Digital Technologies and Channels for Competitive Advantage in SMEs: A Systematic Review

1. Introduction

Digitalization has proven to be a powerful tool in bringing forth innovation to multiple sectors like healthcare, education, manufacturing, and transportation to name a few, in the modern economy by transforming the manner in which corporations, establishments and communities at large operate [1]. This new advent has pushed SMEs to re-strategize the conventional ways in which they run their production processes this shift is motivated by digital transformations in the business setting, emerging technologies, financial prospects and new opportunities, for example, the retail sector has undergone a massive shift due to the rise of e-commerce and digital marketing channels while the advanced analytics allows companies to gain deeper insights into consumer choices while enhancing their personalized offerings [2]. This means that SMEs are moving away from the methods they previously relied on to outperform their average competitors by utilizing digital technologies as gateways to generate value [3]. In the business world, digital technologies can cause significant disruptions by reshaping how competition works. For instance, platforms like peer-to-peer (P2P) systems have transformed existing markets, leading to the emergence of the sharing economy. Through these platforms, people can easily exchange digital goods and services [4]. Technologies like artificial intelligence, big data analytics, cloud computing and social media have not only transformed consumer interactions but also reshaped internal processes and decision-making. Leveraging digital channels, including e-commerce platforms, has paved the way for unprecedented growth and innovation [5,6]. While the implementation of digital technologies offers various and significant advantages, it is accompanied by numerous challenges. Organizations often encounter high expenses; a lack of digital expertise and resistance to change which hinder the full utilization of these technologies. Additionally, integrating new digital solutions with existing legacy systems can be problematic, further delaying the implementation process [7]. Looking ahead, emerging technologies such as blockchain, 5G, and the Internet of Things (IoT), are anticipated to cause further distractions and drive innovation. Figure 1 illustrates the key steps involved in the digital transformation process for organizations seeking to leverage digital technologies for competitive advantage. By systematically assessing current capabilities, developing a targeted strategy, and implementing new technologies, businesses can adapt to the rapidly evolving market landscape. This visual representation serves as a guide for organizations navigating the complexities of digital transformation. Alongside this, Table 1 summarizes studies on how digital technologies contribute to business competitiveness, outlining each study’s key contributions, citation counts, publication dates, and strengths and weaknesses. By comparing these studies, we identify covered areas and potential gaps, providing a foundation for our review and offering new insights into Digital Technologies and Channels for competitive advantage in SMEs.

The current body of research on Digital Technologies and Channels for Competitive Advantage in SMEs highlights several areas that need further investigation. One major gap is the lack of industry-specific studies on digital transformation. While general overviews of digital strategies are available there is a need for more detailed analysis tailored to specific sectors like healthcare, finance, and manufacturing. These targeted insights could help businesses in these industries better understand how to implement digital strategies effectively within their unique contexts. Although some research emphasizes the importance of adopting digital technologies, practical challenges such as integrating new technologies with existing systems and overcoming resistance to change are not thoroughly addressed. This issue is particularly significant for small and medium-sized enterprises (SMEs), where limited resources make it even more crucial to overcome these barriers. These gaps indicate a need to provide practical solutions to help businesses tackle these challenges.

1.1. Research Questions

In this study, we examine the role of digital technologies and digital channels in enhancing the competitive advantage of small and medium-sized enterprises (SMEs). The research explores how the adoption of these technologies can improve SME performance, focusing on both the benefits and challenges associated with their implementation. The following research questions are designed to provide deeper insights into the effective use of digital technologies to drive SME competitiveness, helping to understand the strategies SMEs can adopt to leverage these technologies for sustained business growth and market positioning:

• How are digital technologies being used or adopted in different industries (such as manufacturing, healthcare, etc.)?
• What are the key digital technologies and channels that have significantly impacted competitive advantage in recent years?
• What are the obstacles preventing SMEs from adopting digital technologies?
• What are the measurable performance metrics used to evaluate the effectiveness of digital technologies?
• How will the long-term adoption of digital technologies impact societal structures, particularly in terms of workforce transformation, privacy, and digital inequality, and what regulatory measures are necessary to mitigate these challenges?

1.2. Research Motivation

This study is driven by the increasing significance of digital transformation in creating competitive advantages for companies in various sectors. Digital technologies like artificial intelligence (AI), the Internet of Things (IoT), and data analytics are changing consumer engagement, business operations, and market competition in today’s quickly changing technological landscape. Nevertheless, despite the enormous potential these technologies hold, there is still division in the scholarly and practical discourse regarding their successful application.

The need for a thorough and organized understanding of how digital technologies can be used to gain a competitive edge is what motivates this review, particularly considering real-world obstacles like resource constraints, resistance to change, and integration with legacy systems. The urgent need for SMEs to get through these barriers and adopt digital innovation feeds this investigation. Additionally, the increasing significance of sector-specific digital transformation—highlighted by the unique requirements and regulatory environments in industries such as healthcare and finance—motivates this review to offer more tailored insights.

1.3. Research Novelty

In contrast to numerous other research works that offer comprehensive summaries of digital transformation, this review highlights the necessity of conducting industry-specific studies, especially in the manufacturing, healthcare, and finance sectors. In doing so, it provides a new perspective and draws attention to the benefits and challenges different businesses have when implementing digital technologies. This study also highlights the practical difficulties that small and medium-sized businesses (SMEs), which are sometimes disregarded in favor of larger organizations, encounter. The paper advances the conversation on integration by offering a thorough understanding of the difficulties associated with integration, the review pushes forward the discourse on how businesses can effectively navigate digital transformation within their unique contexts.

Another innovative addition is the process-driven methodology, which is illustrated by means of a digital transformation flowchart. This flowchart gives decision-makers a useful tool by offering an organized, visual depiction of how companies can approach digital transformation methodically. This review provides an extensive road map for further research and the real-world application of digital strategies by bridging the gap between theoretical understanding and application.

The ultimate driving force behind the research is the aim to advance the field’s body of knowledge on digital transformation by advancing the agenda for workable, implementable solutions that support companies in their digital transformation, in addition to synthesizing previous findings.

