constraints and future research directions for big data analytics were not discussed effectively.

Ifeyinwa et al., 2019
To examine big data analytics methodologies and how they can The report provides an in-depth examination of big data and business analytics technologies, applications, data sources, and problems.In addition, the paper discusses the advantages and disadvantages of various big data contribute to company success.
techniques, as well as open research topics that need to be explored further.Ho et al., 2021 To investigate Big Data applications: exploratory data analytics of public safety Public engagement and data analytics can be used jointly in the big data era, this case study shows that even with its great promise, Big Data applications cannot replace careful evaluative design and thoughtful consideration of ethical issues and public value questions in policymaking and program management Oatley, 2021 This paper examines the impact of new AI-related technologies in data mining and big data on important research questions in crime analytics.
While big data analytics offers great potential for a better society, there are many factors that need serious consideration such as bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems.

Henriques et al., 2020
The aim of this study to identify major debates on big data analytics, presenting its evolution over the past years and identifying its research tendencies.
The findings suggest that big data analytics is apparently reaching a high level of development various fields, which might be confirmed by publications in the following years.They concluded that other perspectives on big data analytics might include a new wave of studies and that new paths beyond productivity gains can be Source(s): The study's authors The previous arguments reveal that big data analytics gave deep data-driven insights into the competitive advantages earned by large organizations (Wang et al., 2018).Notably, some researchers regard big data analytics as the "fourth paradigm of science" (Agrawal and Choudhary, 2016).Similarly, Hagstrom (2012) asserted that big data analytics is a "new paradigm of knowledge assets", other claimed that it is the next edge of innovation, competitiveness, and productivity (Wedel and Kannan, 2016).Other scholars considered big data analytics as a critical differentiator and a key to growth by high-performing organizations (Thirathon, 2015).
In addition to that, big data analytics may boost organizational outputs and foster industries in a variety of ways.These include enhanced health-care delivery, educational standards, national security, and the ability to engage in good government (Grover et al., 2018).Furthermore, big data analytics has the potential to help policymakers gain insight into enabling policies that will provide a safe playground for investors (Chahal et al., 2019).Furthermore, an education monitoring agency can use big data and business analytics methodologies to analyze teacher effectiveness and improve work attitude.Additionally, mobile network position data can be utilized for traffic management in order to avoid traffic bottlenecks in major cities or to better organize the public transportation system (Ifeyinwa et al., 2019).

The Relationship Between Big Data Analytics and Predictive Policing
The review of literature shows that big data analytics is linked to each other and when police departments utilize big data analytics in law enforcement, they improve their ability in crime prediction, as well as prepare for security crisis at the right time.The empirical evidences found from the literature confirm the association between big data analytics and predictive policing, whereas algorithmic analysis of big data about crimes trends is used in a predictive policing systems (Sandhu and Fussey, 2021).The emergence of 'intelligence-led policing' can be linked to the concept of replacing subjective police discretion with the effectiveness of big data analytics (Hung and Yen, 2021).Several authors in this field suggested that big data analytics has a direct effect towards predictive policing as will be explained in this section.
Carrie and James (2017, p.7) stated that "predictive policing relies on computer algorithms to see patterns, predict the occurrence of future events based on large quantities of data, and aims to carefully target police presence to the necessary minimum to achieve desired results".The significance about predictive policing is the increased power of the big data analytics which in order to achieve mobilization of categorical criminal suspicion activities (Ericson., 2007).
The mission of law enforcement has been changed dramatically by technology advancement over the past two centuries.Today, big data has revolutionized many industries, including the police and law enforcement (Aruni, 2018).The collection and use of data have always been an aspect of police work, technological advancement and increased availability of policing data have led to a shift from predominantly reactionary police work towards a more proactive policing (Jansen 2018;Sandhu and Fussey, 2021).
It is evident that data are used for predictive in many businesses and industries, in addition to other purposes like explanatory goals (Brayne, 2017).Chan and Bennett (2016) claimed that the interest of predictive policing software in the United States with the usage of big data has created a new horizon in policing, whereas big data technology can make policing smarter and information-based rather than subject to human interfere which could bias the results in many cases.Thereby, big data analytics can be useful in all law enforcement aspects, such as crisis and emergency prediction either before the initiating unexpected event (e.g., earthquake, tsunami) or as part of the public riots and political crisis (Qadir et al., 2016).The application of big data analytics assists police to be ready in proper time for any kind of crisis.
Thereby, this research formulates the following hypothesis statement: 14 Hypothesis 1: "Big data analytics has a significant effect on predictive policing"

