Intelligence Beyond Crime: AI, EI, and the Quest for Work-Life Balance in Policing

Work-life balance

As stated by Putra (2023), work-life balance, often known as work-life balance, refers to the ability of an individual to manage many demands in their life at the same time in order to meet their obligations to both positions. The common preface is that work-life adjustment arrangements are presented to assist workers in accommodating what they need to do (care) with what they need to do (work). “Work-to-family interference” or “family-to-work interference” are terms used to describe how employees’ employment or workplace conditions can significantly affect their nonwork-related living circumstances and vice versa (Mache et al., 2023). According to a study on the effects of work-life balance strategies, flexible work schedules may assist women employees regain their equilibrium and relax so they can work freely (Mohan & Prabha, 2024).

Police and work-life balance

The term POLICE stands for “Protection Organization for Life and Investment in Civil Establishment”. The word police have been derivative from the Greek word “POLITIA” means the condition of a state or Government. A police job is very hectic in nature and requires physical fitness. The work of the police is to safeguard the public and protect property. The high level of fitness helps the police executives cope with their workload. Police personnel have experienced more conflict between work and personal life due to work pressure, heavy stress, unfixed working hours, etc. Many research studies have found that political pressure, a lack of family time, a negative public image, and low pay are the leading causes of stress among police officers (SAVITHA, 2023).

The idea of emotional intelligence

The umbrella term of emotional intelligence (EI), originally initially emerged by Salovey and Mayer (1990), is “the ability to monitor one’s own feelings and emotions, to discriminate among them, and to use this information to guide one’s thinking and action” (Salovey and Mayer, 1990, p. 189).

The relationship between work-life balance and emotional intelligence

An unbiased judgment Emotional intelligence (EI) has been separated into four different dimensions by Wong and Law (2002):

Assessment of emotional intelligence

Research indicates that EI significantly influences individual performance and work-life balance. For instance, Barsade and Gibson (2007a, b) highlight that affect plays a critical role in organizational dynamics, shaping employee engagement and collaboration. Bell et al. (2012) found that higher EI correlates with reduced job stress and improved employee well-being. The ability to use emotions to solve issues and think more creatively are made possible by emotional intelligence. A critical set of psychological abilities linked to success in general and work-life balance is emotional intelligence, according to Daniel Goleman. Your ability to balance your professional and personal life will depend heavily on your communication, social, and leadership skills (Ahmad, S.R., Prasad, K.D.V., Bhakuni, S., Hedau, A., Narayan, P.B.S., Parameswari, P., 2023) ( Table 1).

The concept of AI

McCarthy defined AI as “the science and engineering of making intelligent machines”.

John McCarthy, a computer scientist and emeritus Stanford professor, coined the term “artificial intelligence” (AI) in 1955. AI refers to the simulation of human intelligence by a system or a machine. The aim of artificial intelligence (AI) is to create a machine that can think and behave like a human, including seeing, thinking, learning, planning, predicting, and so forth.

Although artificial intelligence (AI) has been a hot topic for decades, the literature has yet to agree on a definition. The lack of a concept to support empirical research on AI has resulted in a fundamental issue with recognizing AI as a whole. C. Geyer, J.C. Weyerer, and B.W. Wirtz (2019).

“The ability to communicate, acquire knowledge, adapt, and resort to information from experiences, as well as to deal with uncertainty” is how Legg and Hutter (2007) define intelligence in their comprehensive approach.

The impacts of AI on the equilibrium between work and personal life.

Task allocation, employee autonomy, and job intensity have all been studied in relation to AI-driven automation by Demerouti et al. (2019) and Bosch et al. (2020). AI’s potential to boost job satisfaction is evident in its ability to reduce mundane work aspects, allowing employees to focus on more meaningful tasks. This shift improves job performance and satisfaction, particularly when employees perceive that AI technologies support their work-life balance (Kumar, Patel, & Singh, 2024). Furthermore, according to Deloitte, 82% of workers think that using AI technology will enhance their productivity and job satisfaction [2023].

