Literature Reviews of RBV and KBV Theories Reimagined: A Technological Approach Using Text Analysis and Power BI Visualization

— Over the years, the Resource-Based View (RBV) and Knowledge-Based View (KBV) have solidified their roles as pivotal paradigms in strategic management literature. With an emphasis on Small and Medium Enterprises (SMEs), this study uses text analysis and Microsoft Power BI to explore these concepts innovatively. The study implements a systematic literature review, extracting data from Scopus, Web of Science, and DOAJ databases to assemble a comprehensive literature corpus. The methodology incorporates text analysis to draw out key themes, relationships, and trends, and these are subsequently visualized using Power BI to create an engaging, interactive representation of data. Components like word clouds, co-occurrence networks, and trend lines are generated, while Power BI's dynamic filtering and drill-down functionalities facilitate thorough data investigation. The results display significant overlap between RBV and KBV, denoting possible integration junctures for these theories within the domain of strategic management. Additionally, the study underscores the relevance of these insights for SMEs, emphasizing the part played by unique resources, encompassing knowledge assets, in catalyzing innovation and fostering a competitive edge. The study concludes by recognizing the significant theoretical and practical implications of integrating text analysis and Power BI in conducting literature reviews. This methodology bolsters our understanding of RBV and KBV, offering small and medium-sized enterprises a beneficial instrument to traverse these intricate theories. The study suggests that future research could broaden the application of this methodological approach to encompass other strategic management theories.


I. INTRODUCTION
This article aims to explore the concepts of Resource-Based View (RBV) and Knowledge-Based View (KBV) and their integration for Small and Medium-sized Enterprises (SMEs) through text analysis and Microsoft Power BI.The goal is to create a Power BI report and dashboard to help readers select the required literature for their research purposes.Systematic literature reviews have become an indispensable tool in the research arsenal, offering a means to review existing scholarly work comprehensively, objectively, and reproducibly on a particular topic [1]- [3].The meticulous method sets systematic reviews apart from their traditional counterparts by including all relevant research, thereby reducing bias and expanding the breadth and depth of knowledge capture [2], [3].Systematic literature reviews serve as a lighthouse, illuminating the vast sea of existing research and providing a robust base for further academic exploration.
Nevertheless, as the corpus of literature expands, traditional analytical methods can be overwhelmed.Text analysis, or text mining, is a powerful ally in this context [4], [5].By leveraging techniques from natural language processing, computational linguistics, and machine learning, text analysis facilitates the extraction of meaningful insights from large volumes of text, metamorphosing unstructured data into a structured format suitable for nuanced analysis [4], [6], [7].
Even so, the transformation of data into digestible insights presents another challenge.Microsoft Power BI, a suite of business analytics tools, provides a powerful solution.It empowers users to transform raw data into accessible, insightful, interactive visualizations and dashboards.With Power BI, researchers can interact dynamically with data, unearthing patterns, trends, and insights that can be readily understood and communicated-a critical capability when navigating the complex and voluminous data associated with systematic literature reviews.
In this article, we navigate the confluence of systematic literature reviews, text analysis, and Power BI.We delve into their collective power to investigate and distill insights from the wealth of literature on the Resource-Based View (RBV) and Knowledge-Based View (KBV) within the context of small and medium-sized enterprises (SMEs) [8]- [12].By integrating these tools and approaches, we seek to offer a novel perspective through which academics and practitioners can access, interpret, and apply the rich theoretical insights of RBV and KBV.
The Resource-Based View (RBV) and the Knowledge-Based View (KBV) occupy a pivotal role in contemporary business and management literature, providing unique perspectives on how firms acquire, maintain, and leverage competitive advantages.RBV, initially articulated by Wernerfelt [10] and further expounded by Barney [13], suggests that a firm's resources, particularly those that are valuable, rare, inimitable, and non-substitutable (VRIN), are instrumental in sustaining its competitive advantage.While early applications of RBV focused primarily on large organizations, SMEs are increasingly recognized as adept at capitalizing on their unique resources to compete effectively, even amidst larger rivals [8], [10], [12]- [14].The RBV, thus, offers a nuanced understanding of how SMEs can harness their distinct resource configurations for success.Complementing the RBV is the KBV of the firm, as Grant [9], [11], [15] proposed.The Knowledge-Based View (KBV) posits that knowledge is a company's most strategic asset, providing crucial perspectives on how firms can utilize their intellectual wealth to secure a competitive edge.This theoretical framework is especially relevant to small and medium-sized enterprises (SMEs), which depend on their team members' specialized knowledge and abilities to innovate, adjust, and thrive in rapidly evolving market environments [16].
