Original Research

Artificial intelligence-enabled e-services for justice system services delivery in Namibia

Hedvig Mendonca, Tiko Iyamu, Efosa Idemudia

Received: 19 Feb. 2026; Accepted: 14 July 2026; Published: 29 Sept. 2026

Copyright: © 2026. The Authors. Licensee: AOSIS.
This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

Abstract

Background: People living in villages and rural communities across many countries in Africa have limited financial and educational resources to access the services that are provided by the justice system. Thus, making e-services particularly significant. To date, unfortunately, few scholars and researchers have investigated e-services from the justice systems in the context of villages and rural communities in Africa.

Objectives: To fill gaps in the literature and address limitations, we conducted this study to empirically investigate how artificial intelligence (AI) can be used to improve and enhance the e-services and deliverables of the justice system in villages and rural communities.

Method: The theoretical background for the study is the actor-network theory. The core concepts of the theory, such as translation and immutable mobiles, focus on transformation, processes and events involving actors and actor-networks. The case study approach was applied to the Ministry of Justice (MOJ) and a rural community in Namibia, using the qualitative method. Qualitative data were collected from 18 participants through semi-structured interviews.

Results: The findings from the data analysis show that AI can have a positive and significant impact on the justice system e-services and reveal some of the key factors that are fundamentally influencing the process and events.

Conclusion: The research has many theoretical and practical implications for the government, the MOJ, rural communities and academic institutions. Additionally, the findings provide valuable insights, knowledge, and understanding for future research and managerial implications for improving and enhancing the e-services justice delivery in villages and rural Namibian communities.

Contribution: The study highlights and provides some of the benefits, opportunities, strengths and challenges of applying AI to e-services in the justice system in villages and rural communities.

Keywords: artificial intelligence; justice systems; e-services; actor-network theory; case study; Namibia; information technology.

Introduction

The constant advancement in information technology (IT) has significantly improved the features and functions of artificial intelligence (AI) systems. This has led to the widespread adoption of AI technologies, which have significantly impacted all fields, disciplines and professions, including the field of justice systems. As the justice system’s deliverables face increasing expectations and demands for efficiency and effectiveness, AI tools emerge as a more efficient, reliable and robust solution. Artificial intelligence technologies are altering legal work and the justice system by significantly improving speed, reliability, accuracy, efficiency, and the effective handling and management of massive data volumes for human practitioners (Ejjami 2024). In developing countries, there have been discussions in various quarters about how AI affects the arts and science of the justice system procedures and outcomes. The concepts and methodologies of AI have not been embedded in the justice systems of rural areas in most countries in Africa.

Cromartie and Bucholtz (2008) discuss that the ‘term’ rural widely refers to images of farms, villages, small towns, ranches and open spaces. The definition of rural involves multidimensional concepts (Cromartie & Bucholtz 2008). Rural refers to a small population size (Cromartie & Bucholtz 2008). In general, citizens living in African rural areas are deprived of legal and justice systems because of the lack of infrastructure, political instability and poor economic development. Hartung et al. (2022) argue that AI will drastically transform future digital justice systems.

Globally, the digital transformation of the justice system is generally lagging far behind the rest of society (Hartung et al. 2022). The gap between the expected user experiences and the actual services provided by the court is widening daily. The gap in the justice system can be understood by applying the three-layer framework (i.e. enabler, process support and substantive law solutions) (Hartung et al. 2022). To date, among legal and justice systems, the courts and public offices are several years behind in adopting a wide range of technological platforms (Hartung et al. 2022). During the coronavirus disease 2019 (COVID-19) pandemic, economically strong and developed countries realised that their justice systems were far behind in their use of technology and lacked future readiness (i.e. insufficient hardware, lack of software infrastructure, budget issues and fear of accepting change) (Hartung et al. 2022). The key traits for assessing the successful implementation of digital transformation in justice systems are the adoption of software development, decisive leadership, openness to process optimisation, user-centricity and database strategies (Hartung et al. 2022).

Some of the benefits of digital justice systems include faster processing, more efficient case management, more effective legal conflict resolution, a better court environment and working conditions, and greater access to the legal system (Hartung et al. 2022). Advanced and developed countries are seeing a paradigm shift as they tap into younger generations of lawyers, clerks, judges and civil servants to embrace the daily and constant changes in a wide range of technologies with joy, excitement and happiness. Hartung et al. (2022) present that the five success factors that are critical for a successful implementation of digital justice are: (1) governance with clear goals, vision and responsibility, (2) supporting legal framework and paradigm for justice digitalisation, (3) holistic digitalisation strategy at the top level, (4) sufficient budget for legal digitalisation initiatives, and (5) buy-in from all stakeholders.

