Business strategy is widely recognized in the academic literature as a central determining factor of firm performance and long-term competitive advantage. Early foundational work by [23] established that organizational structure follows strategic decisions, while [7] formulated strategy as a deliberate and analytical approach to growth and expansion. Similarly, these early contributions positioned business strategy as a critical function, emphasizing the alignment between external opportunities and organizational resources. Over the years, strategic management has developed into an increasingly complex field of study, including diverse theoretical perspectives, and expanding in both scope and methodological rigor ([52]). Whereas understood as increasingly complex, business strategy has been consistently viewed as an essential element of organizational success and it has remained a central concern for both academic research and managerial practice.

A leading theme within the literature is that business strategy is inherently success-oriented, with the primary goal of achieving and sustaining competitive advantage. [83] highlighted that firms benefit from analyzing industry structure and competitive forces to position themselves more effectively, while subsequently [84] argued that strategy involves choosing a unique and valuable position that differentiates a firm from its competitors. [12] also examined the resource-based view which suggests that sustained competitive advantage arises from internal resources that are rare, valuable, distinctive, and non-substitutable. Taken together, these perspectives demonstrate business strategy is essentially designed to improve firm performance and enable organizations to achieve enhanced outcomes.

Beyond the success-oriented focus, the literature consistently characterized business strategy as future-focused, requiring firms to anticipate and respond to changing environments over time. Strategic decision-making involves forecasting shifts in technology, consumer preferences, and competitive dynamics to maintain relevance and competitiveness. [75] envisioned strategy as a plan and a pattern that emerges over time. Such a perspective highlights the double-sidedness of strategic thinking. This view is echoed by [94] when they emphasized the importance of dynamic capabilities, arguing that firms benefit from continuously adapting and realigning resources in reply to environmental change. These perspectives reinforce the idea that strategy is inherently forward-looking and essential for navigating uncertainty in modern business environments.

The existing literature also demonstrated that despite its centrality to organizational success, business strategy is often imperfect and incomplete. Scholars have debated the definition and measurement of key strategic constructs, including competitive advantage and firm performance. [87] spotlighted the presence of unresolved theoretical and empirical challenges within the field, suggesting that strategy lacks complete conceptual clarity. Furthermore, the existence of multiple competing definitions of strategy reflects the complexity of the discipline and indicates that business strategy remains an evolving area of study. Because business environments are dynamic and unpredictable, even well-designed strategies may fail to produce expected outcomes, reinforcing the limitations and incompleteness of strategic planning.

Extending this argument further reveals business strategy can be flawed or constrained by real-world conditions. Traditional models of strategy often assume rational decision-making and relatively stable environments; however, these assumptions are usually violated in practice. [76] introduced the concept of emerging strategy, arguing that strategy develops over time through learning and adaption rather than being the result of deliberate planning. From that perspective, one enacts strategic decisions which evolve in response to unfolding events rather than strictly following predetermined plans. This perspective highlights the role of uncertainty, learning, and adaption in shaping strategic outcomes. As a result, strategy is better understood as a repeated and evolving process rather than a fixed or perfectly executed plan.

The existent literature demonstrates that business strategy is a continuously evolving field, shaped by both theoretical advancements and changes in the external environments. Research in strategic management has progressed from early emphasis on industry structure to more recent focus on firm-specific resources, capabilities, and adaptability. [52] described the dynamic nature of the discipline. Despite its imperfections, business strategy remains essential for guiding organizational decision-making, improving performance, and enabling firms to compete effectively in complex and rapidly changing markets. With these fundamentals of business strategy established, it is possible to turn attention to research associated with textual analysis.

Textual analysis has become an increasingly important methodological approach across a wide range of academic disciplines, including sociology ([10]; [77]), organizational studies ([55]; [59]), political science ([15]; [57]), and philosophy ([19]; [58]). Effective applications of textual analysis techniques are allowing researchers to systematically analyze languages and extract meaningful patterns from large bodies of text. Advances in computational methods and natural language process (NLP) have significantly expanded the scope of textual analysis, enabling scholars to move beyond traditional qualitative interpretation toward large-scale, data-driven analysis. Quantitative text analysis allows researchers to analyze extensive composition, such as political speeches, social media posts, and academic literature, at a scale that would not be a feasible through manual methods alone ([50]; [45]).