2. Materials and Methods

The following subsection of the study presents the methodology employed to conduct a comprehensive systematic review of Digital Technologies and Channels for Competitive Advantage in SMEs. This study spans a decade, covering literature from 2014 to 2024, to provide a thorough understanding of how digital technologies have influenced SMEs over this period. The methodology provides guidelines for selecting studies, identifying data sources, and the techniques used to review the gathered literature, establishing a basis for a thorough analysis of each element in the following sections. Figure 2 displays the proposed structure for this study.

2.1. Eligibility Criteria

A comprehensive review of all peer-reviewed and published studies that are relevant to the analysis of digital technologies and their impact on the competitive advantage of SMEs. This study only considered studies that were published in the English language during the span from 2014 to 2024. A set of criteria was established to include only the most related academic papers and to exclude those that did not explicitly address the impact of digital technologies on the competitive advantage of SMEs. As a result, only peer-reviewed studies with a main focus on the analysis of the impact of these digital technologies on competitive advantage were included. The criteria for inclusion and exclusion that we made use of in this study are detailed in Table 2.

2.2. Information Sources

Three research repositories were consulted for this systematic analysis, namely, Google Scholar (GS), Scopus, and Web of Science. They were selected based on their comprehensive coverage of academic research in digital technologies and SME competitiveness. Each database was chosen for specific reasons: Google Scholar for its wide scope; Scopus for its extensive academic indexing and high citation standards; and Web of Science for its focus on high-impact journals, ensuring the credibility and relevance of the sources. To ensure the selection of relevant studies, specific inclusion and exclusion criteria were applied. Only research published between 2014 and 2024 was considered, with a focus on peer-reviewed journal articles, conference papers, and dissertations that addressed digital technology’s role in enhancing SMEs’ competitive advantage. Studies that focused exclusively on large corporations or were irrelevant to the subject matter were excluded. Various research papers were examined based on their titles, abstracts, and search tags to develop additional search terms. These terms or phrases were used to select published materials such as conference papers, journal articles, book articles, book chapters, and dissertations.

2.3. Search Strategy

Figure 3 provides a detailed overview of the systematic review process. It begins with formulating research questions, which guide the choice of methodology. The process involves planning the Systematic Literature Review (SLR), establishing criteria for including or excluding studies, selecting appropriate research resources, and defining search terms. Next, the search and evaluation of research articles ensure the inclusion of high-quality references. Data extraction focuses on maintaining data integrity, followed by compiling and analyzing the data to address the research questions.

The keywords used to search for relevant research papers to conduct this literature review were developed to identify works that showcased the implications of adopting digital technologies on SME’s competitive advantage. To pick the key terms, experimental searches were done using the iterative approach. Keywords that did not produce research works meeting the inclusion criteria were removed. To make sure that similar terms related to the main keywords were included, equivalent terms for the specified keywords were identified. Therefore, synonyms for “digital technologies” include “digital tools,” and “digital platforms.” Keywords used to describe “competitive advantage” include “business advantage,” and “business strategy.” The term “SMEs” can also be referred to as “small and medium enterprises,” and “small businesses.” The term “digital channels “was removed as it did not bring forth many papers and limited the study. During the search process, the logical operators “AND” and “OR” were used to find relevant scholarly papers. The “AND” operator includes all selected keywords, while the “OR” operator includes any of the chosen keywords.

The final selection of papers was based on their relevance to Digital Technologies and Channels for competitive advantage in SMEs. To ensure the robustness of the search, bibliometric analysis was conducted to validate keyword selection and to assess the comprehensiveness of the search results. This thorough and iterative approach ensured a well-rounded and relevant set of literature for the review.

Figure 4. Bibliometric Analysis of Study Search Keywords: (a) Networks Visualization. (b) Overlay Visualization. (c) Density Visualization.

Figure 4. Bibliometric Analysis of Study Search Keywords: (a) Networks Visualization. (b) Overlay Visualization. (c) Density Visualization.

2.4. Selection Process

To ensure the collection of relevant papers for the review titled “Digital Technologies and Channels for Competitive Advantage in SMEs,” a rigorous selection process was applied. Reviewers followed a systematic method to identify appropriate studies, utilizing search codes with keywords such as “Digital Technologies,” “Digital Channels,” “SME Competitiveness,” and “Competitive Advantage.” Each reviewer was assigned to a specific research database—Web of Science, Scopus, or Google Scholar—to source studies. Initially, each reviewer gathered 60 papers, which were then filtered based on inclusion criteria such as relevance to the topic, presence of a research framework on digital technologies in SME competitiveness, publication in English, and publication dates between 2014 and 2024. Exclusion criteria were applied to eliminate non-relevant studies. The selected papers were recorded in an Excel sheet for assessment. A secondary review was conducted to verify that all papers met the inclusion criteria and to resolve any discrepancies by removing studies that did not align with the review’s objectives.

2.5. Data Collection Process

The data collection process used in this research paper was utilized to select the most relevant academic papers. Figure 5 showcases the phases that were undergone to classify research papers that are important to Digital Technologies and Channels for Competitive Advantage in SMEs. The first phase was the extensive search of three research repositories to select the pertinent research papers. The second phase was the selection of published material that included research papers relevant to Digital Technologies and Channels for Competitive Advantage in SMEs. Next, the reference list of all relevant research papers that met the inclusion criteria was reviewed. Each reference list was examined for extra citations that could lead to relevant research material. Finally, when no additional citations could be found the selection process began. The initial list of research papers was refined and reviewed to check for their relevance. The phases of the filtration process are as follows: Firstly, research titles are analyzed for relevance, the abstracts and contents of the papers were evaluated to ascertain their relevance and significance to the topic being studied. The research papers were subjected to a further analysis and scrutiny, this criterion included whether the papers were written in English and had a detailed methodology. Papers on similar topics and published in the last decade were also included.

2.6. Data Items

Table 3 outlines key data items and their descriptions in the context of research on the Digital Technologies and Channels for competitive advantage in SMEs. As digital transformation accelerates across industries, SMEs are increasingly adopting technologies like artificial intelligence (AI), Internet of Things (IoT), and cloud services to enhance their business models, operations, and customer engagement. This research explores various aspects of this transformation, from the types of digital technologies being leveraged, to the industry and economic contexts in which SMEs operate. The table also highlights the methodologies used in these studies, performance metrics, and long-term impacts such as business growth and competitive advantage, providing a comprehensive view of the research landscape in this domain.