The Resource-Based View (RBV) and the Knowledge-Based View (KBV) serve as useful perspectives for comprehending the distinct dynamics at play within small and medium-sized enterprises (SMEs).Their application to this sector aids in developing effective strategies that can enhance competitiveness and drive growth.By delving deeper into the rich literature on these theories, we seek to uncover insights that can bolster the theoretical and practical understanding of SME management.
Emerging technologies have expanded the horizon of possibilities in academic research, notably in conducting and presenting literature reviews.Advanced text analysis techniques are revolutionizing traditional manual literature review methods, enhancing the ability to mine insights from massive text corpora [5], [17].Text analysis can automate identifying themes, trends, and patterns in the literature using algorithms based on natural language processing, computational linguistics, and machine learning, making it an invaluable tool for systematic reviews.Moreover, the advent of sophisticated data visualization tools, such as Microsoft Power BI, has enriched the presentation of literature review findings.Power BI enables the transformation of complex data into interactive, insightful visualizations and dashboards that allow for dynamic engagement with the data.These visual representations facilitate a deeper understanding of the data, highlighting patterns and trends that may not be immediately evident from textual or tabular presentations.
By integrating text analysis and Power BI, researchers can undertake more thorough and insightful literature reviews while presenting their findings in a manner that is accessible and engaging for diverse audiences.This intersection of technology and research methodology heralds a new era in literature review practice, where the potential of these tools harnesses the advancement of knowledge.
The intersection of two critical theories in modern business literature-Resource-Based View (RBV) and Knowledge-Based View (KBV)-and their application to Small and Medium-sized Enterprises (SMEs) represent a burgeoning area of scholarly interest.However, the rich tapestry of existing literature in this domain necessitates a systematic and rigorous examination to fully appreciate the breadth and depth of the insights available [9], [11], [13], [16].
This study aims to bridge this gap by leveraging innovative text analysis and data visualization methods, providing a holistic, objective, and understandable overview of SMEs' RBV and KBV literature landscape.The crux of our methodology hinges on applying text analysis to conduct a systematic literature review, offering an efficient and practical approach to distilling critical insights from the extensive body of text [4], [5].Subsequently, using Microsoft Power BI, we endeavor to present these insights engagingly, interactively, and intuitively, enhancing understanding and fostering ease of interpretation.
In essence, our objectives are twofold.First, we aim to systematically review and analyze the literature on RBV and KBV as they relate to SMEs, yielding a nuanced understanding of the theories and their applications.Second, we seek to demonstrate the potential of text analysis and Power BI as innovative tools for conducting and presenting systematic literature reviews.The ultimate goal is to pioneer a novel approach to literature reviews, which has the potential to revolutionize both the practice and consumption of academic research in the digital age [19]- [21].
This study is a work in the Industrial Engineering (IE) field.IE is fundamentally about designing and optimizing systems, often intending to improve productivity, efficiency, and overall performance [22]- [25].Both the Resource-Based View (RBV) and Knowledge-Based View (KBV) offer theoretical underpinnings that align closely with the principles of IE, primarily through the lens of resource optimization and knowledge management, respectively.Understanding these perspectives can aid in developing strategies and models that leverage a firm's resources and knowledge to enhance operational efficiency, particularly in SMEs [8]- [13], [26]- [30].This study underscores the importance of integrating advanced technologiesspecifically text analysis and Microsoft Power BI-into IE.Text analysis can allow industrial engineers to distill vast amounts of unstructured data into actionable insights, an increasingly essential ability in an era characterized by information overload [4], [5].Similarly, Power BI facilitates the visualization and interpretation of complex data, a critical component in decision-making processes within IE.
In the context of IE, this study provides a pioneering exploration of the intersection of RBV and KBV within SMEs, using innovative technologies to conduct a systematic literature review.It demonstrates how contemporary digital tools can be harnessed to undertake more comprehensive, insightful, and accessible literature reviews.By doing so, this study not only presents the state of the art in applying RBV and KBV to SMEs but also introduces novel methodologies that could shape the future of literature reviews within IE and beyond.