To date, few studies have investigated the impact of AI on the justice system’s deliverable mandate using electronic services (e-services). Thus, the objective of this study was to examine how AI can be used to enable the Ministry of Justice (MOJ) to deliver e-services to the rural communities of Namibia. To achieve this objective, we employed appropriate methodologies, including a qualitative method, a case study approach, and an interpretive technique. Završnik (2020) argues that AI systems have sophisticated features and functions that may help to reduce case backlogs and have important consequences for criminal justice systems. Artificial intelligence systems can help explain the meaning of the grounds for judgment and fair trials, including making judicial decisions transparent (Završnik 2020).

The remainder of the article is organised into five main sections. It begins with a literature review, which covers the core aspects of the study. This is followed by a theoretical framework that underpins the study. The framework was developed based on the study’s objective: to examine how AI can be used to support the justice system’s deliverables in a developing country. Thereafter, the research methodology is discussed, and the findings are presented. The article is concluded in the last section.

Literature review

To date, the global current uses of AI in the judicial systems are: (1) Indian software using AI, (2) predictive analytics in the United States, (3) smart courts in China, (4) digital case system in the United Kingdom, (5) the Columbia ChatGPT judgment (Siani 2024). In Europe, some of the important aspects of AI in the criminal justice systems are: (1) efficiency, (2) bias reduction, (3) improved decision-making, and data analysis (Ahmed, Fatima & Abbas 2024). The implementation of AI is greatly influenced by culture (Murire 2024). Human behaviour and cognition are shaped by culture, norms, traditions and beliefs (Yam et al. 2023). Culture includes norms, traditions, values and beliefs that define who we are (Yam et al. 2023). Cultures influence attitudes and behaviours towards technologies and machines (Yam et al. 2023). The key components in any culture include institutions, environments, knowledge and objects (Bogdanovych et al. 2010). The Ministry of Culture should be involved in the development and implementation of AI platforms and strategies (Kulesz 2018). Cultural expressions play a critical role in the development and implementation of algorithms and automated platforms (Kulesz 2018). The pioneer of AI implementation and development is culture (Kulesz 2018). Culture helps technologies both solve problems and provide meanings (Kulesz 2018). The lack of culture, inclusion and diversity in AI development and implementation leads to biases (Prabhakaran, Qadri & Hutchinson 2022). The culture that shapes and develops AI should match the global culture of the interactions between humans and AI (Prabhakaran et al. 2022). The successful creation, development and implementation of AI systems depend on the cultures of the humans who use and adopt them (Prabhakaran et al. 2022). The development of AI systems interacts with culture through data, human behaviours and resources (Prabhakaran et al. 2022). Culture is a complex, dynamic system that changes daily and is transmitted and transformed during AI implementations (Prabhakaran et al. 2022).

Isensee, Griese, and Teuteberg (2021) argue that corporate culture positively and significantly impacts the adoption and use of AI relating to creating, implementing and developing sustainable resources and environments. Corporate culture positively affects AI outputs and results (Isensee et al. 2021). The values of AI are a part of corporate culture. Corporate culture on AI influences corporate strategies. Corporate culture positively influences the adoption of digital technologies (Isensee et al. 2021). Corporate culture impacts sustainable AI development (Isensee et al. 2021). Corporate culture is one of the major drivers of sustainable AI (Isensee et al. 2021). Corporate culture consists of assumptions, norms, values, behaviours, actions and observable artefacts (Isensee et al. 2021). Corporate culture should embrace AI-related change. Corporate culture should encourage interaction between designers and AI users (Isensee et al. 2021). The successful implementation and development of corporate culture depend on communication. Isensee et al. (2021) argue that top management and leaders play a key role in the successful implementation and development of corporate culture.

To date, advanced countries have used AI tools and techniques to successfully transform their digital justice systems (Ferris, Min & Nayak-Oliver 2021). Judicial bodies in some countries are several years behind in implementing innovative technology compared with other disciplines or fields due to poor infrastructure, financial constraints, conservative thinking and a poor educational structure (Hartung et al. 2022). Artificial intelligence optimises and improves the performance and decision-making of judges and courts (Teremetskyi & Kovalchuk 2024). The United States is one of the most progressive countries in the use of AI-based systems in the justice system (Teremetskyi & Kovalchuk 2024). The United States is using AI tools and technologies to provide information support for court proceedings and hearings in civil and criminal cases (Jung et al. 2020; Teremetskyi & Kovalchuk 2024).