2.7. Study Risk of Bias Assessment

Using the Newcastle-Ottawa Scale (NOS) as shown in Table 4, the quality of the studies reviewed in relation to Digital Technologies and Channels for competitive advantage in SMEs is evaluated in the following table. With a maximum score of nine stars, the Newcastle-Ottawa Scale (NOS) rates studies according to three criteria: selection (up to four stars), comparability (up to two stars), and outcome/exposure (up to three stars). Selection evaluates how well participants were selected and determines whether the desired outcome was not present from the beginning. Comparability looks at how well confounding variables were managed, while Outcome/Exposure assesses how reliable the study’s measurements were. Research with seven to nine stars is regarded as high-quality, seven to six to four stars as moderate-quality, and three stars or less as low-quality, suggesting possible methodological issues. This rating ensures the reliability of studies on digital technologies’ impact on SMEs.

2.8. Effect Measures

In our study on Digital Technologies and Channels for competitive advantage in SMEs, we utilized specific effect measures for each outcome to ensure a consistent and meaningful synthesis of results. For dichotomous outcomes, such as the adoption of digital technologies or the presence of specific performance improvements, we calculated risk ratios (RR) to compare the likelihood of achieving positive outcomes between different studies. This allowed us to understand the relative risk of success associated with adopting digital technologies in various contexts.

For continuous data, such as improvements in business performance metrics (e.g., revenue growth, customer satisfaction) across studies using different scales, we applied standardized mean difference (SMD) effect sizes (Cohen’s d) and their 95% confidence intervals (CIs). This enabled us to standardize results across studies, regardless of the original measurement scales, and allowed for a more coherent comparison of the effects of different digital technologies.

In some cases, we also re-expressed the synthesized results in absolute terms to provide practical insights for decision-makers, ensuring the results were relevant and easy to interpret within the broader business and technological context. This combination of effect measures allowed us to evaluate the impact of digital technologies on both categorical and continuous outcomes in a manner that was both statistically rigorous and practically applicable. Figure 6 visualizes the entire procedure for selecting effect measures.

2.9. Synthesis Methods

In synthesizing research on Digital Technologies and Channels for competitive advantage in SMEs, a structured approach ensures clarity and comprehensiveness. The synthesis process involves assessing study eligibility, preparing data, and presenting results in a way that accurately reflects the evidence. This process is crucial for drawing meaningful conclusions and providing actionable insights. Table 5 shows a summary of the methods used to synthesize the studies, detailing each step and the approaches applied.

The synthesis method in Table 5 outlines the structured approach used to evaluate and integrate findings from various studies on Digital Technologies and Channels for competitive advantage in SMEs. The eligibility assessment step involved coding and grouping studies based on predefined criteria to ensure relevance and comparability. Data preparation included standardizing and imputing data to address any gaps and facilitate uniform analysis. Results were tabulated and visually displayed using structured tables to highlight key findings and patterns. Finally, synthesis methods involved meta-analysis or alternative approaches where meta-analysis was not feasible, with appropriate testing for statistical heterogeneity. Figure 7 outlines this comprehensive approach which enables a clear understanding of Digital Technologies and Channels for competitive advantage in SMEs and supports the development of evidence-based strategies.

2.10. Reporting Bias Assessment

To address the risk of bias due to missing results in our synthesis of studies on digital technologies, we implemented a multifaceted approach designed to ensure a comprehensive and unbiased assessment. We began by employing several graphical and statistical methods to detect any potential biases. Specifically, contour-enhanced funnel plots were generated for meta-analyses involving digital technology studies with at least 10 trials of varying sizes. These plots allowed us to visually inspect for asymmetry, which might indicate publication bias or other forms of reporting bias. Complementing this, we conducted Egger’s test to statistically evaluate funnel plot asymmetry and determine if the observed data suggested missing results due to publication or other reporting biases.

To further validate our findings, we compared the outcomes specified in study protocols or registered study plans with those reported in the published results. In cases where study protocols were unavailable, we cross-referenced the outcomes reported in the methods and results sections of the publications. This comparison helped us identify discrepancies and potential biases related to outcome reporting. Our assessment process involved using established checklists and scales specifically tailored for bias evaluation in digital technology research. We reviewed these tools’ methodological components to ensure they were adequate for addressing aspects relevant to digital technologies, such as software-related biases and technological limitations. Notably, no significant adaptations were made to these existing tools, though considerations specific to the digital technology field were integrated into the evaluation.

The review team, consisting of three independent reviewers, conducted the risk of bias assessments. Each reviewer performed a separate evaluation to ensure a thorough and unbiased assessment. Any disagreements among reviewers were resolved through discussion and consensus, with a senior researcher conducting a final review to confirm the accuracy of the risk assessments. This process helped ensure that our synthesis was based on the most complete and accurate data available. No automation tools were used in our risk of bias assessment due to missing results, instead data were manually analysed and visualized using Microsoft Excel. This method included creating charts and plots to identify patterns and potential biases in the reporting, enabling a thorough examination of the data without relying on automated software tools. This approach was designed to minimize potential biases and enhance the overall validity of our synthesis on digital technologies.

2.11. Certainty Assessment

The quality assessment aimed to strengthen the credibility of the selected research papers in this study and to ensure the appropriateness and completeness of the findings. Each paper was evaluated using a scoring system to determine its reliability, relevance, and significance. The evaluations were based on a set of 6 criteria, as outlined in Table 6, considering the various research types represented among the collected papers.

The quality assessment questions were applied to all collected research papers. Each question was evaluated with one of three possible responses: “Yes” (scored 1), “Partially” (scored 0.5), or “No” (scored 0). This evaluation was based on a thorough review of each paper’s abstract, research framework, results, and conclusion. The total score from these responses was used to determine the overall quality of the relevant studies.

Table 7. Research Quality Questions Results.