While this study aims to provide a comprehensive overview of RBV and KBV literature of SMEs through innovative text analysis and Power BI visualization, it is crucial to acknowledge the potential limitations intrinsic to the study design and methodology.First, the reliance on text analysis, while allowing for a large-scale literature review, inherently operates under the constraints of the chosen algorithms.The subtleties and nuances of academic literature, mainly theoretical or philosophical, may not be fully captured by automated text analysis.At the same time, this study, supplemented by algorithmic efficiency, human interpretation, and intuition, remains a crucial aspect of qualitative analysis.Second, the study is limited to literature written in English, which may exclude significant contributions from non-English speaking researchers, potentially introducing a language bias into our findings.
Similarly, we rely on the availability and accessibility of the literature, which could inadvertently omit some relevant works not included in our chosen databases or be obscured by paywalls [31]- [33].Third, while Power BI offers impressive capabilities for data visualization, the clarity and usefulness of these visualizations are contingent upon the data's complexity and the analyst's proficiency.Furthermore, the interactive nature of Power BI requires readers to have a basic level of digital and subject literacy to engage with the results thoroughly.
In recognizing these limitations, we encourage further study to improve and refine the methods used in this study, continuing the iterative process of knowledge-building and refinement in this critical area of inquiry.Our preliminary literature review identified several gaps that have motivated this study.First, while both the Resource-Based View (RBV) and Knowledge-Based View (KBV) are well-established theories in the realm of strategic management, there is a distinct gap in the literature that integrates these two perspectives, particularly in the context of small and mediumsized enterprises (SMEs.These theories tend to be applied independently, and the potential synergies and overlaps between them need more exploration, creating further research opportunities.Second, using text analysis and visualization tools like Microsoft Power BI in conducting and presenting literature reviews is relatively new in industrial engineering and business management.This study strives to fill this methodological gap by leveraging these innovative tools to synthesize and present a comprehensive review of RBV and KBV literature concerning SMEs.
Regarding the state of the art, SMEs' utilization of RBV and KBV theories is still a rapidly evolving research area.The contemporary discourse recognizes the importance of resources and knowledge in driving SMEs' competitiveness and performance.However, the nuanced interactions between the two theories and their combined effect on SMEs are yet to be thoroughly explored [8], [12], [14], [23], [26], [27], [34]- [38].
Additionally, the adoption of advanced technologies such as text analysis and Microsoft Power BI in literature review methodologies is getting more recognition as a state-of-theart practice.These tools enable the processing of vast volumes of data and offer insightful, interactive visualizations, enhancing the comprehensiveness and accessibility of literature reviews [4], [5], [39], [40].
The current study aims to contribute to the state of the art by bridging the identified gaps, providing valuable insights into the synergistic application of RBV and KBV in SMEs, and introducing a novel, technology-aided approach to conducting literature reviews in industrial engineering and business management studies.

II. MATERIAL AND METHOD
To achieve this goal, we conducted a comprehensive literature review using various databases such as Scopus, Web of Science (WoS), and the Directory of Open Access Journals (DOAJ).The articles were analyzed using text analysis tools, and the relevant data was collected for further analysis.The collected data was then used to create a Power BI report and dashboard.Figure 1 summarizes the workflow of the method used in this study.
Selecting a suitable corpus forms the bedrock of any text analysis-based literature review.In our study, we undertook an exhaustive, meticulous, and systematic process to compile a body of literature focused on the Resource-Based View (RBV) and Knowledge-Based View (KBV), particularly with an emphasis on small and medium-sized enterprises (SMEs).
As mentioned, our literature selection procedure started by pinpointing key business and management research databases, including Scopus, Web of Science, and DOAJ.We chose these databases because they comprehensively cover highquality academic literature across various scholarly disciplines [31]- [33].The data extraction from the chosen databases necessitated a systematic workflow that comprised several steps, from devising the initial search strategy to ultimately exporting the relevant literature metadata in a format compatible with text analysis and visualization using Power BI.
At the outset, we formulated an exhaustive search query using the specified keywords and Boolean operators, concentrating on the documents' titles, abstracts, and keywords.This search query was tailored to each database's unique syntax, considering variations in search operators, fields, and other search parameters [40]- [42].