One of the most widely used techniques in textual analysis is word frequency analysis, which focuses on measuring how often specific words or terms appear within a text. This method enables researchers to identify dominant themes, track linguistic patterns, and assess the relative importance of key concepts. [74] demonstrated the application of large-scale word frequency analysis through culturomics, showing how digitized texts can be used to track cultural and linguistic changes over time. Similarly, [39] highlighted the growing role of computational text analysis in the social sciences, emphasizing how word frequency methods can reveal patterns in social behavior and cultural trends. These approaches illustrate how frequency-based analysis provides a foundational tool for understanding both historical and contemporary textual data.

Building on word frequency analysis, researchers often use bigrams and n-grams to capture relationships between words and understand better context within, and between, texts. Unlike single-word frequency analysis, n-gram methods examine the sequence of words, allowing researchers to identify recurring phrases and patterns of meaning. [61] emphasized the importance of n-gram modeling in natural language processing, noting that these methods improve the accuracy of text classification and interpretation. In political science, bigram analysis has been used to examine rhetorical patterns in political communication, including speeches and debates, helping researchers understand how language shapes political narratives ([33]). By analyzing word combinations rather than isolated terms, n-gram approaches provide deeper insight into how meaning is constructed within text.

Another key method in textual analysis is sentiment analysis, which focuses on identifying and measuring the emotional tone of text. Sentiment analysis is widely used across disciplines to assess attitudes, opinions, and emotional responses within large datasets. [80] provided foundational work in sentiment analysis, outlining methods for classifying text based on positive and negative sentiment. In political science, sentiment analysis has been applied to study political communication and public opinion, allowing researchers to evaluate how language influences voter perceptions and behavior ([51]). Similarly, in finance and economics, sentiment analysis has been used to examine how textual information, such as news articles and financial reports, impacts market behavior and investor sentiment ([70]). These applications demonstrate the broad relevance of sentiment analysis across both social science and business-related contexts.

The effectiveness of sentiment analysis depends on a variety of methodological approaches, including lexicon-based techniques and machine learning models ([85]; [95]). Lexicon-based approaches rely on predefined dictionaries of positive and negative words, while machine learning methods train algorithms to detect patterns in language and classify sentiment more accurately. [68] highlighted the growing importance of machine learning in sentiment analysis, emphasizing its ability to improve classification accuracy and handle complex language structures. In addition, preprocessing techniques, such as stop-word removal, normalization, and tokenization, play a critical role in ensuring the relatability of textual analysis results ([17]; [43]). These methodological considerations demonstrate that while sentiment analysis is a powerful tool, it requires careful implementation and continuous refinement.

It is beneficial to note that textual analysis methods have been applied across a wide range of disciplines, further demonstrating their versatility. In sociology, computational text analysis enables researchers to study cultural trends, social behavior, and communication patterns at scale ([39]). In political science, scholars use textual data from speeches, debates, and social media to analyze political messaging public opinion, and electoral dynamic ([50]). Textual analysis has been applied to philosophical and literary texts, allowing researchers to examine themes, interpretations, and historical shifts in meaning. In addition, textual analysis in economics and finance has been used to evaluate market sentiment and predict financial outcomes, highlighting its relevance in business-related research ([70]). Textual analysis techniques have been applied extensively in the field of communication as well ([8]; [88]; [98]).

Collectively, this literature demonstrates that textual analysis, through methods such as word frequency analysis, bigram modeling, and sentiment analysis, has become a useful tool for understanding language, behavior, and social phenomena across disciplines These methods allow researchers to uncover patters, analyze meaning, and process large-scale textual data in ways that were previously unattainable. As computational tools and data availability continue to expand, textual analysis is likely to play an increasingly important role in academic research, further solidifying its positions as a foundational methodology across fields of study. For the purpose of this study, its application to the field of business strategy is particularly relevant. Existent research on textual analysis applications to business is presented next.

Textual analysis has increasingly been applied within business-related research, offering valuable insights into how firms communicate, position themselves, and respond to external environments. While earlier work in strategic management primarily relied on quantitative financial data and case-based analysis, more recent studies incorporate textual data to understand better firm behavior, managerial decision-making, and market dynamics. This shift reflects a broader recognition that language, found in corporate disclosures, earnings calls, and organizational communication, contains a meaningful information that can enhance traditional approaches to business research ([56]; [70]; [67]).