Scientists from the Stanford Computational Policy Lab developed an algorithm that enables assessment of the risks and endangerment associated with keeping significantly fewer people in custody (Teremetskyi & Kovalchuk 2024). The United States is a leader in implementing AI systems in the judicial system (Teremetskyi & Kovalchuk 2024). The AI-based legal assistant Casetext can analyse, assess, check and verify the relevance of documents to a specific legal case and issue (Wilkins 2023). Large law firms and companies in the United States are using the AI-based Luminance program to assess, analyse and check contracts to identify risks (Nair 2023). Global technology giant IBM created one of the first legal AI assistants, ROSS, that helps lawyers and attorneys search for legal information and documents (Arruda, Ovbiagele & Dall’Oglio 2021).

Lawyers and attorneys in the United States are using the AI-based service Lawgeex to draft and verify legal contracts automatically (Chambers 2024). China is competing with the United States for global leadership in technological innovation and AI. China has a digital court system empowered to hear disputes in copyright, e-commerce violations, business ethics violations and online business disputes (Wininger 2023). Austria has an infrastructure level that supports video and virtual hearings, a justice system, e-learning and free online resources for legal information systems and court decisions (Hartung et al. 2022). Greenstein (2022) presents strategies and guidelines for preserving the rule of law when implementing AI. Justice of the future is a combination of both justice and AI (Choung, David & Ross 2023; Hanseth & Monteiro 1998). Turkanova (2023) argues that implementing AI and machine learning algorithms can effectively and efficiently resolve civil disputes. Saud (2024) discusses some criminal liabilities related to the use of AI in the justice system.

Theoretical framework

We developed a theoretical model based on two concepts from actor-network theory (ANT), translation and immutable mobiles, as shown in Figure 1. The model is intended to understand better how AI can be used to enhance the services of the justice system for the rural community. In the context of this study, the concepts complement one another. Also, recent research has successfully applied ANT to analyse the implementation of AI in other public service contexts, but in other disciplines (Kannelønning 2024; Sperling et al. 2022).

FIGURE 1: Theoretical model based on actor-network theory.

In ANT, the concept of translation is the process that allows a network to be represented by a single entity (Callon 1986; Tatnall 2019). Translation is process-oriented, and it entails social and collaborative practice towards a common interest. The process encompasses shifting negotiations (Latour 2007). Such negotiations allow the service providers and the served community to reach a common understanding of how services should be provided and received. According to Law (1992), translation implies transformation and the possibility of equivalence. Therefore, translation can help reinterpret and represent an entity and align actors around a common interest (Aanestad et al. 2024; Hanseth & Monteiro 1998) using AI. In doing so, the originality is not distorted or manipulated using the concept of immutable mobiles.

The translation process is necessary to restore the balance of justice system services to the community. Tatnall (2005) explains how what has become the common interest is strengthened due to the translation of the innovation. Bignetti, De Souza and Petrini (2023) suggest this is a progressive passage from the micro to the macro level. To enable justice system services in Namibia, such a progression is needed to cover both rural and urban areas. The process helps interpret texts and understand the interplay between signs and objects (Callon 1986). Translation allows entities to be identified and roles to be defined, including the establishment of relationships between the actors (Aanestad et al. 2024).

Entities are immutable because the objects hold their shape as a network, and they are mobile because movement is involved (Law 2002), for example, from Windhoek to Oshakati, in Namibia. This means that books, pamphlets and articles, among other printed materials, could be produced and transferred without the possibility of alteration in the process. Therefore, court papers could be printed and mobilised in their immutable state across locations irrespective of the distance. Artificial intelligence can enable the traces of immutable mobility in their heterogeneous networks. Latour (1987) argues that events, places, people and things are made mobile while remaining stable so that they can move back and forth without distortion, corruption or decay.

Immutable mobiles (in this case, text and audio) are objects that allow knowledge to be used beyond its development origin (Latham 2002; Latour 1988). Development spaces are shaped by movements, which ANT refers to as immutable mobiles (Callon 1986). In the context of this study, for example, a legislative bill passed that moves from the City of Windhoek to regional districts is an immutable mobile that shapes the space of many localities. This means that immutable mobiles can become the method and material of long-distance control (Smith 2003; Wright & Parchoma 2013). Artificial intelligence can translate or move things to automation or artificiality, and back into their original state, without distortion (Chen 2024; Strobel et al. 2023). Legal system documentation, such as text and audio can be transferred to various locations without distortion using AI technologies. This shows the strength of translation by intertwining both social and technical elements.

