The South African Computer Journal (SACJ) is a DHET (South Africa) accredited specialist open access academic journal, publishing research articles and communications in English in the Computer Science, Computer Systems and Information Systems domains. The journal is published by the South African Institute of Computer Scientists and Information Technologists (SAICSIT) and hosted on the Khulisa Journals platform (Academy of Science of South Africa (ASSAf)). The journal - being included on Scopus since 2016 - appears on the DHET sub-list of Scopus-indexed journals that qualify for subsidy for articles published by South African academics at public South African universities.
South African Institute of Computer Scientists and Information Technologists (SAICSIT)
Copyright (c) 2026 Katherine Malan https://creativecommons.org/licenses/by-nc/4.0
Revolutionizing blockchain ecosystems: InternxtChain’s sharded storage & zk-SNARK security for secure, scalable, and decentralized solutions
The inherent challenge of balancing scalability, security, and decentralization – commonly termed the blockchain trilemma – continues to hinder the adoption of distributed systems. This paper presents InternxtChain, a decentralized storage framework designed to address this trilemma through a novel integration of erasure-coded sharding, zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs), and a sharded Proof-of-Storage consensus mechanism. By leveraging aggregated BLS-381 signatures and distributed redundancy protocols, the framework achieves a throughput of 2,800 transactions per second with a latency of 420 milliseconds across 1,024 nodes, surpassing Filecoin by a factor of 3.5 and Ethereum’s capacity by 165 times. The system maintains 99.9% data integrity even under adversarial conditions involving 30% Byzantine nodes. Additionally, InternxtChain reduces storage costs to $0.002 per gigabyte, representing an 85% reduction compared to centralized alternatives like AWS S3. Empirical evaluations demonstrate linear scalability to 4,200 transactions per second with 2,048 nodes, alongside hardware affordability at $180 per node. These advancements not only outperform decentralized platforms in throughput by 2.8 times but also ensure GDPR-compliant data sovereignty, positioning InternxtChain as a pioneering solution for Web3 ecosystems seeking to harmonize enterprise-grade performance with decentralized trustlessness.
Copyright (c) 2026 Saha Reno https://creativecommons.org/licenses/by-nc/4.0
Enablers and constraints of analytics-supported personalised learning in South African higher education
The growing digitisation of teaching and learning in higher education has intensified interest in learning analytics as a means of supporting personalised learning through data-informed pedagogical decision-making. In South African higher education, this interest is shaped by massification, inequality, and pressure to improve student success and completion, positioning learning analytics as a strategic response to structural challenges. Despite institutional investment in analytics platforms and data strategies, personalised learning remains unevenly enacted in practice. This study examines how institutional, technological, and instructional conditions shape analytics supported personalised learning in a South African higher education institution. Using a qualitative case study design, semi-structured interviews were conducted with seven participants involved in analytics, instructional design, academic support, and teaching. Data were analysed using reflexive thematic analysis. The findings show that personalised learning emerges from the interaction of three interdependent conditions: institutional commitment and governance, technological integration and data coherence, and lecturers’ interpretive and ethical capacities to translate analytics into pedagogical action. While leadership support legitimised the use of analytics, fragmented data systems and uneven data literacy constrained coordinated instructional responses. Conceptualising these dynamics as a socio-technical configuration highlights how the design and governance of analytics systems shape pedagogical possibilities for equitable personalised learning.