One of the most prominent applications of textual analysis in business research is the examination of corporate disclosures and financial reporting. Studies analyzing annual reports, earnings announcements, and 10-K filings demonstrate that textual characteristics such as tone, readability, and word choice provide significant insights into firm performance and managerial intent. [70] developed domain-specific dictionaries to improve accuracy of sentiment analysis in financial texts, highlighting that traditional sentiment measures may misclassify business terminology. Similarly, [67] found that the readability of annual reports is associated with firm performance, suggesting that more complex language may signal poorer financial outcomes. These findings illustrate that textual analysis can reveal information that is not immediately apparent from an analysis of numerical data alone.

In addition to financial reporting, textual analysis has been widely used to study earnings calls and managerial communication ([25]; [42]). Research shows the tone and linguistic patterns used by executives during earnings calls can influence investor perceptions and market reactions. For example, [30] investigated the tones of earnings press releases and found positive or negative language can impact significantly stock returns. In addition, [53]([53] showed managerial tone in earnings calls contains predictive information about future firm performance. These studies highlight the importance of language, understood as a strategic tool, showing firms actively shape narratives to influence stakeholders and manage market expectations.

Beyond financial communication, textual analysis has also been applied to corporate strategy and organizational behavior. Research shows textual data has been used to analyze how firms express strategic priorities, innovation efforts, and competitive positioning. For example, [63] used text analysis to show technological innovation and firm strategy, demonstrating language reflects shifts in organizational focus and industry trends. In addition, [73] highlighted the growing use of computer-aided text analysis in strategic management research, emphasizing its ability to systematically analyze large amounts of qualitative data. These approaches have allowed researchers to move beyond subjective interpretations and identify consistent patterns in how firms communicate strategy.

Textual analysis has also been applied in a broader way within business research to examine entrepreneurship, marketing, and organizational identity. In entrepreneurship studies, textual data from business plans and investor pitches has been analyzed to understand how language influences funding decisions and venture success ([82]). In marketing, sentiment analysis of customer reviews and social media content provided insights into consumer perceptions and brand reputation (Tirunillai & Tellis, 2012). These applications demonstrate textual analysis is not limited to narrow business contexts but is also a versatile tool for understanding a wide range of strategic elements associated with business operations.

The application of textual analysis specifically to business strategy as an academic discipline remains relatively underdeveloped despite these advancements. Most of the existing literature focused on firm-level outcomes like financial performance, investor behavior, and market reactions rather than examining how strategy itself is conceived and discussed within academic research. While studies such as [73] emphasized the methodological potential of textual analysis in strategic management, there is limited work that analyzes systematically the language of strategy scholarship itself. This suggests an opportunity to extend textual analysis beyond its current applications and apply it directly to the study of business strategy as a unique field of inquiry.

Additionally, existing research demonstrates textual analysis can reveal patterns in language that reflect underlying priorities, assumptions, and theoretical frameworks. Applying these same methods to academic literature on business strategy could provide valuable insights into how the field defines key concepts, emphasizes certain themes, and evolves over time. With the prior research that successfully used in textual analysis to study communication in marketing, finance, and organizational behavior, it is reasonable to expect that similar analytic approaches could enhance understanding of business strategy as an academic discipline.

This literature reviewed here highlights the interconnected contributions of business strategy research, textual analysis methodologies, and the application of textual analysis within business contexts. Prior research demonstrates that business strategy is a foundational yet evolving discipline, characterized by a focus on achieving competitive advantage, its forward-looking nature, and its inherent limitations due to uncertainty and changing market conditions. At the same time, textual analysis has emerged as a powerful methodological tool across disciplines, enabling researchers to analyze systematically language through techniques such as word frequency analysis, bigram modeling, and sentiment analysis. These methods have been applied in fields such as sociology, political science, and economics, illustrating their versatility and ability to uncover meaningful patterns in large-scale textual data.

