Skip to Content
MDPIMDPI

Advanced

SustainabilitySustainability
PDF
  • Systematic Review
  • Open Access

21 November 2024

54 Pages

A Roadmap to Systematic Review: Evaluating the Role of Data Networks and Application Programming Interfaces in Enhancing Operational Efficiency in Small and Medium Enterprises

,
,
,
and
1
Department of Electrical Engineering Technology, University of Johannesburg, Johannesburg 2092, South Africa
2
Department of Nursing, Medical and Surgical Nursing, University of Johannesburg, Johannesburg 2092, South Africa
*
Author to whom correspondence should be addressed.
Sustainability2024, 16(23), 10192;https://doi.org/10.3390/su162310192
This article belongs to the Special Issue Small and Medium Enterprises (SMEs), Public Policies and Sustainability in a Challenging World
Version Notes
Order Reprints

Abstract

The adoption of Data Networks and Application Programming Interfaces (APIs) has become crucial for small and medium enterprises (SMEs) to streamline operations, improve efficiency, and reduce costs. However, SMEs often face challenges such as resource limitations and security vulnerabilities, which hinder their ability to fully leverage these technologies. This systematic review examines the role of Data Networks and APIs in enhancing operational efficiency within SMEs, focusing on key metrics such as speed, cost reduction, scalability, and security challenges. Following PRISMA 2020 guidelines, we conducted a systematic search across multiple databases including Web of Science, Scopus, IEEE Xplore, and Google Scholar. Studies published between 2014 and 2024, focused on SMEs, and addressing the role of Data Networks and APIs in operational efficiency were included. A total of 49 studies met the inclusion criteria and were analyzed for key outcomes related to operational efficiency, cost-effectiveness, and security risks. The review found that Data Networks and APIs significantly improve operational efficiency by increasing process speed (12% increase), reducing operational costs (8% reduction), and enhancing overall productivity. However, security challenges, particularly related to API vulnerabilities, were a major concern, with cyberattacks on APIs increasing by 400% in Q1 2023 alone. Despite these risks, the benefits of implementing Data Networks and APIs in SMEs, particularly in terms of scalability and real-time data processing, were evident across industries. Data Networks and APIs offer substantial improvements in operational efficiency for SMEs, although security remains a significant challenge. Future efforts should focus on developing security frameworks tailored to SMEs while maintaining the operational benefits of these technologies. Further research is needed to explore scalable and secure API models for SMEs.

1. Introduction

Cloud computing has rapidly advanced in recent years, largely driven by the proliferation of Data Networks and Application Programming Interfaces (APIs). These technologies have enabled seamless connectivity between millions of internet-enabled devices, such as smartphones and tablets, transforming how businesses and consumers interact with digital platforms [1]. The introduction of Amazon Web Services (AWS) in 2006 significantly altered the landscape of large-scale data storage and retrieval, bringing APIs to the forefront of cloud-based applications [2]. APIs are now integral to operations such as managing virtual machine workloads in cloud environments, and leading providers like Microsoft Azure, AWS, and Google Cloud Platform rely heavily on API-based approaches and robust network infrastructures for efficient user operations [3]. The global COVID-19 pandemic in 2020 accelerated this shift towards digital transformation, compelling a vast number of small and medium-sized enterprises (SMEs) to adopt network technologies and APIs as essential tools for conducting business and delivering services remotely [4,5]. This surge in digital adoption extended beyond commercial sectors, profoundly impacting the healthcare industry, where real-time data access became crucial for managing patient admissions, tracking infection rates, and coordinating national and global responses [6]. However, the adoption of these technologies has been uneven, with some regions and industries lagging behind due to geographical constraints and disparities in technological development [7,8].
APIs and network infrastructures are closely linked to operational efficiency, facilitating seamless data exchange and integration across platforms. However, these technologies also introduce new complexities and vulnerabilities, particularly in the face of rising cybersecurity threats. APIs, if inadequately secured, present significant risks, especially when handling sensitive data. The increasing use of APIs has expanded the attack surface for cybercriminals, with reports indicating a 400% surge in API-related attacks in the first quarter of 2023, targeting critical sectors such as finance, healthcare, and e-commerce [9,10,11,12,13]. The financial consequences of these attacks can be severe, potentially leading to losses amounting to millions or even billions of dollars [14]. To mitigate these risks, organizations must adopt a security-first approach, particularly in the context of network infrastructures and serverless API environments. This entails implementing the best practices in API design, leveraging machine learning and artificial intelligence to detect threats, and investing in continuous education and skill development for IT teams [15,16,17].

1.1. Research Problem Statement

This paper conducts a systematic review of the use of network technologies and APIs to enhance operational efficiencies in SMEs. It also examines the security vulnerabilities that arise as a result of accelerated operational efficiency, offering insights into protective measures for SMEs in cloud computing environments. By consolidating existing research, this review aims to identify gaps in the literature and propose novel approaches for future developments in the field [18,19,20]. Table 1 presents a comparative analysis of existing reviews and identifies critical gaps that underscore the importance of further research into the development of APIs and Data Networks for SMEs.
Table 1. Comparative analysis of the existing review works and proposed systematic review.
Table 1 compares the work to show that many studies contribute to the understanding of those needed capabilities but always focus on one technology or one solution as opposed to what the needs of SMEs are overall. Key gaps identified include the following:
  • Most previous work does not acknowledge the specific operational challenges and resource constraints that SMEs face when setting up Data Networks and APIs.
  • API security is discussed, but solution implementation is short on pragmatic, scalable security that best fits SMEs.
  • Only a few studies offer holistic models of operational efficiency along with security imbibed within the SME context.
  • Actionable strategies to help SMEs become smarter and save time in the process whilst managing their risk profile.
While there has been a plethora of research conducted at international levels regarding the integration of Data Networks and APIs worldwide over the last decade or so, you will hardly find any comparison performed to understand how these would have an impact on the operational efficiency within SMEs. The purpose is to go deeper into SME operational efficiency analysis thanks to Data Networks and APIs. To meet the study objectives, the authors have structured their enquiry in terms of the following research questions:
  • Which key metrics are utilized to evaluate the effectiveness of Data Networks and APIs in enhancing operational efficiency within SMEs?
  • What factors contribute to differences in the performance and cost-effectiveness of Data Networks and APIs in various industries and regions, specifically for SMEs?
  • What are the primary challenges in implementing Data Networks and APIs in SMEs, and how do they impact operational feasibility and deployment?
  • How do Data Networks and APIs significantly improve business operations in SMEs by increasing speed, reducing costs, and enhancing overall efficiency?
  • What impact do advancements in data technologies and API standards have on enhancing operational efficiency in SMEs?

1.2. Research Motivation

This systematic review is motivated by two reasons. To start with, the renewed emphasis on Data Networks and APIs offering broader operational efficiency to small and medium-sized enterprises (SMEs) has taken on a pronounced role in the digital economy post-COVID-19. Limited resources and rapidly evolving/emerging technologies pose significant obstacles for SMEs. The likely effect of cultural change on the operational efficiency of SMEs remains largely unexplored in the literature despite significant research into Data Networks and APIs. This review intends to offer an overall perspective, by searching for lapses in the existing work and by concentrating on SMEs’ needs, providing a comprehensive understanding of how these firms can adopt effectively digital technologies and still compete in the present vision of digitally driven markets.

1.3. Research Contribution

This paper presents a comprehensive systematic review of Data Networks and Application Programming Interfaces (APIs) in improving business productivity for small and medium enterprises (SMEs). We identify critical constraints and answer unanswered research questions in their practical implementation. The research contributions of the work are summarized below:
  • To that end, we performed a systematic review of the literature and identified certain pressing shortcomings in the use of Data Networks and APIs at SMEs across diverse industries and geographies.
  • In this systematic review, we propose an innovative model that targets the key performance indicators for systems like network uptime, the timing of response, resource utilization and cost-effectiveness.
  • Informed by this analysis, we propose practical guidance and strategies for SMEs to seamlessly assimilate Data Networks and APIs. We discuss problems such as integration complexity, security vulnerabilities, and scalability issues together with the way to solve these challenges.
  • This research advances theoretical discourse by investigating the advantages as well as hurdles to using Data Networks and APIs, providing further academic insight into this issue and contributing to future developments in this area.
Figure 1 presents the API Efficiency Model for SMEs, illustrating how Data Networks and APIs enhance operational efficiency metrics such as network uptime, cost savings, and more. These factors are collectively enhancing operation efficiency, reducing costs, and contributing to a competitive advantage for SMEs.
Figure 1. API efficiency model for SMEs.

1.4. Research Novelty

This current research intends to provide a distinct systematic review that examines the influence of Data Networks and APIs on operational efficiency in SMEs around security challenges. Contradictory to the available literature, we focus on how the adoption of these technologies impacts fundamental performance measurements such as response time, resource utilization, cost-effectiveness, and scalability in SMEs. We provide new evaluation processes, which include operational efficiency and security aspects that address an existing lack in the related work. This article moves the field forward by offering SME-focused insights and an opening to additional research into how operations can be streamlined yet security risks minimized.

2. Materials and Methods

In this section, based on Figure 2, this research presents methodology to determine the proposed systematic review founded on the role of Data Networks and APIs in enhancing operational efficiency in SMEs. This research is based on a 10-year review (2014–2024). Figure 2 illustrates the systematic review flow chart, outlining the core stages of the study framework. Figure 2 shows the stages, review planning, article selection, data collection, and organization, forming a structured approach for conducting a comprehensive review.
Figure 2. The systematic review flow chart.

2.1. Eligibility Criteria

The purpose of this current study was to systematically review peer-reviewed literature regarding the use of Data Networks and API and how these facilitation tools can be utilized for the sake of operational efficiency enhancement trying to focus particularly on small or medium-sized enterprises. We developed a clear research inclusion criterion to select appropriate articles and exclude other articles which were not in this study. The inclusion and exclusion criteria are summarized in Table 2.
Table 2. Proposed inclusion and exclusion criteria.