Copyright (c) 2026 Mashite Tshidi https://creativecommons.org/licenses/by-nc/4.0
Copyright (c) 2025 Katherine M. Malan https://creativecommons.org/licenses/by-nc/4.0
Copyright (c) 2025 Irwin Brown, Lisa Seymour, Michael Hart, Michael Eccles https://creativecommons.org/licenses/by-nc/4.0
Copyright (c) 2025 Jan H. Kroeze https://creativecommons.org/licenses/by-nc/4.0
Heavy, phone, and light gamers: Adolescent gaming profiles in South Africa and their academic and psychosocial correlates
The amount of time adolescents spend on video games has steadily increased over the past five decades. Recently, the role of gaming in adolescent wellbeing has received growing scholarly attention, particularly following the inclusion of Internet Gaming Disorder as a condition for further study in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). However, data from African contexts remain extremely limited, and little is known about South African adolescents’ gaming behaviours and their effects. This study provides an important first step toward addressing this gap. Drawing on survey data from 960 high-school learners, we examine patterns of gaming behaviour, symptoms and prevalence of gaming addiction, and the interplay between gaming behaviours, academic performance and psychosocial outcomes. Our findings indicate that although South African adolescents tend to spend fewer minutes per day gaming than peers in North America and Europe, they report comparatively high levels of problematic gaming symptoms and prevalence. We identify three distinct gamer profiles: Heavy gamers, Phone gamers and Light gamers. Heavy gamers perform significantly worse academically than Light gamers. In terms of psychosocial wellbeing, Heavy gamers show higher levels of engagement than Light gamers but lower feelings of connectedness than Phone gamers.
Copyright (c) 2026 Francois J. Van Wyk, Daniel B. Le Roux https://creativecommons.org/licenses/by-nc/4.0
Information architecture for first-year modules on Moodle: A Nelson Mandela University case study
Students enrolled in first-year programming modules at higher education institutions often struggle to master the material, resulting in poor performance and high dropout rates. Although blended learning and implementation of various pedagogical approaches have been introduced to address this challenge, the structure and organisation of online course content are underexplored and may affect student engagement and learning. To address this gap, this paper examines the effectiveness of utilising an Information Architecture (IA) specifically designed to structure various types of content in online courses, thereby supporting students’ diverse learning styles. Grounded in the Cognitive Theory of Multimedia Learning (CTML), the research employed a multi-method approach: a questionnaire completed by 64 students and focus group discussions with six students. The findings indicated that inadequate navigation and labelling, poorly structured module content, and a lack of engaging learning materials contribute to student frustration, reduced motivation, and higher dropout rates. Building on our earlier work, which proposed information architecture (IA) guidelines for online course content, this paper demonstrates how these IA guidelines can be applied to the design of online course content.
Copyright (c) 2026 Asanda Kafile, Lynette Barnard, Dumani Kunjuzwa https://creativecommons.org/licenses/by-nc/4.0
Towards practical digital health designs: A single electronic health record for South Africa
Since announcing the implementation of a single electronic health record for all South Africans, the government has not yet informed healthcare facilities of how this would be accomplished. The siloed South African healthcare system would have to be redesigned to accommodate a single electronic health record. A systematic literature review conducted across three databases returned 9 790 results. By applying ten filters, 22 documents were eventually retrieved for analysis. The analysis showed that existing research focuses on healthcare architectures from a theoretical perspective. Therefore, the literature review revealed a practically based research deficiency and a lack of theoretical studies merged with practical cases. Seeking to enhance the understanding of designing a single electronic health record, the documents were analysed using a qualitative inductive content analysis technique, revealing that a single electronic health record cannot be formulated using a fully centralised architecture as this is not practical. A fully decentralised architecture, such as blockchain, is equally infeasible because this requires significant changes to the existing systems and infrastructure and would require re-skilling system builders. Since the South African healthcare architecture is already decentralised, hybrid architecture incorporating edge computing with clusters of systems and information that connect using middleware should be considered.
Copyright (c) 2026 Wesley Moonsamy https://creativecommons.org/licenses/by-nc/4.0
WeaveChain: A decentralized storage framework using Arweave to address scalability, security, and decentralization challenges
Blockchain systems persistently struggle to balance scalability, security, and decentralization - the fundamental trilemma hindering enterprise adoption. Existing solutions like Filecoin compromise decentralization when scaling throughput or face security vulnerabilities under adversarial conditions. Motivated by the urgent need for practical trilemma resolution in storage-intensive applications (e.g., medical records, IoT coordination), this study introduces WeaveChain: a novel framework leveraging Arweave’s permanent storage to simultaneously optimize all three dimensions. Our primary aim is to architect a decentralized storage system that achieves: (1) high-throughput transaction processing, (2) provable security against Byzantine failures (malicious or faulty nodes acting arbitrarily), and (3) egalitarian network participation—without mutual trade-offs. By implementing compact 48-byte transaction anchors and adaptive block sizing (1-200MB), the system processes 7,200 transactions per megabyte block, reaching 162 TPS. Comprehensive testing demonstrates 0.82 chain quality under 40% adversarial influence, 99% lower storage than Bitcoin, and Sybil attack resistance. These results establish a new paradigm for trilemma resolution where permanent data anchoring enables scalable, secure, and truly decentralized blockchain storage.