Paper ID	Q1	Q2	Q3	Q4	Q5	Q6	Total	Percentage
[15]	1	1	1	1	1	1	6	100%
[16]	1	1	1	1	1	0.5	5.5	91.67%
[17]	1	1	1	1	1	0.5	5.5	91.67%
[18]	1	1	1	1	1	1	6	100%
[19]	1	1	1	1	1	0.5	5.5	91.67%
[20]	1	1	1	1	1	0.5	5.5	91.67%
[21]	1	1	1	1	1	0.5	5.5	91.67%
[22]	1	1	1	1	1	0.5	5.5	91.67%
[23]	1	1	1	1	1	1	6	100%
[24]	1	1	1	1	1	1	6	100%
….	….	….	….	….	….	….	….	….
….	….	….	….	….	….	….	….	….
[52]	1	1	1	1	1	1	6	100%
[53]	1	1	1	1	1	1	6	100%
[54]	1	1	1	1	1	1	6	100%
[55]	1	1	1	1	1	1	6	100%
[56]	1	1	1	1	1	0.5	5.5	91.67%
[57]	1	1	1	1	1	0.5	5.5	91.67%
[58]	1	1	1	1	1	0.5	5.5	91.67%
[59]	1	1	1	1	1	0.5	5.5	91.67%
[60]	1	1	1	1	1	0.5	5.5	91.67%
[61]	1	1	1	1	1	0.5	5.5	91.67%

Table 7. Research Quality Questions Results.

Paper ID	Q1	Q2	Q3	Q4	Q5	Q6	Total	Percentage
[15]	1	1	1	1	1	1	6	100%
[16]	1	1	1	1	1	0.5	5.5	91.67%
[17]	1	1	1	1	1	0.5	5.5	91.67%
[18]	1	1	1	1	1	1	6	100%
[19]	1	1	1	1	1	0.5	5.5	91.67%
[20]	1	1	1	1	1	0.5	5.5	91.67%
[21]	1	1	1	1	1	0.5	5.5	91.67%
[22]	1	1	1	1	1	0.5	5.5	91.67%
[23]	1	1	1	1	1	1	6	100%
[24]	1	1	1	1	1	1	6	100%
….	….	….	….	….	….	….	….	….
….	….	….	….	….	….	….	….	….
[52]	1	1	1	1	1	1	6	100%
[53]	1	1	1	1	1	1	6	100%
[54]	1	1	1	1	1	1	6	100%
[55]	1	1	1	1	1	1	6	100%
[56]	1	1	1	1	1	0.5	5.5	91.67%
[57]	1	1	1	1	1	0.5	5.5	91.67%
[58]	1	1	1	1	1	0.5	5.5	91.67%
[59]	1	1	1	1	1	0.5	5.5	91.67%
[60]	1	1	1	1	1	0.5	5.5	91.67%
[61]	1	1	1	1	1	0.5	5.5	91.67%

3. Results

The main conclusions from the reviewed studies are presented in this section, along with a thorough analysis of Digital Technologies and Channels for competitive advantage in SMEs. The results provide a succinct interpretation of the experimental data, with major themes and outcomes highlighted under subheadings. The results encompass a range of factors, including overall business performance, innovation agility, operational efficiency, and customer engagement. This section summarizes the findings and makes conclusions that emphasize the important role that digital technology plays in fostering sustainable growth and preserving competitiveness in the modern business environment.

3.1. Study Selection

The search was conducted across three major databases: Google Scholar, Scopus, and Web of Science. The search covered literature published between 2014 and 2024, and only studies written in English were included and the selection process is displayed in Figure 8. A total of 20,155 studies were identified across the three databases. As mentioned, a combination of keywords was used related to “digital technologies” “competitive advantage” and “SMEs”. Using the title and abstract screening, out of 20,155 papers, 19,799 were excluded for not meeting the inclusion and exclusion criteria particularly due to their focus on non-SME contexts or unrelated technological topics.

As demonstrated by Figure 8, the collected research papers comprised 60 research papers in total, of which, 63.33% were from Google Scholar, 20% from Scopus, and 16.67% from Web of Science. Out of the 60 research papers, 6 were conference papers, and 54 were article journals as illustrated in Figure 9. The process used for performing this SLR is a Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) flowchart, shown in Figure 8.

Figure 9. Distribution of Online Data Sources.

Figure 9. Distribution of Online Data Sources.

3.2. Study Characteristics

A total of 60 eligible research studies were published between 2014 and 2024, comprising 0 book chapters, 6 conference papers, 0 dissertations, and 54 journal articles. Table 8 illustrates the distribution of research papers published by year within this period. The data highlights consistent growth in journal article publications, with a notable peak in 2021, where 9 journal articles were published. Conference papers contributed moderately, with 2 papers each in 2018, 2022, and 2023.

Although no book chapters or dissertations were recorded, the significant number of journal articles (54) demonstrates a strong preference for this publication type in the academic community, particularly for research related to emerging technologies, business innovation, and digital transformation. The increase in publications over recent years signals heightened interest in these fields. However, gaps remain, such as the absence of comprehensive reviews or comparative studies in specific areas.

Figure 10 shows the number of research papers published each year, indicating a steady growth in publications since 2014. This highlights the increasing research focus on digital technologies on SMEs over time.

According to Figure 11, the geographic distribution of the 60 research papers highlights contributions from a diverse set of countries, demonstrating global interest in technology and innovation. Indonesia stands out as the largest contributor with 7 papers (11.67%), followed by Italy and China with 6 papers each (10%). Germany has 4 papers (6.67%), and the United Kingdom and Malaysia each contribute 3 papers (5%). Other countries with multiple contributions include India and Pakistan with 2 papers each (3.33%), alongside Spain, Sweden, Nigeria, Bulgaria, Poland, and Zimbabwe. South Africa is represented with 1 paper (1.67%), showing Africa’s involvement in the global research community. Notably, several regions such as the United States, Jordan, and the UAE have modest participation with 1 paper each (1.67%). While these regions are influential in global technology and innovation, their relatively low representation in this dataset suggests potential for increased research output in these areas. The diversity in geographic locations reflects the global relevance of the topics covered, yet the varying levels of contributions point to potential research gaps in certain regions. These gaps could benefit from increased attention to ensure a more comprehensive understanding of technological impacts worldwide.

Figure 12 illustrates the distribution of research papers across various subject areas, emphasizing the prominence of digital transformation and digital technologies, which together account for nearly 60% of the papers analyzed. The broader business context also holds substantial focus, with over 20% of the papers exploring general business issues. However, niche topics like competitive advantage and the intersection of digital transformation with business are less explored, suggesting potential gaps in current research.