The process of digitalization is socio-technical and involves utilizing digitization potential to accomplish the organization’s strategic and operational goals, which improve WLB (Strohmeier, 2020). 90% of workers believe or strongly agree that AI has successfully increased organizational equity and, consequently, project performance, according to an IBM research report [Zhang, H.; Feinzig, S.; Raisbeck, L.; Mccombe 2023].

The possible negative effects on human development are a concern as technology developments, especially in the area of artificial intelligence (AI), change societies [Y. Qin, Z. Xu, X. Wang, M. Skare, 2023]. Challenges brought about by AI’s impact on employment markets, economic activity, and social structures include biases in AI systems, job displacement, and the creation of a digital divide. Making sure AI development complies with moral principles and enhances human welfare is a challenge for policymakers. With its important role in sustainable economic growth being more widely acknowledged across sectors and attracting the attention of corporations, academia, and government, artificial intelligence (AI) is emerging as a crucial technical instrument for everyday assistance in social and economic activities [F. Heylighen, 2017]. According to research, artificial intelligence (AI) promotes economic expansion [P. Aghion, B.F. Jones, C.I. Jones, 2018].

Workflow management and employee productivity have been greatly impacted by artificial intelligence in a variety of businesses (Vrontis et al., 2022). Qu et al. (2024) claim that such insights and suggestions from AI technologies could enhance an organization’s operations with regard to innovation, decision-making, and enhancing workplace efficiency generally ( Table 2).

AI in police work

In recent years, artificial intelligence in law enforcement has become an important aspect of police work globally. As AI based police technology becomes increasingly essential to law enforcement, areas like crime prevention and prediction are going through major changes. Several policing techniques have undergone major changes in the name of public safety, with predictive policing being just one outcome of this shift.

AI in several important ways

Facial Recognition: According to reports, AI in law enforcement can identify faces more precisely than humans and save officers time. Machines are able to distinguish faces based on parameters that go beyond what humans can often notice. Facial recognition technology is crucial for police forces. Police personnel use facial recognition and picture data to find missing persons and fugitives.

Cameras: In addition to using facial recognition on these photos, AI is also able to recognize objects and intricate events like auto accidents. For law enforcement officials attempting to keep an eye on major events, like music festivals or marathons, object identification is particularly crucial. Drone cameras are also being used by law enforcement organizations, enabling them to conduct faster search and rescue operations and cover more ground.

Policing using Prediction: The ability to anticipate crime scenes, the perpetrators, the types of crimes, and the victims is known as AI predictive policing. Police departments and businesses are only now beginning to experiment with predictive policing technology. Eventually, these systems may offer notable advancements in crime prediction and, preferably, prevention.

Non-violent Crimes: Artificial Intelligence (AI) has the potential to detect non-violent crimes such as fraud and money laundering because it is adept at seeing patterns that don’t match. By analyzing photographs and spotting details that the human eye would miss, artificial intelligence (AI) can detect counterfeit goods and currencies with a high degree of accuracy.

Pre-trial Release & Parole: During the pre-trial stage of the criminal justice system, AI is utilized to decide an offender’s parole terms. By evaluating intricate data sets, these AI systems determine whether an offender should be released on parole and the likelihood that they will flee. Because AI doesn’t make mistakes like people do, it promises to be more efficient than humans at helping.

Because AI eliminates the need for labor-intensive duties, officers may focus on more difficult jobs. Additionally, AI may solve crimes that would otherwise go unnoticed and apprehend offenders who would otherwise escape punishment.

Figure 1. Categorization of EI, AI and WLB factors.