The Relationship Between Big Data Analytics and Crisis Management
The association between big data analytics and crisis management has been examined in the past.The outcome of previous studies reveals a significant correlation between these two variables.Hence, to confirm this relationship, this section reviews the empirical evidences to link big data analytics with crisis management.This study has surveyed this relationship in the literature and summarized the findings and conclusions reached by scholars in the past.
According to the literature, big data analytics has a direct impact on crisis management in a variety of sectors and industries, and this include organizations worked in the field of law enforcement (Kosciejew, 2020).With the introduction of big analytical tools, vast unstructured text, such as papers, internet blog, social posts and comments, audios in social media, may be simply evaluated for crisis management (Han et al., 2017).According to crisis management theory, big data may guide the course of a possible crisis and has generated opportunities for enhanced control over unexpected events.
Many scholars are working on using big data to read trends and agendas in order to gain insights into big data using various data mining techniques for responding to unknown crises and future events in this regard (Koronis and Ponis, 2018;Park and Alenezi, 2018).There is strong claim that big data techniques may be utilized to handle enormous amounts of crisisrelated data in order to provide insight into the rapidly changing situation and help drive an effective catastrophe response (Qadir et al., 2016).Furthermore, the effectiveness of big data analytics is critical to ensuring that unexpected situations are deemed destructive and harmful in political, economic, societal, or environmental affairs involving security issues, particularly when they occur suddenly and unexpectedly, with little or no warning.As a result, in a crisis that causes social upheaval, it is critical to develop a crisis management plan quickly (Doka et al., 2017).While Watson et al. (2017) provided findings from a case study of a big data survey, which support prior findings that big data can contribute to crisis response efforts by providing effective information for decision-makers.They concluded that expanded utilization of vast volumes of datasets, particularly big data analytics, can positively influence crisis preparation steps in the right time and help organizations to respond properly.
In the same vein, Bellomoa et al. (2016) opted that leveraging big data to understand crisis management in extreme conditions and security threats is the best choice for crisis response.They further suggested that the presentation of an overview of community dynamics 15 and safety issues demonstrated that the literature in this sector can make useful contributions to crisis management in evacuation circumstances during times of crisis.In the same context, Ma and Zhang (2017) proposed that big data analytics be integrated into the knowledge management process to increase data processing and crisis response capabilities.Their case study demonstrates that the proposed knowledge management method improves situation awareness and decision making while dealing with a social security crisis.While Doka et al. (2017) discovered a strong relationship between big data applications and crisis management, particularly crisis connected with riots in the community.Based on empirical data and previous research findings.This research assumes that big data analytics and crisis management are linked to each other.Based on the preceding explanation, the following hypothesis is elaborated: Hypothesis 2: "Big data analytics has a significant effect on crisis management"

The Relationship Between Crisis Management and Predictive Policing
The literature review reveal that crisis management has a significant impact on predictive policing.This claim needs further examination and providing empirical evidence to confirm the connection between crisis management and predictive policing.Researchers in the past have evaluated the link between crisis management with predictive policing.However, this study will attempt to validate this relationship and compare it with the results from other research projects.As mentioned earlier in this chapter, the overall crisis life cycle can be divided and analyzed into three stages namely before, during, and after a crisis (Twigg et al. 2004).Any efforts focused on preventing impending crises will also be part of the first stage in the crisis management.The various different big crisis analytics tasks that can be performed on big crisis data such as (a) various discovery tasks (e.g., clustering outlier detection, or correlation analysis (to detect repeating patterns); and (b) various predictive tasks (e.g., classification, regression, and finally recommendation (whereas some preference is predicted) (Qadir et al., 2016).Some scholars found that effective, smart and proactive policing is clearly desirable to simply reacting to criminal acts.Although there are many methods and techniques to help police officers respond quickly to crime and conduct accurate investigations, crisis management tools are essential for predictive policing so that to identify where and when a crime is expected to occur, and who is the individual likely to be responsible for committing the crimes (Waller et al., 2013).