Copyright (c) 2025 Saha Reno https://creativecommons.org/licenses/by-nc/4.0
A vector-based computational framework for comparative analysis of Kenyan Sign Language and American Sign Language phonology
This paper introduces a new vector-space computational approach for comparative phonology of Kenyan Sign Language (KSL) and American Sign Language (ASL) using minimal pairs contrasts. Using formal SignWriting (FSW) notation, the framework compiles handshape, location, movement, orientation and non-manual features into structured phonological vectors. These vectors enable quantitative assessment of phonological distance between KSL and ASL signs that correspond over matched items in the lexicon. We calculate pairwise, phonologically-based component and global Euclidean distances for a set of minimal pairs, pointing to systematic differences in phonological organisation of the glosses in the dataset. This research highlights the importance of language specific models in sign recognition systems because direct model transfer from ASL to KSL yields significant representational mismatches. Centering on a dual symbolic and geometric representation of sign language data, this work presents a scalable, linguistically motivated approach to cross-linguistic sign analysis that has the potential to find uses in natural language processing, low-resource language modelling and accessible sign technologies.
Copyright (c) 2026 Catherine Wangari, Lawrence Muchemi, Peter Waiganjo https://creativecommons.org/licenses/by-nc/4.0
Towards a ubiquitous health system through integration of heterogeneous data sources using relevant attributes without identification keys
Considerable advances in software and hardware technologies now enable information systems to integrate heterogeneous data from multiple sources. Such integration helps address data scarcity by combining related datasets. However, the absence of standard formats and the unstructured nature of many datasets limit automated processing, interoperability, and practical analysis. Heterogeneity also arises because each facility manages its own data using different information systems, creating a major challenge for integration. The objective of this study is to integrate heterogeneous data sources using relevant attributes (e.g., name, date of birth, e-mail), which distinguishes our approach, rather than relying on identification keys that make integration manual and hard to generalise. The purpose of our method is to integrate all heterogeneous data for each individual into a unified database. For patients, it centralises medical data, supports care continuity, and cuts redundant examination costs. This approach can also be applied to education (academic records), commerce (customer data), and epidemiological studies. A case study illustrates the merging of data into a standardised XML (eXtensible Markup Language) format via a data processing pipeline, demonstrating the applicability of the proposed approach, and discusses the main challenges encountered and possible solutions. This methodology enhances efficiency, optimises resources, and informs decision-making.
Copyright (c) 2026 Khaled Kechida, Hamoudi Kalla, Mourad Brik https://creativecommons.org/licenses/by-nc/4.0
A system for linking and integrating heterogeneous datasets in health and demographic surveillance systems
To overcome the constraints of data silos within data-driven organizations, the linking and integration of data from several sources becomes even more crucial. Data linking and integration not only improve the precision of the analysis, but also discover hidden trends, patterns, and relationships that can be overlooked when working with separate datasets. Taking this into account, this study is aimed at linking and integrating data across disparate data sources in the Health and Demographic Surveillance System (HDSS). Data linkage was achieved by implementing a fuzzy pattern-matching algorithm (Levenshtein distance) written in structured query language (SQL). A user-friendly graphical user interface (GUI) was developed using the C# programming language to facilitate the process of data matching and linking across various datasets. The Pentaho data integration (PDI) tool facilitated the integration of data from various data sources into a common database. These open-source technologies facilitated efficient linking and integration of data, enabling researchers to holistically discover patterns and trends in the data. The proposed algorithm achieved 98.1% precision and recall of 88.6% while maintaining 93.0% of good balance between avoiding false matches and missed matches.