2.2. Search Strategy

We conducted a comprehensive search across credible databases to investigate the ways in which Data Networks and Application Programming Interfaces (APIs) support operational efficiency within small and medium-sized enterprises (SMEs). Web of Science, Scopus, IEEE Xplore, and Google Scholar databases have been applied for this study. Given the aim of this review to cover a wide range of literature from different disciplines, these databases were selected based on their broad coverage of high-quality peer-reviewed articles in Information Technology and related areas (e.g., engineering, and business management), allowing us to ensure as comprehensive and focused retrieval as possible. The search terms were combined using Boolean operators “AND” and “OR” to refine the search and ensure the inclusion of studies that address the intersection of these concepts. The final search string was constructed as follows: (“Data Network” OR “network infrastructure” OR “communication network*”) AND (“API” OR “Application Programming Interface”) AND (“operational efficiency” OR “business performance” OR “process optimization” OR “efficiency improvement”) AND (“SME” OR “small and medium-sized enterprise”) AND (“cloud computing” OR “IoT” OR “Internet of Things” OR “digital transformation” OR “cybersecurity”).
Our search was limited to papers published from 2014 onwards to depict any novel advances and new Data Network and API technology trends corresponding with the scenario for SMEs. The starting year was chosen as 2014 due to much progress in cloud computing, API development, and advancement in digital transformation initiatives specifically within SMEs. This time frame would help make sure that the studies included in this report reflect today’s technological landscape and its effect on operations productivity. The quality filters chosen only peer-reviewed journal articles and conference papers written in English to make the reviewed literature as highly consistent as possible. We also excluded those studies that were not in English and or not peer-reviewed to minimize a possible language barrier and credibility, respectively. The relevance of studies was evaluated by title and abstract initially. We excluded studies that did not meet the inclusion criteria such as those on large enterprises or unrelated technologies or were irrelevant to operational efficiency in SMEs. Table 3 illustrates the collected on different databases that fit the inclusion and exclusion criteria.
Table 3. Results received from the databases.

2.3. Selection Process

The relevance of studies was evaluated by title and abstract initially. We excluded studies that did not meet the inclusion criteria such as those on large enterprises or unrelated technologies or were irrelevant to operational efficiency in SMEs. The full text and detailed information evaluation process occurred as follows. After the initial screening, studies that passed were reviewed entirely to confirm their relevance and obtain specific data related to our research questions. Systematic reviews of studies conducted in accordance with the PRISMA guidelines were included to provide transparency and quality assurance. We initially identified 338 records after comprehensive searching in Web of Science, Scopus, IEEE Xplore, and Google Scholar. Exclusion criteria were pilot tests or gray literature predating 2014, and those that passed this criterion moved to the next stage, duplicates in our database (n = 0), by reviewing titles and abstracts related to operational efficiency related to Data Networks/operational APIs in SME enterprises. After the exclusion of studies not meeting the inclusion criteria, those that passed this first step were reviewed in full text against our predefined eligibility criteria, with a careful assessment of the overall methodological quality and relevance. Two independent reviewers screened search results for eligible studies, resolving any disagreements through discussion or consultation with a third reviewer to minimize bias. Figure 3 shows the method and structure used in the collection of a total of 49 research studies that met all the criteria and were ultimately involved in our systematic reviews of data synthesis and result interpretation.
Figure 3. Proposed selection process.

2.4. Data Collection Process

The methods used to collect data from the included studies are depicted in Figure 4.
Figure 4. Data collection and organization process.
A structured extraction form was particularly designed for this review, detailing information on the study characteristics; methodologies; Data Network and API-centric interventions; efficiency metrics observed in the operational context; and SME-relevant key findings. Data extraction was performed independently by three different reviewers to minimize bias and increase objectivity. The reviewers examined data and compared their examinations after extraction, with discrepancies solved by discussion. If agreement was not achieved, a fourth reviewer was consulted. We used Microsoft Excel 6.2.14 2019 to easily organize and manage the data. Where there was unclear or missing information, we made a note of the absence of data and appropriately documented it. There were no automation tools except for Excel. This systematic process was designed to ensure that the raw data were reliable, valid, and appropriate for responding to our research questions.

2.5. Data Items

This section describes the specific outcomes and variables for which data were collected in this systematic review, including detailed definitions and criteria for selecting results relevant to each outcome. This section further describes the methodology that will be applied to deal with data not reported or unclear with the aim of making our research methodology transparent and replicable.

2.5.1. Data Collection Method

We gathered data directly pertinent to the pre-defined outcomes of our research objectives. These objectives assessed the ways in which Data Networks and APIs improve operational efficiencies in SMEs. All findings consistent with these measures were sought for each of these outcome domains over several time points, using multiple methods and analyses. When there was more than one result in the same domain, prioritization was achieved by considering only the most reliable and pertinent data through a systematic process, using such criteria as methodological strength, sample size, and relevance to SMEs. This therefore ensured that our analysis was comprehensive enough to give a methodical review for each outcome. Figure 5 depicts the data collection method used in this review.
Figure 5. Proposed data collection method.

2.5.2. Variable Data Collection

Data were collected on all pre-defined and listed variables—study characteristics, participant demographics, technological interventions, and measured outcomes. Indeed, such in-depth data collection enabled an intensive analysis and synthesis with deep insights and practical recommendations. When information was not clear or missing, standard practices were resorted to make reasonable inferences based on available data. Any assumptions thus made were stated with a view to transparency. Table 4 shows a summary of all the variables collected during data extraction.
Table 4. Variables collected.

2.6. Study Risk of Bias Assessment

This section describes the methods used for assessing the risk of bias for the studies included in this review. It includes information on the tools used for the assessment, as well as how an in-depth, unbiased evaluation of the study was guaranteed. Moreover, several reviewers were assigned to independently assess each dataset, as a means of efficiently handling the datasets and minimizing the risk of missing some biases that might have been hard to notice if the assessment had been conducted manually. This method minimized personal biases and guaranteed wide analysis through subsequent discussions and consensus building. An integrated approach made sure that the review of the risk of bias was comprehensive, reliable, and reproducible for all studies.
Since it pertains to reviewing the role that Data Networks and APIs play in enhancing operational efficiency in SMEs, we prepared a detailed risk of bias assessment for each included study in this systematic review to ensure its validity and reliability. A customized assessment framework, adapted from the Cochrane “Risk of Bias” tool, was prepared for assessing mixed-method studies related to our topic. This evaluation ranged over five (5) distinctive domains of bias: (1) bias due to study design and methodological aspects; (2) bias concerning data collection and analysis techniques; (3) bias from technological interventions; (4) bias pertaining to industry and geographical context; and (5) bias due to selective reporting of results. Three authors of each study reviewed and recorded the Supporting Information independently, together with their justification of judging the risk of bias as low, moderate, high, or unclear. Any disagreement regarding the assessment was resolved by discussion between the same reviewers, with the involvement of a fourth author when necessary. This rigorous process enabled us to comprehensively assess the impact of Data Networks and APIs on operational efficiency for SMEs, identify key developments, and provide insight into any gaps that currently exist. The process is summarized in Table 5.
Table 5. Study risk of bias process.
In our review, we evaluated how likely it was that the studies we included could be biased. Three reviewers checked each study to make sure it was conducted fairly. Each reviewer review rated the studies based on specific rules as shown in Table 6, and then they discussed their ratings to agree on how much bias each study might have. We did not use any software for this process; everything was performed manually. We focused on factors like how the study was designed, how the data were collected, and how clearly the methods were explained. Studies that were well documented were considered to have less risk of bias. We also checked if the studies had conflicts of interest, like funding from sources that might influence the results. The authors saw the need to do this because papers that receive funding tend to be promoted more than the papers that do not receive any funding. We also compared the studies to check for any unusual findings, like Data Networks and APIs decreasing operational efficiency.
Table 6. Study risk of bias process.

2.7. Synthesis Methods

The synthesis methods for this systematic review on the impact of Data Networks and Application Programming Interfaces (APIs) on operational efficiency in SMEs were designed in a way to ensure that any aggregation of results across the selected studies was robust, transparent, and reproducible. The eligibility of the studies for inclusion in each synthesis was systematically and rigorously assessed against review objectives, focusing on the role that Data Networks and APIs can play in operational efficiency enhancement in SMEs. The data collection was informed by the synthesis of eligibility, as outlined in Table 7. Accordingly, careful selections were made regarding relevance with Data Networks and APIs and the objectives set for this review. A systematic comparison, using set criteria, ensured that only the most relevant studies formed the backbone of the work, reducing bias and increasing methodological strength in conducting the review. After that, data from various studies were standardized to make meaningful comparisons; gaps in data were addressed by techniques such as data imputation or contacting authors when necessary. This completed the dataset and ensured its reliability for analysis. Correspondingly, methods of data preparation and their applications were analyzed.
Table 7. Proposed synthesis method.
These were then systematically organized into tabular forms and visualized using appropriate graphical formats, such as forest plots and thematic maps, which were important in depicting patterns and assuring clarity and transparency of the findings. Figure 6 presents scenarios assessed in sensitivity analyses and their results. These tested the effect of different assumptions and methodological decisions, for instance, the exclusion of the studies with a high risk of bias, altering the inclusion criteria.
Figure 6. Synthesis method.
The model selection was documented properly in a well-defined manner to capture the accuracy and reproducibility of the findings. Regarding Table 7, a comparison was made of a fixed-effects model with a random-effects model to present graphically the effects both had on SME operational efficiency outcomes. Subgroup analyses and meta-regression were conducted for possible heterogeneity to investigate how different factors such as the size of the SME, the industry type, or the geographical location would affect the effectiveness of Data Networks and APIs.

2.8. Reporting Bias Assessment

We subsequently conducted an extensive review of publication bias as a means of investigating the risk of missing results due to selective non-publication or non-reporting. Since such biases have a great potential to affect the validity of our findings regarding the role Data Networks and APIs play in promoting operational efficiency within SMEs, we employed a statistical and graphical method for comprehensive evaluation. Contour-enhanced funnel plots provided visual detection of asymmetries that might indicate publication bias. These are funnel plots with contours of statistical significance superimposed, thus helping us to differentiate between contours where studies might be missing because of publication bias and those missing for other reasons, such as heterogeneity or chance. For statistical assessment of funnel plot asymmetry, Egger’s regression test was also applied; it gives a quantitative measure of possible small-study effects or publication bias.
No new tool development was carried out for the assessment; standard tools and techniques recommended in the methodology for systematic reviews were used. Analyses that involved subjective judgment were performed by several reviewers independently, with disagreements resolved by discussion or by consulting a methodological expert. This provided a reliable interpretation of the results, without over-reliance on automated processes. We conducted the analysis and visualization manually in our review. We used software such as Review Manager (RevMan 5.4) and Microsoft Microsoft Excel 6.2.14 2019 to develop plots and carry out statistical tests. No automation tool specifically designed for the assessment of reporting bias was used. Extensive manual searches in databases like Web of Science, Scopus, IEEE Xplore, and Google Scholar helped us obtain a comprehensive set of relevant studies, thus reducing the likelihood of reporting bias in this review.