Within business research, textual analysis has been applied successfully to areas such as financial reporting, managerial communication, and consumer behavior, demonstrating its value in understanding how language influences decision-making and organizational outcomes. However, despite these advancements, there remains a notable gap in applying textual analysis directly to the study of business strategy as an academic discipline. Whereas existing studies have focused primarily on firm-level outcomes, limited attention has been given to analyzing how strategy itself is framed, communicated, and developed within scholarly literature. This gap suggests an opportunity to extend textual analysis methods to examine business strategy more directly. By applying these approaches to strategic management literature, researchers can gain enhanced insight into the themes, language, and evolving priorities that define the field, ultimately contributing to a more comprehensive understanding of business strategy as both a theoretical and practical domain. The method used in this study to undertake that application is presented in the following section (Section 3).

Word frequency analysis was used to identify the most commonly occurring words across the author-created corpus of the conclusion sections of the top 50 academic articles identified for this study. This method highlighted dominant concepts, repeated academic framings, and recurring thematic foci within business strategy. The results indicated that the identified conclusion sections emphasized strategic decision making, research contributions, and business performances outcomes among other concepts and topics. The top 25 words of merit identified through this analysis are presented in Table 1.

Table 1.Top 25 most frequent words of merit in the business strategy corpus.

The word frequencies are presented in Table 1. The first theme identified was that of business strategy. Given the focus of this research, this theme and finding was not surprising. This theme consisted of the most frequently occurring word within the corpus, business (n = 274), and the second most frequent term, strategy (n = 187), along with its plural form, strategies (n = 106). These results suggests that conclusions in the identified business strategy research consistently return to the core framing of business strategy within contexts developed within the research. One could reasonably argue that the dominance of these terms, and subsequent theme, is circular, as business strategy was the key search phrase in this study. There is certainly at least a degree of validity to such an argument. However, these terms were not excluded because it was considered desirable to ascertain the degree to which these terms would be amplified through rhetorical repetition. Beyond this primary theme, the second, nearly self-referential theme identified as that of a pronounced research focus. Words within this theme included the terms: research (n = 131), study (n = 109), model (n = 58), results (n = 58), and studies (n =40). This theme reflected the tendency for academic conclusions to emphasize the scholarly contribution, summarize findings, and extend research. Of the 25 identified words of merit, 3 (12%) were associated with the theme of business strategy and 5 (20%) were associated with the theme of research focus. Collectively, nearly a third of the most frequent terms (32%) were largely self-referential. The remaining three themes identified in this study provide more insight into how business strategy is contextualized within the conclusion sections of academic research.

The largest theme, in terms of the number of words of merit included within it, was the identified theme of organization. The theme organization, as constructed in this study, included the terms: companies (n = 75), performance (n = 70), market(n = 69), competitive (n = 63), management(n = 52), firms (n = 51), managers (n = 51), process (n = 43), activities (n = 42), and company (n = 42). This theme consisted of 10 of the top 25 words of merit (40%), and was by far the most densely populated theme identified in this study. It is perhaps not surprising research related to business strategy would make frequent reference to organizational concerns, as business strategies are developed, implemented, and executed within organizations. Since organizations are the existential milieu for business strategy, there are benefits derived in making these connections explicit in the conclusion sections of academic research.

The last two themes identified in this study comprise the heart of the potentially useful findings of the analysis of the most frequent words of merit. It is not surprising that academic research related to business strategy would reference business strategy, research, or organizations. That these themes were identified through the analysis of the author-created corpus was suggestive its content is reasonably consistent with what one would expect. Based on this reassurance, the two remaining themes are all the more insightful. The first of these themes, advancement, was comprised of the words: digital (n = 85), innovation (n = 61), future (n = 53), and AI (n = 45). This theme accounted for roughly 16% of the 25 most frequent words of merit. The composition of this theme suggests business strategy is future-focused, and is broadly focused on innovation, often through the application of technology. These findings pair well with the last theme, competitive edge, which was comprised of the terms: advantage (n = 47) and knowledge (n = 45), which accounted for roughly 8% of the analyzed words of merit. Collectively, these two themes suggest that business strategy is frequently focused on advancement as a basis of establishing a competitive edge. Of the top 25 most frequent words of merit, all but the term based (n = 56) was allocated to a corresponding theme. This term did not fall consistently within the identified themes. Its omission from allocation is not detrimental to the findings.