The literature search hinged on a blend of specific keywords and Boolean operators to ensure accuracy and inclusiveness.Our primary keywords included "Resource-Based View," "RBV," "Knowledge-Based View," "KBV," "Small and Medium-sized Enterprises," and "SMEs."Boolean operators, such as AND and OR, were used to form combinations of these keywords.
Once the search query was executed in each database, we screened the returned results for relevance based on their titles and abstracts.To ensure rigor and consistency in this process, two reviewers independently screened the results, with any disagreements resolved through discussion or the involvement of a third reviewer [3], [43].
Following the screening process, the relevant articles' bibliographic information (metadata) was exported from each database.This information typically includes the title, authors, abstract, keywords, publication year, journal name, and other details.It is worth noting that different databases offer different options for exporting this data, but most commonly, formats like CSV, BibTeX, RIS, and EndNote are supported [31], [32], [44].
This exported metadata forms the raw data cleaned and prepared for the subsequent text analysis and visualization steps.Ensuring the data is correctly formatted and cleaned, including handling missing values, inconsistent entries, and other potential issues [16].
This comprehensive and systematic approach to data extraction from the databases ensured the creation of a robust and reliable dataset, serving as the foundation for our text analysis and Power BI-based review of the RBV and KBV literature.Figure 1 summarizes the workflow mentioned in this section.
Fig. 1 The method's workflow Furthermore, we used filters such as language (English), document type (journal articles), and publication period to fine-tune our search (See Table 1).We kept the publication period open-ended to incorporate the earliest relevant literature on RBV and KBV until the most current studies are available for our review.The composition of the corpus was routinely scrutinized and updated throughout the research to guarantee its representativeness and currency.Ultimately, our goal was to construct a sturdy and extensive corpus, providing a fertile ground for text analysis and the ensuing development of our Power BI-enabled literature review.Text analysis comprises techniques designed to glean valuable information from unstructured text data.In this study, we applied several text analysis methods to methodically examine the literature corpus centered on RBV and KBV in the context of SMEs.Initially, we commenced with text preprocessing, a vital step for tidying and structuring the data.This stage involves eliminating stop words, performing stemming and lemmatization to trim words down to their base or root form, and converting the text to lowercase to ensure consistency [45], [46].
Following that, we employ topic modeling.Topic modeling uncovers the underlying topics or themes in the corpus.This method proves especially useful in detecting the abstract "topics" present in a document collection, and it can assist in identifying the recurring themes discussed in the RBV and KBV literature [45]- [47].Network analysis will analyze the relationships between different text entities, which could involve understanding the relationships between authors, institutions, or concepts within the RBV and KBV literature [33], [39], [48].Our approach to text analysis combines these multiple techniques to ensure a comprehensive and multifaceted literature examination.The goal is to extract critical insights and information from the unstructured text systematically and rigorously.
Our study necessitated a thorough and deep-dive approach to the comprehensive body of literature surrounding the Resource-Based View (RBV) and Knowledge-Based View (KBV) theories, which required a robust tool capable of handling the vast volume of data and extracting meaningful insights.We turned to Microsoft Power BI, an innovative, user-friendly business analytics tool that allowed us to create powerful visualizations and interactive dashboards from the literature corpus (See Table 2).There are several business intelligence applications on the market today.Microsoft Power BI has steadily gained recognition in the business intelligence market for its robust capabilities and affordability.Power BI's intuitive interface makes it accessible to novices and experienced users.Its powerful DAX language, various data connectors, integration with other Microsoft products, and cloud-based architecture are some of its distinguishing features.However, Power BI's limitations surface when handling complex queries and large data volumes, where performance may be somewhat affected.Also, while it boasts a good range of visuals, there is room for more complex visualizations [40], [41].
Tableau, another industry titan, is primarily recognized for its high-powered data visualization capabilities [21].Its user interface is intuitive, and it provides advanced interactive dashboard creation abilities.Tableau's strength lies in its visual analytics robustness and ability to handle large datasets efficiently.However, it lags slightly behind Power BI's integration with other platforms and is less effective in collaboration and sharing.Additionally, it is more expensive than Power BI, making it less suitable for smaller businesses or budget-constrained enterprises [49].
QlikView stands out for its unique associative data modeling, which provides a robust environment for userdriven, interactive analytics.Its top-notch data discovery capabilities enable users to explore data associations in a multidimensional space.Nevertheless, QlikView has a steeper learning curve than Power BI, particularly in developing analytics apps.Given its on-premises orientation, it is generally more expensive and may require more maintenance.