Copyright (c) 2026 Joseph Tlouyamma, Sello Mokwena, Cairo Bruce Ntimana, Masenyani Oupa Mbombi, Kagiso Peace Seakamela, Peter Mphekgwana, Katlego Mothapo, Reneilwe Given Mashaba, Eric Maimela https://creativecommons.org/licenses/by-nc/4.0
A digital health design model for a live healthcare console from a South African public healthcare perspective
Digital health systems can be effective when they are connected to each other and are able to share relevant information with stakeholders. In South Africa, however, not all digital health systems within the public healthcare environment are connected. The public healthcare system could therefore benefit from the implementation of a Live Healthcare Console which could connect disparate systems and combine and disseminate healthcare information such as hospital bed occupancy and ambulance diverts in real-time. The study proposes a graphical Design Model for a Live Healthcare Console. An initial first version was designed based on a review of the literature. Thirty-one semi-structured online interviews were then conducted with healthcare stakeholders from Gauteng’s public healthcare system. The interviews were analysed using an interpretive approach involving three cycles of coding. This resulted in the formulation of six themes (connect, combine, communicate, contain, collaborate and cooperate). The themes were applied to a revised second version, which was presented to a subset of the research participants. These participants provided feedback which was incorporated into the final and third version of this study.
Copyright (c) 2026 Wesley Moonsamy, Shawren Singh https://creativecommons.org/licenses/by-nc/4.0
Editorial: Impact of innovative IT solutions
Improving greybox fuzzing with dictionary-based mutations: A systematic literature review
Detecting deep bugs that are guided by complex conditions, based on specific byte sequences of the input, often requires input structure-aware or grammar-aware fuzzing strategies. However, the grammar or specification of the input may not be readily available. In this regard, there exists anecdotal evidence that dictionary-based mutations contribute to preserving the syntactic structure of input test cases and may approximate the efficacy of grammar-aware fuzzing. It is not yet clear as to which is the best strategy for automatically extracting fuzzing dictionary tokens from the codebase of the program under test. In this study we conduct a systematic review of the impact of dictionary-based mutations on the fuzzing process. We further review strategies for automatically extracting dictionary tokens and optimizing dictionary-based mutations. Our findings are that current strategies for extracting fuzzing dictionary are not optimised for highly structured input. Furthermore, about 58% of the reviewed state-of-the art fuzzing tools rely on the random mutation operator distribution of respective baseline fuzzer. Moreover, the evaluation of these fuzzing tools report on aggregated performance of mutation operator scheduling algorithms, and not specific individual operators such as dictionary-based mutation operators.
Copyright (c) 2025 Enock L. Dube, Boluwaji A. Akinnuwesi, Stephen G. Fashoto, Petros M. Mashwama, Vusi W. Tsabedze https://creativecommons.org/licenses/by-nc/4.0
A 45-year review of the South African Computer Journal (1979 – 2023)
This single journal study provides a comprehensive overview of the history and development of the South African Computer Journal (SACJ, formerly Quaestiones Informaticae), combined with a bibliometric and altmetric analysis spanning 45 years from 1979 to 2023. Citation data were sourced from Google Scholar and Scopus, while Mendeley readership counts were used as an alternative metric indicator. A total of 768 articles were analysed to assess journal productivity, authorship patterns and collaboration trends. On average, 17 articles were published per year, with an average of 8.3 Google Scholar citations per article. Citation data reveal that 60% of articles had Google Scholar citations, while 40% remained uncited. The journal exhibits a notable shift from single authorship to multi-authorship, with co-authored articles increasing from 22% (1979 to 1989) to 70% (1990 to 2023). The results indicate a strong association between collaboration and scholarly impact, as articles with two or three authors consistently achieved higher citation counts and Mendeley readership than single-authored articles. The most productive authors and most cited papers were identified. The SACJ journal metrics were also compared with selected international journals in the Computing discipline.