2.9. Certainty Assessment

This section describes the methods used to establish the level of certainty or confidence in the evidence collected for each outcome. This lends credence to the strength of our findings on the impact which Data Networks and APIs have on operational efficiency in SMEs. The literature reviewed was assessed against a set of five QA checks that were obtained from our research questions, as mentioned in Table 8.
Table 8. Proposed research quality assessment questions.
The answers to the questions are scored on a scale ranging from zero to one, where “No” scores “0” points, partial fulfillment of the criteria receives “0.5” points, and “Yes” receives “1” point. All five QAs are scored by this criterion, permitting each study a total score to range from 0 to 5 points. Studies receiving higher scores have more certainty and thus stronger evidence for our review. The results of the QA of the retrieved literature are summarized in Table 9.
Table 9. Findings from the literature quality assessment.

3. Results

Figure 7 illustrates the key components that shape the findings of this systematic review, including the selection of research studies, their characteristics, and the assessment of potential biases. These factors are essential in determining the credibility of the results, Figure 7 also underscores the importance of synthesizing data from individual research studies to draw comprehensive conclusions. Also, it highlights the need to address reporting biases and evaluate the certainty of the evidence to ensure that the outcomes presented are both accurate and reliable. Each of these elements is crucial in interpreting the overall results, providing a more transparent and insightful view of how Data Networks and APIs enhance operational efficiency in SMEs.
Figure 7. Essential components in evaluating systematic review results.

3.1. Results of Study Selection

The study selection process was carried out based on the exclusion and exclusion criteria illustrated in Table 2. The research studies were assembled from different types of SMEs-based papers that were focused on the role of Data Networks and APIs in their role of enhancing operational efficiency. To perfectly obtain the required research studies or papers, a search code that consists of all the topic’s keywords and synonyms was used and it gave hundreds of research studies. During the topic screening of the resultant papers, we were able to find that only 49 of these research studies were within our inclusion criteria. Out of these research studies, 28 (57.14%) were journal articles, 2 (4.08%) were book chapters, and 19 (38.78%) were conference papers. The use of an Excel sheet gave us insight into all the research studies that were deemed to be duplicated, and we were able to exclude any duplicate research studies in order to not have any duplicate research studies within the systematic review. This allowed us to conclude that these thirty research studies fit our inclusion criteria for the final review, and they were included in the systematic review results analyses.
Figure 8 illustrates the systematic review procedure for finding and adding research studies using databases and registrations. The flow chart, which highlights important exclusion criteria at each stage, describes the identification of research papers from many sources, the screening and retrieval procedure, and the ultimate inclusion of suitable studies in the review.
Figure 8. Proposed PRISMA flowchart.
The research distribution by online databases is demonstrated in Figure 9.
Figure 9. Distribution of online databases.

3.2. Study Characteristics

Figure 10 and Table 10 provide a detailed overview of the publication trends related to studies on the role of Data Networks and APIs in enhancing operational efficiency in SMEs. Figure 10 illustrates the yearly distribution of publications from 2014 to 2024, showing a steady increase in output, with a significant peak of 13 papers in 2023. Table 10 categorizes these publications into journal articles, conference papers, and book chapters, highlighting the dominance of journal articles, particularly in the more recent years. A notable rise in conference papers is observed starting in 2021, indicating increased discourse on this topic within academic and professional forums. These study characteristics demonstrate the growing academic interest and expanding body of research in this field over the last decade.
Figure 10. The research papers and their year of publication.
Table 10. Brief view of research studies with respect to publishing year.
The results on the graph in Figure 10 show the publications of papers within the inclusion criteria over a 10-year period, from 2014 to 2024. The graph shows a fluctuating pattern, with a relatively low number of publications between 2014 and 2020, peaking with four papers before dropping to just one in 2020. However, starting in 2021, there is a marked increase in research studies, with seven research studies published that year, followed by eight in 2022. The most significant surge occurred in 2023 when the number of publications reached a peak of 13. This trend indicates a growing academic interest in the topic although the slight decline to five publications in 2024 suggests that the momentum may be tapering off. The overall trend highlights an increasing recognition of the importance of this field, particularly in the last few years.
Figure 11 illustrates the distribution of publications by country, highlighting the global spread of research on the role of Data Networks and APIs in enhancing operational efficiency in SMEs. China leads the highest number of publications, accounting for 12 of the totals. The USA has eight, while South Korea and India have four and five, respectively. The other countries each have 1–3 of the totals.
Figure 11. The research papers and their publication countries.
Figure 12 presents the distribution of different types of Data Networks commonly utilized in enhancing operational efficiency within SMEs. The chart indicates that Virtual Private Networks (VPNs) account for the largest share at 33%, followed closely by the Internet of Things (IoT) with 30%. Local Area Networks (LANs) represent 20% of the total, while Wide Area Networks (WANs) contribute 17%. This figure highlights the varying roles these Data Networks play in supporting SMEs, with VPNs and the IoT emerging as the most prominent technologies for improving connectivity and operational performance.
Figure 12. The types of Data Networks.
Figure 13 and Table 11 provide an overview of the various API configurations used to enhance operational efficiency in SMEs. Table 11 categorizes the API configurations into REST, SOAP, GraphQL, other configurations, and unspecified types. The data in Table 11 show that the most used API is REST at 26.53%, followed by SOAP at 8.16% and GraphQL, ranking last, as it sits at 2.04%. The APIs represented by others were mixed or more than one was used and not specified for the papers that did not mention which API was being used during the research period.
Figure 13. Types of APIs.
Table 11. Different types of APIs.

3.3. Results of Individual Studies

Figure 14 illustrates how different enterprises’ performances have been seen to enhance operational efficiency using the integration of Data Networks and APIs based on the 49 research studies included.
Figure 14. Distribution and operational efficiency metrics.
The findings show that 19% of the research focused on increasing productivity, underscoring the critical role that these technologies play in improving production and workflows. Furthermore, process improvement, which is essential for simplifying operations, was mentioned in 12% of the research. A total of 8% of costs were saved, highlighting the financial advantages that businesses receive from these technology integrations. Ten per cent of the studies showed enhanced data-driven creativity, demonstrating how Data Networks and APIs spur more creative business solutions. A further noteworthy element that accounted for 12% of the research was security; however, 39% of the studies did not provide specific measurements. These observations highlight the various ways that APIs and Data Networks are changing how businesses operate in several sectors.

3.4. Results of Synthesis

Figure 15 turns to illustrates the systematics process followed in synthesizing the results of the included research studies on the role of Data Networks and APIs in enhancing operational efficiency in SMEs. The process starts with the initiation step of reporting and categorizing synthesis results; this is followed by a proper examination of the research study’s characteristics, including the research design geographic location, and Data Networks or API type. This goes together with an evaluation of potential biases and sensitive analyses to assess the robustness of conclusions. This visual provides a clear and well-structured approach to comprehending how the results were systematically synthesized to derive reliable conclusions.
Figure 15. Result synthesis process.

3.4.1. Study Characteristics and Bias Assessment

This systematic review combined the findings from multiple research studies on how Data Networks and APIs contribute to operational efficiency. We considered research studies based on different study designs—for example, journals and articles—which added a unique perspective to the analysis. Targeting various kinds of organizations at different geographical levels, from small businesses to medium enterprises, these research studies were based on other methods. They investigated how APIs and Data Networks could help make operations faster, cost-effective, and with a low error rate. A few research studies focused on other factors such as user satisfaction and the scalability of the solutions. However, we noticed some issues that might affect the outcome. The research studies were conducted on different types of economic development. Table 12 shows the difference in percentages of developed and developing countries that have shown interest in the research of APIs and Data Networks. By contrast, Figure 16 shows a visual graphical form of the analyses.
Table 12. Different types of APIs.
Figure 16. Economic development.

3.4.2. Statistical Synthesis Results

Figure 17 below illustrates the analysis methods that have been used in the research studies of Data Networks and APIs in SMEs. The pie chart shows that statistical analysis makes up 24.49% of the methods, emphasizing a quantitative approach in assessing operational efficiency. The thematic analysis accounts for 42.86%, illustrating the use of qualitative methods to assemble and capture broader insights. This blend of quantitative and qualitative analysis provides a knowledgeable standpoint on how Data Networks and APIs influence SME performance.
Figure 17. Data analysis techniques.
The Figure emphasizes the dominance of statistical methods, which play a vital role in assessing the quantitative elements of the research study outcome. By showcasing the focus on statistical analysis, it assists in evaluating the strength of statistical syntheses, like meta-analyses, and their influence on the overall findings. Additionally, the inclusion of thematic analysis highlights the integration of qualitative perspectives, offering a more complete view of how various data types were integrated and combined to reach the study’s conclusions. This type of approach ensures a proper interpretation of the results, considering both quantitative and qualitative data in the overall analysis.

3.4.3. Result Variability Factors

Figure 18 illustrates the factors that contribute to the variation across studies, with the industry context playing a significant role. The diversity of industries examined includes healthcare, manufacturing, finance, and education sectors. This introduces unique challenges and opportunities that impact the overall findings. Each sector has distinct operational needs, data handling practices, and regulatory requirements, all of which influence how Data Networks and APIs affect operational efficiency. As such, the industry context becomes a critical factor in understanding the variability in study results, offering insight into the sector-specific dynamics that shape the implementation and effectiveness of these technologies.
Figure 18. The result variability factors (industrial context).

3.4.4. Sensitivity Analyses

Additionally, we meticulously reviewed the completeness of reported outcomes by comparing outcomes outlined in study protocols or registries with those reported in published papers. This method enabled us to identify instances of result non-disclosure that might have introduced bias into our synthesis. When we noticed that some research studies had results, we flagged them and conducted sensitivity analyses to see how these could affect our overall conclusions. We carefully compared the results of these sensitivity analyses, with the analysis paying attention to any differences.