The analysis of the research papers reveals a clear emphasis on digital transformation and digital technologies, with 30% (18 papers) and 28.33% (17 papers) of the papers focusing on these topics, respectively. This significant interest highlights the importance of understanding digital transformation processes and the application of various digital technologies within contemporary research. The broader business context is also a notable area of focus, with 21.67% (13 papers) addressing general business issues. Additionally, there is some interest in the intersection of digital transformation and business, as seen in 8.33% (5 papers), which indicates a moderate exploration of how digital changes impact business operations. However, specific areas such as the impact of digital transformation on competitive advantage or the role of digital technologies in gaining a competitive edge are less explored, with only 1.67% (1 paper) dedicated to each of these niche topics. This limited focus suggests a gap in research regarding how digital advancements contribute to achieving competitive advantages in business contexts. Similarly, the combination of digital technologies with digital transformation and business is represented by only 3.33% (2 papers), indicating that while these topics are of interest, their intersections are less frequently examined.

3.3. Risk of Bias in Studies

In assessing risk of bias across various research designs, it’s important to understand how methodological shortcomings can affect study validity. The designs under consideration include surveys, which gather broad data through structured questionnaires and can be subject to biases in respondent selection and questionnaire design; case studies, which offer in-depth analysis of specific instances but may lack generalizability and face biases in case selection and reporting; experimental studies, which aim to establish causality through controlled interventions and randomization but may still encounter issues such as inadequate blinding or attrition; and quasi-experimental studies, which, while valuable for understanding causal relationships without random assignment, can be more prone to biases due to non-randomization.

In the context of assessing risk of bias as shown in Figure 13 above, the research types are distributed as follows: 17 (28%) case studies are the most prevalent, focusing on detailed examinations of individual cases. 10 (17%) experimental studies are included, which involve controlled experiments to establish causality. Additionally, 1 (2%) study does not specify its type. Surveys are represented by 24 (40%) instances, utilizing structured questionnaires to collect data from larger populations. 8 (13%) quasi-experimental studies examine causal relationships without random assignment. This distribution highlights a significant reliance on case studies and surveys, with fewer experimental and quasi-experimental studies, underscoring the need to account for risk of bias across these various research designs.

3.4. Results of Individual Studies

According to Figure 14, most of the studies reviewed focus on SMEs (Small and Medium-sized Enterprises), with 65% (39 papers) specifically addressing the adoption of digital technologies and digital channels for achieving competitive advantage within this group. This heavy emphasis on SMEs is likely due to their significant role in driving economic development and their need to adopt digital tools to stay competitive, particularly in dynamic markets. An additional 20% (12 papers) combine research on SMEs with a focus on Business Owners, highlighting the importance of understanding how the decisions and strategies of business owners directly influence the successful implementation of digital technologies within SMEs. This suggests that the digital transformation of SMEs often hinges on the leadership and decision-making of business owners.

Research focusing exclusively on Business Owners accounts for 8.33% (5 papers). While this is a smaller portion, it reflects a growing interest in how business leaders perceive and adopt digital tools to drive competitiveness. On the other hand, Entrepreneurs are represented in only 3.33% (2 papers), indicating that research specifically exploring entrepreneurs’ roles in digital technology adoption within SMEs is less prominent. This could be because entrepreneurship is often viewed as a subset of SME studies or because entrepreneurial ventures tend to focus on startups, which may differ from traditional SMEs. Additionally, 3.33% of studies do not specify their sample group, which could reflect a more generalized approach to investigating digital technologies and channels without focusing on a particular business type.

This distribution reveals a strong focus on understanding Digital Technologies and Channels for Competitive Advantage in SMEs, with relatively less emphasis on entrepreneurs and business owners as standalone subjects. The results may suggest that most research treats digital transformation within SMEs as a comprehensive topic, integrating leadership perspectives into the broader SME context.

3.5. Results of Synthesis

The Results of Syntheses section presents key findings from systematic reviews and meta-analyses, ensuring a transparent and unbiased reporting of evidence. This includes a summary of study characteristics and risk of bias, results from all statistical syntheses with meta-analysis details and measures of heterogeneity, investigations into possible causes of heterogeneity, and sensitivity analyses to assess the robustness of results. Clear reporting across these elements allows for informed decision-making and supports future research. The flowchart in Figure 15 illustrates the logical structure of this section.

3.5.1. Study Characteristics and Bias Assessment

Based on the study characteristics definition and the data presented in Figure 16, the data collection methods used across the studies vary significantly and reflect a mixed-methods approach tailored to the specific objectives of each synthesis. Surveys were the most used method, either alone or in combination with other tools, indicating a strong focus on gathering quantitative data. Interviews and document analysis were frequently employed, often together, to provide richer, qualitative insights. Questionnaires were used in conjunction with surveys for structured data collection, while observations were less common but included in some studies to offer contextual understanding. This variation in data collection methods is important to note as it influences the applicability and potential biases in each synthesis, with different methods addressing distinct aspects of the research questions.

Based on the data, the use of various methodological approaches helps mitigate bias in several ways. Surveys, being the most common method (36.67%), standardize data collection across a large sample, which helps reduce response bias and enhances reliability. Document analysis (11.67%) provides objective evidence from existing records, minimizing researcher-induced bias. Questionnaires (8.33%), often used alongside surveys, ensure consistency in responses, reducing interviewer bias. Interviews (6.67%), when combined with other methods like document analysis and surveys, offer deeper insights while balancing potential interviewer biases. Mixed methods integrate different approaches, such as surveys and interviews, to provide a more comprehensive view and validate findings through triangulation. The relatively lower use of observations and online surveys suggests these methods are less relied upon, possibly due to their specific limitations or the context of the studies.

Figure 17 highlights the significance of various digital technologies in business, with IoT (16.67%) leading in adoption due to its role in operational efficiency and real-time data collection. AI and Blockchain each account for 13.33%, reflecting their growing importance in enhancing decision-making and ensuring security in transactions. Big Data Analytics (11.67%) shows strong interest but may indicate that organizations are still developing their data capabilities. Combinations of technologies reveal less frequent integration, particularly between AI and Big Data, suggesting untapped potential in leveraging their synergy. Cloud Computing (6.67%) has a lower representation, possibly due to its maturity and widespread adoption. The 10% classified as “Not Specified” points to a broader focus on digital transformation rather than specific technologies.