Bibliometric analysis is a research technique that employs quantitative and statistical methods to examine research patterns within a specified period. It has become a prominent method for evaluation, and systematic literature review (SLR) follows a structured, transparent, and replicable process (Zupic & Cater, 2015). This study utilizes an SLR based on bibliometric analysis to explore the current research landscape on Intelligence and Work-Life Balance, providing a comprehensive overview and identifying future research opportunities.

A systematic search was conducted using academic databases such as Google Scholar and Scopus. The process involved identifying appropriate search keywords, selecting relevant search engines, and extracting studies based on predefined inclusion criteria. Research papers selected for this study were analyzed using bibliometric methods, which have been widely applied in management research (Kataria et al., 2020). Bibliometric analysis enables researchers to assess intellectual contributions and gain a comprehensive understanding of a research domain. Bibliographic mapping techniques helped identify key research articles, influential authors, and major thematic areas in this field.

This study specifically examines Intelligence and Work-Life Balance, with a focus on police personnel, using VOSviewer software to construct and visualize bibliometric networks. These networks, built through citation analysis, bibliographic coupling, and co-citation techniques, help investigate publication patterns, the knowledge base, and the impact of research articles.

To refine the scope, previous research papers were examined to identify relevant keywords. The selected search terms included “Intelligence,” “Intelligence AND Work-Life Balance,” “Work-Life Balance,” and “Work-Family Balance.” Work-Life Balance (WLB) is an evolving concept that broadens discussions beyond gendered perspectives, promoting a more inclusive approach (Akanji et al., 2020).

Figure 2. PRISMA flowchart showing screening and exclusion process.

The research utilized bibliometric techniques, employing VOSviewer software alongside Scopus’s analytical functions to analyze search results (Purnomo, Septianto, et al.; Purnomo, Rosyidah, et al.). Bibliometric reviews are a specialized form of systematic reviews that offer a transparent, unbiased, and empirically grounded method for examining knowledge production patterns within specific disciplines (Hallinger & Kovacevic, in press; Zupic & Cater, 2015). This approach is particularly valuable in identifying influential authors and helping scholars recognize existing knowledge gaps (Aria & Cuccurullo, 2017).

Unlike traditional review methods—such as integrative, scoping, and meta-analysis—that synthesize findings from a limited number of studies, bibliometric reviews analyze bibliographic data from a broader set of research documents (Zupic & Cater, 2015). Software tools enable this expansive analysis, uncovering overarching trends in knowledge production over time (Hallinger & Kovacevic, in press).

This study developed a comprehensive bibliometric network, visualized using VOSviewer, incorporating elements such as researchers, publication volume, academic affiliations, geographic distribution, research areas, keywords, and author collaborations (Natakusumah). This visualization provides a clearer understanding of the field’s dynamics and key contributors.

Annual trends in publications

Figure 3. Annual publication trends.

From Figure 3, it can be seen that the increase of international academic document publications in the Intelligence & Work-life balance area has increased every year. The peak of the publication of international academic documents in the field of Work-life balance Innovation was the highest in 2023 with 20 documents. The first paper was published in 2007. Up to the year 2008, paper published are in single digit, but from 2015 it gained momentum and in 2024 the growth almost doubled from 9 publications in 2022 to 19 publications in 2024.

Document per year by source

Figure 4. Document distribution by source.

In the annual number of sources of Intelligence & Work-life balance publications is “International Journal Of Services And Operations Management” with 4 documents. Followed by “International Journal Of Environmental Research And Public Health” with 2 documents, “Psychology Research And Behavior Management” with 2 documents, “AANA Journal” and “Administrative Sciences” with 1 document, as shown in Figure.

Distribution by nation

Figure 5. Country-wise publication distribution.

India with 31 academic documents was the leading research nation, Then, with 19 articles, the United States followed, United Kingdom with 8 documents, Malaysia with 7 documents, China with 4 documents, Egypt with 3, France with 3 Nigeria and Thailand with 3 documents.

Intelligence & Work-life balance publications most individual researcher

Figure 6. Most productive researchers.