3.5. Risk of Bias

When examining the role of Data Networks and APIs in enhancing the operational efficiency of SMEs, it is essential to understand the research methods employed in studies, as these greatly influence the validity and relevance of the findings. Figure 19 below illustrates the distribution of research methods used in studies on this topic, highlighting the potential bias risks associated with each approach. A range of methods, including case studies, surveys, and experimental design, was used. These approaches have been employed, each offering their advantages and limitations when exploring Data Networks and APIs’ impact on SMEs.
Figure 19. Distribution and implication of research design.
The data presented in Figure 19 illustrate the distribution of research designs used in the research studies, with experimental designs accounting for the majority, comprising 46% of the studies, providing a robust approach for establishing causality. However, experimental methods may suffer from limited external validity, as the controlled conditions might not fully reflect real-world SME environments. The “Not Specified” category makes up 17% of the studies, indicating a lack of clarity in research design, which could affect the reliability of findings. Surveys, making up 8%, offer an efficient way to gather extensive data but may introduce bias due to self-reporting and the difficulty of capturing the full complexity of API and Data Network integration in SMEs. Case studies, comprising 6%, offer in-depth insights into specific contexts but may lack generalizability.
This is evident from the varied methodologies depicted in the figure that indicate a complex approach to addressing this area of study while each design has its own inherently possible sources of bias. The use of such designs indicates the interest in tightly controlled conditions whereas the lack of other methods, such as questionnaires and cases, may reduce the external validity of the findings. Such risks can be minimized in future research with a more balanced use of a combination of research designs and the more frequent use of mixed methods and statistical analysis to increase the understanding of how Data Networks and APIs affect the performance of SMEs.

4. Key Findings and Strategic Implications for Business Leaders

The review’s findings reveal how Data Networks and APIs significantly impact the operational efficiency and scalability of small and medium enterprises (SMEs). For business leaders, these technologies provide opportunities to optimize processes, reduce costs, and improve customer satisfaction. However, they also present challenges, particularly related to security risks and the need for technical integration. By understanding these key metrics, SME leaders can make informed decisions about implementing Data Networks and APIs to align with their strategic goals.
Table 13 provides a comprehensive analysis of key findings and their implications across various industries. Each entry highlights the core findings, strategic implications for leaders, opportunities and challenges, relevance to the systematic review, strategic drivers, and expected outcomes.
Table 13. Key findings and strategic implications for business leaders in implementing Data Networks and APIs.
Across industries, the integration of APIs and Data Networks offers clear strategic benefits for SMEs, including improved operational efficiency, cost savings, enhanced customer satisfaction, and better scalability. However, successful implementation requires leaders to navigate challenges such as security risks, compliance with regulations, and high initial investments. The table illustrates how different industries can leverage these technologies to drive growth while addressing industry-specific challenges, such as compliance in healthcare or synchronization issues in hospitality. The findings also emphasize the importance of long-term sustainability, with key investment priorities focusing on real-time data integration, secure API systems, and scalable infrastructure.

5. Decision-Making Framework for Implementing Proposed Study Topic

The implementation of Data Networks and APIs is a crucial step for SMEs aiming to enhance their operational efficiency, scalability, and competitiveness. A structured decision-making framework helps businesses plan, choose, and implement these technologies in a manner that aligns with their strategic goals. This framework ensures that each stage of adoption—from initial assessment to full integration—delivers tangible business benefits, minimizes risks, and provides a clear path to return on investment. Table 14 outlines the decision-making framework, detailing the key steps for each industry, focusing on framework elements such as strategic drivers, expected outcomes, and relevance to the systematic review on operational efficiency in SMEs.
Table 14. Decision-making framework for implementing Data Networks and APIs in SMEs.
The decision-making framework for implementing Data Networks and APIs in SMEs outlines a structured, industry-specific approach that guides leaders through each critical stage, from assessment to full-scale implementation. The framework focuses on aligning the technology with strategic business goals, ensuring that systems are scalable, and addressing the unique challenges of each industry. For instance, retail SMEs benefit from real-time inventory and customer data management, while manufacturing SMEs optimize production with IoT-enabled predictive maintenance.

6. Best Practices for Successful Study Topic Implementation

Business leaders looking to adopt Data Networks and APIs must follow a strategic approach to ensure successful implementation. These technologies provide significant benefits, including smoother operations, cost savings, and enhanced profitability. By following industry-specific best practices, businesses can optimize resource usage, improve compliance, build customer trust, and offer advanced services that boost their competitiveness. Table 15 presents the proposed key best practices for using Data Networks and APIs in SMEs, categorized by industry. This revised framework includes columns for SME types to capture different SME business models, operational challenges to highlight specific issues addressed by the technology, strategic drivers that guide implementation, expected impact of the practice, and ties to systematic review findings to show how each best practice aligns with evidence from the review.
Table 15. Key practices for using Data Networks and APIs in SMEs.
The proposed framework highlights the best practices for successful implementation of Data Networks and APIs in SMEs, across a range of industries. For retail SMEs, real-time API integration improves inventory management and customer engagement, while healthcare SMEs benefit from secure APIs to ensure compliance and better patient data management. Financial services SMEs gain from secure payment processing and fraud detection systems, reducing risks and enhancing customer trust.

7. Proposed Metrics and KPIs for Measuring Study Topic Performance

In evaluating the performance of Data Networks and APIs, SMEs across various industries must focus on specific key metrics and strategic drivers that are critical to their operational success. For example, retail SMEs benefit significantly from prioritizing transaction processing time and inventory accuracy, which streamline e-commerce operations and improve customer satisfaction. By contrast, manufacturing SMEs focus on machine downtime and production throughput to optimize equipment efficiency and reduce operational costs. The inclusion of technology integration complexity, cost of implementation, and long-term scalability further highlights the unique challenges SMEs face when adopting these systems as shown in Table 16. Each industry has its own set of priorities, as shown by the varied Key Metrics/KPIs and expected outcomes, such as fraud detection rate in financial services and on-time delivery rate in logistics. The prioritization of these metrics helps business leaders strategically align their investments with long-term growth, ensuring that technology implementations lead to tangible improvements in operational efficiency (Table 16).
Table 16. Proposed metrics and KPIs for measuring performance.
The table highlights the critical metrics and KPIs for measuring the performance of Data Networks and APIs across different SME sectors. Industries such as retail and logistics prioritize transaction processing time and on-time delivery rates to drive customer satisfaction and operational efficiency. By contrast, sectors like healthcare and financial services focus on data access time and fraud detection rates to ensure compliance and security. The additional columns provide a comprehensive view of the integration challenges, costs, and scalability associated with these systems. The prioritization of metrics ensures that businesses can focus on the most impactful areas, while the insights on implementation complexity guide SMEs in planning and resource allocation, ultimately supporting their growth and competitiveness in a dynamic market.

8. Proposed Industry-Specific Frameworks for the Study Topic

Small and medium enterprises (SMEs) across various industries face unique operational challenges, such as limited resources, scalability issues, and rising cybersecurity threats. In response, the integration of Data Networks and Application Programming Interfaces (APIs) has become increasingly crucial for optimizing operations and improving efficiency. However, the specific application and impact of these technologies vary significantly across industries due to differences in infrastructure needs, regulatory requirements, and market demands. This section introduces a detailed framework that breaks down the strategic implications, opportunities, challenges, and expected outcomes of API and Data Network integration across key sectors, including retail, manufacturing, healthcare, financial services, hospitality, logistics, education, energy, agriculture, and technology. As outlined in Table 17, these frameworks provide insights into how SMEs can leverage APIs to address sector-specific challenges while enhancing their operational capabilities. Each industry is examined through several key dimensions: the primary findings on API and Data Network impacts, strategic opportunities for business leaders, potential challenges during implementation, and how these insights relate to the broader systematic review on operational efficiency. Additionally, the frameworks emphasize the strategic drivers that can support successful technology integration, along with the expected outcomes for each sector.
Table 17. Industry-specific frameworks for implementing Data Networks and APIs in SMEs.
The proposed industry-specific frameworks presented highlight the diverse benefits and challenges that SMEs face when implementing Data Networks and APIs. Retail SMEs can use APIs to optimize inventory management, increasing customer satisfaction and reducing stockouts, while manufacturing SMEs benefit from IoT integration for predictive maintenance and production efficiency improvements. Healthcare and financial services SMEs must prioritize security and regulatory compliance, especially when dealing with sensitive data, but stand to gain significant operational efficiencies from real-time communication and secure payment APIs. Other sectors, such as logistics and hospitality, can enhance customer service and streamline operations through real-time tracking and booking systems, respectively.