Looker, a more recent entrant to the BI landscape, is firm in handling large datasets thanks to its in-database architecture.Its data modeling language, LookML, allows for high customization.However, compared to Power BI, Looker's main weaknesses include a less intuitive user interface and higher costs.It also does not offer as much integration with other platforms and tools, reducing its flexibility compared to Power BI.
Although Tableau, QlikView, and Looker have unique strengths and use cases, Microsoft Power BI is the most suitable tool for this study.Its integration capabilities, ease of use, and versatile functionality offer a balanced mix of capabilities that align with the needs of this study.
After the search phase to publication databases, the process followed with importing the carefully cleaned and structured data into Power BI.A few key steps were involved: first, the CSV files containing our structured literature data were loaded into the software.Once loaded, Power BI's query editor refined and transformed the data, renaming columns for clarity, replacing missing values, and other data cleansing tasks.
Following data cleansing, we moved on to creating a structured data model.Data modeling is critical in enabling complex analysis and creating meaningful relationships among various data elements.The process included defining the relationships between data elements such as authors, publications, and keywords and designing measures that facilitated comprehensive data analysis.Power BI's intuitive interface and powerful DAX (Data Analysis Expressions) language made this process streamlined and efficient, ensuring that the resulting model could provide deep insights [20].
With the data model established, the next phase involved leveraging Power BI's robust visualization capabilities (See Figure 2).Power BI provides a range of visualization options, from straightforward bar charts and line graphs to more intricate plots and geographic maps.We used these instruments to create dynamic visual representations of our data.For instance, word clouds were used to visualize the most frequently used keywords in the literature, while bar charts depicted the dispersion of publications over time [2], [17], [33], [50]- [52].
Fig. 1 The Power BI text analysis and visualization processes One remarkable feature of Power BI is its capacity to construct interactive dashboards and facilitate question-andanswer dialogues about the data.This functionality amalgamated our various visualizations into a unified, interactive interface.These dashboards empowered users to filter and delve into specific data subsets, uncovering more nuanced insights tailored to their interests.For example, readers could explore the temporal distribution of certain keywords or scrutinize the influence of specific authors in the field.
Applying Power BI in our literature review facilitated a more structured and encompassing exploration of the RBV and KBV literature.It ushered in an inventive, technologyaugmented approach to literature reviews.Blending text analysis and data visualization strengths, this method can transform how literature reviews are carried out and presented in academic research.
The endeavor to pinpoint the integration junctures between the Resource-Based View (RBV) and the Knowledge-Based View (KBV) for application in Small and Medium Enterprises (SMEs) necessitated an exhaustive and precise methodology.This complex process was rooted in the principles of theoretical integration, an approach that facilitates researchers to uncover the synergistic aspects of diverse theories.
The initial phase involved a comprehensive literature review.As detailed earlier, the literature corpus extracted from reputable databases revolved around RBV and KBV.The selection process guaranteed the inclusion of only the most pertinent articles and publications, leading to a balanced and encompassing mix of sources.This array of literature was crucial for a profound, multifaceted comprehension of both theories.
Subsequently, this voluminous collection of literature was scrutinized using sophisticated text analysis tools.The text analysis procedure was tailored to highlight the distinct elements of RBV and KBV and their intersections.The analysis unveiled overlapping ideas, common threads, and possible linkages between the two theories, culminating in a richly intertwined understanding of RBV and KBV.As the analysis progressed, the iterative process allowed for ongoing refinement and deepening of insights, yielding a progressively complete view of integrating the two theories.
Upon completing the analytical process, the next stage involved constructing an initial theoretical framework.This framework emerged from the shared themes and cross-points between RBV and KBV, as brought to light by the text analysis.The synergistic points and areas of overlap that were identified held particular relevance for SMEs, as they signified potential areas of integration that could benefit such entities.However, it's crucial to note that the framework didn't solely rely on the frequency of overlapping themes.It also considered the importance of these overlaps within the broader discourse on RBV and KBV, ensuring a well-rounded, context-driven perspective [18].