16
Despite there are limited studies that examined the direct relationship between crisis management and predictive policing, there lots of debates on these variables which required further investigation to verify the association between them.In the light of the previous claim, and other supporting findings on the relationship between crisis management and predictive policing, the following hypothesis is proposed: Hypothesis 3: "Crisis management has a significant effect on predictive policing"

The Mediation Role of Crisis Management
Despite there are few studies that examined the direct relationship between big data analytics and predictive policing, the role of crise management to mediate this relationship has not been reported in the domain of police there which required further investigation to verify the association between them.Police officers who engage in the law enforcement are constantly looking for efficient crime-reduction methods, either through big data analytics or by the adoption of crisis management tools.Whereas reducing crimes is the ultimate goal of every police so that to decrease social harm, strengthens communities, and so promoting homeland security.The use of big data analytics provides excellent resources for achieving this goal, but is it enough to prevent or reduce the effect of crisis.Various studies have previously validated the use of big data analytics in policing, but with the mediation role of crisis management the performance of police will be enhance and can predict not only crimes but also crisis before it happens.Many scholars support the claim that big data analytics is essential for responding to unknown crises and anticipate crimes at the same time (Park and Alenezi, 2018;Gonzalez, 2020;Aruni, 2018).Aruni (2018) discovered that the use of big data analytics in law enforcement assisted police officers in predicting crimes in a short period of time.Many major police departments in industrialized nations now rely on the algorithms for the predicting crimes.However, the same algorithms could be used in the early stages of crisis management.Mitroff's crisis management model has proposed early stages to anticipate the crisis based on information and reports, which in turn very useful for the mission of policing, while big data is the source for police departments to work in this stage and supporting predictive policing as well.In other words, both crisis management tools and big data analytics are necessary for achieving high level of predictive policing.
Although the algorithms used through big data analytics are typically well-intended for specific purposes, the role of crisis management would enhance the decision-making process for better policing performance (Gonzalez, 2020;Aruni, 2018).As found in the literature, the crisis management theory has not been used to explain how law enforcement agencies deal with crises through five essential stages (i.e.signal detection; probing and prevention; damage containment; recovery; and learning) and indirectly influence the effect of big data analytics on predictive policing.In other words, to control the consequences of security situation in the country during crises such as pandemic, or political crises, the police need crises management to improve forecasting tools and predict crimes before happen in a large scale violation to the safety of the country (Boeke, 2018).It appears justified to associate the national security system with crisis management as a function of multifaceted national security management the support the anticipation of future crimes and threats on the national security (Bsoul-Kopowska, 2018).It has been demonstrated that it is required for the establishment of an integral sector of national security, as well as strengthening strategic planning, analysis, and crisis management; developing the resilience potential of national security; the adoption of these strategic crisis management by law enforcement agencies will ensure the country's long-term development in the security of the nation (Bondarenko et al., 2021).Hence, the study of the crisis management framework on which big data technology is built is necessary to have a better understanding of how crisis management theory (i.e.Mitroff model) uses big data to avoid the worst impacts of the aforementioned crises (Alexander and Marion, 2020).As a result, this study will investigate the mediation role of crisis management in modeling the relationship between big data analytics and predictive policing.The following hypothesis statement is elaborated to validate this claim.

FRAMEWORK OF THE RESEARCH
The proposed framework illustrates how the strength of big data analytics used by the police departments (e.g., large volume of data consisting of numbers) and unstructured (e.g., text data) data which processing such data to produce information and analytics (i.e., big data analytics) could promote predictive policing.In this paper the concepts of big data analytics, predicting policing, crisis management have been introduced, whereas the relationships between them have been identified from the previous studies and reports.A new conceptual framework based on various theories is developed in this study to explain how big data analytics enhance the predictive policing in UAE with the mediation influence of crisis management.
Accordingly, this study adopt the Mitroff's five-stages crisis management model (e.g.signal detection; probing and prevention; damage containment; recovery; learning) which will serve as the backdrop for this study to understand how police benefit from crisis management to foster the influence of big data analytics towards predictive policing in UAE The conceptual model of the study, as shown in the Figure 2. below, the constructs of the conceptual framework consists of an independent variable (big data analytics), and a dependent variable (predictive policing), and a mediator variable (crisis management).