Overall, thematic analysis based on the determined word frequencies suggests business strategy conclusions frequently revolved around the key themes of organizational dynamics, advancement, and the establishment of a meaningful, competitive edge. These results were contextualized by those of the frequency analysis of the bigrams of merit.

Extending the insights established through an analysis of the thematic patterns based on individual word counts (Section 4.1), bigram analysis was conducted to identify the most frequently occurring two-word phrases across all conclusion sections. Bigram results provide additional insight into how academics structured their concluding remarks and what two-word phrases were commonly used. The top 29 most frequent bigrams of merit are presented in Table 2.

As indicated in the method section (Section 3), if the top 25 bigrams of merit end at a point at which there are bigrams that are equally frequent to the 25^th term, the additional terms will be included until that frequency of occurrence was exhausted. As a result, 29 bigrams were included in the results (Table 2), terminating at a bigram frequency of 7. The allocation of bigrams into themes mapped consistently with the themes identified in the analysis of the words of merit (Section 4.1). The first theme, business strategy, contained the bigrams: business strategy (n = 97), and business strategies (n = 47), along with the bigrams digital strategies (n = 12), differentiation strategy (n = 7), and strategy type (n= 7). When analyzed at the bigram level, business strategy is sometimes broken down to its strategy type, and when it is, it might be uniquely focused on digital strategy or differentiation strategy. This theme accounts for nearly 17% of the 29 bigrams analyzed. Likewise, the theme of research focus analyzed as a bigram was conceptually similar to that as a simple word frequency. The theme of research focus, included the bigrams: future research (n = 21), business model (n = 14), ban model (n = 13), business models (n = 12), future studies (n = 11), Ahmad2010 (n = 8), and theoretical framework (n = 7). The reference to both the ban model (n = 13) and Ahmad2010 (n = 8) are from the source [3]. The BAN model stands for “Business strategy development model for Applying kNowledge management in construction” (p. 253), and Ahmad is a citation reference. These results are idiosyncratic to a single source, and therefore are not generalizable. A case could be made that an iterative, preprocessing approach should be used in studies of this nature, in which initial results are run, idiosyncratic results identified, and then those words and phrases could be excluded from analysis through a subsequent preprocessing step, with the model then rerun and the results reanalyzed. Since this approach was not adopted at the beginning of this analysis, these results are retained and reported. Future studies would likely benefit from such an iterative process.

Table 2. Top 29 most frequent bigrams of merit in the business strategy corpus.

The theme of organization was the largest for the bigrams of merit, in terms of number of observations included in the theme (n = 8), which is about 28% of the bigrams analyzed (Table 2). At the word level, this theme was focused on aspects like managementand firms. At the bigram level, this theme had more granularity on concepts like tournament incentives (n= 12) and executive compensation (n = 7), along with the bigrams of contracting companies (n = 10), britishbusiness (n = 7), financial performance (n = 7), firm performance (n = 7), institutional distance (n = 7), and tax policy (n = 7). When examined at the bigram level, more peculiar aspects of organizational dynamics like executive compensation and tax policy come into focus. These findings are largely consistent with those established earlier (Section 4.1), and provide nuance to how business strategy research is contextualized within organizations. Similar nuances emerge in the analysis of the themes of advancement and competitive edge.

For bigrams, the theme of advancement includes the bigrams: digital business (n = 15), digital transformation (n = 14), and innovative activities (n = 9). The theme competitive edge has the bigram competitive advantage (n = 36). Again, the results of the thematic analysis of bigrams suggest an underlying coherence with those established through the thematic analysis of the words of merit. In terms of strategy, academic articles focused on business strategy have a pronounced focus on the themes of advancement and competitive advantage. These themes accounted for approximately 10% and 3.4% of the observed bigrams respectively.

An additional theme emerged when analyzing the results of the bigrams of merit. The theme of media accounted for approximately 10% of the observed bigrams of merit and consisted of the phrases: daily news (n = 8), media outlets (n = 7) and social media (n = 7). Two bigrams remained unallocated to themes: maturity mismatched (n = 8) and mismatched investment (n = 8). These two bigrams actually comprise the trigram maturity mismatched investment (n = 8), and come from a single source, [100]. Given the two bigrams are actually a trigram, and the trigram is idiosyncratic to a single source, the fact these two bigrams were not allocated to a theme is not considered problematic.