Microsoft Power BI ultimately translated The integration framework into a visual form.This action turned the theoretical cross-connections into a dynamic visual tool that presents the possible areas of overlap between RBV and KBV.This Power BI tool empowers users to engage with the data, understand the intersection points, and derive their own insights, thereby offering a potent resource for SMEs to harness the synergistic possibilities of RBV and KBV.
The suggested methodology signified a groundbreaking, solid strategy to investigate the possible fusion of RBV and KBV.Sophisticated analytical tools such as text analysis and Power BI were key in dealing with the vast amounts of literature and exhibiting the findings in an understandable, interactive fashion.

A. Text Analysis Outcomes
Our in-depth examination of our selected body of literature, emphasizing the Resource-Based View (RBV) and the Knowledge-Based View (KBV), was thorough and insightful.By carefully scrutinizing the text, we unveiled various consistent themes and intriguing connections between RBV and KBV, which are the foundational perspectives of strategic management.
In the heart of the RBV literature, we identified an unmistakable imprint of Barney's [10], [12], [13], [27]- [29] seminal work.Barney underscored the value of internal resources as the primary determinant of competitive advantage in firms.These resources, unique to each firm and often challenging for competitors to imitate or substitute, can provide a sustained competitive edge.Our analysis reflected how this theory had permeated the discourse in strategic management, remaining as potent today as it was when initially proposed.Parallel to the strong narrative of RBV, we found an equally profound emphasis on KBV in our literature corpus.Rooted in Grant's pioneering work [9], [11], [16], KBV posits that knowledge, above all other resources, holds the highest strategic significance for firms.Knowledge becomes essential in a rapidly changing business environment characterized by technological advances and information-driven economies.It can fuel innovation, bolster efficiency, and forge a unique path for a firm that is hard for competitors to replicate.Our text analysis indicated this perspective's enduring relevance and influence in contemporary management literature.
Our text analysis unveiled intriguing intersections between RBV and KBV as we dug deeper.The overlapping themes centered around organizational learning, innovation, and competitive advantage, signaling a unique interplay between the two perspectives.These overlaps illuminated that RBV and KBV are not mutually exclusive but complementary views.They offer different lenses to view the same objective-leveraging a firm's resources to achieve a sustained competitive advantage.This common ground hinted at potential integration points for RBV and KBV, opening up possibilities for a unified approach that encapsulates both resource types (tangible and intangible) in strategic management [53]- [60].
The resulting text analysis connected the dots between two major strategic management theories, providing a potential roadmap for future research and practice integration.Drawing from our comprehensive text analysis, we unearthed invaluable insights that bring to light the intertwined nature of the RBV and KBV theories.These insights deepen our understanding of these two pivotal strategic management theories and chart a potential pathway for their integration in future research and practice.
For instance, the theme of organizational learning consistently appeared as a shared concern across both RBV and KBV literature.The RBV perspective recognizes organizational learning as a valuable, unique resource that enhances a firm's competitive advantage.Simultaneously, the KBV views organizational learning as a conduit for knowledge creation and application, crucial for firm innovation and strategic competitiveness.This congruence suggests an integrative approach where learning is viewed as a crucial internal resource (RBV) facilitating knowledge creation (KBV), ultimately driving competitive advantage.
Further, themes of innovation also resonated across both theoretical lenses.While RBV might emphasize innovation due to a firm's unique resources, such as patented technology or specialized manufacturing processes, KBV would argue that innovation springs from the firm's unique knowledge reservoir.Here, the opportunity for integration becomes evident.We could consider innovation the outcome of the interplay between unique tangible resources (RBV) and unique intangible knowledge (KBV) within a firm.
Moreover, both RBV and KBV converge on the ultimate business goal: achieving and maintaining a competitive advantage.While RBV underscores the role of firm-specific resources, KBV focuses on knowledge as the pivotal driver of this advantage.The integration point could be viewing a competitive advantage as a multifaceted outcome driven by a firm's unique resources (RBV), including its specific knowledge (KBV).
Through this enlightening journey of text analysis, we were able to draw meaningful connections between two significant strategic management theories.In doing so, we have provided an insightful roadmap for their potential integration.This path could pave the way for a more nuanced, comprehensive understanding of strategic management in future research and business practice.This approach may be precious for Small and Medium Enterprises (SMEs), where resources are often limited, and the ability to integrate and leverage both tangible and intangible assets can be a significant driver for success.