9. Real-World Case Studies on How Data Networks and APIs Enhance Operational Efficiency in SMEs

In fact, digital transformation is something businesses in today’s time must undergo. It is more crucial in the case of an SME. Cloud computing, AI, Data Networks, and APIs are a few advanced technologies that have been adopted largely by many organizations with the motive of bringing efficiency and growth into operations. In the backdrop of a systematic review, how Data Networks and APIs provide relevant contributions to these efforts becomes significant and compelling, especially among SMEs. This review demonstrates, with the help of examples, how these technologies are being implemented in real life and their measurable outcomes in optimizing business performance for a wide range of sectors and regions.
Case 1: China’s SME Adoption of Data Networks and Machine Learning in Healthcare
This is how large amounts of big data and machine learning applications enabled SMEs in the healthcare industry in China to work on patient data management and service delivery. Moving forward, it presented an efficient framework within the integration of data and knowledge reasoning, thereby bringing a major improvement in decision making and operational efficiency. The integration of the IoT increased powers of data processing and allowed SMEs to undertake streamlined operations with respect to ensuring data security and regulatory compliances [98].
Case 2: Data-driven API Recommendations for Web Application Development
In China, SMEs utilized data-driven API recommendation systems for decreasing the development burden related to web applications. The WAR framework allows app developers to navigate through the process of discovery, verification, and selection of compatible APIs through keyword-based searches to reduce the complexity of going through extensive web APIs manually. The simplification of the selection process of APIs made the cycles of app development efficient, as SMEs could minimize the time required to integrate the external APIs into their platforms [32].
Case 3: An Approach for an Efficient Execution of SPMD Applications on Multi-core Environments
In Spain, achieving a good balance between speed and computational efficiency is thus an extremely challenging job on the part of parallel programmers for traditional MPI applications on multi-core clusters. This work puts the spotlight on SPMD applications marked by high volume and synchrony in communication while proposing a technique for managing heterogeneity in communication on homogeneous multi-core platforms. The aim is to find, through analytics, the number of cores that yields the highest speedup while keeping the computational efficiency above some threshold; that is what strong scalability entails [46].
Case 4: API and Permission-Based Classification for Android Application Security
In Thailand, a design is presented to classify Android applications into three categories—Benign, Suspicious, and Malicious—based on their APIs and permissions. The classification system works in three tiers of analysis: Level 1 consists of 19 broad categories like network and system summary, while Level 2 expands to 113 detailed classifications. Level 3 does the matching of API interfaces, classes, and public methods with permissions. It makes use of YARA Rules to draw out information from AndroidManifest.xml and classes Dex for deep diving into application behavior. This will improve user awareness, in that users will be provided with insights regarding app behaviors, helping users make their own informed decisions in downloading any app [81].
Case 5: Embedded System with GPS and API Integration for Road Safety in India
This case has proposed an embedded system for improved road safety in view of the current increase in population and demand for safer and more efficient transportation in India. Approximately 1 million deaths annually result from road accidents, which calls for an enhanced accident detection and response system. It uses a GPS and GSM module to capture the location of accident spots and sends data to Web APIs when a network is available; it stores data locally and sends them if the network is not available. Along with that, a gyroscope measures vehicle tilt in case of a turn, which helps in assessing the damage to a vehicle. The system allows for real-time data acquisition and is quite practical for improvement in transportation safety [99].
Case 6: Mashup Service API Recommendation Model Using Graph Attention Networks (GATs)
The development of Web APIs in India has been rapid in recent years, and it has become much easier to create Mashups from several API sources. However, the right choice of APIs remains a challenge. Most traditional recommender systems based on collaborative filtering usually produce one-sided results, depending on historical user data alone. In this paper, the authors propose a new Mashup service recommendation model using a Graph Attention Network (GAT) that integrates functional semantics and non-functional features and service invocation behavior to provide better API recommendations [100].
Case 7: API-Driven Business Model Transformation in Amadeus Corporation
Digital transformation has been making firms develop new strategies and business models to enhance value creation. This study focuses on the case of the Amadeus Corporation, regarded as a leading player within the travel industry, discussing its transformation toward an API-driven business model. Content analysis is based on publicly available documents and gray literature. The research attempts to show how Amadeus has utilized Public APIs in innovation and optimization processes internally while opening toward third-party developers [101].
Case 8: A Pragmatic Framework for Digital Transformation
Economies are changing at a rapid pace, with ever-increasing growth in computing resources and emergent technologies such as analytics, social media, and mobile computing. The case study investigates the existing DT frameworks, while at the same time proposing a practical framework that is bound to help organizations match current trends by increasing efficiency and flexibility. The proposed framework involves the assessment of various architectures, stacks of technologies, and development, testing, deployment, and operational processes with cultural and business changes accordingly required. With the help of some metrics and KPIs that are custom defined, DT’s impact is assessed on a continuous basis during the journey of transformation. Lastly, discussions on the implications of the proposed model conclude the case study and raise questions for future research to consider in order to validate the effectiveness of the model across different business domains [102].

10. Proposed Roadmap for SME Businesses and Policy Recommendations

The roadmap for SMEs, therefore, in the wake of the digital transformation journey, recommends adopting a structured approach to overcome complexities emanating from emerging technologies. Recognizing the critical role of digital capabilities, this roadmap identifies key strategic areas, including investment in technology, workforce training, collaboration with stakeholders, and adherence to industry best practices. Emphasis in these areas can help SMEs enhance their operational efficiency and competitive advantage even further in a more dynamic market. The roadmap thus acts as a reference point in defining ways that address the specific challenges of SMEs in capturing business goals through leveraging innovation.
Table 18 summarizes this roadmap in some detail, listing specific suggestions for SMEs and related policy measures to support SMEs in their digital transformation. Furthermore, it outlines policy recommendations that constitute an enabling environment for innovation and growth of the SME economy. Table 18 addresses a wide array of critical areas, from funding to infrastructure development and support, among regulatory development, in effective strategy development and implementation to harness the full value of Data Networks and APIs in driving value creation and improved business performance within the digital economy.
Table 18. Proposed roadmap for SME businesses and policy recommendations.

11. Discussion

This systematic review delves into the crucial role of Data Networks and APIs in enhancing the operational efficiency of small and medium enterprises (SMEs). It emphasizes this role’s ramifications, obstacles, and vital methodological facets. The review shows that the performance of SMEs is greatly enhanced by integrating Data Networks and APIs in several areas, including cost reduction, operational efficiency, and business decision making. Additionally, by promoting innovation, streamlining procedures, and maximizing resource use, these technologies support economic progress.
RQ 1: Which key metrics are utilized to evaluate the effectiveness of Data Networks and APIs in enhancing operational efficiency within SMEs?
Figure 14 addresses this question, and Figure 11 shows some of the most used metrics when it comes to assessing the performance of Data Networks and APIs within SMEs. As shown, the main metric is process improvement at 12.24%, showing how these technologies make operational routines easier and more productive. Productivity metrics are 18.36%, and cost savings are at 8.16%, underlining the financial and operational importance it holds for SMEs to integrate these technologies. Security-linked metrics stand at 12.24%, and data-driven innovation stands at 10.20%, indicating data protection concerns and innovative solutions used to drive operational results. Figure 18 emphasizes that due consideration is to be provided for the adaption of metrics in SMEs to provide an accurate and reliable measure of operational efficiency and underpins how judicious use of metrics can all along affect the performance evaluation.
RQ 2: What factors contribute to differences in the performance and cost-effectiveness of Data Networks and APIs in various industries and regions, specifically for SMEs?
Figure 11 and Figure 18 show in detail the causes of the variation in performance and cost-effectiveness across industries and regions. Figure 18 postulates that different contexts of industries, such as health, manufacturing, and finance, have different influences on the effectiveness of Data Networks and APIs. Regulations, standards in these industries, and the level of resource availability are big determinants of this influence. Figure 11 also depicts that regional differences are observed, such as the US and China, with higher success rates relative to others, attributed to larger investments in infrastructure and technology. It is from this that the results highlighted the industry and regional elements being important determinants of how the Data Networks and APIs would influence operational efficiency and cost-effectiveness within the SMEs.
RQ 3: What are the primary challenges in implementing Data Networks and APIs in SMEs, and how do they impact operational feasibility and deployment?
Figure 17 give the overall challenges faced by SMEs in implementing Data Networks and APIs. Thematic analyses, at 43%, and statistical analyses, at 24%, as explained in Figure 17, are the major barriers in deployment related to Data Networks and APIs in most SMEs that especially have underdeveloped IT infrastructures. These challenges further delay deployment and restrain SMEs from benefitting most from Data Networks and APIs. Figure 17 also shows integration difficulties of these technologies with legacy systems, which increases the complexity of operational feasibility and prolongs the deployment timeline. The findings imply that for better feasibility and success rates in Data Network and API implementations, SMEs should also address resource limitations and technical challenges.
RQ 4: How do Data Networks and APIs significantly improve business operations in SMEs by increasing speed, reducing costs, and enhancing overall efficiency?
Figure 14 and Figure 19 represent two key improvements that Data Networks and APIs create for SMEs. Figure 14 very succinctly points out that the most significant advantages are in process improvement at 12% and in cost savings at 8% due to automation and the smoothing of data flows. Figure 19 completes the argument, as it shows that APIs allow for making decisions faster and quicker than their manual versions do. These developments make SMEs more operationally agile and decrease their operations costs, while consequently allowing them to compete more effectively in their markets. Considered all together, these figures show how Data Networks and APIs increase speed, efficiency, and cost-effectiveness in the business processes of SMEs.
RQ 5: What impact do advancements in data technologies and API standards have on enhancing operational efficiency in SMEs?
The implication of advances in data technologies and API standards are assessed in Figure 18 and Figure 19. Figure 18 suggests that with the development of data technologies, SMEs believe there is improvement in their data quality, reliability, and, hence, operational efficiency. This has crucial implications for developing standardized APIs that facilitate seamless integration and thereby compatibility with each other to reduce friction in operations. Furthermore, Figure 19 indicates that emerging data technologies like real-time data processing and blockchain integration open new frontiers for SMEs in the matter of better decision making and smoothing operational workflows. These findings again point to the fact that changes in technology are essential for improving growth and operational efficiency in SMEs.

12. Conclusions

The systematic review assessed the role of Data Networks and APIs in enhancing operational efficiency, centered on their integration within small and medium enterprises (SMEs). A total of 49 studies were reviewed, with outcomes indicating a significant positive impact on efficiency in the implementation of Data Networks and APIs. These technologies provide faster data processing, resource optimization, and much better scalability. However, their widespread adoption also introduces major security concerns, especially with the growing risk of cyberattacks targeting vulnerable APIs. The review underlines that while advanced security protocols and management strategies are necessary to reduce these risks, the overall benefits to SMEs remain compelling. With proper security measures and management, Data Networks and APIs offer transformative potential in streamlining business operations, reducing costs, and enhancing flexibility in a dynamic market environment.

13. Limitations and Suggestions for Future Research

Limitations within this research was that it is a challenge obtaining papers that had both Data Networks and APIs within the same research paper. Most of the papers found were not focused on the business sector but on Data Networks and general API use. Future research should focus on refining API security models and developing frameworks tailored specifically to the SME sector to maximize operational efficiency while minimizing security vulnerabilities. Furthermore, future research can also focus on enhancing research that covers both Data Networks and APIs for impact on SMEs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su162310192/s1. Reference [103] is citied in the Supplementary Materials.

Author Contributions

M.B.K., S.N.X., and N.A.M. performed data collection, conducted investigations, and wrote and prepared the article under the supervision of B.T., and L.M., who were responsible for conceptualizing, reviewing, and editing the article. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data is contained within the article or Supplementary Materials.