Figure 2
The conceptual framework of predictive policing

DATA AND METHODOLOGY
During the survey phase, the researchers has distributed questionnaires to a sample of respondents consists of 450 individuals in all police departments of Dubai.Out of the 450 questionnaires distributed to the study sample, 389 valid questionnaires have been considered for the analysis.This implies a response rate of 86.44%.Scholars in statistics found that a response rate should be more exceed the defined sample to make a correct generalization of results with respect to the whole population.The remaining 61 questionnaires are redundant because they identified with missing data or totally incomplete.Missing data is one of the most persistent problems during data analysis procedure.Moreover, the missing data could deviate the conclusions and should not be included in any analysis.Therefore, 56 incomplete questionnaires, such as missing data or blank are filtered and then removed from data analysis.19 Nonetheless, the minimum sample requirement for generalisation was met.A period of six ( 6) weeks was allocated to collect data.The results were downloaded from the online data collection platform and analyzed with Analysis of (AMOS).The collected data was originally entered into the online data collection platform based on the weights of the various responses.

DEMOGRAPHIC ANALYSIS
Table 1 shows the respondents profile for those participated in the research.
Demographic analysis includes the statistics that explain the characteristics of study sample.
Hence, this section demonstrate the population dynamics by investigating the main demographic data such as age, gender, academic level, work experience of the respondents as shown in Table 1.With respect to gender, the number of males is higher than females in the Dubai police, while those aged 30-39 years are the highest number of employees, and employee older than 60 years are the lowest group.It is interesting to know that the Majority of police officers have fair experience on using the application of big data analytics.The majority of respondents (91.00%) confirmed that predictive policing in Dubai is effective.This result is identical with a recent report that confirm Dubai Police using AI to predict crime led to reduce the serious crime rate down to 25%.Additionally, Dubai police force has implemented a proactive approach to policing, where officers are deployed to highrisk areas based on the analysis of data.All these factors make predictive policing in Dubai effective and useful in enhancing the safety in the city so that 91% of respondents confirmed that predictive policing reduced alarming crime rates.
It is evident that predictive policing helped the Dubai police form effective task forces (94.09%).This is mainly because the predictive technique uses data analysis and machine learning algorithms to identify patterns and predict potential criminal activity.By using this approach, the Dubai police have been able to allocate their resources more efficiently and effectively, resulting in a reduction in crime rates.

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The success of predictive policing in Dubai has led to its adoption in other cities around the world.In other words, the implementation of predictive policing in Dubai has been a gamechanger in the fight against crime.Its success has not only resulted in a reduction in crime rates but has also led to its adoption in other cities around the world.This technology has proven to be effective in identifying patterns and predicting potential criminal activity, enabling law enforcement agencies to allocate their resources more efficiently and effectively.As a result, predictive policing has become an essential tool in modern law enforcement.In other sense, the implementation of predictive policing in Dubai has been a significant success story, with benefits that have extended beyond the city's borders.Its impact on reducing crime rates and improving resource allocation has made it an essential tool in modern law enforcement, and its adoption in other cities around the world is a testament to its effectiveness.
Predictive policing indications are very promising, whereas the police officers confirmed that it was effective in reducing alarming crime rates, rapidly handling reports, reducing crimes in the city, and forming effective task forces.A study by Al Boom and Bin Thani (2022) built forecasting model in SAS and it showed us that in the upcoming years, the crime rates in Dubai will decrease dramatically based on the pattern of crimes that predictive policing provide using in the historical data of criminal activities in the city.
Moreover the majority of police officers in Dubai confirmed that Dubai police was very good in responding to recent crises in the country.Overall, it seems that the Dubai police force has received positive feedback from both residents and officers for their quick and effective response to recent crises in the country.The Dubai police force has been highly praised for their efficient and prompt response to recent crises in the country.Both residents and police officers have expressed their satisfaction with the police force's performance, highlighting their professionalism and effectiveness in handling emergency situations.This positive feedback is a testament to the dedication and hard work of the Dubai police force in ensuring the safety and security of the community..  2, it is evident that AVEs for all dimension within the standard threshold (≥ 0.50), with a condition that the composite reliability ≥ 0.7.In conclusion, each dimension is adequate to convey sufficient variance for indicators to converge into a single dimension.

Table 2
The amount of AVE and composite reliability of dimensions Next step is measuring the discriminant validity of constructs in order to know how far the dimensions that are related to a single construct distant from each other.In other words, the discriminant validity refers reveal the extent of differences between the dimensions of a single construct, as well as the level of non-correlation between them.The condition is that the AVEs related to each dimension must be greater than all the correlation between the dimensions of a single construct and less than 0.70.As shown in Table 3, all correlations are less then (0.70) the threshold point which ensure that discriminant validity of constructs has been obtained.