Copyright (c) 2025 Filistéa Naudé, Jan Hendrik Kroeze https://creativecommons.org/licenses/by-nc/4.0
Reinforcement learning algorithms for adaptive load-balancing for web applications
This research investigates the application of reinforcement learning (RL) to optimise load balancing in Nginx web applications. We developed a simulation environment on AWS to evaluate three enhanced RL algorithms: Epsilon-greedy, Upper Confidence Bound, and Proximal Policy Optimization (PPO) against classic methods (round-robin and Least Connections) under diverse load conditions, including normal loads, burst loads, server failures, and heterogeneous server instances. Our results demonstrate that RL, particularly PPO, significantly outperforms classic methods. Notably, PPO achieved up to a 30% increase in throughput, a 20% reduction in latency, and a 5% improvement in the successful message rate compared to the best-performing classic algorithm. These improvements were most pronounced under challenging conditions such as burst loads and server failures, highlighting the adaptability and resilience of RL-based load balancing.
Copyright (c) 2025 Rana Zuhair Al-Shaikh, Muna M. Jawad Al-Nayar, Ahmed M. Hasan https://creativecommons.org/licenses/by-nc/4.0
Investigating the support of proprioception and visual guidance for menu selection in virtual reality
Virtual reality (VR) technology enhances spatial awareness in the virtual environment, yet current menu design research seldom explores how this can support interaction. This study investigates how proprioception, users’ sense of body position, can guide menu selection in immersive VR environments. A menu system was developed to test how users interact with menu items using non-visual senses, supported by visual cues. Across three usability sessions, participants engaged with the system, and their evolving behaviours were examined through performance metrics, observations, interviews, and focus groups. Thematic analysis revealed that while users can learn to use proprioceptive cues for menu selection, they rarely rely on them without visual guidance. Effective use requires systems that support spatial awareness and familiarity, allowing interactions to become intuitive and memorised over time. The study also identifies types of visual guidance linked to varying levels of familiarity with surrounding virtual objects. These findings contribute to VR interaction design by demonstrating how visual and proprioceptive cues affect user behaviour and support efficient, embodied menu selection.
Copyright (c) 2025 Kwan Sui Dave Ka, Isak de Villers Bosman, Theo Bothma https://creativecommons.org/licenses/by-nc/4.0
Copyright (c) 2024 Katherine Malan https://creativecommons.org/licenses/by-nc/4.0
A multilevel analysis of digital technology to support teachers to improve their professional practice
Within the education sector, digital technology has increasingly entered into school subjects, curricula, assessment and teaching methods. The common features of technology-orientated and supported programmes are the independence from geographic boundaries and time constraints. Covid-19 pandemic accelerated the debates about digitalisation in the education sector. Therefore, the purpose of this study was to conduct a multilevel analysis of evidence from a large-scale randomised control trial complemented with the systematic collection of implementation information to compare the impact of a conventional structured pedagogic programme to that of a pedagogically similar programme built with a digital technology overlay. The technology acceptance model framed our analysis. Results from the multilevel analysis provided an opportunity to advance knowledge about teachers’ acceptance of digital technology to improve student learning and their professional practice. The evidence pointed to two hypotheses: While much of the literature on teachers’ acceptance of digital technology concentrated on the subjective element of teachers’ behavioural intentions, the current study suggests the extent to which professional accountability may be a key factor in successful digital technology interventions for teachers; and the results points to substantial differentiated uptake that is not linked to either perceived usefulness or perceived ease of use.
Copyright (c) 2025 Reuben Dlamini, Brahm Fleisch https://creativecommons.org/licenses/by-nc/4.0
An investigation into the feasibility of using virtual environments as an induction method in SHIP® therapy
Virtual reality contributes to the successful treatment of patients by assisting those who have difficulty with the process of imagining the required visual images needed during psychotherapy. SHIP® is a form of psychotherapy that suggests that spontaneous healing is a natural tendency that emerges from within a person. It identifies certain activator images as essential pathways for accessing unconscious trauma material that needs healing. The purpose of this study is to examine whether virtual reality can be used as a medium to induce memories through the utilisation of neutral images based on the SHIP® Frame. Two groups of participants were gathered: one group underwent a traditional SHIP® session while the other group underwent the virtual induction with the aid of a head mounted display. A random clinical trial was used to determine the level of induction and identify the helpful aspects that contributed to the induction. The results indicated that virtual reality was able to assist as a cognitive stimulus as well as a cognitive proxy in the overall process of SHIP®.