B. Power BI Visuals
The data translation process into an interactive and insightful format with Microsoft Power BI commenced with data preprocessing and cleaning.These are critical stages in text analysis [61], [62].It involved filtering irrelevant data, handling missing or null values, and ensuring uniformity and consistency in the textual data.The formatted, cleaned data was then ready for the next stage, which involved importing it into Power BI.
One of the unique features of Power BI is its ability to handle both structured and unstructured data, making it an ideal choice for our study [2], [63].We imported the preprocessed data into Power BI and created diverse visualizations to illuminate our text analysis findings.These visualizations included word clouds, co-occurrence networks, and trend lines, which all catered to different aspects of the data and contributed uniquely to the final insights.
Fig. 3 The dashboard with structured and unstructured data A word cloud (Figure 5) was our primary tool for visualizing keyword frequency.The larger the word's appearance in the cloud, the higher its frequency within our literature.These word clouds offered a quick visual summary of the prevailing themes within the selected literature, aligning with studies by Liu et al. [23] that affirm the effectiveness of word clouds in reflecting keyword importance.
We map the authors' locations to utilize the mapping capabilities of the report (Figure 6).The results show that researchers discussing the RBV and KBV concepts are mainly from North America and Europe.We utilized co-occurrence networks to investigate and demonstrate the relationships between different themes and terms within the literature.These networks let us visualize the connections between keywords based on their simultaneous appearance in the same contexts [33], [39], [64].This allowed us to identify dominant themes and observe their interplay within the RBV and KBV contexts.
Trend lines (Figure 7) added a temporal dimension to our data visualization, enabling us to track the evolution and prevalence of RBV and KBV themes over time.This feature was critical in understanding the historical context and development trajectory of these strategic management theories, similar to studies by Chen [4], [33] that utilized trend lines to study scientific paradigm shifts.
Further enhancing our Power BI experience was the dynamic filtering and drill-down capabilities.Figure 8 gives this example in publishers' publication data.All of the visualizations dynamically change the corresponding information and other visuals.This interactive feature allowed for an in-depth and focused data exploration, allowing users to study specific themes or periods closely.This supports the findings of Perer and Shneiderman [2], [65], [66], who argue that interactive visualizations like those provided by Power BI enhance user engagement and comprehension.The report also can discuss the data using a question-and-answer interface (Figure 9).Users can ask questions regarding the data and receive the answers visually.In sum, our approach to employing Power BI in this study underscores the significant potential of business intelligence tools in simplifying and enhancing the accessibility of complex datasets.By integrating these tools into the literature review process, researchers and practitioners can more efficiently derive valuable insights from vast academic knowledge.

C. RBV and KBV Integration Insights
The exploration of the Resource-Based View (RBV) and Knowledge-Based View (KBV) conducted in our systematic literature review, augmented by text analysis, has facilitated the discernment of considerable overlap between these two strategic management theories.Although their primary focus areas differ, both RBV and KBV underline the critical role of unique and challenging-to-replicate resources in forging a sustainable competitive edge.The RBV traditionally prioritizes tangible resources such as financial assets, physical infrastructure, and human resources [9], [13], [67]- [71].However, it is critical to recognize that RBV does not exclusively favor the tangible.A pivotal extension of RBV theory acknowledges the integral role of intangible resources, such as organizational culture and reputation, in shaping a firm's competitiveness.
On the other hand, KBV dives deeper into the realm of intangibles.It puts forth the argument that among all resources, knowledge is the most strategically impactful [9], [11], [18].Nonaka's [29] seminal work plays a profound role in the KBV, emphasizing the organizational processes of knowledge creation, dissemination, and integration as key determinants of competitive advantage.As suggested by our text analysis, the overlap implies a substantial potential for the symbiotic integration of RBV and KBV.Such integration could serve as a more comprehensive lens to view, understand, and apply strategic management.This resonates with Sveiby and Simons [34] and Grant, who previously proposed that an amalgamation of the RBV and KBV could provide a holistic understanding of competitive advantage.
This integration bridges the gap between tangible and intangible resources, offering a more inclusive and robust strategic model.In the rapidly evolving business landscapes where tangible resources often form the basis for intangible assets and vice versa, such a unified approach holds significant merit.Our study findings provide compelling evidence for integrating RBV and KBV into a coherent strategic management model.The practical implications of such integration can provide managers with a holistic approach to effective resource utilization, thereby contributing to competitive advantage.