STRUCTURAL EQUATION MODELING
Reading the output data in Table 4 shows that the magnitude of CMN/DF range in between 1.596 -1.758.To achieve a good fitness with the observed data, the Normed-ratio (CMIN/D) is preferred to be less than 3.00.CFI magnitude ranges in between 0.968 -0.984 (cut-off ≥ 0.90).TLI magnitude ranges in between 0.964 -0.980 (cut-off ≥ 0.90).While PCLOSE is non-significant for all measures (Non-Sig.≥ 0.05), and RMSEA ≤ 0.08 for all constructs which indicates a good data fit with the measurement model.In conclusion, all fitindices are matching the cut-off points of SEM standards.The second step in CFA analysis is evaluating the strength of relationships (i.e., beta) between the dimensions and their associated construct, as well as ensuring that all these relationships are significant as shown in Table 5.In conclusion, the CFA analysis is consistent with the result of EFA which reveal that all constructs are second order (consisting of a multiple sub-constructs or dimensions).Each sub-construct (dimension) is measured using certain number of indicators.Whereas big data analytics is linked to four latent dimensions (volume, velocity, variety, and veracity), crisis management is linked to five latent dimensions (signal detection, probing and prevention, damage containment, recovery, learning from crises).Finally, predictive policing is linked to three latent dimensions (predictive accuracy, predictive fairness, crime rates).

HYPOTHESES TESTING
In Figure 3   This section presents the result of hypotheses testing for direct effect.The direct effect test aims to examine the relationships between the independent variables and the dependent variable.The results are presented in table 6 and explained in the following conclusions.To support the statements of hypotheses, the Critical Ration (C.R) is applied to evaluate the significance level of unstandardized regression coefficients (Hair et al., 2017).The Hypothesis (H1) is tested.As reported in Table 6, this hypothesis is accepted which states that "Big data analytics has a significant effect on predictive policing" (Sig.= 0.00, C.R = 2.75 ≥ 1.96).In line with this findings, previous studies reported similar results.One reason why big data analytics has a significant effect on predictive policing is that it allows law enforcement agencies to identify patterns and trends in criminal behavior (Ragini et al., 2018).
By analyzing vast amounts of data from various sources, including social media, crime databases, and surveillance footage, police can better understand how criminals operate and anticipate where and when crimes are likely to occur using special algorithms (Raghunathan, 2015).This can help police agencies allocate resources more effectively and respond to incidents more quickly, potentially reducing crime rates and improving public safety.
Additionally, big data analytics can provide insights into the root causes of crime, such as poverty, unemployment, and social inequality, which can inform policy decisions aimed at addressing these underlying issues and reducing crime in the long term (Oatley, 2022).Overall, the findings in this chapter show that the use of big data in law enforcement has the potential to revolutionize how police departments operate and ultimately make communities safer.Thus, the implementation of big data analytics in law enforcement can significantly enhance the efficiency and effectiveness of police departments in preventing and solving crimes.The insights gained from analyzing large volumes of data can help police departments make datadriven decisions and allocate resources more effectively.Ultimately, the use of big data in law enforcement has the potential to revolutionize how police departments operate and improve their overall effectiveness in preventing and solving crimes.
Hypothesis (H2) is tested.As reported in

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were used to track the spread of the virus and identify hotspots, allowing governments to implement targeted measures such as lockdowns and contact tracing (Park, 2022).Additionally, organizations used data analysis to adapt during (Koronis and Ponis., 2018)

Hypothesis Testing (Mediation Effect)
As stated above, Table 7 which report the significance of direct relationships between the constructs.If one of these relationships is non-significant, then a mediation effect does not exist.Otherwise proceeding for testing the significance of indirect and total effect using bootstrapping technique.The next step in mediation analysis is testing the indirect and total effects between the constructs so that to confirm the mediation role of crisis management.
Bootstrapping is a popular and reliable approach in empirical studies for testing mediation effects.AMOS software is utilized for conducting bootstrapping.The "total effect" = "indirect effect" + "direct effect".It is necessary that all indirect and total effects are statistically significant in order to consider the mediation role of crisis management.Preacher and Hayes (2008) indicated that when the indirect impact of big data analytics on predictive policing through crisis management, with 95% bootstrapping confidence interval does not overlap a 0 in between, this indicates a mediation effect.Table 7 indicates the output of bootstrapping statistics for the mediating path (big data analytics→ crisis management → predictive policing).