Copyright (c) 2025 Weich Malan, Isak de Villers Bosman, Theo Bothma https://creativecommons.org/licenses/by-nc/4.0
Identifying factors affecting the adoption of big data analytics in South African rural-based hospitals to improve service delivery
The healthcare sector is progressively recognising the potential of big data analytics to revolutionise patient care, operational efficiency and decision-making processes. However, rural-based hospitals face challenges in embracing big data analytical tools to improve their service delivery to their patients. The adoption of big data analytics will assist rural-based hospitals in predicting and making informed decisions to manage the limited resources that they have and allocate them appropriately. However, there is a lack of literature that explores and identifies the factors that affect the adoption of big data analytics in rural-based hospitals to improve service delivery. This research employs a quantitative research methodology using surveys to identify the factors that affect the adoption of big data analytics in rural-based hospitals. This study found that rural-based hospitals prefer a hybrid method to collect data and it impedes the adoption of big data analytics in rural-based hospitals. Therefore, there is a need to implement modern infrastructure to integrate various data collection methods to promote the adoption of big data analytics in rural-based hospitals.
Copyright (c) 2025 Thifhindulwi Maxwell Rambau, Willard Munyoka, Nkhangweni Lawrence Mashau https://creativecommons.org/licenses/by-nc/4.0
Predicting the performance of ORB-SLAM3 on embedded platforms
Simultaneous Localization and Mapping (SLAM) is a crucial component to the push towards full autonomy of robotic systems, yet it is computationally expensive and can rarely achieve real-time execution speeds on embedded platforms. Therefore, a need exists to evaluate the performance of SLAM algorithms in practical embedded environments – this paper addresses this need by creating prediction models to estimate the performance that ORB-SLAM3 can achieve on embedded platforms. The paper uses three embedded platforms: Nvidia Jetson TX2, Raspberry Pi 3B+ and the Raspberry Pi 4B, to generate a dataset that is used in training and testing performance prediction models. The process of profiling ORB-SLAM3 aids in the selection of inputs to the prediction model as well as benchmarking the embedded platforms’ performances by using PassMark. The EuRoC micro aerial vehicle (MAV) dataset is used to generate the average tracking time that the embedded platforms can achieve when executing ORB-SLAM3, which is the target of the prediction model. The best-performing model has the following results 2.84%, 3.93%, and 0.95 for MAE, RMSE and R2 score respectively. The results show the feasibility of predicting the performance that SLAM applications can achieve on embedded platforms.
Copyright (c) 2024 Jacques Matthee, Kenneth Uren, George van Schoor, Corne van Daalen https://creativecommons.org/licenses/by-nc/4.0
This paper addresses the challenge of designing digital sharing platforms that align with the local sharing practices of SMEs in resource-constrained countries, with a specific focus on Ethiopian SMEs. The research utilises the elaborated action design research (EADR) methodology and begins with the diagnosis stage, employing thematic analysis with activity theory (AT) to gain an understanding of the local sharing practices. Results revealed elements such as activities, stakeholders, motives, resources, goals, actions, rules, and tools that inform the design requirements of the digital sharing platform. Building on the diagnosis stage, the design cycle established key principles and the architecture of 13 local sharing models that form the features of the platform. In the implementation cycle, the paper presented a specific instance of a “property rental package.” Results indicated the positive acceptance of the digital platform. Factors such as technological compatibility, perceived direct benefit, ease of use, and perceived indirect benefit contributed to this acceptance. The study contributes to the existing literature by providing valuable insights on how to contextualise the design of digital platforms in the local sharing practice contexts. Additionally, it showcases the effective utilisation of the EADR methodology and activity theory.