Research methods and design

This study employed a qualitative research method to examine how AI can be used to enable the justice system deliverables through an e-services approach. This is primarily because qualitative methods are well suited to gaining an in-depth understanding of participants’ perspectives and experiences, as argued in the literature, including Iyamu (2018) and Creswell (2013). Within the qualitative methods, the semi-structured interview technique was selected and used to collect data. The technique allows for deep investigation while maintaining the flexibility to probe and ask follow-up questions, leading to richer data (Naz, Gulab & Aslam 2022).

Two distinct cases were selected to provide a holistic understanding from both the service provider’s and service recipient’s points of view. These cases are the MOJ and the Hilltop (pseudonym), which is a rural community in Namibia. Participants were selected based on pre-defined criteria to ensure that individuals with relevant experience and knowledge of the context were selected. This approach was also affirmed in Palinkas et al. (2015). Based on the criteria, a total of 18 participants were selected and interviewed: 11 from MOJ and 7 from the Hilltop rural community. The interviews stopped at 18 participants because no new information was emerging. Although new information stopped coming in at the 15th participant, we continued and conducted three more interviews. This was to confirm a point of saturation. In the literature, saturation has been discussed as a point at which no new insights emerge (Saunders et al. 2018).

Before data collection, permission was obtained from the relevant authorities, to whom all required documentation relating to the study was submitted. Two separate interview guides were developed, one for each case. The questions in these guides were derived directly from the research objective, which is revisited herein: to examine how AI can be used to enable e-service delivery by the MOJ to the rural community in Namibia. During the interviews, follow-up questions were used to facilitate more in-depth discussions. Permissions were sought and granted by each participant to audio-record the interviews, which were transcribed verbatim. For participants who spoke in a language other than English, transcripts were translated into English. Following translation, the data were cleaned and prepared for analysis.

Participant confidentiality was maintained using pseudonyms. Ministry of Justice participants were assigned MOJ01 through MOJ11, while participants from the Hilltop rural community were assigned RC001 through RC007. Individual interview transcripts were documented separately and formatted with page and line numbers to facilitate referencing. A consistent citation format was formulated; for instance, MOJ01, 1:33–34, refers to an extract from the interview transcript of MOJ participant 01, page 1, lines 33–34. Transcripts were thoroughly reviewed and coded. Similar codes were grouped, and through this process, six key challenges emerged naturally from the data. These challenges were not imposed on the data but rather emerged organically from the participants’ own lived experiences. To strengthen credibility, interview data were supplemented with relevant documents and policies from within MOJ to ensure corroboration. Actor-network theory helped make sense of why certain challenges existed and how different actors influenced the delivery of justice system services in rural areas.

The theoretical framework based on ANT (Figure 1), guided the data analysis and the interpretation of the findings. The theoretical framework focused mainly on translation and immutable mobiles, and it was an iterative process, as shown in Figure 1. In the data analysis, we focused on three areas that are fundamental to achieving the study’s objective: (1) attempted to gain a better understanding of the complex interplay between AI, e-services and community; (2) moved beyond simply viewing AI as a tool wielded by humans and instead recognise its active role in shaping social practices as a nonhuman actor; and (3) based on these understandings (1 and 2), gain insights into how the translation of delegated devices (such as texts and audios) of MOJ deliverables can remain immutable mobiles, for wider and easier access to the rural communities of the country using Hilltop rural community as a case study.

Ethical considerations

Ethical clearance to conduct this study was obtained from the Cape Peninsula University of Technology Research Ethics Committee (Ref. No. 213101181/2023/27).

Results and discussion

Employing ANT as a theoretical framework, specifically the tenets of translation and immutable mobiles, the study explored the challenges of interacting with the justice system from two perspectives (MOJ and Hilltop Rural Community). Actor-network theory’s focus on the relationality of human and non-human actors (Iyamu & Sekgweleo 2013) provides valuable insights into how AI could reshape existing practices and potential future practices. Data analysis revealed six key challenges: language barriers, cultural affiliation and its impact, technology infrastructure, technology integration, alternative delivery mechanisms and automated document verification. Based on the findings, a framework was developed, as shown in Figure 2. The factors found can be used to strengthen and guide the implementation of AI for MOJ services in Namibia. The following discussion delves into these findings, explaining how they illuminate the complex interplay between human and non-human actors. This includes the search for equitable and inclusive justice system services for rural community members.