Table 7
The summary of bootstrapping of total and indirect effects It is found that the indirect (mediated) effect of big data analytics on predictive policing is significant (Sig.= 0.01), and 0 out of interval (0.245, 0.892).These two conditions are essential to consider the indirect path big data analytics→ predictive policing is partially mediated by crisis management.The same assumption is applied to assess the total effect between the independent and dependent constructs.In brief, the result from bootstrapping reveal that both effects (indirect/total) due to the partial mediating influence of crisis management are statistically significant (Sig.≤ 0.05).Furthermore, the evaluation of output data in Table 4.13 shows that the (0-null) bound falls out of confidence intervals for all paths.
Based on this result, hypotheses 4 which states that "Crisis management mediate the relationship between big data analytics and predictive policing" is accepted and supported.

DISCUSSION AND CONCLUSION
The review of literature on the topic of policing reveal that big data analytics is linked to crisis management, while big data analytics is third variable interact between them.In theory, the review of previous academic works shows that Mitroff's crisis management theory has not been used to explain how law enforcement agencies deal with crises through predefined stages (i.e.signal detection; probing and prevention; damage containment; recovery, learning).There have been lots of study that used Mitroff's theoretical model in industries, business, and manufacturing (Mitroff, 1994).However, none of them in the field of law enforcement and policing.As explained in this paper, the capability of police is driven by limited volume of data in the past, while at the present time the performance of modern police is largely depending on big data to fight the most complicated crimes and offenders who by themselves rely on the science of big data to commit new types of crimes (e.g.cybercrimes, hacking, social network 29 traps, financial frauds, identity illegal information, theft and sale of corporate data).These crimes require advanced predictive methods in parallel with effective crises management strategy to deal with all kinds of crises (financial, technological, natural, community, pandemics, human-made disasters, acts of malevolence).However, the researching on the connections between these variables was not widely covered, with relatively little focus on the underlying criminological theory from the perspective of big data.The study of crisis management framework on which the big data technology is based is essential to have a bigger picture on the how crises management utilize big data escape the worst consequences of aforementioned crises (Alexander and Marion, 2020).As the findings in the literature reveal that big data analytics afford the possibility of reframing the performance of almost every organization at the current technological era (Kitchin, 2014), this topic was not studied in UAE policing, while the focus by most scholars in the past was mainly on the role of innovation tools in fostering the performance of police (Alshamsi and Isaac, 2019;Alosani et al., 2020;Almazrooei et al., 2021).The literature review indicates of dearth of studies that highlight the role of big data analytics on predictive policing in UAE, as well as examining the mediating role of five stages of Mitroff's crisis management theory on this relationship.
In summary, big data analytics showed great results to foster the performance of police in almost all developed countries.Today, the power of big data cannot be denied in controlling the level of crimes, especially those rely on technologies and sophisticated malicious software.
However, the research in this discipline is still evolving and not yet established well in developing countries such as UAE.Nevertheless, a comprehensible understanding of big data analytics, and its definition and influence on police work in developing countries is still not widely covered (Uthayasankar et al., 2017), while examining the relationship between the application of big data analytics and crisis management remained scarce (Mikalef, 2018;Qasim et al, 2020).In addition to that, big data analytics is still considered an innovation tool in the early stages of development and research which require more academic investigation and further empirical evidences (Kitchin, 2014), especially in the domain of law enforcement and policing The findings in this study reveal the reasons why big data analytics, crisis management, and predictive policing are linked to each other, one of the reasons is the ability to leverage data to prevent and respond to emergencies.By analyzing large amounts of data, law enforcement agencies can identify patterns and predict potential criminal activity, allowing them to take proactive measures to prevent crime before it happens.Additionally, big data analytics can help police agencies to respond for crises quickly, such as natural disasters or terrorist attacks, by This allows them to deploy resources more effectively and proactively, rather than simply responding to incidents after they occur.Additionally, predictive policing can help reduce bias and discrimination in law enforcement by using data-driven approaches rather than relying solely on subjective judgments.By implementing effective crisis management techniques, law enforcement can identify potential risks and develop strategies to mitigate them before they escalate into crises.This can include monitoring social media for potential threats, conducting risk assessments, and developing emergency response plans.By being proactive in their approach, law enforcement can enhance public safety and prevent disasters from occurring.
The application of crises management will help the community in many ways.As one of the key components of a crisis management national plan is the safety and well-being of the citizens.The outcome of this study will help government authorities to achieve the goals behind applying crisis management such as preventing the damage to the market and help organizations in the UAE to survive and sustain reputation, restore stability, return the GDP to its normal rate, keep unemployment to the minimum, and retain the operations of businesses as quickly as possible.In sum, any study that provide empirical evidence on crises management to the public or government agencies will ensure the safety of people and long-term prosperity.

LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH
Moreover, big data analytics as a study discipline are still evolving and not yet established.Thus, understanding the phenomenon and the variables that interact with big data and crisis management still needs more investigation and in-depth quantitative analysis.At the same time, a framework that correlates big data analytics, risk management, and crisis management are yet to be established.The actual progress made in big data analytics revealed a lack of management study in the field and a distinct lack of theoretical constructs and academic consistency perhaps a function of an underlying methodological rather than an educational challenge.In other words, there has also been a lack of study studies that broadly address the critical challenges of big data in the UAE or investigate opportunities for new theoretical models.Thus, studies in this field should identify the big data challenges and associate big data analytics methods with crisis management to understand how these two variables are associated (AL- Ma'aitah , 2020).It is important to note that the availability of big data alone does not stop crisis or provide complete protection from the consequences of crisis (Bacon., 2013).A good example is the existence of a vast amount of data on earthquakes, but the lack of a consistent model that can precisely predict earthquakes (Watson, 2017).Some existing studies found that the challenges of crisis management are related to a hypothesis, testing and frameworks utilized for big data predicting (Watson, 2017), whilst Cohen et al, (2013) recognized more concern on the absence of theory to complement big data in a wide range of industries.Besides, the diverse challenges linked to predicting big data should be given more consideration (Abdul Samee and Tayyibah, 2010).

3. 2
CONSTRUCT VALIDITY ANALYSIS Construct validity is essential to the perceived overall validity of the empirical data in this study.Convergent validity, along with discriminant validity, is a subtype of construct validity.It is a primary analysis that should be conducted before CFA.In this analysis the associations of the indicators are evaluated in relation to their related dimensions of big data analytics, risk management, and crisis management.The first step is exploring the true structure The Effect of Big Data Analytics on Predictive Policing: The Mediation Role of Crisis Management ___________________________________________________________________________ Rev. Gest.Soc.Ambient.| Miami | v.18.n.2 | p.1-38 | e06033 | 2024.22 of indicators with their related dimension by evaluating the initial measurement models based on the dataset.In other sense, these steps are necessary to improve the fit of data with the measurement model before building the structure model.The convergent validity is associated with the indicators belong to a single dimension and reveals how close those indicators to determine a particular dimension.To establish convergent validity, the Average Variance Extracted (AVE) should exceed (0.50).Referring the output data in Table report that all fit-indices are compatible with the cut-off points for SEM standards.Starting with PCLOSE = 1.00 (perfect non-significant), while RMSEA = 0.035 (≤0.08) which reflects a high degree of model-fit.Moreover, CMIN/DF = 1.486 (≤ 3.00), CFI = 0.949 (≥ 0.80), TLI = 0.946 (≥ 0.80), the typical range for TLI and CFI lies between zero and one, whereas TLI and CFI values close to 1 indicate a very good model fit.These are the standard fit-indices in SEM standards that should be used to assess the validity of a theoretical model with an empirical data.
Figure 3Study Framework

The
Effect of Big Data Analytics on Predictive Policing: The Mediation Role of Crisis Management ___________________________________________________________________________ Rev. Gest.Soc.Ambient.| Miami | v.18.n.2 | p.1-38 | e06033 | 2024.26 following two criteria are used to validate the hypotheses: (1) if C.R ≥ 1.96 for a specific relationship, then a hypothesis is accepted, otherwise the hypothesis should be rejected, and (2) the estimate path coefficient for a relationship is significant at the 0.05 cut-off point (Sig.≤ 0.05).