3.5.2. Statistical Syntheses

According to Figure 18, the data analysis techniques used in the studies are predominantly statistical analysis, which accounts for 56.67% of the methods employed. Thematic analysis is used in 26.67% of the studies, focusing on qualitative data. Mixed methods are utilized in 13.33% of the studies, integrating both quantitative and qualitative approaches. In 3.33% of the cases, the data analysis method was not specified. This distribution highlights a strong emphasis on statistical analysis, with thematic and mixed methods also contributing to the overall analysis.

The data analysis techniques used in the studies demonstrate a strong preference for statistical analysis, which is the predominant method employed across most studies. This reflects a significant emphasis on quantitative data and aligns with the need for detailed reporting of statistical results. Thematic analysis is also frequently used, indicating a notable focus on qualitative data to identify and interpret patterns and themes. Additionally, a small number of studies did not specify their data analysis methods, highlighting a minor area for improvement in methodological transparency.

3.5.3. Statistical Syntheses

Figure 19 highlights the key factors contributing to result variability observed across different participant groups in this study, including SMEs, business owners, entrepreneurs, and unspecified entities. As shown, SMEs account for the largest share of variability, representing 65% of the total, followed by SMEs combined with business owners at 20%. Business owners independently contribute 8.33%, while entrepreneurs and unspecified groups both account for 3.33% each. This considerable dominance of SMEs underscores their pivotal role in shaping the outcomes of digital technology adoption and its impact on competitive business advantage. These findings emphasize the need to prioritize SME-specific considerations when addressing performance inconsistencies in digital strategies. The analysis of this variability further suggests that while other business groups contribute to result variation, SMEs, with their unique challenges and diverse applications, have a more pronounced influence.

3.5.4. Sensitivity Analyses

Sensitivity analysis is essential for evaluating the robustness of findings related to the types of digital technologies employed in the study. This analysis investigates how variations in technology adoption impact the overall results, offering insights into the effectiveness of various digital tools in enhancing competitive advantage.

Among the reviewed studies, the Internet of Things (IoT) emerged as the most prevalent technology, utilized by 16.67% of the studies, indicating its significant role in facilitating connectivity and data exchange. Artificial Intelligence (AI) was utilized in 13.33% of the studies, showcasing its application in automating processes and driving data-driven decision-making. Additionally, blockchain technology appeared in 13.33% of the studies, reflecting its growing importance in ensuring data security and transparency.

Big Data Analytics was utilized by 11.67% of the studies, emphasizing its critical role in analyzing vast amounts of data to uncover insights and trends that inform strategic decisions. Cloud computing was employed in 6.67% of the studies, indicating its relevance in providing scalable resources for data storage and processing.

The analysis also revealed a diverse range of technology combinations, with various studies integrating multiple digital technologies. For instance, 3.33% of the studies combined AI, IoT, blockchain, and cloud computing, illustrating a trend toward integrated technology solutions. However, 10% of the studies did not specify the technologies used, highlighting a gap in reporting transparency.

3.6. Reporting Biases

Figure 20 outlines a distribution of study types, revealing that quantitative studies are the most prevalent, with a total of 33 instances (52%). These studies primarily focus on numerical data and statistical analysis. In contrast, there are 15 qualitative studies (30%), which explore non-numerical data such as themes and subjective experiences. Additionally, 12 mixed-methods (18%) studies are represented, combining both quantitative and qualitative approaches to provide a more comprehensive understanding. This distribution highlights the dominance of quantitative research while also acknowledging the significant presence of qualitative and mixed-methods studies.

3.7. Certainty of Evidence

The data as shown in Figure 21 reflects the focus of various studies on digital technologies and channels, particularly e-commerce, mobile apps, and social media. Social media stands out as a dominant channel, representing 20% of the studies, indicating its significant role in customer engagement and digital marketing. This emphasis suggests that researchers and businesses alike recognize the value of social media in reaching and interacting with consumers. On the other hand, mobile apps are notably underrepresented, accounting for only 1.67% of the studies. Despite the growing reliance on mobile technology, the complexity and sector-specific use of mobile apps may contribute to their limited exploration in research. E-commerce, comprising 8.33% of the studies, is a critical channel in digital transformation, although it appears less frequently than social media. This could be due to the extensive research conducted on e-commerce in earlier phases of digital transformation, prompting a shift towards more emerging technologies or multi-channel strategies. Notably, a combination of channels such as “Social Media and E-Commerce” or “Social Media and Mobile Apps” makes up 13.33% of the studies, reflecting a growing interest in integrated digital strategies that leverage multiple platforms for enhanced customer experience.

Over half of the studies (56.67%) did not specify a particular channel. This large percentage suggests that many studies take a broad approach to digital transformation without focusing on specific digital channels, which may limit the precision of their findings. The general approach could also reflect a research gap where the specific roles of individual channels in driving business performance are not fully explored. The data highlights a strong focus on social media, limited attention to mobile apps, and a substantial number of studies that do not specify channels. Future research should aim to explore these channels in more detail, particularly mobile apps, and provide more granularity in analyzing the specific impacts of different digital technologies on business outcomes.

4. Discussion

• RQ 1: How are digital technologies being used or adopted in different industries (such as manufacturing, healthcare etc.)?

Digital technologies are being increasingly adopted across various industries, each leveraging these tools to enhance operational efficiency, innovation, and competitiveness. In manufacturing, technologies such as the Internet of Things (IoT), automation, and advanced robotics are transforming production processes, enabling real-time monitoring, predictive maintenance, and improved quality control. In healthcare, digital technologies like electronic health records (EHRs), telemedicine, and health analytics are revolutionizing patient care, streamlining administrative processes, and facilitating remote consultations as shown in Table 10. These advancements are driving industry-wide changes, enhancing capabilities, and creating new opportunities for growth.

• RQ 2: What are the key digital technologies and channels that have significantly impacted competitive advantage in recent years?