FIGURE 2: Artificial intelligence adoption framework for e-services.

Cultural affiliations and their impact

Currently, many members of the Hilltop community are unaware of the services that MOJ is supposed to provide, as defined by the country’s constitution. The use of AI promotes inclusivity, enabling community members, regardless of cultural affiliation, to draw closer to the MOJ and better understand the services the Ministry provides. One prevalent challenge, echoed by a participant is that ‘people do not understand the service’ (MOJ10, 6:179). This challenge has been shaped by culture, among other contributing factors. Inclusivity and better understanding can improve service delivery and restore trust between the community and the MOJ.

Culture is observed through individuals and communities, reflecting certain values and practices. Culture shapes how IT solutions can be used in an environment. Thus, culture and technology use form a feedback loop, where people develop an interest in accepting or rejecting the solution. It is on this basis that one of the participants suggests that:

‘People need to understand and trust the system to ensure it effectively delivers justice.’ (MOJ02, 4:127–128)

The understanding is distorted because of cultural affiliation. It is critically important to digitalise traditional culture to improve the inclusiveness of community members (Sun et al. 2023). Therefore, the interlink between culture and technology can have positive and negative impacts on people and service delivery.

Traditionally, in the Hilltop community, older adults were prioritised over others in their need for services. Artificial intelligence profoundly changes popular culture by altering and disregarding the established sequence of queues. It facilitates attendance in this queue system, which the service provider must adhere to, and constructs a new approach to democratise access to MOJ services. The use of AI to improve MOJ service delivery to the community leads to a shift in cultural norms from the perspectives of the service receiver and the provider. Artificial intelligence helps redefine the value of culture through digitalisation, enabling a shift in mindset and fostering inclusivity and broader access for more actors. (Sun et al. 2023).

Language barrier

English is the official language in the country. The MOJ therefore uses English in various communications to provide services to communities. However, most rural community members are either not fluent in English or do not speak it at all. As a result, an interpreter is often required when accessing MOJ services. The use of an interpreter compounds the problem. The problem is twofold for both MOJ and the Hilltop rural community, in providing and receiving services, respectively. One of the participants explains:

‘Language is one of the biggest barriers. Most people in rural communities do not understand the official language very well, and our courts primarily operate using the official language.’ (MOJ01, 2:61–63)

When using an interpreter, the translation is not always accurate, and meanings are distorted. The other problem is that some African native words cannot be found or translated into or out of English. According to Makovhololo and Iyamu (2020):

In the Tshivenda language, they do not have the correct semantics or a word for ‘cancer’, as the natives generally refer to it as ‘Tshilonda’ which means ‘wound’ when translated literally into English. (p. 13)

Increasingly, ANT has proven to be a useful tool for translation. According to Law (1992:5–6), translation ‘is a verb which implies transformation and the possibility of equivalence, the possibility that one thing (for example an actor) may stand for another’. This is done by continually negotiating meanings and putting sociocultural context into unique perspectives. Artificial intelligence has been accurate in mimicking humans. Artificial intelligence can mimic human communication and language, including the semiotics involved (Gao et al. 2023; He et al. 2024). This maintains the immutability of materials and the preservation of knowledge. Heaton (2024) suggests that immutable mobiles are produced to create stability across contexts and to anchor knowledge claims.

Technology infrastructure

Technology infrastructure remains a resource challenge in many rural communities (Rolandi et al. 2021). In the Hilltop community, the technology infrastructure is either obsolete or non-existent. This hinders the delivery of technology-enabled justice system services. The challenge extends beyond usability to resource allocation, as one participant stated:

‘… the challenge would be funding, personnel, and infrastructure.’ (MOJ01, 5:147–148)

This aligns with broader discussions on the digital divide and unequal access to technology and resources (Mannuru et al. 2023). Addressing these disparities requires translating both technical and non-technical requirements into community-centric technology solutions. This reinforces the appropriateness of selecting and implementing technological solutions, such as AI, that are accessible and sustainable (Farahani & Ghasemi 2024).