Copyright (c) 2025 Sertse Abebe, Hossana Twinomurinzi https://creativecommons.org/licenses/by-nc/4.0
The use of detective analytics for mitigating financial crimes: A South African perspective
South African organisations are increasingly exploring the use of detective analytics in mitigating financial crimes. However, many organisations are struggling with how to employ the tool in detecting and preventing financial crimes. This study aimed to conceptualise how detective analytics can be used to mitigate financial crimes in organisations. Qualitative data were gathered from different sources, from peer-reviewed to grey literature. Actor-network theory (ANT) was employed as a lens, through its mantra ‘follow the actors’, to gain insights on how activities can be identified, traced, and tracked, in mitigating financial crime in institutions. The interpretive approach was applied. The findings revealed seamless integration of incidents, cybersecurity detection, in-house fraud detection, external infiltrate detection, and image-matching data into one cohesive system. The study highlights the need for gaining a deeper understanding of networks of actors, following the actors, and passage points within an organisation. The findings have significant implications for improving the efficiency and effectiveness of the use of detective analytics, from both technical and non-technical perspectives.
Copyright (c) 2025 Nontobeko Mlambo, Tiko Iyamu https://creativecommons.org/licenses/by-nc/4.0
There is a lack of acceptance, adoption and sustained use of mobile phones for agricultural purposes by smallholder farmers in Zimbabwe, but the reasons for this have not been critically explored. In this study, the authors used a naturalistic methodology to help uncover the nuances of adoption dynamics among smallholder farmers in Gokwe South District, Zimbabwe. Data was gathered from in-depth interviews and focus groups. The findings suggest that gender, age, land ownership size, farming experience, mobile device ownership, and the period of owning a mobile device are critical precursors to adoption and use. Furthermore, digital proficiency and literacy, affluence, mobile technology services cost and telecommunication infrastructure availability are vital in achieving continuous, sustainable use of mobile technology in the Zimbabwean agricultural sector. This research has practical implications for policy and practice and may inform national legislation encouraging the increased use and affordability of mobile devices in the local agricultural sector. The research also makes a theoretical contribution in terms of unpacking the key factors that inhibit the adoption of mobile technology in marginalised settings.
Copyright (c) 2024 Samuel Musungwini, Izak van Zyl, Jan Kroeze https://creativecommons.org/licenses/by-nc/4.0
Formalising solutions to network availability issues in low-resource environments
In most software systems operating within low-resource environments, the persistently encountered challenges related to network availability often result in compromised performance or even complete system failure. To address this issue, software developers frequently resort to ad hoc measures to mitigate these challenges. To offer a more comprehensive solution, this paper introduces an all-encompassing approach in the form of a design pattern. This design pattern uses offline functionalities with localised storage, emphasising the distinction between offline storage and synchronisation, presenting a versatile and high-level methodology for overcoming the recurrent network-related challenges intrinsic to low-resource environments. We describe the use of the design pattern in some real-world use cases, thereby illustrating how a single design pattern is the foundation for many seemingly disparate software engineering approaches. This design pattern holds the potential to significantly redefine the manner in which network availability challenges are approached and addressed.
Copyright (c) 2024 Abayomi Agbeyangi, Hussein Suleman https://creativecommons.org/licenses/by-nc/4.0
Integrating the TOE framework and DOI theory to dissect and understand the key elements of AI adoption in sociotechnical systems
This study is interested in increasing the likelihood of achieving the organisational adoption of artificial intelligence that has a positive outcome. We argue that the technological-organisational-environmental framework provides a sound theoretical lens for analysing how an organisation’s context influences the adoption and integration of artificial intelligence solutions. Furthermore, the diffusion of innovation theory is proposed to identify enablers for transforming organisations. Together with the combination of technological-organisational-environmental and diffusion of innovation, the stages of diffusion are proposed as an evaluation paradigm to evaluate the effectiveness of the enabling factors. Furthermore, the elements and objectives of artificial intelligence adoption in the context of data-driven organisations are included. From this, the research develops a comprehensive framework for studying the technical and social AI adoption elements in an organisational environment where complex symbiotic relationships prevail. This study uses generative artificial intelligence as a novel approach to exploring the framework’s usability. The evidence from our research indicates that the developed framework can advance our comprehension of what drives the success or failure of artificial intelligence adoption in organisations. Theoretically, it provides a tool for dissecting and understanding the key elements influencing this process.
Copyright (c) 2024 Danie Smit, Sunet Eybers, Alta van der Merwe, Rene Wies, Nica Human, Julia Pielmeier https://creativecommons.org/licenses/by-nc/4.0