The
Effect of Big Data Analytics on Predictive Policing: The Mediation Role of Crisis Management ___________________________________________________________________________ Rev. Gest.Soc.Ambient.| Miami | v.18.n.2 | p.1-38 | e06033 | 2024.30providing real-time information about the situation and the needs of those affected.Overall, the respondents confirmed that the integration of big data analytics into crisis management and predictive policing strategies has the potential to improve public safety and save lives in Dubai.However, it is important to balance the use of technology with privacy concerns and ethical considerations to ensure that it is used responsibly and effectively.One potential concern with the use of big data in crisis management and policing is the potential for bias in the data and algorithms used.If data is collected from certain sources or demographics, it may not accurately represent the entire population affected by a crisis.In general, adopting big data analytics by Dubai police has dramatically improved proactive policing.Ultimately, the responsible use of big data can lead to more effective and efficient policing practices while protecting the rights of individuals.The review of respondents' answers reveal that Dubai police, like many law enforcement agencies around the world, are turning to big data analytics to enhance their ability to predict and prevent crime.By analyzing large sets of data, including crime statistics, social media activity, and even weather patterns, police can identify patterns and trends that may indicate potential criminal activity.
This software is unique in its capacity to reliably recognize intricate patterns of criminal behavior in seemingly unconnected occurrences and then estimate the likelihood of recurrence.The result is improving the performance of Dubai police in the arrest of over 100 wanted criminals since the beginning of applying predictive policing in 2018.His Excellency Major General Khalil Ibrahim Al Mansouri, Assistant Commander-in-Chief of Criminal Investigation, affirmed that the AI The Effect of Big Data Analytics on Predictive Policing: The Mediation Role of Crisis Management ___________________________________________________________________________ Rev. Gest.Soc.Ambient.|Miami|v.18.n.2 | p.1-38 | e06033 | 2024.7 teams about which districts may require greater resources to combat possible criminal activities (SIME, 2016).Space Imaging Middle East (SIME) is a market-leading complete solutions provider that incorporates all aspects of the satellite imagery value chain.SIME collaborated with Dubai Police on the application of predictive software on this major project.project in police comprises advanced smart devices to help reduce crime rates and improve people's safety and security(SIME, 2016).In summary, data has long been used by police departments in modern countries like UAE, whether it's to track crimes on a map, track terrorist groups, or maintain track of repeat offenders.Authorities are now eager to apply cutting-edge analytical techniques to minimize crime and solve prior crimes in the country.This analytical practice is primarily concerned with identifying potential criminals for police intervention.Law enforcement in Dubai utilized The Effect of Big Data Analytics on Predictive Policing: The Mediation Role of Crisis Management ___________________________________________________________________________ Rev. Gest.Soc.Ambient.| Miami | v.18.n.2 | p.1-38 | e06033 | 2024.

Table 3
Correlations between Constructs

Table 4
Fit indices of measurement models

Table 5
Significance and strength of relationships between constructs and dimensions

Table 6
Standardized regression weights

Table 6
situations where time is of the essence, such as natural disasters, cyberattacks, or public health emergencies.With big data analytics, decision-makers can make more informed decisions, allocate resources more effectively, and mitigate the impact of crises on people and communities.For example, during the COVID-19 pandemic, big data analyticsThe Effect of Big Data Analytics on Predictive Policing: The Mediation Role of Crisis Management ___________________________________________________________________________ Rev. Gest.Soc.Ambient.| Miami | v.18.n.2 | p.1-38 | e06033 | 2024.
humanitarian and disaster operations.Overall, the use of big data analytics has transformed crisis management by providing faster and more accurate insights, allowing organizations to respond more efficiently and effectively to crises.This has resulted in better outcomes for individuals and communities affected by crises.Hypothesis (H3) is tested.As reported in Table6, this hypothesis is accepted which states that "Crisis management has a significant effect on predictive policing" (Sig.= 0.00, C.R = 4.86 ≥ 1.96).This study provide a new evidence that crisis management plays a crucial role in supporting predictive policing efforts.In fact, crisis management and predictive policing are often intertwined, as crisis management can help law enforcement agencies anticipate and respond to potential threats before they escalate into full-blown crises.By using advanced data analytics and other tools, crisis management professionals can help police identify patterns and trends in criminal activity and use this information to develop more effective strategies for preventing crime and keeping communities safe.For example, crisis management can provide valuable support to Abu Dhabi police during times of crisis, such as natural disasters or terrorist attacks, helping to coordinate emergency response efforts and ensure that resources are allocated efficiently.