D. Applicability
Our findings from this study could have significant implications for Small and Medium Enterprises (SMEs).As suggested by Hitt [28], [72], SMEs can stimulate innovation and establish a solid competitive edge by recognizing and leveraging their unique resources, including knowledgebased ones.Indeed, this aligns with the Resource-Based View (RBV), which posits that a firm's tangible or intangible human or non-human resources play a central role in its strategic positioning and potential to achieve competitive advantage.In small and medium-sized enterprises (SMEs), such resources could encompass entrepreneurial zeal, adaptability, close customer relations, or specialized technological proficiency [26].
Furthermore, the Knowledge-Based View (KBV) asserts that knowledge is a company's most vital strategic asset.This perspective underscores the critical importance of knowledge within organizations, particularly small and medium-sized enterprises (SMEs), which frequently function within intensely competitive and swiftly evolving business landscapes.Procuring, cultivating, and administrating knowledge can equip SMEs with fresh possibilities and the capacity to adapt efficiently to shifts in their operating environment [73].
Therefore, our research suggests that fusing the Resource-Based View (RBV) and the Knowledge-Based View (KBV) may offer a resilient framework for small and medium-sized enterprises (SMEs) to leverage their distinctive resources and knowledge competencies.This amalgamation could assist SMEs in comprehending and tackling the intricate and diverse obstacles presented in today's business environment [28], [72].
Additionally, the Power BI dashboard crafted during our research holds great promise as a beneficial instrument for SMEs.The dashboard's interactive layout and captivating visuals make sifting through the extensive RBV and KBV literature a breeze, facilitating SMEs in making informed strategic decisions.This finding is consistent with a study by Pauwels et al. [70], which posits that graphical representations of data can streamline decision-making in business contexts, especially for SMEs, by diminishing complexity and augmenting comprehension.
To sum it up, our research casts a spotlight on the potential advantages that the integration of RBV and KBV holds for SMEs.It demonstrates how Power BI can serve as a potent ally in aiding SMEs to traverse and decipher the intricate labyrinth of strategic management literature efficiently.

IV. CONCLUSIONS
Our study has unearthed captivating revelations about literature encompassing the Resource-Based View (RBV) and the Knowledge-Based View (KBV) theories.A careful dissection of our meticulously compiled literary collection revealed persistent themes and correlations between these theoretical frameworks, spotlighting overlapping facets such as organizational learning, innovation, and obtaining a competitive edge.Moreover, the Power BI visual illustrations offered a dynamic and interactive display of our findings, allowing for a more profound examination of the themes and patterns in the RBV and KBV narratives.
This study contributes considerably to the pre-existing body of literature concerning RBV and KBV.It has accentuated the possibility of a harmonious fusion of RBV and KBV, providing SMEs with an all-encompassing method to exploit their unique resources and knowledge capital to maintain a competitive advantage over time.Therefore, our research augments the scholarly conversation revolving around these two crucial strategic management theories, making a strong argument for their combined use within the SME context.
On the practical side, the Power BI dashboard can serve as an indispensable tool for researchers, industry practitioners, particularly managers of SMEs, and decision-makers.By presenting a visually appealing and interactive portrayal of the broad RBV and KBV literature, the dashboard can aid users in making well-informed strategic decisions grounded on the insights extracted from these theories.Furthermore, our study models how text analysis and business intelligence tools can be used in systematic literature reviews, demonstrating their capacity to uncover intricate patterns and relationships within large bodies of text.
Building on this study's findings, future research could further explore the integration of RBV and KBV in different contexts beyond SMEs.For instance, the synergistic use of these theories could be investigated within large multinational corporations or non-profit organizations.Future studies could also experiment with other business intelligence tools or text analysis techniques to further advance the methodological approach undertaken in this study.Finally, longitudinal studies could be conducted to track the evolution of themes within the RBV and KBV literature over more extended periods, providing a more dynamic understanding of these strategic management theories.
In conclusion, our study showcases the power of text analysis and Microsoft Power BI in conducting systematic literature reviews.It offers a fresh perspective on the interplay between RBV and KBV, with important implications for theory and practice.We hope this study will spur further exploration into integrating these strategic management theories and the innovative use of digital tools in literature review processes.

Fig. 6
Fig. 6 Trend and Projected Documents by Year