These findings developed here show that business strategy conclusions consistently emphasize strategic frameworks, competitive advantage, and future academic development while also reflecting emerging emphasis on digital transformation and evolving business models. With these themes developed, it is possible to examine the overarching sentiments associated with business strategy.

Sentiment analysis was conducted to measure the average emotional tone or semantic polarity of each conclusion section. Each conclusion was assigned an average sentiment score, with higher positive scores representing more positive language and higher negative scores representing more negative language. Lower scores represent less intense semantic polarity, with zero values representing a neutral sentiment. These average sentiment scores were used to identify whether business strategy conclusions tend to emphasize positivity, negativity, or neutrality. The average sentiment scores across the 50 articles indicated that conclusions were generally written in a positive tone. A boxplot of the average sentiment scores for the 50 articles identified is presented as Figure 2.

Figure 2. Boxplot analysis of average sentiment across business strategy corpus.

As indicated in Figure 2, the mean average sentiment score across the sample was 1.15, meaning that conclusions tend to exhibit a positive sentiment. The median sentiment score was 1.26 which again suggests that the conclusion sections of academic research on business strategy are written with a positive tone. The quartile values were 0.74 (Q1) and 1.58 (Q3), with a minimum value of −0.5, indicating a slightly negative sentiment, and a maximum value of 2.0, indicating a significantly positive sentiment. Only 2 articles out of 50 (4%) had a negative average sentiment score: a) [13] with an average sentiment score of −0.1, and b) [107] with an average sentiment score of −0.5. These findings suggest academic articles on business strategy tend to have conclusion sections containing a positive sentiment. This finding is further substantiated by the results of the hypothesis test (H1).

As indicated in the introduction (Section 1), the null hypothesis for this study was the average mean sentiment score for the conclusion section of academic journal articles covering business strategy is less than or equal to 0. Based on a one-tailed, single same t-test, the null hypothesis was rejected (M = 1.15, SD = 0.55), t (49) = 14.785, p < .001. This result suggests the average, mean sentiment score for the conclusion section of academic research focused on business strategy contains a positive sentiment. Given the word and bigram frequencies, themes, and sentiments presented, it is possible to provide some summary insights into the integrated findings across these analyses.

When considering the word frequency, bigram analysis, and sentiment results together, several thematic patterns of interest emerge. First, the repeated dominance of terms such as strategy, business, and strategies suggests that conclusions are used by authors of academic research in this field of study to consistently reinforce the foundational purpose of strategic management research. Second, the frequent appearance of academic framing terms such as research, study, results, and modelindicates business strategy conclusions emphasize contributions to scholarly literature. This potentially reflects the traditional academic structure in which authors summarize findings, reinforce theoretical significance, and justify methodological value. Third, the prominence of words and phrases related to the future such as future, future research, and future studies suggests conclusion sections are strongly oriented toward recommending additional research to extend a given study. Such a finding reinforces the idea that business strategy is an evolving field with ongoing exploring opportunities. Finally, the presence of digital terminology across both word frequency and bigram analysis suggests modern business strategy research increasingly emphasizes technological change. Phrases such as digital transformation, digital business, and digital strategies indicate that digitalization in particular is becoming a central theme in how scholars interpret strategy and competitive advantage in current markets.

The results from this textual analysis of 50 business strategy conclusion sections revealed consistent patterns in language, structure, and tone. The word frequency analysis showed business strategy conclusions are commonly centered around themes of business strategy, research focus, organization, advancement, and competitive edge. When the results of the bigram analysis were included, the same five themes emerged, along with the additional thematic category of media (i.e., daily news, media outlets, and social media). Sentiment analysis revealed conclusion sections are generally written with a positive semantic tone with an average sentiment score of 1.15. Which was determined to be positive statistically. These results showed that strategy researchers often conclude their work positively, by emphasizing things like its contribution, opportunities, and future implications, rather than focusing on concerns prompting a negative sentiment. Overall, these results suggest business strategy conclusions follow a structured pattern: summarizing findings, emphasizing strategic value, highlighting competitive implications, and recommending directions for future research. With these results in mind, it is possible to move toward the conclusion of this study (Section 5).