The researcher in the area of Intelligence & Work-life balance to the most writings was Vasumathi, A. with 5 documents. Pursued by Sagaya, M.T. with 4 documents, Anandh, K.S. with 2 documents, Elkbuli, A. with 2 documents, Kumarasamy, M.M., with 2 documents, Pangil, F. with 2 documents and Subashini, R., Warrier, U., with 2 documents, ABDULLAH, M.S., and Abdelsalam, E., with 1 document each.

The largest frequency of publication of intelligence & work-life balance by subject area

Figure 7. Subject-area distribution.

Among the subjects most frequently seen in international publications on intelligence and work-life balance, business, management, and accounting accounted for 17.5 percent, or 25 documents. The next most common categories are Social Science (17.5%), Medicine (15.4%), Engineering (7.0%), Psychology (7.0%), Computer Science (6.3%), Decision Sciences and Economics, Econometrics, and Finance (4.2%), Nursing (3.5%), Arts and Humanities (2.8%), and Others (14.0%).

Most common research on sustainable leadership by type of document

Figure 8. Document type distribution.

The Largest Frequent Document Scientific Type in Intelligence and work-life Balance Publication is Article (100%) with 91 documents ( Table 3).

As seen in Figure 8, we used the author keywords to do a co-occurrence analysis and found seven major clusters of work-life balance and intelligence (EI & AI) literature. The requirement for the minimal quantity of documents relating to the keyword was five repetitions. Thus, out of 901 keywords, 31 keywords met the requirements.

An examination of research on work-life balance and intelligence (EI & AI) shows that three keywords are prevalent: article, work-life balance, and human, as shown in Figure 8.

Keyword network diagrams assist researchers in identifying barren research areas.

Figure 9. Keyword co-occurrence network.

Density visualization

We conducted the co-occurrence analysis using the author keywords and identified five main density visualizations of Intelligence (EI & AI) and work-life balance literature, as shown in Figure 9. Five repetitions were the criterion for the minimum number of keyword-related documents. Therefore, 5 keywords among 339 reached the thresholds.

Figure 9 illustrates the prevalence of five terms in an examination of research on intelligence (EI & AI) and work-life balance: emotional intelligence, artificial intelligence, work-life balance, and work-life balance.

Figure 10. Density visualization.

Co-authorship analysis

This study has identified the major contributors, academic institutions, and international partners involved in significant collaborative efforts in the field of intelligence (EI & AI) & work-life balance, as represented in network visualizations, by employing co-authorship analysis. This study of co-authorship has targeted the researchers who have the most collaborative engagement in the field of work-life balance and intelligence (EI & AI). Due to this thorough examination, it was determined that 296 authors were covered by this scope. 9 researchers clearly met the predetermined threshold when the condition of at least two publications and citations was used. The network map presented in Figure 9 above shows the names of scholars who engage in the most active collaborations with one another.

With five published publications and twenty-one citations, Vasumathi, A. is without a doubt the most productive researcher in the subject of intelligence (EI & AI) & work-life balance leadership, as shown in Table 2. Sagaya, Mary T., who has received 17 citations and 4 published articles. Later, other well-known scholars in the topic include Subashini, R., Anandh, K.S., Soundarya Priya, M.G., Kumarasamy, and Mokana Muthu, in descending order. They each contributed two published articles and have amassed 11, 3, 3, and 15 citations, respectively ( Table 4).

Co-citation analysis

Co-citation analysis is a novel technique used to understand the cognitive structure of a scientific field. This method of analysis involves recording pairs of source articles that are co-referenced within other source articles. The formation of research clusters occurs when particular pairings of publications are co-referenced by several writers. For this study, co-citation analysis of intelligence research entails the development of network maps that illustrate the connections among cited references, source publications, and authors (Gerçek & Gerçek, 2022).

The network map of co-cited references on intelligence (AI & EI) is determined by applying a minimum citation count threshold of 20.