Acknowledgments

The authors would like to thank all the researchers for their contribution in the database.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Prajapati, A.; Sakadasariya, A.; Patel, J. Software defined network: Future of networking. In Proceedings of the 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 19–20 January 2018; pp. 1351–1354. [Google Scholar] [CrossRef] [Scilit]
  2. Tipu, A.J.S.; Conbhuí, P.Ó.; Howley, E. Artificial neural networks based predictions towards the auto-tuning and optimization of parallel IO bandwidth in HPC system. Clust. Comput. 2024, 27, 71–90. [Google Scholar] [CrossRef] [Scilit]
  3. Wittig, A.; Wittig, M. Amazon Web Services in Action: An In-Depth Guide to AWS; Simon and Schuster; Manning Publications: New York, NY, USA, 2003; Available online: https://books.google.com/books?hl=en&lr=&id=joK3EAAAQBAJ&oi=fnd&pg=PA1&dq=Leading+cloud+services+providers+such+as+Microsoft+Azure,+AWS,+and+Google+Cloud+Plat-form+use+API-based+approach+and+network+infrastructures+for+handling+different+operations+associated+with+users+quickly.&ots=DsOpUp6zsV&sig=jsi0yy7zPtJl5RQGe9uZNGNmI94 (accessed on 12 September 2024).
  4. Guo, H.; Yang, Z.; Huang, R.; Guo, A. The digitalization and public crisis responses of small and medium enterprises: Implications from a COVID-19 survey. Front. Bus. Res. China 2020, 14, 19. [Google Scholar] [CrossRef] [Scilit] [PubMed Central]
  5. Ahmadinejad, S.H.; Fong, P.W. An Enforcement Model for Preventing Inference Attacks in Social Computing Platforms. In Proceedings of the 22nd ACM on Symposium on Access Control Models and Technologies, Indianapolis, IN, USA, 7 June 2017; pp. 55–66. [Google Scholar] [CrossRef] [Scilit]
  6. Chaparadza, R.; Elkotob, M.; Radier, B.; Meriem, T.B.; Choi, T.; Bohn, R.B.; Battou, A.; Zhang, T. The Era of Knowledge Plane (KP) Platforms Driven Networking—Anchor for Federation of Autonomic/Autonomous Networks (ANs) Across Industry Sectors. In Proceedings of the 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea, 19–21 October 2022; pp. 950–955. [Google Scholar] [CrossRef] [Scilit]
  7. Bagherian, H.; Sattari, M. Health Information System in Developing Countries: A Review on the Challenges and Causes of Success and Failure. Med. J. Islam. Repub. Iran. 2022, 36, 111. [Google Scholar] [CrossRef] [Scilit] [PubMed] [PubMed Central]
  8. Kviesis, A.; Zacepins, A.; Durgun, M.; Tekin, S. Application of wireless sensor networks in precision apiculture. Eng. Rural. Dev. 2015, 20, 440–445. [Google Scholar]
  9. Bagale, G.S.; Vandadi, V.R.; Singh, D.; Sharma, D.K.; Garlapati, D.V.K.; Bommisetti, R.K.; Sengan, S. Small and Medium-Sized Enterprises’ Contribution in Digital Technology (Doctoral dissertation, Petra Christian University). 2021. Available online: https://link.springer.com/article/10.1007/s10479-021-04235-5 (accessed on 12 September 2024).
  10. Ullah, I.; Adhikari, D.; Su, X.; Palmieri, F.; Wu, C.; Choi, C. Integration of data science with the intelligent IoT (IIoT): Current challenges and future perspectives. Digit. Commun. Netw. 2024, in press. [CrossRef] [Scilit]
  11. Hyat-Khan, S. 8 Towards a more secure IT driven future: The necessity and promise of IT operations management governance. In Cybersecurity Risk Management: Enhancing Leadership and Expertise; Walter de Gruyter: Berlin, Germany, 2004; p. 143. [Google Scholar] [CrossRef] [Scilit]
  12. Jacobson, D.; Brail, G.; Woods, D. APIs: A Strategy Guide; O’Reilly Media, Inc.: Sebastopol, CA, USA, 2012; Available online: https://books.google.co.za/books?hl=en&lr=&id=pLN7BxMTg7IC&oi=fnd&pg=PR3&dq=Companies+may+have+hundreds+of+APIs+and+each+with+dozens+or+more+vulnerabilities+per+year+&ots=SA_vps5ww2&sig=LLNF_UlwG8qePvqCn8G99OI0aVQ&redir_esc=y#v=onepage&q&f=false (accessed on 12 September 2024).
  13. Labs, S. Latest State of API Security Report: 400% Increase in Attackers and More; Salt Labs: New York, NY, USA, 2023; Available online: https://salt.security/blog/latest-state-of-api-security-report-400-increase-in-attackers-and-more (accessed on 12 September 2024).
  14. Adelmann, F.; Ergen, I.; Gaidosch, T.; Jenkinson, N.; Khiaonarong, M.T.; Morozova, A.; Wilson, C. Cyber Risk and Financial Stability: It’s a Small World After All; International Monetary Fund: Washington, DC, USA, 2020; Available online: https://www.elibrary.imf.org/downloadpdf/journals/006/2020/007/006.2020.issue-007-en.pdf (accessed on 12 September 2024).
  15. Díaz-Rojas, J.A.; Ocharán-Hernández, J.O.; Pérez-Arriaga, J.C.; Limón, X. Web API Security Vulnerabilities and Mitigation Mechanisms: A Systematic Mapping Study. In Proceedings of the 2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT), San Diego, CA, USA, 25–29 October 2021; pp. 207–218. [Google Scholar] [CrossRef] [Scilit]
  16. Birje, M.N.; Challagidad, P.S.; Goudar, R.H.; Tapale, M.T. Cloud computing review: Concepts, technology, challenges and security. Int. J. Cloud Comput. 2017, 6, 32–57. [Google Scholar] [CrossRef] [Scilit]
  17. Botta, A.; De Donato, W.; Persico, V.; Pescapé, A. Integration of cloud computing and internet of things: A survey. Future Gener. Comput. Syst. 2016, 56, 684–700. [Google Scholar] [CrossRef] [Scilit]
  18. Jayeola, O.; Sidek, S.; Rahman, A.A.; Mahomed, A.S.B. Cloud Computing Adoption in Small and Medium Enterprises (SMEs): A Systematic Literature Review and Directions for Future Research. Int. J. Bus. Soc. 2022, 23, 226–243. [Google Scholar] [CrossRef] [Scilit]
  19. Dhayanidhi, G. Research on IoT Threats & Implementation of AI/ML to Address Emerging Cybersecurity Issues in IoT with Cloud Computing. Master’s Thesis, University of Alberta, Edmonton, Canada, 2022. [Google Scholar] [CrossRef]
  20. Abdulaziz, A.A. A Systematic Review of Financial Factors Affecting Implementation of Lean Principle in Small and Medium Enterprises of Saudi Arabia. Manag. Syst. Prod. Eng. 2024, 32, 265–279. [Google Scholar] [CrossRef] [Scilit]
  21. Fang, S.; Da Xu, L.; Zhu, Y.; Ahati, J.; Pei, H.; Yan, J.; Liu, Z. An Integrated System for Regional Environmental Monitoring and Management Based on Internet of Things. IEEE Trans. Ind. Inform. 2014, 10, 1596–1605. [Google Scholar] [CrossRef] [Scilit]
  22. Yan, H.; Li, Y.; Dong, W.; Jin, D. Software-Defined WAN via Open APIs. IEEE Access 2018, 6, 33752–33765. [Google Scholar] [CrossRef] [Scilit]
  23. Nadar, A.; Härri, J. Enhancing Network Data Analytics Functions: Integrating AIaaS with ML Model Provisioning. In Proceedings of the 2024 22nd Mediterranean Communication and Computer Networking Conference (MedComNet), Nice, France, 11–13 June 2024; pp. 1–4. [Google Scholar]
  24. Hsu, K. Big data analysis and optimization and platform components. J. King Saud Univ.—Sci. 2022, 34, 101945. [Google Scholar] [CrossRef] [Scilit]
  25. Huang, D.; Tong, X.; Yang, H. Web Service Recommendation based on Graph Attention Network (GAT-WSR). In Proceedings of the 2022 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 25–27 January 2022; pp. 1–5. [Google Scholar]
  26. Huang, Y. Mixed Training Mode of Business English Cloud Classroom Based on Mobile APP with Shared SDK. In Proceedings of the 2022 International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, 16–18 March 2022; pp. 792–795. [Google Scholar]
  27. Hytti, U.; Kuoppakangas, P.; Suomi, K.; Chapleo, C.; Giovanardi, M. Challenges in delivering brand promise—Focusing on municipal healthcare organisations. Int. J. Public Sect. Manag. 2015, 28, 254–272. [Google Scholar] [CrossRef] [Scilit]
  28. Park, J.; Chun, H.; Jung, S. API and Permission-based Classification System for Android Malware Analysis. In Proceedings of the 2018 International Conference on Information Networking (ICOIN), Chiang Mai, Thailand, 10–12 January 2018; pp. 930–935. [Google Scholar]
  29. Li, K. Research on Network Digital Information Security Defense System under Computer Big Data Technology. In Proceedings of the 2023 IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE), Jinzhou, China, 18–20 August 2023; pp. 1112–1116. [Google Scholar]
  30. Li, M.; Zhou, G.; Li, Z. Fast recognition system for Tree images based on dual-task Gabor convolutional neural network. Multimed. Tools Appl. 2022, 81, 28607–28631. [Google Scholar] [CrossRef] [Scilit]
  31. Muthohar, M.F.; Nugraha, I.G.D.; Choi, D. Mobile application access design for user-defined network infrastructure. In Proceedings of the 2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS), Busan, Republic of Korea, 19–21 August 2015; pp. 577–579. [Google Scholar]
  32. Qi, L.; He, Q.; Chen, F.; Zhang, X.; Dou, W.; Ni, Q. Data-Driven Web APIs Recommendation for Building Web Applications. IEEE Trans. Big Data 2022, 8, 685–698. [Google Scholar] [CrossRef] [Scilit]
  33. Baucke, S.; Ali, R.B.; Kempf, J.; Mishra, R.; Ferioli, F.; Carossino, A. Cloud Atlas: A Software Defined Networking Abstraction for Cloud to WAN Virtual Networking. In Proceedings of the 2013 IEEE Sixth International Conference on Cloud Computing, Santa Clara, CA, USA, 28 June–3 July October 2021; pp. 895–902. [Google Scholar] [CrossRef] [Scilit]