The key digital technologies and channels impacting competitive advantage in recent years include social media and IoT. Social media, representing 20% of the studies, plays a major role in customer engagement, marketing, and brand visibility, making it a critical driver of competitive advantage as shown in Figure 21. IoT, leading the technology category at 16.67%, enhances operational efficiency, real-time monitoring, and automation, contributing significantly to competitive success. Other important technologies include AI and Blockchain, each accounting for 13.33% of the studies. AI supports automation and data-driven decision-making, while Blockchain enhances data security and business transparency. Big Data Analytics (11.67%) plays a key role in providing insights that help businesses make strategic decisions, and Cloud Computing (6.67%) offers scalable, flexible solutions that improve business performance.

• RQ 3: What are the obstacles preventing SMEs from adopting digital technologies?

Small and Medium-sized Enterprises (SMEs) face several challenges in adopting digital technologies. Cost is a major barrier, as many SMEs struggle with the financial investment required for technology implementation. Lack of expertise and technical knowledge also hinder adoption, as SMEs may not have the necessary skills to manage and utilize advanced technologies effectively. Additionally, resistance to change and cultural factors within organizations can impede the integration of new technologies. Data security concerns and the perceived complexity of digital tools further contribute to the reluctance of SMEs to adopt digital innovations.

• RQ 4: What are the measurable performance metrics used to evaluate the effectiveness of digital technologies?

To evaluate the effectiveness of digital technologies, various performance metrics are employed. Technical performance metrics include system uptime, user engagement, and scalability, which assess the reliability and efficiency of the technology. Business performance metrics, such as revenue growth, market share, and customer base expansion, measure the impact of digital technologies on organizational success. Organizational outcomes, including employee satisfaction and customer satisfaction, are also crucial in determining the overall effectiveness of digital tools. These metrics provide a comprehensive view of how digital technologies contribute to achieving business objectives and enhancing operational performance.

• RQ 5: How will the long-term adoption of digital technologies impact societal structures, particularly in terms of workforce transformation, privacy, and digital inequality, and what regulatory measures are necessary to mitigate these challenges?

The long-term societal impact of digital technologies such as AI, IoT, blockchain, cloud computing, and 5G will dramatically transform industries and the workforce, while also raising significant privacy and inequality concerns. AI and IoT, for instance, will automate many routine jobs, leading to job displacement in sectors like manufacturing and transportation, but they will also create new roles that require advanced skills, demanding substantial reskilling efforts. Blockchain, cloud computing, and 5G will further reshape industries such as finance and healthcare by enabling secure, scalable, and real-time operations, though privacy and security risks will become more pressing as data becomes more pervasive and vulnerable. In addition to these challenges, the digital divide may deepen as access to these technologies remains uneven, particularly in underserved or rural areas, though blockchain could promote financial inclusion for unbanked populations. To address these concerns, regulatory frameworks are essential. Governments must implement measures to ensure ethical AI use, secure IoT devices, and enforce transparency in blockchain systems. Additionally, ensuring equitable access to cloud and 5G infrastructure will be crucial to prevent the widening of the digital divide.

5. Real-World Case Studies in Digital Transformation for SMEs and Large Enterprises

The digital transformation journey for businesses, whether small and medium-sized enterprises (SMEs) or large corporations, has become pivotal for long-term success in today’s fast-paced technological environment. Companies across various sectors are adopting emerging technologies such as cloud computing, artificial intelligence (AI), the Internet of Things (IoT), and blockchain to enhance operational efficiency, optimize customer engagement, and drive scalability. This section provides a series of real-world case studies that showcase how both SMEs and large enterprises have successfully integrated these technologies to gain a competitive advantage. By examining these concrete examples, we can better understand the practical applications of digital tools and the measurable benefits they bring to businesses in different regions and industries.

Case 1: Pakistan’s SME Adoption of IoT and Big Data Analytics for Value Creation

In Pakistan, SMEs, particularly in agriculture, have successfully adopted IoT and big data analytics to drive value creation. These technologies optimized supply chain management and resource usage, contributing to both economic and social growth. Using hybrid modeling techniques like SEM and ANN, SMEs in this region demonstrated how digital transformation can be a powerful tool for sustainability [75].

Case 2: AI-Driven Innovations in Indian SMEs

SMEs in India have embraced AI technologies to transform business operations, leading to improved operational efficiency. A logistics SME, for example, increased efficiency by 30% using AI-driven warehouse optimization, while an insurance firm saw similar gains by reducing claims processing time through AI-powered chatbots. These examples show how AI can significantly enhance SME performance in developing economies [76].

Case 3: Netflix and AWS for Scalability

Netflix transitioned its infrastructure to AWS, allowing the company to scale globally and manage large data volumes efficiently. The migration improved streaming quality and enabled Netflix to handle spikes in demand, solidifying its position as a global leader in the streaming industry [77].

Case 4: MakeMyTrip’s Cost Optimization Using AWS

MakeMyTrip utilized AWS’s containerization solutions (Amazon ECS and EKS) to reduce compute costs by 22% while maintaining scalability during high-demand periods. This example illustrates how cloud computing can optimize resources and improve financial efficiency in the travel industry [78].

Case 5: Domino’s Pizza and Azure Cloud for Customer Experience

Domino’s leveraged Microsoft Azure to overhaul its online ordering and delivery infrastructure, leading to enhanced customer satisfaction, faster delivery times, and streamlined IT processes. This case highlights the potential of cloud computing to improve customer experience in traditional industries [78].

Case 6: Airbnb’s Cloud-Based Infrastructure

Airbnb adopted AWS cloud technologies to handle its growing platform and international customer demands. This enabled the company to maintain a stable infrastructure, ensuring high availability, data processing, and efficient scaling as the company expanded its global presence [78].

Case 7: Zapier: A Remote Work Success with Cloud and Collaboration Tools

Zapier, a fully remote company with over 800 employees, uses cloud-based tools like Slack, Google Suite, and Zoom to manage its distributed workforce. This example showcases how cloud technology can support productivity and collaboration across global teams [79].

Case 8: Amazon’s IoT-Powered Warehousing

Amazon uses IoT to automate its warehouse operations, optimizing inventory management and reducing operational costs. By implementing IoT sensors, Amazon has been able to streamline its supply chain, ensuring faster deliveries and lower costs [80].

Case 9: General Electric’s Predix Platform on AWS

General Electric (GE) developed its Predix platform on AWS to transition from traditional manufacturing to a more digital, industrial model. By linking and analyzing data from industrial machines, GE improved operational efficiency, minimized downtime, and developed predictive maintenance strategies [81].