Beyond infrastructure, usability and trust are crucial for using AI in rural communities to aid access to justice system services. One participant stated:

‘… challenges such as language barriers and the complexity of technology might arise. People need to understand and trust the system to ensure it effectively delivers justice.’ (MOJ01, 4:127–128)

This highlights the importance of translation in ANT, which emphasises the negotiation between different actors to make technology functional and accessible (Mendonca & Iyamu 2024). Artificial intelligence tools can serve as immutable mobiles (Gonçalves & Figueiredo 2012), ensuring consistent and equitable access to justice system services. However, if these systems are not designed with local languages and digital literacy levels in mind, they risk becoming mutable mobiles, reinforcing exclusion rather than bridging gaps. If equitable design and resource distribution are prioritised, AI can help bridge gaps and transform access to justice system services in rural communities.

Technology integration

Technology integration refers to the combination of manual and electronic processes into an IT solution:

‘There is a need for decentralization of services. For instance, in Windhoek [the capital city], the maintenance office serves a large region with a huge population.’ (MOJ02, 3:87–88)

This makes it a bit difficult to drill down to the locals, such as the Hilltop community. The integration will not only uniformise the IT solutions but also introduce coherence and rigidity across functions within the MOJ and among its staff. Artificial intelligence tools have the capability for this type of scalability. This solves the problems of the physical presence of some actors, such as the one briefly explained by one of the Hilltop community members:

‘Some of the things need to be approved by the people who are in Windhoek, and the process is just delayed.’ (RC003, 2:51–52)

Artificial intelligence demonstrates why integration is critical, as it serves to optimise processes and IT solutions (Kumar 2024).

The integration of IT solutions, including associated artefacts and processes such as automated tasks, increases operational effectiveness and efficiency. When asked what seems to be the challenge, one of the research participants responded:

‘They [community members] need to understand their rights in terms of justice, but the current system is not fully effective in reaching everyone.’ (MOJ02, 2:41–42)

Hence, integration of the processes, including the manual, is increasingly important. Additionally, the integration effort will help the Hilltop community from a documentation verification perspective:

‘The people are sent back for documents, or they did not finish the process, and they still need to come back tomorrow.’ (RC003, 2:49–50)

Goel (2024) argues that the integration supports services while maintaining system reliability and enabling operational scaling.

Alternative delivery mechanism

Delivery mechanism refers to the means of delivering justice system services to the rural communities in the country. The MOJ system streamlines and manages its services to the communities. However, service delivery is poor and devastating for many rural Hilltop community members. Services such as access to information or law enforcement personnel are either delayed or distorted. From firsthand experience, an employee of MOJ vocalises:

‘… the challenges we face are, as much as we have a slogan of bringing services to the community, the reality does not align with what we preach. Ideally, our offices should be closer to the communities.’ (MOJ2, 2:69–71; 74–75)

In affirmation, an older adult in the Hilltop community explains:

‘… most of the community members need information because, without information, it is hard to know what to do and where to go, and people travel long distances out of the village to seek information.’ (RC003, 1:26–27)

Given the overwhelming challenges, an alternative delivery mechanism using AI to automate certain services, such as information dissemination, could be considered. An employee of the MOJ who seems to understand the plight of the rural communities offered his view:

‘… the lack of alternative delivery mechanisms for rural communities is a significant gap that needs to be addressed.’ (MOJ01, 1:33–34)

Using AI for automation will have three fundamental significances. Firstly, it increases access and maintains the immutability of the services. Secondly, it improves data optimisation through real-time tracking and performance enhancement. Thirdly, it removes constraints such as time constraints, distance barriers, physical disability and financial difficulties. An AI-enabled alternative delivery mechanism expands access and flexibility to services beyond the current traditional approach. It allows shifting negotiations between the actors. Additionally, the alternative mechanism increases operational efficiency and effectiveness, thereby improving the quality of service delivery to the community.

Automated document verification

Rural community members struggle to access justice system services due to logistical obstacles in verifying documents. One participant highlighted that:

‘… the offices should have photocopy machines available to make copies of documents instead of sending people away to make copies elsewhere. This often forces people to spend extra money because copy services are far away and charge fees.’ (RC002, 4:110–113)

Travelling to off-site copy services incurs additional costs and inconvenience, making it harder for rural community members to navigate the justice system. Another participant stated that:

‘… the offices should have a police desk to certify documents on-site. This would save people from having to travel to a police station just for a signature and stamp, which are required for every copy.’ (RC002, 4:113–116)

These challenges align with research that emphasises the importance of minimising barriers to accessing justice system services, especially for vulnerable groups (Mendonca & Iyamu 2024).