  34. Qazi, F. Application Programming Interface (API) Security in Cloud Applications. EAI Endorsed Trans. Cloud Syst. 2023, 7, e1. [Google Scholar] [CrossRef] [Scilit]
  35. Xie, N.; Zeng, F.; Qin, X.; Zhang, Y.; Zhou, M.; Lv, C. Repassdroid: Automatic detection of android malware based on essential permissions and semantic features of sensitive apis. In Proceedings of the 2018 International Symposium on Theoretical Aspects of Software Engineering (TASE), Guangzhou, China, 29–31 August 2018; pp. 52–59. [Google Scholar] [CrossRef] [Scilit]
  36. Ravi, H. Blockchain: An EOM approach to reconciliation in banking. Innov. Manag. Rev. 2023, 20, 17–27. [Google Scholar]
  37. Wang, H.; Belitskaya-Levy, I.; Wu, F.; Lee, J.S.; Shih, M.-C.; Tsao, P.S.; Lu, Y. A statistical quality assessment method for longitudinal observations in electronic health record data with an application to the VA million veteran program. BMC Med. Inform. Decis. Mak. 2021, 21, 289. [Google Scholar] [CrossRef] [Scilit]
  38. Vijayvargiya, S.; Kumar, L.; Neti LB, M.; Misra, S.; Krishna, A.; Padmanabhuni, S. Empirical Analysis for Investigating the Effect of Machine Learning Techniques on Malware Prediction. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering, Prague, Czech Republic, 24–25 April 2023; pp. 453–460. Available online: https://www.scitepress.org/Papers/2023/118582/118582.pdf (accessed on 12 September 2024).
  39. Tavagnutti, A.A.; Vicenzutti, A.; Comugnaro, D.; Bosich, D.; Chiandone, M.; Sulligoi, G. Reduced Order Model of Zonal DC Microgrid for Open Source Real-time Emulation. In Proceedings of the 2024 Open Source Modelling and Simulation of Energy Systems (OSMSES), Vienna, Austria, 3–4 September 2024; pp. 1–6. [Google Scholar]
  40. Radha, K.; Parameswari, R. Reducing the Effects of DDos Attacks in Software Defined Networks Using Cloud Computing. In Proceedings of the 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS), Kochi, India, 1–3 February 2023; pp. 1–6. [Google Scholar] [CrossRef] [Scilit]
  41. Clester, I.; Freeman, J. Composing the Network with Streams. In Proceedings of the 16th International Audio Mostly Conference, Trento Italy, 1–3 September 2021; Association for Computing Machinery: New York, NY, USA, 2021; pp. 196–199. [Google Scholar] [CrossRef] [Scilit]
  42. Li, L.; Chou, W.; Zhou, W.; Luo, M. Design Patterns and Extensibility of REST API for Networking Applications. IEEE Trans. Netw. Serv. Manag. 2016, 13, 154–167. [Google Scholar] [CrossRef] [Scilit]
  43. Adeleye, O.; Yu, J.; Wang, G.; Yongchareon, S. Constructing and Evaluating Evolving Web-API Networks A Complex Network Perspective. IEEE Trans. Serv. Comput. 2023, 16, 177–190. [Google Scholar] [CrossRef] [Scilit]
  44. Tanizawa, Y.; Takahashi, R.; Dixon, A.R. A routing method designed for a quantum key distribution network. In Proceedings of the 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria, 5–8 July 2016; pp. 208–214. [Google Scholar] [CrossRef] [Scilit]
  45. Yang, S.; Wang, Y. A hybrid MPI-CUDA approach for nonequispaced discrete Fourier transformation. Comput. Phys. Commun. 2020, 258, 107513. [Google Scholar] [CrossRef] [Scilit]
  46. Latif, R.M.; Jamil, M.; He, J.; Farhan, M. A Novel Authentication and Communication Protocol for Urban Traffic Monitoring in VANETs Based on Cluster Management. Systems 2023, 11, 322. [Google Scholar] [CrossRef] [Scilit]
  47. Arora, A.; Peddoju, S.K. NTPDroid: A hybrid android malware detector using network traffic and system permissions. In Proceedings of the 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), New York, NY, USA, 1–3 August 2018; pp. 808–813. [Google Scholar] [CrossRef] [Scilit]
  48. Tsiu, S.; Ngobeni, M.; Mathabela, L.; Thango, B. Applications and Competitive Advantages of Data Mining and Business Intelligence in SMEs Performance: A Systematic Review. Preprints 2024, 2024090940. [Google Scholar] [CrossRef] [Scilit]
  49. Qi, L.; He, Q.; Chen, F.; Dou, W.; Wan, S.; Zhang, X.; Xu, X. Finding all you need: Web APIs recommendation in web of things through keywords search. IEEE Trans. Comput. Soc. Syst. 2019, 6, 1063–1072. [Google Scholar] [CrossRef] [Scilit]
  50. Lins, S.; Pandl, K.D.; Teigeler, H. Artificial Intelligence as a Service. Bus. Inf. Syst. Eng. 2021, 63, 441–456. [Google Scholar] [CrossRef] [Scilit]
  51. Tang, M.; Xie, F.; Lian, S.; Mai, J.; Li, S. Mashup-oriented API recommendation via pre-trained heterogeneous information networks. Inf. Softw. Technol. 2024, 169, 107428. [Google Scholar] [CrossRef] [Scilit]
  52. Zhang, P. Cloud computing English teaching application platform based on machine learning algorithm. Soft Comput. 2023, 1–13. [Google Scholar] [CrossRef] [Scilit]
  53. Chen, J.L.; Ma, Y.W.; Kuo, H.Y.; Hung, W.C. Enterprise visor: A Software-Defined enterprise network resource management engine. In Proceedings of the 2014 IEEE/SICE International Symposium on System Integration, Tokyo, Japan, 14–17 December 2014; pp. 381–384. [Google Scholar] [CrossRef] [Scilit]
  54. Molete, O.B.; Mokhele, S.E.; Ntombela, S.D.; Thango, B.A. The Impact of IT Strategic Planning Process on SME Performance: A Systematic Review. Preprints 2024, 2024091024. [Google Scholar] [CrossRef] [Scilit]
  55. Andreev, S.; Moltchanov, D.; Galinina, O.; Pyattaev, A.; Ometov, A.; Koucheryavy, Y. Network-assisted device-to-device connectivity: Contemporary vision and open challenges. In Proceedings of the European Wireless 2015; 21th European Wireless Conference, Budapest, Hungary, 20–22 May 2015; pp. 1–8. [Google Scholar]
  56. Dinita, R.-I.; Wilson, G.; Cirstea, M. Novel Autonomous Software for Enhanced Data Center Operational Efficiency and Botnet Detection. In Proceedings of the IECON 2023—49th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 16–19 October 2023; pp. 1–9. [Google Scholar] [CrossRef] [Scilit]
  57. Rabbani, A.; Musarat, M.A.; Alaloul, W.S.; Maqsoom, A.; Bukhari, H.; Waqas, R. Road traffic accident data analysis and its visualization. Civ. Eng. Archit. 2021, 9, 1603–1614. [Google Scholar] [CrossRef] [Scilit]
  58. Martin, R.; Lopez, B.; Vidal, I.; Valera, F.; Nogales, B. Service for Deploying Digital Twins of QKD Networks. Appl. Sci. 2023, 14, 1018. [Google Scholar] [CrossRef] [Scilit]
  59. Keränen, A.-M. Rising Cyber Threats Targeting the Banking Sector; University of Jyväskylä: Jyväskylän yliopisto, Finland, 2022. [Google Scholar]
  60. Takahashi, R.; Tanizawa, Y.; Dixon, A. A high-speed key management method for quantum key distribution network. In Proceedings of the 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), Zagreb, Croatia, 2–5 July 2019; pp. 437–442. [Google Scholar] [CrossRef] [Scilit]
  61. Du, Y.; Cui, M.; Cheng, X. A Mobile Malware Detection Method Based on Malicious Subgraphs Mining. Secur. Commun. Netw. 2020, 2021, 5593178. [Google Scholar] [CrossRef] [Scilit]
  62. Ngcobo, K.; Bhengu, S.; Mudau, A.; Thango, B.; Matshaka, L. Enterprise Data Management: Types, Sources, and Real-Time Applications to Enhance Business Performance—A Systematic Review. Preprints 2024, 2024091913. [Google Scholar] [CrossRef] [Scilit]
  63. Muresano, R.; Meyer, H.; Rexachs, D.; Luque, E. An approach for an efficient execution of SPMD applications on Multi-core environments. Future Gener. Comput. Syst. 2016, 66, 11–26. [Google Scholar] [CrossRef] [Scilit]
  64. Imoize, A.L.; Odeyemi, S.D.; Adebisi, J.A. Development of a low-cost wireless bee-hive temperature and sound monitoring system. Indones. J. Electr. Eng. Inform. (IJEEI) 2020, 8, 476–485. [Google Scholar]
  65. Liu, Z.; Zhang, C.; Wu, H.; Fang, J.; Peng, L.; Ye, G.; Tang, Z. Optimizing HPC I/O Performance with Regression Analysis and Ensemble Learning. In Proceedings of the 2023 IEEE International Conference on Cluster Computing (CLUSTER), Santa Fe, NM, USA, 31 October–3 November 2023; pp. 234–246. [Google Scholar] [CrossRef] [Scilit]
  66. Hasenjager, M.J.; Hoppitt, W.; Cunningham-Eurich, I.; Franks, V.R.; Leadbeater, E. Coupled information networks drive honeybee (Apis mellifera) collective foraging. J. Anim. Ecol. 2023, 93, 71–82. [Google Scholar] [CrossRef] [Scilit]
  67. Mkhize, A.; Mokhothu, K.; Tshikhotho, M.; Thango, B. Evaluating the Impact of Cloud Computing on SMEs Performance: A Systematic Review. Preprints 2024, 2024090882. [Google Scholar] [CrossRef] [Scilit]
  68. Kgakatsi, M.; Galeboe, O.; Molelekwa, K.; Thango, B. The Impact of Big Data on SME Performance: A Systematic Review. Preprints 2024, 632–695. [Google Scholar] [CrossRef] [Scilit]
  69. Lagos Jenschke, T.; Dias de Amorim, M.; Fdida, S. Nearby connections strategies: Features, usage, and empirical performance evaluation. Internet Things 2023, 23, 100895. [Google Scholar] [CrossRef] [Scilit]
  70. Wenzel, M.; Meinel, C. Parallel network data processing in client side JavaScript applications. In Proceedings of the 2015 International Conference on Collaboration Technologies and Systems (CTS), Atlanta, GA, USA, 1–5 June 2015; pp. 140–147. [Google Scholar] [CrossRef] [Scilit]