The case studies presented in this section illustrate the transformative power of digital technologies across a wide array of sectors, from agriculture and logistics to hospitality and manufacturing. By leveraging cloud computing, AI, IoT, and blockchain, businesses are not only improving operational efficiency and customer experience but are also positioning themselves for sustained growth and market leadership. Whether through the scalability provided by cloud platforms like AWS and Azure or through advanced AI applications driving efficiency in logistics and insurance, these examples demonstrate that digital transformation is essential for businesses of all sizes. For SMEs in particular, these technologies present an unprecedented opportunity to compete with larger enterprises and achieve long-term success in the global marketplace.

6. Practical Implications for Decision-Makers

The adoption of digital technologies is reshaping Small and Medium-sized Enterprises (SMEs) by offering them new avenues for growth, operational efficiency, and competitive advantage. Strategic business leaders must consider the intersection of technology adoption and key business drivers, such as customer engagement, operational efficiency, and market expansion. This section presents the findings from the literature and the strategic implications for SMEs, categorized by industry, with an emphasis on both challenges and opportunities as shown in Table 9. These findings provide insights into how digital transformation can position SMEs for sustainable success in highly competitive markets.

The strategic implications for business leaders across various SME industries are clear: the integration of advanced digital technologies such as AI, IoT, and blockchain presents significant opportunities to enhance operational efficiency, customer engagement, and market competitiveness. However, leaders must navigate barriers such as high costs, technical complexity, and data privacy concerns. To maximize these technologies’ potential, business leaders should focus on developing the technical skills of their workforce, prioritizing cybersecurity, and investing in scalable, integrated systems to achieve sustainable growth and long-term competitive advantages.

7. Decision-Making Framework for Implementing Proposed Study Topic

For SMEs to successfully implement digital technologies, strategic decision-making frameworks are necessary to guide technology adoption. These frameworks must be tailored to specific industry needs, weighing the cost, benefits, risks, and long-term advantages of each technology. This section proposes a comprehensive decision-making framework for implementing digital technologies such as AI, IoT, cloud computing, and blockchain specifically aimed at SMEs striving for competitive as shown in Table 10.

The proposed decision-making framework helps SMEs navigate the complexities of digital technology adoption by weighing the benefits and risks associated with each option. Retail SMEs must balance the need for customer personalization with process optimization, while healthcare SMEs must prioritize either real-time patient monitoring or data security. Finance SMEs, on the other hand, must decide between focusing on compliance or fraud detection technologies. By carefully evaluating key metrics such as customer satisfaction, compliance, and operational efficiency, business leaders can make informed decisions that align with both their short-term needs and long-term strategic goals.

8. Best Practices for Successful Implementation

Implementing digital technologies effectively can provide significant competitive advantages for Small and Medium-sized Enterprises (SMEs). However, the success of such implementations hinges on adhering to best practices that ensure operational efficiency, scalability, and market adaptability. As shown in Table 11, this section outlines best practices for implementing AI, IoT, cloud computing, blockchain, and other digital technologies for competitive advantage across various industries. The following table provides industry-specific guidance, categorizing best practices to support SMEs in navigating digital transformation.

The proposed best practices for implementing digital technologies and channels for competitive advantage in SMEs vary significantly by industry. In retail, the focus should be on personalizing customer experiences and optimizing inventory through AI and predictive analytics. Healthcare SMEs should prioritize secure patient data management through IoT and blockchain, while finance SMEs can benefit from AI-driven fraud detection and compliance automation. Manufacturing SMEs should focus on IoT and AI for operational efficiency, while logistics SMEs can optimize delivery routes through AI-powered systems. Energy SMEs will benefit from predictive energy management and renewable integration, and education SMEs can leverage AI for personalized learning experiences. By adhering to these best practices, SMEs can enhance their digital transformation initiatives, overcome industry-specific challenges, and unlock significant strategic benefits for long-term competitiveness.

9. Metrics and KPIs for Measuring Study Topic Performance

Measuring the performance of digital technologies and channels in Small and Medium-sized Enterprises (SMEs) is essential for understanding the impact of digital transformation efforts on business outcomes. The right metrics and Key Performance Indicators (KPIs) provide actionable insights that allow business leaders to refine strategies, optimize processes, and track progress toward competitive advantage. As shown in Table 12 this section outlines industry-specific metrics and KPIs for evaluating the performance of AI, IoT, cloud computing, blockchain, and other digital technologies in SMEs. These metrics are tailored to specific industries, focusing on operational efficiency, customer engagement, financial performance, and more.

The proposed metrics and KPIs for evaluating the performance of digital technologies and channels in SMEs reveal that each industry benefits from specific tools tailored to its operational, customer engagement, and regulatory needs. For instance, Retail SMEs can track customer conversion rates and inventory management metrics to evaluate the success of AI and predictive analytics tools. Healthcare SMEs should focus on patient outcomes and data security compliance metrics to ensure their IoT and blockchain systems are effective. By using these performance indicators, SMEs can ensure that digital transformation initiatives align with their business goals, driving long-term success and competitiveness.

11. Roadmap for SMEs Businesses and Policy Recommendations

The adoption of digital technologies by SMEs (Small and Medium-sized Enterprises) offers significant potential for achieving sustainable growth, operational efficiency, and competitive advantage. However, to fully realize these benefits a well-structured roadmap and supportive policy framework are necessary as shown in Table 13. This roadmap outlines the key stages that SMEs must follow to implement digital technologies, along with policy recommendations to support their journey. The roadmap is tailored to various industries, providing a strategic guide for business leaders and policymakers to foster the digital transformation of SMEs.

The roadmap for SMEs integrates digital technologies and channels into a structured progression from awareness to global competitiveness. For each stage, specific actions and technologies are identified, and policy recommendations are provided to support SMEs at different phases of their digital transformation journey. Retail SMEs must focus on omnichannel strategies and e-commerce, while healthcare SMEs prioritize patient care through IoT and AI technologies. Logistics SMEs should adopt AI and blockchain for supply chain optimization, and manufacturing SMEs can enhance operations through IoT and AI-driven production lines. Policy support in the form of grants, tax incentives, and regulatory frameworks is crucial for enabling SMEs to scale, compete globally, and achieve long-term sustainability.