Furthermore, issues with incorrect documentation suggest a need for improved communication and support, as indicated by a participant that:

‘… many of them arrive with the wrong documents, and we cannot just process their cases without them.’ (MOJ02, 3:78–79)

These logistical challenges may be overwhelming for rural community members when interacting with the justice system and may require innovations, such as automating document verification process.

From an ANT perspective, we can understand how AI can address these challenges. Translation is key: realising AI’’s potential requires effectively translating it into a usable reality for rural communities, one that involves diverse actors (Mendonca & Iyamu 2024). Artificial intelligence-powered tools can become immutable mobiles for justice system information and services, but this depends on bridging the digital divide and ensuring equitable access and trust (Gonçalves & Figueiredo 2012). If designed thoughtfully, AI can be transformative for rural access to justice, but this requires a careful and inclusive approach as supported by Farahani and Ghasemi (2024).

Implications of the study

Governments, companies and institutions are investing, spending and budgeting significant amounts of money on e-justice and e-services systems to exceed customers’ and users’ expectations and satisfaction. Generally, investments in e-justice service systems are considered wasted if the systems fail to improve customer and user satisfaction, exceed existing levels, and increase profits, income, revenue and sales to achieve a competitive advantage. In addition, e-justice service systems should be able to reduce costs related to transportation, physical spaces, water and/or electric bills, virtual meetings, training, education, security, among others. Government, companies and institutions benefit greatly from e-justice service systems when they understand the key factors, variables and antecedents that significantly and positively influence the use and adoption of these systems. The study creates opportunities for future research to investigate the core factors that positively and significantly influence the use and adoption of e-justice service systems and justice services.

The findings of the study provide insights into the dynamics of implementing AI-enabled e-services in the Namibian justice system with a particular focus on rural communities. Countries implementing or planning to implement the e-justice services system can apply the findings to improve and exceed customers’ expectations and satisfaction. In addition, the study and proposed adoption framework open the door for researchers to investigate the relationships among the components of e-justice services systems and the critical factors that positively and significantly influence the adoption of a wide range of e-justice services platforms. Future research should investigate the relationship among the impact of AI on culture, language, technology infrastructure, technology integration, delivery mechanisms and automated document verification. The implications of the study and research framework are significant within the context of e-justice services, automated document verification, alternative delivery mechanisms, technology integration, technology infrastructure, cultural impact and language barriers.

The study opens the door for future research to apply ANT to e-justice service systems and justice services. The adoption framework, grounded in ANT and developed in this study, offers valuable guidance to policymakers, senior administrators and innovators seeking to enhance access to justice system services, particularly in rural areas and villages. Practically, the study highlights the need for a holistic approach to e-services adoption, recognising immutability, automating tasks and processes, the interconnectedness of diverse actors, bridging the digital divide, and adopting careful, inclusive approaches to solution design. Furthermore, the findings contribute to the development of policies, training, bylaws and guidelines for digital transformation in Namibia’s justice sector, which other countries can implement. Policymakers and top administrators should consider the adoption framework when designing, training and implementing policies for e-justice services initiatives and the adoption of justice system services to ensure that these initiatives are accessible, easy to use, useful, secure, of high system and information quality, inclusive and equitable.

Theoretically, this study contributes to the greater discourse on a wide variety of e-services and justice system adoption, as well as contributes to the growing literature on implementing AI in the justice systems of developing and developed countries. Additionally, the use of ANT demonstrates how it can be applied to analyse the socio-technical networks that influence the adoption of various technology platforms in rural communities. Tracing the relationships between the human and nonhuman actors and other factors, the study underscores the interconnectedness of these elements in shaping the adoption process for a wide range of information systems platforms.

Further research is recommended on how to maintain e-services and justice system services, as well as the impact of AI on access to justice system services in rural Namibia and villages, with a special focus on user satisfaction, efficiency and usability. The research models motivate and encourage future researchers to investigate and validate the strengths, pathways and significance of all components of the adoption framework. Additional research can help to explain and clarify the concepts and gaps in the e-justice services systems, adoption framework and justice system services literature. Future research should also investigate the long-term training of people in rural communities to ensure the viability of the adopted AI-enabled e-services for fair access to justice.