  71. Chabalala, K.; Boyana, S.; Kolisi, L.; Thango, B.A.; Matshaka, L. Digital Technologies and Channels for Competitive Advantage in SMEs: A Systematic Review. Preprints 2024, 2024100020. [Google Scholar] [CrossRef] [Scilit]
  72. Jiang, L.; Ren, Y. A Network Information Security Prevention Model Base on Web Data Mining. In Proceedings of the 2016 International Conference on Robots & Intelligent System (ICRIS), Zhangjiajie, China, 27–28 August 2016; pp. 26–29. [Google Scholar]
  73. Shen, L.; Wang, Y.; Zhang, S.; Chen, Z. Similarity and Complementarity Attention-Based Graph Neural Networks for Mashup-Oriented Cloud API Recommendation. Electronics 2022, 12, 4436. [Google Scholar] [CrossRef] [Scilit]
  74. Fokkema, I.F.; Kroon, M.; López Hernández, J.A.; Asscheman, D.; Lugtenburg, I.; Hoogenboom, J.; den Dunnen, J.T. The LOVD3 platform: Efficient genome-wide sharing of genetic variants. Eur. J. Hum. Genet. 2021, 29, 1796–1803. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  75. Coutinho-Almeida, J.; Saez, C.; Correia, R.; Rodrigues, P.P. Development and Initial Validation of a Data Quality Evaluation Tool in Obstetrics Real-World Data Through HL7-FHIR Interoperable Bayesian Networks and Expert Rules. In JAMIA Open; Oxford University Press: Oxford, UK, 2024. [Google Scholar]
  76. Kshetri, N. The emerging role of Big Data in key development issues: Opportunities, challenges, and concerns. Proc. Natl. Acad. Sci. USA 2014, 1, 10975–10980. [Google Scholar] [CrossRef] [Scilit]
  77. Chiandone, M.; Vicenzutti, A.; Bosich, D.; Tavagnutti, A.A.; Barbini, N.; Sulligoi, G. Open-Source Hardware in the Loop Real-time Simulation of Zonal DC systems. In Proceedings of the 2022 Open Source Modelling and Simulation of Energy Systems (OSMSES), Aachen, Germany, 4–5 April 2022; pp. 1–6. [Google Scholar]
  78. Santoro, M.; Vaccari, L.; Mavridis, D.; Smith, R.; Posada, M.; Gattwinkel, D. Web Application Programming Interfaces (Apis): General Purpose Standards, Terms and European Commission Initiatives. Publications Office of the European Union: Rue de Reims, Luxembourg, 2019. Available online: https://publications.jrc.ec.europa.eu/repository/bitstream/JRC118082/jrc118082_api-landscape-standards.pdf (accessed on 12 September 2024).
  79. Qazi, F.A. Insecure Application Programming Interfaces (APIs) in Zero-Trust Networks; Capitol Technology University: Laurel, MD, USA, 2022; Available online: https://search.proquest.com/openview/93d809dffbd1a584b18ee126cd81d4b9/1?pq-origsite=gscholar&cbl=18750&diss=y (accessed on 12 September 2024).
  80. Ahmadinejad, S.H.; Fong, P.W.L. On the feasibility of inference attacks by third-party extensions to social network systems. In Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security, Hangzhou, China, 8–10 May 2013; pp. 161–166. [Google Scholar]
  81. Salam, A. Internet of things for environmental sustainability and climate change. In Internet of Things for Sustainable Community Development: Wireless Communications, Sensing, and Systems; Springer International Publishing: Cham, Switzerland, 2024; pp. 33–69. [Google Scholar] [CrossRef] [Scilit]
  82. Mohlala, T.T.; Mehlwana, L.L.; Nekhavhambe, U.P.; Thango, B.; Matshaka, L. Strategic Innovation in HRIS and AI for Enhancing Workforce Productivity in SMEs: A Systematic Review. Preprints 2024, 2024091996. [Google Scholar] [CrossRef] [Scilit]
  83. Ahmad, W.; Tayara, H.; Chong, K.T. Attention-based graph neural network for molecular solubility prediction. ACS Omega 2023, 8, 3236–3244. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  84. Naganathan, V. Comparative analysis of Big data, Big data analytics: Challenges and trends. Int. Res. J. Eng. Technol. (IRJET). 2018, 5, 1948–1964. [Google Scholar]
  85. Adeleye, O.; Yu, J.; Yongchareon, S.; Han, Y. Constructing and Evaluating an Evolving Web-API Network for Service Discovery. In Service-Oriented Computing. ICSOC 2018; Lecture Notes in Computer Science; Pahl, C., Vukovic, M., Yin, J., Yu, Q., Eds.; Springer: Berlin/Heidelberg, Germany, 2018; Volume 11236. [Google Scholar] [CrossRef] [Scilit]
  86. Muroyama, M.; Tanaka, S. Development of an Application Programming Interface for a Multi-Sensing System Based on Sensor Platform LSIs. In Proceedings of the 2021 6th International Conference on Business and Industrial Research (ICBIR), Bangkok, Thailand, 20–21 May 2021; pp. 109–112. [Google Scholar] [CrossRef] [Scilit]
  87. Mothapo, M.; Thango, B.; Matshaka, L. Tracking and Measuring Social Media Activity: Key Metrics for SME Strategic Success—A Systematic Review. Preprints 2024, 2024091757. [Google Scholar] [CrossRef] [Scilit]
  88. Farahpoor, M.; Esparza, O.; Soriano, M. Comprehensive IoT-driven Fleet Management System for Industrial Vehicles. IEEE Access 2023. [Google Scholar] [CrossRef] [Scilit]
  89. Hou, S.; Ye, Y.; Song, Y.; Abdulhayoglu, M. Make evasion harder: An intelligent Android malware detection system. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018, Stockholm, Sweden, 13–19 July 2018; pp. 5279–5283. Available online: https://www.ijcai.org/Proceedings/2018/0737.pdf (accessed on 12 September 2024).
  90. Sahoo, K.S.; Mohanty, S.; Tiwary, M.; Mishra, B.K.; Sahoo, B. A comprehensive tutorial on software defined network: The driving force for the future internet technology. In Proceedings of the International Conference on Advances in Information Communication Technology & Computing, Thai Nguyen City, Vietnam, 12–13 December 2016; pp. 1–6. [Google Scholar]
  91. Dixon, C.; Olshefski, D.; Jain, V.; DeCusatis, C.; Felter, W.; Carter, J.; Banikazemi, M.; Mann, V.; Tracey, J.M.; Recio, R. Software defined networking to support the software defined environment. IBM J. Res. Dev. 2014, 58, 3:1–3:14. [Google Scholar] [CrossRef] [Scilit]
  92. Fokkema, I.F.; van der Velde, K.J.; Slofstra, M.K.; Ruivenkamp, C.A.; Vogel, M.J.; Pfundt, R.; Blok, M.J.; Lekanne Deprez, R.H.; Waisfisz, Q.; Abbott, K.M.; et al. Dutch genome diagnostic laboratories accelerated and improved variant interpretation and increased accuracy by sharing data. Hum. Mutat. 2019, 40, 2230–2238. [Google Scholar] [CrossRef] [Scilit]
  93. Cao, B.; Peng, M.; Zhang, L.; Qing, Y.; Tang, B.; Kang, G.; Liu, J. Web Service Recommendation via Integrating Heterogeneous Graph Attention Network Representation and FiBiNET Score Prediction. IEEE Trans. Serv. Comput. 2023, 16, 3837–3850. [Google Scholar] [CrossRef] [Scilit]
  94. Kumaresan, A.; Liberona, D.; Gnanamurthy, R.K. A Case Study on API-Centric Big Data Architecture. In Knowledge Management in Organizations. KMO 2017; Communications in Computer and Information Science; Uden, L., Lu, W., Ting, I.H., Eds.; Springer: Cham, Switzerland, 2017; Volume 731. [Google Scholar] [CrossRef] [Scilit]
  95. Jha, R. Analyzing the Impact of Digital Transformation on Business. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2020. Available online: https://hdl.handle.net/1721.1/145228 (accessed on 12 September 2024).
  96. Zhou, W.; Li, L.; Luo, M.; Chou, W. REST API design patterns for SDN northbound API. In Proceedings of the 2014 28th International Conference on Advanced Information Networking and Applications Workshops, Victoria, BC, Canada, 13–16 May 2014; pp. 358–365. [Google Scholar]
  97. Buddhika, T.; Pallickara, S.L.; Pallickara, S. Pebbles: Leveraging Sketches for Processing Voluminous, High Velocity Data Streams. IEEE Trans. Parallel Distrib. Syst. 2021, 32, 2005–2020. [Google Scholar] [CrossRef] [Scilit]
  98. Heshmatisafa, S.; Seppänen, M. Exploring API-driven business models: Lessons learned from Amadeus’s digital transformation. Digit. Bus. 2023, 3, 100055. [Google Scholar] [CrossRef] [Scilit]
  99. Chaparadza, R.; Elkotob, M.; Radier, B.; Meriem, T.B.; Hammad, E.; Choi, T. Autonomic/Autonomous Networking (AN): Federations & Governance of ANs, Progress & Open Challenges in Standards: Analysis Report. In Proceedings of the 2022 IEEE Future Networks World Forum (FNWF), Montreal, QC, Canada, 10–14 October 2022; pp. 176–183. [Google Scholar] [CrossRef] [Scilit]
  100. Samvrudhi, K.; Gudodagi, R.; Ahmed, M.R.; Vinay, S.V. Two-Wheeler Location Tracking and Tilt Monitoring System. In Proceedings of the 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India, 26–27 August 2022; pp. 1–4. [Google Scholar] [CrossRef] [Scilit]
  101. Vaccari, L.; Posada, M.; Boyd, M.; Santoro, M. APIs for EU Governments: A Landscape Analysis on Policy Instruments, Standards, Strategies and Best Practices. Data, 2021; 6, p. 59. [CrossRef] [Scilit]
  102. Perrone, G.; Romano, S.P. The Docker Security Playground: A hands-on approach to the study of network security. In Proceedings of the 2017 Principles, Systems and Applications of IP Telecommunications (IPTComm), Chicago, IL, USA, 25–28 September 2017; pp. 1–8. [Google Scholar] [CrossRef] [Scilit]
  103. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
The Impact of Student Evaluation of Teaching Staff on Enhancing the Quality of Teaching in Higher Education in Romania
Previous
Measuring Up? The Illusion of Sustainability and the Limits of Big Tech Self-Regulation
Next