Limitations of artificial intelligence

The limitations of the study from an AI perspective include costly, lengthy training processes for building machine learning algorithms, the need to hire specialised staff to train models, the complexity of maintaining AI systems and the cybersecurity risks they pose (Bi et al. 2025). Artificial intelligence is just that: artificial. It is the automated formulation of the works created and distributed by humans (Fui-Hoon Nah et al. 2023). Although we have discussed how AI can be both useful and efficient, there are limitations to AI that must be considered when integrating it into everyday work and life. Introducing AI entails a tedious, time-consuming process of developing, training and maintaining the systems necessary to make it effective for the business (Idemudia 2025). Whether it is infrastructure, coding or AI training personnel, this can be quite costly to start up (Bi et al. 2025). This limits the accessibility of AI adoption for smaller companies, which can put a serious strain on any company that may need to free up capacity to undertake this project (Bi et al. 2025). Additionally, companies will need to undergo training for employees to learn to use AI properly (Chen 2023). According to ScienceDirect, scholars have cautioned that excessive use of AI may encourage cognitive offloading, reduce critical evaluation and deplete independent thinking over time (Tian & Zhang 2025). Consequently, employees will need to be trained not to be dependent on AI but rather to use it as a tool to enhance their work (Chen 2023).

Artificial intelligence has the potential to diminish people’s capacity for work as their jobs become more intertwined with technology. It can also erode people’s actual mental capacity through prolonged overuse (Chirayath, Premamalini & Joseph 2025). This can be detrimental not only to the employer but also to the employee’s ability to exercise critical thinking skills (Tian & Zhang 2025). Additionally, this can lead to errors in the company’s data (Tian & Zhang 2025). It is also necessary to remind employees that they cannot rely on AI for the quality and accuracy of the data (Idemudia 2025). Employees must ensure that the quality of their work matches or exceeds that of AI, which includes verifying AI’s output for errors or inaccuracies (Idemudia 2025). Moreover, many employees may be resistant to the integration of an AI system in their workplace. There is often a lack of trust in these new technological advances due to their novelty, but there is also a possibility of disdain towards the integration of AI due to the job displacement people have experienced (Afroogh et al. 2024). Employee morale is just as important to the company’s efficiency and productivity as the tools and processes it uses (Choung et al. 2023). Employees must bridge the gaps between AI and the company’s goals, and if their morale is low, their effort and innovation are likely to be as well (Choung et al. 2023).

Conclusion

The study provides insights, explanations and understanding of how to successfully create, develop and implement AI in justice system services, enabling justice system e-services in rural communities in Africa and other rural villages to achieve competitive advantages. The theoretical background, ANT, helps us understand the relationships, effects and impacts of all the key actors and stakeholders in justice system services. In addition, the study provides strategies, guidelines and policies for how government and top management can address challenges to e-services in the justice system. Some of the key challenges related to the justice system in rural communities include language barriers, cultural impacts, technology infrastructure, technology integration, alternative delivery mechanisms and automated document verification. The study presents a theoretical framework for understanding the variables that have significant and positive effects on the justice system’s e-services. Empirically, the study helps us strengthen our perspective that AI in the justice system entails translating the goals and intentions of various actors into the immutable mobilities of MOJ deliverables. Among other things, the theoretical framework demonstrates the relevance of understanding the sociotechnical complexities of AI implementation, integration and infrastructure.

Despite the understanding of the study, there is room for further research. Firstly, the participant pool can be expanded to empirically investigate how age, education, work experience, culture, among others, positively and significantly impact AI justice system services. On the one hand, more rural communities from around the world should be included, and quantitative methods should be employed. On the other hand, AI specialists should be involved to expand views and complement notions and concepts. Secondly, the findings of this research need to be evaluated and validated, which another study can explore.

Acknowledgements

We would like to thank the Department of Information Technology at Cape Peninsula University Technology for its support. To our colleagues in the research forum, we appreciate your support.

The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article. The author, Tiko Iyamu, serves as an editorial board member of this journal. The peer review process for this submission was handled independently, and the author had no involvement in the editorial decision-making process for this article. The authors have no other competing interests to declare.

Hedvig Mendonca: Conceptualisation, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing. Tiko Iyamu: Data curation, Formal analysis, Methodology, Writing – original draft. Efosa Idemudia: Conceptualisation, Writing – original draft. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and take responsibility for the integrity of its findings.

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

The data that supports the findings of this study are available from the corresponding author, Tiko Iyamu, upon reasonable request.

The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency, or that of the publisher. The authors are responsible for this article’s results, findings, and content.

References